diff --git a/master/.buildinfo b/master/.buildinfo index c6c2a8882..70e411042 100644 --- a/master/.buildinfo +++ b/master/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 4f3df2d59ec98742a805ef8a33da0300 +config: 4bb4c4bcc276393a0692c2c4822e1f6b tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree index a4d25efc1..027c3e424 100644 Binary files a/master/.doctrees/cleanlab/benchmarking/index.doctree and b/master/.doctrees/cleanlab/benchmarking/index.doctree differ diff --git a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree index 9c9587f1e..66e022d8b 100644 Binary files a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree and b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree differ diff --git a/master/.doctrees/cleanlab/classification.doctree b/master/.doctrees/cleanlab/classification.doctree index ee71eb4c1..369f6dbb2 100644 Binary files a/master/.doctrees/cleanlab/classification.doctree and b/master/.doctrees/cleanlab/classification.doctree differ diff --git a/master/.doctrees/cleanlab/count.doctree b/master/.doctrees/cleanlab/count.doctree index 05d1ac346..1b94fe457 100644 Binary files a/master/.doctrees/cleanlab/count.doctree and b/master/.doctrees/cleanlab/count.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/datalab.doctree b/master/.doctrees/cleanlab/datalab/datalab.doctree index 4177c5c22..be1dc725f 100644 Binary files a/master/.doctrees/cleanlab/datalab/datalab.doctree and b/master/.doctrees/cleanlab/datalab/datalab.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree index 6b8cac718..f54d8218f 100644 Binary files a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree and b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree index 7dbd7cace..4fb86f0ae 100644 Binary files a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree and b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/guide/index.doctree b/master/.doctrees/cleanlab/datalab/guide/index.doctree index f2a6f80f7..4b023c8f5 100644 Binary files a/master/.doctrees/cleanlab/datalab/guide/index.doctree and b/master/.doctrees/cleanlab/datalab/guide/index.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree index a8dd1aa6e..24096580f 100644 Binary files a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree and b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/index.doctree b/master/.doctrees/cleanlab/datalab/index.doctree index 4ab580823..d1f468c4a 100644 Binary files a/master/.doctrees/cleanlab/datalab/index.doctree and b/master/.doctrees/cleanlab/datalab/index.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree index f07bdefbc..a7ae19ef7 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/data.doctree and b/master/.doctrees/cleanlab/datalab/internal/data.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree index c722fe8d6..9d5d29cd1 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree and b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/factory.doctree b/master/.doctrees/cleanlab/datalab/internal/factory.doctree index eed132827..d06b3922d 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/factory.doctree and b/master/.doctrees/cleanlab/datalab/internal/factory.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/index.doctree b/master/.doctrees/cleanlab/datalab/internal/index.doctree index 32d25807a..41e065568 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/index.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree index 7ffdc190e..1501a082a 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree index 7d0ce042c..970a41c91 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree index b297af3a5..8ae54ed5a 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree index 2fcf23ea2..8fc8df0f4 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree index 6b8c641db..fe2187c0a 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree index b00a8664c..24a815016 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree index 3b00ca329..fd24e5d9f 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree index 476001193..949011725 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree index a4ea0a53f..3cd1b9aac 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree index 98442b1c8..a37828ae3 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree index 79f6d24db..beab1b036 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree index 9572893d2..00adeb8da 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree index e5763dadb..d3e825195 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree index f938aba95..50d12297c 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/internal/report.doctree b/master/.doctrees/cleanlab/datalab/internal/report.doctree index 6dd7ff5ba..5d8eb1e85 100644 Binary files a/master/.doctrees/cleanlab/datalab/internal/report.doctree and b/master/.doctrees/cleanlab/datalab/internal/report.doctree differ diff --git a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree index 0b5cdaa2f..6638bac73 100644 Binary files a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree and b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree differ diff --git a/master/.doctrees/cleanlab/dataset.doctree b/master/.doctrees/cleanlab/dataset.doctree index 677518326..140ba798f 100644 Binary files a/master/.doctrees/cleanlab/dataset.doctree and b/master/.doctrees/cleanlab/dataset.doctree differ diff --git a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree index e1da4c620..db665db39 100644 Binary files a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree and b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree differ diff --git a/master/.doctrees/cleanlab/experimental/coteaching.doctree b/master/.doctrees/cleanlab/experimental/coteaching.doctree index 95e4fc00c..2a4c9af2f 100644 Binary files a/master/.doctrees/cleanlab/experimental/coteaching.doctree and b/master/.doctrees/cleanlab/experimental/coteaching.doctree differ diff --git a/master/.doctrees/cleanlab/experimental/index.doctree b/master/.doctrees/cleanlab/experimental/index.doctree index 0aa51d1fe..31e807ec1 100644 Binary files a/master/.doctrees/cleanlab/experimental/index.doctree and b/master/.doctrees/cleanlab/experimental/index.doctree differ diff --git a/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree b/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree index 3536c6818..a71df045d 100644 Binary files a/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree and b/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree differ diff --git a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree index b4886aaff..70b38dd30 100644 Binary files a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree and b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree differ diff --git a/master/.doctrees/cleanlab/filter.doctree b/master/.doctrees/cleanlab/filter.doctree index a4a6b04ec..fbee53cd3 100644 Binary files a/master/.doctrees/cleanlab/filter.doctree and b/master/.doctrees/cleanlab/filter.doctree differ diff --git a/master/.doctrees/cleanlab/internal/index.doctree b/master/.doctrees/cleanlab/internal/index.doctree index 5866e5c6b..75ef57092 100644 Binary files a/master/.doctrees/cleanlab/internal/index.doctree and b/master/.doctrees/cleanlab/internal/index.doctree differ diff --git a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree index 2b55ec72b..06aac0f67 100644 Binary files a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree and b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree differ diff --git a/master/.doctrees/cleanlab/internal/latent_algebra.doctree b/master/.doctrees/cleanlab/internal/latent_algebra.doctree index ca8629888..7f5a2dfc8 100644 Binary files a/master/.doctrees/cleanlab/internal/latent_algebra.doctree and b/master/.doctrees/cleanlab/internal/latent_algebra.doctree differ diff --git a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree index df475316c..be2cbc079 100644 Binary files a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree and b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree differ diff --git a/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree b/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree index dd7d3ce5a..e0c596d59 100644 Binary files a/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree and b/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree differ diff --git a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree index 952da8ce3..9380e189e 100644 Binary files a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree and b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree differ diff --git a/master/.doctrees/cleanlab/internal/outlier.doctree b/master/.doctrees/cleanlab/internal/outlier.doctree index db27e5921..1b1562a0d 100644 Binary files a/master/.doctrees/cleanlab/internal/outlier.doctree and b/master/.doctrees/cleanlab/internal/outlier.doctree differ diff --git a/master/.doctrees/cleanlab/internal/token_classification_utils.doctree b/master/.doctrees/cleanlab/internal/token_classification_utils.doctree index 1608440cc..2c49d2bc0 100644 Binary files a/master/.doctrees/cleanlab/internal/token_classification_utils.doctree and b/master/.doctrees/cleanlab/internal/token_classification_utils.doctree differ diff --git a/master/.doctrees/cleanlab/internal/util.doctree b/master/.doctrees/cleanlab/internal/util.doctree index 113ab9f35..29e395e9f 100644 Binary files a/master/.doctrees/cleanlab/internal/util.doctree and b/master/.doctrees/cleanlab/internal/util.doctree differ diff --git a/master/.doctrees/cleanlab/internal/validation.doctree b/master/.doctrees/cleanlab/internal/validation.doctree index fa0e83f82..11d5048a4 100644 Binary files a/master/.doctrees/cleanlab/internal/validation.doctree and b/master/.doctrees/cleanlab/internal/validation.doctree differ diff --git a/master/.doctrees/cleanlab/models/fasttext.doctree b/master/.doctrees/cleanlab/models/fasttext.doctree index 253d84af0..f1248b1bd 100644 Binary files a/master/.doctrees/cleanlab/models/fasttext.doctree and b/master/.doctrees/cleanlab/models/fasttext.doctree differ diff --git a/master/.doctrees/cleanlab/models/index.doctree b/master/.doctrees/cleanlab/models/index.doctree index e86f9b327..1f165cfbc 100644 Binary files a/master/.doctrees/cleanlab/models/index.doctree and b/master/.doctrees/cleanlab/models/index.doctree differ diff --git a/master/.doctrees/cleanlab/models/keras.doctree b/master/.doctrees/cleanlab/models/keras.doctree index 98b62dbee..ed719d140 100644 Binary files a/master/.doctrees/cleanlab/models/keras.doctree and b/master/.doctrees/cleanlab/models/keras.doctree differ diff --git a/master/.doctrees/cleanlab/multiannotator.doctree b/master/.doctrees/cleanlab/multiannotator.doctree index 473cf986c..eae157068 100644 Binary files a/master/.doctrees/cleanlab/multiannotator.doctree and b/master/.doctrees/cleanlab/multiannotator.doctree differ diff --git a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree index accfba0de..7b8365a18 100644 Binary files a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree and b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree differ diff --git a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree index 4ee5505e1..dcf4cdf53 100644 Binary files a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree and b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree differ diff --git a/master/.doctrees/cleanlab/multilabel_classification/index.doctree b/master/.doctrees/cleanlab/multilabel_classification/index.doctree index 2e842a19b..b264e464f 100644 Binary files a/master/.doctrees/cleanlab/multilabel_classification/index.doctree and b/master/.doctrees/cleanlab/multilabel_classification/index.doctree differ diff --git a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree index 99158d0fa..3f9141641 100644 Binary files a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree and b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree differ diff --git a/master/.doctrees/cleanlab/object_detection/filter.doctree b/master/.doctrees/cleanlab/object_detection/filter.doctree index c32aaad4d..de119631a 100644 Binary files a/master/.doctrees/cleanlab/object_detection/filter.doctree and b/master/.doctrees/cleanlab/object_detection/filter.doctree differ diff --git a/master/.doctrees/cleanlab/object_detection/index.doctree b/master/.doctrees/cleanlab/object_detection/index.doctree index 5af2fc40f..34695c97d 100644 Binary files a/master/.doctrees/cleanlab/object_detection/index.doctree and b/master/.doctrees/cleanlab/object_detection/index.doctree differ diff --git a/master/.doctrees/cleanlab/object_detection/rank.doctree b/master/.doctrees/cleanlab/object_detection/rank.doctree index ed5982bb3..378b40d55 100644 Binary files a/master/.doctrees/cleanlab/object_detection/rank.doctree and b/master/.doctrees/cleanlab/object_detection/rank.doctree differ diff --git a/master/.doctrees/cleanlab/object_detection/summary.doctree b/master/.doctrees/cleanlab/object_detection/summary.doctree index a88b5633d..3fd4deb22 100644 Binary files a/master/.doctrees/cleanlab/object_detection/summary.doctree and b/master/.doctrees/cleanlab/object_detection/summary.doctree differ diff --git a/master/.doctrees/cleanlab/outlier.doctree b/master/.doctrees/cleanlab/outlier.doctree index b580db0ba..4cc6b3626 100644 Binary files a/master/.doctrees/cleanlab/outlier.doctree and b/master/.doctrees/cleanlab/outlier.doctree differ diff --git a/master/.doctrees/cleanlab/rank.doctree b/master/.doctrees/cleanlab/rank.doctree index 5dbb05e03..efc32621f 100644 Binary files a/master/.doctrees/cleanlab/rank.doctree and b/master/.doctrees/cleanlab/rank.doctree differ diff --git a/master/.doctrees/cleanlab/regression/index.doctree b/master/.doctrees/cleanlab/regression/index.doctree index da4de0808..19d00581c 100644 Binary files a/master/.doctrees/cleanlab/regression/index.doctree and b/master/.doctrees/cleanlab/regression/index.doctree differ diff --git a/master/.doctrees/cleanlab/regression/learn.doctree b/master/.doctrees/cleanlab/regression/learn.doctree index afbbba08b..95ddb53f1 100644 Binary files a/master/.doctrees/cleanlab/regression/learn.doctree and b/master/.doctrees/cleanlab/regression/learn.doctree differ diff --git a/master/.doctrees/cleanlab/regression/rank.doctree b/master/.doctrees/cleanlab/regression/rank.doctree index c29ac57a3..66f7961b1 100644 Binary files a/master/.doctrees/cleanlab/regression/rank.doctree and b/master/.doctrees/cleanlab/regression/rank.doctree differ diff --git a/master/.doctrees/cleanlab/segmentation/filter.doctree b/master/.doctrees/cleanlab/segmentation/filter.doctree index f710e1ea3..25fd6c34c 100644 Binary files a/master/.doctrees/cleanlab/segmentation/filter.doctree and b/master/.doctrees/cleanlab/segmentation/filter.doctree differ diff --git a/master/.doctrees/cleanlab/segmentation/index.doctree b/master/.doctrees/cleanlab/segmentation/index.doctree index 50d55bdd7..37ea9c180 100644 Binary files a/master/.doctrees/cleanlab/segmentation/index.doctree and b/master/.doctrees/cleanlab/segmentation/index.doctree differ diff --git a/master/.doctrees/cleanlab/segmentation/rank.doctree b/master/.doctrees/cleanlab/segmentation/rank.doctree index 9776352b3..4eca47729 100644 Binary files a/master/.doctrees/cleanlab/segmentation/rank.doctree and b/master/.doctrees/cleanlab/segmentation/rank.doctree differ diff --git a/master/.doctrees/cleanlab/segmentation/summary.doctree b/master/.doctrees/cleanlab/segmentation/summary.doctree index 82b03f245..cadaba353 100644 Binary files a/master/.doctrees/cleanlab/segmentation/summary.doctree and b/master/.doctrees/cleanlab/segmentation/summary.doctree differ diff --git a/master/.doctrees/cleanlab/token_classification/filter.doctree b/master/.doctrees/cleanlab/token_classification/filter.doctree index 4d336aaa8..0d5d83b8a 100644 Binary files a/master/.doctrees/cleanlab/token_classification/filter.doctree and b/master/.doctrees/cleanlab/token_classification/filter.doctree differ diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree index 6bd51058f..e310989ff 100644 Binary files a/master/.doctrees/cleanlab/token_classification/index.doctree and b/master/.doctrees/cleanlab/token_classification/index.doctree differ diff --git a/master/.doctrees/cleanlab/token_classification/rank.doctree b/master/.doctrees/cleanlab/token_classification/rank.doctree index 93b3f2d0c..93a692311 100644 Binary files a/master/.doctrees/cleanlab/token_classification/rank.doctree and b/master/.doctrees/cleanlab/token_classification/rank.doctree differ diff --git a/master/.doctrees/cleanlab/token_classification/summary.doctree b/master/.doctrees/cleanlab/token_classification/summary.doctree index 0d87b0296..b3e86ae72 100644 Binary files a/master/.doctrees/cleanlab/token_classification/summary.doctree and b/master/.doctrees/cleanlab/token_classification/summary.doctree differ diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle index 501dfd5d1..fa9d86554 100644 Binary files a/master/.doctrees/environment.pickle and b/master/.doctrees/environment.pickle differ diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree index a46ecc8be..0d5014600 100644 Binary files a/master/.doctrees/index.doctree and b/master/.doctrees/index.doctree differ diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree index 4928a252b..d0f204142 100644 Binary files a/master/.doctrees/migrating/migrate_v2.doctree and b/master/.doctrees/migrating/migrate_v2.doctree differ diff --git a/master/.doctrees/nbsphinx/tutorials/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/audio.ipynb index 5d67baf75..a64acbf25 100644 --- a/master/.doctrees/nbsphinx/tutorials/audio.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:03:56.246621Z", - "iopub.status.busy": "2024-01-17T18:03:56.246426Z", - "iopub.status.idle": "2024-01-17T18:03:59.462158Z", - "shell.execute_reply": "2024-01-17T18:03:59.461536Z" + "iopub.execute_input": "2024-01-17T23:06:03.241225Z", + "iopub.status.busy": "2024-01-17T23:06:03.241029Z", + "iopub.status.idle": "2024-01-17T23:06:06.464107Z", + "shell.execute_reply": "2024-01-17T23:06:06.463420Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:03:59.465517Z", - "iopub.status.busy": "2024-01-17T18:03:59.464868Z", - "iopub.status.idle": "2024-01-17T18:03:59.468353Z", - "shell.execute_reply": "2024-01-17T18:03:59.467776Z" + "iopub.execute_input": "2024-01-17T23:06:06.467168Z", + "iopub.status.busy": "2024-01-17T23:06:06.466788Z", + "iopub.status.idle": "2024-01-17T23:06:06.470304Z", + "shell.execute_reply": "2024-01-17T23:06:06.469673Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:03:59.470782Z", - "iopub.status.busy": "2024-01-17T18:03:59.470350Z", - "iopub.status.idle": "2024-01-17T18:03:59.475553Z", - "shell.execute_reply": "2024-01-17T18:03:59.475067Z" + "iopub.execute_input": "2024-01-17T23:06:06.472648Z", + "iopub.status.busy": "2024-01-17T23:06:06.472216Z", + "iopub.status.idle": "2024-01-17T23:06:06.477236Z", + "shell.execute_reply": "2024-01-17T23:06:06.476626Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-17T18:03:59.477904Z", - "iopub.status.busy": "2024-01-17T18:03:59.477550Z", - "iopub.status.idle": "2024-01-17T18:04:01.172783Z", - "shell.execute_reply": "2024-01-17T18:04:01.171901Z" + "iopub.execute_input": "2024-01-17T23:06:06.479737Z", + "iopub.status.busy": "2024-01-17T23:06:06.479248Z", + "iopub.status.idle": "2024-01-17T23:06:07.960092Z", + "shell.execute_reply": "2024-01-17T23:06:07.959366Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:01.175803Z", - "iopub.status.busy": "2024-01-17T18:04:01.175584Z", - "iopub.status.idle": "2024-01-17T18:04:01.187892Z", - "shell.execute_reply": "2024-01-17T18:04:01.187256Z" + "iopub.execute_input": "2024-01-17T23:06:07.963314Z", + "iopub.status.busy": "2024-01-17T23:06:07.962895Z", + "iopub.status.idle": "2024-01-17T23:06:07.975189Z", + "shell.execute_reply": "2024-01-17T23:06:07.974586Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:01.221136Z", - "iopub.status.busy": "2024-01-17T18:04:01.220534Z", - "iopub.status.idle": "2024-01-17T18:04:01.227460Z", - "shell.execute_reply": "2024-01-17T18:04:01.226813Z" + "iopub.execute_input": "2024-01-17T23:06:08.007234Z", + "iopub.status.busy": "2024-01-17T23:06:08.006813Z", + "iopub.status.idle": "2024-01-17T23:06:08.013562Z", + "shell.execute_reply": "2024-01-17T23:06:08.013029Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:01.230129Z", - "iopub.status.busy": "2024-01-17T18:04:01.229643Z", - "iopub.status.idle": "2024-01-17T18:04:01.936049Z", - "shell.execute_reply": "2024-01-17T18:04:01.935379Z" + "iopub.execute_input": "2024-01-17T23:06:08.015975Z", + "iopub.status.busy": "2024-01-17T23:06:08.015602Z", + "iopub.status.idle": "2024-01-17T23:06:08.737027Z", + "shell.execute_reply": "2024-01-17T23:06:08.736373Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:01.938750Z", - "iopub.status.busy": "2024-01-17T18:04:01.938361Z", - "iopub.status.idle": "2024-01-17T18:04:02.839523Z", - "shell.execute_reply": "2024-01-17T18:04:02.838812Z" + "iopub.execute_input": "2024-01-17T23:06:08.739545Z", + "iopub.status.busy": "2024-01-17T23:06:08.739230Z", + "iopub.status.idle": "2024-01-17T23:06:10.120689Z", + "shell.execute_reply": "2024-01-17T23:06:10.120102Z" }, "id": "vL9lkiKsHvKr" }, @@ -472,10 +472,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:02.842451Z", - "iopub.status.busy": "2024-01-17T18:04:02.842179Z", - "iopub.status.idle": "2024-01-17T18:04:02.864885Z", - "shell.execute_reply": "2024-01-17T18:04:02.864263Z" + "iopub.execute_input": "2024-01-17T23:06:10.123621Z", + "iopub.status.busy": "2024-01-17T23:06:10.123219Z", + "iopub.status.idle": "2024-01-17T23:06:10.145672Z", + "shell.execute_reply": "2024-01-17T23:06:10.145076Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -555,10 +555,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:02.867335Z", - "iopub.status.busy": "2024-01-17T18:04:02.866965Z", - "iopub.status.idle": "2024-01-17T18:04:02.870237Z", - "shell.execute_reply": "2024-01-17T18:04:02.869668Z" + "iopub.execute_input": "2024-01-17T23:06:10.148144Z", + "iopub.status.busy": "2024-01-17T23:06:10.147843Z", + "iopub.status.idle": "2024-01-17T23:06:10.151186Z", + "shell.execute_reply": "2024-01-17T23:06:10.150643Z" }, "id": "I8JqhOZgi94g" }, @@ -580,10 +580,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:02.872601Z", - "iopub.status.busy": "2024-01-17T18:04:02.872244Z", - "iopub.status.idle": "2024-01-17T18:04:21.805805Z", - "shell.execute_reply": "2024-01-17T18:04:21.805139Z" + "iopub.execute_input": "2024-01-17T23:06:10.153485Z", + "iopub.status.busy": "2024-01-17T23:06:10.153191Z", + "iopub.status.idle": "2024-01-17T23:06:28.541137Z", + "shell.execute_reply": "2024-01-17T23:06:28.540500Z" }, "id": "2FSQ2GR9R_YA" }, @@ -615,10 +615,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:21.809284Z", - "iopub.status.busy": "2024-01-17T18:04:21.808690Z", - "iopub.status.idle": "2024-01-17T18:04:21.813229Z", - "shell.execute_reply": "2024-01-17T18:04:21.812575Z" + "iopub.execute_input": "2024-01-17T23:06:28.544247Z", + "iopub.status.busy": "2024-01-17T23:06:28.543816Z", + "iopub.status.idle": "2024-01-17T23:06:28.548440Z", + "shell.execute_reply": "2024-01-17T23:06:28.547908Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -677,10 +677,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:21.815784Z", - "iopub.status.busy": "2024-01-17T18:04:21.815395Z", - "iopub.status.idle": "2024-01-17T18:04:27.285963Z", - "shell.execute_reply": "2024-01-17T18:04:27.285265Z" + "iopub.execute_input": "2024-01-17T23:06:28.550975Z", + "iopub.status.busy": "2024-01-17T23:06:28.550597Z", + "iopub.status.idle": "2024-01-17T23:06:34.059947Z", + "shell.execute_reply": "2024-01-17T23:06:34.059266Z" }, "id": "i_drkY9YOcw4" }, @@ -714,10 +714,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.289545Z", - "iopub.status.busy": "2024-01-17T18:04:27.289071Z", - "iopub.status.idle": "2024-01-17T18:04:27.294502Z", - "shell.execute_reply": "2024-01-17T18:04:27.293903Z" + "iopub.execute_input": "2024-01-17T23:06:34.063475Z", + "iopub.status.busy": "2024-01-17T23:06:34.062997Z", + "iopub.status.idle": "2024-01-17T23:06:34.068792Z", + "shell.execute_reply": "2024-01-17T23:06:34.068163Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -764,10 +764,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.297552Z", - "iopub.status.busy": "2024-01-17T18:04:27.297119Z", - "iopub.status.idle": "2024-01-17T18:04:27.391354Z", - "shell.execute_reply": "2024-01-17T18:04:27.390637Z" + "iopub.execute_input": "2024-01-17T23:06:34.071846Z", + "iopub.status.busy": "2024-01-17T23:06:34.071416Z", + "iopub.status.idle": "2024-01-17T23:06:34.185550Z", + "shell.execute_reply": "2024-01-17T23:06:34.184822Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.394410Z", - "iopub.status.busy": "2024-01-17T18:04:27.394019Z", - "iopub.status.idle": "2024-01-17T18:04:27.404313Z", - "shell.execute_reply": "2024-01-17T18:04:27.403781Z" + "iopub.execute_input": "2024-01-17T23:06:34.188375Z", + "iopub.status.busy": "2024-01-17T23:06:34.188110Z", + "iopub.status.idle": "2024-01-17T23:06:34.198290Z", + "shell.execute_reply": "2024-01-17T23:06:34.197648Z" }, "scrolled": true }, @@ -862,10 +862,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.406721Z", - "iopub.status.busy": "2024-01-17T18:04:27.406410Z", - "iopub.status.idle": "2024-01-17T18:04:27.414725Z", - "shell.execute_reply": "2024-01-17T18:04:27.414071Z" + "iopub.execute_input": "2024-01-17T23:06:34.200848Z", + "iopub.status.busy": "2024-01-17T23:06:34.200521Z", + "iopub.status.idle": "2024-01-17T23:06:34.208862Z", + "shell.execute_reply": "2024-01-17T23:06:34.208245Z" } }, "outputs": [ @@ -969,10 +969,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.417120Z", - "iopub.status.busy": "2024-01-17T18:04:27.416724Z", - "iopub.status.idle": "2024-01-17T18:04:27.421580Z", - "shell.execute_reply": "2024-01-17T18:04:27.420916Z" + "iopub.execute_input": "2024-01-17T23:06:34.211398Z", + "iopub.status.busy": "2024-01-17T23:06:34.211050Z", + "iopub.status.idle": "2024-01-17T23:06:34.215872Z", + "shell.execute_reply": "2024-01-17T23:06:34.215362Z" } }, "outputs": [ @@ -1010,10 +1010,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.423983Z", - "iopub.status.busy": "2024-01-17T18:04:27.423639Z", - "iopub.status.idle": "2024-01-17T18:04:27.429656Z", - "shell.execute_reply": "2024-01-17T18:04:27.429010Z" + "iopub.execute_input": "2024-01-17T23:06:34.218162Z", + "iopub.status.busy": "2024-01-17T23:06:34.217800Z", + "iopub.status.idle": "2024-01-17T23:06:34.224079Z", + "shell.execute_reply": "2024-01-17T23:06:34.223545Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1133,10 +1133,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.432082Z", - "iopub.status.busy": "2024-01-17T18:04:27.431713Z", - "iopub.status.idle": "2024-01-17T18:04:27.545999Z", - "shell.execute_reply": "2024-01-17T18:04:27.545312Z" + "iopub.execute_input": "2024-01-17T23:06:34.226655Z", + "iopub.status.busy": "2024-01-17T23:06:34.226193Z", + "iopub.status.idle": "2024-01-17T23:06:34.342868Z", + "shell.execute_reply": "2024-01-17T23:06:34.342208Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1190,10 +1190,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.548644Z", - "iopub.status.busy": "2024-01-17T18:04:27.548226Z", - "iopub.status.idle": "2024-01-17T18:04:27.658900Z", - "shell.execute_reply": "2024-01-17T18:04:27.658242Z" + "iopub.execute_input": "2024-01-17T23:06:34.345463Z", + "iopub.status.busy": "2024-01-17T23:06:34.345103Z", + "iopub.status.idle": "2024-01-17T23:06:34.452029Z", + "shell.execute_reply": "2024-01-17T23:06:34.451378Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1238,10 +1238,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.661353Z", - "iopub.status.busy": "2024-01-17T18:04:27.661065Z", - "iopub.status.idle": "2024-01-17T18:04:27.770988Z", - "shell.execute_reply": "2024-01-17T18:04:27.770337Z" + "iopub.execute_input": "2024-01-17T23:06:34.454818Z", + "iopub.status.busy": "2024-01-17T23:06:34.454245Z", + "iopub.status.idle": "2024-01-17T23:06:34.561699Z", + "shell.execute_reply": "2024-01-17T23:06:34.561010Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1282,10 +1282,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.773635Z", - "iopub.status.busy": "2024-01-17T18:04:27.773261Z", - "iopub.status.idle": "2024-01-17T18:04:27.880054Z", - "shell.execute_reply": "2024-01-17T18:04:27.879383Z" + "iopub.execute_input": "2024-01-17T23:06:34.564138Z", + "iopub.status.busy": "2024-01-17T23:06:34.563922Z", + "iopub.status.idle": "2024-01-17T23:06:34.673537Z", + "shell.execute_reply": "2024-01-17T23:06:34.672870Z" } }, "outputs": [ @@ -1333,10 +1333,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.882841Z", - "iopub.status.busy": "2024-01-17T18:04:27.882447Z", - "iopub.status.idle": "2024-01-17T18:04:27.885966Z", - "shell.execute_reply": "2024-01-17T18:04:27.885407Z" + "iopub.execute_input": "2024-01-17T23:06:34.675986Z", + "iopub.status.busy": "2024-01-17T23:06:34.675761Z", + "iopub.status.idle": "2024-01-17T23:06:34.679368Z", + "shell.execute_reply": "2024-01-17T23:06:34.678729Z" }, "nbsphinx": "hidden" }, @@ -1377,65 +1377,37 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "04545eb82c614214a57ff48711e5ef69": { + "030585901c0a49c3976e336a1ab51ee1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e38a6eaeba49436d880afec59461ee06", - "placeholder": "​", - "style": "IPY_MODEL_9a471f99ebec460f82f340da4bf084a3", - "value": "embedding_model.ckpt: 100%" - } - }, - "0fd11d936da54fa68096671994e1275f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_617fa189f462470a937beee9505799fa", - "placeholder": "​", - "style": "IPY_MODEL_9fca2e61f7d64cea930c8b07bdcbc59e", - "value": " 3.20k/3.20k [00:00<00:00, 549kB/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "159b496215244dc799dd25dae2e0470d": { + "0ff00007ea674a8496fed6616e438639": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "1acfc2e65c71473497f43eb9e974bc92": { + "1bdf3168328746ddb97b58d22771e706": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -1450,65 +1422,66 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_db86af8139ff41018b497ff1758537de", - "IPY_MODEL_fc52934f923d48f6815ef3ea863cb962", - "IPY_MODEL_ebe5bf1237e54a5cb31a7953735ed040" + "IPY_MODEL_8d69a36aa0ef4ac398e5473efe8facaf", + "IPY_MODEL_4013766ebdd64bcdaa57852522f87774", + "IPY_MODEL_7b3c7b951bc643fea2c2d55d50e02569" ], - "layout": "IPY_MODEL_7fda371c9fba45549d352696e38b0c16" - } - }, - "1cca100b2cea4a77bbbcc5a23f19e1d7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_da8efe510bcf414b9d2c76f5f791e9ea", - "placeholder": "​", - "style": "IPY_MODEL_220c65a6f5bb442e8b509643c78298cb", - "value": "label_encoder.txt: 100%" - } - }, - "220c65a6f5bb442e8b509643c78298cb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "layout": "IPY_MODEL_46c6f6135bb9449482ba0294d1515087" } }, - "24e87b41e33643a0a449e4e5f4a04a21": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "1eb624a3dad54e7fa6b146681c26c1fd": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "25691bde0c394a299f774959e32e539d": { + "2105d31075094aec8143dc36c2cc4c8a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -1524,7 +1497,7 @@ "description_width": "" } }, - "25c36bd0682a44a1af90eecaec670b14": { + "21a9a1365c8149ebafde1882681607b7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1576,28 +1549,7 @@ "width": null } }, - "2fb3c50dfd544cf297447c4dd5e4bdfa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_5836728e785d44ce8ebe512cb1ffe735", - "placeholder": "​", - "style": "IPY_MODEL_dae854f92b7548ddb4b4981bb4a8c9d2", - "value": " 16.9M/16.9M [00:00<00:00, 96.3MB/s]" - } - }, - "32761d387fb34f55a8662c19898e2e19": { + "2415134083d142f18e75d9c1fd90ad9e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1649,68 +1601,102 @@ "width": null } }, - "41336a38204142909d6d6151660b30ef": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "26707147e6f24b498f499aec25d3879a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "42cde93ea0f44516bebd28767667c2fa": { + "289a6e5f2aa24222a97d08ebc952a72e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_ccb07543f38c422eba6e58deccf2a389", - "max": 2041.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_55ca6bb4d3414d2091e2d6cc744d0227", - "value": 2041.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f2c87a26b3e843afa6644bed13f5508c", + "IPY_MODEL_7694a31ea9a04edcb6864f25b28b15cb", + "IPY_MODEL_959ba9c2758b476f942db7be7ea533c2" + ], + "layout": "IPY_MODEL_965eba21261d4a2e9ee7513ec333adc2" } }, - "4a8b0b00351a48b1bd3c4ffb02422045": { + "320bad48fde04048bd4f6c3c7723b882": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_649eb4698e3443adb0fd32629e36ece6", - "IPY_MODEL_c9aad8223fed4fd79a1fa84e675e9c34", - "IPY_MODEL_0fd11d936da54fa68096671994e1275f" - ], - "layout": "IPY_MODEL_c3b13273fa614276aa8cd31a0d3ba6a0" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_da437028c14a404b9d0753eab94e3c55", + "placeholder": "​", + "style": "IPY_MODEL_df6099d96cc04692aa1804b28f0d3165", + "value": " 129k/129k [00:00<00:00, 12.9MB/s]" } }, - "4bdbef4700e34979a7835f1c9cb5f63b": { + "3b6ccb1c3d224e82864d450d361cb50f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1762,7 +1748,46 @@ "width": null } }, - "4d632ff2b07b4dffa2ead315843c6e6c": { + "3bfd515955e24904b4dc7bac5512c42f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4013766ebdd64bcdaa57852522f87774": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ff2eea9324ab4fdebb914b6830cf4e4e", + "max": 16887676.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b391f6736d6440f5977ea7e4075132b1", + "value": 16887676.0 + } + }, + "424d86c2319543f0b0d18ccae78cd508": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1777,7 +1802,7 @@ "description_width": "" } }, - "5105284d7b9346b09f3d678a6f15a3cb": { + "46c6f6135bb9449482ba0294d1515087": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1829,44 +1854,65 @@ "width": null } }, - "53e5f5516c1940eb84c59305f879b33d": { + "48805febf856433c84dda4034fb9c0c6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_53a59a607d394f2e832ba2a5f8cd33f2", + "placeholder": "​", + "style": "IPY_MODEL_0ff00007ea674a8496fed6616e438639", + "value": " 3.20k/3.20k [00:00<00:00, 511kB/s]" + } + }, + "4a7fe1b9aff24fc7a7e179b7f11d5bbd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_5105284d7b9346b09f3d678a6f15a3cb", - "placeholder": "​", - "style": "IPY_MODEL_db5c6c408f9147df9a5d44f8f886ab61", - "value": " 2.04k/2.04k [00:00<00:00, 352kB/s]" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8d8b99dd01c64a7ca9041d32353eac13", + "IPY_MODEL_e364bdc739a344f9b5a54749543225af", + "IPY_MODEL_320bad48fde04048bd4f6c3c7723b882" + ], + "layout": "IPY_MODEL_d9416e2443dc46e89ea2b97a96b892cb" } }, - "55ca6bb4d3414d2091e2d6cc744d0227": { + "4aba10cb04694ab4bf360dfefe9871d7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "5836728e785d44ce8ebe512cb1ffe735": { + "508a9cef5621429ca2ad08b2e5a5b906": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1918,7 +1964,7 @@ "width": null } }, - "617fa189f462470a937beee9505799fa": { + "53a59a607d394f2e832ba2a5f8cd33f2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1970,7 +2016,44 @@ "width": null } }, - "61d06a92b987421cb46d8a89d1975bb7": { + "5fd842770f3c46198750ec0bc065bbec": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_851a3e78f4cb49c28fa6d9d3f6ad533c", + "IPY_MODEL_b3b1a6b802c2438aa6b7345347be1daa", + "IPY_MODEL_48805febf856433c84dda4034fb9c0c6" + ], + "layout": "IPY_MODEL_cc06c8dc25a146d1949bbc6f284744e5" + } + }, + "609ef4dd1eb940adb8bd7ff656af9b5e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "766477efa9484d7b8b8cc8a77a1b6f7d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2022,112 +2105,52 @@ "width": null } }, - "649eb4698e3443adb0fd32629e36ece6": { + "7694a31ea9a04edcb6864f25b28b15cb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_4bdbef4700e34979a7835f1c9cb5f63b", - "placeholder": "​", - "style": "IPY_MODEL_41336a38204142909d6d6151660b30ef", - "value": "mean_var_norm_emb.ckpt: 100%" - } - }, - "64de8f2a3f18422084ccd72434dca5cc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1cca100b2cea4a77bbbcc5a23f19e1d7", - "IPY_MODEL_7137378c46a441c9b8f6dd4cd7cd9ee2", - "IPY_MODEL_7aaa5224bfe44036a333755a894e18b5" - ], - "layout": "IPY_MODEL_25c36bd0682a44a1af90eecaec670b14" - } - }, - "660387b90c454207a12955625f60a888": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "69f5f977755e4a4b82b5ca684955dd9e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_04545eb82c614214a57ff48711e5ef69", - "IPY_MODEL_886f26d855ae49049a473788aaf13fdd", - "IPY_MODEL_2fb3c50dfd544cf297447c4dd5e4bdfa" - ], - "layout": "IPY_MODEL_77fefa233aa04e3ba284da2418760799" + "layout": "IPY_MODEL_508a9cef5621429ca2ad08b2e5a5b906", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2105d31075094aec8143dc36c2cc4c8a", + "value": 15856877.0 } }, - "7137378c46a441c9b8f6dd4cd7cd9ee2": { + "7b3c7b951bc643fea2c2d55d50e02569": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_78e330e8413b4c638b5f5ad1892ffc7c", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_25691bde0c394a299f774959e32e539d", - "value": 128619.0 + "layout": "IPY_MODEL_3b6ccb1c3d224e82864d450d361cb50f", + "placeholder": "​", + "style": "IPY_MODEL_3bfd515955e24904b4dc7bac5512c42f", + "value": " 16.9M/16.9M [00:00<00:00, 74.2MB/s]" } }, - "77fefa233aa04e3ba284da2418760799": { + "7eb2a98375b74c91b45dc5b5accd9bca": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2179,7 +2202,7 @@ "width": null } }, - "78e330e8413b4c638b5f5ad1892ffc7c": { + "83f7b9bff2ec40f19a358ae6e9071f3a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2231,7 +2254,7 @@ "width": null } }, - "799d78d15e1549e58376f474b256b3f1": { + "851a3e78f4cb49c28fa6d9d3f6ad533c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2246,13 +2269,28 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_8c2848fd80e94dfeba2260153670f5b5", + "layout": "IPY_MODEL_ee5f56e9b26d44c29b98957642b5cbe6", "placeholder": "​", - "style": "IPY_MODEL_d73d40dffae24b919506395235104b28", - "value": "hyperparams.yaml: 100%" + "style": "IPY_MODEL_4aba10cb04694ab4bf360dfefe9871d7", + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "8699a0a7cb124a369027b929c14aca25": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "7aaa5224bfe44036a333755a894e18b5": { + "8d69a36aa0ef4ac398e5473efe8facaf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2267,89 +2305,55 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_61d06a92b987421cb46d8a89d1975bb7", + "layout": "IPY_MODEL_2415134083d142f18e75d9c1fd90ad9e", "placeholder": "​", - "style": "IPY_MODEL_f1827a2afaa3497796df337825e00c5d", - "value": " 129k/129k [00:00<00:00, 13.1MB/s]" + "style": "IPY_MODEL_424d86c2319543f0b0d18ccae78cd508", + "value": "embedding_model.ckpt: 100%" } }, - "7fda371c9fba45549d352696e38b0c16": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "8d8b99dd01c64a7ca9041d32353eac13": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_766477efa9484d7b8b8cc8a77a1b6f7d", + "placeholder": "​", + "style": "IPY_MODEL_d3cc29ff5820423989ce16d9d70bcd31", + "value": "label_encoder.txt: 100%" } }, - "886f26d855ae49049a473788aaf13fdd": { + "959ba9c2758b476f942db7be7ea533c2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_bbb0a42cbeb44cd2807289cb84e06c66", - "max": 16887676.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_660387b90c454207a12955625f60a888", - "value": 16887676.0 + "layout": "IPY_MODEL_21a9a1365c8149ebafde1882681607b7", + "placeholder": "​", + "style": "IPY_MODEL_030585901c0a49c3976e336a1ab51ee1", + "value": " 15.9M/15.9M [00:00<00:00, 89.8MB/s]" } }, - "8c2848fd80e94dfeba2260153670f5b5": { + "965eba21261d4a2e9ee7513ec333adc2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2401,7 +2405,7 @@ "width": null } }, - "95589bfbae6545eea65cdda502cb8591": { + "aa7002ac34ad461c971cb567fbaed7c7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -2417,59 +2421,83 @@ "description_width": "" } }, - "9a471f99ebec460f82f340da4bf084a3": { + "b391f6736d6440f5977ea7e4075132b1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "9fca2e61f7d64cea930c8b07bdcbc59e": { + "b3b1a6b802c2438aa6b7345347be1daa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d3db5175210c48839bd72a54bbd9e709", + "max": 3201.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_aa7002ac34ad461c971cb567fbaed7c7", + "value": 3201.0 } }, - "a2cd93bedff346d4b94c8c101e88352e": { + "b6341a7e935242c2adc1364ec71311bf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_83f7b9bff2ec40f19a358ae6e9071f3a", + "placeholder": "​", + "style": "IPY_MODEL_bf1dc44a81b64e408396f375ab6db707", + "value": " 2.04k/2.04k [00:00<00:00, 337kB/s]" + } + }, + "bf1dc44a81b64e408396f375ab6db707": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_799d78d15e1549e58376f474b256b3f1", - "IPY_MODEL_42cde93ea0f44516bebd28767667c2fa", - "IPY_MODEL_53e5f5516c1940eb84c59305f879b33d" - ], - "layout": "IPY_MODEL_32761d387fb34f55a8662c19898e2e19" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "a70c7781a1174beb8b08bdb996fa2bc8": { + "c4ebdef1b4f14614814debc42af581c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2521,7 +2549,29 @@ "width": null } }, - "bbb0a42cbeb44cd2807289cb84e06c66": { + "c5b91c7d529d4e1a9a36a41238ba0244": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ed7a2be85ae641c2b6cb2f1e8fb8054b", + "IPY_MODEL_cc503c61cdb6438ea915c9aa1032c63c", + "IPY_MODEL_b6341a7e935242c2adc1364ec71311bf" + ], + "layout": "IPY_MODEL_fa264daf6009497ca63787f2eb2c7503" + } + }, + "cc06c8dc25a146d1949bbc6f284744e5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2573,7 +2623,62 @@ "width": null } }, - "c0de636f211e43f793ee2c8ccaccead2": { + "cc503c61cdb6438ea915c9aa1032c63c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_26707147e6f24b498f499aec25d3879a", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d21e1fb2327a464b8b743384ffa08cc0", + "value": 2041.0 + } + }, + "d21e1fb2327a464b8b743384ffa08cc0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "d3cc29ff5820423989ce16d9d70bcd31": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "d3db5175210c48839bd72a54bbd9e709": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2625,7 +2730,7 @@ "width": null } }, - "c30853559a364583b1f650a150b6654a": { + "d9416e2443dc46e89ea2b97a96b892cb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2677,7 +2782,7 @@ "width": null } }, - "c3b13273fa614276aa8cd31a0d3ba6a0": { + "da437028c14a404b9d0753eab94e3c55": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2729,7 +2834,22 @@ "width": null } }, - "c9aad8223fed4fd79a1fa84e675e9c34": { + "df6099d96cc04692aa1804b28f0d3165": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "e364bdc739a344f9b5a54749543225af": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -2745,82 +2865,36 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c0de636f211e43f793ee2c8ccaccead2", - "max": 3201.0, + "layout": "IPY_MODEL_c4ebdef1b4f14614814debc42af581c4", + "max": 128619.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_95589bfbae6545eea65cdda502cb8591", - "value": 3201.0 - } - }, - "ccb07543f38c422eba6e58deccf2a389": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "style": "IPY_MODEL_f8c0ae7aca4c47fdb99824d3a5c6c79b", + "value": 128619.0 } }, - "d73d40dffae24b919506395235104b28": { + "ed7a2be85ae641c2b6cb2f1e8fb8054b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1eb624a3dad54e7fa6b146681c26c1fd", + "placeholder": "​", + "style": "IPY_MODEL_8699a0a7cb124a369027b929c14aca25", + "value": "hyperparams.yaml: 100%" } }, - "da8efe510bcf414b9d2c76f5f791e9ea": { + "ee5f56e9b26d44c29b98957642b5cbe6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2872,58 +2946,44 @@ "width": null } }, - "dae854f92b7548ddb4b4981bb4a8c9d2": { + "f2c87a26b3e843afa6644bed13f5508c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7eb2a98375b74c91b45dc5b5accd9bca", + "placeholder": "​", + "style": "IPY_MODEL_609ef4dd1eb940adb8bd7ff656af9b5e", + "value": "classifier.ckpt: 100%" } }, - "db5c6c408f9147df9a5d44f8f886ab61": { + "f8c0ae7aca4c47fdb99824d3a5c6c79b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "db86af8139ff41018b497ff1758537de": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_a70c7781a1174beb8b08bdb996fa2bc8", - "placeholder": "​", - "style": "IPY_MODEL_4d632ff2b07b4dffa2ead315843c6e6c", - "value": "classifier.ckpt: 100%" - } - }, - "e38a6eaeba49436d880afec59461ee06": { + "fa264daf6009497ca63787f2eb2c7503": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2975,7 +3035,7 @@ "width": null } }, - "e52c6bc80973455ab0806594b5177ef7": { + "ff2eea9324ab4fdebb914b6830cf4e4e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3026,66 +3086,6 @@ "visibility": null, "width": null } - }, - "ebe5bf1237e54a5cb31a7953735ed040": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_c30853559a364583b1f650a150b6654a", - "placeholder": "​", - "style": "IPY_MODEL_24e87b41e33643a0a449e4e5f4a04a21", - "value": " 15.9M/15.9M [00:00<00:00, 302MB/s]" - } - }, - "f1827a2afaa3497796df337825e00c5d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "fc52934f923d48f6815ef3ea863cb962": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e52c6bc80973455ab0806594b5177ef7", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_159b496215244dc799dd25dae2e0470d", - "value": 15856877.0 - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb index 1d05a4ac7..90cafbf27 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:33.483819Z", - "iopub.status.busy": "2024-01-17T18:04:33.483262Z", - "iopub.status.idle": "2024-01-17T18:04:34.563147Z", - "shell.execute_reply": "2024-01-17T18:04:34.562532Z" + "iopub.execute_input": "2024-01-17T23:06:39.342500Z", + "iopub.status.busy": "2024-01-17T23:06:39.342321Z", + "iopub.status.idle": "2024-01-17T23:06:40.409562Z", + "shell.execute_reply": "2024-01-17T23:06:40.408997Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -118,10 +118,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:34.566022Z", - "iopub.status.busy": "2024-01-17T18:04:34.565584Z", - "iopub.status.idle": "2024-01-17T18:04:34.568829Z", - "shell.execute_reply": "2024-01-17T18:04:34.568346Z" + "iopub.execute_input": "2024-01-17T23:06:40.412370Z", + "iopub.status.busy": "2024-01-17T23:06:40.412089Z", + "iopub.status.idle": "2024-01-17T23:06:40.415239Z", + "shell.execute_reply": "2024-01-17T23:06:40.414697Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:34.571312Z", - "iopub.status.busy": "2024-01-17T18:04:34.571026Z", - "iopub.status.idle": "2024-01-17T18:04:34.580263Z", - "shell.execute_reply": "2024-01-17T18:04:34.579715Z" + "iopub.execute_input": "2024-01-17T23:06:40.417686Z", + "iopub.status.busy": "2024-01-17T23:06:40.417328Z", + "iopub.status.idle": "2024-01-17T23:06:40.426661Z", + "shell.execute_reply": "2024-01-17T23:06:40.426085Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:34.582624Z", - "iopub.status.busy": "2024-01-17T18:04:34.582267Z", - "iopub.status.idle": "2024-01-17T18:04:34.587260Z", - "shell.execute_reply": "2024-01-17T18:04:34.586787Z" + "iopub.execute_input": "2024-01-17T23:06:40.429048Z", + "iopub.status.busy": "2024-01-17T23:06:40.428682Z", + "iopub.status.idle": "2024-01-17T23:06:40.433294Z", + "shell.execute_reply": "2024-01-17T23:06:40.432812Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:34.589869Z", - "iopub.status.busy": "2024-01-17T18:04:34.589376Z", - "iopub.status.idle": "2024-01-17T18:04:34.868264Z", - "shell.execute_reply": "2024-01-17T18:04:34.867632Z" + "iopub.execute_input": "2024-01-17T23:06:40.435792Z", + "iopub.status.busy": "2024-01-17T23:06:40.435424Z", + "iopub.status.idle": "2024-01-17T23:06:40.706242Z", + "shell.execute_reply": "2024-01-17T23:06:40.705511Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:34.871315Z", - "iopub.status.busy": "2024-01-17T18:04:34.870886Z", - "iopub.status.idle": "2024-01-17T18:04:35.179091Z", - "shell.execute_reply": "2024-01-17T18:04:35.178424Z" + "iopub.execute_input": "2024-01-17T23:06:40.709010Z", + "iopub.status.busy": "2024-01-17T23:06:40.708793Z", + "iopub.status.idle": "2024-01-17T23:06:41.078542Z", + "shell.execute_reply": "2024-01-17T23:06:41.077853Z" } }, "outputs": [ @@ -568,10 +568,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:35.181786Z", - "iopub.status.busy": "2024-01-17T18:04:35.181405Z", - "iopub.status.idle": "2024-01-17T18:04:35.205575Z", - "shell.execute_reply": "2024-01-17T18:04:35.205040Z" + "iopub.execute_input": "2024-01-17T23:06:41.081271Z", + "iopub.status.busy": "2024-01-17T23:06:41.081043Z", + "iopub.status.idle": "2024-01-17T23:06:41.105700Z", + "shell.execute_reply": "2024-01-17T23:06:41.105204Z" } }, "outputs": [], @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:35.208176Z", - "iopub.status.busy": "2024-01-17T18:04:35.207800Z", - "iopub.status.idle": "2024-01-17T18:04:35.219387Z", - "shell.execute_reply": "2024-01-17T18:04:35.218891Z" + "iopub.execute_input": "2024-01-17T23:06:41.108273Z", + "iopub.status.busy": "2024-01-17T23:06:41.107900Z", + "iopub.status.idle": "2024-01-17T23:06:41.119557Z", + "shell.execute_reply": "2024-01-17T23:06:41.119070Z" } }, "outputs": [], @@ -641,10 +641,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:35.221936Z", - "iopub.status.busy": "2024-01-17T18:04:35.221576Z", - "iopub.status.idle": "2024-01-17T18:04:36.514513Z", - "shell.execute_reply": "2024-01-17T18:04:36.513826Z" + "iopub.execute_input": "2024-01-17T23:06:41.121850Z", + "iopub.status.busy": "2024-01-17T23:06:41.121645Z", + "iopub.status.idle": "2024-01-17T23:06:42.395090Z", + "shell.execute_reply": "2024-01-17T23:06:42.394350Z" } }, "outputs": [ @@ -708,10 +708,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.517319Z", - "iopub.status.busy": "2024-01-17T18:04:36.516739Z", - "iopub.status.idle": "2024-01-17T18:04:36.540062Z", - "shell.execute_reply": "2024-01-17T18:04:36.539415Z" + "iopub.execute_input": "2024-01-17T23:06:42.397825Z", + "iopub.status.busy": "2024-01-17T23:06:42.397474Z", + "iopub.status.idle": "2024-01-17T23:06:42.421815Z", + "shell.execute_reply": "2024-01-17T23:06:42.421176Z" } }, "outputs": [ @@ -820,10 +820,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.542755Z", - "iopub.status.busy": "2024-01-17T18:04:36.542357Z", - "iopub.status.idle": "2024-01-17T18:04:36.565338Z", - "shell.execute_reply": "2024-01-17T18:04:36.564636Z" + "iopub.execute_input": "2024-01-17T23:06:42.424433Z", + "iopub.status.busy": "2024-01-17T23:06:42.424030Z", + "iopub.status.idle": "2024-01-17T23:06:42.444464Z", + "shell.execute_reply": "2024-01-17T23:06:42.443784Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.568303Z", - "iopub.status.busy": "2024-01-17T18:04:36.567786Z", - "iopub.status.idle": "2024-01-17T18:04:36.582311Z", - "shell.execute_reply": "2024-01-17T18:04:36.581766Z" + "iopub.execute_input": "2024-01-17T23:06:42.446887Z", + "iopub.status.busy": "2024-01-17T23:06:42.446518Z", + "iopub.status.idle": "2024-01-17T23:06:42.461229Z", + "shell.execute_reply": "2024-01-17T23:06:42.460605Z" } }, "outputs": [ @@ -1068,17 +1068,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.584583Z", - "iopub.status.busy": "2024-01-17T18:04:36.584386Z", - "iopub.status.idle": "2024-01-17T18:04:36.606116Z", - "shell.execute_reply": "2024-01-17T18:04:36.605481Z" + "iopub.execute_input": "2024-01-17T23:06:42.463847Z", + "iopub.status.busy": "2024-01-17T23:06:42.463265Z", + "iopub.status.idle": "2024-01-17T23:06:42.485045Z", + "shell.execute_reply": "2024-01-17T23:06:42.484382Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce0df2e674ad49f289b9c6629eb9a93b", + "model_id": "f9c446a6ae0e481a8f4425281acd2812", "version_major": 2, "version_minor": 0 }, @@ -1114,10 +1114,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.608360Z", - "iopub.status.busy": "2024-01-17T18:04:36.608121Z", - "iopub.status.idle": "2024-01-17T18:04:36.623341Z", - "shell.execute_reply": "2024-01-17T18:04:36.622732Z" + "iopub.execute_input": "2024-01-17T23:06:42.487982Z", + "iopub.status.busy": "2024-01-17T23:06:42.487461Z", + "iopub.status.idle": "2024-01-17T23:06:42.502778Z", + "shell.execute_reply": "2024-01-17T23:06:42.502139Z" } }, "outputs": [ @@ -1235,10 +1235,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.625892Z", - "iopub.status.busy": "2024-01-17T18:04:36.625457Z", - "iopub.status.idle": "2024-01-17T18:04:36.631828Z", - "shell.execute_reply": "2024-01-17T18:04:36.631217Z" + "iopub.execute_input": "2024-01-17T23:06:42.505343Z", + "iopub.status.busy": "2024-01-17T23:06:42.504854Z", + "iopub.status.idle": "2024-01-17T23:06:42.511266Z", + "shell.execute_reply": "2024-01-17T23:06:42.510747Z" } }, "outputs": [], @@ -1295,10 +1295,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.634265Z", - "iopub.status.busy": "2024-01-17T18:04:36.633885Z", - "iopub.status.idle": "2024-01-17T18:04:36.652691Z", - "shell.execute_reply": "2024-01-17T18:04:36.652133Z" + "iopub.execute_input": "2024-01-17T23:06:42.513763Z", + "iopub.status.busy": "2024-01-17T23:06:42.513319Z", + "iopub.status.idle": "2024-01-17T23:06:42.532164Z", + "shell.execute_reply": "2024-01-17T23:06:42.531605Z" } }, "outputs": [ @@ -1430,52 +1430,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "016da6d5adb14dfcab43c2faeffdcac2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_1bec11a906f14351943811da375f1b68", - "placeholder": "​", - "style": "IPY_MODEL_328dc3e51eef4f61ada827866ade843a", - "value": " 132/132 [00:00<00:00, 11203.37 examples/s]" - } - }, - "051bf949fb4a4c34977914b2fdbd1a46": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_8524043e6e114e199d262dc88bfc22da", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4a135c98d58f4354bffda310f2a613d9", - "value": 132.0 - } - }, - "1bec11a906f14351943811da375f1b68": { + "11eac3b2f4164e5694fdb5152fff54f1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1527,53 +1482,7 @@ "width": null } }, - "328dc3e51eef4f61ada827866ade843a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "4a135c98d58f4354bffda310f2a613d9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "5f0f68db532f4983830e8d3d0e1d9c55": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "6ea64771986e42b6b69a518953aae518": { + "2c9ed523e681457c97733b484f5d2302": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1625,7 +1534,7 @@ "width": null } }, - "8524043e6e114e199d262dc88bfc22da": { + "67e30ead652c4e178b1a2fa25ba4149c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1677,7 +1586,43 @@ "width": null } }, - "b3956bd3c1b34e468eb324a8aa73cd4e": { + "7c7a8b3a377f4891984c98cc48b480b5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_67e30ead652c4e178b1a2fa25ba4149c", + "placeholder": "​", + "style": "IPY_MODEL_de85a0cc75b345a8b0257d3379f3b927", + "value": " 132/132 [00:00<00:00, 11349.20 examples/s]" + } + }, + "9d1d43035b6d471cbf16064de0792a70": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "bdeb99c1040f435d9ea59551e490a482": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1729,7 +1674,59 @@ "width": null } }, - "ce0df2e674ad49f289b9c6629eb9a93b": { + "d48408d0b5804d8c8f562072e9542410": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_11eac3b2f4164e5694fdb5152fff54f1", + "placeholder": "​", + "style": "IPY_MODEL_9d1d43035b6d471cbf16064de0792a70", + "value": "Saving the dataset (1/1 shards): 100%" + } + }, + "dded8d49e1ea43ad9f1ab1944f0fa28e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "de85a0cc75b345a8b0257d3379f3b927": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f9c446a6ae0e481a8f4425281acd2812": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -1744,32 +1741,35 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_f247eef24dd44de39c6212b2720239c1", - "IPY_MODEL_051bf949fb4a4c34977914b2fdbd1a46", - "IPY_MODEL_016da6d5adb14dfcab43c2faeffdcac2" + "IPY_MODEL_d48408d0b5804d8c8f562072e9542410", + "IPY_MODEL_ffd5efcaf7a945d984571d2a9f2057ad", + "IPY_MODEL_7c7a8b3a377f4891984c98cc48b480b5" ], - "layout": "IPY_MODEL_b3956bd3c1b34e468eb324a8aa73cd4e" + "layout": "IPY_MODEL_bdeb99c1040f435d9ea59551e490a482" } }, - "f247eef24dd44de39c6212b2720239c1": { + "ffd5efcaf7a945d984571d2a9f2057ad": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_6ea64771986e42b6b69a518953aae518", - "placeholder": "​", - "style": "IPY_MODEL_5f0f68db532f4983830e8d3d0e1d9c55", - "value": "Saving the dataset (1/1 shards): 100%" + "layout": "IPY_MODEL_2c9ed523e681457c97733b484f5d2302", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_dded8d49e1ea43ad9f1ab1944f0fa28e", + "value": 132.0 } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb index 1697ceead..d3df4ed89 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:41.467132Z", - "iopub.status.busy": "2024-01-17T18:04:41.466518Z", - "iopub.status.idle": "2024-01-17T18:04:42.555591Z", - "shell.execute_reply": "2024-01-17T18:04:42.554980Z" + "iopub.execute_input": "2024-01-17T23:06:47.569861Z", + "iopub.status.busy": "2024-01-17T23:06:47.569660Z", + "iopub.status.idle": "2024-01-17T23:06:48.641275Z", + "shell.execute_reply": "2024-01-17T23:06:48.640563Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -116,10 +116,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:42.558508Z", - "iopub.status.busy": "2024-01-17T18:04:42.558086Z", - "iopub.status.idle": "2024-01-17T18:04:42.561388Z", - "shell.execute_reply": "2024-01-17T18:04:42.560867Z" + "iopub.execute_input": "2024-01-17T23:06:48.644454Z", + "iopub.status.busy": "2024-01-17T23:06:48.643822Z", + "iopub.status.idle": "2024-01-17T23:06:48.647145Z", + "shell.execute_reply": "2024-01-17T23:06:48.646571Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:42.563994Z", - "iopub.status.busy": "2024-01-17T18:04:42.563549Z", - "iopub.status.idle": "2024-01-17T18:04:42.573492Z", - "shell.execute_reply": "2024-01-17T18:04:42.572963Z" + "iopub.execute_input": "2024-01-17T23:06:48.649505Z", + "iopub.status.busy": "2024-01-17T23:06:48.649176Z", + "iopub.status.idle": "2024-01-17T23:06:48.658971Z", + "shell.execute_reply": "2024-01-17T23:06:48.658347Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:42.575559Z", - "iopub.status.busy": "2024-01-17T18:04:42.575357Z", - "iopub.status.idle": "2024-01-17T18:04:42.579953Z", - "shell.execute_reply": "2024-01-17T18:04:42.579468Z" + "iopub.execute_input": "2024-01-17T23:06:48.661358Z", + "iopub.status.busy": "2024-01-17T23:06:48.661025Z", + "iopub.status.idle": "2024-01-17T23:06:48.666186Z", + "shell.execute_reply": "2024-01-17T23:06:48.665652Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:42.582528Z", - "iopub.status.busy": "2024-01-17T18:04:42.582149Z", - "iopub.status.idle": "2024-01-17T18:04:42.863268Z", - "shell.execute_reply": "2024-01-17T18:04:42.862639Z" + "iopub.execute_input": "2024-01-17T23:06:48.668478Z", + "iopub.status.busy": "2024-01-17T23:06:48.668137Z", + "iopub.status.idle": "2024-01-17T23:06:48.950866Z", + "shell.execute_reply": "2024-01-17T23:06:48.950180Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:42.866012Z", - "iopub.status.busy": "2024-01-17T18:04:42.865802Z", - "iopub.status.idle": "2024-01-17T18:04:43.236893Z", - "shell.execute_reply": "2024-01-17T18:04:43.236235Z" + "iopub.execute_input": "2024-01-17T23:06:48.953576Z", + "iopub.status.busy": "2024-01-17T23:06:48.953323Z", + "iopub.status.idle": "2024-01-17T23:06:49.325530Z", + "shell.execute_reply": "2024-01-17T23:06:49.324859Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:43.239627Z", - "iopub.status.busy": "2024-01-17T18:04:43.239261Z", - "iopub.status.idle": "2024-01-17T18:04:43.242241Z", - "shell.execute_reply": "2024-01-17T18:04:43.241645Z" + "iopub.execute_input": "2024-01-17T23:06:49.328443Z", + "iopub.status.busy": "2024-01-17T23:06:49.327940Z", + "iopub.status.idle": "2024-01-17T23:06:49.331128Z", + "shell.execute_reply": "2024-01-17T23:06:49.330575Z" } }, "outputs": [], @@ -601,10 +601,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:43.244798Z", - "iopub.status.busy": "2024-01-17T18:04:43.244450Z", - "iopub.status.idle": "2024-01-17T18:04:43.282859Z", - "shell.execute_reply": "2024-01-17T18:04:43.282243Z" + "iopub.execute_input": "2024-01-17T23:06:49.333565Z", + "iopub.status.busy": "2024-01-17T23:06:49.333216Z", + "iopub.status.idle": "2024-01-17T23:06:49.371453Z", + "shell.execute_reply": "2024-01-17T23:06:49.370801Z" } }, "outputs": [ @@ -646,10 +646,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:43.285162Z", - "iopub.status.busy": "2024-01-17T18:04:43.284947Z", - "iopub.status.idle": "2024-01-17T18:04:44.608959Z", - "shell.execute_reply": "2024-01-17T18:04:44.608186Z" + "iopub.execute_input": "2024-01-17T23:06:49.373991Z", + "iopub.status.busy": "2024-01-17T23:06:49.373529Z", + "iopub.status.idle": "2024-01-17T23:06:50.680046Z", + "shell.execute_reply": "2024-01-17T23:06:50.679378Z" } }, "outputs": [ @@ -701,10 +701,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.611888Z", - "iopub.status.busy": "2024-01-17T18:04:44.611313Z", - "iopub.status.idle": "2024-01-17T18:04:44.636218Z", - "shell.execute_reply": "2024-01-17T18:04:44.635671Z" + "iopub.execute_input": "2024-01-17T23:06:50.682695Z", + "iopub.status.busy": "2024-01-17T23:06:50.682347Z", + "iopub.status.idle": "2024-01-17T23:06:50.707371Z", + "shell.execute_reply": "2024-01-17T23:06:50.706854Z" } }, "outputs": [ @@ -878,10 +878,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.638722Z", - "iopub.status.busy": "2024-01-17T18:04:44.638518Z", - "iopub.status.idle": "2024-01-17T18:04:44.646754Z", - "shell.execute_reply": "2024-01-17T18:04:44.646231Z" + "iopub.execute_input": "2024-01-17T23:06:50.709806Z", + "iopub.status.busy": "2024-01-17T23:06:50.709606Z", + "iopub.status.idle": "2024-01-17T23:06:50.716253Z", + "shell.execute_reply": "2024-01-17T23:06:50.715721Z" } }, "outputs": [ @@ -985,10 +985,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.649051Z", - "iopub.status.busy": "2024-01-17T18:04:44.648690Z", - "iopub.status.idle": "2024-01-17T18:04:44.654993Z", - "shell.execute_reply": "2024-01-17T18:04:44.654393Z" + "iopub.execute_input": "2024-01-17T23:06:50.718504Z", + "iopub.status.busy": "2024-01-17T23:06:50.718307Z", + "iopub.status.idle": "2024-01-17T23:06:50.724463Z", + "shell.execute_reply": "2024-01-17T23:06:50.723963Z" } }, "outputs": [ @@ -1055,10 +1055,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.657457Z", - "iopub.status.busy": "2024-01-17T18:04:44.657064Z", - "iopub.status.idle": "2024-01-17T18:04:44.667959Z", - "shell.execute_reply": "2024-01-17T18:04:44.667358Z" + "iopub.execute_input": "2024-01-17T23:06:50.726704Z", + "iopub.status.busy": "2024-01-17T23:06:50.726464Z", + "iopub.status.idle": "2024-01-17T23:06:50.737224Z", + "shell.execute_reply": "2024-01-17T23:06:50.736715Z" } }, "outputs": [ @@ -1231,10 +1231,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.670277Z", - "iopub.status.busy": "2024-01-17T18:04:44.670077Z", - "iopub.status.idle": "2024-01-17T18:04:44.679556Z", - "shell.execute_reply": "2024-01-17T18:04:44.679053Z" + "iopub.execute_input": "2024-01-17T23:06:50.739595Z", + "iopub.status.busy": "2024-01-17T23:06:50.739250Z", + "iopub.status.idle": "2024-01-17T23:06:50.748736Z", + "shell.execute_reply": "2024-01-17T23:06:50.748121Z" } }, "outputs": [ @@ -1350,10 +1350,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.681741Z", - "iopub.status.busy": "2024-01-17T18:04:44.681546Z", - "iopub.status.idle": "2024-01-17T18:04:44.689258Z", - "shell.execute_reply": "2024-01-17T18:04:44.688717Z" + "iopub.execute_input": "2024-01-17T23:06:50.751142Z", + "iopub.status.busy": "2024-01-17T23:06:50.750687Z", + "iopub.status.idle": "2024-01-17T23:06:50.758345Z", + "shell.execute_reply": "2024-01-17T23:06:50.757710Z" }, "scrolled": true }, @@ -1478,10 +1478,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.691411Z", - "iopub.status.busy": "2024-01-17T18:04:44.691219Z", - "iopub.status.idle": "2024-01-17T18:04:44.701261Z", - "shell.execute_reply": "2024-01-17T18:04:44.700743Z" + "iopub.execute_input": "2024-01-17T23:06:50.760695Z", + "iopub.status.busy": "2024-01-17T23:06:50.760357Z", + "iopub.status.idle": "2024-01-17T23:06:50.770230Z", + "shell.execute_reply": "2024-01-17T23:06:50.769519Z" } }, "outputs": [ diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index 4d347a0cb..89e9e8e2e 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb @@ -74,10 +74,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:49.517933Z", - "iopub.status.busy": "2024-01-17T18:04:49.517732Z", - "iopub.status.idle": "2024-01-17T18:04:50.545443Z", - "shell.execute_reply": "2024-01-17T18:04:50.544850Z" + "iopub.execute_input": "2024-01-17T23:06:55.582283Z", + "iopub.status.busy": "2024-01-17T23:06:55.581731Z", + "iopub.status.idle": "2024-01-17T23:06:56.607603Z", + "shell.execute_reply": "2024-01-17T23:06:56.606957Z" }, "nbsphinx": "hidden" }, @@ -87,7 +87,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.548446Z", - "iopub.status.busy": "2024-01-17T18:04:50.547847Z", - "iopub.status.idle": "2024-01-17T18:04:50.564313Z", - "shell.execute_reply": "2024-01-17T18:04:50.563695Z" + "iopub.execute_input": "2024-01-17T23:06:56.610616Z", + "iopub.status.busy": "2024-01-17T23:06:56.610107Z", + "iopub.status.idle": "2024-01-17T23:06:56.627639Z", + "shell.execute_reply": "2024-01-17T23:06:56.627094Z" } }, "outputs": [], @@ -155,10 +155,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.566981Z", - "iopub.status.busy": "2024-01-17T18:04:50.566555Z", - "iopub.status.idle": "2024-01-17T18:04:50.730069Z", - "shell.execute_reply": "2024-01-17T18:04:50.729433Z" + "iopub.execute_input": "2024-01-17T23:06:56.630524Z", + "iopub.status.busy": "2024-01-17T23:06:56.630127Z", + "iopub.status.idle": "2024-01-17T23:06:56.764165Z", + "shell.execute_reply": "2024-01-17T23:06:56.763459Z" } }, "outputs": [ @@ -265,10 +265,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.732586Z", - "iopub.status.busy": "2024-01-17T18:04:50.732381Z", - "iopub.status.idle": "2024-01-17T18:04:50.736190Z", - "shell.execute_reply": "2024-01-17T18:04:50.735672Z" + "iopub.execute_input": "2024-01-17T23:06:56.766653Z", + "iopub.status.busy": "2024-01-17T23:06:56.766289Z", + "iopub.status.idle": "2024-01-17T23:06:56.770134Z", + "shell.execute_reply": "2024-01-17T23:06:56.769507Z" } }, "outputs": [], @@ -289,10 +289,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.738648Z", - "iopub.status.busy": "2024-01-17T18:04:50.738199Z", - "iopub.status.idle": "2024-01-17T18:04:50.746501Z", - "shell.execute_reply": "2024-01-17T18:04:50.746013Z" + "iopub.execute_input": "2024-01-17T23:06:56.772612Z", + "iopub.status.busy": "2024-01-17T23:06:56.772309Z", + "iopub.status.idle": "2024-01-17T23:06:56.780263Z", + "shell.execute_reply": "2024-01-17T23:06:56.779767Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.749021Z", - "iopub.status.busy": "2024-01-17T18:04:50.748698Z", - "iopub.status.idle": "2024-01-17T18:04:50.751419Z", - "shell.execute_reply": "2024-01-17T18:04:50.750879Z" + "iopub.execute_input": "2024-01-17T23:06:56.782699Z", + "iopub.status.busy": "2024-01-17T23:06:56.782326Z", + "iopub.status.idle": "2024-01-17T23:06:56.785122Z", + "shell.execute_reply": "2024-01-17T23:06:56.784591Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.753826Z", - "iopub.status.busy": "2024-01-17T18:04:50.753466Z", - "iopub.status.idle": "2024-01-17T18:04:54.360252Z", - "shell.execute_reply": "2024-01-17T18:04:54.359622Z" + "iopub.execute_input": "2024-01-17T23:06:56.787751Z", + "iopub.status.busy": "2024-01-17T23:06:56.787454Z", + "iopub.status.idle": "2024-01-17T23:07:00.383276Z", + "shell.execute_reply": "2024-01-17T23:07:00.382535Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:54.363603Z", - "iopub.status.busy": "2024-01-17T18:04:54.363144Z", - "iopub.status.idle": "2024-01-17T18:04:54.373021Z", - "shell.execute_reply": "2024-01-17T18:04:54.372497Z" + "iopub.execute_input": "2024-01-17T23:07:00.386618Z", + "iopub.status.busy": "2024-01-17T23:07:00.386346Z", + "iopub.status.idle": "2024-01-17T23:07:00.396061Z", + "shell.execute_reply": "2024-01-17T23:07:00.395412Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:54.375544Z", - "iopub.status.busy": "2024-01-17T18:04:54.375164Z", - "iopub.status.idle": "2024-01-17T18:04:55.734156Z", - "shell.execute_reply": "2024-01-17T18:04:55.733429Z" + "iopub.execute_input": "2024-01-17T23:07:00.398720Z", + "iopub.status.busy": "2024-01-17T23:07:00.398246Z", + "iopub.status.idle": "2024-01-17T23:07:01.717594Z", + "shell.execute_reply": "2024-01-17T23:07:01.716841Z" } }, "outputs": [ @@ -475,10 +475,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.738536Z", - "iopub.status.busy": "2024-01-17T18:04:55.737191Z", - "iopub.status.idle": "2024-01-17T18:04:55.765750Z", - "shell.execute_reply": "2024-01-17T18:04:55.765138Z" + "iopub.execute_input": "2024-01-17T23:07:01.722190Z", + "iopub.status.busy": "2024-01-17T23:07:01.720796Z", + "iopub.status.idle": "2024-01-17T23:07:01.749134Z", + "shell.execute_reply": "2024-01-17T23:07:01.748523Z" }, "scrolled": true }, @@ -624,10 +624,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.770227Z", - "iopub.status.busy": "2024-01-17T18:04:55.769054Z", - "iopub.status.idle": "2024-01-17T18:04:55.781862Z", - "shell.execute_reply": "2024-01-17T18:04:55.781261Z" + "iopub.execute_input": "2024-01-17T23:07:01.753395Z", + "iopub.status.busy": "2024-01-17T23:07:01.752274Z", + "iopub.status.idle": "2024-01-17T23:07:01.764948Z", + "shell.execute_reply": "2024-01-17T23:07:01.764266Z" } }, "outputs": [ @@ -731,10 +731,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.786261Z", - "iopub.status.busy": "2024-01-17T18:04:55.785118Z", - "iopub.status.idle": "2024-01-17T18:04:55.799914Z", - "shell.execute_reply": "2024-01-17T18:04:55.799303Z" + "iopub.execute_input": "2024-01-17T23:07:01.769149Z", + "iopub.status.busy": "2024-01-17T23:07:01.768032Z", + "iopub.status.idle": "2024-01-17T23:07:01.782499Z", + "shell.execute_reply": "2024-01-17T23:07:01.781898Z" } }, "outputs": [ @@ -863,10 +863,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.804376Z", - "iopub.status.busy": "2024-01-17T18:04:55.803247Z", - "iopub.status.idle": "2024-01-17T18:04:55.816361Z", - "shell.execute_reply": "2024-01-17T18:04:55.815747Z" + "iopub.execute_input": "2024-01-17T23:07:01.786729Z", + "iopub.status.busy": "2024-01-17T23:07:01.785586Z", + "iopub.status.idle": "2024-01-17T23:07:01.798260Z", + "shell.execute_reply": "2024-01-17T23:07:01.797656Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.820790Z", - "iopub.status.busy": "2024-01-17T18:04:55.819659Z", - "iopub.status.idle": "2024-01-17T18:04:55.832665Z", - "shell.execute_reply": "2024-01-17T18:04:55.832191Z" + "iopub.execute_input": "2024-01-17T23:07:01.802498Z", + "iopub.status.busy": "2024-01-17T23:07:01.801367Z", + "iopub.status.idle": "2024-01-17T23:07:01.814051Z", + "shell.execute_reply": "2024-01-17T23:07:01.813478Z" } }, "outputs": [ @@ -1094,10 +1094,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.835067Z", - "iopub.status.busy": "2024-01-17T18:04:55.834854Z", - "iopub.status.idle": "2024-01-17T18:04:55.842208Z", - "shell.execute_reply": "2024-01-17T18:04:55.841666Z" + "iopub.execute_input": "2024-01-17T23:07:01.816878Z", + "iopub.status.busy": "2024-01-17T23:07:01.816671Z", + "iopub.status.idle": "2024-01-17T23:07:01.823646Z", + "shell.execute_reply": "2024-01-17T23:07:01.823005Z" } }, "outputs": [ @@ -1181,10 +1181,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.844882Z", - "iopub.status.busy": "2024-01-17T18:04:55.844409Z", - "iopub.status.idle": "2024-01-17T18:04:55.851860Z", - "shell.execute_reply": "2024-01-17T18:04:55.851312Z" + "iopub.execute_input": "2024-01-17T23:07:01.826161Z", + "iopub.status.busy": "2024-01-17T23:07:01.825755Z", + "iopub.status.idle": "2024-01-17T23:07:01.832679Z", + "shell.execute_reply": "2024-01-17T23:07:01.832073Z" } }, "outputs": [ @@ -1277,10 +1277,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.854459Z", - "iopub.status.busy": "2024-01-17T18:04:55.854085Z", - "iopub.status.idle": "2024-01-17T18:04:55.861606Z", - "shell.execute_reply": "2024-01-17T18:04:55.860960Z" + "iopub.execute_input": "2024-01-17T23:07:01.835198Z", + "iopub.status.busy": "2024-01-17T23:07:01.834825Z", + "iopub.status.idle": "2024-01-17T23:07:01.842060Z", + "shell.execute_reply": "2024-01-17T23:07:01.841527Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index 4e71fc4dd..9f92665d5 100644 --- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:00.739310Z", - "iopub.status.busy": "2024-01-17T18:05:00.739115Z", - "iopub.status.idle": "2024-01-17T18:05:03.063769Z", - "shell.execute_reply": "2024-01-17T18:05:03.063206Z" + "iopub.execute_input": "2024-01-17T23:07:06.736514Z", + "iopub.status.busy": "2024-01-17T23:07:06.736135Z", + "iopub.status.idle": "2024-01-17T23:07:09.007449Z", + "shell.execute_reply": "2024-01-17T23:07:09.006832Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cf63fee22bbf401492c9f2f6f74d206a", + "model_id": "735576d8959e46f3826a38708cf752de", "version_major": 2, "version_minor": 0 }, @@ -118,7 +118,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -143,10 +143,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.066797Z", - "iopub.status.busy": "2024-01-17T18:05:03.066295Z", - "iopub.status.idle": "2024-01-17T18:05:03.069771Z", - "shell.execute_reply": "2024-01-17T18:05:03.069245Z" + "iopub.execute_input": "2024-01-17T23:07:09.010579Z", + "iopub.status.busy": "2024-01-17T23:07:09.009974Z", + "iopub.status.idle": "2024-01-17T23:07:09.013430Z", + "shell.execute_reply": "2024-01-17T23:07:09.012886Z" } }, "outputs": [], @@ -167,10 +167,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.072156Z", - "iopub.status.busy": "2024-01-17T18:05:03.071788Z", - "iopub.status.idle": "2024-01-17T18:05:03.075200Z", - "shell.execute_reply": "2024-01-17T18:05:03.074554Z" + "iopub.execute_input": "2024-01-17T23:07:09.015859Z", + "iopub.status.busy": "2024-01-17T23:07:09.015504Z", + "iopub.status.idle": "2024-01-17T23:07:09.018792Z", + "shell.execute_reply": "2024-01-17T23:07:09.018271Z" }, "nbsphinx": "hidden" }, @@ -200,10 +200,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.077484Z", - "iopub.status.busy": "2024-01-17T18:05:03.077149Z", - "iopub.status.idle": "2024-01-17T18:05:03.143138Z", - "shell.execute_reply": "2024-01-17T18:05:03.142505Z" + "iopub.execute_input": "2024-01-17T23:07:09.021102Z", + "iopub.status.busy": "2024-01-17T23:07:09.020714Z", + "iopub.status.idle": "2024-01-17T23:07:09.058694Z", + "shell.execute_reply": "2024-01-17T23:07:09.058068Z" } }, "outputs": [ @@ -293,10 +293,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.145703Z", - "iopub.status.busy": "2024-01-17T18:05:03.145221Z", - "iopub.status.idle": "2024-01-17T18:05:03.149435Z", - "shell.execute_reply": "2024-01-17T18:05:03.148814Z" + "iopub.execute_input": "2024-01-17T23:07:09.061088Z", + "iopub.status.busy": "2024-01-17T23:07:09.060763Z", + "iopub.status.idle": "2024-01-17T23:07:09.064868Z", + "shell.execute_reply": "2024-01-17T23:07:09.064298Z" } }, "outputs": [ @@ -305,7 +305,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'apple_pay_or_google_pay', 'getting_spare_card', 'visa_or_mastercard', 'cancel_transfer', 'beneficiary_not_allowed', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'change_pin'}\n" + "Classes: {'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'getting_spare_card', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'cancel_transfer', 'beneficiary_not_allowed', 'change_pin', 'visa_or_mastercard'}\n" ] } ], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.151986Z", - "iopub.status.busy": "2024-01-17T18:05:03.151547Z", - "iopub.status.idle": "2024-01-17T18:05:03.155066Z", - "shell.execute_reply": "2024-01-17T18:05:03.154495Z" + "iopub.execute_input": "2024-01-17T23:07:09.067157Z", + "iopub.status.busy": "2024-01-17T23:07:09.066816Z", + "iopub.status.idle": "2024-01-17T23:07:09.070335Z", + "shell.execute_reply": "2024-01-17T23:07:09.069700Z" } }, "outputs": [ @@ -387,17 +387,17 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.157519Z", - "iopub.status.busy": "2024-01-17T18:05:03.157070Z", - "iopub.status.idle": "2024-01-17T18:05:12.348322Z", - "shell.execute_reply": "2024-01-17T18:05:12.347594Z" + "iopub.execute_input": "2024-01-17T23:07:09.072907Z", + "iopub.status.busy": "2024-01-17T23:07:09.072479Z", + "iopub.status.idle": "2024-01-17T23:07:19.691854Z", + "shell.execute_reply": "2024-01-17T23:07:19.691230Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "45ac3af2e1ed4f4c8dfdabe525afe3d2", + "model_id": "eae3f2bc15824aa1945e4a9709a7cb7c", "version_major": 2, "version_minor": 0 }, @@ -411,7 +411,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e185e5cf5c0548fbbc30b1069d606797", + "model_id": "aebe7c43a94b411b875f55c85e5bbf99", "version_major": 2, "version_minor": 0 }, @@ -425,7 +425,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b94b659172c4c808567a150cf38153c", + "model_id": "ba2d5d53d9a94b1896142b5a30bbd514", "version_major": 2, "version_minor": 0 }, @@ -439,7 +439,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cac01943eb874158907dd23885f1ee1a", + "model_id": "0065750b88fd406396533e490ff9a0ca", "version_major": 2, "version_minor": 0 }, @@ -453,7 +453,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3d414f4be0554dc48490d27044d55d7b", + "model_id": "3b25cf0897af4c389e5bb84dce0e453a", "version_major": 2, "version_minor": 0 }, @@ -467,7 +467,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "696fed9227a24944a318a7e13ec558c1", + "model_id": "116e4a0b97db4d0c884059f25c064c6a", "version_major": 2, "version_minor": 0 }, @@ -481,7 +481,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "48cf8f69b83648bc957e73f69c88e66a", + "model_id": "253f1e63067b4f78a688136462184c85", "version_major": 2, "version_minor": 0 }, @@ -535,10 +535,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:12.351622Z", - "iopub.status.busy": "2024-01-17T18:05:12.351228Z", - "iopub.status.idle": "2024-01-17T18:05:13.520684Z", - "shell.execute_reply": "2024-01-17T18:05:13.519985Z" + "iopub.execute_input": "2024-01-17T23:07:19.695009Z", + "iopub.status.busy": "2024-01-17T23:07:19.694579Z", + "iopub.status.idle": "2024-01-17T23:07:20.868638Z", + "shell.execute_reply": "2024-01-17T23:07:20.867960Z" }, "scrolled": true }, @@ -570,10 +570,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:13.524656Z", - "iopub.status.busy": "2024-01-17T18:05:13.524249Z", - "iopub.status.idle": "2024-01-17T18:05:13.527395Z", - "shell.execute_reply": "2024-01-17T18:05:13.526816Z" + "iopub.execute_input": "2024-01-17T23:07:20.873083Z", + "iopub.status.busy": "2024-01-17T23:07:20.871780Z", + "iopub.status.idle": "2024-01-17T23:07:20.876509Z", + "shell.execute_reply": "2024-01-17T23:07:20.875949Z" } }, "outputs": [], @@ -593,10 +593,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:13.531364Z", - "iopub.status.busy": "2024-01-17T18:05:13.530212Z", - "iopub.status.idle": "2024-01-17T18:05:14.861750Z", - "shell.execute_reply": "2024-01-17T18:05:14.860991Z" + "iopub.execute_input": "2024-01-17T23:07:20.880805Z", + "iopub.status.busy": "2024-01-17T23:07:20.879680Z", + "iopub.status.idle": "2024-01-17T23:07:22.198191Z", + "shell.execute_reply": "2024-01-17T23:07:22.197409Z" }, "scrolled": true }, @@ -640,10 +640,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.866391Z", - "iopub.status.busy": "2024-01-17T18:05:14.865013Z", - "iopub.status.idle": "2024-01-17T18:05:14.901758Z", - "shell.execute_reply": "2024-01-17T18:05:14.901142Z" + "iopub.execute_input": "2024-01-17T23:07:22.201762Z", + "iopub.status.busy": "2024-01-17T23:07:22.201081Z", + "iopub.status.idle": "2024-01-17T23:07:22.236485Z", + "shell.execute_reply": "2024-01-17T23:07:22.235879Z" }, "scrolled": true }, @@ -808,10 +808,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.906114Z", - "iopub.status.busy": "2024-01-17T18:05:14.904971Z", - "iopub.status.idle": "2024-01-17T18:05:14.917983Z", - "shell.execute_reply": "2024-01-17T18:05:14.917381Z" + "iopub.execute_input": "2024-01-17T23:07:22.239702Z", + "iopub.status.busy": "2024-01-17T23:07:22.239312Z", + "iopub.status.idle": "2024-01-17T23:07:22.249593Z", + "shell.execute_reply": "2024-01-17T23:07:22.249016Z" }, "scrolled": true }, @@ -921,10 +921,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.920990Z", - "iopub.status.busy": "2024-01-17T18:05:14.920788Z", - "iopub.status.idle": "2024-01-17T18:05:14.925853Z", - "shell.execute_reply": "2024-01-17T18:05:14.925068Z" + "iopub.execute_input": "2024-01-17T23:07:22.252711Z", + "iopub.status.busy": "2024-01-17T23:07:22.252340Z", + "iopub.status.idle": "2024-01-17T23:07:22.256985Z", + "shell.execute_reply": "2024-01-17T23:07:22.256523Z" } }, "outputs": [ @@ -962,10 +962,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.928490Z", - "iopub.status.busy": "2024-01-17T18:05:14.928107Z", - "iopub.status.idle": "2024-01-17T18:05:14.935276Z", - "shell.execute_reply": "2024-01-17T18:05:14.934787Z" + "iopub.execute_input": "2024-01-17T23:07:22.259203Z", + "iopub.status.busy": "2024-01-17T23:07:22.258912Z", + "iopub.status.idle": "2024-01-17T23:07:22.264975Z", + "shell.execute_reply": "2024-01-17T23:07:22.264510Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.937647Z", - "iopub.status.busy": "2024-01-17T18:05:14.937445Z", - "iopub.status.idle": "2024-01-17T18:05:14.945173Z", - "shell.execute_reply": "2024-01-17T18:05:14.944393Z" + "iopub.execute_input": "2024-01-17T23:07:22.267168Z", + "iopub.status.busy": "2024-01-17T23:07:22.266883Z", + "iopub.status.idle": "2024-01-17T23:07:22.272743Z", + "shell.execute_reply": "2024-01-17T23:07:22.272288Z" } }, "outputs": [ @@ -1168,10 +1168,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.947361Z", - "iopub.status.busy": "2024-01-17T18:05:14.947162Z", - "iopub.status.idle": "2024-01-17T18:05:14.953478Z", - "shell.execute_reply": "2024-01-17T18:05:14.952831Z" + "iopub.execute_input": "2024-01-17T23:07:22.274875Z", + "iopub.status.busy": "2024-01-17T23:07:22.274585Z", + "iopub.status.idle": "2024-01-17T23:07:22.280068Z", + "shell.execute_reply": "2024-01-17T23:07:22.279615Z" } }, "outputs": [ @@ -1279,10 +1279,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.955611Z", - "iopub.status.busy": "2024-01-17T18:05:14.955414Z", - "iopub.status.idle": "2024-01-17T18:05:14.965109Z", - "shell.execute_reply": "2024-01-17T18:05:14.964575Z" + "iopub.execute_input": "2024-01-17T23:07:22.282238Z", + "iopub.status.busy": "2024-01-17T23:07:22.281951Z", + "iopub.status.idle": "2024-01-17T23:07:22.290181Z", + "shell.execute_reply": "2024-01-17T23:07:22.289592Z" } }, "outputs": [ @@ -1393,10 +1393,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.967441Z", - "iopub.status.busy": "2024-01-17T18:05:14.967073Z", - "iopub.status.idle": "2024-01-17T18:05:14.972810Z", - "shell.execute_reply": "2024-01-17T18:05:14.972298Z" + "iopub.execute_input": "2024-01-17T23:07:22.292538Z", + "iopub.status.busy": "2024-01-17T23:07:22.292335Z", + "iopub.status.idle": "2024-01-17T23:07:22.475829Z", + "shell.execute_reply": "2024-01-17T23:07:22.475150Z" } }, "outputs": [ @@ -1464,10 +1464,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.975149Z", - "iopub.status.busy": "2024-01-17T18:05:14.974766Z", - "iopub.status.idle": "2024-01-17T18:05:15.144852Z", - "shell.execute_reply": "2024-01-17T18:05:15.144184Z" + "iopub.execute_input": "2024-01-17T23:07:22.478452Z", + "iopub.status.busy": "2024-01-17T23:07:22.478023Z", + "iopub.status.idle": "2024-01-17T23:07:22.484232Z", + "shell.execute_reply": "2024-01-17T23:07:22.483639Z" } }, "outputs": [ @@ -1546,10 +1546,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:15.147389Z", - "iopub.status.busy": "2024-01-17T18:05:15.147036Z", - "iopub.status.idle": "2024-01-17T18:05:15.151030Z", - "shell.execute_reply": "2024-01-17T18:05:15.150478Z" + "iopub.execute_input": "2024-01-17T23:07:22.486793Z", + "iopub.status.busy": "2024-01-17T23:07:22.486423Z", + "iopub.status.idle": "2024-01-17T23:07:22.490377Z", + "shell.execute_reply": "2024-01-17T23:07:22.489770Z" } }, "outputs": [ @@ -1597,10 +1597,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:15.153611Z", - "iopub.status.busy": "2024-01-17T18:05:15.153229Z", - "iopub.status.idle": "2024-01-17T18:05:15.159064Z", - "shell.execute_reply": "2024-01-17T18:05:15.158439Z" + "iopub.execute_input": "2024-01-17T23:07:22.492585Z", + "iopub.status.busy": "2024-01-17T23:07:22.492384Z", + "iopub.status.idle": "2024-01-17T23:07:22.498186Z", + "shell.execute_reply": "2024-01-17T23:07:22.497549Z" }, "nbsphinx": "hidden" }, @@ -1650,73 +1650,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "04bf7ee7a9c34eeeaa4d9b286f8d7f9d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_27abf396b79145cd917bff0ccd201c7e", - "placeholder": "​", - "style": "IPY_MODEL_4af4550bfcf047a5b3c8e1e3d3cd4b87", - "value": " 466k/466k [00:00<00:00, 29.4MB/s]" - } - }, - "0753cc2aba574e4da97f9bf330792b5f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "075782102ee04e45ac26c0953b93dd10": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "093ca90351304fdc8d2445f60be4b830": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "0b94b659172c4c808567a150cf38153c": { + "0065750b88fd406396533e490ff9a0ca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -1731,14 +1665,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_a40a416e6a3b4891b9b8d98a976ad7ed", - "IPY_MODEL_631251cb36704a7e85d67bbb798aaeec", - "IPY_MODEL_1f21207692b5457abe47bd5134b48d96" + "IPY_MODEL_c3a4ce2dd9534c0dae13baffc4bd8242", + "IPY_MODEL_a6798e924bf145afa6fb5a4f09ee6212", + "IPY_MODEL_f01c428443be4e759a8006b89c1f9698" ], - "layout": "IPY_MODEL_681e924901d943949a488b29a505cff0" + "layout": "IPY_MODEL_016aa72c5a294f7d8d81a1b8202fd993" } }, - "0fbbf4fdb51b413294041b296f5b2a74": { + "016aa72c5a294f7d8d81a1b8202fd993": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1790,59 +1724,105 @@ "width": null } }, - "13ff262aa4b44a0e8255af196d81fd7d": { + "06bdb1c3f8ce403faee0d760e00db57e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_ad72a839c6e047a1827fda170388e992", - "placeholder": "​", - "style": "IPY_MODEL_d44a2401009343aeae443f41faabbafa", - "value": "tokenizer_config.json: 100%" + "layout": "IPY_MODEL_98d9d39b1f014399a851cb3246835792", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b73ee3bd3bc9482f9e9a6daf96958b25", + "value": 665.0 } }, - "156b2e945a6b44ddbdf2da4443a15de5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "0ec758d764544f0c95b7109cd53210d6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "1cd9e82053dd4f6e9275c8fa64b04d24": { + "116e4a0b97db4d0c884059f25c064c6a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6c4d8a52a4a0431d9bf8fa248904a023", + "IPY_MODEL_81190e134e0f4f15b3e4fd3bbe41be0c", + "IPY_MODEL_989066e3c5524adaa8b8a66f2f5ad9e5" + ], + "layout": "IPY_MODEL_3b8bd17f043a430fb0597488d2d6d5f2" } }, - "1e756c095e0949b1a3e12c3fd5657b28": { + "167dfa55e2104cc999179284d62f3179": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1894,70 +1874,46 @@ "width": null } }, - "1ec07e59e71d462894df6ddac86d79db": { + "17086bcb496d418a808e0606b0e2a048": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_56eb788d2ed34127ad6946586c6057e4", - "placeholder": "​", - "style": "IPY_MODEL_0753cc2aba574e4da97f9bf330792b5f", - "value": " 391/391 [00:00<00:00, 49.9kB/s]" - } - }, - "1f21207692b5457abe47bd5134b48d96": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e55f2cd15ee649ac846a97aaf09d6567", - "placeholder": "​", - "style": "IPY_MODEL_075782102ee04e45ac26c0953b93dd10", - "value": " 665/665 [00:00<00:00, 82.4kB/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "24800faa4aa049179ae0e1a942a1bdf6": { + "1be99f53fcc64c409edb1ddbd07de2b0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_f5e134f5de9e4373a4455cf6307bb806", - "placeholder": "​", - "style": "IPY_MODEL_7736ef16b7ec4b909c412b03cfe285dc", - "value": " 29.0/29.0 [00:00<00:00, 3.70kB/s]" + "layout": "IPY_MODEL_b1fbb37072854152a3a3b16caf0ab204", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ba078714339f4da593e623adf2222bd7", + "value": 2211.0 } }, - "27abf396b79145cd917bff0ccd201c7e": { + "1e09d7da26ff491d9d5a848cc7f15be9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2009,7 +1965,7 @@ "width": null } }, - "2a69838d719f4df6a92e28d9ece1f08d": { + "2367326c6c9a474ab889cf6d74faf9aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2045,195 +2001,23 @@ "left": null, "margin": null, "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "33db2753de344f9b9a86f2af38acd19e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_690fccf3b2e142c4aab722075bd4f649", - "placeholder": "​", - "style": "IPY_MODEL_5d8a1655567a40f081ab0f97f931d0fd", - "value": ".gitattributes: 100%" - } - }, - "37196e3822914cc9a99aadf1179c6517": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "3b4ab4c5daaf45d7b33c2799316d07cf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "3d414f4be0554dc48490d27044d55d7b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_98ec89e168f64dfbafb57d9076bd2975", - "IPY_MODEL_ecd151fdeecb4f5393b6dc51ca91a59e", - "IPY_MODEL_04bf7ee7a9c34eeeaa4d9b286f8d7f9d" - ], - "layout": "IPY_MODEL_7c80669ce4b44eb091bc16d3833d05ef" - } - }, - "45ac3af2e1ed4f4c8dfdabe525afe3d2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_33db2753de344f9b9a86f2af38acd19e", - "IPY_MODEL_4ca1fe21051a4502b3b9f11c97612ceb", - "IPY_MODEL_1ec07e59e71d462894df6ddac86d79db" - ], - "layout": "IPY_MODEL_648615497a3f4b90ad7c7b1c678d5f5f" - } - }, - "48cf8f69b83648bc957e73f69c88e66a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_abf34ac7904346f0af3ff73344ab92dd", - "IPY_MODEL_ab6d14ed062546a7bc3a37486ca8e7f2", - "IPY_MODEL_99287ca3d43a478fa3a68b0140e5b6cc" - ], - "layout": "IPY_MODEL_e88376b3ed14410a951ad244ba1c84a3" - } - }, - "4af4550bfcf047a5b3c8e1e3d3cd4b87": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "4afd2fbb4d864842b5974978b9c8d333": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4ca1fe21051a4502b3b9f11c97612ceb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_d4076baf5b434bb2adf7f2348373cb42", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_976b837ca52c4769859ed9e693f46300", - "value": 391.0 + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "553f932b2e7648b68c7c9aa3f3257737": { + "247e42b59dc0445aaaa5f7084dd20cf8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2285,23 +2069,44 @@ "width": null } }, - "55be5ce9aa9e4a548b0ad61273477bd1": { + "253f1e63067b4f78a688136462184c85": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4efdd1e0ae254291ba5a3223a84bc808", + "IPY_MODEL_5fd1f24864bd4064949a3e592679a012", + "IPY_MODEL_ccb22378b232470890186d60d7e8a7ea" + ], + "layout": "IPY_MODEL_9904071098db436e985611826d978697" + } + }, + "2e0c8591af124e1e862ae0bbee13ab5a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "56eb788d2ed34127ad6946586c6057e4": { + "30aa87d3494840c4aa464ef74d9e0db1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2353,22 +2158,74 @@ "width": null } }, - "5d8a1655567a40f081ab0f97f931d0fd": { + "3164b830fbfe43a3b05f9dcada4619b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3dcbe3b63dd44a6ca4dc5a4e53ac44d9", + "max": 1.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e4ce483df8b44f459c167932952d75d6", + "value": 0.0 + } + }, + "34de74288e484d6392837444b0960b3b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8ab3108545484488b542fe77f6de60f6", + "placeholder": "​", + "style": "IPY_MODEL_3c9c5368618d46b89d1f97ee8461b90b", + "value": "config.json: 100%" + } + }, + "3b25cf0897af4c389e5bb84dce0e453a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_932c39ca775540ec91faed2d09952439", + "IPY_MODEL_ff6d854ab28e41d9b10ad56ffdd1d632", + "IPY_MODEL_41d630690c6c48ef936615876fa0652c" + ], + "layout": "IPY_MODEL_30aa87d3494840c4aa464ef74d9e0db1" } }, - "601383fd35904b3d948030127529c5b7": { + "3b8bd17f043a430fb0597488d2d6d5f2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2420,52 +2277,37 @@ "width": null } }, - "62c627b523ce4d0680d7ef46df4046b2": { + "3c9c5368618d46b89d1f97ee8461b90b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6eb61a2631024a97b38409576940d076", - "placeholder": "​", - "style": "IPY_MODEL_6f7185636dfd445bbcfa590f6f91decd", - "value": " 2.21k/2.21k [00:00<00:00, 288kB/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "631251cb36704a7e85d67bbb798aaeec": { + "3d7b642ec81c487dab799950929baded": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_1e756c095e0949b1a3e12c3fd5657b28", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4afd2fbb4d864842b5974978b9c8d333", - "value": 665.0 + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "648615497a3f4b90ad7c7b1c678d5f5f": { + "3dcbe3b63dd44a6ca4dc5a4e53ac44d9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2514,10 +2356,25 @@ "right": null, "top": null, "visibility": null, - "width": null + "width": "20px" + } + }, + "3dd0e3791e974382a9f78275796c9be1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "681e924901d943949a488b29a505cff0": { + "3e17f05dde9d401b9fbb2d95c15d1fba": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2569,7 +2426,49 @@ "width": null } }, - "68aac3d30f1c485fb01431c3acce067f": { + "41a6a383eaa443cba8c9a8e78ec48ecd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_95b27ef1300348efbc7ad67edd3d9b7a", + "placeholder": "​", + "style": "IPY_MODEL_dae97db7d3c64df7896a80c486b3450f", + "value": " 2.21k/2.21k [00:00<00:00, 299kB/s]" + } + }, + "41d630690c6c48ef936615876fa0652c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_57d3bf10275b4091992aa1a8a28265d8", + "placeholder": "​", + "style": "IPY_MODEL_6504d792bf274abaadea897aedb15878", + "value": " 466k/466k [00:00<00:00, 33.2MB/s]" + } + }, + "431d2da6f0804f3790d3cc8f87036067": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2621,7 +2520,100 @@ "width": null } }, - "690fccf3b2e142c4aab722075bd4f649": { + "43c253f77bd74d59bb9b5eb8c0dad2f8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "46c6854e27ef4c689e054b875c34f02d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3e17f05dde9d401b9fbb2d95c15d1fba", + "placeholder": "​", + "style": "IPY_MODEL_826c4413b0c040c9b8a8c022223e7ab0", + "value": " 0/0 [00:00<?, ?it/s]" + } + }, + "494ad30da7f845f995cfe5a7b7812ca2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6d6c91a6a97947bca8cef6f298aee849", + "placeholder": "​", + "style": "IPY_MODEL_3d7b642ec81c487dab799950929baded", + "value": ".gitattributes: 100%" + } + }, + "49def5d883e3468bb75ff61ae24e8374": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4efdd1e0ae254291ba5a3223a84bc808": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0ec758d764544f0c95b7109cd53210d6", + "placeholder": "​", + "style": "IPY_MODEL_2e0c8591af124e1e862ae0bbee13ab5a", + "value": "vocab.txt: 100%" + } + }, + "51b14007f9df43c2ae6ae64965212985": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2673,7 +2665,38 @@ "width": null } }, - "6950f4f6ccd84fec8817d0612d30f52c": { + "5521b2020c564b9892aa36630243ff48": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "55a8e271f7084ea88ce61eb6cea457ad": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "579ddcecd4ad41ff943769c38a61dcc1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2725,29 +2748,7 @@ "width": null } }, - "696fed9227a24944a318a7e13ec558c1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_13ff262aa4b44a0e8255af196d81fd7d", - "IPY_MODEL_b99461c80ba342a19bb61225b5a1c6ab", - "IPY_MODEL_24800faa4aa049179ae0e1a942a1bdf6" - ], - "layout": "IPY_MODEL_2a69838d719f4df6a92e28d9ece1f08d" - } - }, - "6cdbd6e2878d4ad0a30ac4e4f00206cb": { + "57d3bf10275b4091992aa1a8a28265d8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2796,26 +2797,10 @@ "right": null, "top": null, "visibility": null, - "width": "20px" - } - }, - "6df9e8714cf44f9cb0c6adc1e2801a7d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "width": null } }, - "6eb61a2631024a97b38409576940d076": { + "5da50ac709c04ceebc042c02d11780bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2867,7 +2852,31 @@ "width": null } }, - "6f7185636dfd445bbcfa590f6f91decd": { + "5fd1f24864bd4064949a3e592679a012": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a59b8ca3d88645ca9287af80e876815b", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6985d8eda2514934b005c470e8949eb2", + "value": 231508.0 + } + }, + "6504d792bf274abaadea897aedb15878": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -2882,74 +2891,59 @@ "description_width": "" } }, - "705ab5d8864749feba2e94565f234f24": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "653bb2ad40a74c2392bb1d2735dea51e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" } }, - "7736ef16b7ec4b909c412b03cfe285dc": { + "6985d8eda2514934b005c470e8949eb2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "7a8a403f88844204b88b6b499f25620b": { + "6a18b3dd9d7049169469a3a4798f15b9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c2f3f723831e4429ba055d875902387a", + "placeholder": "​", + "style": "IPY_MODEL_c8a57340288d4909ade8e4646100002d", + "value": "" + } + }, + "6baeb4add86f4ccea1fa84f9697170d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3001,7 +2995,28 @@ "width": null } }, - "7c80669ce4b44eb091bc16d3833d05ef": { + "6c4d8a52a4a0431d9bf8fa248904a023": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9174cdb1a46c4b888353513a0a8b4639", + "placeholder": "​", + "style": "IPY_MODEL_7e47396caa2d4e8ba0db916be172b240", + "value": "tokenizer_config.json: 100%" + } + }, + "6cad7a8d33ba491babcc8162595fad2a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3053,7 +3068,7 @@ "width": null } }, - "7da1806fce7e4909bc926a3bb7935ad7": { + "6d6c91a6a97947bca8cef6f298aee849": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3105,28 +3120,29 @@ "width": null } }, - "8717845bbc894b72935b209a75bb2f16": { + "735576d8959e46f3826a38708cf752de": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e5067484ef674c0380e65aaa243e5a78", - "placeholder": "​", - "style": "IPY_MODEL_d5bd9e0905d947c6b4a03b3be26daf04", - "value": "pytorch_model.bin: 100%" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6a18b3dd9d7049169469a3a4798f15b9", + "IPY_MODEL_3164b830fbfe43a3b05f9dcada4619b7", + "IPY_MODEL_46c6854e27ef4c689e054b875c34f02d" + ], + "layout": "IPY_MODEL_579ddcecd4ad41ff943769c38a61dcc1" } }, - "8e2803fbb174478d972207388a2cb9ff": { + "741743a689ed408fabb5c8b7ae249ad3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3141,44 +3157,61 @@ "description_width": "" } }, - "90d2e4a16ad541c380a1dff4cfde1f90": { + "7e47396caa2d4e8ba0db916be172b240": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "9407545d118c45a89c4d6605a80d0fff": { + "81190e134e0f4f15b3e4fd3bbe41be0c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c3b4b53286594ff5bb5a5fd3d6d3eef3", - "placeholder": "​", - "style": "IPY_MODEL_093ca90351304fdc8d2445f60be4b830", - "value": " 0/0 [00:00<?, ?it/s]" + "layout": "IPY_MODEL_431d2da6f0804f3790d3cc8f87036067", + "max": 29.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_971a763adca04a4898d2ea87d1e3b531", + "value": 29.0 + } + }, + "826c4413b0c040c9b8a8c022223e7ab0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "976b837ca52c4769859ed9e693f46300": { + "890e919775c548d29334fc80abf8dd41": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -3194,7 +3227,59 @@ "description_width": "" } }, - "97a28fb79228459592c99ca4714195d8": { + "8ab3108545484488b542fe77f6de60f6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9174cdb1a46c4b888353513a0a8b4639": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3246,73 +3331,7 @@ "width": null } }, - "982f196d95cf48388e934100fe105449": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6cdbd6e2878d4ad0a30ac4e4f00206cb", - "max": 1.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_156b2e945a6b44ddbdf2da4443a15de5", - "value": 0.0 - } - }, - "98ec89e168f64dfbafb57d9076bd2975": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_601383fd35904b3d948030127529c5b7", - "placeholder": "​", - "style": "IPY_MODEL_37196e3822914cc9a99aadf1179c6517", - "value": "tokenizer.json: 100%" - } - }, - "99287ca3d43a478fa3a68b0140e5b6cc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6950f4f6ccd84fec8817d0612d30f52c", - "placeholder": "​", - "style": "IPY_MODEL_a4b986613dd3460fb27e399d856b5baf", - "value": " 232k/232k [00:00<00:00, 26.4MB/s]" - } - }, - "9b6a79f738174dc4a1d6e6c7665fb9c5": { + "92cc65daed6c4dd6a66605efa7d7c4f8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3364,38 +3383,7 @@ "width": null } }, - "9b70999254274e98a23fd7d37ed2000f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "a30da11f6f264d78801d7be28ba245f6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "a40a416e6a3b4891b9b8d98a976ad7ed": { + "932c39ca775540ec91faed2d09952439": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3410,28 +3398,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_eaf16cd449ad4d9ba049f1736192243c", + "layout": "IPY_MODEL_1e09d7da26ff491d9d5a848cc7f15be9", "placeholder": "​", - "style": "IPY_MODEL_3b4ab4c5daaf45d7b33c2799316d07cf", - "value": "config.json: 100%" - } - }, - "a4b986613dd3460fb27e399d856b5baf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "style": "IPY_MODEL_741743a689ed408fabb5c8b7ae249ad3", + "value": "tokenizer.json: 100%" } }, - "a92177c88acf40c5ababe4a7a348a09e": { + "95b27ef1300348efbc7ad67edd3d9b7a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3483,52 +3456,7 @@ "width": null } }, - "ab6d14ed062546a7bc3a37486ca8e7f2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_cc2919ce84274a69aae9ff4e90a80906", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_55be5ce9aa9e4a548b0ad61273477bd1", - "value": 231508.0 - } - }, - "abf34ac7904346f0af3ff73344ab92dd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_0fbbf4fdb51b413294041b296f5b2a74", - "placeholder": "​", - "style": "IPY_MODEL_e797f27a584b45b88287c7dfb530657c", - "value": "vocab.txt: 100%" - } - }, - "ad72a839c6e047a1827fda170388e992": { + "9694ffca6f9a4f48be09b09321d00477": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3580,7 +3508,7 @@ "width": null } }, - "b31ba758e76b4e649b3bc97ebfcfb764": { + "971a763adca04a4898d2ea87d1e3b531": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -3596,7 +3524,28 @@ "description_width": "" } }, - "b773da097d5d41d2bd390767173f453b": { + "989066e3c5524adaa8b8a66f2f5ad9e5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a369cc74356749659c4fdffbd4415a89", + "placeholder": "​", + "style": "IPY_MODEL_5521b2020c564b9892aa36630243ff48", + "value": " 29.0/29.0 [00:00<00:00, 3.64kB/s]" + } + }, + "98d9d39b1f014399a851cb3246835792": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3648,31 +3597,7 @@ "width": null } }, - "b99461c80ba342a19bb61225b5a1c6ab": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_cb539705caae4b2888bf430cbdae00f9", - "max": 29.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9b70999254274e98a23fd7d37ed2000f", - "value": 29.0 - } - }, - "c3b4b53286594ff5bb5a5fd3d6d3eef3": { + "9904071098db436e985611826d978697": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3724,50 +3649,7 @@ "width": null } }, - "c44c22e3abfa4e00b6be10158490dcc5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_553f932b2e7648b68c7c9aa3f3257737", - "placeholder": "​", - "style": "IPY_MODEL_1cd9e82053dd4f6e9275c8fa64b04d24", - "value": "" - } - }, - "cac01943eb874158907dd23885f1ee1a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8717845bbc894b72935b209a75bb2f16", - "IPY_MODEL_d26bd1de66324c479d2abcd64629946d", - "IPY_MODEL_f5732d71a1fa4ad285feb8ad5d719a36" - ], - "layout": "IPY_MODEL_705ab5d8864749feba2e94565f234f24" - } - }, - "cb539705caae4b2888bf430cbdae00f9": { + "a369cc74356749659c4fdffbd4415a89": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3819,7 +3701,7 @@ "width": null } }, - "cc2919ce84274a69aae9ff4e90a80906": { + "a59b8ca3d88645ca9287af80e876815b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3861,39 +3743,17 @@ "object_fit": null, "object_position": null, "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "cf63fee22bbf401492c9f2f6f74d206a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c44c22e3abfa4e00b6be10158490dcc5", - "IPY_MODEL_982f196d95cf48388e934100fe105449", - "IPY_MODEL_9407545d118c45a89c4d6605a80d0fff" - ], - "layout": "IPY_MODEL_b773da097d5d41d2bd390767173f453b" + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "d26bd1de66324c479d2abcd64629946d": { + "a6798e924bf145afa6fb5a4f09ee6212": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -3909,39 +3769,37 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_a92177c88acf40c5ababe4a7a348a09e", + "layout": "IPY_MODEL_6baeb4add86f4ccea1fa84f9697170d3", "max": 54245363.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_90d2e4a16ad541c380a1dff4cfde1f90", + "style": "IPY_MODEL_890e919775c548d29334fc80abf8dd41", "value": 54245363.0 } }, - "d2af69c7dfa5442bae97f456950de975": { + "aebe7c43a94b411b875f55c85e5bbf99": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_68aac3d30f1c485fb01431c3acce067f", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6df9e8714cf44f9cb0c6adc1e2801a7d", - "value": 2211.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e0f7b67a7ef14e7f81bcde4581d41777", + "IPY_MODEL_1be99f53fcc64c409edb1ddbd07de2b0", + "IPY_MODEL_41a6a383eaa443cba8c9a8e78ec48ecd" + ], + "layout": "IPY_MODEL_fe5812eb7c1945458f0517ab7e8b8982" } }, - "d4076baf5b434bb2adf7f2348373cb42": { + "b1fbb37072854152a3a3b16caf0ab204": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3993,37 +3851,39 @@ "width": null } }, - "d44a2401009343aeae443f41faabbafa": { + "b73ee3bd3bc9482f9e9a6daf96958b25": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "d5bd9e0905d947c6b4a03b3be26daf04": { + "ba078714339f4da593e623adf2222bd7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "e185e5cf5c0548fbbc30b1069d606797": { + "ba2d5d53d9a94b1896142b5a30bbd514": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -4038,14 +3898,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_eebfeb338a314755a90465c813e21dc1", - "IPY_MODEL_d2af69c7dfa5442bae97f456950de975", - "IPY_MODEL_62c627b523ce4d0680d7ef46df4046b2" + "IPY_MODEL_34de74288e484d6392837444b0960b3b", + "IPY_MODEL_06bdb1c3f8ce403faee0d760e00db57e", + "IPY_MODEL_c8688c529ee943f0a17f150a07147e22" ], - "layout": "IPY_MODEL_7a8a403f88844204b88b6b499f25620b" + "layout": "IPY_MODEL_92cc65daed6c4dd6a66605efa7d7c4f8" } }, - "e5067484ef674c0380e65aaa243e5a78": { + "c2eb7e0981334b8d9993c8d3ef5a5d69": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4097,7 +3957,7 @@ "width": null } }, - "e55f2cd15ee649ac846a97aaf09d6567": { + "c2f3f723831e4429ba055d875902387a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4149,7 +4009,49 @@ "width": null } }, - "e797f27a584b45b88287c7dfb530657c": { + "c3a4ce2dd9534c0dae13baffc4bd8242": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_51b14007f9df43c2ae6ae64965212985", + "placeholder": "​", + "style": "IPY_MODEL_3dd0e3791e974382a9f78275796c9be1", + "value": "pytorch_model.bin: 100%" + } + }, + "c8688c529ee943f0a17f150a07147e22": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_cbef96f2bab2484681f3c8921fa2c6ec", + "placeholder": "​", + "style": "IPY_MODEL_ea74d452fbb34576ab157f19520a727a", + "value": " 665/665 [00:00<00:00, 84.2kB/s]" + } + }, + "c8a57340288d4909ade8e4646100002d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4164,7 +4066,7 @@ "description_width": "" } }, - "e88376b3ed14410a951ad244ba1c84a3": { + "cbef96f2bab2484681f3c8921fa2c6ec": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4216,83 +4118,80 @@ "width": null } }, - "eaf16cd449ad4d9ba049f1736192243c": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "ccb22378b232470890186d60d7e8a7ea": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_247e42b59dc0445aaaa5f7084dd20cf8", + "placeholder": "​", + "style": "IPY_MODEL_653bb2ad40a74c2392bb1d2735dea51e", + "value": " 232k/232k [00:00<00:00, 27.0MB/s]" + } + }, + "dae97db7d3c64df7896a80c486b3450f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" } }, - "ecd151fdeecb4f5393b6dc51ca91a59e": { + "e0f7b67a7ef14e7f81bcde4581d41777": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_7da1806fce7e4909bc926a3bb7935ad7", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b31ba758e76b4e649b3bc97ebfcfb764", - "value": 466062.0 + "layout": "IPY_MODEL_167dfa55e2104cc999179284d62f3179", + "placeholder": "​", + "style": "IPY_MODEL_49def5d883e3468bb75ff61ae24e8374", + "value": "README.md: 100%" + } + }, + "e4ce483df8b44f459c167932952d75d6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "eebfeb338a314755a90465c813e21dc1": { + "e98bd145421044fba60d1778da8b0bc6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4307,13 +4206,74 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_9b6a79f738174dc4a1d6e6c7665fb9c5", + "layout": "IPY_MODEL_5da50ac709c04ceebc042c02d11780bb", "placeholder": "​", - "style": "IPY_MODEL_a30da11f6f264d78801d7be28ba245f6", - "value": "README.md: 100%" + "style": "IPY_MODEL_43c253f77bd74d59bb9b5eb8c0dad2f8", + "value": " 391/391 [00:00<00:00, 44.5kB/s]" + } + }, + "ea74d452fbb34576ab157f19520a727a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "eae3f2bc15824aa1945e4a9709a7cb7c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_494ad30da7f845f995cfe5a7b7812ca2", + "IPY_MODEL_efd392f0de6b42a3b5b343acd7c8211a", + "IPY_MODEL_e98bd145421044fba60d1778da8b0bc6" + ], + "layout": "IPY_MODEL_6cad7a8d33ba491babcc8162595fad2a" + } + }, + "efd392f0de6b42a3b5b343acd7c8211a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9694ffca6f9a4f48be09b09321d00477", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f18d2ca563c44c24a00e3946ef1e6c94", + "value": 391.0 } }, - "f5732d71a1fa4ad285feb8ad5d719a36": { + "f01c428443be4e759a8006b89c1f9698": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4328,13 +4288,29 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_97a28fb79228459592c99ca4714195d8", + "layout": "IPY_MODEL_2367326c6c9a474ab889cf6d74faf9aa", "placeholder": "​", - "style": "IPY_MODEL_8e2803fbb174478d972207388a2cb9ff", - "value": " 54.2M/54.2M [00:00<00:00, 250MB/s]" + "style": "IPY_MODEL_17086bcb496d418a808e0606b0e2a048", + "value": " 54.2M/54.2M [00:01<00:00, 41.9MB/s]" + } + }, + "f18d2ca563c44c24a00e3946ef1e6c94": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f5e134f5de9e4373a4455cf6307bb806": { + "fe5812eb7c1945458f0517ab7e8b8982": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4385,6 +4361,30 @@ "visibility": null, "width": null } + }, + "ff6d854ab28e41d9b10ad56ffdd1d632": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c2eb7e0981334b8d9993c8d3ef5a5d69", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_55a8e271f7084ea88ce61eb6cea457ad", + "value": 466062.0 + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index dee038810..b276fa39c 100644 --- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb @@ -68,10 +68,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:19.808883Z", - "iopub.status.busy": "2024-01-17T18:05:19.808324Z", - "iopub.status.idle": "2024-01-17T18:05:20.839884Z", - "shell.execute_reply": "2024-01-17T18:05:20.839261Z" + "iopub.execute_input": "2024-01-17T23:07:27.832889Z", + "iopub.status.busy": "2024-01-17T23:07:27.832698Z", + "iopub.status.idle": "2024-01-17T23:07:28.833865Z", + "shell.execute_reply": "2024-01-17T23:07:28.833240Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -108,10 +108,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:20.842981Z", - "iopub.status.busy": "2024-01-17T18:05:20.842479Z", - "iopub.status.idle": "2024-01-17T18:05:20.845564Z", - "shell.execute_reply": "2024-01-17T18:05:20.844934Z" + "iopub.execute_input": "2024-01-17T23:07:28.836757Z", + "iopub.status.busy": "2024-01-17T23:07:28.836298Z", + "iopub.status.idle": "2024-01-17T23:07:28.839253Z", + "shell.execute_reply": "2024-01-17T23:07:28.838765Z" }, "id": "_UvI80l42iyi" }, @@ -201,10 +201,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:20.848194Z", - "iopub.status.busy": "2024-01-17T18:05:20.847848Z", - "iopub.status.idle": "2024-01-17T18:05:20.860567Z", - "shell.execute_reply": "2024-01-17T18:05:20.860055Z" + "iopub.execute_input": "2024-01-17T23:07:28.841805Z", + "iopub.status.busy": "2024-01-17T23:07:28.841328Z", + "iopub.status.idle": "2024-01-17T23:07:28.854079Z", + "shell.execute_reply": "2024-01-17T23:07:28.853457Z" }, "nbsphinx": "hidden" }, @@ -283,10 +283,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:20.862951Z", - "iopub.status.busy": "2024-01-17T18:05:20.862575Z", - "iopub.status.idle": "2024-01-17T18:05:25.011623Z", - "shell.execute_reply": "2024-01-17T18:05:25.011065Z" + "iopub.execute_input": "2024-01-17T23:07:28.856642Z", + "iopub.status.busy": "2024-01-17T23:07:28.856314Z", + "iopub.status.idle": "2024-01-17T23:07:31.662775Z", + "shell.execute_reply": "2024-01-17T23:07:31.662089Z" }, "id": "dhTHOg8Pyv5G" }, @@ -297,6 +297,9 @@ "text": [ "\n", "🎯 Caltech256 🎯\n", + "\n", + "\n", + "Loaded the 'caltech256' dataset with predicted probabilities of shape (29780, 256)\n", "\n" ] }, @@ -304,9 +307,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "Loaded the 'caltech256' dataset with predicted probabilities of shape (29780, 256)\n", - "\n", "-------------------------------------------------------------\n", "| Generating a Cleanlab Dataset Health Summary |\n", "| for your dataset with 29,780 examples and 256 classes. |\n", @@ -692,13 +692,7 @@ "\n", "\n", "🎯 Mnist_test_set 🎯\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "\n", "Loaded the 'mnist_test_set' dataset with predicted probabilities of shape (10000, 10)\n", "\n", @@ -2182,13 +2176,7 @@ "\n", "\n", "🎯 Cifar100_test_set 🎯\n", - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "\n", "Loaded the 'cifar100_test_set' dataset with predicted probabilities of shape (10000, 100)\n", "\n", diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index 24103a62b..c60bdad38 100644 --- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:29.575294Z", - "iopub.status.busy": "2024-01-17T18:05:29.575098Z", - "iopub.status.idle": "2024-01-17T18:05:30.601638Z", - "shell.execute_reply": "2024-01-17T18:05:30.600990Z" + "iopub.execute_input": "2024-01-17T23:07:36.495882Z", + "iopub.status.busy": "2024-01-17T23:07:36.495688Z", + "iopub.status.idle": "2024-01-17T23:07:37.512326Z", + "shell.execute_reply": "2024-01-17T23:07:37.511707Z" }, "nbsphinx": "hidden" }, @@ -97,10 +97,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:30.604812Z", - "iopub.status.busy": "2024-01-17T18:05:30.604317Z", - "iopub.status.idle": "2024-01-17T18:05:30.607977Z", - "shell.execute_reply": "2024-01-17T18:05:30.607450Z" + "iopub.execute_input": "2024-01-17T23:07:37.515517Z", + "iopub.status.busy": "2024-01-17T23:07:37.514954Z", + "iopub.status.idle": "2024-01-17T23:07:37.518606Z", + "shell.execute_reply": "2024-01-17T23:07:37.517983Z" } }, "outputs": [], @@ -136,10 +136,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:30.610519Z", - "iopub.status.busy": "2024-01-17T18:05:30.610067Z", - "iopub.status.idle": "2024-01-17T18:05:32.632789Z", - "shell.execute_reply": "2024-01-17T18:05:32.632102Z" + "iopub.execute_input": "2024-01-17T23:07:37.521046Z", + "iopub.status.busy": "2024-01-17T23:07:37.520609Z", + "iopub.status.idle": "2024-01-17T23:07:39.512127Z", + "shell.execute_reply": "2024-01-17T23:07:39.511435Z" } }, "outputs": [], @@ -162,10 +162,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.636274Z", - "iopub.status.busy": "2024-01-17T18:05:32.635511Z", - "iopub.status.idle": "2024-01-17T18:05:32.677507Z", - "shell.execute_reply": "2024-01-17T18:05:32.676720Z" + "iopub.execute_input": "2024-01-17T23:07:39.515319Z", + "iopub.status.busy": "2024-01-17T23:07:39.514761Z", + "iopub.status.idle": "2024-01-17T23:07:39.553653Z", + "shell.execute_reply": "2024-01-17T23:07:39.552872Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.680506Z", - "iopub.status.busy": "2024-01-17T18:05:32.680097Z", - "iopub.status.idle": "2024-01-17T18:05:32.719749Z", - "shell.execute_reply": "2024-01-17T18:05:32.719066Z" + "iopub.execute_input": "2024-01-17T23:07:39.556731Z", + "iopub.status.busy": "2024-01-17T23:07:39.556457Z", + "iopub.status.idle": "2024-01-17T23:07:39.590911Z", + "shell.execute_reply": "2024-01-17T23:07:39.590128Z" } }, "outputs": [], @@ -213,10 +213,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.722770Z", - "iopub.status.busy": "2024-01-17T18:05:32.722366Z", - "iopub.status.idle": "2024-01-17T18:05:32.725463Z", - "shell.execute_reply": "2024-01-17T18:05:32.724891Z" + "iopub.execute_input": "2024-01-17T23:07:39.593846Z", + "iopub.status.busy": "2024-01-17T23:07:39.593576Z", + "iopub.status.idle": "2024-01-17T23:07:39.596849Z", + "shell.execute_reply": "2024-01-17T23:07:39.596247Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.727932Z", - "iopub.status.busy": "2024-01-17T18:05:32.727487Z", - "iopub.status.idle": "2024-01-17T18:05:32.730280Z", - "shell.execute_reply": "2024-01-17T18:05:32.729759Z" + "iopub.execute_input": "2024-01-17T23:07:39.599261Z", + "iopub.status.busy": "2024-01-17T23:07:39.598893Z", + "iopub.status.idle": "2024-01-17T23:07:39.601723Z", + "shell.execute_reply": "2024-01-17T23:07:39.601200Z" } }, "outputs": [], @@ -298,10 +298,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.732729Z", - "iopub.status.busy": "2024-01-17T18:05:32.732328Z", - "iopub.status.idle": "2024-01-17T18:05:32.759985Z", - "shell.execute_reply": "2024-01-17T18:05:32.759356Z" + "iopub.execute_input": "2024-01-17T23:07:39.604155Z", + "iopub.status.busy": "2024-01-17T23:07:39.603812Z", + "iopub.status.idle": "2024-01-17T23:07:39.631912Z", + "shell.execute_reply": "2024-01-17T23:07:39.631291Z" } }, "outputs": [ @@ -315,7 +315,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2907ab9e816444c921cba3edd090d1c", + "model_id": "c8d58b7026a04e969e05f5cbd2b99e14", "version_major": 2, "version_minor": 0 }, @@ -329,7 +329,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e1401ba3b9414469b83ae25c9cddabf3", + "model_id": "8fc0f21110364ef8b4a28d24e2bd55e7", "version_major": 2, "version_minor": 0 }, @@ -387,10 +387,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.767178Z", - "iopub.status.busy": "2024-01-17T18:05:32.766941Z", - "iopub.status.idle": "2024-01-17T18:05:32.774377Z", - "shell.execute_reply": "2024-01-17T18:05:32.773883Z" + "iopub.execute_input": "2024-01-17T23:07:39.638587Z", + "iopub.status.busy": "2024-01-17T23:07:39.638170Z", + "iopub.status.idle": "2024-01-17T23:07:39.644966Z", + "shell.execute_reply": "2024-01-17T23:07:39.644435Z" }, "nbsphinx": "hidden" }, @@ -421,10 +421,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.776777Z", - "iopub.status.busy": "2024-01-17T18:05:32.776413Z", - "iopub.status.idle": "2024-01-17T18:05:32.780193Z", - "shell.execute_reply": "2024-01-17T18:05:32.779640Z" + "iopub.execute_input": "2024-01-17T23:07:39.647202Z", + "iopub.status.busy": "2024-01-17T23:07:39.646994Z", + "iopub.status.idle": "2024-01-17T23:07:39.650818Z", + "shell.execute_reply": "2024-01-17T23:07:39.650290Z" }, "nbsphinx": "hidden" }, @@ -447,10 +447,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.782656Z", - "iopub.status.busy": "2024-01-17T18:05:32.782291Z", - "iopub.status.idle": "2024-01-17T18:05:32.789381Z", - "shell.execute_reply": "2024-01-17T18:05:32.788810Z" + "iopub.execute_input": "2024-01-17T23:07:39.653116Z", + "iopub.status.busy": "2024-01-17T23:07:39.652913Z", + "iopub.status.idle": "2024-01-17T23:07:39.659830Z", + "shell.execute_reply": "2024-01-17T23:07:39.659314Z" } }, "outputs": [], @@ -500,10 +500,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.791749Z", - "iopub.status.busy": "2024-01-17T18:05:32.791389Z", - "iopub.status.idle": "2024-01-17T18:05:32.834263Z", - "shell.execute_reply": "2024-01-17T18:05:32.833469Z" + "iopub.execute_input": "2024-01-17T23:07:39.662042Z", + "iopub.status.busy": "2024-01-17T23:07:39.661827Z", + "iopub.status.idle": "2024-01-17T23:07:39.700248Z", + "shell.execute_reply": "2024-01-17T23:07:39.699558Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.837464Z", - "iopub.status.busy": "2024-01-17T18:05:32.836999Z", - "iopub.status.idle": "2024-01-17T18:05:32.879044Z", - "shell.execute_reply": "2024-01-17T18:05:32.878377Z" + "iopub.execute_input": "2024-01-17T23:07:39.703097Z", + "iopub.status.busy": "2024-01-17T23:07:39.702829Z", + "iopub.status.idle": "2024-01-17T23:07:39.740445Z", + "shell.execute_reply": "2024-01-17T23:07:39.739778Z" }, "nbsphinx": "hidden" }, @@ -602,10 +602,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.882160Z", - "iopub.status.busy": "2024-01-17T18:05:32.881824Z", - "iopub.status.idle": "2024-01-17T18:05:33.000399Z", - "shell.execute_reply": "2024-01-17T18:05:32.999631Z" + "iopub.execute_input": "2024-01-17T23:07:39.743730Z", + "iopub.status.busy": "2024-01-17T23:07:39.743262Z", + "iopub.status.idle": "2024-01-17T23:07:39.857929Z", + "shell.execute_reply": "2024-01-17T23:07:39.857221Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:33.003322Z", - "iopub.status.busy": "2024-01-17T18:05:33.003098Z", - "iopub.status.idle": "2024-01-17T18:05:35.514808Z", - "shell.execute_reply": "2024-01-17T18:05:35.514063Z" + "iopub.execute_input": "2024-01-17T23:07:39.860566Z", + "iopub.status.busy": "2024-01-17T23:07:39.860345Z", + "iopub.status.idle": "2024-01-17T23:07:42.349955Z", + "shell.execute_reply": "2024-01-17T23:07:42.349281Z" } }, "outputs": [ @@ -761,10 +761,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.517610Z", - "iopub.status.busy": "2024-01-17T18:05:35.517166Z", - "iopub.status.idle": "2024-01-17T18:05:35.576901Z", - "shell.execute_reply": "2024-01-17T18:05:35.576188Z" + "iopub.execute_input": "2024-01-17T23:07:42.352843Z", + "iopub.status.busy": "2024-01-17T23:07:42.352472Z", + "iopub.status.idle": "2024-01-17T23:07:42.410242Z", + "shell.execute_reply": "2024-01-17T23:07:42.409702Z" } }, "outputs": [ @@ -802,7 +802,7 @@ }, { "cell_type": "markdown", - "id": "37949d7a", + "id": "78363458", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -810,7 +810,7 @@ }, { "cell_type": "markdown", - "id": "dfe41b86", + "id": "d2a5e8b7", "metadata": {}, "source": [ "When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n", @@ -823,13 +823,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "17100cb9", + "id": "c950fb91", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.579654Z", - "iopub.status.busy": "2024-01-17T18:05:35.579165Z", - "iopub.status.idle": "2024-01-17T18:05:35.693837Z", - "shell.execute_reply": "2024-01-17T18:05:35.692940Z" + "iopub.execute_input": "2024-01-17T23:07:42.412848Z", + "iopub.status.busy": "2024-01-17T23:07:42.412446Z", + "iopub.status.idle": "2024-01-17T23:07:42.526424Z", + "shell.execute_reply": "2024-01-17T23:07:42.525736Z" } }, "outputs": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "a03bf6f2", + "id": "8ab3357a", "metadata": {}, "source": [ "For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:" @@ -879,13 +879,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "6ef2cce4", + "id": "1c9ad48b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.697709Z", - "iopub.status.busy": "2024-01-17T18:05:35.696542Z", - "iopub.status.idle": "2024-01-17T18:05:35.771866Z", - "shell.execute_reply": "2024-01-17T18:05:35.771185Z" + "iopub.execute_input": "2024-01-17T23:07:42.530165Z", + "iopub.status.busy": "2024-01-17T23:07:42.529379Z", + "iopub.status.idle": "2024-01-17T23:07:42.607790Z", + "shell.execute_reply": "2024-01-17T23:07:42.607189Z" } }, "outputs": [ @@ -921,7 +921,7 @@ }, { "cell_type": "markdown", - "id": "8ca4b358", + "id": "4fc657f4", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by cleanlab?\n", @@ -932,13 +932,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "66ce860e", + "id": "8fa90df4", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.774640Z", - "iopub.status.busy": "2024-01-17T18:05:35.774188Z", - "iopub.status.idle": "2024-01-17T18:05:35.782861Z", - "shell.execute_reply": "2024-01-17T18:05:35.782309Z" + "iopub.execute_input": "2024-01-17T23:07:42.610437Z", + "iopub.status.busy": "2024-01-17T23:07:42.610059Z", + "iopub.status.idle": "2024-01-17T23:07:42.618357Z", + "shell.execute_reply": "2024-01-17T23:07:42.617793Z" } }, "outputs": [], @@ -1040,7 +1040,7 @@ }, { "cell_type": "markdown", - "id": "5f1c9f23", + "id": "cf99e781", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1055,13 +1055,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "7dce32d6", + "id": "98118892", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.785295Z", - "iopub.status.busy": "2024-01-17T18:05:35.784899Z", - "iopub.status.idle": "2024-01-17T18:05:35.805010Z", - "shell.execute_reply": "2024-01-17T18:05:35.804458Z" + "iopub.execute_input": "2024-01-17T23:07:42.620700Z", + "iopub.status.busy": "2024-01-17T23:07:42.620256Z", + "iopub.status.idle": "2024-01-17T23:07:42.638982Z", + "shell.execute_reply": "2024-01-17T23:07:42.638447Z" } }, "outputs": [ @@ -1104,13 +1104,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "47046a1c", + "id": "e6faf2ef", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.807439Z", - "iopub.status.busy": "2024-01-17T18:05:35.807048Z", - "iopub.status.idle": "2024-01-17T18:05:35.811530Z", - "shell.execute_reply": "2024-01-17T18:05:35.811000Z" + "iopub.execute_input": "2024-01-17T23:07:42.641306Z", + "iopub.status.busy": "2024-01-17T23:07:42.640932Z", + "iopub.status.idle": "2024-01-17T23:07:42.645229Z", + "shell.execute_reply": "2024-01-17T23:07:42.644699Z" } }, "outputs": [ @@ -1205,38 +1205,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "2effe8a32eaa4e92abaa5cfd66d81dcb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "36e767e7e95d43ffb851228c4f91edbe": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4af3a63525c440a4abc52d769e9017dd": { + "01401acd053d494aa90753fa1b8c29ba": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1288,37 +1257,68 @@ "width": null } }, - "522f0096addf4e0cbafbc1d1348408a1": { + "07a3f5a6aaad401a972197c77e87bf79": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "5e22bd91d1694cfd8c3fe6d8d8c39dff": { + "0a9f4395be2b49e484d8a8639bea82b5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9cc15f84dcd54b2ca4ee56b56c2a8d26", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_50bbf71c81b94d23aeb3814fd48c052f", + "value": 50.0 + } + }, + "1f5c5a8eafb94b9a9b11d9a2524373d7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d62ab3c07fc64753b2a9c29aea8050a2", + "placeholder": "​", + "style": "IPY_MODEL_63793e7ce97b43db816b5f644c926e7f", + "value": "number of examples processed for checking labels: " } }, - "5f05395507ac4367ae64c6ba9a41e376": { + "265276564615465aae7792742a910cf0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1370,7 +1370,74 @@ "width": null } }, - "6100ae8ba0d54e21a507da746704062e": { + "381f4ed9e1cc4936a05b827e22b7d226": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4c99538a52524c55b6909964ce7c9d68": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c5b4c3e45e8848ffbc301b4507e649f1", + "placeholder": "​", + "style": "IPY_MODEL_ad51724cc1d6431d904bb6e18167ea25", + "value": " 10000/? [00:00<00:00, 1165602.49it/s]" + } + }, + "50bbf71c81b94d23aeb3814fd48c052f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "63793e7ce97b43db816b5f644c926e7f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "8187bd04584f405d9507f19902206d11": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1422,7 +1489,29 @@ "width": null } }, - "63d1a448b6a746f18ee2f037a92ae878": { + "8fc0f21110364ef8b4a28d24e2bd55e7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1f5c5a8eafb94b9a9b11d9a2524373d7", + "IPY_MODEL_ce759ae3b428466aa400b60523204cbc", + "IPY_MODEL_4c99538a52524c55b6909964ce7c9d68" + ], + "layout": "IPY_MODEL_8187bd04584f405d9507f19902206d11" + } + }, + "91b32b8f1849447099270e102d55f348": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1437,13 +1526,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_a80a6611d34f4705b4f07ebf44d92707", + "layout": "IPY_MODEL_960a5752066f4191bb38b7c279cd71ee", "placeholder": "​", - "style": "IPY_MODEL_2effe8a32eaa4e92abaa5cfd66d81dcb", - "value": " 10000/? [00:00<00:00, 1183093.76it/s]" + "style": "IPY_MODEL_381f4ed9e1cc4936a05b827e22b7d226", + "value": " 10000/? [00:00<00:00, 913831.54it/s]" } }, - "67bf9df6950044db8352f04efbecf277": { + "960a5752066f4191bb38b7c279cd71ee": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1495,7 +1584,7 @@ "width": null } }, - "69ec8fb5a49d4072a4b0ee16e18334df": { + "9bee374c69a64257bb771b86156bbde2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1510,13 +1599,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_5f05395507ac4367ae64c6ba9a41e376", + "layout": "IPY_MODEL_01401acd053d494aa90753fa1b8c29ba", "placeholder": "​", - "style": "IPY_MODEL_522f0096addf4e0cbafbc1d1348408a1", - "value": " 10000/? [00:00<00:00, 948101.00it/s]" + "style": "IPY_MODEL_a813a7209e3749ad8bce32b87d8a1474", + "value": "number of examples processed for estimating thresholds: " } }, - "984a0aef565c492c93122b0c6cd2e272": { + "9cc15f84dcd54b2ca4ee56b56c2a8d26": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1568,23 +1657,37 @@ "width": null } }, - "a60031b8b82f49fbafcdb6359ff9ad02": { + "a813a7209e3749ad8bce32b87d8a1474": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "ad51724cc1d6431d904bb6e18167ea25": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "a80a6611d34f4705b4f07ebf44d92707": { + "c5b4c3e45e8848ffbc301b4507e649f1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1636,53 +1739,53 @@ "width": null } }, - "c481e1fa4e204113b169f35e94dcd64d": { + "c8d58b7026a04e969e05f5cbd2b99e14": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6100ae8ba0d54e21a507da746704062e", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a60031b8b82f49fbafcdb6359ff9ad02", - "value": 50.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_9bee374c69a64257bb771b86156bbde2", + "IPY_MODEL_0a9f4395be2b49e484d8a8639bea82b5", + "IPY_MODEL_91b32b8f1849447099270e102d55f348" + ], + "layout": "IPY_MODEL_e591d29a3749442799df27c12ffbaf94" } }, - "d2907ab9e816444c921cba3edd090d1c": { + "ce759ae3b428466aa400b60523204cbc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_e69126c6e0524cfcafeb8b4ee9fa96bc", - "IPY_MODEL_f89f3d1d85684857aa38609ce181730f", - "IPY_MODEL_69ec8fb5a49d4072a4b0ee16e18334df" - ], - "layout": "IPY_MODEL_ee0899a9ff57472586211760d9d7a753" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_265276564615465aae7792742a910cf0", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_07a3f5a6aaad401a972197c77e87bf79", + "value": 50.0 } }, - "e1135200e6bc4c8d898c8dc36eb2cd8e": { + "d62ab3c07fc64753b2a9c29aea8050a2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1734,50 +1837,7 @@ "width": null } }, - "e1401ba3b9414469b83ae25c9cddabf3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_f7eb91c94209438b8648fd260292716e", - "IPY_MODEL_c481e1fa4e204113b169f35e94dcd64d", - "IPY_MODEL_63d1a448b6a746f18ee2f037a92ae878" - ], - "layout": "IPY_MODEL_e1135200e6bc4c8d898c8dc36eb2cd8e" - } - }, - "e69126c6e0524cfcafeb8b4ee9fa96bc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4af3a63525c440a4abc52d769e9017dd", - "placeholder": "​", - "style": "IPY_MODEL_5e22bd91d1694cfd8c3fe6d8d8c39dff", - "value": "number of examples processed for estimating thresholds: " - } - }, - "ee0899a9ff57472586211760d9d7a753": { + "e591d29a3749442799df27c12ffbaf94": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1828,66 +1888,6 @@ "visibility": null, "width": null } - }, - "ee272707d3f34718a00e655664ab8731": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "f7eb91c94209438b8648fd260292716e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_67bf9df6950044db8352f04efbecf277", - "placeholder": "​", - "style": "IPY_MODEL_ee272707d3f34718a00e655664ab8731", - "value": "number of examples processed for checking labels: " - } - }, - "f89f3d1d85684857aa38609ce181730f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_984a0aef565c492c93122b0c6cd2e272", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_36e767e7e95d43ffb851228c4f91edbe", - "value": 50.0 - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/image.ipynb b/master/.doctrees/nbsphinx/tutorials/image.ipynb index e02fa69d1..fbad8e084 100644 --- a/master/.doctrees/nbsphinx/tutorials/image.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:41.067246Z", - "iopub.status.busy": "2024-01-17T18:05:41.067031Z", - "iopub.status.idle": "2024-01-17T18:05:43.291099Z", - "shell.execute_reply": "2024-01-17T18:05:43.290418Z" + "iopub.execute_input": "2024-01-17T23:07:47.931457Z", + "iopub.status.busy": "2024-01-17T23:07:47.931264Z", + "iopub.status.idle": "2024-01-17T23:07:50.035403Z", + "shell.execute_reply": "2024-01-17T23:07:50.034793Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:43.294169Z", - "iopub.status.busy": "2024-01-17T18:05:43.293669Z", - "iopub.status.idle": "2024-01-17T18:05:43.297424Z", - "shell.execute_reply": "2024-01-17T18:05:43.296885Z" + "iopub.execute_input": "2024-01-17T23:07:50.038356Z", + "iopub.status.busy": "2024-01-17T23:07:50.037866Z", + "iopub.status.idle": "2024-01-17T23:07:50.041629Z", + "shell.execute_reply": "2024-01-17T23:07:50.041029Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:43.299869Z", - "iopub.status.busy": "2024-01-17T18:05:43.299508Z", - "iopub.status.idle": "2024-01-17T18:05:45.744718Z", - "shell.execute_reply": "2024-01-17T18:05:45.744111Z" + "iopub.execute_input": "2024-01-17T23:07:50.043951Z", + "iopub.status.busy": "2024-01-17T23:07:50.043515Z", + "iopub.status.idle": "2024-01-17T23:07:53.352869Z", + "shell.execute_reply": "2024-01-17T23:07:53.352216Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9218ba50145b4c1abb05285d39489f7b", + "model_id": "79ccb0a1555b42b5a881bd1c68892db9", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "411e7b1250d04fceabc4a56d3aa7c5ba", + "model_id": "3b8311a276a84a2b810f57fb87ac7a1c", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "34ddbf7efb384ee4960a77089187f278", + "model_id": "19d4a001660c44e8a52d8d2f5e1ea989", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "13b1818046c2493686e01a759bad0eef", + "model_id": "7ce4749d923744c09bd675ee944baaad", "version_major": 2, "version_minor": 0 }, @@ -246,10 +246,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:45.747577Z", - "iopub.status.busy": "2024-01-17T18:05:45.747182Z", - "iopub.status.idle": "2024-01-17T18:05:45.751308Z", - "shell.execute_reply": "2024-01-17T18:05:45.750698Z" + "iopub.execute_input": "2024-01-17T23:07:53.355203Z", + "iopub.status.busy": "2024-01-17T23:07:53.354998Z", + "iopub.status.idle": "2024-01-17T23:07:53.359311Z", + "shell.execute_reply": "2024-01-17T23:07:53.358704Z" } }, "outputs": [ @@ -274,17 +274,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:45.753514Z", - "iopub.status.busy": "2024-01-17T18:05:45.753224Z", - "iopub.status.idle": "2024-01-17T18:05:58.059408Z", - "shell.execute_reply": "2024-01-17T18:05:58.058802Z" + "iopub.execute_input": "2024-01-17T23:07:53.361599Z", + "iopub.status.busy": "2024-01-17T23:07:53.361259Z", + "iopub.status.idle": "2024-01-17T23:08:05.481673Z", + "shell.execute_reply": "2024-01-17T23:08:05.480925Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "76f30844c4444b9cb566e2bb926b7b40", + "model_id": "cf1bbec90cc743c19c044238bc5cd410", "version_major": 2, "version_minor": 0 }, @@ -322,10 +322,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:58.062215Z", - "iopub.status.busy": "2024-01-17T18:05:58.061961Z", - "iopub.status.idle": "2024-01-17T18:06:20.156233Z", - "shell.execute_reply": "2024-01-17T18:06:20.155534Z" + "iopub.execute_input": "2024-01-17T23:08:05.484696Z", + "iopub.status.busy": "2024-01-17T23:08:05.484433Z", + "iopub.status.idle": "2024-01-17T23:08:26.795293Z", + "shell.execute_reply": "2024-01-17T23:08:26.794657Z" } }, "outputs": [], @@ -358,10 +358,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:06:20.159472Z", - "iopub.status.busy": "2024-01-17T18:06:20.159075Z", - "iopub.status.idle": "2024-01-17T18:06:20.164287Z", - "shell.execute_reply": "2024-01-17T18:06:20.163771Z" + "iopub.execute_input": "2024-01-17T23:08:26.798340Z", + "iopub.status.busy": "2024-01-17T23:08:26.797902Z", + "iopub.status.idle": "2024-01-17T23:08:26.804100Z", + "shell.execute_reply": "2024-01-17T23:08:26.803564Z" } }, "outputs": [], @@ -399,10 +399,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:06:20.166658Z", - "iopub.status.busy": "2024-01-17T18:06:20.166314Z", - "iopub.status.idle": "2024-01-17T18:06:20.170443Z", - "shell.execute_reply": "2024-01-17T18:06:20.169973Z" + "iopub.execute_input": "2024-01-17T23:08:26.806531Z", + "iopub.status.busy": "2024-01-17T23:08:26.806178Z", + "iopub.status.idle": "2024-01-17T23:08:26.810192Z", + "shell.execute_reply": "2024-01-17T23:08:26.809670Z" }, "nbsphinx": "hidden" }, @@ -539,10 +539,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:06:20.172709Z", - "iopub.status.busy": "2024-01-17T18:06:20.172347Z", - "iopub.status.idle": "2024-01-17T18:06:20.181874Z", - "shell.execute_reply": "2024-01-17T18:06:20.181379Z" + "iopub.execute_input": "2024-01-17T23:08:26.812487Z", + "iopub.status.busy": "2024-01-17T23:08:26.812126Z", + "iopub.status.idle": "2024-01-17T23:08:26.821683Z", + "shell.execute_reply": "2024-01-17T23:08:26.821162Z" }, "nbsphinx": "hidden" }, @@ -667,10 +667,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:06:20.184367Z", - "iopub.status.busy": "2024-01-17T18:06:20.183884Z", - "iopub.status.idle": "2024-01-17T18:06:20.214975Z", - "shell.execute_reply": "2024-01-17T18:06:20.214272Z" + "iopub.execute_input": "2024-01-17T23:08:26.823908Z", + "iopub.status.busy": "2024-01-17T23:08:26.823540Z", + "iopub.status.idle": "2024-01-17T23:08:26.852714Z", + "shell.execute_reply": "2024-01-17T23:08:26.852213Z" } }, "outputs": [], @@ -707,10 +707,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:06:20.218114Z", - "iopub.status.busy": "2024-01-17T18:06:20.217484Z", - "iopub.status.idle": "2024-01-17T18:06:52.341728Z", - "shell.execute_reply": "2024-01-17T18:06:52.340867Z" + "iopub.execute_input": "2024-01-17T23:08:26.855055Z", + "iopub.status.busy": "2024-01-17T23:08:26.854682Z", + "iopub.status.idle": "2024-01-17T23:08:57.530989Z", + "shell.execute_reply": "2024-01-17T23:08:57.530120Z" } }, "outputs": [ @@ -726,14 +726,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.896\n" + "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.560\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.667\n", + "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.349\n", "Computing feature embeddings ...\n" ] }, @@ -750,7 +750,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:04, 9.04it/s]" + " 2%|▎ | 1/40 [00:00<00:03, 9.97it/s]" ] }, { @@ -758,7 +758,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 8/40 [00:00<00:00, 42.57it/s]" + " 22%|██▎ | 9/40 [00:00<00:00, 48.20it/s]" ] }, { @@ -766,7 +766,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 16/40 [00:00<00:00, 57.36it/s]" + " 42%|████▎ | 17/40 [00:00<00:00, 60.18it/s]" ] }, { @@ -774,7 +774,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 24/40 [00:00<00:00, 64.11it/s]" + " 62%|██████▎ | 25/40 [00:00<00:00, 65.74it/s]" ] }, { @@ -782,7 +782,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 32/40 [00:00<00:00, 68.31it/s]" + " 82%|████████▎ | 33/40 [00:00<00:00, 69.32it/s]" ] }, { @@ -790,7 +790,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 62.78it/s]" + "100%|██████████| 40/40 [00:00<00:00, 62.65it/s]" ] }, { @@ -820,7 +820,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 2/40 [00:00<00:01, 19.18it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 18.01it/s]" ] }, { @@ -828,7 +828,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 10/40 [00:00<00:00, 52.54it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 50.14it/s]" ] }, { @@ -836,7 +836,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 18/40 [00:00<00:00, 63.06it/s]" + " 45%|████▌ | 18/40 [00:00<00:00, 60.51it/s]" ] }, { @@ -844,7 +844,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 26/40 [00:00<00:00, 68.34it/s]" + " 62%|██████▎ | 25/40 [00:00<00:00, 62.34it/s]" ] }, { @@ -852,7 +852,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 34/40 [00:00<00:00, 71.92it/s]" + " 82%|████████▎ | 33/40 [00:00<00:00, 67.31it/s]" ] }, { @@ -860,7 +860,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 65.99it/s]" + "100%|██████████| 40/40 [00:00<00:00, 63.76it/s]" ] }, { @@ -882,14 +882,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.738\n" + "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.550\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.632\n", + "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.337\n", "Computing feature embeddings ...\n" ] }, @@ -906,7 +906,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 2/40 [00:00<00:01, 19.17it/s]" + " 8%|▊ | 3/40 [00:00<00:01, 25.95it/s]" ] }, { @@ -914,7 +914,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 47.23it/s]" + " 28%|██▊ | 11/40 [00:00<00:00, 53.82it/s]" ] }, { @@ -922,7 +922,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 59.67it/s]" + " 48%|████▊ | 19/40 [00:00<00:00, 63.69it/s]" ] }, { @@ -930,7 +930,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 65.28it/s]" + " 65%|██████▌ | 26/40 [00:00<00:00, 64.34it/s]" ] }, { @@ -938,7 +938,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 68.98it/s]" + " 82%|████████▎ | 33/40 [00:00<00:00, 65.40it/s]" ] }, { @@ -946,7 +946,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 62.09it/s]" + "100%|██████████| 40/40 [00:00<00:00, 64.01it/s]" ] }, { @@ -976,7 +976,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:05, 7.50it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 17.33it/s]" ] }, { @@ -984,7 +984,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 43.21it/s]" + " 22%|██▎ | 9/40 [00:00<00:00, 43.06it/s]" ] }, { @@ -992,7 +992,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 56.82it/s]" + " 40%|████ | 16/40 [00:00<00:00, 52.93it/s]" ] }, { @@ -1000,7 +1000,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 63.54it/s]" + " 57%|█████▊ | 23/40 [00:00<00:00, 57.07it/s]" ] }, { @@ -1008,7 +1008,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 68.22it/s]" + " 78%|███████▊ | 31/40 [00:00<00:00, 62.51it/s]" ] }, { @@ -1016,7 +1016,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 61.30it/s]" + "100%|██████████| 40/40 [00:00<00:00, 59.78it/s]" ] }, { @@ -1038,14 +1038,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.767\n" + "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.749\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.551\n", + "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.346\n", "Computing feature embeddings ...\n" ] }, @@ -1062,7 +1062,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 2/40 [00:00<00:02, 17.49it/s]" + " 8%|▊ | 3/40 [00:00<00:01, 27.26it/s]" ] }, { @@ -1070,7 +1070,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 10/40 [00:00<00:00, 50.44it/s]" + " 28%|██▊ | 11/40 [00:00<00:00, 55.24it/s]" ] }, { @@ -1078,7 +1078,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 18/40 [00:00<00:00, 60.96it/s]" + " 48%|████▊ | 19/40 [00:00<00:00, 64.49it/s]" ] }, { @@ -1086,7 +1086,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 63.96it/s]" + " 68%|██████▊ | 27/40 [00:00<00:00, 69.00it/s]" ] }, { @@ -1094,7 +1094,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 67.69it/s]" + " 90%|█████████ | 36/40 [00:00<00:00, 73.84it/s]" ] }, { @@ -1102,7 +1102,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 63.32it/s]" + "100%|██████████| 40/40 [00:00<00:00, 67.59it/s]" ] }, { @@ -1132,7 +1132,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:04, 9.55it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 19.00it/s]" ] }, { @@ -1140,7 +1140,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 8/40 [00:00<00:00, 44.04it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 52.63it/s]" ] }, { @@ -1148,7 +1148,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 15/40 [00:00<00:00, 54.25it/s]" + " 45%|████▌ | 18/40 [00:00<00:00, 63.02it/s]" ] }, { @@ -1156,7 +1156,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▊ | 23/40 [00:00<00:00, 62.12it/s]" + " 65%|██████▌ | 26/40 [00:00<00:00, 68.31it/s]" ] }, { @@ -1164,7 +1164,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 31/40 [00:00<00:00, 67.11it/s]" + " 88%|████████▊ | 35/40 [00:00<00:00, 73.71it/s]" ] }, { @@ -1172,15 +1172,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 39/40 [00:00<00:00, 69.06it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 40/40 [00:00<00:00, 60.60it/s]" + "100%|██████████| 40/40 [00:00<00:00, 67.51it/s]" ] }, { @@ -1257,10 +1249,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:06:52.344865Z", - "iopub.status.busy": "2024-01-17T18:06:52.344261Z", - "iopub.status.idle": "2024-01-17T18:06:52.360939Z", - "shell.execute_reply": "2024-01-17T18:06:52.360351Z" + "iopub.execute_input": "2024-01-17T23:08:57.533946Z", + "iopub.status.busy": "2024-01-17T23:08:57.533629Z", + "iopub.status.idle": "2024-01-17T23:08:57.550228Z", + "shell.execute_reply": "2024-01-17T23:08:57.549699Z" } }, "outputs": [], @@ -1285,10 +1277,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:06:52.364131Z", - "iopub.status.busy": "2024-01-17T18:06:52.363638Z", - "iopub.status.idle": "2024-01-17T18:06:52.849592Z", - "shell.execute_reply": "2024-01-17T18:06:52.848859Z" + "iopub.execute_input": "2024-01-17T23:08:57.552782Z", + "iopub.status.busy": "2024-01-17T23:08:57.552226Z", + "iopub.status.idle": "2024-01-17T23:08:57.986311Z", + "shell.execute_reply": "2024-01-17T23:08:57.985707Z" } }, "outputs": [], @@ -1308,10 +1300,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:06:52.852535Z", - "iopub.status.busy": "2024-01-17T18:06:52.852301Z", - "iopub.status.idle": "2024-01-17T18:10:13.364394Z", - "shell.execute_reply": "2024-01-17T18:10:13.363707Z" + "iopub.execute_input": "2024-01-17T23:08:57.989260Z", + "iopub.status.busy": "2024-01-17T23:08:57.988841Z", + "iopub.status.idle": "2024-01-17T23:12:17.592442Z", + "shell.execute_reply": "2024-01-17T23:12:17.591804Z" } }, "outputs": [ @@ -1350,7 +1342,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4788f80918f340f199482171f3731dd8", + "model_id": "c1f8c848d7954850a757e5e8fa83f920", "version_major": 2, "version_minor": 0 }, @@ -1389,10 +1381,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:13.367055Z", - "iopub.status.busy": "2024-01-17T18:10:13.366655Z", - "iopub.status.idle": "2024-01-17T18:10:13.890209Z", - "shell.execute_reply": "2024-01-17T18:10:13.889535Z" + "iopub.execute_input": "2024-01-17T23:12:17.595341Z", + "iopub.status.busy": "2024-01-17T23:12:17.594687Z", + "iopub.status.idle": "2024-01-17T23:12:18.109017Z", + "shell.execute_reply": "2024-01-17T23:12:18.108362Z" } }, "outputs": [ @@ -1604,10 +1596,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:13.893666Z", - "iopub.status.busy": "2024-01-17T18:10:13.893098Z", - "iopub.status.idle": "2024-01-17T18:10:13.957139Z", - "shell.execute_reply": "2024-01-17T18:10:13.956469Z" + "iopub.execute_input": "2024-01-17T23:12:18.112328Z", + "iopub.status.busy": "2024-01-17T23:12:18.111889Z", + "iopub.status.idle": "2024-01-17T23:12:18.175100Z", + "shell.execute_reply": "2024-01-17T23:12:18.174534Z" } }, "outputs": [ @@ -1711,10 +1703,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:13.959807Z", - "iopub.status.busy": "2024-01-17T18:10:13.959450Z", - "iopub.status.idle": "2024-01-17T18:10:13.968741Z", - "shell.execute_reply": "2024-01-17T18:10:13.968232Z" + "iopub.execute_input": "2024-01-17T23:12:18.177637Z", + "iopub.status.busy": "2024-01-17T23:12:18.177428Z", + "iopub.status.idle": "2024-01-17T23:12:18.186532Z", + "shell.execute_reply": "2024-01-17T23:12:18.185876Z" } }, "outputs": [ @@ -1844,10 +1836,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:13.971381Z", - "iopub.status.busy": "2024-01-17T18:10:13.970900Z", - "iopub.status.idle": "2024-01-17T18:10:13.976151Z", - "shell.execute_reply": "2024-01-17T18:10:13.975669Z" + "iopub.execute_input": "2024-01-17T23:12:18.188808Z", + "iopub.status.busy": "2024-01-17T23:12:18.188604Z", + "iopub.status.idle": "2024-01-17T23:12:18.193610Z", + "shell.execute_reply": "2024-01-17T23:12:18.193087Z" }, "nbsphinx": "hidden" }, @@ -1893,10 +1885,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:13.978642Z", - "iopub.status.busy": "2024-01-17T18:10:13.978133Z", - "iopub.status.idle": "2024-01-17T18:10:14.471555Z", - "shell.execute_reply": "2024-01-17T18:10:14.470916Z" + "iopub.execute_input": "2024-01-17T23:12:18.195809Z", + "iopub.status.busy": "2024-01-17T23:12:18.195608Z", + "iopub.status.idle": "2024-01-17T23:12:18.652330Z", + "shell.execute_reply": "2024-01-17T23:12:18.651636Z" } }, "outputs": [ @@ -1931,10 +1923,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:14.474235Z", - "iopub.status.busy": "2024-01-17T18:10:14.473823Z", - "iopub.status.idle": "2024-01-17T18:10:14.482948Z", - "shell.execute_reply": "2024-01-17T18:10:14.482455Z" + "iopub.execute_input": "2024-01-17T23:12:18.655261Z", + "iopub.status.busy": "2024-01-17T23:12:18.654743Z", + "iopub.status.idle": "2024-01-17T23:12:18.663764Z", + "shell.execute_reply": "2024-01-17T23:12:18.663258Z" } }, "outputs": [ @@ -2101,10 +2093,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:14.485528Z", - "iopub.status.busy": "2024-01-17T18:10:14.485164Z", - "iopub.status.idle": "2024-01-17T18:10:14.492848Z", - "shell.execute_reply": "2024-01-17T18:10:14.492364Z" + "iopub.execute_input": "2024-01-17T23:12:18.666187Z", + "iopub.status.busy": "2024-01-17T23:12:18.665737Z", + "iopub.status.idle": "2024-01-17T23:12:18.674326Z", + "shell.execute_reply": "2024-01-17T23:12:18.673700Z" }, "nbsphinx": "hidden" }, @@ -2180,10 +2172,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:14.496054Z", - "iopub.status.busy": "2024-01-17T18:10:14.495663Z", - "iopub.status.idle": "2024-01-17T18:10:14.964665Z", - "shell.execute_reply": "2024-01-17T18:10:14.963989Z" + "iopub.execute_input": "2024-01-17T23:12:18.676786Z", + "iopub.status.busy": "2024-01-17T23:12:18.676313Z", + "iopub.status.idle": "2024-01-17T23:12:19.151323Z", + "shell.execute_reply": "2024-01-17T23:12:19.150735Z" } }, "outputs": [ @@ -2220,10 +2212,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:14.967054Z", - "iopub.status.busy": "2024-01-17T18:10:14.966847Z", - "iopub.status.idle": "2024-01-17T18:10:14.982866Z", - "shell.execute_reply": "2024-01-17T18:10:14.982341Z" + "iopub.execute_input": "2024-01-17T23:12:19.153906Z", + "iopub.status.busy": "2024-01-17T23:12:19.153526Z", + "iopub.status.idle": "2024-01-17T23:12:19.169696Z", + "shell.execute_reply": "2024-01-17T23:12:19.169068Z" } }, "outputs": [ @@ -2380,10 +2372,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:14.985211Z", - "iopub.status.busy": "2024-01-17T18:10:14.984998Z", - "iopub.status.idle": "2024-01-17T18:10:14.990999Z", - "shell.execute_reply": "2024-01-17T18:10:14.990478Z" + "iopub.execute_input": "2024-01-17T23:12:19.172357Z", + "iopub.status.busy": "2024-01-17T23:12:19.172040Z", + "iopub.status.idle": "2024-01-17T23:12:19.178064Z", + "shell.execute_reply": "2024-01-17T23:12:19.177456Z" }, "nbsphinx": "hidden" }, @@ -2428,10 +2420,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:14.993471Z", - "iopub.status.busy": "2024-01-17T18:10:14.993012Z", - "iopub.status.idle": "2024-01-17T18:10:15.582338Z", - "shell.execute_reply": "2024-01-17T18:10:15.581670Z" + "iopub.execute_input": "2024-01-17T23:12:19.180611Z", + "iopub.status.busy": "2024-01-17T23:12:19.180056Z", + "iopub.status.idle": "2024-01-17T23:12:19.840971Z", + "shell.execute_reply": "2024-01-17T23:12:19.840346Z" } }, "outputs": [ @@ -2513,10 +2505,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:15.585070Z", - "iopub.status.busy": "2024-01-17T18:10:15.584860Z", - "iopub.status.idle": "2024-01-17T18:10:15.594353Z", - "shell.execute_reply": "2024-01-17T18:10:15.593626Z" + "iopub.execute_input": "2024-01-17T23:12:19.844161Z", + "iopub.status.busy": "2024-01-17T23:12:19.843784Z", + "iopub.status.idle": "2024-01-17T23:12:19.853652Z", + "shell.execute_reply": "2024-01-17T23:12:19.853100Z" } }, "outputs": [ @@ -2541,47 +2533,47 @@ " \n", " \n", " \n", - " dark_score\n", " is_dark_issue\n", + " dark_score\n", " \n", " \n", " \n", " \n", " 34848\n", - " 0.203922\n", " True\n", + " 0.203922\n", " \n", " \n", " 50270\n", - " 0.204588\n", " True\n", + " 0.204588\n", " \n", " \n", " 3936\n", - " 0.213098\n", " True\n", + " 0.213098\n", " \n", " \n", " 733\n", - " 0.217686\n", " True\n", + " 0.217686\n", " \n", " \n", " 8094\n", - " 0.230118\n", " True\n", + " 0.230118\n", " \n", " \n", "\n", "" ], "text/plain": [ - " dark_score is_dark_issue\n", - "34848 0.203922 True\n", - "50270 0.204588 True\n", - "3936 0.213098 True\n", - "733 0.217686 True\n", - "8094 0.230118 True" + " is_dark_issue dark_score\n", + "34848 True 0.203922\n", + "50270 True 0.204588\n", + "3936 True 0.213098\n", + "733 True 0.217686\n", + "8094 True 0.230118" ] }, "execution_count": 26, @@ -2644,10 +2636,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:15.597141Z", - "iopub.status.busy": "2024-01-17T18:10:15.596921Z", - "iopub.status.idle": "2024-01-17T18:10:15.602211Z", - "shell.execute_reply": "2024-01-17T18:10:15.601473Z" + "iopub.execute_input": "2024-01-17T23:12:19.856574Z", + "iopub.status.busy": "2024-01-17T23:12:19.856209Z", + "iopub.status.idle": "2024-01-17T23:12:19.862408Z", + "shell.execute_reply": "2024-01-17T23:12:19.861843Z" }, "nbsphinx": "hidden" }, @@ -2684,10 +2676,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:15.604750Z", - "iopub.status.busy": "2024-01-17T18:10:15.604550Z", - "iopub.status.idle": "2024-01-17T18:10:15.778107Z", - "shell.execute_reply": "2024-01-17T18:10:15.777326Z" + "iopub.execute_input": "2024-01-17T23:12:19.865248Z", + "iopub.status.busy": "2024-01-17T23:12:19.864885Z", + "iopub.status.idle": "2024-01-17T23:12:20.064339Z", + "shell.execute_reply": "2024-01-17T23:12:20.063776Z" } }, "outputs": [ @@ -2729,10 +2721,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:15.781004Z", - "iopub.status.busy": "2024-01-17T18:10:15.780794Z", - "iopub.status.idle": "2024-01-17T18:10:15.789706Z", - "shell.execute_reply": "2024-01-17T18:10:15.788980Z" + "iopub.execute_input": "2024-01-17T23:12:20.066913Z", + "iopub.status.busy": "2024-01-17T23:12:20.066523Z", + "iopub.status.idle": "2024-01-17T23:12:20.074947Z", + "shell.execute_reply": "2024-01-17T23:12:20.074451Z" } }, "outputs": [ @@ -2818,10 +2810,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:15.792222Z", - "iopub.status.busy": "2024-01-17T18:10:15.791846Z", - "iopub.status.idle": "2024-01-17T18:10:15.991891Z", - "shell.execute_reply": "2024-01-17T18:10:15.991225Z" + "iopub.execute_input": "2024-01-17T23:12:20.077342Z", + "iopub.status.busy": "2024-01-17T23:12:20.076942Z", + "iopub.status.idle": "2024-01-17T23:12:20.273417Z", + "shell.execute_reply": "2024-01-17T23:12:20.272758Z" } }, "outputs": [ @@ -2861,10 +2853,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:15.994477Z", - "iopub.status.busy": "2024-01-17T18:10:15.994081Z", - "iopub.status.idle": "2024-01-17T18:10:15.998830Z", - "shell.execute_reply": "2024-01-17T18:10:15.998287Z" + "iopub.execute_input": "2024-01-17T23:12:20.275994Z", + "iopub.status.busy": "2024-01-17T23:12:20.275783Z", + "iopub.status.idle": "2024-01-17T23:12:20.280537Z", + "shell.execute_reply": "2024-01-17T23:12:20.280012Z" }, "nbsphinx": "hidden" }, @@ -2901,39 +2893,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "0422f06140e1429bb9df4d5e22a01ac2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "06b2bf8222ef4b6db00a974dc321d1d2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "09b79e0723394c75a9295ba283a8fd52": { + "0397c83a64194b3e8f302bed7a8d9f8c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2948,13 +2908,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_6c248b337d0c458ead2d440b3472437f", + "layout": "IPY_MODEL_e6236cd92f06491a83ea7c38bf31a7e7", "placeholder": "​", - "style": "IPY_MODEL_0ddd9623827d4a419d5c51467297b6bd", - "value": " 60000/0 [00:00<00:00, 817475.64 examples/s]" + "style": "IPY_MODEL_2bfaa3482cd24c6f8a7f4c4e881e2ca7", + "value": "Generating train split: " } }, - "0a024c8dbb214427b3250ba948806841": { + "045208935e1f4bd6a391b26b3983d049": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -2970,31 +2930,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_19629acb9010471ba50b5b29e00183c6", - "max": 1.0, + "layout": "IPY_MODEL_7b8800a4904440598f0e45fab5fbd35a", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_f72cf07726314106aaa73a8aa0dbb3d9", - "value": 1.0 - } - }, - "0d884ce3ab3b4e51bd48a78309442fe2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "style": "IPY_MODEL_98fe2af32bbc4615b9fd39e09236fd2f", + "value": 60000.0 } }, - "0ddd9623827d4a419d5c51467297b6bd": { + "06760201f05c4000afc116288d98641b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3009,53 +2953,28 @@ "description_width": "" } }, - "1236cf2a98e4455b930f467d1e95e56f": { + "0a530e5055f0497090b37f9ec22dadae": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_a649432ed8c04a3b87f55c6e1cbc806d", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6616b30d88f3472290070dde94c3d289", - "value": 60000.0 - } - }, - "13b1818046c2493686e01a759bad0eef": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_3dd51d9190ef4190bd322c16bed1fb06", - "IPY_MODEL_5c339eb01faf4cf5a621c0edb6c44b68", - "IPY_MODEL_cf40c5cc5c3e42069f474bc592b94779" - ], - "layout": "IPY_MODEL_5d82cde87f5c4f48b90eca1c0c7219d2" + "layout": "IPY_MODEL_3bf8c457de024516a37553baea232c37", + "placeholder": "​", + "style": "IPY_MODEL_1ed5c49b1ddb4b7dbf0853bcd1a3778a", + "value": "Downloading data: 100%" } }, - "13e232e4e5e148d8a3faa3c2f89ea970": { + "0c58d5d607b74c128d2fed5574d2b2f0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3107,7 +3026,7 @@ "width": null } }, - "14970ee75a2d4d2b84651021085a0bab": { + "179264665b3d4da192240f1ac09ac5e5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3122,37 +3041,35 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_2dbb0555cbba41679ae0e974d1f96ac2", + "layout": "IPY_MODEL_c3549b4dff2849a49f4c582646e80469", "placeholder": "​", - "style": "IPY_MODEL_94474ce5fa7c49859fb2ba027b64aef7", - "value": "Computing checksums: 100%" + "style": "IPY_MODEL_99d94d93be9e48819c0c81e7e63b86e8", + "value": "Map (num_proc=4): 100%" } }, - "18e94be5479c4f7bade2bb296c771679": { + "19d4a001660c44e8a52d8d2f5e1ea989": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_cf90eb9f6391477bbf013ddee2032497", - "max": 2.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9af6f90090b44f5a994f4d2ef2c28c38", - "value": 2.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_0397c83a64194b3e8f302bed7a8d9f8c", + "IPY_MODEL_231dea10656441a1a0519b295f0d7bfd", + "IPY_MODEL_84270cee9acd46f7b5711cc0cb1d1790" + ], + "layout": "IPY_MODEL_80393a3a390947c29ad3e5befeee9f1a" } }, - "19629acb9010471ba50b5b29e00183c6": { + "1a9050a96ba840b1981a446cc79d9a2a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3201,25 +3118,62 @@ "right": null, "top": null, "visibility": null, - "width": "20px" + "width": null } }, - "1e1ecba39033420591ada0b76d527bf1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "1a99e1fffc83437580ec94981db5304f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "20170352b29c453e997c9a95c3a33b80": { + "1c435eee98164c94875a24dc9213ab07": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3271,7 +3225,22 @@ "width": null } }, - "25cccd29d96c4634a04b0cb4c0ae7a99": { + "1ed5c49b1ddb4b7dbf0853bcd1a3778a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "231dea10656441a1a0519b295f0d7bfd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -3287,58 +3256,61 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_adfa225ec8f64e7880afb3cb066db908", - "max": 60000.0, + "layout": "IPY_MODEL_f2f5ea97fde04ab1992313b89ce1e044", + "max": 1.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_f7614c62ca994b079963f2061f90aec2", - "value": 60000.0 + "style": "IPY_MODEL_995e09d244bd49bd8d7ac45836ec79f9", + "value": 1.0 } }, - "293d1b710eea4f9fa017893a7a977f6d": { + "2bfaa3482cd24c6f8a7f4c4e881e2ca7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_14970ee75a2d4d2b84651021085a0bab", - "IPY_MODEL_18e94be5479c4f7bade2bb296c771679", - "IPY_MODEL_41a78828843c457294fc38e7f334ef9a" - ], - "layout": "IPY_MODEL_819ad8fec46043248eb022c81bb1b5b6" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "2b8505492b124436bf13b9605b3b7dd8": { + "2f977c6c9def44e5a688a578fdbe8f46": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_9e9f3c1977bc4cc18ccf7c0091220fdd", - "placeholder": "​", - "style": "IPY_MODEL_a8a2c7b2b22842f5be552d65b32d2f6c", - "value": " 60000/60000 [00:12<00:00, 6511.95 examples/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "38f79432d4ea43f3ad8e5a6a05b9cfd7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "2dbb0555cbba41679ae0e974d1f96ac2": { + "39cced6b09354583a41a423a7f3697f2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3390,50 +3362,59 @@ "width": null } }, - "34ddbf7efb384ee4960a77089187f278": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "3b5c42a953034dfbb32825a03164a0f7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_daf93e367bcd41009e494ece90f80fde", - "IPY_MODEL_0a024c8dbb214427b3250ba948806841", - "IPY_MODEL_09b79e0723394c75a9295ba283a8fd52" - ], - "layout": "IPY_MODEL_e57d19b9905849f58864c0e2ae9f2c73" - } - }, - "3dd51d9190ef4190bd322c16bed1fb06": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_67575008a0c84c898646dedc03acf942", - "placeholder": "​", - "style": "IPY_MODEL_fe2e11a13fdb46e8b74279e56b89a038", - "value": "Generating test split: " + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "411e7b1250d04fceabc4a56d3aa7c5ba": { + "3b8311a276a84a2b810f57fb87ac7a1c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -3448,35 +3429,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_5fc122f493644e7ca3b284b5bf922da7", - "IPY_MODEL_6af063d78a0b4668bb022ad287881b3a", - "IPY_MODEL_6a561cf815bc411bbf8042951da94f0f" + "IPY_MODEL_0a530e5055f0497090b37f9ec22dadae", + "IPY_MODEL_4975b121fca840d69ee14918cd0d217d", + "IPY_MODEL_fd2ab6faeeeb4055bbcc5fe64bac125d" ], - "layout": "IPY_MODEL_8388e5c0bcdd44658f6c1d9fe032adde" - } - }, - "41a78828843c457294fc38e7f334ef9a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_20170352b29c453e997c9a95c3a33b80", - "placeholder": "​", - "style": "IPY_MODEL_af1dd8d8ecef45a48f86fa83500577b4", - "value": " 2/2 [00:00<00:00, 346.19it/s]" + "layout": "IPY_MODEL_94881b422b624471974fef993ac1b9e3" } }, - "43118ea6bbea4a20b0f36f67ad05ec28": { + "3bf8c457de024516a37553baea232c37": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3528,29 +3488,83 @@ "width": null } }, - "4788f80918f340f199482171f3731dd8": { + "3dceb99795994c2c9faef0d8437d26df": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "3f168cb3d1c94b1ba4a217cd6467dbc6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_d80873115a3d48b4a783d8a26120c697", - "IPY_MODEL_1236cf2a98e4455b930f467d1e95e56f", - "IPY_MODEL_56ab71b0a1ee4eb8b1ab047505bc017c" - ], - "layout": "IPY_MODEL_b950eeaac0b8423fbc3d696ee313e0f7" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0c58d5d607b74c128d2fed5574d2b2f0", + "placeholder": "​", + "style": "IPY_MODEL_481f7c373c054bd1a8ed7c673293ca90", + "value": " 30.9M/30.9M [00:01<00:00, 16.7MB/s]" + } + }, + "406fe6cbb9a34dca80a0ecaff800df90": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1a9050a96ba840b1981a446cc79d9a2a", + "max": 2.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_38f79432d4ea43f3ad8e5a6a05b9cfd7", + "value": 2.0 + } + }, + "41327ec0739e4dcd957c5ee2898e050a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "56ab71b0a1ee4eb8b1ab047505bc017c": { + "44346c7bc1cf426680dd5db30374b0c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3565,13 +3579,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_13e232e4e5e148d8a3faa3c2f89ea970", + "layout": "IPY_MODEL_a392060d75f04f79b300b2254520405a", "placeholder": "​", - "style": "IPY_MODEL_59b669434d304300ae56a27d14f49bf5", - "value": " 60000/60000 [00:34<00:00, 1771.33it/s]" + "style": "IPY_MODEL_2f977c6c9def44e5a688a578fdbe8f46", + "value": "Computing checksums: 100%" } }, - "58930589e42646c7a4adf4cd6bcd6371": { + "44d4dca1913340ce8b9c507cd312f8c9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3623,7 +3637,46 @@ "width": null } }, - "59b669434d304300ae56a27d14f49bf5": { + "481f7c373c054bd1a8ed7c673293ca90": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4975b121fca840d69ee14918cd0d217d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3b5c42a953034dfbb32825a03164a0f7", + "max": 5175617.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_9fafe542e2ed4c18aab17f4dc9200a15", + "value": 5175617.0 + } + }, + "4ece4d1132ab47b39c47d91a5c14bc90": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3638,7 +3691,7 @@ "description_width": "" } }, - "5a1b5f56c91c4c2aa69d687eae32d787": { + "4f39a6854c894be9b84b9424dc291601": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3690,31 +3743,44 @@ "width": null } }, - "5c339eb01faf4cf5a621c0edb6c44b68": { + "522cf4a497fa4a07ae98cbe1f5c211c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "57b755a1482c49a2be45568b6bdd0f3e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_ae038169fdbf4ff78f655a94650c3c2b", - "max": 1.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_0d884ce3ab3b4e51bd48a78309442fe2", - "value": 1.0 + "layout": "IPY_MODEL_c83a7ea94765464f9172b04328ecf7cb", + "placeholder": "​", + "style": "IPY_MODEL_9ae996a06a074f598ffdd1896fc06e5f", + "value": " 60000/60000 [00:34<00:00, 1762.24it/s]" } }, - "5d82cde87f5c4f48b90eca1c0c7219d2": { + "58928fb735cd44a8ab6e8c0edf078872": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3766,44 +3832,31 @@ "width": null } }, - "5fc122f493644e7ca3b284b5bf922da7": { + "59a3fa3e95574a8b814d146dfed3c4b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_fd329fbe811246e8a0566112ee0206c9", - "placeholder": "​", - "style": "IPY_MODEL_fdd50d7c13524c3c882b9c849c0fb910", - "value": "Downloading data: 100%" - } - }, - "6616b30d88f3472290070dde94c3d289": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "layout": "IPY_MODEL_5e5f26489aa44b649b2a01102f0c989a", + "max": 1.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_522cf4a497fa4a07ae98cbe1f5c211c0", + "value": 1.0 } }, - "67575008a0c84c898646dedc03acf942": { + "5e5f26489aa44b649b2a01102f0c989a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3852,10 +3905,10 @@ "right": null, "top": null, "visibility": null, - "width": null + "width": "20px" } }, - "6a561cf815bc411bbf8042951da94f0f": { + "6f35b4075f004e12901e63c69bb0356a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3870,13 +3923,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_85537fd6b7504ad187504946935cc321", + "layout": "IPY_MODEL_8e7dbb79e5a543c7885714426be3c75c", "placeholder": "​", - "style": "IPY_MODEL_8b814b38f0104752b04bd4a5310bb6b1", - "value": " 5.18M/5.18M [00:00<00:00, 52.6MB/s]" + "style": "IPY_MODEL_e9e90e1e28aa4e3588e479ab25012fbf", + "value": " 60000/60000 [00:12<00:00, 5989.87 examples/s]" } }, - "6af063d78a0b4668bb022ad287881b3a": { + "748f41179a3a40d5bfb7a4edfe8d13ff": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -3892,15 +3945,37 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_58930589e42646c7a4adf4cd6bcd6371", - "max": 5175617.0, + "layout": "IPY_MODEL_e35a74ec5bd6445e96da7f49abfe6c0c", + "max": 60000.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_0422f06140e1429bb9df4d5e22a01ac2", - "value": 5175617.0 + "style": "IPY_MODEL_41327ec0739e4dcd957c5ee2898e050a", + "value": 60000.0 + } + }, + "79ccb0a1555b42b5a881bd1c68892db9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8a776487ef584aacb78e7a4c24fc20f0", + "IPY_MODEL_f9341d1dc6404c1194db44e60a6c230e", + "IPY_MODEL_3f168cb3d1c94b1ba4a217cd6467dbc6" + ], + "layout": "IPY_MODEL_1c435eee98164c94875a24dc9213ab07" } }, - "6c248b337d0c458ead2d440b3472437f": { + "7b8800a4904440598f0e45fab5fbd35a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3952,7 +4027,7 @@ "width": null } }, - "76f30844c4444b9cb566e2bb926b7b40": { + "7ce4749d923744c09bd675ee944baaad": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -3967,14 +4042,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_fc146736a7804106a33260d3a605aae1", - "IPY_MODEL_25cccd29d96c4634a04b0cb4c0ae7a99", - "IPY_MODEL_2b8505492b124436bf13b9605b3b7dd8" + "IPY_MODEL_fba3a6dbeac647a58b86ca3c2387dea1", + "IPY_MODEL_59a3fa3e95574a8b814d146dfed3c4b7", + "IPY_MODEL_9122abc397764bf2888a6ee466622011" ], - "layout": "IPY_MODEL_da553b2e323e4d9e9a3dfc9c117bcd77" + "layout": "IPY_MODEL_857bf7c092c443779ed47daba1a805c1" } }, - "78bbe03efe744a16b7517c70dee50f2a": { + "80393a3a390947c29ad3e5befeee9f1a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4026,7 +4101,7 @@ "width": null } }, - "7d62cb9c305943eab8fadf60d62315d0": { + "80ebd1685921401db08c4590b9baa83c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4041,59 +4116,28 @@ "description_width": "" } }, - "819ad8fec46043248eb022c81bb1b5b6": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "84270cee9acd46f7b5711cc0cb1d1790": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_eac75786bd0747edbc8069694b24a47f", + "placeholder": "​", + "style": "IPY_MODEL_c200da24b8d24af2a8729220de3fc9f5", + "value": " 60000/0 [00:00<00:00, 822434.05 examples/s]" } }, - "8388e5c0bcdd44658f6c1d9fe032adde": { + "857bf7c092c443779ed47daba1a805c1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4145,7 +4189,28 @@ "width": null } }, - "85537fd6b7504ad187504946935cc321": { + "8a776487ef584aacb78e7a4c24fc20f0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_be2fb1d47c9b4ddca4f55cd862ed528f", + "placeholder": "​", + "style": "IPY_MODEL_9f3c94882ba84a3188e74bcaadb84860", + "value": "Downloading data: 100%" + } + }, + "8e7dbb79e5a543c7885714426be3c75c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4197,58 +4262,29 @@ "width": null } }, - "886d87adc7e14bb181d14fe73356712c": { + "8f5c519e8d9d42d7a8b129396063163d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_ecec36d46b93457e812be0045f430615", - "placeholder": "​", - "style": "IPY_MODEL_92480a4d5aa542e4b3c245d5eaef0719", - "value": " 30.9M/30.9M [00:00<00:00, 54.1MB/s]" - } - }, - "8a59d3978538419bb1a7ae4f2c634c57": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "8b814b38f0104752b04bd4a5310bb6b1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_44346c7bc1cf426680dd5db30374b0c0", + "IPY_MODEL_406fe6cbb9a34dca80a0ecaff800df90", + "IPY_MODEL_8f6e5f7512ee4ff7ad72e4cc5dca9461" + ], + "layout": "IPY_MODEL_58928fb735cd44a8ab6e8c0edf078872" } }, - "8ec077adf7584cd58c173b40862ef286": { + "8f6e5f7512ee4ff7ad72e4cc5dca9461": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4263,81 +4299,34 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_d080bdb537ff418c9cc35048f6e03b22", + "layout": "IPY_MODEL_e5a47d68a2cd473f951c59fcf76d01f1", "placeholder": "​", - "style": "IPY_MODEL_7d62cb9c305943eab8fadf60d62315d0", - "value": "Downloading data: 100%" + "style": "IPY_MODEL_4ece4d1132ab47b39c47d91a5c14bc90", + "value": " 2/2 [00:00<00:00, 299.27it/s]" } }, - "9218ba50145b4c1abb05285d39489f7b": { + "9122abc397764bf2888a6ee466622011": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8ec077adf7584cd58c173b40862ef286", - "IPY_MODEL_dc3e760d54c94143afafcc4928690c1e", - "IPY_MODEL_886d87adc7e14bb181d14fe73356712c" - ], - "layout": "IPY_MODEL_43118ea6bbea4a20b0f36f67ad05ec28" - } - }, - "92480a4d5aa542e4b3c245d5eaef0719": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "94474ce5fa7c49859fb2ba027b64aef7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "9af6f90090b44f5a994f4d2ef2c28c38": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_44d4dca1913340ce8b9c507cd312f8c9", + "placeholder": "​", + "style": "IPY_MODEL_3dceb99795994c2c9faef0d8437d26df", + "value": " 10000/0 [00:00<00:00, 481500.65 examples/s]" } }, - "9e9f3c1977bc4cc18ccf7c0091220fdd": { + "94881b422b624471974fef993ac1b9e3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4389,7 +4378,7 @@ "width": null } }, - "a649432ed8c04a3b87f55c6e1cbc806d": { + "9531113f8f814de2b0a6e754c456bf1f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4441,22 +4430,7 @@ "width": null } }, - "a8a2c7b2b22842f5be552d65b32d2f6c": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "adfa225ec8f64e7880afb3cb066db908": { + "9700769e28304398b3004f50582ae2ae": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4508,59 +4482,39 @@ "width": null } }, - "ae038169fdbf4ff78f655a94650c3c2b": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "98fe2af32bbc4615b9fd39e09236fd2f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": "20px" + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "995e09d244bd49bd8d7ac45836ec79f9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "af1dd8d8ecef45a48f86fa83500577b4": { + "99d94d93be9e48819c0c81e7e63b86e8": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4575,59 +4529,68 @@ "description_width": "" } }, - "b950eeaac0b8423fbc3d696ee313e0f7": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "9ae996a06a074f598ffdd1896fc06e5f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" + } + }, + "9d080255b4c84e7c91b438dd5562134b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "9f3c94882ba84a3188e74bcaadb84860": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "9fafe542e2ed4c18aab17f4dc9200a15": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "c8c2dd564bcd47159c5d7da2cd990067": { + "a392060d75f04f79b300b2254520405a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4679,22 +4642,23 @@ "width": null } }, - "cee108b3a0fc446daf791fcfa739d5f9": { + "b36fa317529346c8837fe628e9b58225": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "cf40c5cc5c3e42069f474bc592b94779": { + "b94cbe2658d747a2b3051c57bb7a1145": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4709,13 +4673,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c8c2dd564bcd47159c5d7da2cd990067", + "layout": "IPY_MODEL_1a99e1fffc83437580ec94981db5304f", "placeholder": "​", - "style": "IPY_MODEL_cee108b3a0fc446daf791fcfa739d5f9", - "value": " 10000/0 [00:00<00:00, 518981.42 examples/s]" + "style": "IPY_MODEL_06760201f05c4000afc116288d98641b", + "value": "100%" } }, - "cf90eb9f6391477bbf013ddee2032497": { + "be2fb1d47c9b4ddca4f55cd862ed528f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4767,7 +4731,44 @@ "width": null } }, - "d0382716c80644e3ba550bc5ed756161": { + "c1f8c848d7954850a757e5e8fa83f920": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_b94cbe2658d747a2b3051c57bb7a1145", + "IPY_MODEL_748f41179a3a40d5bfb7a4edfe8d13ff", + "IPY_MODEL_57b755a1482c49a2be45568b6bdd0f3e" + ], + "layout": "IPY_MODEL_4f39a6854c894be9b84b9424dc291601" + } + }, + "c200da24b8d24af2a8729220de3fc9f5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "c3549b4dff2849a49f4c582646e80469": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4819,7 +4820,7 @@ "width": null } }, - "d080bdb537ff418c9cc35048f6e03b22": { + "c83a7ea94765464f9172b04328ecf7cb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4871,28 +4872,29 @@ "width": null } }, - "d80873115a3d48b4a783d8a26120c697": { + "cf1bbec90cc743c19c044238bc5cd410": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_5a1b5f56c91c4c2aa69d687eae32d787", - "placeholder": "​", - "style": "IPY_MODEL_de829f78730844509bffcc9f0cf8f531", - "value": "100%" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_179264665b3d4da192240f1ac09ac5e5", + "IPY_MODEL_045208935e1f4bd6a391b26b3983d049", + "IPY_MODEL_6f35b4075f004e12901e63c69bb0356a" + ], + "layout": "IPY_MODEL_39cced6b09354583a41a423a7f3697f2" } }, - "da553b2e323e4d9e9a3dfc9c117bcd77": { + "e35a74ec5bd6445e96da7f49abfe6c0c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4944,67 +4946,7 @@ "width": null } }, - "daf93e367bcd41009e494ece90f80fde": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_d0382716c80644e3ba550bc5ed756161", - "placeholder": "​", - "style": "IPY_MODEL_1e1ecba39033420591ada0b76d527bf1", - "value": "Generating train split: " - } - }, - "dc3e760d54c94143afafcc4928690c1e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_78bbe03efe744a16b7517c70dee50f2a", - "max": 30931277.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_06b2bf8222ef4b6db00a974dc321d1d2", - "value": 30931277.0 - } - }, - "de829f78730844509bffcc9f0cf8f531": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "e57d19b9905849f58864c0e2ae9f2c73": { + "e5a47d68a2cd473f951c59fcf76d01f1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5056,7 +4998,7 @@ "width": null } }, - "ecec36d46b93457e812be0045f430615": { + "e6236cd92f06491a83ea7c38bf31a7e7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5108,60 +5050,74 @@ "width": null } }, - "f72cf07726314106aaa73a8aa0dbb3d9": { + "e9e90e1e28aa4e3588e479ab25012fbf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "f7614c62ca994b079963f2061f90aec2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "eac75786bd0747edbc8069694b24a47f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "fc146736a7804106a33260d3a605aae1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_fcad613391c24914b64a652e2c514fcb", - "placeholder": "​", - "style": "IPY_MODEL_8a59d3978538419bb1a7ae4f2c634c57", - "value": "Map (num_proc=4): 100%" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "fcad613391c24914b64a652e2c514fcb": { + "f21c6c99bca84e06b8d1299f3b53db6d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5213,7 +5169,7 @@ "width": null } }, - "fd329fbe811246e8a0566112ee0206c9": { + "f2f5ea97fde04ab1992313b89ce1e044": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5262,37 +5218,73 @@ "right": null, "top": null, "visibility": null, - "width": null + "width": "20px" } }, - "fdd50d7c13524c3c882b9c849c0fb910": { + "f9341d1dc6404c1194db44e60a6c230e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_f21c6c99bca84e06b8d1299f3b53db6d", + "max": 30931277.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b36fa317529346c8837fe628e9b58225", + "value": 30931277.0 } }, - "fe2e11a13fdb46e8b74279e56b89a038": { + "fba3a6dbeac647a58b86ca3c2387dea1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9531113f8f814de2b0a6e754c456bf1f", + "placeholder": "​", + "style": "IPY_MODEL_9d080255b4c84e7c91b438dd5562134b", + "value": "Generating test split: " + } + }, + "fd2ab6faeeeb4055bbcc5fe64bac125d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9700769e28304398b3004f50582ae2ae", + "placeholder": "​", + "style": "IPY_MODEL_80ebd1685921401db08c4590b9baa83c", + "value": " 5.18M/5.18M [00:00<00:00, 38.7MB/s]" } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index f96a2b6cb..36e58d2a0 100644 --- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:21.766507Z", - "iopub.status.busy": "2024-01-17T18:10:21.766309Z", - "iopub.status.idle": "2024-01-17T18:10:22.883924Z", - "shell.execute_reply": "2024-01-17T18:10:22.882998Z" + "iopub.execute_input": "2024-01-17T23:12:25.645173Z", + "iopub.status.busy": "2024-01-17T23:12:25.644719Z", + "iopub.status.idle": "2024-01-17T23:12:26.712765Z", + "shell.execute_reply": "2024-01-17T23:12:26.712142Z" }, "nbsphinx": "hidden" }, @@ -68,7 +68,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -95,10 +95,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:22.886848Z", - "iopub.status.busy": "2024-01-17T18:10:22.886478Z", - "iopub.status.idle": "2024-01-17T18:10:23.166976Z", - "shell.execute_reply": "2024-01-17T18:10:23.166265Z" + "iopub.execute_input": "2024-01-17T23:12:26.715639Z", + "iopub.status.busy": "2024-01-17T23:12:26.715237Z", + "iopub.status.idle": "2024-01-17T23:12:26.981319Z", + "shell.execute_reply": "2024-01-17T23:12:26.980689Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:23.170233Z", - "iopub.status.busy": "2024-01-17T18:10:23.169703Z", - "iopub.status.idle": "2024-01-17T18:10:23.182165Z", - "shell.execute_reply": "2024-01-17T18:10:23.181510Z" + "iopub.execute_input": "2024-01-17T23:12:26.984263Z", + "iopub.status.busy": "2024-01-17T23:12:26.983847Z", + "iopub.status.idle": "2024-01-17T23:12:26.995890Z", + "shell.execute_reply": "2024-01-17T23:12:26.995368Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:23.184824Z", - "iopub.status.busy": "2024-01-17T18:10:23.184445Z", - "iopub.status.idle": "2024-01-17T18:10:23.391408Z", - "shell.execute_reply": "2024-01-17T18:10:23.390738Z" + "iopub.execute_input": "2024-01-17T23:12:26.998151Z", + "iopub.status.busy": "2024-01-17T23:12:26.997887Z", + "iopub.status.idle": "2024-01-17T23:12:27.219836Z", + "shell.execute_reply": "2024-01-17T23:12:27.219174Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:23.394226Z", - "iopub.status.busy": "2024-01-17T18:10:23.393866Z", - "iopub.status.idle": "2024-01-17T18:10:23.421258Z", - "shell.execute_reply": "2024-01-17T18:10:23.420553Z" + "iopub.execute_input": "2024-01-17T23:12:27.222804Z", + "iopub.status.busy": "2024-01-17T23:12:27.222337Z", + "iopub.status.idle": "2024-01-17T23:12:27.249283Z", + "shell.execute_reply": "2024-01-17T23:12:27.248679Z" } }, "outputs": [], @@ -427,10 +427,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:23.423997Z", - "iopub.status.busy": "2024-01-17T18:10:23.423593Z", - "iopub.status.idle": "2024-01-17T18:10:24.787226Z", - "shell.execute_reply": "2024-01-17T18:10:24.786489Z" + "iopub.execute_input": "2024-01-17T23:12:27.251883Z", + "iopub.status.busy": "2024-01-17T23:12:27.251444Z", + "iopub.status.idle": "2024-01-17T23:12:28.555194Z", + "shell.execute_reply": "2024-01-17T23:12:28.554473Z" } }, "outputs": [ @@ -473,10 +473,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:24.790031Z", - "iopub.status.busy": "2024-01-17T18:10:24.789628Z", - "iopub.status.idle": "2024-01-17T18:10:24.814871Z", - "shell.execute_reply": "2024-01-17T18:10:24.814207Z" + "iopub.execute_input": "2024-01-17T23:12:28.558034Z", + "iopub.status.busy": "2024-01-17T23:12:28.557654Z", + "iopub.status.idle": "2024-01-17T23:12:28.582599Z", + "shell.execute_reply": "2024-01-17T23:12:28.581962Z" }, "scrolled": true }, @@ -641,10 +641,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:24.817303Z", - "iopub.status.busy": "2024-01-17T18:10:24.816930Z", - "iopub.status.idle": "2024-01-17T18:10:25.718347Z", - "shell.execute_reply": "2024-01-17T18:10:25.717676Z" + "iopub.execute_input": "2024-01-17T23:12:28.584982Z", + "iopub.status.busy": "2024-01-17T23:12:28.584634Z", + "iopub.status.idle": "2024-01-17T23:12:29.463581Z", + "shell.execute_reply": "2024-01-17T23:12:29.462958Z" }, "id": "AaHC5MRKjruT" }, @@ -763,10 +763,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:25.721086Z", - "iopub.status.busy": "2024-01-17T18:10:25.720678Z", - "iopub.status.idle": "2024-01-17T18:10:25.735438Z", - "shell.execute_reply": "2024-01-17T18:10:25.734893Z" + "iopub.execute_input": "2024-01-17T23:12:29.466240Z", + "iopub.status.busy": "2024-01-17T23:12:29.465885Z", + "iopub.status.idle": "2024-01-17T23:12:29.480184Z", + "shell.execute_reply": "2024-01-17T23:12:29.479506Z" }, "id": "Wy27rvyhjruU" }, @@ -815,10 +815,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:25.738066Z", - "iopub.status.busy": "2024-01-17T18:10:25.737688Z", - "iopub.status.idle": "2024-01-17T18:10:25.834468Z", - "shell.execute_reply": "2024-01-17T18:10:25.833833Z" + "iopub.execute_input": "2024-01-17T23:12:29.482535Z", + "iopub.status.busy": "2024-01-17T23:12:29.482170Z", + "iopub.status.idle": "2024-01-17T23:12:29.563194Z", + "shell.execute_reply": "2024-01-17T23:12:29.562462Z" }, "id": "Db8YHnyVjruU" }, @@ -925,10 +925,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:25.837117Z", - "iopub.status.busy": "2024-01-17T18:10:25.836764Z", - "iopub.status.idle": "2024-01-17T18:10:26.040217Z", - "shell.execute_reply": "2024-01-17T18:10:26.039537Z" + "iopub.execute_input": "2024-01-17T23:12:29.565726Z", + "iopub.status.busy": "2024-01-17T23:12:29.565468Z", + "iopub.status.idle": "2024-01-17T23:12:29.770924Z", + "shell.execute_reply": "2024-01-17T23:12:29.770408Z" }, "id": "iJqAHuS2jruV" }, @@ -965,10 +965,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.042998Z", - "iopub.status.busy": "2024-01-17T18:10:26.042584Z", - "iopub.status.idle": "2024-01-17T18:10:26.060028Z", - "shell.execute_reply": "2024-01-17T18:10:26.059516Z" + "iopub.execute_input": "2024-01-17T23:12:29.773611Z", + "iopub.status.busy": "2024-01-17T23:12:29.773086Z", + "iopub.status.idle": "2024-01-17T23:12:29.790463Z", + "shell.execute_reply": "2024-01-17T23:12:29.789935Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -1030,10 +1030,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.062644Z", - "iopub.status.busy": "2024-01-17T18:10:26.062148Z", - "iopub.status.idle": "2024-01-17T18:10:26.072694Z", - "shell.execute_reply": "2024-01-17T18:10:26.072095Z" + "iopub.execute_input": "2024-01-17T23:12:29.792922Z", + "iopub.status.busy": "2024-01-17T23:12:29.792556Z", + "iopub.status.idle": "2024-01-17T23:12:29.802644Z", + "shell.execute_reply": "2024-01-17T23:12:29.802136Z" }, "id": "0lonvOYvjruV" }, @@ -1180,10 +1180,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.075434Z", - "iopub.status.busy": "2024-01-17T18:10:26.074917Z", - "iopub.status.idle": "2024-01-17T18:10:26.191355Z", - "shell.execute_reply": "2024-01-17T18:10:26.190725Z" + "iopub.execute_input": "2024-01-17T23:12:29.805003Z", + "iopub.status.busy": "2024-01-17T23:12:29.804543Z", + "iopub.status.idle": "2024-01-17T23:12:29.898840Z", + "shell.execute_reply": "2024-01-17T23:12:29.898192Z" }, "id": "MfqTCa3kjruV" }, @@ -1264,10 +1264,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.194148Z", - "iopub.status.busy": "2024-01-17T18:10:26.193847Z", - "iopub.status.idle": "2024-01-17T18:10:26.344135Z", - "shell.execute_reply": "2024-01-17T18:10:26.343417Z" + "iopub.execute_input": "2024-01-17T23:12:29.901475Z", + "iopub.status.busy": "2024-01-17T23:12:29.901217Z", + "iopub.status.idle": "2024-01-17T23:12:30.040827Z", + "shell.execute_reply": "2024-01-17T23:12:30.040112Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1327,10 +1327,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.347074Z", - "iopub.status.busy": "2024-01-17T18:10:26.346637Z", - "iopub.status.idle": "2024-01-17T18:10:26.350908Z", - "shell.execute_reply": "2024-01-17T18:10:26.350355Z" + "iopub.execute_input": "2024-01-17T23:12:30.043470Z", + "iopub.status.busy": "2024-01-17T23:12:30.043221Z", + "iopub.status.idle": "2024-01-17T23:12:30.047307Z", + "shell.execute_reply": "2024-01-17T23:12:30.046685Z" }, "id": "0rXP3ZPWjruW" }, @@ -1368,10 +1368,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.353694Z", - "iopub.status.busy": "2024-01-17T18:10:26.352956Z", - "iopub.status.idle": "2024-01-17T18:10:26.358205Z", - "shell.execute_reply": "2024-01-17T18:10:26.357689Z" + "iopub.execute_input": "2024-01-17T23:12:30.049676Z", + "iopub.status.busy": "2024-01-17T23:12:30.049240Z", + "iopub.status.idle": "2024-01-17T23:12:30.053883Z", + "shell.execute_reply": "2024-01-17T23:12:30.053284Z" }, "id": "-iRPe8KXjruW" }, @@ -1426,10 +1426,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.360522Z", - "iopub.status.busy": "2024-01-17T18:10:26.360138Z", - "iopub.status.idle": "2024-01-17T18:10:26.400534Z", - "shell.execute_reply": "2024-01-17T18:10:26.399805Z" + "iopub.execute_input": "2024-01-17T23:12:30.056405Z", + "iopub.status.busy": "2024-01-17T23:12:30.055960Z", + "iopub.status.idle": "2024-01-17T23:12:30.095504Z", + "shell.execute_reply": "2024-01-17T23:12:30.094989Z" }, "id": "ZpipUliyjruW" }, @@ -1480,10 +1480,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.403784Z", - "iopub.status.busy": "2024-01-17T18:10:26.403295Z", - "iopub.status.idle": "2024-01-17T18:10:26.450103Z", - "shell.execute_reply": "2024-01-17T18:10:26.449493Z" + "iopub.execute_input": "2024-01-17T23:12:30.097960Z", + "iopub.status.busy": "2024-01-17T23:12:30.097577Z", + "iopub.status.idle": "2024-01-17T23:12:30.144004Z", + "shell.execute_reply": "2024-01-17T23:12:30.143423Z" }, "id": "SLq-3q4xjruX" }, @@ -1552,10 +1552,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.452754Z", - "iopub.status.busy": "2024-01-17T18:10:26.452361Z", - "iopub.status.idle": "2024-01-17T18:10:26.564403Z", - "shell.execute_reply": "2024-01-17T18:10:26.563611Z" + "iopub.execute_input": "2024-01-17T23:12:30.146635Z", + "iopub.status.busy": "2024-01-17T23:12:30.146169Z", + "iopub.status.idle": "2024-01-17T23:12:30.252527Z", + "shell.execute_reply": "2024-01-17T23:12:30.251862Z" }, "id": "g5LHhhuqFbXK" }, @@ -1587,10 +1587,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.567555Z", - "iopub.status.busy": "2024-01-17T18:10:26.567278Z", - "iopub.status.idle": "2024-01-17T18:10:26.680006Z", - "shell.execute_reply": "2024-01-17T18:10:26.679288Z" + "iopub.execute_input": "2024-01-17T23:12:30.255773Z", + "iopub.status.busy": "2024-01-17T23:12:30.255280Z", + "iopub.status.idle": "2024-01-17T23:12:30.355493Z", + "shell.execute_reply": "2024-01-17T23:12:30.354791Z" }, "id": "p7w8F8ezBcet" }, @@ -1647,10 +1647,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.682699Z", - "iopub.status.busy": "2024-01-17T18:10:26.682408Z", - "iopub.status.idle": "2024-01-17T18:10:26.885900Z", - "shell.execute_reply": "2024-01-17T18:10:26.885214Z" + "iopub.execute_input": "2024-01-17T23:12:30.358291Z", + "iopub.status.busy": "2024-01-17T23:12:30.358031Z", + "iopub.status.idle": "2024-01-17T23:12:30.561214Z", + "shell.execute_reply": "2024-01-17T23:12:30.560494Z" }, "id": "WETRL74tE_sU" }, @@ -1685,10 +1685,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:26.888642Z", - "iopub.status.busy": "2024-01-17T18:10:26.888408Z", - "iopub.status.idle": "2024-01-17T18:10:27.119260Z", - "shell.execute_reply": "2024-01-17T18:10:27.118551Z" + "iopub.execute_input": "2024-01-17T23:12:30.563781Z", + "iopub.status.busy": "2024-01-17T23:12:30.563569Z", + "iopub.status.idle": "2024-01-17T23:12:30.778738Z", + "shell.execute_reply": "2024-01-17T23:12:30.778106Z" }, "id": "kCfdx2gOLmXS" }, @@ -1850,10 +1850,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:27.121834Z", - "iopub.status.busy": "2024-01-17T18:10:27.121615Z", - "iopub.status.idle": "2024-01-17T18:10:27.128106Z", - "shell.execute_reply": "2024-01-17T18:10:27.127583Z" + "iopub.execute_input": "2024-01-17T23:12:30.781569Z", + "iopub.status.busy": "2024-01-17T23:12:30.781097Z", + "iopub.status.idle": "2024-01-17T23:12:30.787611Z", + "shell.execute_reply": "2024-01-17T23:12:30.787090Z" }, "id": "-uogYRWFYnuu" }, @@ -1907,10 +1907,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:27.130325Z", - "iopub.status.busy": "2024-01-17T18:10:27.130128Z", - "iopub.status.idle": "2024-01-17T18:10:27.340313Z", - "shell.execute_reply": "2024-01-17T18:10:27.339598Z" + "iopub.execute_input": "2024-01-17T23:12:30.789959Z", + "iopub.status.busy": "2024-01-17T23:12:30.789573Z", + "iopub.status.idle": "2024-01-17T23:12:30.995874Z", + "shell.execute_reply": "2024-01-17T23:12:30.995216Z" }, "id": "pG-ljrmcYp9Q" }, @@ -1957,10 +1957,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:27.342786Z", - "iopub.status.busy": "2024-01-17T18:10:27.342557Z", - "iopub.status.idle": "2024-01-17T18:10:28.416510Z", - "shell.execute_reply": "2024-01-17T18:10:28.415789Z" + "iopub.execute_input": "2024-01-17T23:12:30.998644Z", + "iopub.status.busy": "2024-01-17T23:12:30.998256Z", + "iopub.status.idle": "2024-01-17T23:12:32.077472Z", + "shell.execute_reply": "2024-01-17T23:12:32.076842Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index cfe891b73..7aab0ca60 100644 --- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb @@ -89,10 +89,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:34.066939Z", - "iopub.status.busy": "2024-01-17T18:10:34.066726Z", - "iopub.status.idle": "2024-01-17T18:10:35.107469Z", - "shell.execute_reply": "2024-01-17T18:10:35.106848Z" + "iopub.execute_input": "2024-01-17T23:12:37.119091Z", + "iopub.status.busy": "2024-01-17T23:12:37.118893Z", + "iopub.status.idle": "2024-01-17T23:12:38.145112Z", + "shell.execute_reply": "2024-01-17T23:12:38.144486Z" }, "nbsphinx": "hidden" }, @@ -102,7 +102,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -136,10 +136,10 @@ "id": "c4efd119", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:35.110492Z", - "iopub.status.busy": "2024-01-17T18:10:35.110165Z", - "iopub.status.idle": "2024-01-17T18:10:35.113533Z", - "shell.execute_reply": "2024-01-17T18:10:35.112907Z" + "iopub.execute_input": "2024-01-17T23:12:38.148219Z", + "iopub.status.busy": "2024-01-17T23:12:38.147778Z", + "iopub.status.idle": "2024-01-17T23:12:38.151072Z", + "shell.execute_reply": "2024-01-17T23:12:38.150569Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:35.116141Z", - "iopub.status.busy": "2024-01-17T18:10:35.115710Z", - "iopub.status.idle": "2024-01-17T18:10:35.124258Z", - "shell.execute_reply": "2024-01-17T18:10:35.123628Z" + "iopub.execute_input": "2024-01-17T23:12:38.153453Z", + "iopub.status.busy": "2024-01-17T23:12:38.153123Z", + "iopub.status.idle": "2024-01-17T23:12:38.161595Z", + "shell.execute_reply": "2024-01-17T23:12:38.160994Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:35.126617Z", - "iopub.status.busy": "2024-01-17T18:10:35.126181Z", - "iopub.status.idle": "2024-01-17T18:10:35.174782Z", - "shell.execute_reply": "2024-01-17T18:10:35.174277Z" + "iopub.execute_input": "2024-01-17T23:12:38.163930Z", + "iopub.status.busy": "2024-01-17T23:12:38.163445Z", + "iopub.status.idle": "2024-01-17T23:12:38.215430Z", + "shell.execute_reply": "2024-01-17T23:12:38.214906Z" } }, "outputs": [], @@ -380,10 +380,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:35.177183Z", - "iopub.status.busy": "2024-01-17T18:10:35.176965Z", - "iopub.status.idle": "2024-01-17T18:10:35.196729Z", - "shell.execute_reply": "2024-01-17T18:10:35.196205Z" + "iopub.execute_input": "2024-01-17T23:12:38.217958Z", + "iopub.status.busy": "2024-01-17T23:12:38.217555Z", + "iopub.status.idle": "2024-01-17T23:12:38.236753Z", + "shell.execute_reply": "2024-01-17T23:12:38.236121Z" } }, "outputs": [ @@ -598,10 +598,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:35.199192Z", - "iopub.status.busy": "2024-01-17T18:10:35.198837Z", - "iopub.status.idle": "2024-01-17T18:10:35.202844Z", - "shell.execute_reply": "2024-01-17T18:10:35.202255Z" + "iopub.execute_input": "2024-01-17T23:12:38.239132Z", + "iopub.status.busy": "2024-01-17T23:12:38.238775Z", + "iopub.status.idle": "2024-01-17T23:12:38.242790Z", + "shell.execute_reply": "2024-01-17T23:12:38.242281Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:35.205415Z", - "iopub.status.busy": "2024-01-17T18:10:35.204931Z", - "iopub.status.idle": "2024-01-17T18:10:35.232341Z", - "shell.execute_reply": "2024-01-17T18:10:35.231693Z" + "iopub.execute_input": "2024-01-17T23:12:38.245093Z", + "iopub.status.busy": "2024-01-17T23:12:38.244847Z", + "iopub.status.idle": "2024-01-17T23:12:38.275707Z", + "shell.execute_reply": "2024-01-17T23:12:38.275228Z" } }, "outputs": [], @@ -699,10 +699,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:35.234865Z", - "iopub.status.busy": "2024-01-17T18:10:35.234654Z", - "iopub.status.idle": "2024-01-17T18:10:35.262331Z", - "shell.execute_reply": "2024-01-17T18:10:35.261831Z" + "iopub.execute_input": "2024-01-17T23:12:38.277989Z", + "iopub.status.busy": "2024-01-17T23:12:38.277604Z", + "iopub.status.idle": "2024-01-17T23:12:38.304773Z", + "shell.execute_reply": "2024-01-17T23:12:38.304291Z" } }, "outputs": [], @@ -739,10 +739,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:35.264679Z", - "iopub.status.busy": "2024-01-17T18:10:35.264478Z", - "iopub.status.idle": "2024-01-17T18:10:36.634552Z", - "shell.execute_reply": "2024-01-17T18:10:36.633890Z" + "iopub.execute_input": "2024-01-17T23:12:38.307191Z", + "iopub.status.busy": "2024-01-17T23:12:38.306725Z", + "iopub.status.idle": "2024-01-17T23:12:39.660035Z", + "shell.execute_reply": "2024-01-17T23:12:39.659311Z" } }, "outputs": [], @@ -772,10 +772,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:36.637556Z", - "iopub.status.busy": "2024-01-17T18:10:36.637138Z", - "iopub.status.idle": "2024-01-17T18:10:36.644742Z", - "shell.execute_reply": "2024-01-17T18:10:36.644122Z" + "iopub.execute_input": "2024-01-17T23:12:39.663258Z", + "iopub.status.busy": "2024-01-17T23:12:39.662839Z", + "iopub.status.idle": "2024-01-17T23:12:39.670307Z", + "shell.execute_reply": "2024-01-17T23:12:39.669734Z" }, "scrolled": true }, @@ -886,10 +886,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:36.647074Z", - "iopub.status.busy": "2024-01-17T18:10:36.646869Z", - "iopub.status.idle": "2024-01-17T18:10:36.661069Z", - "shell.execute_reply": "2024-01-17T18:10:36.660517Z" + "iopub.execute_input": "2024-01-17T23:12:39.672727Z", + "iopub.status.busy": "2024-01-17T23:12:39.672379Z", + "iopub.status.idle": "2024-01-17T23:12:39.686172Z", + "shell.execute_reply": "2024-01-17T23:12:39.685544Z" } }, "outputs": [ @@ -1139,10 +1139,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:36.663330Z", - "iopub.status.busy": "2024-01-17T18:10:36.663130Z", - "iopub.status.idle": "2024-01-17T18:10:36.670332Z", - "shell.execute_reply": "2024-01-17T18:10:36.669799Z" + "iopub.execute_input": "2024-01-17T23:12:39.688633Z", + "iopub.status.busy": "2024-01-17T23:12:39.688179Z", + "iopub.status.idle": "2024-01-17T23:12:39.694998Z", + "shell.execute_reply": "2024-01-17T23:12:39.694392Z" }, "scrolled": true }, @@ -1316,10 +1316,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:36.672921Z", - "iopub.status.busy": "2024-01-17T18:10:36.672547Z", - "iopub.status.idle": "2024-01-17T18:10:36.675600Z", - "shell.execute_reply": "2024-01-17T18:10:36.675078Z" + "iopub.execute_input": "2024-01-17T23:12:39.697375Z", + "iopub.status.busy": "2024-01-17T23:12:39.696998Z", + "iopub.status.idle": "2024-01-17T23:12:39.699962Z", + "shell.execute_reply": "2024-01-17T23:12:39.699338Z" } }, "outputs": [], @@ -1341,10 +1341,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:36.677889Z", - "iopub.status.busy": "2024-01-17T18:10:36.677688Z", - "iopub.status.idle": "2024-01-17T18:10:36.682112Z", - "shell.execute_reply": "2024-01-17T18:10:36.681569Z" + "iopub.execute_input": "2024-01-17T23:12:39.702455Z", + "iopub.status.busy": "2024-01-17T23:12:39.702117Z", + "iopub.status.idle": "2024-01-17T23:12:39.706331Z", + "shell.execute_reply": "2024-01-17T23:12:39.705698Z" }, "scrolled": true }, @@ -1396,10 +1396,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:36.684614Z", - "iopub.status.busy": "2024-01-17T18:10:36.684230Z", - "iopub.status.idle": "2024-01-17T18:10:36.687195Z", - "shell.execute_reply": "2024-01-17T18:10:36.686658Z" + "iopub.execute_input": "2024-01-17T23:12:39.708839Z", + "iopub.status.busy": "2024-01-17T23:12:39.708410Z", + "iopub.status.idle": "2024-01-17T23:12:39.711306Z", + "shell.execute_reply": "2024-01-17T23:12:39.710774Z" } }, "outputs": [], @@ -1423,10 +1423,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:36.689699Z", - "iopub.status.busy": "2024-01-17T18:10:36.689303Z", - "iopub.status.idle": "2024-01-17T18:10:36.693988Z", - "shell.execute_reply": "2024-01-17T18:10:36.693350Z" + "iopub.execute_input": "2024-01-17T23:12:39.713686Z", + "iopub.status.busy": "2024-01-17T23:12:39.713257Z", + "iopub.status.idle": "2024-01-17T23:12:39.717980Z", + "shell.execute_reply": "2024-01-17T23:12:39.717452Z" } }, "outputs": [ @@ -1481,10 +1481,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:36.696492Z", - "iopub.status.busy": "2024-01-17T18:10:36.696127Z", - "iopub.status.idle": "2024-01-17T18:10:36.730677Z", - "shell.execute_reply": "2024-01-17T18:10:36.730063Z" + "iopub.execute_input": "2024-01-17T23:12:39.720498Z", + "iopub.status.busy": "2024-01-17T23:12:39.720135Z", + "iopub.status.idle": "2024-01-17T23:12:39.753497Z", + "shell.execute_reply": "2024-01-17T23:12:39.753003Z" } }, "outputs": [], @@ -1527,10 +1527,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:36.733736Z", - "iopub.status.busy": "2024-01-17T18:10:36.733290Z", - "iopub.status.idle": "2024-01-17T18:10:36.739015Z", - "shell.execute_reply": "2024-01-17T18:10:36.738419Z" + "iopub.execute_input": "2024-01-17T23:12:39.755769Z", + "iopub.status.busy": "2024-01-17T23:12:39.755564Z", + "iopub.status.idle": "2024-01-17T23:12:39.760459Z", + "shell.execute_reply": "2024-01-17T23:12:39.759917Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index 345c02d68..870f9da70 100644 --- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb @@ -63,10 +63,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:41.555662Z", - "iopub.status.busy": "2024-01-17T18:10:41.555447Z", - "iopub.status.idle": "2024-01-17T18:10:42.641450Z", - "shell.execute_reply": "2024-01-17T18:10:42.640766Z" + "iopub.execute_input": "2024-01-17T23:12:45.381897Z", + "iopub.status.busy": "2024-01-17T23:12:45.381706Z", + "iopub.status.idle": "2024-01-17T23:12:46.455370Z", + "shell.execute_reply": "2024-01-17T23:12:46.454763Z" }, "nbsphinx": "hidden" }, @@ -78,7 +78,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -104,10 +104,10 @@ "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:42.644299Z", - "iopub.status.busy": "2024-01-17T18:10:42.643939Z", - "iopub.status.idle": "2024-01-17T18:10:42.934236Z", - "shell.execute_reply": "2024-01-17T18:10:42.933614Z" + "iopub.execute_input": "2024-01-17T23:12:46.458206Z", + "iopub.status.busy": "2024-01-17T23:12:46.457873Z", + "iopub.status.idle": "2024-01-17T23:12:46.742282Z", + "shell.execute_reply": "2024-01-17T23:12:46.741647Z" } }, "outputs": [], @@ -269,10 +269,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:42.937365Z", - "iopub.status.busy": "2024-01-17T18:10:42.936893Z", - "iopub.status.idle": "2024-01-17T18:10:42.950979Z", - "shell.execute_reply": "2024-01-17T18:10:42.950488Z" + "iopub.execute_input": "2024-01-17T23:12:46.745006Z", + "iopub.status.busy": "2024-01-17T23:12:46.744797Z", + "iopub.status.idle": "2024-01-17T23:12:46.758584Z", + "shell.execute_reply": "2024-01-17T23:12:46.758067Z" }, "nbsphinx": "hidden" }, @@ -408,10 +408,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:42.953251Z", - "iopub.status.busy": "2024-01-17T18:10:42.953039Z", - "iopub.status.idle": "2024-01-17T18:10:45.626467Z", - "shell.execute_reply": "2024-01-17T18:10:45.625783Z" + "iopub.execute_input": "2024-01-17T23:12:46.761055Z", + "iopub.status.busy": "2024-01-17T23:12:46.760585Z", + "iopub.status.idle": "2024-01-17T23:12:49.437361Z", + "shell.execute_reply": "2024-01-17T23:12:49.436698Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:45.629098Z", - "iopub.status.busy": "2024-01-17T18:10:45.628708Z", - "iopub.status.idle": "2024-01-17T18:10:47.211836Z", - "shell.execute_reply": "2024-01-17T18:10:47.211233Z" + "iopub.execute_input": "2024-01-17T23:12:49.440037Z", + "iopub.status.busy": "2024-01-17T23:12:49.439727Z", + "iopub.status.idle": "2024-01-17T23:12:51.019074Z", + "shell.execute_reply": "2024-01-17T23:12:51.018452Z" } }, "outputs": [], @@ -498,10 +498,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:47.214903Z", - "iopub.status.busy": "2024-01-17T18:10:47.214448Z", - "iopub.status.idle": "2024-01-17T18:10:47.220079Z", - "shell.execute_reply": "2024-01-17T18:10:47.219546Z" + "iopub.execute_input": "2024-01-17T23:12:51.021987Z", + "iopub.status.busy": "2024-01-17T23:12:51.021563Z", + "iopub.status.idle": "2024-01-17T23:12:51.026386Z", + "shell.execute_reply": "2024-01-17T23:12:51.025743Z" } }, "outputs": [ @@ -543,10 +543,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:47.222591Z", - "iopub.status.busy": "2024-01-17T18:10:47.222214Z", - "iopub.status.idle": "2024-01-17T18:10:48.654551Z", - "shell.execute_reply": "2024-01-17T18:10:48.653628Z" + "iopub.execute_input": "2024-01-17T23:12:51.028795Z", + "iopub.status.busy": "2024-01-17T23:12:51.028423Z", + "iopub.status.idle": "2024-01-17T23:12:52.362154Z", + "shell.execute_reply": "2024-01-17T23:12:52.361435Z" } }, "outputs": [ @@ -584,10 +584,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:48.658404Z", - "iopub.status.busy": "2024-01-17T18:10:48.657649Z", - "iopub.status.idle": "2024-01-17T18:10:51.504151Z", - "shell.execute_reply": "2024-01-17T18:10:51.503505Z" + "iopub.execute_input": "2024-01-17T23:12:52.365370Z", + "iopub.status.busy": "2024-01-17T23:12:52.364687Z", + "iopub.status.idle": "2024-01-17T23:12:55.189939Z", + "shell.execute_reply": "2024-01-17T23:12:55.189338Z" } }, "outputs": [ @@ -622,10 +622,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:51.506885Z", - "iopub.status.busy": "2024-01-17T18:10:51.506474Z", - "iopub.status.idle": "2024-01-17T18:10:51.511559Z", - "shell.execute_reply": "2024-01-17T18:10:51.510930Z" + "iopub.execute_input": "2024-01-17T23:12:55.192359Z", + "iopub.status.busy": "2024-01-17T23:12:55.192152Z", + "iopub.status.idle": "2024-01-17T23:12:55.197237Z", + "shell.execute_reply": "2024-01-17T23:12:55.196716Z" } }, "outputs": [ @@ -662,10 +662,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:51.514207Z", - "iopub.status.busy": "2024-01-17T18:10:51.513861Z", - "iopub.status.idle": "2024-01-17T18:10:51.518178Z", - "shell.execute_reply": "2024-01-17T18:10:51.517551Z" + "iopub.execute_input": "2024-01-17T23:12:55.199466Z", + "iopub.status.busy": "2024-01-17T23:12:55.199269Z", + "iopub.status.idle": "2024-01-17T23:12:55.203414Z", + "shell.execute_reply": "2024-01-17T23:12:55.202885Z" } }, "outputs": [], @@ -688,10 +688,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:51.520433Z", - "iopub.status.busy": "2024-01-17T18:10:51.520221Z", - "iopub.status.idle": "2024-01-17T18:10:51.523664Z", - "shell.execute_reply": "2024-01-17T18:10:51.523119Z" + "iopub.execute_input": "2024-01-17T23:12:55.205569Z", + "iopub.status.busy": "2024-01-17T23:12:55.205370Z", + "iopub.status.idle": "2024-01-17T23:12:55.208734Z", + "shell.execute_reply": "2024-01-17T23:12:55.208218Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index dbc911529..8bd932980 100644 --- a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:56.270011Z", - "iopub.status.busy": "2024-01-17T18:10:56.269487Z", - "iopub.status.idle": "2024-01-17T18:10:57.381241Z", - "shell.execute_reply": "2024-01-17T18:10:57.380625Z" + "iopub.execute_input": "2024-01-17T23:13:00.015965Z", + "iopub.status.busy": "2024-01-17T23:13:00.015766Z", + "iopub.status.idle": "2024-01-17T23:13:01.088856Z", + "shell.execute_reply": "2024-01-17T23:13:01.088251Z" }, "nbsphinx": "hidden" }, @@ -83,7 +83,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -109,10 +109,10 @@ "id": "c90449c8", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:57.384088Z", - "iopub.status.busy": "2024-01-17T18:10:57.383700Z", - "iopub.status.idle": "2024-01-17T18:10:58.753470Z", - "shell.execute_reply": "2024-01-17T18:10:58.752605Z" + "iopub.execute_input": "2024-01-17T23:13:01.091695Z", + "iopub.status.busy": "2024-01-17T23:13:01.091313Z", + "iopub.status.idle": "2024-01-17T23:13:02.386388Z", + "shell.execute_reply": "2024-01-17T23:13:02.385615Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:58.756374Z", - "iopub.status.busy": "2024-01-17T18:10:58.756152Z", - "iopub.status.idle": "2024-01-17T18:10:58.759433Z", - "shell.execute_reply": "2024-01-17T18:10:58.758883Z" + "iopub.execute_input": "2024-01-17T23:13:02.389277Z", + "iopub.status.busy": "2024-01-17T23:13:02.388865Z", + "iopub.status.idle": "2024-01-17T23:13:02.392078Z", + "shell.execute_reply": "2024-01-17T23:13:02.391529Z" } }, "outputs": [], @@ -165,10 +165,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:58.761637Z", - "iopub.status.busy": "2024-01-17T18:10:58.761440Z", - "iopub.status.idle": "2024-01-17T18:10:58.766806Z", - "shell.execute_reply": "2024-01-17T18:10:58.766335Z" + "iopub.execute_input": "2024-01-17T23:13:02.394243Z", + "iopub.status.busy": "2024-01-17T23:13:02.394041Z", + "iopub.status.idle": "2024-01-17T23:13:02.399439Z", + "shell.execute_reply": "2024-01-17T23:13:02.398974Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:58.768917Z", - "iopub.status.busy": "2024-01-17T18:10:58.768711Z", - "iopub.status.idle": "2024-01-17T18:10:59.379953Z", - "shell.execute_reply": "2024-01-17T18:10:59.379277Z" + "iopub.execute_input": "2024-01-17T23:13:02.401585Z", + "iopub.status.busy": "2024-01-17T23:13:02.401389Z", + "iopub.status.idle": "2024-01-17T23:13:03.000882Z", + "shell.execute_reply": "2024-01-17T23:13:03.000200Z" }, "scrolled": true }, @@ -237,10 +237,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:59.383016Z", - "iopub.status.busy": "2024-01-17T18:10:59.382511Z", - "iopub.status.idle": "2024-01-17T18:10:59.388670Z", - "shell.execute_reply": "2024-01-17T18:10:59.388096Z" + "iopub.execute_input": "2024-01-17T23:13:03.004104Z", + "iopub.status.busy": "2024-01-17T23:13:03.003684Z", + "iopub.status.idle": "2024-01-17T23:13:03.009713Z", + "shell.execute_reply": "2024-01-17T23:13:03.009212Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:59.391089Z", - "iopub.status.busy": "2024-01-17T18:10:59.390893Z", - "iopub.status.idle": "2024-01-17T18:10:59.394956Z", - "shell.execute_reply": "2024-01-17T18:10:59.394468Z" + "iopub.execute_input": "2024-01-17T23:13:03.011996Z", + "iopub.status.busy": "2024-01-17T23:13:03.011640Z", + "iopub.status.idle": "2024-01-17T23:13:03.015800Z", + "shell.execute_reply": "2024-01-17T23:13:03.015298Z" } }, "outputs": [ @@ -552,10 +552,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:10:59.397137Z", - "iopub.status.busy": "2024-01-17T18:10:59.396930Z", - "iopub.status.idle": "2024-01-17T18:11:00.095222Z", - "shell.execute_reply": "2024-01-17T18:11:00.094559Z" + "iopub.execute_input": "2024-01-17T23:13:03.018314Z", + "iopub.status.busy": "2024-01-17T23:13:03.017851Z", + "iopub.status.idle": "2024-01-17T23:13:03.630150Z", + "shell.execute_reply": "2024-01-17T23:13:03.629427Z" } }, "outputs": [ @@ -611,10 +611,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:00.098096Z", - "iopub.status.busy": "2024-01-17T18:11:00.097682Z", - "iopub.status.idle": "2024-01-17T18:11:00.189490Z", - "shell.execute_reply": "2024-01-17T18:11:00.188840Z" + "iopub.execute_input": "2024-01-17T23:13:03.632920Z", + "iopub.status.busy": "2024-01-17T23:13:03.632509Z", + "iopub.status.idle": "2024-01-17T23:13:03.740338Z", + "shell.execute_reply": "2024-01-17T23:13:03.739687Z" } }, "outputs": [ @@ -655,10 +655,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:00.192112Z", - "iopub.status.busy": "2024-01-17T18:11:00.191749Z", - "iopub.status.idle": "2024-01-17T18:11:00.196425Z", - "shell.execute_reply": "2024-01-17T18:11:00.195812Z" + "iopub.execute_input": "2024-01-17T23:13:03.742826Z", + "iopub.status.busy": "2024-01-17T23:13:03.742438Z", + "iopub.status.idle": "2024-01-17T23:13:03.746983Z", + "shell.execute_reply": "2024-01-17T23:13:03.746385Z" } }, "outputs": [ @@ -695,10 +695,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:00.198699Z", - "iopub.status.busy": "2024-01-17T18:11:00.198356Z", - "iopub.status.idle": "2024-01-17T18:11:00.573742Z", - "shell.execute_reply": "2024-01-17T18:11:00.573082Z" + "iopub.execute_input": "2024-01-17T23:13:03.749393Z", + "iopub.status.busy": "2024-01-17T23:13:03.749035Z", + "iopub.status.idle": "2024-01-17T23:13:04.126430Z", + "shell.execute_reply": "2024-01-17T23:13:04.125638Z" } }, "outputs": [ @@ -757,10 +757,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:00.576878Z", - "iopub.status.busy": "2024-01-17T18:11:00.576638Z", - "iopub.status.idle": "2024-01-17T18:11:00.916426Z", - "shell.execute_reply": "2024-01-17T18:11:00.915786Z" + "iopub.execute_input": "2024-01-17T23:13:04.129102Z", + "iopub.status.busy": "2024-01-17T23:13:04.128646Z", + "iopub.status.idle": "2024-01-17T23:13:04.466798Z", + "shell.execute_reply": "2024-01-17T23:13:04.466140Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:00.919738Z", - "iopub.status.busy": "2024-01-17T18:11:00.919345Z", - "iopub.status.idle": "2024-01-17T18:11:01.302132Z", - "shell.execute_reply": "2024-01-17T18:11:01.301169Z" + "iopub.execute_input": "2024-01-17T23:13:04.469970Z", + "iopub.status.busy": "2024-01-17T23:13:04.469557Z", + "iopub.status.idle": "2024-01-17T23:13:04.855437Z", + "shell.execute_reply": "2024-01-17T23:13:04.854746Z" } }, "outputs": [ @@ -857,10 +857,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:01.305447Z", - "iopub.status.busy": "2024-01-17T18:11:01.305205Z", - "iopub.status.idle": "2024-01-17T18:11:01.743265Z", - "shell.execute_reply": "2024-01-17T18:11:01.742611Z" + "iopub.execute_input": "2024-01-17T23:13:04.858407Z", + "iopub.status.busy": "2024-01-17T23:13:04.858151Z", + "iopub.status.idle": "2024-01-17T23:13:05.320000Z", + "shell.execute_reply": "2024-01-17T23:13:05.319330Z" } }, "outputs": [ @@ -920,10 +920,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:01.747782Z", - "iopub.status.busy": "2024-01-17T18:11:01.747564Z", - "iopub.status.idle": "2024-01-17T18:11:02.177235Z", - "shell.execute_reply": "2024-01-17T18:11:02.176546Z" + "iopub.execute_input": "2024-01-17T23:13:05.324336Z", + "iopub.status.busy": "2024-01-17T23:13:05.323918Z", + "iopub.status.idle": "2024-01-17T23:13:05.792579Z", + "shell.execute_reply": "2024-01-17T23:13:05.791924Z" } }, "outputs": [ @@ -966,10 +966,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:02.180504Z", - "iopub.status.busy": "2024-01-17T18:11:02.180026Z", - "iopub.status.idle": "2024-01-17T18:11:02.496037Z", - "shell.execute_reply": "2024-01-17T18:11:02.495341Z" + "iopub.execute_input": "2024-01-17T23:13:05.795897Z", + "iopub.status.busy": "2024-01-17T23:13:05.795682Z", + "iopub.status.idle": "2024-01-17T23:13:06.121035Z", + "shell.execute_reply": "2024-01-17T23:13:06.120426Z" } }, "outputs": [ @@ -1012,10 +1012,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:02.499471Z", - "iopub.status.busy": "2024-01-17T18:11:02.498870Z", - "iopub.status.idle": "2024-01-17T18:11:02.702199Z", - "shell.execute_reply": "2024-01-17T18:11:02.701491Z" + "iopub.execute_input": "2024-01-17T23:13:06.123681Z", + "iopub.status.busy": "2024-01-17T23:13:06.123465Z", + "iopub.status.idle": "2024-01-17T23:13:06.322246Z", + "shell.execute_reply": "2024-01-17T23:13:06.321624Z" } }, "outputs": [ @@ -1050,10 +1050,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:02.704604Z", - "iopub.status.busy": "2024-01-17T18:11:02.704393Z", - "iopub.status.idle": "2024-01-17T18:11:02.709155Z", - "shell.execute_reply": "2024-01-17T18:11:02.708516Z" + "iopub.execute_input": "2024-01-17T23:13:06.325012Z", + "iopub.status.busy": "2024-01-17T23:13:06.324597Z", + "iopub.status.idle": "2024-01-17T23:13:06.328427Z", + "shell.execute_reply": "2024-01-17T23:13:06.327899Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index 4968673b9..bd88b2fad 100644 --- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb @@ -109,10 +109,10 @@ "id": "2bbebfc8", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:05.142211Z", - "iopub.status.busy": "2024-01-17T18:11:05.141650Z", - "iopub.status.idle": "2024-01-17T18:11:07.187769Z", - "shell.execute_reply": "2024-01-17T18:11:07.187028Z" + "iopub.execute_input": "2024-01-17T23:13:08.406000Z", + "iopub.status.busy": "2024-01-17T23:13:08.405789Z", + "iopub.status.idle": "2024-01-17T23:13:10.340846Z", + "shell.execute_reply": "2024-01-17T23:13:10.340212Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "id": "4396f544", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:07.191099Z", - "iopub.status.busy": "2024-01-17T18:11:07.190494Z", - "iopub.status.idle": "2024-01-17T18:11:07.529231Z", - "shell.execute_reply": "2024-01-17T18:11:07.528438Z" + "iopub.execute_input": "2024-01-17T23:13:10.343878Z", + "iopub.status.busy": "2024-01-17T23:13:10.343422Z", + "iopub.status.idle": "2024-01-17T23:13:10.660493Z", + "shell.execute_reply": "2024-01-17T23:13:10.659800Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:07.532317Z", - "iopub.status.busy": "2024-01-17T18:11:07.531754Z", - "iopub.status.idle": "2024-01-17T18:11:07.536174Z", - "shell.execute_reply": "2024-01-17T18:11:07.535550Z" + "iopub.execute_input": "2024-01-17T23:13:10.663317Z", + "iopub.status.busy": "2024-01-17T23:13:10.663104Z", + "iopub.status.idle": "2024-01-17T23:13:10.667300Z", + "shell.execute_reply": "2024-01-17T23:13:10.666820Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:07.538835Z", - "iopub.status.busy": "2024-01-17T18:11:07.538474Z", - "iopub.status.idle": "2024-01-17T18:11:11.753022Z", - "shell.execute_reply": "2024-01-17T18:11:11.752366Z" + "iopub.execute_input": "2024-01-17T23:13:10.669596Z", + "iopub.status.busy": "2024-01-17T23:13:10.669232Z", + "iopub.status.idle": "2024-01-17T23:13:14.971485Z", + "shell.execute_reply": "2024-01-17T23:13:14.970803Z" } }, "outputs": [ @@ -242,7 +242,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "94ffd2c8ec814e019f2b36b808770db9", + "model_id": "6047426b013f47c49a17843cd40c0b2e", "version_major": 2, "version_minor": 0 }, @@ -361,10 +361,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:11.755600Z", - "iopub.status.busy": "2024-01-17T18:11:11.755226Z", - "iopub.status.idle": "2024-01-17T18:11:11.762083Z", - "shell.execute_reply": "2024-01-17T18:11:11.759899Z" + "iopub.execute_input": "2024-01-17T23:13:14.974264Z", + "iopub.status.busy": "2024-01-17T23:13:14.973841Z", + "iopub.status.idle": "2024-01-17T23:13:14.978950Z", + "shell.execute_reply": "2024-01-17T23:13:14.978415Z" }, "nbsphinx": "hidden" }, @@ -415,10 +415,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:11.764664Z", - "iopub.status.busy": "2024-01-17T18:11:11.764465Z", - "iopub.status.idle": "2024-01-17T18:11:12.328987Z", - "shell.execute_reply": "2024-01-17T18:11:12.328323Z" + "iopub.execute_input": "2024-01-17T23:13:14.981395Z", + "iopub.status.busy": "2024-01-17T23:13:14.980946Z", + "iopub.status.idle": "2024-01-17T23:13:15.518999Z", + "shell.execute_reply": "2024-01-17T23:13:15.518328Z" } }, "outputs": [ @@ -451,10 +451,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:12.331547Z", - "iopub.status.busy": "2024-01-17T18:11:12.331336Z", - "iopub.status.idle": "2024-01-17T18:11:12.972317Z", - "shell.execute_reply": "2024-01-17T18:11:12.971642Z" + "iopub.execute_input": "2024-01-17T23:13:15.521673Z", + "iopub.status.busy": "2024-01-17T23:13:15.521449Z", + "iopub.status.idle": "2024-01-17T23:13:16.161297Z", + "shell.execute_reply": "2024-01-17T23:13:16.160609Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:12.974776Z", - "iopub.status.busy": "2024-01-17T18:11:12.974562Z", - "iopub.status.idle": "2024-01-17T18:11:12.978442Z", - "shell.execute_reply": "2024-01-17T18:11:12.977910Z" + "iopub.execute_input": "2024-01-17T23:13:16.164039Z", + "iopub.status.busy": "2024-01-17T23:13:16.163636Z", + "iopub.status.idle": "2024-01-17T23:13:16.167332Z", + "shell.execute_reply": "2024-01-17T23:13:16.166795Z" } }, "outputs": [], @@ -518,10 +518,10 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:12.980858Z", - "iopub.status.busy": "2024-01-17T18:11:12.980485Z", - "iopub.status.idle": "2024-01-17T18:11:25.431905Z", - "shell.execute_reply": "2024-01-17T18:11:25.431175Z" + "iopub.execute_input": "2024-01-17T23:13:16.169667Z", + "iopub.status.busy": "2024-01-17T23:13:16.169312Z", + "iopub.status.idle": "2024-01-17T23:13:28.179162Z", + "shell.execute_reply": "2024-01-17T23:13:28.178540Z" } }, "outputs": [ @@ -580,10 +580,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:25.434666Z", - "iopub.status.busy": "2024-01-17T18:11:25.434434Z", - "iopub.status.idle": "2024-01-17T18:11:26.998669Z", - "shell.execute_reply": "2024-01-17T18:11:26.997927Z" + "iopub.execute_input": "2024-01-17T23:13:28.182004Z", + "iopub.status.busy": "2024-01-17T23:13:28.181558Z", + "iopub.status.idle": "2024-01-17T23:13:29.717779Z", + "shell.execute_reply": "2024-01-17T23:13:29.716989Z" } }, "outputs": [ @@ -627,10 +627,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:27.001780Z", - "iopub.status.busy": "2024-01-17T18:11:27.001137Z", - "iopub.status.idle": "2024-01-17T18:11:27.269493Z", - "shell.execute_reply": "2024-01-17T18:11:27.268620Z" + "iopub.execute_input": "2024-01-17T23:13:29.720707Z", + "iopub.status.busy": "2024-01-17T23:13:29.720296Z", + "iopub.status.idle": "2024-01-17T23:13:29.954475Z", + "shell.execute_reply": "2024-01-17T23:13:29.953698Z" } }, "outputs": [ @@ -666,10 +666,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:27.272731Z", - "iopub.status.busy": "2024-01-17T18:11:27.272064Z", - "iopub.status.idle": "2024-01-17T18:11:27.957611Z", - "shell.execute_reply": "2024-01-17T18:11:27.956708Z" + "iopub.execute_input": "2024-01-17T23:13:29.957342Z", + "iopub.status.busy": "2024-01-17T23:13:29.957131Z", + "iopub.status.idle": "2024-01-17T23:13:30.610677Z", + "shell.execute_reply": "2024-01-17T23:13:30.610003Z" } }, "outputs": [ @@ -719,10 +719,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:27.961024Z", - "iopub.status.busy": "2024-01-17T18:11:27.960734Z", - "iopub.status.idle": "2024-01-17T18:11:28.481913Z", - "shell.execute_reply": "2024-01-17T18:11:28.481164Z" + "iopub.execute_input": "2024-01-17T23:13:30.613503Z", + "iopub.status.busy": "2024-01-17T23:13:30.613295Z", + "iopub.status.idle": "2024-01-17T23:13:31.091930Z", + "shell.execute_reply": "2024-01-17T23:13:31.091231Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:28.485267Z", - "iopub.status.busy": "2024-01-17T18:11:28.484715Z", - "iopub.status.idle": "2024-01-17T18:11:28.738717Z", - "shell.execute_reply": "2024-01-17T18:11:28.737951Z" + "iopub.execute_input": "2024-01-17T23:13:31.094432Z", + "iopub.status.busy": "2024-01-17T23:13:31.094207Z", + "iopub.status.idle": "2024-01-17T23:13:31.340997Z", + "shell.execute_reply": "2024-01-17T23:13:31.340291Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:28.742501Z", - "iopub.status.busy": "2024-01-17T18:11:28.741853Z", - "iopub.status.idle": "2024-01-17T18:11:28.828263Z", - "shell.execute_reply": "2024-01-17T18:11:28.827679Z" + "iopub.execute_input": "2024-01-17T23:13:31.344339Z", + "iopub.status.busy": "2024-01-17T23:13:31.343982Z", + "iopub.status.idle": "2024-01-17T23:13:31.429054Z", + "shell.execute_reply": "2024-01-17T23:13:31.428488Z" } }, "outputs": [], @@ -853,10 +853,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:11:28.831018Z", - "iopub.status.busy": "2024-01-17T18:11:28.830693Z", - "iopub.status.idle": "2024-01-17T18:12:06.785768Z", - "shell.execute_reply": "2024-01-17T18:12:06.784990Z" + "iopub.execute_input": "2024-01-17T23:13:31.431988Z", + "iopub.status.busy": "2024-01-17T23:13:31.431565Z", + "iopub.status.idle": "2024-01-17T23:14:09.484257Z", + "shell.execute_reply": "2024-01-17T23:14:09.483536Z" } }, "outputs": [ @@ -893,10 +893,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:06.788676Z", - "iopub.status.busy": "2024-01-17T18:12:06.788248Z", - "iopub.status.idle": "2024-01-17T18:12:08.033479Z", - "shell.execute_reply": "2024-01-17T18:12:08.032852Z" + "iopub.execute_input": "2024-01-17T23:14:09.487213Z", + "iopub.status.busy": "2024-01-17T23:14:09.486711Z", + "iopub.status.idle": "2024-01-17T23:14:10.689009Z", + "shell.execute_reply": "2024-01-17T23:14:10.688273Z" } }, "outputs": [ @@ -927,10 +927,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:08.036962Z", - "iopub.status.busy": "2024-01-17T18:12:08.036208Z", - "iopub.status.idle": "2024-01-17T18:12:08.232791Z", - "shell.execute_reply": "2024-01-17T18:12:08.232181Z" + "iopub.execute_input": "2024-01-17T23:14:10.692325Z", + "iopub.status.busy": "2024-01-17T23:14:10.691749Z", + "iopub.status.idle": "2024-01-17T23:14:10.883450Z", + "shell.execute_reply": "2024-01-17T23:14:10.882715Z" } }, "outputs": [], @@ -944,10 +944,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:08.235701Z", - "iopub.status.busy": "2024-01-17T18:12:08.235355Z", - "iopub.status.idle": "2024-01-17T18:12:08.238889Z", - "shell.execute_reply": "2024-01-17T18:12:08.238378Z" + "iopub.execute_input": "2024-01-17T23:14:10.886396Z", + "iopub.status.busy": "2024-01-17T23:14:10.886034Z", + "iopub.status.idle": "2024-01-17T23:14:10.889447Z", + "shell.execute_reply": "2024-01-17T23:14:10.888823Z" } }, "outputs": [], @@ -969,10 +969,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:08.241261Z", - "iopub.status.busy": "2024-01-17T18:12:08.241030Z", - "iopub.status.idle": "2024-01-17T18:12:08.250335Z", - "shell.execute_reply": "2024-01-17T18:12:08.249660Z" + "iopub.execute_input": "2024-01-17T23:14:10.891982Z", + "iopub.status.busy": "2024-01-17T23:14:10.891602Z", + "iopub.status.idle": "2024-01-17T23:14:10.900135Z", + "shell.execute_reply": "2024-01-17T23:14:10.899657Z" }, "nbsphinx": "hidden" }, @@ -1017,7 +1017,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "44fda716909e43628352ea6c49c2ff41": { + "2f9ad96375eb43b79434c86af5089657": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1069,132 +1069,105 @@ "width": null } }, - "4acca3f4f43345808ed1160cde907360": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "3115519043034972b905b0c53494f868": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a13d518f6ab14929a70a4234ad51590d", + "placeholder": "​", + "style": "IPY_MODEL_f57e4dbd7d6c44deadc2f7b94af1255d", + "value": "100%" } }, - "4b5c786cd0e44554a0b38a7e4e5e83b0": { + "3802f1bde2bf44b184140a42223935a4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "6a7a1a6e1cd0483ba26f35bf8df07850": { + "3919f9bbffe84644b5c28740e1b586f4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "79cb6e9a3ba74463934eec2b0b276fa5": { + "549fd896bf6d492d98bc17a3cadcec46": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_ecae7d3da8404315949ba1e79252c7e9", - "placeholder": "​", - "style": "IPY_MODEL_9cfcbc6781604ce8bf02eff15dd91a94", - "value": "100%" + "layout": "IPY_MODEL_e2da266fb3f04ca79e2519f380d7a53a", + "max": 170498071.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3919f9bbffe84644b5c28740e1b586f4", + "value": 170498071.0 } }, - "888ff16121af4053a4f316377395b6e9": { + "6047426b013f47c49a17843cd40c0b2e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_44fda716909e43628352ea6c49c2ff41", - "placeholder": "​", - "style": "IPY_MODEL_6a7a1a6e1cd0483ba26f35bf8df07850", - "value": " 170498071/170498071 [00:01<00:00, 113387794.23it/s]" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3115519043034972b905b0c53494f868", + "IPY_MODEL_549fd896bf6d492d98bc17a3cadcec46", + "IPY_MODEL_a16534c9be87478dbdc9bfae4ae7566b" + ], + "layout": "IPY_MODEL_c2c04344033844fca8dd2955e2dce8d8" } }, - "8a1ccbf8e4d1474ca5594d36a574b20a": { + "a13d518f6ab14929a70a4234ad51590d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1246,68 +1219,80 @@ "width": null } }, - "94ffd2c8ec814e019f2b36b808770db9": { + "a16534c9be87478dbdc9bfae4ae7566b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_79cb6e9a3ba74463934eec2b0b276fa5", - "IPY_MODEL_a012aefd9d514d26aacefc9b267c3824", - "IPY_MODEL_888ff16121af4053a4f316377395b6e9" - ], - "layout": "IPY_MODEL_8a1ccbf8e4d1474ca5594d36a574b20a" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2f9ad96375eb43b79434c86af5089657", + "placeholder": "​", + "style": "IPY_MODEL_3802f1bde2bf44b184140a42223935a4", + "value": " 170498071/170498071 [00:01<00:00, 97316208.25it/s]" } }, - "9cfcbc6781604ce8bf02eff15dd91a94": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "c2c04344033844fca8dd2955e2dce8d8": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "a012aefd9d514d26aacefc9b267c3824": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4acca3f4f43345808ed1160cde907360", - "max": 170498071.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4b5c786cd0e44554a0b38a7e4e5e83b0", - "value": 170498071.0 + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "ecae7d3da8404315949ba1e79252c7e9": { + "e2da266fb3f04ca79e2519f380d7a53a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1358,6 +1343,21 @@ "visibility": null, "width": null } + }, + "f57e4dbd7d6c44deadc2f7b94af1255d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index eafa96774..3de0c3413 100644 --- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb @@ -94,10 +94,10 @@ "id": "2e1af7d8", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:12.975780Z", - "iopub.status.busy": "2024-01-17T18:12:12.975329Z", - "iopub.status.idle": "2024-01-17T18:12:14.087240Z", - "shell.execute_reply": "2024-01-17T18:12:14.086667Z" + "iopub.execute_input": "2024-01-17T23:14:15.960182Z", + "iopub.status.busy": "2024-01-17T23:14:15.959654Z", + "iopub.status.idle": "2024-01-17T23:14:17.062152Z", + "shell.execute_reply": "2024-01-17T23:14:17.061449Z" }, "nbsphinx": "hidden" }, @@ -109,7 +109,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -135,10 +135,10 @@ "id": "4fb10b8f", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:14.090216Z", - "iopub.status.busy": "2024-01-17T18:12:14.089748Z", - "iopub.status.idle": "2024-01-17T18:12:14.105993Z", - "shell.execute_reply": "2024-01-17T18:12:14.105493Z" + "iopub.execute_input": "2024-01-17T23:14:17.065102Z", + "iopub.status.busy": "2024-01-17T23:14:17.064826Z", + "iopub.status.idle": "2024-01-17T23:14:17.081276Z", + "shell.execute_reply": "2024-01-17T23:14:17.080798Z" } }, "outputs": [], @@ -157,10 +157,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:14.108511Z", - "iopub.status.busy": "2024-01-17T18:12:14.108131Z", - "iopub.status.idle": "2024-01-17T18:12:14.111275Z", - "shell.execute_reply": "2024-01-17T18:12:14.110730Z" + "iopub.execute_input": "2024-01-17T23:14:17.083754Z", + "iopub.status.busy": "2024-01-17T23:14:17.083378Z", + "iopub.status.idle": "2024-01-17T23:14:17.086439Z", + "shell.execute_reply": "2024-01-17T23:14:17.085896Z" }, "nbsphinx": "hidden" }, @@ -191,10 +191,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:14.113627Z", - "iopub.status.busy": "2024-01-17T18:12:14.113255Z", - "iopub.status.idle": "2024-01-17T18:12:14.211397Z", - "shell.execute_reply": "2024-01-17T18:12:14.210759Z" + "iopub.execute_input": "2024-01-17T23:14:17.088732Z", + "iopub.status.busy": "2024-01-17T23:14:17.088379Z", + "iopub.status.idle": "2024-01-17T23:14:17.163683Z", + "shell.execute_reply": "2024-01-17T23:14:17.163047Z" } }, "outputs": [ @@ -367,10 +367,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:14.214337Z", - "iopub.status.busy": "2024-01-17T18:12:14.213937Z", - "iopub.status.idle": "2024-01-17T18:12:14.499529Z", - "shell.execute_reply": "2024-01-17T18:12:14.498832Z" + "iopub.execute_input": "2024-01-17T23:14:17.166444Z", + "iopub.status.busy": "2024-01-17T23:14:17.166096Z", + "iopub.status.idle": "2024-01-17T23:14:17.450591Z", + "shell.execute_reply": "2024-01-17T23:14:17.449863Z" }, "nbsphinx": "hidden" }, @@ -410,10 +410,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:14.502466Z", - "iopub.status.busy": "2024-01-17T18:12:14.502090Z", - "iopub.status.idle": "2024-01-17T18:12:14.756785Z", - "shell.execute_reply": "2024-01-17T18:12:14.756135Z" + "iopub.execute_input": "2024-01-17T23:14:17.453500Z", + "iopub.status.busy": "2024-01-17T23:14:17.453276Z", + "iopub.status.idle": "2024-01-17T23:14:17.712310Z", + "shell.execute_reply": "2024-01-17T23:14:17.711570Z" } }, "outputs": [ @@ -449,10 +449,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:14.759685Z", - "iopub.status.busy": "2024-01-17T18:12:14.759113Z", - "iopub.status.idle": "2024-01-17T18:12:14.763916Z", - "shell.execute_reply": "2024-01-17T18:12:14.763292Z" + "iopub.execute_input": "2024-01-17T23:14:17.715207Z", + "iopub.status.busy": "2024-01-17T23:14:17.714563Z", + "iopub.status.idle": "2024-01-17T23:14:17.719694Z", + "shell.execute_reply": "2024-01-17T23:14:17.719140Z" } }, "outputs": [], @@ -470,10 +470,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:14.766230Z", - "iopub.status.busy": "2024-01-17T18:12:14.766027Z", - "iopub.status.idle": "2024-01-17T18:12:14.772493Z", - "shell.execute_reply": "2024-01-17T18:12:14.771995Z" + "iopub.execute_input": "2024-01-17T23:14:17.722020Z", + "iopub.status.busy": "2024-01-17T23:14:17.721636Z", + "iopub.status.idle": "2024-01-17T23:14:17.727795Z", + "shell.execute_reply": "2024-01-17T23:14:17.727294Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:14.774939Z", - "iopub.status.busy": "2024-01-17T18:12:14.774604Z", - "iopub.status.idle": "2024-01-17T18:12:14.777371Z", - "shell.execute_reply": "2024-01-17T18:12:14.776796Z" + "iopub.execute_input": "2024-01-17T23:14:17.730435Z", + "iopub.status.busy": "2024-01-17T23:14:17.729964Z", + "iopub.status.idle": "2024-01-17T23:14:17.732895Z", + "shell.execute_reply": "2024-01-17T23:14:17.732419Z" } }, "outputs": [], @@ -538,10 +538,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:14.779648Z", - "iopub.status.busy": "2024-01-17T18:12:14.779287Z", - "iopub.status.idle": "2024-01-17T18:12:24.954125Z", - "shell.execute_reply": "2024-01-17T18:12:24.953475Z" + "iopub.execute_input": "2024-01-17T23:14:17.735253Z", + "iopub.status.busy": "2024-01-17T23:14:17.734894Z", + "iopub.status.idle": "2024-01-17T23:14:27.746313Z", + "shell.execute_reply": "2024-01-17T23:14:27.745534Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:24.957367Z", - "iopub.status.busy": "2024-01-17T18:12:24.956688Z", - "iopub.status.idle": "2024-01-17T18:12:24.964247Z", - "shell.execute_reply": "2024-01-17T18:12:24.963650Z" + "iopub.execute_input": "2024-01-17T23:14:27.749932Z", + "iopub.status.busy": "2024-01-17T23:14:27.749187Z", + "iopub.status.idle": "2024-01-17T23:14:27.757016Z", + "shell.execute_reply": "2024-01-17T23:14:27.756392Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:24.966582Z", - "iopub.status.busy": "2024-01-17T18:12:24.966381Z", - "iopub.status.idle": "2024-01-17T18:12:24.970309Z", - "shell.execute_reply": "2024-01-17T18:12:24.969793Z" + "iopub.execute_input": "2024-01-17T23:14:27.759616Z", + "iopub.status.busy": "2024-01-17T23:14:27.759241Z", + "iopub.status.idle": "2024-01-17T23:14:27.763155Z", + "shell.execute_reply": "2024-01-17T23:14:27.762627Z" } }, "outputs": [], @@ -689,10 +689,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:24.972387Z", - "iopub.status.busy": "2024-01-17T18:12:24.972195Z", - "iopub.status.idle": "2024-01-17T18:12:24.976119Z", - "shell.execute_reply": "2024-01-17T18:12:24.975605Z" + "iopub.execute_input": "2024-01-17T23:14:27.765475Z", + "iopub.status.busy": "2024-01-17T23:14:27.765102Z", + "iopub.status.idle": "2024-01-17T23:14:27.768912Z", + "shell.execute_reply": "2024-01-17T23:14:27.768381Z" } }, "outputs": [ @@ -727,10 +727,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:24.978399Z", - "iopub.status.busy": "2024-01-17T18:12:24.978063Z", - "iopub.status.idle": "2024-01-17T18:12:24.981351Z", - "shell.execute_reply": "2024-01-17T18:12:24.980720Z" + "iopub.execute_input": "2024-01-17T23:14:27.771349Z", + "iopub.status.busy": "2024-01-17T23:14:27.770935Z", + "iopub.status.idle": "2024-01-17T23:14:27.774415Z", + "shell.execute_reply": "2024-01-17T23:14:27.773861Z" } }, "outputs": [], @@ -749,10 +749,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:24.983590Z", - "iopub.status.busy": "2024-01-17T18:12:24.983226Z", - "iopub.status.idle": "2024-01-17T18:12:24.991687Z", - "shell.execute_reply": "2024-01-17T18:12:24.991176Z" + "iopub.execute_input": "2024-01-17T23:14:27.776712Z", + "iopub.status.busy": "2024-01-17T23:14:27.776344Z", + "iopub.status.idle": "2024-01-17T23:14:27.785189Z", + "shell.execute_reply": "2024-01-17T23:14:27.784641Z" } }, "outputs": [ @@ -894,10 +894,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:24.993898Z", - "iopub.status.busy": "2024-01-17T18:12:24.993700Z", - "iopub.status.idle": "2024-01-17T18:12:25.140874Z", - "shell.execute_reply": "2024-01-17T18:12:25.140179Z" + "iopub.execute_input": "2024-01-17T23:14:27.787704Z", + "iopub.status.busy": "2024-01-17T23:14:27.787338Z", + "iopub.status.idle": "2024-01-17T23:14:27.937319Z", + "shell.execute_reply": "2024-01-17T23:14:27.936622Z" } }, "outputs": [ @@ -936,10 +936,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:25.143690Z", - "iopub.status.busy": "2024-01-17T18:12:25.143247Z", - "iopub.status.idle": "2024-01-17T18:12:25.277016Z", - "shell.execute_reply": "2024-01-17T18:12:25.276372Z" + "iopub.execute_input": "2024-01-17T23:14:27.940162Z", + "iopub.status.busy": "2024-01-17T23:14:27.939715Z", + "iopub.status.idle": "2024-01-17T23:14:28.073020Z", + "shell.execute_reply": "2024-01-17T23:14:28.072323Z" } }, "outputs": [ @@ -995,10 +995,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:25.280112Z", - "iopub.status.busy": "2024-01-17T18:12:25.279620Z", - "iopub.status.idle": "2024-01-17T18:12:25.869524Z", - "shell.execute_reply": "2024-01-17T18:12:25.868796Z" + "iopub.execute_input": "2024-01-17T23:14:28.075914Z", + "iopub.status.busy": "2024-01-17T23:14:28.075472Z", + "iopub.status.idle": "2024-01-17T23:14:28.663372Z", + "shell.execute_reply": "2024-01-17T23:14:28.662637Z" } }, "outputs": [], @@ -1014,10 +1014,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:25.872698Z", - "iopub.status.busy": "2024-01-17T18:12:25.872237Z", - "iopub.status.idle": "2024-01-17T18:12:25.955671Z", - "shell.execute_reply": "2024-01-17T18:12:25.954697Z" + "iopub.execute_input": "2024-01-17T23:14:28.666533Z", + "iopub.status.busy": "2024-01-17T23:14:28.666263Z", + "iopub.status.idle": "2024-01-17T23:14:28.748208Z", + "shell.execute_reply": "2024-01-17T23:14:28.747515Z" } }, "outputs": [ @@ -1055,10 +1055,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:25.958459Z", - "iopub.status.busy": "2024-01-17T18:12:25.958243Z", - "iopub.status.idle": "2024-01-17T18:12:25.968660Z", - "shell.execute_reply": "2024-01-17T18:12:25.968158Z" + "iopub.execute_input": "2024-01-17T23:14:28.750983Z", + "iopub.status.busy": "2024-01-17T23:14:28.750598Z", + "iopub.status.idle": "2024-01-17T23:14:28.760804Z", + "shell.execute_reply": "2024-01-17T23:14:28.760302Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 277db8411..6376839e6 100644 --- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb @@ -61,10 +61,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:31.125213Z", - "iopub.status.busy": "2024-01-17T18:12:31.124971Z", - "iopub.status.idle": "2024-01-17T18:12:32.801405Z", - "shell.execute_reply": "2024-01-17T18:12:32.800636Z" + "iopub.execute_input": "2024-01-17T23:14:34.017881Z", + "iopub.status.busy": "2024-01-17T23:14:34.017503Z", + "iopub.status.idle": "2024-01-17T23:14:36.066016Z", + "shell.execute_reply": "2024-01-17T23:14:36.065243Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:12:32.804526Z", - "iopub.status.busy": "2024-01-17T18:12:32.804011Z", - "iopub.status.idle": "2024-01-17T18:13:26.043168Z", - "shell.execute_reply": "2024-01-17T18:13:26.042378Z" + "iopub.execute_input": "2024-01-17T23:14:36.068785Z", + "iopub.status.busy": "2024-01-17T23:14:36.068574Z", + "iopub.status.idle": "2024-01-17T23:15:29.432226Z", + "shell.execute_reply": "2024-01-17T23:15:29.431512Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:13:26.046070Z", - "iopub.status.busy": "2024-01-17T18:13:26.045857Z", - "iopub.status.idle": "2024-01-17T18:13:27.779303Z", - "shell.execute_reply": "2024-01-17T18:13:27.778617Z" + "iopub.execute_input": "2024-01-17T23:15:29.435338Z", + "iopub.status.busy": "2024-01-17T23:15:29.434915Z", + "iopub.status.idle": "2024-01-17T23:15:30.460838Z", + "shell.execute_reply": "2024-01-17T23:15:30.460233Z" }, "nbsphinx": "hidden" }, @@ -111,7 +111,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -137,10 +137,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:13:27.782397Z", - "iopub.status.busy": "2024-01-17T18:13:27.781842Z", - "iopub.status.idle": "2024-01-17T18:13:27.785561Z", - "shell.execute_reply": "2024-01-17T18:13:27.784975Z" + "iopub.execute_input": "2024-01-17T23:15:30.463806Z", + "iopub.status.busy": "2024-01-17T23:15:30.463391Z", + "iopub.status.idle": "2024-01-17T23:15:30.467069Z", + "shell.execute_reply": "2024-01-17T23:15:30.466497Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:13:27.787782Z", - "iopub.status.busy": "2024-01-17T18:13:27.787576Z", - "iopub.status.idle": "2024-01-17T18:13:27.791812Z", - "shell.execute_reply": "2024-01-17T18:13:27.791287Z" + "iopub.execute_input": "2024-01-17T23:15:30.469583Z", + "iopub.status.busy": "2024-01-17T23:15:30.469112Z", + "iopub.status.idle": "2024-01-17T23:15:30.473211Z", + "shell.execute_reply": "2024-01-17T23:15:30.472586Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:13:27.794277Z", - "iopub.status.busy": "2024-01-17T18:13:27.793928Z", - "iopub.status.idle": "2024-01-17T18:13:27.797585Z", - "shell.execute_reply": "2024-01-17T18:13:27.797052Z" + "iopub.execute_input": "2024-01-17T23:15:30.475590Z", + "iopub.status.busy": "2024-01-17T23:15:30.475289Z", + "iopub.status.idle": "2024-01-17T23:15:30.479220Z", + "shell.execute_reply": "2024-01-17T23:15:30.478614Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:13:27.799879Z", - "iopub.status.busy": "2024-01-17T18:13:27.799457Z", - "iopub.status.idle": "2024-01-17T18:13:27.802639Z", - "shell.execute_reply": "2024-01-17T18:13:27.802012Z" + "iopub.execute_input": "2024-01-17T23:15:30.481531Z", + "iopub.status.busy": "2024-01-17T23:15:30.481169Z", + "iopub.status.idle": "2024-01-17T23:15:30.484282Z", + "shell.execute_reply": "2024-01-17T23:15:30.483769Z" } }, "outputs": [], @@ -333,10 +333,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:13:27.804808Z", - "iopub.status.busy": "2024-01-17T18:13:27.804477Z", - "iopub.status.idle": "2024-01-17T18:14:55.334560Z", - "shell.execute_reply": "2024-01-17T18:14:55.333875Z" + "iopub.execute_input": "2024-01-17T23:15:30.486493Z", + "iopub.status.busy": "2024-01-17T23:15:30.486198Z", + "iopub.status.idle": "2024-01-17T23:16:54.759811Z", + "shell.execute_reply": "2024-01-17T23:16:54.759104Z" } }, "outputs": [ @@ -350,7 +350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ec615bcedf144713a74c0755f4d4a017", + "model_id": "4fea53dd3c354db89dbd413a514598b1", "version_major": 2, "version_minor": 0 }, @@ -364,7 +364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b6cb19a81c2a486b82141204a442d67b", + "model_id": "3addf8ea78984dc3bf5ed29c07556bb9", "version_major": 2, "version_minor": 0 }, @@ -407,10 +407,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:14:55.337672Z", - "iopub.status.busy": "2024-01-17T18:14:55.337209Z", - "iopub.status.idle": "2024-01-17T18:14:56.101511Z", - "shell.execute_reply": "2024-01-17T18:14:56.100932Z" + "iopub.execute_input": "2024-01-17T23:16:54.762950Z", + "iopub.status.busy": "2024-01-17T23:16:54.762598Z", + "iopub.status.idle": "2024-01-17T23:16:55.528775Z", + "shell.execute_reply": "2024-01-17T23:16:55.528081Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:14:56.104021Z", - "iopub.status.busy": "2024-01-17T18:14:56.103660Z", - "iopub.status.idle": "2024-01-17T18:14:58.199350Z", - "shell.execute_reply": "2024-01-17T18:14:58.198693Z" + "iopub.execute_input": "2024-01-17T23:16:55.531668Z", + "iopub.status.busy": "2024-01-17T23:16:55.531169Z", + "iopub.status.idle": "2024-01-17T23:16:57.626271Z", + "shell.execute_reply": "2024-01-17T23:16:57.625582Z" } }, "outputs": [ @@ -526,10 +526,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:14:58.202064Z", - "iopub.status.busy": "2024-01-17T18:14:58.201668Z", - "iopub.status.idle": "2024-01-17T18:15:26.871400Z", - "shell.execute_reply": "2024-01-17T18:15:26.870736Z" + "iopub.execute_input": "2024-01-17T23:16:57.628737Z", + "iopub.status.busy": "2024-01-17T23:16:57.628517Z", + "iopub.status.idle": "2024-01-17T23:17:27.065044Z", + "shell.execute_reply": "2024-01-17T23:17:27.064412Z" } }, "outputs": [ @@ -546,7 +546,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 17362/4997817 [00:00<00:28, 173608.58it/s]" + " 0%| | 16923/4997817 [00:00<00:29, 169217.65it/s]" ] }, { @@ -554,7 +554,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 34895/4997817 [00:00<00:28, 174610.72it/s]" + " 1%| | 33993/4997817 [00:00<00:29, 170085.88it/s]" ] }, { @@ -562,7 +562,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 52475/4997817 [00:00<00:28, 175150.77it/s]" + " 1%| | 51002/4997817 [00:00<00:29, 169503.62it/s]" ] }, { @@ -570,7 +570,7 @@ "output_type": "stream", "text": [ "\r", - " 1%|▏ | 69991/4997817 [00:00<00:28, 173938.91it/s]" + " 1%|▏ | 68012/4997817 [00:00<00:29, 169735.92it/s]" ] }, { @@ -578,7 +578,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 87813/4997817 [00:00<00:27, 175471.95it/s]" + " 2%|▏ | 84993/4997817 [00:00<00:28, 169759.89it/s]" ] }, { @@ -586,7 +586,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 105688/4997817 [00:00<00:27, 176578.81it/s]" + " 2%|▏ | 101970/4997817 [00:00<00:28, 169628.61it/s]" ] }, { @@ -594,7 +594,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 123543/4997817 [00:00<00:27, 177216.89it/s]" + " 2%|▏ | 118944/4997817 [00:00<00:28, 169663.19it/s]" ] }, { @@ -602,7 +602,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 141289/4997817 [00:00<00:27, 177290.20it/s]" + " 3%|▎ | 135911/4997817 [00:00<00:29, 163001.52it/s]" ] }, { @@ -610,7 +610,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 159076/4997817 [00:00<00:27, 177466.31it/s]" + " 3%|▎ | 152918/4997817 [00:00<00:29, 165147.19it/s]" ] }, { @@ -618,7 +618,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▎ | 176918/4997817 [00:01<00:27, 177754.44it/s]" + " 3%|▎ | 169974/4997817 [00:01<00:28, 166782.07it/s]" ] }, { @@ -626,7 +626,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 194876/4997817 [00:01<00:26, 178308.24it/s]" + " 4%|▎ | 187064/4997817 [00:01<00:28, 168021.67it/s]" ] }, { @@ -634,7 +634,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 212708/4997817 [00:01<00:26, 178223.48it/s]" + " 4%|▍ | 204038/4997817 [00:01<00:28, 168537.30it/s]" ] }, { @@ -642,7 +642,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 230531/4997817 [00:01<00:26, 178136.92it/s]" + " 4%|▍ | 220938/4997817 [00:01<00:28, 168672.47it/s]" ] }, { @@ -650,7 +650,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 248505/4997817 [00:01<00:26, 178619.00it/s]" + " 5%|▍ | 238130/4997817 [00:01<00:28, 169647.75it/s]" ] }, { @@ -658,7 +658,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 266377/4997817 [00:01<00:26, 178647.43it/s]" + " 5%|▌ | 255262/4997817 [00:01<00:27, 170147.90it/s]" ] }, { @@ -666,7 +666,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 284425/4997817 [00:01<00:26, 179193.60it/s]" + " 5%|▌ | 272284/4997817 [00:01<00:27, 170019.26it/s]" ] }, { @@ -674,7 +674,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 302378/4997817 [00:01<00:26, 179290.92it/s]" + " 6%|▌ | 289291/4997817 [00:01<00:27, 169797.69it/s]" ] }, { @@ -682,7 +682,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▋ | 320347/4997817 [00:01<00:26, 179407.53it/s]" + " 6%|▌ | 306465/4997817 [00:01<00:27, 170377.22it/s]" ] }, { @@ -690,7 +690,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 338297/4997817 [00:01<00:25, 179431.36it/s]" + " 6%|▋ | 323703/4997817 [00:01<00:27, 170975.52it/s]" ] }, { @@ -698,7 +698,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 356241/4997817 [00:02<00:25, 179348.77it/s]" + " 7%|▋ | 340851/4997817 [00:02<00:27, 171125.25it/s]" ] }, { @@ -706,7 +706,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 374208/4997817 [00:02<00:25, 179441.48it/s]" + " 7%|▋ | 357965/4997817 [00:02<00:27, 170735.16it/s]" ] }, { @@ -714,7 +714,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 392153/4997817 [00:02<00:26, 175413.81it/s]" + " 8%|▊ | 375079/4997817 [00:02<00:27, 170854.49it/s]" ] }, { @@ -722,7 +722,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 410009/4997817 [00:02<00:26, 176338.11it/s]" + " 8%|▊ | 392275/4997817 [00:02<00:26, 171182.95it/s]" ] }, { @@ -730,7 +730,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▊ | 427853/4997817 [00:02<00:25, 176958.47it/s]" + " 8%|▊ | 409485/4997817 [00:02<00:26, 171454.31it/s]" ] }, { @@ -738,7 +738,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 445826/4997817 [00:02<00:25, 177779.06it/s]" + " 9%|▊ | 426631/4997817 [00:02<00:26, 171181.61it/s]" ] }, { @@ -746,7 +746,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 463613/4997817 [00:02<00:25, 177528.30it/s]" + " 9%|▉ | 443750/4997817 [00:02<00:26, 170479.76it/s]" ] }, { @@ -754,7 +754,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 481507/4997817 [00:02<00:25, 177946.35it/s]" + " 9%|▉ | 460916/4997817 [00:02<00:26, 170828.33it/s]" ] }, { @@ -762,7 +762,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 499307/4997817 [00:02<00:25, 177661.06it/s]" + " 10%|▉ | 478000/4997817 [00:02<00:27, 167094.77it/s]" ] }, { @@ -770,7 +770,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 517142/4997817 [00:02<00:25, 177861.59it/s]" + " 10%|▉ | 495446/4997817 [00:02<00:26, 169263.15it/s]" ] }, { @@ -778,7 +778,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 535037/4997817 [00:03<00:25, 178184.28it/s]" + " 10%|█ | 512890/4997817 [00:03<00:26, 170794.12it/s]" ] }, { @@ -786,7 +786,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 552923/4997817 [00:03<00:24, 178382.09it/s]" + " 11%|█ | 530383/4997817 [00:03<00:25, 172020.72it/s]" ] }, { @@ -794,7 +794,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█▏ | 570763/4997817 [00:03<00:25, 171086.58it/s]" + " 11%|█ | 547934/4997817 [00:03<00:25, 173058.88it/s]" ] }, { @@ -802,7 +802,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 588402/4997817 [00:03<00:25, 172627.47it/s]" + " 11%|█▏ | 565248/4997817 [00:03<00:25, 171739.41it/s]" ] }, { @@ -810,7 +810,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 606160/4997817 [00:03<00:25, 174079.34it/s]" + " 12%|█▏ | 582430/4997817 [00:03<00:25, 171635.25it/s]" ] }, { @@ -818,7 +818,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 623955/4997817 [00:03<00:24, 175220.83it/s]" + " 12%|█▏ | 599599/4997817 [00:03<00:25, 171434.03it/s]" ] }, { @@ -826,7 +826,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 641860/4997817 [00:03<00:24, 176352.97it/s]" + " 12%|█▏ | 616746/4997817 [00:03<00:25, 171242.34it/s]" ] }, { @@ -834,7 +834,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 659728/4997817 [00:03<00:24, 177041.83it/s]" + " 13%|█▎ | 633873/4997817 [00:03<00:25, 171131.74it/s]" ] }, { @@ -842,7 +842,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▎ | 677498/4997817 [00:03<00:24, 177235.53it/s]" + " 13%|█▎ | 650988/4997817 [00:03<00:25, 167888.38it/s]" ] }, { @@ -850,7 +850,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 695298/4997817 [00:03<00:24, 177460.17it/s]" + " 13%|█▎ | 667980/4997817 [00:03<00:25, 168487.13it/s]" ] }, { @@ -858,7 +858,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 713108/4997817 [00:04<00:24, 177648.89it/s]" + " 14%|█▎ | 685095/4997817 [00:04<00:25, 169274.32it/s]" ] }, { @@ -866,7 +866,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 731013/4997817 [00:04<00:23, 178065.71it/s]" + " 14%|█▍ | 702365/4997817 [00:04<00:25, 170291.66it/s]" ] }, { @@ -874,7 +874,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 748824/4997817 [00:04<00:23, 177652.38it/s]" + " 14%|█▍ | 719508/4997817 [00:04<00:25, 170628.16it/s]" ] }, { @@ -882,7 +882,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 766599/4997817 [00:04<00:23, 177677.08it/s]" + " 15%|█▍ | 736576/4997817 [00:04<00:25, 169810.36it/s]" ] }, { @@ -890,7 +890,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 784420/4997817 [00:04<00:23, 177831.64it/s]" + " 15%|█▌ | 753562/4997817 [00:04<00:24, 169771.99it/s]" ] }, { @@ -898,7 +898,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 802279/4997817 [00:04<00:23, 178056.61it/s]" + " 15%|█▌ | 770715/4997817 [00:04<00:24, 170294.50it/s]" ] }, { @@ -906,7 +906,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▋ | 820264/4997817 [00:04<00:23, 178591.12it/s]" + " 16%|█▌ | 787747/4997817 [00:04<00:24, 169972.43it/s]" ] }, { @@ -914,7 +914,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 838197/4997817 [00:04<00:23, 178808.60it/s]" + " 16%|█▌ | 804872/4997817 [00:04<00:24, 170350.25it/s]" ] }, { @@ -922,7 +922,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 856111/4997817 [00:04<00:23, 178905.08it/s]" + " 16%|█▋ | 821909/4997817 [00:04<00:24, 170342.59it/s]" ] }, { @@ -930,7 +930,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 874002/4997817 [00:04<00:23, 178854.94it/s]" + " 17%|█▋ | 838945/4997817 [00:04<00:24, 170326.57it/s]" ] }, { @@ -938,7 +938,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 891888/4997817 [00:05<00:22, 178769.86it/s]" + " 17%|█▋ | 856345/4997817 [00:05<00:24, 171423.84it/s]" ] }, { @@ -946,7 +946,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 909766/4997817 [00:05<00:22, 177999.88it/s]" + " 17%|█▋ | 873794/4997817 [00:05<00:23, 172340.75it/s]" ] }, { @@ -954,7 +954,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▊ | 927567/4997817 [00:05<00:23, 175332.09it/s]" + " 18%|█▊ | 891355/4997817 [00:05<00:23, 173318.17it/s]" ] }, { @@ -962,7 +962,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 945109/4997817 [00:05<00:23, 172027.61it/s]" + " 18%|█▊ | 908761/4997817 [00:05<00:23, 173536.69it/s]" ] }, { @@ -970,7 +970,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 962858/4997817 [00:05<00:23, 173626.11it/s]" + " 19%|█▊ | 926115/4997817 [00:05<00:23, 173374.72it/s]" ] }, { @@ -978,7 +978,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 980440/4997817 [00:05<00:23, 174271.59it/s]" + " 19%|█▉ | 943534/4997817 [00:05<00:23, 173615.78it/s]" ] }, { @@ -986,7 +986,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 998071/4997817 [00:05<00:22, 174873.76it/s]" + " 19%|█▉ | 960987/4997817 [00:05<00:23, 173888.18it/s]" ] }, { @@ -994,7 +994,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 1015609/4997817 [00:05<00:22, 175019.90it/s]" + " 20%|█▉ | 978376/4997817 [00:05<00:23, 173759.83it/s]" ] }, { @@ -1002,7 +1002,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1033146/4997817 [00:05<00:22, 175119.66it/s]" + " 20%|█▉ | 995883/4997817 [00:05<00:22, 174150.86it/s]" ] }, { @@ -1010,7 +1010,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1050707/4997817 [00:05<00:22, 175263.37it/s]" + " 20%|██ | 1013363/4997817 [00:05<00:22, 174342.29it/s]" ] }, { @@ -1018,7 +1018,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██▏ | 1068310/4997817 [00:06<00:22, 175489.78it/s]" + " 21%|██ | 1030818/4997817 [00:06<00:22, 174402.88it/s]" ] }, { @@ -1026,7 +1026,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1086006/4997817 [00:06<00:22, 175925.40it/s]" + " 21%|██ | 1048303/4997817 [00:06<00:22, 174534.90it/s]" ] }, { @@ -1034,7 +1034,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1103856/4997817 [00:06<00:22, 176693.74it/s]" + " 21%|██▏ | 1065757/4997817 [00:06<00:22, 174515.39it/s]" ] }, { @@ -1042,7 +1042,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1121527/4997817 [00:06<00:21, 176653.73it/s]" + " 22%|██▏ | 1083209/4997817 [00:06<00:22, 174141.01it/s]" ] }, { @@ -1050,7 +1050,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1139194/4997817 [00:06<00:21, 175610.49it/s]" + " 22%|██▏ | 1100652/4997817 [00:06<00:22, 174222.63it/s]" ] }, { @@ -1058,7 +1058,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1156758/4997817 [00:06<00:21, 175340.14it/s]" + " 22%|██▏ | 1118075/4997817 [00:06<00:22, 173614.04it/s]" ] }, { @@ -1066,7 +1066,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1174423/4997817 [00:06<00:21, 175726.52it/s]" + " 23%|██▎ | 1135437/4997817 [00:06<00:22, 173273.09it/s]" ] }, { @@ -1074,7 +1074,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1192038/4997817 [00:06<00:21, 175849.70it/s]" + " 23%|██▎ | 1152765/4997817 [00:06<00:22, 173078.85it/s]" ] }, { @@ -1082,7 +1082,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1209681/4997817 [00:06<00:21, 176020.95it/s]" + " 23%|██▎ | 1170074/4997817 [00:06<00:22, 172769.04it/s]" ] }, { @@ -1090,7 +1090,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1227284/4997817 [00:06<00:21, 175879.77it/s]" + " 24%|██▍ | 1187352/4997817 [00:06<00:22, 166605.50it/s]" ] }, { @@ -1098,7 +1098,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1244924/4997817 [00:07<00:21, 176033.24it/s]" + " 24%|██▍ | 1204651/4997817 [00:07<00:22, 168465.26it/s]" ] }, { @@ -1106,7 +1106,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 1262528/4997817 [00:07<00:21, 175800.45it/s]" + " 24%|██▍ | 1221930/4997817 [00:07<00:22, 169735.51it/s]" ] }, { @@ -1114,7 +1114,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1280128/4997817 [00:07<00:21, 175857.50it/s]" + " 25%|██▍ | 1239241/4997817 [00:07<00:22, 170729.82it/s]" ] }, { @@ -1122,7 +1122,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1297844/4997817 [00:07<00:20, 176243.66it/s]" + " 25%|██▌ | 1256336/4997817 [00:07<00:21, 170593.49it/s]" ] }, { @@ -1130,7 +1130,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▋ | 1315598/4997817 [00:07<00:20, 176628.10it/s]" + " 25%|██▌ | 1273736/4997817 [00:07<00:21, 171604.75it/s]" ] }, { @@ -1138,7 +1138,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1333326/4997817 [00:07<00:20, 176821.58it/s]" + " 26%|██▌ | 1291095/4997817 [00:07<00:21, 172192.76it/s]" ] }, { @@ -1146,7 +1146,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1351011/4997817 [00:07<00:20, 176827.24it/s]" + " 26%|██▌ | 1308323/4997817 [00:07<00:21, 171512.26it/s]" ] }, { @@ -1154,7 +1154,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1368694/4997817 [00:07<00:20, 176802.46it/s]" + " 27%|██▋ | 1325481/4997817 [00:07<00:21, 170300.82it/s]" ] }, { @@ -1162,7 +1162,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1386390/4997817 [00:07<00:20, 176847.35it/s]" + " 27%|██▋ | 1342592/4997817 [00:07<00:21, 170538.44it/s]" ] }, { @@ -1170,7 +1170,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1404075/4997817 [00:07<00:20, 176737.99it/s]" + " 27%|██▋ | 1359667/4997817 [00:07<00:21, 170596.95it/s]" ] }, { @@ -1178,7 +1178,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1421749/4997817 [00:08<00:20, 176144.34it/s]" + " 28%|██▊ | 1376730/4997817 [00:08<00:21, 170584.52it/s]" ] }, { @@ -1186,7 +1186,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1439364/4997817 [00:08<00:20, 175538.08it/s]" + " 28%|██▊ | 1393808/4997817 [00:08<00:21, 170640.10it/s]" ] }, { @@ -1194,7 +1194,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1456919/4997817 [00:08<00:20, 175153.28it/s]" + " 28%|██▊ | 1410890/4997817 [00:08<00:21, 170689.62it/s]" ] }, { @@ -1202,7 +1202,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 1474435/4997817 [00:08<00:20, 170969.49it/s]" + " 29%|██▊ | 1428041/4997817 [00:08<00:20, 170932.83it/s]" ] }, { @@ -1210,7 +1210,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 1491823/4997817 [00:08<00:20, 171820.74it/s]" + " 29%|██▉ | 1445136/4997817 [00:08<00:20, 170912.74it/s]" ] }, { @@ -1218,7 +1218,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|███ | 1509255/4997817 [00:08<00:20, 172556.44it/s]" + " 29%|██▉ | 1462229/4997817 [00:08<00:20, 170913.08it/s]" ] }, { @@ -1226,7 +1226,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1526644/4997817 [00:08<00:20, 172949.51it/s]" + " 30%|██▉ | 1479321/4997817 [00:08<00:20, 170499.01it/s]" ] }, { @@ -1234,7 +1234,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1544068/4997817 [00:08<00:19, 173330.43it/s]" + " 30%|██▉ | 1496372/4997817 [00:08<00:20, 170482.52it/s]" ] }, { @@ -1242,7 +1242,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1561511/4997817 [00:08<00:19, 173656.01it/s]" + " 30%|███ | 1513421/4997817 [00:08<00:20, 170395.25it/s]" ] }, { @@ -1250,7 +1250,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1578882/4997817 [00:08<00:19, 173600.68it/s]" + " 31%|███ | 1530461/4997817 [00:08<00:20, 167319.27it/s]" ] }, { @@ -1258,7 +1258,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1596246/4997817 [00:09<00:19, 173421.86it/s]" + " 31%|███ | 1547481/4997817 [00:09<00:20, 168168.22it/s]" ] }, { @@ -1266,7 +1266,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1613759/4997817 [00:09<00:19, 173930.01it/s]" + " 31%|███▏ | 1564657/4997817 [00:09<00:20, 169231.06it/s]" ] }, { @@ -1274,7 +1274,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1631217/4997817 [00:09<00:19, 174122.33it/s]" + " 32%|███▏ | 1581588/4997817 [00:09<00:20, 169022.41it/s]" ] }, { @@ -1282,7 +1282,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1648631/4997817 [00:09<00:19, 170877.37it/s]" + " 32%|███▏ | 1598553/4997817 [00:09<00:20, 169205.69it/s]" ] }, { @@ -1290,7 +1290,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1666045/4997817 [00:09<00:19, 171838.32it/s]" + " 32%|███▏ | 1615478/4997817 [00:09<00:20, 168976.78it/s]" ] }, { @@ -1298,7 +1298,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▎ | 1683650/4997817 [00:09<00:19, 173084.46it/s]" + " 33%|███▎ | 1632488/4997817 [00:09<00:19, 169311.06it/s]" ] }, { @@ -1306,7 +1306,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1701219/4997817 [00:09<00:18, 173858.01it/s]" + " 33%|███▎ | 1649496/4997817 [00:09<00:19, 169537.42it/s]" ] }, { @@ -1314,7 +1314,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1718873/4997817 [00:09<00:18, 174654.48it/s]" + " 33%|███▎ | 1666516/4997817 [00:09<00:19, 169734.29it/s]" ] }, { @@ -1322,7 +1322,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 1736344/4997817 [00:09<00:18, 174622.09it/s]" + " 34%|███▎ | 1683770/4997817 [00:09<00:19, 170570.66it/s]" ] }, { @@ -1330,7 +1330,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1753942/4997817 [00:09<00:18, 175023.95it/s]" + " 34%|███▍ | 1700828/4997817 [00:09<00:19, 170107.59it/s]" ] }, { @@ -1338,7 +1338,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1771448/4997817 [00:10<00:18, 174993.57it/s]" + " 34%|███▍ | 1718095/4997817 [00:10<00:19, 170870.72it/s]" ] }, { @@ -1346,7 +1346,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1788950/4997817 [00:10<00:18, 174751.67it/s]" + " 35%|███▍ | 1735554/4997817 [00:10<00:18, 171982.68it/s]" ] }, { @@ -1354,7 +1354,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1806535/4997817 [00:10<00:18, 175076.18it/s]" + " 35%|███▌ | 1752844/4997817 [00:10<00:18, 172253.27it/s]" ] }, { @@ -1362,7 +1362,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▋ | 1824044/4997817 [00:10<00:18, 174947.92it/s]" + " 35%|███▌ | 1770192/4997817 [00:10<00:18, 172616.19it/s]" ] }, { @@ -1370,7 +1370,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1841882/4997817 [00:10<00:17, 175973.50it/s]" + " 36%|███▌ | 1787471/4997817 [00:10<00:18, 172664.15it/s]" ] }, { @@ -1378,7 +1378,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1859592/4997817 [00:10<00:17, 176306.73it/s]" + " 36%|███▌ | 1804738/4997817 [00:10<00:18, 172460.42it/s]" ] }, { @@ -1386,7 +1386,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1877279/4997817 [00:10<00:17, 176473.74it/s]" + " 36%|███▋ | 1822303/4997817 [00:10<00:18, 173414.99it/s]" ] }, { @@ -1394,7 +1394,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1894943/4997817 [00:10<00:17, 176520.23it/s]" + " 37%|███▋ | 1839775/4997817 [00:10<00:18, 173804.36it/s]" ] }, { @@ -1402,7 +1402,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1912596/4997817 [00:10<00:17, 176219.44it/s]" + " 37%|███▋ | 1857168/4997817 [00:10<00:18, 173839.96it/s]" ] }, { @@ -1410,7 +1410,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▊ | 1930263/4997817 [00:10<00:17, 176352.95it/s]" + " 38%|███▊ | 1874674/4997817 [00:10<00:17, 174204.13it/s]" ] }, { @@ -1418,7 +1418,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1947942/4997817 [00:11<00:17, 176481.33it/s]" + " 38%|███▊ | 1892095/4997817 [00:11<00:17, 173973.19it/s]" ] }, { @@ -1426,7 +1426,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1965591/4997817 [00:11<00:17, 175961.68it/s]" + " 38%|███▊ | 1909493/4997817 [00:11<00:17, 173652.09it/s]" ] }, { @@ -1434,7 +1434,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 1983188/4997817 [00:11<00:17, 175748.07it/s]" + " 39%|███▊ | 1926956/4997817 [00:11<00:17, 173942.04it/s]" ] }, { @@ -1442,7 +1442,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 2000787/4997817 [00:11<00:17, 175815.61it/s]" + " 39%|███▉ | 1944351/4997817 [00:11<00:17, 173676.68it/s]" ] }, { @@ -1450,7 +1450,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 2018369/4997817 [00:11<00:17, 168730.26it/s]" + " 39%|███▉ | 1961719/4997817 [00:11<00:17, 173544.32it/s]" ] }, { @@ -1458,7 +1458,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2035840/4997817 [00:11<00:17, 170467.93it/s]" + " 40%|███▉ | 1979205/4997817 [00:11<00:17, 173936.66it/s]" ] }, { @@ -1466,7 +1466,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2053380/4997817 [00:11<00:17, 171912.16it/s]" + " 40%|███▉ | 1996632/4997817 [00:11<00:17, 174033.21it/s]" ] }, { @@ -1474,7 +1474,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████▏ | 2070874/4997817 [00:11<00:16, 172804.36it/s]" + " 40%|████ | 2014036/4997817 [00:11<00:17, 173984.33it/s]" ] }, { @@ -1482,7 +1482,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2088397/4997817 [00:11<00:16, 173519.94it/s]" + " 41%|████ | 2031435/4997817 [00:11<00:17, 173931.39it/s]" ] }, { @@ -1490,7 +1490,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2105931/4997817 [00:11<00:16, 174057.90it/s]" + " 41%|████ | 2048829/4997817 [00:11<00:16, 173817.50it/s]" ] }, { @@ -1498,7 +1498,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2123526/4997817 [00:12<00:16, 174619.28it/s]" + " 41%|████▏ | 2066211/4997817 [00:12<00:16, 173089.88it/s]" ] }, { @@ -1506,7 +1506,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2140999/4997817 [00:12<00:16, 174636.87it/s]" + " 42%|████▏ | 2083521/4997817 [00:12<00:16, 173048.80it/s]" ] }, { @@ -1514,7 +1514,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2158470/4997817 [00:12<00:16, 174638.99it/s]" + " 42%|████▏ | 2100866/4997817 [00:12<00:16, 173165.42it/s]" ] }, { @@ -1522,7 +1522,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▎ | 2175968/4997817 [00:12<00:16, 174737.78it/s]" + " 42%|████▏ | 2118183/4997817 [00:12<00:16, 173142.49it/s]" ] }, { @@ -1530,7 +1530,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2193446/4997817 [00:12<00:16, 174633.60it/s]" + " 43%|████▎ | 2135542/4997817 [00:12<00:16, 173274.37it/s]" ] }, { @@ -1538,7 +1538,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2211104/4997817 [00:12<00:15, 175212.99it/s]" + " 43%|████▎ | 2152870/4997817 [00:12<00:16, 172932.60it/s]" ] }, { @@ -1546,7 +1546,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2228891/4997817 [00:12<00:15, 176005.69it/s]" + " 43%|████▎ | 2170164/4997817 [00:12<00:16, 172786.75it/s]" ] }, { @@ -1554,7 +1554,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2246705/4997817 [00:12<00:15, 176642.54it/s]" + " 44%|████▍ | 2187443/4997817 [00:12<00:16, 172758.80it/s]" ] }, { @@ -1562,7 +1562,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 2264371/4997817 [00:12<00:15, 176477.26it/s]" + " 44%|████▍ | 2204719/4997817 [00:12<00:16, 172489.32it/s]" ] }, { @@ -1570,7 +1570,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2282020/4997817 [00:12<00:15, 176270.92it/s]" + " 44%|████▍ | 2221969/4997817 [00:12<00:16, 172209.27it/s]" ] }, { @@ -1578,7 +1578,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2299648/4997817 [00:13<00:15, 176268.65it/s]" + " 45%|████▍ | 2239236/4997817 [00:13<00:16, 172342.87it/s]" ] }, { @@ -1586,7 +1586,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▋ | 2317443/4997817 [00:13<00:15, 176769.77it/s]" + " 45%|████▌ | 2256471/4997817 [00:13<00:15, 171712.51it/s]" ] }, { @@ -1594,7 +1594,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2335287/4997817 [00:13<00:15, 177266.82it/s]" + " 45%|████▌ | 2273643/4997817 [00:13<00:15, 171115.35it/s]" ] }, { @@ -1602,7 +1602,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2353014/4997817 [00:13<00:14, 177237.67it/s]" + " 46%|████▌ | 2290884/4997817 [00:13<00:15, 171500.07it/s]" ] }, { @@ -1610,7 +1610,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2370811/4997817 [00:13<00:14, 177454.13it/s]" + " 46%|████▌ | 2308082/4997817 [00:13<00:15, 171641.01it/s]" ] }, { @@ -1618,7 +1618,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2388644/4997817 [00:13<00:14, 177713.67it/s]" + " 47%|████▋ | 2325509/4997817 [00:13<00:15, 172425.40it/s]" ] }, { @@ -1626,7 +1626,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2406515/4997817 [00:13<00:14, 178008.59it/s]" + " 47%|████▋ | 2342779/4997817 [00:13<00:15, 172505.46it/s]" ] }, { @@ -1634,7 +1634,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▊ | 2424382/4997817 [00:13<00:14, 178202.69it/s]" + " 47%|████▋ | 2360266/4997817 [00:13<00:15, 173212.74it/s]" ] }, { @@ -1642,7 +1642,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2442203/4997817 [00:13<00:14, 176953.39it/s]" + " 48%|████▊ | 2377746/4997817 [00:13<00:15, 173684.69it/s]" ] }, { @@ -1650,7 +1650,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2460064/4997817 [00:13<00:14, 177445.01it/s]" + " 48%|████▊ | 2395275/4997817 [00:13<00:14, 174160.30it/s]" ] }, { @@ -1658,7 +1658,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|████▉ | 2478011/4997817 [00:14<00:14, 178046.47it/s]" + " 48%|████▊ | 2412692/4997817 [00:14<00:14, 173545.01it/s]" ] }, { @@ -1666,7 +1666,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|████▉ | 2495859/4997817 [00:14<00:14, 178170.66it/s]" + " 49%|████▊ | 2430069/4997817 [00:14<00:14, 173608.65it/s]" ] }, { @@ -1674,7 +1674,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|█████ | 2513719/4997817 [00:14<00:13, 178294.71it/s]" + " 49%|████▉ | 2447462/4997817 [00:14<00:14, 173701.94it/s]" ] }, { @@ -1682,7 +1682,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2531550/4997817 [00:14<00:13, 177984.78it/s]" + " 49%|████▉ | 2464833/4997817 [00:14<00:14, 173310.39it/s]" ] }, { @@ -1690,7 +1690,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2549350/4997817 [00:14<00:13, 177765.45it/s]" + " 50%|████▉ | 2482165/4997817 [00:14<00:14, 172931.85it/s]" ] }, { @@ -1698,7 +1698,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████▏ | 2567127/4997817 [00:14<00:13, 177467.75it/s]" + " 50%|█████ | 2499563/4997817 [00:14<00:14, 173242.08it/s]" ] }, { @@ -1706,7 +1706,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2584875/4997817 [00:14<00:13, 177135.41it/s]" + " 50%|█████ | 2516888/4997817 [00:14<00:14, 173011.66it/s]" ] }, { @@ -1714,7 +1714,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2602589/4997817 [00:14<00:13, 176761.69it/s]" + " 51%|█████ | 2534190/4997817 [00:14<00:14, 172835.54it/s]" ] }, { @@ -1722,7 +1722,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2620266/4997817 [00:14<00:13, 176400.10it/s]" + " 51%|█████ | 2551474/4997817 [00:14<00:14, 172684.40it/s]" ] }, { @@ -1730,7 +1730,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2637907/4997817 [00:14<00:13, 176353.99it/s]" + " 51%|█████▏ | 2568743/4997817 [00:14<00:14, 171725.95it/s]" ] }, { @@ -1738,7 +1738,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2655543/4997817 [00:15<00:13, 175946.08it/s]" + " 52%|█████▏ | 2586056/4997817 [00:15<00:14, 172142.42it/s]" ] }, { @@ -1746,7 +1746,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2673138/4997817 [00:15<00:13, 175874.49it/s]" + " 52%|█████▏ | 2603272/4997817 [00:15<00:13, 172009.73it/s]" ] }, { @@ -1754,7 +1754,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2690726/4997817 [00:15<00:13, 175848.48it/s]" + " 52%|█████▏ | 2620507/4997817 [00:15<00:13, 172106.86it/s]" ] }, { @@ -1762,7 +1762,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2708319/4997817 [00:15<00:13, 175869.29it/s]" + " 53%|█████▎ | 2637748/4997817 [00:15<00:13, 172193.71it/s]" ] }, { @@ -1770,7 +1770,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 2725906/4997817 [00:15<00:13, 170165.16it/s]" + " 53%|█████▎ | 2655228/4997817 [00:15<00:13, 172969.91it/s]" ] }, { @@ -1778,7 +1778,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 2743530/4997817 [00:15<00:13, 171941.91it/s]" + " 53%|█████▎ | 2672594/4997817 [00:15<00:13, 173172.78it/s]" ] }, { @@ -1786,7 +1786,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2761055/4997817 [00:15<00:12, 172913.09it/s]" + " 54%|█████▍ | 2689912/4997817 [00:15<00:13, 172973.25it/s]" ] }, { @@ -1794,7 +1794,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2778599/4997817 [00:15<00:12, 173657.51it/s]" + " 54%|█████▍ | 2707210/4997817 [00:15<00:13, 172673.04it/s]" ] }, { @@ -1802,7 +1802,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2796132/4997817 [00:15<00:12, 174152.13it/s]" + " 55%|█████▍ | 2724649/4997817 [00:15<00:13, 173183.95it/s]" ] }, { @@ -1810,7 +1810,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▋ | 2813713/4997817 [00:15<00:12, 174642.51it/s]" + " 55%|█████▍ | 2742045/4997817 [00:15<00:13, 173411.99it/s]" ] }, { @@ -1818,7 +1818,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2831215/4997817 [00:16<00:12, 174751.85it/s]" + " 55%|█████▌ | 2759387/4997817 [00:16<00:12, 172797.03it/s]" ] }, { @@ -1826,7 +1826,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2848781/4997817 [00:16<00:12, 175020.18it/s]" + " 56%|█████▌ | 2776675/4997817 [00:16<00:12, 172818.44it/s]" ] }, { @@ -1834,7 +1834,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2866288/4997817 [00:16<00:12, 174922.90it/s]" + " 56%|█████▌ | 2793958/4997817 [00:16<00:12, 172443.43it/s]" ] }, { @@ -1842,7 +1842,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2883784/4997817 [00:16<00:12, 174867.39it/s]" + " 56%|█████▌ | 2811203/4997817 [00:16<00:12, 172181.49it/s]" ] }, { @@ -1850,7 +1850,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2901274/4997817 [00:16<00:12, 174518.53it/s]" + " 57%|█████▋ | 2828422/4997817 [00:16<00:12, 171875.31it/s]" ] }, { @@ -1858,7 +1858,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2918784/4997817 [00:16<00:11, 174687.97it/s]" + " 57%|█████▋ | 2845610/4997817 [00:16<00:12, 171605.12it/s]" ] }, { @@ -1866,7 +1866,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2936508/4997817 [00:16<00:11, 175446.36it/s]" + " 57%|█████▋ | 2862771/4997817 [00:16<00:12, 171158.71it/s]" ] }, { @@ -1874,7 +1874,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2954190/4997817 [00:16<00:11, 175855.21it/s]" + " 58%|█████▊ | 2880043/4997817 [00:16<00:12, 171620.59it/s]" ] }, { @@ -1882,7 +1882,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2971867/4997817 [00:16<00:11, 176126.05it/s]" + " 58%|█████▊ | 2897206/4997817 [00:16<00:12, 171545.78it/s]" ] }, { @@ -1890,7 +1890,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2989607/4997817 [00:16<00:11, 176504.50it/s]" + " 58%|█████▊ | 2914479/4997817 [00:16<00:12, 171897.64it/s]" ] }, { @@ -1898,7 +1898,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 3007465/4997817 [00:17<00:11, 177122.98it/s]" + " 59%|█████▊ | 2931670/4997817 [00:17<00:12, 171468.11it/s]" ] }, { @@ -1906,7 +1906,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3025371/4997817 [00:17<00:11, 177699.75it/s]" + " 59%|█████▉ | 2948818/4997817 [00:17<00:12, 164907.42it/s]" ] }, { @@ -1914,7 +1914,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3043142/4997817 [00:17<00:11, 177526.55it/s]" + " 59%|█████▉ | 2965819/4997817 [00:17<00:12, 166389.56it/s]" ] }, { @@ -1922,7 +1922,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3060895/4997817 [00:17<00:10, 177261.36it/s]" + " 60%|█████▉ | 2983140/4997817 [00:17<00:11, 168389.32it/s]" ] }, { @@ -1930,7 +1930,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3078730/4997817 [00:17<00:10, 177583.27it/s]" + " 60%|██████ | 3000413/4997817 [00:17<00:11, 169668.46it/s]" ] }, { @@ -1938,7 +1938,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3096489/4997817 [00:17<00:10, 173439.53it/s]" + " 60%|██████ | 3017703/4997817 [00:17<00:11, 170624.06it/s]" ] }, { @@ -1946,7 +1946,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3114319/4997817 [00:17<00:10, 174869.70it/s]" + " 61%|██████ | 3034960/4997817 [00:17<00:11, 171201.38it/s]" ] }, { @@ -1954,7 +1954,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3132167/4997817 [00:17<00:10, 175934.19it/s]" + " 61%|██████ | 3052094/4997817 [00:17<00:11, 171123.09it/s]" ] }, { @@ -1962,7 +1962,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3150036/4997817 [00:17<00:10, 176751.52it/s]" + " 61%|██████▏ | 3069455/4997817 [00:17<00:11, 171864.37it/s]" ] }, { @@ -1970,7 +1970,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3167884/4997817 [00:18<00:10, 177263.84it/s]" + " 62%|██████▏ | 3086755/4997817 [00:18<00:11, 172200.09it/s]" ] }, { @@ -1978,7 +1978,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▎ | 3185698/4997817 [00:18<00:10, 177523.48it/s]" + " 62%|██████▏ | 3104106/4997817 [00:18<00:10, 172590.10it/s]" ] }, { @@ -1986,7 +1986,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3203568/4997817 [00:18<00:10, 177872.39it/s]" + " 62%|██████▏ | 3121414/4997817 [00:18<00:10, 172732.84it/s]" ] }, { @@ -1994,7 +1994,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3221360/4997817 [00:18<00:09, 177711.90it/s]" + " 63%|██████▎ | 3138690/4997817 [00:18<00:10, 172414.93it/s]" ] }, { @@ -2002,7 +2002,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 3239134/4997817 [00:18<00:09, 177577.15it/s]" + " 63%|██████▎ | 3155966/4997817 [00:18<00:10, 172513.49it/s]" ] }, { @@ -2010,7 +2010,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 3256894/4997817 [00:18<00:09, 176368.10it/s]" + " 63%|██████▎ | 3173316/4997817 [00:18<00:10, 172807.21it/s]" ] }, { @@ -2018,7 +2018,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3274697/4997817 [00:18<00:09, 176860.96it/s]" + " 64%|██████▍ | 3190786/4997817 [00:18<00:10, 173371.43it/s]" ] }, { @@ -2026,7 +2026,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3292438/4997817 [00:18<00:09, 177021.85it/s]" + " 64%|██████▍ | 3208124/4997817 [00:18<00:10, 173368.22it/s]" ] }, { @@ -2034,7 +2034,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3310162/4997817 [00:18<00:09, 177081.84it/s]" + " 65%|██████▍ | 3225462/4997817 [00:18<00:10, 173291.90it/s]" ] }, { @@ -2042,7 +2042,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3327911/4997817 [00:18<00:09, 177201.04it/s]" + " 65%|██████▍ | 3242799/4997817 [00:18<00:10, 173312.39it/s]" ] }, { @@ -2050,7 +2050,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3345632/4997817 [00:19<00:09, 177070.11it/s]" + " 65%|██████▌ | 3260131/4997817 [00:19<00:10, 173229.77it/s]" ] }, { @@ -2058,7 +2058,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3363340/4997817 [00:19<00:09, 176560.09it/s]" + " 66%|██████▌ | 3277459/4997817 [00:19<00:09, 173241.78it/s]" ] }, { @@ -2066,7 +2066,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3380997/4997817 [00:19<00:09, 176297.50it/s]" + " 66%|██████▌ | 3294784/4997817 [00:19<00:10, 168720.26it/s]" ] }, { @@ -2074,7 +2074,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3398628/4997817 [00:19<00:09, 175923.15it/s]" + " 66%|██████▋ | 3311836/4997817 [00:19<00:09, 169246.65it/s]" ] }, { @@ -2082,7 +2082,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3416444/4997817 [00:19<00:08, 176583.41it/s]" + " 67%|██████▋ | 3329185/4997817 [00:19<00:09, 170499.74it/s]" ] }, { @@ -2090,7 +2090,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▊ | 3434103/4997817 [00:19<00:08, 176134.50it/s]" + " 67%|██████▋ | 3346525/4997817 [00:19<00:09, 171358.37it/s]" ] }, { @@ -2098,7 +2098,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3451738/4997817 [00:19<00:08, 176195.20it/s]" + " 67%|██████▋ | 3363981/4997817 [00:19<00:09, 172310.29it/s]" ] }, { @@ -2106,7 +2106,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3469358/4997817 [00:19<00:08, 176027.78it/s]" + " 68%|██████▊ | 3381408/4997817 [00:19<00:09, 172891.73it/s]" ] }, { @@ -2114,7 +2114,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|██████▉ | 3486962/4997817 [00:19<00:08, 175916.89it/s]" + " 68%|██████▊ | 3398883/4997817 [00:19<00:09, 173444.12it/s]" ] }, { @@ -2122,7 +2122,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3504554/4997817 [00:19<00:08, 175282.26it/s]" + " 68%|██████▊ | 3416395/4997817 [00:19<00:09, 173944.67it/s]" ] }, { @@ -2130,7 +2130,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3522083/4997817 [00:20<00:08, 175175.68it/s]" + " 69%|██████▊ | 3433866/4997817 [00:20<00:08, 174170.71it/s]" ] }, { @@ -2138,7 +2138,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3539798/4997817 [00:20<00:08, 175762.45it/s]" + " 69%|██████▉ | 3451295/4997817 [00:20<00:08, 174202.78it/s]" ] }, { @@ -2146,7 +2146,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3557423/4997817 [00:20<00:08, 175905.54it/s]" + " 69%|██████▉ | 3468717/4997817 [00:20<00:08, 173871.49it/s]" ] }, { @@ -2154,7 +2154,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3575120/4997817 [00:20<00:08, 176219.37it/s]" + " 70%|██████▉ | 3486106/4997817 [00:20<00:08, 173730.48it/s]" ] }, { @@ -2162,7 +2162,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3592743/4997817 [00:20<00:07, 176124.71it/s]" + " 70%|███████ | 3503480/4997817 [00:20<00:08, 173576.14it/s]" ] }, { @@ -2170,7 +2170,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3610402/4997817 [00:20<00:07, 176260.53it/s]" + " 70%|███████ | 3520839/4997817 [00:20<00:08, 172430.10it/s]" ] }, { @@ -2178,7 +2178,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3628029/4997817 [00:20<00:07, 176148.24it/s]" + " 71%|███████ | 3538084/4997817 [00:20<00:08, 172099.73it/s]" ] }, { @@ -2186,7 +2186,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3645692/4997817 [00:20<00:07, 176289.26it/s]" + " 71%|███████ | 3555296/4997817 [00:20<00:08, 171901.03it/s]" ] }, { @@ -2194,7 +2194,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3663322/4997817 [00:20<00:07, 176222.00it/s]" + " 71%|███████▏ | 3572487/4997817 [00:20<00:08, 171608.38it/s]" ] }, { @@ -2202,7 +2202,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▎ | 3680980/4997817 [00:20<00:07, 176324.07it/s]" + " 72%|███████▏ | 3589757/4997817 [00:20<00:08, 171930.02it/s]" ] }, { @@ -2210,7 +2210,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3698735/4997817 [00:21<00:07, 176687.64it/s]" + " 72%|███████▏ | 3606959/4997817 [00:21<00:08, 171954.77it/s]" ] }, { @@ -2218,7 +2218,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3716404/4997817 [00:21<00:07, 176628.29it/s]" + " 73%|███████▎ | 3624245/4997817 [00:21<00:07, 172222.71it/s]" ] }, { @@ -2226,7 +2226,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 3734091/4997817 [00:21<00:07, 176697.15it/s]" + " 73%|███████▎ | 3641468/4997817 [00:21<00:08, 165178.06it/s]" ] }, { @@ -2234,7 +2234,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 3751761/4997817 [00:21<00:07, 176640.21it/s]" + " 73%|███████▎ | 3658590/4997817 [00:21<00:08, 166933.37it/s]" ] }, { @@ -2242,7 +2242,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 3769457/4997817 [00:21<00:06, 176732.49it/s]" + " 74%|███████▎ | 3675846/4997817 [00:21<00:07, 168580.91it/s]" ] }, { @@ -2250,7 +2250,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3787209/4997817 [00:21<00:06, 176965.92it/s]" + " 74%|███████▍ | 3692949/4997817 [00:21<00:07, 169301.80it/s]" ] }, { @@ -2258,7 +2258,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3804906/4997817 [00:21<00:06, 176115.13it/s]" + " 74%|███████▍ | 3710095/4997817 [00:21<00:07, 169941.03it/s]" ] }, { @@ -2266,7 +2266,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▋ | 3822589/4997817 [00:21<00:06, 176324.35it/s]" + " 75%|███████▍ | 3727212/4997817 [00:21<00:07, 170305.46it/s]" ] }, { @@ -2274,7 +2274,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3840267/4997817 [00:21<00:06, 176456.98it/s]" + " 75%|███████▍ | 3744383/4997817 [00:21<00:07, 170721.03it/s]" ] }, { @@ -2282,7 +2282,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3857949/4997817 [00:21<00:06, 176561.33it/s]" + " 75%|███████▌ | 3761585/4997817 [00:21<00:07, 171106.29it/s]" ] }, { @@ -2290,7 +2290,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3875608/4997817 [00:22<00:06, 176566.31it/s]" + " 76%|███████▌ | 3778703/4997817 [00:22<00:07, 171043.71it/s]" ] }, { @@ -2298,7 +2298,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3893265/4997817 [00:22<00:06, 176410.01it/s]" + " 76%|███████▌ | 3795890/4997817 [00:22<00:07, 171287.44it/s]" ] }, { @@ -2306,7 +2306,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3910907/4997817 [00:22<00:06, 176281.59it/s]" + " 76%|███████▋ | 3813023/4997817 [00:22<00:06, 170783.59it/s]" ] }, { @@ -2314,7 +2314,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▊ | 3928536/4997817 [00:22<00:06, 176164.37it/s]" + " 77%|███████▋ | 3830175/4997817 [00:22<00:06, 171001.66it/s]" ] }, { @@ -2322,7 +2322,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3946173/4997817 [00:22<00:05, 176221.61it/s]" + " 77%|███████▋ | 3847278/4997817 [00:22<00:06, 170875.44it/s]" ] }, { @@ -2330,7 +2330,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3963796/4997817 [00:22<00:05, 175821.79it/s]" + " 77%|███████▋ | 3864410/4997817 [00:22<00:06, 171005.16it/s]" ] }, { @@ -2338,7 +2338,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3981379/4997817 [00:22<00:05, 175679.25it/s]" + " 78%|███████▊ | 3881633/4997817 [00:22<00:06, 171370.89it/s]" ] }, { @@ -2346,7 +2346,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 3999029/4997817 [00:22<00:05, 175919.84it/s]" + " 78%|███████▊ | 3898771/4997817 [00:22<00:06, 170584.23it/s]" ] }, { @@ -2354,7 +2354,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 4016746/4997817 [00:22<00:05, 176291.45it/s]" + " 78%|███████▊ | 3915831/4997817 [00:22<00:06, 169965.52it/s]" ] }, { @@ -2362,7 +2362,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4034376/4997817 [00:22<00:05, 176165.06it/s]" + " 79%|███████▊ | 3932869/4997817 [00:22<00:06, 170087.91it/s]" ] }, { @@ -2370,7 +2370,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4051993/4997817 [00:23<00:05, 176084.45it/s]" + " 79%|███████▉ | 3949879/4997817 [00:23<00:06, 169990.31it/s]" ] }, { @@ -2378,7 +2378,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████▏ | 4069602/4997817 [00:23<00:05, 175885.63it/s]" + " 79%|███████▉ | 3966879/4997817 [00:23<00:06, 169949.51it/s]" ] }, { @@ -2386,7 +2386,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4087191/4997817 [00:23<00:05, 175394.53it/s]" + " 80%|███████▉ | 3983875/4997817 [00:23<00:05, 169772.79it/s]" ] }, { @@ -2394,7 +2394,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4104731/4997817 [00:23<00:05, 175012.49it/s]" + " 80%|████████ | 4000853/4997817 [00:23<00:05, 169718.68it/s]" ] }, { @@ -2402,7 +2402,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4122236/4997817 [00:23<00:05, 175020.26it/s]" + " 80%|████████ | 4017853/4997817 [00:23<00:05, 169799.69it/s]" ] }, { @@ -2410,7 +2410,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4139739/4997817 [00:23<00:04, 174861.56it/s]" + " 81%|████████ | 4034999/4997817 [00:23<00:05, 170291.45it/s]" ] }, { @@ -2418,7 +2418,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4157233/4997817 [00:23<00:04, 174879.84it/s]" + " 81%|████████ | 4052095/4997817 [00:23<00:05, 170489.07it/s]" ] }, { @@ -2426,7 +2426,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▎ | 4174722/4997817 [00:23<00:04, 167593.86it/s]" + " 81%|████████▏ | 4069145/4997817 [00:23<00:05, 170353.71it/s]" ] }, { @@ -2434,7 +2434,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4192083/4997817 [00:23<00:04, 169341.24it/s]" + " 82%|████████▏ | 4086208/4997817 [00:23<00:05, 170433.51it/s]" ] }, { @@ -2442,7 +2442,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4209474/4997817 [00:23<00:04, 170677.23it/s]" + " 82%|████████▏ | 4103252/4997817 [00:23<00:05, 169878.09it/s]" ] }, { @@ -2450,7 +2450,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▍ | 4226976/4997817 [00:24<00:04, 171956.01it/s]" + " 82%|████████▏ | 4120406/4997817 [00:24<00:05, 170373.48it/s]" ] }, { @@ -2458,7 +2458,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▍ | 4244411/4997817 [00:24<00:04, 172661.73it/s]" + " 83%|████████▎ | 4137520/4997817 [00:24<00:05, 170600.48it/s]" ] }, { @@ -2466,7 +2466,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 4261828/4997817 [00:24<00:04, 173107.14it/s]" + " 83%|████████▎ | 4154679/4997817 [00:24<00:04, 170894.40it/s]" ] }, { @@ -2474,7 +2474,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4279356/4997817 [00:24<00:04, 173752.22it/s]" + " 83%|████████▎ | 4171769/4997817 [00:24<00:04, 170333.58it/s]" ] }, { @@ -2482,7 +2482,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4296896/4997817 [00:24<00:04, 174242.38it/s]" + " 84%|████████▍ | 4188803/4997817 [00:24<00:04, 169923.43it/s]" ] }, { @@ -2490,7 +2490,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▋ | 4314328/4997817 [00:24<00:03, 173327.93it/s]" + " 84%|████████▍ | 4205796/4997817 [00:24<00:04, 169523.93it/s]" ] }, { @@ -2498,7 +2498,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4331919/4997817 [00:24<00:03, 174094.65it/s]" + " 84%|████████▍ | 4222817/4997817 [00:24<00:04, 169724.32it/s]" ] }, { @@ -2506,7 +2506,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4349427/4997817 [00:24<00:03, 174384.04it/s]" + " 85%|████████▍ | 4239790/4997817 [00:24<00:04, 169579.63it/s]" ] }, { @@ -2514,7 +2514,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4367051/4997817 [00:24<00:03, 174935.33it/s]" + " 85%|████████▌ | 4256818/4997817 [00:24<00:04, 169786.18it/s]" ] }, { @@ -2522,7 +2522,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4384595/4997817 [00:24<00:03, 175083.73it/s]" + " 86%|████████▌ | 4273871/4997817 [00:24<00:04, 170006.16it/s]" ] }, { @@ -2530,7 +2530,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4402158/4997817 [00:25<00:03, 175244.18it/s]" + " 86%|████████▌ | 4291017/4997817 [00:25<00:04, 170438.96it/s]" ] }, { @@ -2538,7 +2538,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4419786/4997817 [00:25<00:03, 175552.04it/s]" + " 86%|████████▌ | 4308086/4997817 [00:25<00:04, 170511.81it/s]" ] }, { @@ -2546,7 +2546,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4437371/4997817 [00:25<00:03, 175636.52it/s]" + " 87%|████████▋ | 4325138/4997817 [00:25<00:03, 169547.73it/s]" ] }, { @@ -2554,7 +2554,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4455003/4997817 [00:25<00:03, 175837.48it/s]" + " 87%|████████▋ | 4342095/4997817 [00:25<00:04, 163414.55it/s]" ] }, { @@ -2562,7 +2562,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4472798/4997817 [00:25<00:02, 176468.40it/s]" + " 87%|████████▋ | 4359299/4997817 [00:25<00:03, 165924.16it/s]" ] }, { @@ -2570,7 +2570,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 4490560/4997817 [00:25<00:02, 176808.91it/s]" + " 88%|████████▊ | 4376343/4997817 [00:25<00:03, 167246.68it/s]" ] }, { @@ -2578,7 +2578,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 4508294/4997817 [00:25<00:02, 176965.60it/s]" + " 88%|████████▊ | 4393409/4997817 [00:25<00:03, 168254.06it/s]" ] }, { @@ -2586,7 +2586,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4525991/4997817 [00:25<00:02, 176542.13it/s]" + " 88%|████████▊ | 4410560/4997817 [00:25<00:03, 169219.14it/s]" ] }, { @@ -2594,7 +2594,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4543710/4997817 [00:25<00:02, 176732.28it/s]" + " 89%|████████▊ | 4427598/4997817 [00:25<00:03, 169562.46it/s]" ] }, { @@ -2602,7 +2602,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████▏| 4561463/4997817 [00:25<00:02, 176966.16it/s]" + " 89%|████████▉ | 4444705/4997817 [00:25<00:03, 170009.98it/s]" ] }, { @@ -2610,7 +2610,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4579245/4997817 [00:26<00:02, 177220.61it/s]" + " 89%|████████▉ | 4461912/4997817 [00:26<00:03, 170621.62it/s]" ] }, { @@ -2618,7 +2618,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4597064/4997817 [00:26<00:02, 177506.60it/s]" + " 90%|████████▉ | 4478981/4997817 [00:26<00:03, 170378.09it/s]" ] }, { @@ -2626,7 +2626,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4614841/4997817 [00:26<00:02, 177581.31it/s]" + " 90%|████████▉ | 4496051/4997817 [00:26<00:02, 170472.86it/s]" ] }, { @@ -2634,7 +2634,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4632600/4997817 [00:26<00:02, 177414.75it/s]" + " 90%|█████████ | 4513109/4997817 [00:26<00:02, 170500.24it/s]" ] }, { @@ -2642,7 +2642,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4650342/4997817 [00:26<00:01, 176810.52it/s]" + " 91%|█████████ | 4530422/4997817 [00:26<00:02, 171284.56it/s]" ] }, { @@ -2650,7 +2650,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4668024/4997817 [00:26<00:02, 163789.41it/s]" + " 91%|█████████ | 4547617/4997817 [00:26<00:02, 171480.12it/s]" ] }, { @@ -2658,7 +2658,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 4685720/4997817 [00:26<00:01, 167519.88it/s]" + " 91%|█████████▏| 4564863/4997817 [00:26<00:02, 171770.24it/s]" ] }, { @@ -2666,7 +2666,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 4703228/4997817 [00:26<00:01, 169696.70it/s]" + " 92%|█████████▏| 4582041/4997817 [00:26<00:02, 171252.29it/s]" ] }, { @@ -2674,7 +2674,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 4720997/4997817 [00:26<00:01, 172024.75it/s]" + " 92%|█████████▏| 4599168/4997817 [00:26<00:02, 170751.33it/s]" ] }, { @@ -2682,7 +2682,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▍| 4738747/4997817 [00:26<00:01, 173631.53it/s]" + " 92%|█████████▏| 4616245/4997817 [00:26<00:02, 170236.58it/s]" ] }, { @@ -2690,7 +2690,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 4756401/4997817 [00:27<00:01, 174489.53it/s]" + " 93%|█████████▎| 4633367/4997817 [00:27<00:02, 170526.11it/s]" ] }, { @@ -2698,7 +2698,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4774026/4997817 [00:27<00:01, 175007.15it/s]" + " 93%|█████████▎| 4650476/4997817 [00:27<00:02, 170690.98it/s]" ] }, { @@ -2706,7 +2706,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4791625/4997817 [00:27<00:01, 175295.19it/s]" + " 93%|█████████▎| 4667696/4997817 [00:27<00:01, 171138.38it/s]" ] }, { @@ -2714,7 +2714,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4809276/4997817 [00:27<00:01, 175653.18it/s]" + " 94%|█████████▎| 4684811/4997817 [00:27<00:01, 166416.31it/s]" ] }, { @@ -2722,7 +2722,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 4827007/4997817 [00:27<00:00, 176143.70it/s]" + " 94%|█████████▍| 4702063/4997817 [00:27<00:01, 168205.75it/s]" ] }, { @@ -2730,7 +2730,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 4844714/4997817 [00:27<00:00, 176418.08it/s]" + " 94%|█████████▍| 4719283/4997817 [00:27<00:01, 169383.99it/s]" ] }, { @@ -2738,7 +2738,7 @@ "output_type": "stream", "text": [ "\r", - " 97%|█████████▋| 4862493/4997817 [00:27<00:00, 176823.46it/s]" + " 95%|█████████▍| 4736314/4997817 [00:27<00:01, 169655.39it/s]" ] }, { @@ -2746,7 +2746,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4880182/4997817 [00:27<00:00, 176751.65it/s]" + " 95%|█████████▌| 4753543/4997817 [00:27<00:01, 170435.52it/s]" ] }, { @@ -2754,7 +2754,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4897903/4997817 [00:27<00:00, 176884.36it/s]" + " 95%|█████████▌| 4770597/4997817 [00:27<00:01, 170424.55it/s]" ] }, { @@ -2762,7 +2762,7 @@ "output_type": "stream", "text": [ "\r", - " 98%|█████████▊| 4915661/4997817 [00:27<00:00, 177087.36it/s]" + " 96%|█████████▌| 4787647/4997817 [00:27<00:01, 170176.12it/s]" ] }, { @@ -2770,7 +2770,7 @@ "output_type": "stream", "text": [ "\r", - " 99%|█████████▊| 4933428/4997817 [00:28<00:00, 177258.92it/s]" + " 96%|█████████▌| 4804723/4997817 [00:28<00:01, 170346.92it/s]" ] }, { @@ -2778,7 +2778,7 @@ "output_type": "stream", "text": [ "\r", - " 99%|█████████▉| 4951156/4997817 [00:28<00:00, 177082.23it/s]" + " 96%|█████████▋| 4821848/4997817 [00:28<00:01, 170613.16it/s]" ] }, { @@ -2786,7 +2786,7 @@ "output_type": "stream", "text": [ "\r", - " 99%|█████████▉| 4968913/4997817 [00:28<00:00, 177225.04it/s]" + " 97%|█████████▋| 4838912/4997817 [00:28<00:00, 170004.32it/s]" ] }, { @@ -2794,7 +2794,7 @@ "output_type": "stream", "text": [ "\r", - "100%|█████████▉| 4986697/4997817 [00:28<00:00, 177405.38it/s]" + " 97%|█████████▋| 4856169/4997817 [00:28<00:00, 170767.84it/s]" ] }, { @@ -2802,7 +2802,71 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 4997817/4997817 [00:28<00:00, 175774.30it/s]" + " 98%|█████████▊| 4873248/4997817 [00:28<00:00, 170274.10it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 4890277/4997817 [00:28<00:00, 170191.72it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 98%|█████████▊| 4907298/4997817 [00:28<00:00, 170155.86it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▊| 4924593/4997817 [00:28<00:00, 170988.03it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▉| 4941723/4997817 [00:28<00:00, 171077.12it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + " 99%|█████████▉| 4958832/4997817 [00:28<00:00, 170246.67it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|█████████▉| 4975925/4997817 [00:29<00:00, 170448.66it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|█████████▉| 4992971/4997817 [00:29<00:00, 170315.59it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 4997817/4997817 [00:29<00:00, 171134.52it/s]" ] }, { @@ -3041,10 +3105,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:26.873997Z", - "iopub.status.busy": "2024-01-17T18:15:26.873608Z", - "iopub.status.idle": "2024-01-17T18:15:34.500725Z", - "shell.execute_reply": "2024-01-17T18:15:34.500119Z" + "iopub.execute_input": "2024-01-17T23:17:27.067514Z", + "iopub.status.busy": "2024-01-17T23:17:27.067292Z", + "iopub.status.idle": "2024-01-17T23:17:33.987753Z", + "shell.execute_reply": "2024-01-17T23:17:33.987100Z" } }, "outputs": [], @@ -3058,10 +3122,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:34.503481Z", - "iopub.status.busy": "2024-01-17T18:15:34.503250Z", - "iopub.status.idle": "2024-01-17T18:15:37.645465Z", - "shell.execute_reply": "2024-01-17T18:15:37.644802Z" + "iopub.execute_input": "2024-01-17T23:17:33.990761Z", + "iopub.status.busy": "2024-01-17T23:17:33.990234Z", + "iopub.status.idle": "2024-01-17T23:17:37.063049Z", + "shell.execute_reply": "2024-01-17T23:17:37.062361Z" } }, "outputs": [ @@ -3130,17 +3194,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:37.648166Z", - "iopub.status.busy": "2024-01-17T18:15:37.647687Z", - "iopub.status.idle": "2024-01-17T18:15:38.942832Z", - "shell.execute_reply": "2024-01-17T18:15:38.942205Z" + "iopub.execute_input": "2024-01-17T23:17:37.065626Z", + "iopub.status.busy": "2024-01-17T23:17:37.065237Z", + "iopub.status.idle": "2024-01-17T23:17:38.361596Z", + "shell.execute_reply": "2024-01-17T23:17:38.360967Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7dceb22a611b4605b26d5be95c8f7516", + "model_id": "3f014168fa4346d1a5243faf468a81d2", "version_major": 2, "version_minor": 0 }, @@ -3170,10 +3234,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:38.945502Z", - "iopub.status.busy": "2024-01-17T18:15:38.945301Z", - "iopub.status.idle": "2024-01-17T18:15:39.161394Z", - "shell.execute_reply": "2024-01-17T18:15:39.160716Z" + "iopub.execute_input": "2024-01-17T23:17:38.364721Z", + "iopub.status.busy": "2024-01-17T23:17:38.364114Z", + "iopub.status.idle": "2024-01-17T23:17:38.581707Z", + "shell.execute_reply": "2024-01-17T23:17:38.581139Z" } }, "outputs": [], @@ -3187,10 +3251,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:39.164077Z", - "iopub.status.busy": "2024-01-17T18:15:39.163834Z", - "iopub.status.idle": "2024-01-17T18:15:43.933619Z", - "shell.execute_reply": "2024-01-17T18:15:43.932997Z" + "iopub.execute_input": "2024-01-17T23:17:38.584628Z", + "iopub.status.busy": "2024-01-17T23:17:38.584131Z", + "iopub.status.idle": "2024-01-17T23:17:43.223493Z", + "shell.execute_reply": "2024-01-17T23:17:43.222781Z" } }, "outputs": [ @@ -3263,10 +3327,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:43.935930Z", - "iopub.status.busy": "2024-01-17T18:15:43.935733Z", - "iopub.status.idle": "2024-01-17T18:15:43.992260Z", - "shell.execute_reply": "2024-01-17T18:15:43.991692Z" + "iopub.execute_input": "2024-01-17T23:17:43.226244Z", + "iopub.status.busy": "2024-01-17T23:17:43.225777Z", + "iopub.status.idle": "2024-01-17T23:17:43.282179Z", + "shell.execute_reply": "2024-01-17T23:17:43.281446Z" }, "nbsphinx": "hidden" }, @@ -3310,7 +3374,23 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "1772cf6eace14f4f8a042982d8d016a8": { + "017b88b766dd4b1eb6d2b1e38318808d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "11f01422601b4fbfb274288e4cabc8dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3362,7 +3442,7 @@ "width": null } }, - "1a4a06ef208a4aea96192270ec7fb84a": { + "1a11c07d013c43bb8d5c2a9a9f51b77c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3414,22 +3494,31 @@ "width": null } }, - "1cf620e1d88a4e37b2d34ea1fb5e1535": { + "1c9e243742194e9f93f1f1d0f4ade627": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_fcc677509e4047feacf1ee2612eb3bf6", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2d9bd9df2ade4a83ab6231049c45a53b", + "value": 30.0 } }, - "1ed919af8e7844faabecb7a8dd47f285": { + "25a7aae549ff4d1fa03ce9aa556d8fc3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3444,23 +3533,7 @@ "description_width": "" } }, - "254221792aff42ce82df1c22b6a17fe7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "2c30e1b3212d44fcb51934e93bf256f4": { + "2641df8a6b10468c84fd7d0cf8fe4b7f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3512,7 +3585,60 @@ "width": null } }, - "2cef6291580545a795374bc03a6013f9": { + "2d9bd9df2ade4a83ab6231049c45a53b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "337a4acbdc614129ab2ee0180f730c19": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "3addf8ea78984dc3bf5ed29c07556bb9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_540cfd5273004c949a94947f00084b1e", + "IPY_MODEL_6c0f1a6b8b0941eb890e179ab021fa32", + "IPY_MODEL_e15b5be74c0c4d29bdfda502583c695b" + ], + "layout": "IPY_MODEL_606e484d407447bcb09578773cd22d5d" + } + }, + "3bf0e062325848e38433cfd0a1c1e64e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3564,64 +3690,7 @@ "width": null } }, - "3d5158c48b6d4e3587ff508936ad7386": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "4109ef68aa164157869d8efce1002420": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_1a4a06ef208a4aea96192270ec7fb84a", - "placeholder": "​", - "style": "IPY_MODEL_98e1388d51f34102aacc21ba7bc431eb", - "value": " 30/30 [00:01<00:00, 23.76it/s]" - } - }, - "464b5bb258bd409ab318e6ecab337861": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_7b875172cd764fa9ab4b1d7fdcd93b46", - "placeholder": "​", - "style": "IPY_MODEL_3d5158c48b6d4e3587ff508936ad7386", - "value": "images processed using softmin: 100%" - } - }, - "587d5b941fbc465faf4eb95923831929": { + "3eedef9a02374d1e96abed5e91673616": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3673,38 +3742,51 @@ "width": null } }, - "6422f763cda64280af2c3e65257aeadf": { + "3f014168fa4346d1a5243faf468a81d2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ba97605c2b714aaeae151edc68655c4e", + "IPY_MODEL_a29c9371170a4cf195f0623a90a22046", + "IPY_MODEL_b75dd4684dc541648d9d07c71d611dde" + ], + "layout": "IPY_MODEL_3bf0e062325848e38433cfd0a1c1e64e" } }, - "6493300865974b87a00b6d58ce9d45d3": { + "4fea53dd3c354db89dbd413a514598b1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_579262aaf92e43c8b403d67fcdbb69de", + "IPY_MODEL_1c9e243742194e9f93f1f1d0f4ade627", + "IPY_MODEL_f8958214798d4aaea3392a34c1132c86" + ], + "layout": "IPY_MODEL_e66280fc5b9544d3b2997f0895b86556" } }, - "660cb6ce052f47b4b6cbe492bceef787": { + "540cfd5273004c949a94947f00084b1e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3719,29 +3801,34 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_9c7f519c6e4e43a983bbd8764c156460", + "layout": "IPY_MODEL_1a11c07d013c43bb8d5c2a9a9f51b77c", "placeholder": "​", - "style": "IPY_MODEL_6422f763cda64280af2c3e65257aeadf", + "style": "IPY_MODEL_25a7aae549ff4d1fa03ce9aa556d8fc3", "value": "number of examples processed for checking labels: 100%" } }, - "70503413664647f98537eb6ceb24c397": { + "579262aaf92e43c8b403d67fcdbb69de": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_68e0315ddeeb47aaa3eed64bc0a781bb", + "placeholder": "​", + "style": "IPY_MODEL_c0d493b322dc419dbe87e7a6b2af79ce", + "value": "number of examples processed for estimating thresholds: 100%" } }, - "7b875172cd764fa9ab4b1d7fdcd93b46": { + "606e484d407447bcb09578773cd22d5d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3793,50 +3880,7 @@ "width": null } }, - "7daa21d4a2364d38ad2a974e382c6322": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_813659abd0ae4858b70121d8e5e42c7a", - "placeholder": "​", - "style": "IPY_MODEL_1cf620e1d88a4e37b2d34ea1fb5e1535", - "value": "number of examples processed for estimating thresholds: 100%" - } - }, - "7dceb22a611b4605b26d5be95c8f7516": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_464b5bb258bd409ab318e6ecab337861", - "IPY_MODEL_e065034c12da4de986d452fa8e88a13a", - "IPY_MODEL_4109ef68aa164157869d8efce1002420" - ], - "layout": "IPY_MODEL_a4c1ccb6ff5b40f5b2a9448cc2d9a89d" - } - }, - "813659abd0ae4858b70121d8e5e42c7a": { + "68e0315ddeeb47aaa3eed64bc0a781bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3888,7 +3932,7 @@ "width": null } }, - "92db28b2f6594a47bbecaa39c185f8fd": { + "6c0f1a6b8b0941eb890e179ab021fa32": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -3904,30 +3948,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_cdcc7203a1d34765831634720bdbf052", + "layout": "IPY_MODEL_3eedef9a02374d1e96abed5e91673616", "max": 30.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_254221792aff42ce82df1c22b6a17fe7", + "style": "IPY_MODEL_bbeae6affcaf40c7b85b3a80bc472488", "value": 30.0 } }, - "98e1388d51f34102aacc21ba7bc431eb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "9c7f519c6e4e43a983bbd8764c156460": { + "75f17e33a67d4d759b4cd9037c2e49dc": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3979,7 +4008,104 @@ "width": null } }, - "a4c1ccb6ff5b40f5b2a9448cc2d9a89d": { + "851b95992b3f4ad4a8670c0230fcd73f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "a29c9371170a4cf195f0623a90a22046": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2641df8a6b10468c84fd7d0cf8fe4b7f", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_017b88b766dd4b1eb6d2b1e38318808d", + "value": 30.0 + } + }, + "b75dd4684dc541648d9d07c71d611dde": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_bc89283dc7174f93b8b234407daf2a1d", + "placeholder": "​", + "style": "IPY_MODEL_dacdbf7e5c0a4c5a8b089b152056890c", + "value": " 30/30 [00:01<00:00, 23.70it/s]" + } + }, + "ba97605c2b714aaeae151edc68655c4e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_75f17e33a67d4d759b4cd9037c2e49dc", + "placeholder": "​", + "style": "IPY_MODEL_dc529557e2e64fcba50b373e9f823e42", + "value": "images processed using softmin: 100%" + } + }, + "bbeae6affcaf40c7b85b3a80bc472488": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "bc89283dc7174f93b8b234407daf2a1d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4031,7 +4157,7 @@ "width": null } }, - "ab4466bce4fc4e29a5451793442f63f0": { + "c0d493b322dc419dbe87e7a6b2af79ce": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4046,29 +4172,7 @@ "description_width": "" } }, - "b6cb19a81c2a486b82141204a442d67b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_660cb6ce052f47b4b6cbe492bceef787", - "IPY_MODEL_ef63b67382614e458109cee4652d44da", - "IPY_MODEL_d603c968a93c4cc180f3f615cff5aa17" - ], - "layout": "IPY_MODEL_2c30e1b3212d44fcb51934e93bf256f4" - } - }, - "cdcc7203a1d34765831634720bdbf052": { + "c2042fce4b7149fbb89d176082a3c94f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4120,52 +4224,58 @@ "width": null } }, - "d603c968a93c4cc180f3f615cff5aa17": { + "dacdbf7e5c0a4c5a8b089b152056890c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e77f234650f04aceb92afed644f9afbf", - "placeholder": "​", - "style": "IPY_MODEL_ab4466bce4fc4e29a5451793442f63f0", - "value": " 30/30 [00:39<00:00, 1.18s/it]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "e065034c12da4de986d452fa8e88a13a": { + "dc529557e2e64fcba50b373e9f823e42": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "e15b5be74c0c4d29bdfda502583c695b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_1772cf6eace14f4f8a042982d8d016a8", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6493300865974b87a00b6d58ce9d45d3", - "value": 30.0 + "layout": "IPY_MODEL_c2042fce4b7149fbb89d176082a3c94f", + "placeholder": "​", + "style": "IPY_MODEL_851b95992b3f4ad4a8670c0230fcd73f", + "value": " 30/30 [00:34<00:00, 1.15s/it]" } }, - "e77f234650f04aceb92afed644f9afbf": { + "e66280fc5b9544d3b2997f0895b86556": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4217,53 +4327,28 @@ "width": null } }, - "ec615bcedf144713a74c0755f4d4a017": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_7daa21d4a2364d38ad2a974e382c6322", - "IPY_MODEL_92db28b2f6594a47bbecaa39c185f8fd", - "IPY_MODEL_fbe3025786e94c99ab6c633251923c57" - ], - "layout": "IPY_MODEL_f26f2446131342a1b208ddec0b71c771" - } - }, - "ef63b67382614e458109cee4652d44da": { + "f8958214798d4aaea3392a34c1132c86": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_587d5b941fbc465faf4eb95923831929", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_70503413664647f98537eb6ceb24c397", - "value": 30.0 + "layout": "IPY_MODEL_11f01422601b4fbfb274288e4cabc8dd", + "placeholder": "​", + "style": "IPY_MODEL_337a4acbdc614129ab2ee0180f730c19", + "value": " 30/30 [00:00<00:00, 415.09it/s]" } }, - "f26f2446131342a1b208ddec0b71c771": { + "fcc677509e4047feacf1ee2612eb3bf6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4314,27 +4399,6 @@ "visibility": null, "width": null } - }, - "fbe3025786e94c99ab6c633251923c57": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_2cef6291580545a795374bc03a6013f9", - "placeholder": "​", - "style": "IPY_MODEL_1ed919af8e7844faabecb7a8dd47f285", - "value": " 30/30 [00:00<00:00, 414.89it/s]" - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb index 5466270db..17bc8ee87 100644 --- a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb @@ -112,10 +112,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:48.780485Z", - "iopub.status.busy": "2024-01-17T18:15:48.780288Z", - "iopub.status.idle": "2024-01-17T18:15:49.889722Z", - "shell.execute_reply": "2024-01-17T18:15:49.889148Z" + "iopub.execute_input": "2024-01-17T23:17:47.734952Z", + "iopub.status.busy": "2024-01-17T23:17:47.734771Z", + "iopub.status.idle": "2024-01-17T23:17:48.739120Z", + "shell.execute_reply": "2024-01-17T23:17:48.738432Z" }, "nbsphinx": "hidden" }, @@ -125,7 +125,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -150,10 +150,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:49.892898Z", - "iopub.status.busy": "2024-01-17T18:15:49.892481Z", - "iopub.status.idle": "2024-01-17T18:15:49.912635Z", - "shell.execute_reply": "2024-01-17T18:15:49.912039Z" + "iopub.execute_input": "2024-01-17T23:17:48.742367Z", + "iopub.status.busy": "2024-01-17T23:17:48.741768Z", + "iopub.status.idle": "2024-01-17T23:17:48.758449Z", + "shell.execute_reply": "2024-01-17T23:17:48.757950Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:49.915208Z", - "iopub.status.busy": "2024-01-17T18:15:49.914856Z", - "iopub.status.idle": "2024-01-17T18:15:49.961568Z", - "shell.execute_reply": "2024-01-17T18:15:49.960846Z" + "iopub.execute_input": "2024-01-17T23:17:48.760735Z", + "iopub.status.busy": "2024-01-17T23:17:48.760537Z", + "iopub.status.idle": "2024-01-17T23:17:48.800098Z", + "shell.execute_reply": "2024-01-17T23:17:48.799507Z" } }, "outputs": [ @@ -304,10 +304,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:49.964144Z", - "iopub.status.busy": "2024-01-17T18:15:49.963669Z", - "iopub.status.idle": "2024-01-17T18:15:49.967418Z", - "shell.execute_reply": "2024-01-17T18:15:49.966851Z" + "iopub.execute_input": "2024-01-17T23:17:48.802608Z", + "iopub.status.busy": "2024-01-17T23:17:48.802234Z", + "iopub.status.idle": "2024-01-17T23:17:48.805857Z", + "shell.execute_reply": "2024-01-17T23:17:48.805334Z" } }, "outputs": [], @@ -328,10 +328,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:49.969921Z", - "iopub.status.busy": "2024-01-17T18:15:49.969441Z", - "iopub.status.idle": "2024-01-17T18:15:49.978117Z", - "shell.execute_reply": "2024-01-17T18:15:49.977519Z" + "iopub.execute_input": "2024-01-17T23:17:48.808362Z", + "iopub.status.busy": "2024-01-17T23:17:48.807900Z", + "iopub.status.idle": "2024-01-17T23:17:48.817003Z", + "shell.execute_reply": "2024-01-17T23:17:48.816545Z" } }, "outputs": [], @@ -383,10 +383,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:49.980561Z", - "iopub.status.busy": "2024-01-17T18:15:49.980192Z", - "iopub.status.idle": "2024-01-17T18:15:49.983567Z", - "shell.execute_reply": "2024-01-17T18:15:49.983071Z" + "iopub.execute_input": "2024-01-17T23:17:48.819529Z", + "iopub.status.busy": "2024-01-17T23:17:48.819050Z", + "iopub.status.idle": "2024-01-17T23:17:48.821755Z", + "shell.execute_reply": "2024-01-17T23:17:48.821267Z" } }, "outputs": [], @@ -408,10 +408,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:49.985926Z", - "iopub.status.busy": "2024-01-17T18:15:49.985561Z", - "iopub.status.idle": "2024-01-17T18:15:50.575769Z", - "shell.execute_reply": "2024-01-17T18:15:50.575063Z" + "iopub.execute_input": "2024-01-17T23:17:48.824069Z", + "iopub.status.busy": "2024-01-17T23:17:48.823696Z", + "iopub.status.idle": "2024-01-17T23:17:49.401999Z", + "shell.execute_reply": "2024-01-17T23:17:49.401363Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:50.578728Z", - "iopub.status.busy": "2024-01-17T18:15:50.578362Z", - "iopub.status.idle": "2024-01-17T18:15:51.817403Z", - "shell.execute_reply": "2024-01-17T18:15:51.816705Z" + "iopub.execute_input": "2024-01-17T23:17:49.404884Z", + "iopub.status.busy": "2024-01-17T23:17:49.404494Z", + "iopub.status.idle": "2024-01-17T23:17:50.628963Z", + "shell.execute_reply": "2024-01-17T23:17:50.628183Z" } }, "outputs": [ @@ -480,10 +480,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:51.820386Z", - "iopub.status.busy": "2024-01-17T18:15:51.819691Z", - "iopub.status.idle": "2024-01-17T18:15:51.829994Z", - "shell.execute_reply": "2024-01-17T18:15:51.829415Z" + "iopub.execute_input": "2024-01-17T23:17:50.632065Z", + "iopub.status.busy": "2024-01-17T23:17:50.631351Z", + "iopub.status.idle": "2024-01-17T23:17:50.641752Z", + "shell.execute_reply": "2024-01-17T23:17:50.641138Z" } }, "outputs": [ @@ -604,10 +604,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:51.832550Z", - "iopub.status.busy": "2024-01-17T18:15:51.832105Z", - "iopub.status.idle": "2024-01-17T18:15:51.836560Z", - "shell.execute_reply": "2024-01-17T18:15:51.835934Z" + "iopub.execute_input": "2024-01-17T23:17:50.644372Z", + "iopub.status.busy": "2024-01-17T23:17:50.643967Z", + "iopub.status.idle": "2024-01-17T23:17:50.648354Z", + "shell.execute_reply": "2024-01-17T23:17:50.647831Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:51.839202Z", - "iopub.status.busy": "2024-01-17T18:15:51.838732Z", - "iopub.status.idle": "2024-01-17T18:15:51.847634Z", - "shell.execute_reply": "2024-01-17T18:15:51.847116Z" + "iopub.execute_input": "2024-01-17T23:17:50.650701Z", + "iopub.status.busy": "2024-01-17T23:17:50.650342Z", + "iopub.status.idle": "2024-01-17T23:17:50.659466Z", + "shell.execute_reply": "2024-01-17T23:17:50.658916Z" } }, "outputs": [], @@ -657,10 +657,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:51.849889Z", - "iopub.status.busy": "2024-01-17T18:15:51.849543Z", - "iopub.status.idle": "2024-01-17T18:15:51.974011Z", - "shell.execute_reply": "2024-01-17T18:15:51.973351Z" + "iopub.execute_input": "2024-01-17T23:17:50.662055Z", + "iopub.status.busy": "2024-01-17T23:17:50.661674Z", + "iopub.status.idle": "2024-01-17T23:17:50.784615Z", + "shell.execute_reply": "2024-01-17T23:17:50.783929Z" } }, "outputs": [ @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:51.976584Z", - "iopub.status.busy": "2024-01-17T18:15:51.976090Z", - "iopub.status.idle": "2024-01-17T18:15:51.979227Z", - "shell.execute_reply": "2024-01-17T18:15:51.978616Z" + "iopub.execute_input": "2024-01-17T23:17:50.787531Z", + "iopub.status.busy": "2024-01-17T23:17:50.787142Z", + "iopub.status.idle": "2024-01-17T23:17:50.790141Z", + "shell.execute_reply": "2024-01-17T23:17:50.789587Z" } }, "outputs": [], @@ -714,10 +714,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:51.981454Z", - "iopub.status.busy": "2024-01-17T18:15:51.981098Z", - "iopub.status.idle": "2024-01-17T18:15:53.416387Z", - "shell.execute_reply": "2024-01-17T18:15:53.415644Z" + "iopub.execute_input": "2024-01-17T23:17:50.792562Z", + "iopub.status.busy": "2024-01-17T23:17:50.792169Z", + "iopub.status.idle": "2024-01-17T23:17:52.221345Z", + "shell.execute_reply": "2024-01-17T23:17:52.220630Z" } }, "outputs": [], @@ -737,10 +737,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:53.419589Z", - "iopub.status.busy": "2024-01-17T18:15:53.419145Z", - "iopub.status.idle": "2024-01-17T18:15:53.433267Z", - "shell.execute_reply": "2024-01-17T18:15:53.432699Z" + "iopub.execute_input": "2024-01-17T23:17:52.224659Z", + "iopub.status.busy": "2024-01-17T23:17:52.224238Z", + "iopub.status.idle": "2024-01-17T23:17:52.238083Z", + "shell.execute_reply": "2024-01-17T23:17:52.237541Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:53.435679Z", - "iopub.status.busy": "2024-01-17T18:15:53.435296Z", - "iopub.status.idle": "2024-01-17T18:15:53.480212Z", - "shell.execute_reply": "2024-01-17T18:15:53.479691Z" + "iopub.execute_input": "2024-01-17T23:17:52.240512Z", + "iopub.status.busy": "2024-01-17T23:17:52.240154Z", + "iopub.status.idle": "2024-01-17T23:17:52.269107Z", + "shell.execute_reply": "2024-01-17T23:17:52.268431Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/text.ipynb b/master/.doctrees/nbsphinx/tutorials/text.ipynb index af64e12b3..c78dd02a5 100644 --- a/master/.doctrees/nbsphinx/tutorials/text.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/text.ipynb @@ -114,10 +114,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:15:58.681081Z", - "iopub.status.busy": "2024-01-17T18:15:58.680544Z", - "iopub.status.idle": "2024-01-17T18:16:00.775060Z", - "shell.execute_reply": "2024-01-17T18:16:00.774440Z" + "iopub.execute_input": "2024-01-17T23:17:57.742875Z", + "iopub.status.busy": "2024-01-17T23:17:57.742428Z", + "iopub.status.idle": "2024-01-17T23:17:59.830312Z", + "shell.execute_reply": "2024-01-17T23:17:59.829658Z" }, "nbsphinx": "hidden" }, @@ -134,7 +134,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:00.778036Z", - "iopub.status.busy": "2024-01-17T18:16:00.777514Z", - "iopub.status.idle": "2024-01-17T18:16:00.781173Z", - "shell.execute_reply": "2024-01-17T18:16:00.780639Z" + "iopub.execute_input": "2024-01-17T23:17:59.833303Z", + "iopub.status.busy": "2024-01-17T23:17:59.832848Z", + "iopub.status.idle": "2024-01-17T23:17:59.836479Z", + "shell.execute_reply": "2024-01-17T23:17:59.835915Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:00.783257Z", - "iopub.status.busy": "2024-01-17T18:16:00.782964Z", - "iopub.status.idle": "2024-01-17T18:16:00.786185Z", - "shell.execute_reply": "2024-01-17T18:16:00.785673Z" + "iopub.execute_input": "2024-01-17T23:17:59.838723Z", + "iopub.status.busy": "2024-01-17T23:17:59.838522Z", + "iopub.status.idle": "2024-01-17T23:17:59.842419Z", + "shell.execute_reply": "2024-01-17T23:17:59.841936Z" }, "nbsphinx": "hidden" }, @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:00.788633Z", - "iopub.status.busy": "2024-01-17T18:16:00.788231Z", - "iopub.status.idle": "2024-01-17T18:16:00.838714Z", - "shell.execute_reply": "2024-01-17T18:16:00.838154Z" + "iopub.execute_input": "2024-01-17T23:17:59.844709Z", + "iopub.status.busy": "2024-01-17T23:17:59.844354Z", + "iopub.status.idle": "2024-01-17T23:17:59.883168Z", + "shell.execute_reply": "2024-01-17T23:17:59.882480Z" } }, "outputs": [ @@ -311,10 +311,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:00.840975Z", - "iopub.status.busy": "2024-01-17T18:16:00.840666Z", - "iopub.status.idle": "2024-01-17T18:16:00.844276Z", - "shell.execute_reply": "2024-01-17T18:16:00.843733Z" + "iopub.execute_input": "2024-01-17T23:17:59.885656Z", + "iopub.status.busy": "2024-01-17T23:17:59.885435Z", + "iopub.status.idle": "2024-01-17T23:17:59.889383Z", + "shell.execute_reply": "2024-01-17T23:17:59.888880Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:00.846573Z", - "iopub.status.busy": "2024-01-17T18:16:00.846282Z", - "iopub.status.idle": "2024-01-17T18:16:00.849977Z", - "shell.execute_reply": "2024-01-17T18:16:00.849375Z" + "iopub.execute_input": "2024-01-17T23:17:59.891833Z", + "iopub.status.busy": "2024-01-17T23:17:59.891353Z", + "iopub.status.idle": "2024-01-17T23:17:59.895138Z", + "shell.execute_reply": "2024-01-17T23:17:59.894518Z" } }, "outputs": [ @@ -341,7 +341,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'beneficiary_not_allowed', 'supported_cards_and_currencies', 'change_pin', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'getting_spare_card', 'card_payment_fee_charged', 'cancel_transfer', 'lost_or_stolen_phone', 'card_about_to_expire'}\n" + "Classes: {'visa_or_mastercard', 'lost_or_stolen_phone', 'card_about_to_expire', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'cancel_transfer', 'getting_spare_card', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'change_pin'}\n" ] } ], @@ -364,10 +364,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:00.852267Z", - "iopub.status.busy": "2024-01-17T18:16:00.851974Z", - "iopub.status.idle": "2024-01-17T18:16:00.855468Z", - "shell.execute_reply": "2024-01-17T18:16:00.854975Z" + "iopub.execute_input": "2024-01-17T23:17:59.897309Z", + "iopub.status.busy": "2024-01-17T23:17:59.897107Z", + "iopub.status.idle": "2024-01-17T23:17:59.901064Z", + "shell.execute_reply": "2024-01-17T23:17:59.900538Z" } }, "outputs": [ @@ -408,10 +408,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:00.857729Z", - "iopub.status.busy": "2024-01-17T18:16:00.857390Z", - "iopub.status.idle": "2024-01-17T18:16:00.860942Z", - "shell.execute_reply": "2024-01-17T18:16:00.860317Z" + "iopub.execute_input": "2024-01-17T23:17:59.903420Z", + "iopub.status.busy": "2024-01-17T23:17:59.903221Z", + "iopub.status.idle": "2024-01-17T23:17:59.907092Z", + "shell.execute_reply": "2024-01-17T23:17:59.906454Z" } }, "outputs": [], @@ -452,10 +452,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:00.863348Z", - "iopub.status.busy": "2024-01-17T18:16:00.862895Z", - "iopub.status.idle": "2024-01-17T18:16:09.532434Z", - "shell.execute_reply": "2024-01-17T18:16:09.531800Z" + "iopub.execute_input": "2024-01-17T23:17:59.909625Z", + "iopub.status.busy": "2024-01-17T23:17:59.909179Z", + "iopub.status.idle": "2024-01-17T23:18:08.513069Z", + "shell.execute_reply": "2024-01-17T23:18:08.512429Z" } }, "outputs": [ @@ -502,10 +502,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:09.535736Z", - "iopub.status.busy": "2024-01-17T18:16:09.535212Z", - "iopub.status.idle": "2024-01-17T18:16:09.538358Z", - "shell.execute_reply": "2024-01-17T18:16:09.537737Z" + "iopub.execute_input": "2024-01-17T23:18:08.516221Z", + "iopub.status.busy": "2024-01-17T23:18:08.516011Z", + "iopub.status.idle": "2024-01-17T23:18:08.519002Z", + "shell.execute_reply": "2024-01-17T23:18:08.518428Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:09.540756Z", - "iopub.status.busy": "2024-01-17T18:16:09.540303Z", - "iopub.status.idle": "2024-01-17T18:16:09.543296Z", - "shell.execute_reply": "2024-01-17T18:16:09.542675Z" + "iopub.execute_input": "2024-01-17T23:18:08.521479Z", + "iopub.status.busy": "2024-01-17T23:18:08.521036Z", + "iopub.status.idle": "2024-01-17T23:18:08.524037Z", + "shell.execute_reply": "2024-01-17T23:18:08.523424Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:09.545603Z", - "iopub.status.busy": "2024-01-17T18:16:09.545232Z", - "iopub.status.idle": "2024-01-17T18:16:11.774377Z", - "shell.execute_reply": "2024-01-17T18:16:11.773545Z" + "iopub.execute_input": "2024-01-17T23:18:08.526322Z", + "iopub.status.busy": "2024-01-17T23:18:08.525946Z", + "iopub.status.idle": "2024-01-17T23:18:10.782125Z", + "shell.execute_reply": "2024-01-17T23:18:10.781246Z" }, "scrolled": true }, @@ -571,10 +571,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:11.777880Z", - "iopub.status.busy": "2024-01-17T18:16:11.777134Z", - "iopub.status.idle": "2024-01-17T18:16:11.785059Z", - "shell.execute_reply": "2024-01-17T18:16:11.784473Z" + "iopub.execute_input": "2024-01-17T23:18:10.785663Z", + "iopub.status.busy": "2024-01-17T23:18:10.784971Z", + "iopub.status.idle": "2024-01-17T23:18:10.793126Z", + "shell.execute_reply": "2024-01-17T23:18:10.792564Z" } }, "outputs": [ @@ -675,10 +675,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:11.787623Z", - "iopub.status.busy": "2024-01-17T18:16:11.787145Z", - "iopub.status.idle": "2024-01-17T18:16:11.791785Z", - "shell.execute_reply": "2024-01-17T18:16:11.791195Z" + "iopub.execute_input": "2024-01-17T23:18:10.795673Z", + "iopub.status.busy": "2024-01-17T23:18:10.795173Z", + "iopub.status.idle": "2024-01-17T23:18:10.799534Z", + "shell.execute_reply": "2024-01-17T23:18:10.799023Z" } }, "outputs": [], @@ -692,10 +692,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:11.794363Z", - "iopub.status.busy": "2024-01-17T18:16:11.793879Z", - "iopub.status.idle": "2024-01-17T18:16:11.797824Z", - "shell.execute_reply": "2024-01-17T18:16:11.797308Z" + "iopub.execute_input": "2024-01-17T23:18:10.801761Z", + "iopub.status.busy": "2024-01-17T23:18:10.801399Z", + "iopub.status.idle": "2024-01-17T23:18:10.804803Z", + "shell.execute_reply": "2024-01-17T23:18:10.804175Z" } }, "outputs": [ @@ -730,10 +730,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:11.800118Z", - "iopub.status.busy": "2024-01-17T18:16:11.799769Z", - "iopub.status.idle": "2024-01-17T18:16:11.802932Z", - "shell.execute_reply": "2024-01-17T18:16:11.802404Z" + "iopub.execute_input": "2024-01-17T23:18:10.807088Z", + "iopub.status.busy": "2024-01-17T23:18:10.806776Z", + "iopub.status.idle": "2024-01-17T23:18:10.809992Z", + "shell.execute_reply": "2024-01-17T23:18:10.809457Z" } }, "outputs": [], @@ -753,10 +753,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:11.805206Z", - "iopub.status.busy": "2024-01-17T18:16:11.804854Z", - "iopub.status.idle": "2024-01-17T18:16:11.812281Z", - "shell.execute_reply": "2024-01-17T18:16:11.811672Z" + "iopub.execute_input": "2024-01-17T23:18:10.812343Z", + "iopub.status.busy": "2024-01-17T23:18:10.811976Z", + "iopub.status.idle": "2024-01-17T23:18:10.818970Z", + "shell.execute_reply": "2024-01-17T23:18:10.818388Z" } }, "outputs": [ @@ -881,10 +881,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:11.814814Z", - "iopub.status.busy": "2024-01-17T18:16:11.814358Z", - "iopub.status.idle": "2024-01-17T18:16:12.075466Z", - "shell.execute_reply": "2024-01-17T18:16:12.074804Z" + "iopub.execute_input": "2024-01-17T23:18:10.821471Z", + "iopub.status.busy": "2024-01-17T23:18:10.821021Z", + "iopub.status.idle": "2024-01-17T23:18:11.086279Z", + "shell.execute_reply": "2024-01-17T23:18:11.085605Z" }, "scrolled": true }, @@ -923,10 +923,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:12.079661Z", - "iopub.status.busy": "2024-01-17T18:16:12.078502Z", - "iopub.status.idle": "2024-01-17T18:16:12.366162Z", - "shell.execute_reply": "2024-01-17T18:16:12.365485Z" + "iopub.execute_input": "2024-01-17T23:18:11.089474Z", + "iopub.status.busy": "2024-01-17T23:18:11.089026Z", + "iopub.status.idle": "2024-01-17T23:18:11.370620Z", + "shell.execute_reply": "2024-01-17T23:18:11.369959Z" }, "scrolled": true }, @@ -959,10 +959,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:12.370837Z", - "iopub.status.busy": "2024-01-17T18:16:12.369694Z", - "iopub.status.idle": "2024-01-17T18:16:12.375305Z", - "shell.execute_reply": "2024-01-17T18:16:12.374713Z" + "iopub.execute_input": "2024-01-17T23:18:11.373903Z", + "iopub.status.busy": "2024-01-17T23:18:11.373450Z", + "iopub.status.idle": "2024-01-17T23:18:11.377667Z", + "shell.execute_reply": "2024-01-17T23:18:11.377072Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 8f1cbce7d..8126f8625 100644 --- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb +++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:17.683980Z", - "iopub.status.busy": "2024-01-17T18:16:17.683539Z", - "iopub.status.idle": "2024-01-17T18:16:18.840240Z", - "shell.execute_reply": "2024-01-17T18:16:18.839548Z" + "iopub.execute_input": "2024-01-17T23:18:16.631328Z", + "iopub.status.busy": "2024-01-17T23:18:16.631138Z", + "iopub.status.idle": "2024-01-17T23:18:17.707067Z", + "shell.execute_reply": "2024-01-17T23:18:17.706406Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-01-17 18:16:17-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-01-17 23:18:16-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,9 +94,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "169.150.236.99, 2400:52e0:1a00::718:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|169.150.236.99|:443... connected.\r\n", - "HTTP request sent, awaiting response... 200 OK\r\n", + "185.93.1.244, 2400:52e0:1a00::871:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.244|:443... connected.\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "HTTP request sent, awaiting response... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "200 OK\r\n", "Length: 982975 (960K) [application/zip]\r\n", "Saving to: ‘conll2003.zip’\r\n", "\r\n", @@ -109,9 +122,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.07s \r\n", + "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2024-01-17 18:16:17 (14.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-01-17 23:18:17 (6.54 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -131,15 +144,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-01-17 18:16:18-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.69.41, 52.216.35.73, 3.5.25.134, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.69.41|:443... connected.\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "--2024-01-17 23:18:17-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.195.49, 52.216.244.132, 52.217.89.156, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.195.49|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -160,10 +167,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 96%[==================> ] 15.71M 56.8MB/s \r", - "pred_probs.npz 100%[===================>] 16.26M 58.4MB/s in 0.3s \r\n", + "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", "\r\n", - "2024-01-17 18:16:18 (58.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-01-17 23:18:17 (134 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -180,10 +186,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:18.843321Z", - "iopub.status.busy": "2024-01-17T18:16:18.842924Z", - "iopub.status.idle": "2024-01-17T18:16:19.868315Z", - "shell.execute_reply": "2024-01-17T18:16:19.867697Z" + "iopub.execute_input": "2024-01-17T23:18:17.709578Z", + "iopub.status.busy": "2024-01-17T23:18:17.709371Z", + "iopub.status.idle": "2024-01-17T23:18:18.727351Z", + "shell.execute_reply": "2024-01-17T23:18:18.726727Z" }, "nbsphinx": "hidden" }, @@ -194,7 +200,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -220,10 +226,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:19.871321Z", - "iopub.status.busy": "2024-01-17T18:16:19.870738Z", - "iopub.status.idle": "2024-01-17T18:16:19.874378Z", - "shell.execute_reply": "2024-01-17T18:16:19.873888Z" + "iopub.execute_input": "2024-01-17T23:18:18.730451Z", + "iopub.status.busy": "2024-01-17T23:18:18.729940Z", + "iopub.status.idle": "2024-01-17T23:18:18.733621Z", + "shell.execute_reply": "2024-01-17T23:18:18.733015Z" } }, "outputs": [], @@ -273,10 +279,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:19.876654Z", - "iopub.status.busy": "2024-01-17T18:16:19.876350Z", - "iopub.status.idle": "2024-01-17T18:16:19.879488Z", - "shell.execute_reply": "2024-01-17T18:16:19.878934Z" + "iopub.execute_input": "2024-01-17T23:18:18.735946Z", + "iopub.status.busy": "2024-01-17T23:18:18.735620Z", + "iopub.status.idle": "2024-01-17T23:18:18.739293Z", + "shell.execute_reply": "2024-01-17T23:18:18.738779Z" }, "nbsphinx": "hidden" }, @@ -294,10 +300,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:19.881736Z", - "iopub.status.busy": "2024-01-17T18:16:19.881370Z", - "iopub.status.idle": "2024-01-17T18:16:27.772463Z", - "shell.execute_reply": "2024-01-17T18:16:27.771773Z" + "iopub.execute_input": "2024-01-17T23:18:18.741545Z", + "iopub.status.busy": "2024-01-17T23:18:18.741197Z", + "iopub.status.idle": "2024-01-17T23:18:26.628775Z", + "shell.execute_reply": "2024-01-17T23:18:26.628162Z" } }, "outputs": [], @@ -371,10 +377,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:27.775590Z", - "iopub.status.busy": "2024-01-17T18:16:27.775061Z", - "iopub.status.idle": "2024-01-17T18:16:27.781127Z", - "shell.execute_reply": "2024-01-17T18:16:27.780571Z" + "iopub.execute_input": "2024-01-17T23:18:26.631735Z", + "iopub.status.busy": "2024-01-17T23:18:26.631355Z", + "iopub.status.idle": "2024-01-17T23:18:26.637292Z", + "shell.execute_reply": "2024-01-17T23:18:26.636788Z" }, "nbsphinx": "hidden" }, @@ -414,10 +420,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:27.783496Z", - "iopub.status.busy": "2024-01-17T18:16:27.783123Z", - "iopub.status.idle": "2024-01-17T18:16:28.214355Z", - "shell.execute_reply": "2024-01-17T18:16:28.213734Z" + "iopub.execute_input": "2024-01-17T23:18:26.639706Z", + "iopub.status.busy": "2024-01-17T23:18:26.639338Z", + "iopub.status.idle": "2024-01-17T23:18:27.069318Z", + "shell.execute_reply": "2024-01-17T23:18:27.068667Z" } }, "outputs": [], @@ -454,10 +460,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:28.217259Z", - "iopub.status.busy": "2024-01-17T18:16:28.216794Z", - "iopub.status.idle": "2024-01-17T18:16:28.223233Z", - "shell.execute_reply": "2024-01-17T18:16:28.222714Z" + "iopub.execute_input": "2024-01-17T23:18:27.072250Z", + "iopub.status.busy": "2024-01-17T23:18:27.071829Z", + "iopub.status.idle": "2024-01-17T23:18:27.078274Z", + "shell.execute_reply": "2024-01-17T23:18:27.077724Z" } }, "outputs": [ @@ -529,10 +535,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:28.225511Z", - "iopub.status.busy": "2024-01-17T18:16:28.225305Z", - "iopub.status.idle": "2024-01-17T18:16:30.205162Z", - "shell.execute_reply": "2024-01-17T18:16:30.204242Z" + "iopub.execute_input": "2024-01-17T23:18:27.080782Z", + "iopub.status.busy": "2024-01-17T23:18:27.080396Z", + "iopub.status.idle": "2024-01-17T23:18:29.029614Z", + "shell.execute_reply": "2024-01-17T23:18:29.028849Z" } }, "outputs": [], @@ -554,10 +560,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:30.208772Z", - "iopub.status.busy": "2024-01-17T18:16:30.207942Z", - "iopub.status.idle": "2024-01-17T18:16:30.215461Z", - "shell.execute_reply": "2024-01-17T18:16:30.214886Z" + "iopub.execute_input": "2024-01-17T23:18:29.033316Z", + "iopub.status.busy": "2024-01-17T23:18:29.032451Z", + "iopub.status.idle": "2024-01-17T23:18:29.039639Z", + "shell.execute_reply": "2024-01-17T23:18:29.038988Z" } }, "outputs": [ @@ -593,10 +599,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:30.218139Z", - "iopub.status.busy": "2024-01-17T18:16:30.217665Z", - "iopub.status.idle": "2024-01-17T18:16:30.243112Z", - "shell.execute_reply": "2024-01-17T18:16:30.242491Z" + "iopub.execute_input": "2024-01-17T23:18:29.042085Z", + "iopub.status.busy": "2024-01-17T23:18:29.041709Z", + "iopub.status.idle": "2024-01-17T23:18:29.066441Z", + "shell.execute_reply": "2024-01-17T23:18:29.065766Z" } }, "outputs": [ @@ -774,10 +780,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:30.245621Z", - "iopub.status.busy": "2024-01-17T18:16:30.245250Z", - "iopub.status.idle": "2024-01-17T18:16:30.277918Z", - "shell.execute_reply": "2024-01-17T18:16:30.277281Z" + "iopub.execute_input": "2024-01-17T23:18:29.068812Z", + "iopub.status.busy": "2024-01-17T23:18:29.068604Z", + "iopub.status.idle": "2024-01-17T23:18:29.100697Z", + "shell.execute_reply": "2024-01-17T23:18:29.099970Z" } }, "outputs": [ @@ -879,10 +885,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:30.280522Z", - "iopub.status.busy": "2024-01-17T18:16:30.280142Z", - "iopub.status.idle": "2024-01-17T18:16:30.291143Z", - "shell.execute_reply": "2024-01-17T18:16:30.290517Z" + "iopub.execute_input": "2024-01-17T23:18:29.103479Z", + "iopub.status.busy": "2024-01-17T23:18:29.103200Z", + "iopub.status.idle": "2024-01-17T23:18:29.112931Z", + "shell.execute_reply": "2024-01-17T23:18:29.112353Z" } }, "outputs": [ @@ -956,10 +962,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:30.293427Z", - "iopub.status.busy": "2024-01-17T18:16:30.293061Z", - "iopub.status.idle": "2024-01-17T18:16:32.143940Z", - "shell.execute_reply": "2024-01-17T18:16:32.143364Z" + "iopub.execute_input": "2024-01-17T23:18:29.115284Z", + "iopub.status.busy": "2024-01-17T23:18:29.115081Z", + "iopub.status.idle": "2024-01-17T23:18:30.973062Z", + "shell.execute_reply": "2024-01-17T23:18:30.972404Z" } }, "outputs": [ @@ -1131,10 +1137,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:16:32.146497Z", - "iopub.status.busy": "2024-01-17T18:16:32.146092Z", - "iopub.status.idle": "2024-01-17T18:16:32.150481Z", - "shell.execute_reply": "2024-01-17T18:16:32.149957Z" + "iopub.execute_input": "2024-01-17T23:18:30.975447Z", + "iopub.status.busy": "2024-01-17T23:18:30.975239Z", + "iopub.status.idle": "2024-01-17T23:18:30.979718Z", + "shell.execute_reply": "2024-01-17T23:18:30.979194Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/audio.doctree b/master/.doctrees/tutorials/audio.doctree index da6a100ba..ff4dccfb7 100644 Binary files a/master/.doctrees/tutorials/audio.doctree and b/master/.doctrees/tutorials/audio.doctree differ diff --git a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree index ad877e99d..b4ad8df36 100644 Binary files a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree and b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree differ diff --git a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree index 8f84f6c95..4c01f4b14 100644 Binary files a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree and b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree differ diff --git a/master/.doctrees/tutorials/datalab/index.doctree b/master/.doctrees/tutorials/datalab/index.doctree index bd3f92e85..608b5e9b4 100644 Binary files a/master/.doctrees/tutorials/datalab/index.doctree and b/master/.doctrees/tutorials/datalab/index.doctree differ diff --git a/master/.doctrees/tutorials/datalab/tabular.doctree b/master/.doctrees/tutorials/datalab/tabular.doctree index a17ae61b9..d074408f6 100644 Binary files a/master/.doctrees/tutorials/datalab/tabular.doctree and b/master/.doctrees/tutorials/datalab/tabular.doctree differ diff --git a/master/.doctrees/tutorials/datalab/text.doctree b/master/.doctrees/tutorials/datalab/text.doctree index 8180ca07d..2d756bc8c 100644 Binary files a/master/.doctrees/tutorials/datalab/text.doctree and b/master/.doctrees/tutorials/datalab/text.doctree differ diff --git a/master/.doctrees/tutorials/dataset_health.doctree b/master/.doctrees/tutorials/dataset_health.doctree index e23270c9a..278bac603 100644 Binary files a/master/.doctrees/tutorials/dataset_health.doctree and b/master/.doctrees/tutorials/dataset_health.doctree differ diff --git a/master/.doctrees/tutorials/faq.doctree b/master/.doctrees/tutorials/faq.doctree index 665956f18..5c77ae728 100644 Binary files a/master/.doctrees/tutorials/faq.doctree and b/master/.doctrees/tutorials/faq.doctree differ diff --git a/master/.doctrees/tutorials/image.doctree b/master/.doctrees/tutorials/image.doctree index 00378d52e..7e6246568 100644 Binary files a/master/.doctrees/tutorials/image.doctree and b/master/.doctrees/tutorials/image.doctree differ diff --git a/master/.doctrees/tutorials/indepth_overview.doctree b/master/.doctrees/tutorials/indepth_overview.doctree index 3d0b7ea42..471f07994 100644 Binary files a/master/.doctrees/tutorials/indepth_overview.doctree and b/master/.doctrees/tutorials/indepth_overview.doctree differ diff --git a/master/.doctrees/tutorials/index.doctree b/master/.doctrees/tutorials/index.doctree index d3f3f3489..6cac1d280 100644 Binary files a/master/.doctrees/tutorials/index.doctree and b/master/.doctrees/tutorials/index.doctree differ diff --git a/master/.doctrees/tutorials/multiannotator.doctree b/master/.doctrees/tutorials/multiannotator.doctree index b89069d38..5cc7c9f3e 100644 Binary files a/master/.doctrees/tutorials/multiannotator.doctree and b/master/.doctrees/tutorials/multiannotator.doctree differ diff --git a/master/.doctrees/tutorials/multilabel_classification.doctree b/master/.doctrees/tutorials/multilabel_classification.doctree index 4d515cf72..b7aba0798 100644 Binary files a/master/.doctrees/tutorials/multilabel_classification.doctree and b/master/.doctrees/tutorials/multilabel_classification.doctree differ diff --git a/master/.doctrees/tutorials/object_detection.doctree b/master/.doctrees/tutorials/object_detection.doctree index 58c98fa28..fed67dd3a 100644 Binary files a/master/.doctrees/tutorials/object_detection.doctree and b/master/.doctrees/tutorials/object_detection.doctree differ diff --git a/master/.doctrees/tutorials/outliers.doctree b/master/.doctrees/tutorials/outliers.doctree index 9852ed813..1edff8894 100644 Binary files a/master/.doctrees/tutorials/outliers.doctree and b/master/.doctrees/tutorials/outliers.doctree differ diff --git a/master/.doctrees/tutorials/pred_probs_cross_val.doctree b/master/.doctrees/tutorials/pred_probs_cross_val.doctree index f3c52425b..343d5e92a 100644 Binary files a/master/.doctrees/tutorials/pred_probs_cross_val.doctree and b/master/.doctrees/tutorials/pred_probs_cross_val.doctree differ diff --git a/master/.doctrees/tutorials/regression.doctree b/master/.doctrees/tutorials/regression.doctree index 2c2ce2334..cb2aa701b 100644 Binary files a/master/.doctrees/tutorials/regression.doctree and b/master/.doctrees/tutorials/regression.doctree differ diff --git a/master/.doctrees/tutorials/segmentation.doctree b/master/.doctrees/tutorials/segmentation.doctree index 3d40bc76b..57bb53e96 100644 Binary files a/master/.doctrees/tutorials/segmentation.doctree and b/master/.doctrees/tutorials/segmentation.doctree differ diff --git a/master/.doctrees/tutorials/tabular.doctree b/master/.doctrees/tutorials/tabular.doctree index 39af9f6fc..f15bbcd46 100644 Binary files a/master/.doctrees/tutorials/tabular.doctree and b/master/.doctrees/tutorials/tabular.doctree differ diff --git a/master/.doctrees/tutorials/text.doctree b/master/.doctrees/tutorials/text.doctree index 4525c9426..751f3e975 100644 Binary files a/master/.doctrees/tutorials/text.doctree and b/master/.doctrees/tutorials/text.doctree differ diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree index 9cb85838f..cf4f8d80e 100644 Binary files a/master/.doctrees/tutorials/token_classification.doctree and b/master/.doctrees/tutorials/token_classification.doctree differ diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb index e1552e10d..c420f2d59 100644 --- a/master/_sources/tutorials/audio.ipynb +++ b/master/_sources/tutorials/audio.ipynb @@ -91,7 +91,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/datalab_advanced.ipynb b/master/_sources/tutorials/datalab/datalab_advanced.ipynb index 2dd371bb7..1ac09d950 100644 --- a/master/_sources/tutorials/datalab/datalab_advanced.ipynb +++ b/master/_sources/tutorials/datalab/datalab_advanced.ipynb @@ -87,7 +87,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb index bc334ca01..1d6f4d43c 100644 --- a/master/_sources/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/_sources/tutorials/datalab/datalab_quickstart.ipynb @@ -85,7 +85,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb index bbd503791..c81d43815 100644 --- a/master/_sources/tutorials/datalab/tabular.ipynb +++ b/master/_sources/tutorials/datalab/tabular.ipynb @@ -81,7 +81,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/datalab/text.ipynb b/master/_sources/tutorials/datalab/text.ipynb index 9cde2c95b..f51e8a2c8 100644 --- a/master/_sources/tutorials/datalab/text.ipynb +++ b/master/_sources/tutorials/datalab/text.ipynb @@ -90,7 +90,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/dataset_health.ipynb b/master/_sources/tutorials/dataset_health.ipynb index 103271dc7..7dc733191 100644 --- a/master/_sources/tutorials/dataset_health.ipynb +++ b/master/_sources/tutorials/dataset_health.ipynb @@ -77,7 +77,7 @@ "dependencies = [\"cleanlab\", \"requests\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/indepth_overview.ipynb b/master/_sources/tutorials/indepth_overview.ipynb index ec1deba4a..fa3e6cbab 100644 --- a/master/_sources/tutorials/indepth_overview.ipynb +++ b/master/_sources/tutorials/indepth_overview.ipynb @@ -62,7 +62,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/multiannotator.ipynb b/master/_sources/tutorials/multiannotator.ipynb index 695a42eb7..d330117e8 100644 --- a/master/_sources/tutorials/multiannotator.ipynb +++ b/master/_sources/tutorials/multiannotator.ipynb @@ -96,7 +96,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/multilabel_classification.ipynb b/master/_sources/tutorials/multilabel_classification.ipynb index 36eb677d3..3190a928e 100644 --- a/master/_sources/tutorials/multilabel_classification.ipynb +++ b/master/_sources/tutorials/multilabel_classification.ipynb @@ -72,7 +72,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/object_detection.ipynb b/master/_sources/tutorials/object_detection.ipynb index 7319ef77f..ac20fcf37 100644 --- a/master/_sources/tutorials/object_detection.ipynb +++ b/master/_sources/tutorials/object_detection.ipynb @@ -77,7 +77,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/outliers.ipynb b/master/_sources/tutorials/outliers.ipynb index af6f4f866..499e98577 100644 --- a/master/_sources/tutorials/outliers.ipynb +++ b/master/_sources/tutorials/outliers.ipynb @@ -119,7 +119,7 @@ "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/regression.ipynb b/master/_sources/tutorials/regression.ipynb index 3b88a914b..02004fd69 100644 --- a/master/_sources/tutorials/regression.ipynb +++ b/master/_sources/tutorials/regression.ipynb @@ -103,7 +103,7 @@ "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/segmentation.ipynb b/master/_sources/tutorials/segmentation.ipynb index 6e12112ee..b21340580 100644 --- a/master/_sources/tutorials/segmentation.ipynb +++ b/master/_sources/tutorials/segmentation.ipynb @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/tabular.ipynb b/master/_sources/tutorials/tabular.ipynb index cf13abd88..5e8578194 100644 --- a/master/_sources/tutorials/tabular.ipynb +++ b/master/_sources/tutorials/tabular.ipynb @@ -119,7 +119,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/text.ipynb b/master/_sources/tutorials/text.ipynb index f1282fdf3..91b7895b8 100644 --- a/master/_sources/tutorials/text.ipynb +++ b/master/_sources/tutorials/text.ipynb @@ -128,7 +128,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/_sources/tutorials/token_classification.ipynb b/master/_sources/tutorials/token_classification.ipynb index 39da93648..b71884c46 100644 --- a/master/_sources/tutorials/token_classification.ipynb +++ b/master/_sources/tutorials/token_classification.ipynb @@ -95,7 +95,7 @@ "dependencies = [\"cleanlab\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/searchindex.js b/master/searchindex.js index 38e87af2a..017ebcdad 100644 --- a/master/searchindex.js +++ b/master/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/report", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/dataset_health", "tutorials/faq", "tutorials/image", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/tabular", "tutorials/text", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/fasttext.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/image.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/tabular.ipynb", "tutorials/text.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "datalab", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "report", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "outlier", "token_classification_utils", "util", "validation", "fasttext", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "Audio Classification with SpeechBrain and Cleanlab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Find Dataset-level Issues for Dataset Curation", "FAQ", "Image Classification with PyTorch and Cleanlab", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Classification with Tabular Data using Scikit-Learn and Cleanlab", "Text Classification with Noisy Labels", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 73, 75, 76, 83, 85, 86], "helper": [1, 14, 34, 38, 40, 41, 42, 43, 44, 45, 57, 80, 82, 94], "method": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "ar": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 25, 26, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "us": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 72, 73, 75, 80, 84, 89], "benchmark": [1, 31, 72, 73, 75, 76, 83, 85, 86], "cleanlab": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 75, 76, 80, 84, 89], "": [1, 2, 3, 8, 16, 30, 31, 35, 38, 41, 43, 45, 50, 51, 55, 57, 58, 59, 60, 62, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "core": [1, 4, 34, 36, 64, 66, 91], "algorithm": [1, 2, 6, 8, 27, 32, 45, 50, 59, 68, 70, 72, 81, 83, 85, 94], "These": [1, 2, 3, 6, 8, 19, 33, 36, 37, 48, 50, 51, 54, 58, 59, 63, 67, 68, 70, 71, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "introduc": [1, 74, 81, 83], "synthet": [1, 85, 86, 91], "nois": [1, 2, 3, 30, 36, 39, 45, 51, 75, 76, 80, 85], "label": [1, 2, 3, 4, 5, 6, 7, 10, 14, 18, 19, 20, 25, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 45, 46, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 75, 80, 84, 88, 89], "classif": [1, 3, 4, 5, 8, 12, 14, 28, 30, 34, 36, 39, 41, 42, 45, 50, 51, 52, 53, 54, 59, 60, 68, 69, 70, 71, 72, 73, 75, 76, 84, 85, 88, 89, 90, 91], "dataset": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 22, 23, 24, 26, 27, 34, 35, 36, 39, 41, 45, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 77, 78, 84, 85, 89, 92], "specif": [1, 3, 4, 7, 12, 13, 14, 23, 28, 33, 48, 52, 55, 58, 67, 71, 76, 78, 79, 82, 83, 94], "thi": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 38, 39, 41, 42, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "modul": [1, 3, 11, 12, 13, 14, 19, 25, 28, 30, 31, 32, 33, 34, 35, 36, 43, 45, 48, 50, 59, 60, 72, 81, 82, 86], "provid": [1, 2, 3, 4, 5, 6, 8, 12, 14, 16, 21, 26, 30, 31, 32, 34, 35, 36, 39, 45, 49, 50, 51, 52, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 88, 89, 90, 91, 92, 93, 94], "gener": [1, 2, 3, 5, 8, 16, 21, 28, 30, 41, 45, 46, 59, 60, 62, 67, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 89, 90, 91, 93, 94], "valid": [1, 2, 3, 4, 8, 10, 30, 36, 37, 39, 40, 41, 43, 45, 50, 52, 55, 58, 60, 62, 63, 71, 73, 74, 75, 76, 78, 79, 80, 81, 83, 84, 86, 87, 90, 91, 92, 93, 94], "matric": [1, 3, 39, 81], "which": [1, 2, 3, 4, 8, 10, 11, 12, 14, 16, 20, 22, 28, 30, 31, 35, 36, 39, 41, 44, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 66, 67, 68, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "learn": [1, 2, 3, 4, 8, 12, 14, 20, 26, 28, 32, 33, 34, 35, 36, 38, 40, 45, 48, 50, 52, 59, 61, 63, 66, 70, 72, 74, 75, 78, 79, 80, 82, 84, 85, 90, 93], "i": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "possibl": [1, 2, 3, 8, 30, 31, 35, 36, 38, 39, 41, 52, 53, 54, 55, 57, 58, 59, 60, 62, 68, 70, 71, 76, 81, 83, 85, 86, 87, 90, 91, 94], "noisi": [1, 2, 3, 8, 30, 32, 35, 36, 39, 45, 51, 52, 54, 60, 62, 63, 64, 66, 67, 73, 75, 76, 78, 79, 81, 84, 85], "given": [1, 2, 3, 8, 26, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 44, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "matrix": [1, 2, 3, 4, 8, 14, 16, 27, 30, 36, 38, 39, 42, 45, 46, 52, 57, 58, 59, 60, 78, 88], "trace": [1, 75, 76, 83, 85, 86], "valu": [1, 2, 3, 4, 8, 10, 11, 14, 16, 20, 22, 23, 30, 31, 32, 34, 35, 36, 38, 39, 41, 43, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 71, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "more": [1, 2, 3, 4, 5, 8, 11, 14, 16, 22, 30, 31, 34, 35, 38, 41, 43, 45, 50, 51, 52, 53, 54, 55, 57, 58, 60, 62, 63, 66, 67, 68, 70, 72, 74, 75, 78, 79, 80, 81, 82, 85, 86, 87, 88, 91, 94], "function": [1, 2, 3, 4, 5, 11, 12, 14, 21, 22, 26, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 76, 80, 81, 83, 85, 86, 87, 91, 92, 93, 94], "noise_matrix_is_valid": 1, "noise_matrix": [1, 2, 3, 8, 39, 45, 75, 76, 83, 85, 86], "py": [1, 3, 28, 31, 32, 36, 39, 41, 75, 76, 83, 85, 86], "verbos": [1, 2, 4, 5, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 34, 36, 50, 51, 52, 57, 59, 60, 62, 64, 66, 67, 71, 75, 83, 85], "fals": [1, 2, 3, 4, 5, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 34, 35, 36, 40, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 74, 75, 76, 78, 79, 81, 82, 83, 85, 87, 88, 90, 91, 93], "sourc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71], "prior": [1, 2, 3, 30, 36, 39, 41], "repres": [1, 2, 3, 5, 8, 10, 14, 16, 22, 30, 34, 36, 39, 42, 43, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "p": [1, 2, 3, 8, 30, 36, 38, 39, 45, 50, 58, 59, 60, 64, 76, 78, 79, 82, 83, 85, 94], "true_label": [1, 2, 3, 30, 39, 45, 83, 85], "k": [1, 2, 3, 4, 6, 8, 10, 14, 16, 17, 21, 22, 24, 27, 30, 34, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 53, 54, 55, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 74, 75, 76, 81, 83, 85, 86, 87, 88, 91, 92, 94], "check": [1, 2, 4, 7, 8, 10, 14, 23, 31, 34, 35, 40, 46, 49, 55, 58, 62, 72, 74, 75, 76, 81, 82, 83, 85, 86, 90, 92, 93], "learnabl": 1, "mean": [1, 2, 5, 6, 10, 11, 20, 22, 32, 35, 39, 41, 43, 57, 62, 76, 79, 81, 83, 85, 86, 88, 90, 93], "achiev": [1, 2, 31, 32, 35, 62, 81, 85, 94], "better": [1, 4, 36, 50, 52, 60, 62, 63, 72, 74, 76, 78, 79, 81, 83, 86, 87, 88, 93, 94], "than": [1, 2, 3, 5, 8, 22, 24, 27, 30, 36, 45, 49, 50, 55, 57, 59, 60, 62, 66, 70, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 94], "random": [1, 2, 3, 5, 8, 16, 27, 34, 41, 50, 60, 62, 74, 75, 76, 78, 81, 82, 83, 85, 86, 88, 92], "perform": [1, 2, 5, 8, 22, 24, 27, 31, 35, 41, 58, 62, 72, 75, 81, 83, 85, 86, 89, 90, 92, 93], "averag": [1, 3, 8, 20, 24, 30, 31, 35, 41, 43, 50, 51, 58, 59, 60, 81, 85, 88], "amount": [1, 3, 82], "paramet": [1, 2, 3, 4, 7, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 76, 79, 82, 92, 93], "np": [1, 2, 3, 4, 5, 14, 16, 27, 30, 32, 34, 36, 38, 39, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 70, 71, 74, 75, 76, 78, 80, 81, 82, 83, 85, 86, 88, 90, 91, 92, 93, 94], "ndarrai": [1, 2, 3, 4, 14, 21, 22, 26, 27, 30, 32, 34, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 94], "an": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 43, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "arrai": [1, 2, 3, 4, 5, 8, 10, 14, 16, 22, 30, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 75, 76, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "shape": [1, 2, 3, 4, 14, 16, 30, 32, 34, 36, 38, 39, 40, 41, 43, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 80, 81, 83, 86, 87, 88, 91, 94], "condit": [1, 2, 3, 39, 44, 45, 60, 82, 83, 94], "probabl": [1, 2, 3, 4, 6, 8, 14, 21, 24, 30, 34, 35, 36, 38, 39, 41, 42, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 73, 80, 81, 83, 84, 86, 87, 88, 91, 94], "k_": [1, 2, 3, 39, 45], "k_y": [1, 2, 3, 39, 45], "contain": [1, 2, 3, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93], "fraction": [1, 2, 3, 8, 18, 32, 39, 45, 50, 62, 78, 81], "exampl": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 44, 45, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 85, 86, 87, 89, 90, 91, 92, 93, 94], "everi": [1, 2, 3, 4, 14, 31, 35, 36, 39, 44, 45, 52, 60, 62, 63, 74, 75, 76, 78, 79, 81, 82, 85, 87, 89, 91, 92, 94], "class": [1, 2, 3, 4, 5, 7, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 44, 45, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 92, 93, 94], "other": [1, 2, 3, 4, 8, 14, 20, 23, 30, 31, 33, 34, 35, 36, 39, 42, 45, 46, 48, 50, 51, 54, 58, 59, 60, 62, 67, 74, 75, 76, 78, 79, 81, 82, 83, 86, 88, 91, 94], "assum": [1, 2, 3, 10, 36, 39, 44, 45, 60, 64, 67, 81, 88, 91, 94], "column": [1, 2, 3, 4, 8, 10, 11, 26, 30, 34, 36, 39, 41, 42, 44, 45, 50, 51, 52, 54, 55, 58, 59, 60, 62, 67, 68, 70, 71, 74, 75, 76, 79, 80, 81, 82, 83, 85, 87, 90, 91, 92, 93, 94], "sum": [1, 2, 3, 22, 27, 30, 39, 41, 45, 51, 52, 54, 57, 62, 75, 76, 81, 82, 83, 85, 86, 91, 94], "1": [1, 2, 3, 4, 5, 8, 10, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 80, 81, 89], "each": [1, 2, 3, 4, 5, 6, 7, 11, 12, 14, 18, 20, 21, 22, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 45, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "true": [1, 2, 3, 4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 39, 41, 44, 45, 46, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "return": [1, 2, 3, 4, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "type": [1, 2, 3, 4, 5, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 86, 87, 91, 92, 94], "bool": [1, 2, 3, 4, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 34, 35, 36, 41, 44, 45, 50, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71], "is_valid": 1, "whether": [1, 3, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 31, 34, 35, 36, 45, 50, 51, 52, 54, 55, 71, 74, 76, 78, 79, 80, 81, 82, 83, 90, 93, 94], "generate_noisy_label": [1, 75, 76, 83, 85, 86], "from": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 21, 23, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 39, 41, 42, 43, 44, 45, 50, 52, 54, 57, 58, 59, 60, 62, 63, 68, 70, 71, 72, 74, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 91, 94], "perfect": [1, 2, 30, 62, 83, 87], "exactli": [1, 3, 8, 30, 31, 35, 36, 53, 59, 75, 76, 78, 79, 82, 83], "yield": [1, 31, 35], "between": [1, 4, 8, 13, 14, 19, 20, 22, 25, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 43, 48, 50, 51, 54, 57, 59, 60, 62, 63, 66, 70, 71, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "below": [1, 3, 4, 8, 30, 31, 34, 35, 36, 38, 41, 50, 51, 52, 57, 58, 66, 70, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "we": [1, 2, 3, 4, 5, 8, 11, 20, 31, 34, 35, 36, 41, 45, 46, 50, 57, 58, 60, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "loop": [1, 3, 39, 45, 82], "implement": [1, 2, 3, 4, 7, 12, 20, 31, 32, 34, 35, 39, 45, 62, 72, 74, 75, 78, 88, 89, 92], "what": [1, 4, 7, 8, 14, 28, 30, 32, 34, 36, 50, 51, 55, 57, 74, 75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "doe": [1, 2, 3, 8, 34, 35, 36, 41, 46, 57, 58, 62, 64, 66, 70, 74, 75, 76, 78, 79, 82, 86, 90, 91, 93], "do": [1, 2, 4, 8, 30, 34, 35, 45, 46, 59, 60, 64, 74, 75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "fast": 1, "explain": [1, 8], "python": [1, 2, 35, 49, 62, 75, 76, 80, 88], "pseudocod": [1, 89], "happen": [1, 8, 36, 52, 79, 85, 91], "n": [1, 2, 3, 4, 5, 30, 31, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 74, 79, 80, 81, 82, 85, 86, 90, 91, 92, 93, 94], "without": [1, 2, 4, 8, 10, 12, 18, 31, 35, 54, 62, 72, 74, 79, 83, 87, 88, 93], "ani": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 38, 40, 43, 44, 45, 49, 50, 52, 54, 55, 57, 58, 60, 62, 64, 66, 67, 72, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93], "distinct": [1, 16, 45, 94], "natur": [1, 8, 85, 88], "number": [1, 2, 3, 4, 5, 6, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 91, 94], "0": [1, 2, 3, 4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "count_joint": 1, "len": [1, 2, 3, 5, 30, 34, 39, 44, 45, 46, 59, 60, 62, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93, 94], "y": [1, 2, 3, 4, 6, 16, 26, 27, 35, 39, 41, 45, 46, 49, 58, 62, 63, 74, 75, 76, 78, 81, 83, 85, 86, 88, 90, 93], "round": [1, 34, 36, 45, 62, 81, 90], "astyp": [1, 85], "int": [1, 2, 3, 4, 5, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 41, 42, 43, 44, 45, 51, 52, 54, 58, 59, 60, 62, 64, 66, 67, 68, 71, 74, 75, 82, 88], "rang": [1, 3, 4, 5, 10, 39, 41, 43, 45, 58, 62, 63, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 94], "idx_flip": 1, "where": [1, 2, 3, 4, 5, 8, 10, 11, 14, 20, 30, 34, 36, 39, 40, 41, 42, 43, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94], "pragma": 1, "cover": [1, 3, 73, 80], "choic": [1, 6, 36, 43, 81, 82, 86, 88], "replac": [1, 44, 49, 60, 75, 76, 79, 80, 81, 82, 85, 88, 92, 93], "generate_noise_matrix_from_trac": [1, 75, 76, 83, 85, 86], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 60, 74, 75, 76], "05": [1, 8, 22, 26, 44, 58, 62, 68, 70, 78, 80, 81, 82, 83, 87, 91], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 75, 76, 83, 85, 86], "none": [1, 2, 3, 4, 5, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 75, 76, 81, 82, 83, 85, 86, 91], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 8, 22, 35, 41, 62, 74, 75, 76, 78, 80, 83, 85, 86, 92], "max_it": [1, 74, 79, 88, 93], "10000": [1, 34, 80, 81], "x": [1, 2, 3, 4, 8, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 31, 32, 35, 36, 38, 39, 41, 44, 45, 46, 49, 50, 52, 58, 59, 60, 62, 64, 74, 75, 76, 78, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93], "diagon": [1, 3, 4, 36, 39, 45], "equal": [1, 3, 8, 10, 52, 57, 67, 89], "creat": [1, 2, 7, 14, 16, 31, 34, 35, 36, 45, 62, 72, 74, 78, 79, 81, 82, 91, 93, 94], "impli": [1, 8, 30, 51, 58], "float": [1, 2, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 40, 41, 43, 44, 45, 50, 51, 52, 54, 57, 58, 62, 66, 70, 74, 75, 76, 83, 85, 86], "entri": [1, 3, 4, 30, 31, 35, 36, 38, 42, 43, 45, 50, 51, 52, 55, 78, 79, 83, 86, 87, 92, 93], "maximum": [1, 8, 59, 67, 71, 91], "minimum": [1, 6, 8, 18, 36, 38, 52, 57, 70], "noise_r": 1, "non": [1, 2, 3, 4, 7, 14, 22, 31, 35, 36, 57, 62, 75, 81, 83, 85, 87, 88], "default": [1, 2, 3, 4, 5, 8, 12, 14, 24, 26, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 75, 81, 82, 91], "If": [1, 2, 3, 4, 8, 10, 11, 14, 22, 24, 30, 31, 34, 35, 36, 38, 39, 41, 44, 45, 49, 50, 51, 52, 55, 57, 58, 59, 62, 63, 64, 66, 67, 70, 71, 72, 73, 74, 75, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "have": [1, 2, 3, 4, 8, 14, 19, 22, 25, 30, 31, 33, 34, 35, 36, 39, 41, 45, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 67, 71, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "all": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 20, 28, 30, 31, 34, 35, 36, 39, 41, 42, 44, 45, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "necessari": [1, 2, 3, 5, 8, 10, 44, 75], "In": [1, 2, 3, 8, 30, 31, 34, 35, 50, 51, 53, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 88, 89, 90, 91, 92, 93, 94], "particular": [1, 4, 8, 11, 12, 14, 17, 18, 20, 22, 23, 24, 27, 31, 35, 45, 50, 54, 58, 62, 67, 71, 72, 74, 76, 79, 81, 85, 86, 88, 90, 92, 93], "satisfi": [1, 3, 30], "requir": [1, 2, 4, 5, 6, 7, 8, 9, 10, 26, 29, 31, 32, 33, 34, 35, 36, 39, 45, 48, 49, 52, 59, 60, 62, 64, 72, 73, 74, 80, 81, 83, 89], "argument": [1, 2, 3, 4, 8, 14, 21, 23, 26, 27, 31, 34, 35, 36, 41, 46, 49, 50, 51, 52, 54, 57, 58, 59, 60, 62, 66, 67, 68, 70, 76, 79, 80, 81, 82, 87, 90, 93, 94], "when": [1, 2, 3, 4, 8, 10, 12, 21, 22, 31, 35, 36, 39, 41, 45, 49, 52, 54, 55, 57, 59, 60, 62, 63, 75, 76, 78, 79, 82, 85, 89, 90, 91, 92, 93, 94], "The": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 38, 39, 40, 41, 42, 43, 45, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 67, 68, 70, 72, 74, 75, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "rate": [1, 2, 3, 8, 32, 45, 74, 94], "set": [1, 2, 3, 4, 7, 8, 10, 11, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 40, 41, 43, 45, 49, 50, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 75, 76, 78, 79, 81, 85, 86, 88, 89, 90, 91, 92, 93, 94], "note": [1, 2, 3, 5, 6, 8, 23, 27, 31, 34, 35, 36, 41, 45, 50, 55, 57, 58, 59, 60, 62, 63, 67, 73, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "you": [1, 2, 3, 4, 5, 8, 12, 14, 30, 31, 33, 34, 35, 36, 41, 48, 49, 50, 52, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "high": [1, 2, 14, 34, 36, 45, 57, 60, 62, 75, 76, 80, 82, 83, 87, 90, 91, 92, 93, 94], "mai": [1, 2, 3, 4, 8, 11, 19, 20, 25, 30, 31, 33, 34, 35, 36, 39, 41, 45, 50, 51, 55, 57, 58, 59, 60, 62, 64, 67, 71, 73, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 89, 90, 91, 93, 94], "imposs": [1, 8, 83], "also": [1, 2, 3, 4, 5, 8, 20, 30, 31, 34, 35, 36, 44, 49, 50, 59, 62, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "low": [1, 8, 45, 50, 72, 75, 76, 79, 83, 87, 91], "zero": [1, 3, 4, 31, 35, 38, 45, 46, 75, 82, 86, 87, 88], "forc": [1, 2, 3, 4, 35, 75, 94], "instead": [1, 2, 3, 8, 11, 14, 28, 30, 31, 34, 35, 36, 39, 45, 49, 50, 52, 54, 58, 59, 60, 62, 63, 66, 68, 70, 73, 74, 78, 79, 81, 82, 83, 86, 87, 88, 90, 91, 92, 93, 94], "onli": [1, 2, 3, 4, 5, 8, 14, 21, 22, 26, 30, 31, 34, 35, 36, 38, 39, 44, 45, 49, 50, 59, 60, 62, 64, 66, 70, 71, 72, 74, 75, 76, 79, 82, 85, 86, 87, 88, 89, 90, 91, 93, 94], "guarante": [1, 3, 4, 13, 19, 25, 31, 33, 35, 37, 39, 48, 73], "produc": [1, 2, 4, 8, 14, 41, 50, 60, 62, 64, 66, 72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 93, 94], "higher": [1, 4, 8, 30, 36, 38, 39, 41, 43, 50, 51, 62, 76, 79, 81, 87], "opposit": [1, 94], "occur": [1, 3, 8, 30, 44, 57, 75, 76, 81, 82, 88], "small": [1, 3, 8, 30, 34, 41, 45, 51, 58, 79, 80, 82, 86, 88, 93], "numpi": [1, 3, 4, 5, 8, 10, 16, 27, 34, 35, 41, 43, 44, 46, 49, 54, 57, 62, 63, 68, 70, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "max": [1, 36, 59, 60, 76, 82, 88], "tri": [1, 31, 35, 89], "befor": [1, 2, 3, 31, 35, 43, 45, 59, 62, 67, 79, 81, 83, 85, 88, 90, 92, 93], "option": [1, 2, 3, 4, 5, 6, 7, 10, 11, 14, 21, 22, 26, 30, 31, 34, 35, 36, 39, 41, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 72, 74, 75, 76, 78, 81, 82, 83, 90, 91, 92], "left": [1, 2, 36, 38, 43, 45, 52, 55, 58, 75, 76, 86, 87, 88, 91], "stochast": 1, "exceed": 1, "generate_n_rand_probabilities_that_sum_to_m": 1, "m": [1, 4, 31, 35, 40, 41, 50, 55, 57, 58, 59, 75, 76, 80, 85, 86, 87, 94], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 31, 35, 49, 81, 83, 91], "length": [1, 4, 10, 22, 23, 30, 32, 36, 45, 52, 55, 59, 60, 62, 64, 67, 71, 74, 86, 88, 91, 92, 94], "must": [1, 2, 3, 4, 14, 30, 31, 32, 33, 35, 36, 39, 41, 42, 45, 48, 49, 50, 51, 52, 59, 60, 62, 64, 66, 67, 68, 70, 71, 74, 85, 89, 91, 94], "randomly_distribute_n_balls_into_k_bin": 1, "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 8, 10, 30, 34, 36, 42, 45, 46, 50, 52, 58, 64, 66, 67, 68, 70, 71, 74, 81, 85, 86, 87, 91, 92, 93, 94], "ball": [1, 80], "bin": [1, 3, 52, 75, 76, 88], "ensur": [1, 2, 8, 31, 35, 45, 46, 57, 60, 62, 74, 75, 76, 79, 81, 82, 83, 88, 89, 90, 92, 93], "most": [1, 3, 4, 5, 8, 14, 30, 34, 36, 41, 49, 50, 51, 52, 55, 57, 58, 59, 60, 63, 66, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93], "least": [1, 8, 16, 27, 30, 34, 50, 51, 57, 60, 70, 76, 81, 82, 85, 88, 91], "int_arrai": [1, 45], "can": [2, 3, 4, 5, 6, 7, 11, 12, 14, 28, 30, 31, 32, 33, 34, 35, 36, 40, 41, 42, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 71, 72, 73, 74, 75, 78, 79, 82, 86, 87, 88, 89, 90, 91, 92, 93, 94], "model": [2, 3, 4, 8, 14, 16, 26, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 44, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 75, 76, 80, 84, 89, 91, 94], "For": [2, 3, 4, 5, 7, 8, 9, 14, 20, 29, 30, 31, 34, 35, 36, 39, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 68, 70, 71, 72, 74, 76, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 93, 94], "regular": [2, 3, 34, 49], "multi": [2, 3, 8, 30, 31, 34, 35, 36, 40, 41, 42, 45, 46, 51, 52, 53, 54, 59, 60, 72, 81, 83, 84], "task": [2, 4, 5, 8, 10, 12, 13, 14, 26, 28, 30, 34, 39, 41, 42, 43, 45, 50, 52, 60, 62, 72, 74, 79, 80, 81, 83, 86, 88, 91, 93, 94], "cleanlearn": [2, 3, 8, 21, 26, 31, 45, 49, 62, 63, 72, 73, 90, 92, 93], "wrap": [2, 31, 35, 49, 59, 62, 72, 75, 76, 78, 79, 83, 90, 92, 93], "instanc": [2, 3, 4, 5, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 35, 41, 49, 58, 59, 62, 67, 74, 75, 76, 78, 79, 82, 83, 92], "sklearn": [2, 3, 4, 6, 8, 16, 27, 30, 35, 41, 45, 49, 59, 62, 63, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 89, 90, 92, 93], "classifi": [2, 3, 35, 41, 45, 50, 53, 59, 60, 72, 73, 74, 78, 79, 81, 85, 86, 88, 89, 91, 92, 93, 94], "adher": [2, 35, 62], "estim": [2, 3, 4, 7, 11, 20, 30, 34, 35, 36, 39, 45, 50, 51, 52, 57, 59, 62, 64, 66, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 84, 86, 87, 88, 89, 90, 91, 94], "api": [2, 3, 12, 49, 59, 62, 73, 81, 90], "defin": [2, 3, 4, 5, 8, 12, 20, 30, 31, 32, 34, 35, 36, 60, 62, 64, 75, 76, 78, 81, 85, 88, 94], "four": [2, 8, 80, 83, 94], "clf": [2, 3, 4, 41, 62, 72, 78, 81, 83, 86, 92], "fit": [2, 3, 4, 6, 8, 16, 35, 49, 59, 62, 72, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 89, 90, 92, 93, 94], "sample_weight": [2, 35, 62, 83], "predict_proba": [2, 4, 30, 35, 41, 49, 74, 75, 76, 78, 79, 81, 83, 85, 86, 88, 92], "predict": [2, 3, 4, 6, 8, 14, 20, 21, 24, 26, 30, 34, 35, 36, 38, 39, 41, 42, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 80, 81, 83, 84, 88, 90, 91, 93, 94], "score": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 34, 36, 38, 41, 43, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 88, 90, 92, 93], "data": [2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 32, 33, 34, 35, 36, 41, 42, 45, 48, 49, 50, 51, 52, 53, 57, 59, 60, 61, 62, 67, 68, 69, 70, 71, 73, 77, 82, 84, 89, 93], "e": [2, 3, 4, 8, 10, 20, 30, 31, 34, 35, 36, 39, 41, 42, 45, 46, 50, 51, 52, 53, 59, 60, 62, 64, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93], "featur": [2, 3, 4, 6, 8, 14, 17, 21, 22, 23, 24, 26, 27, 41, 45, 59, 62, 72, 75, 76, 78, 79, 81, 83, 85, 90, 92], "element": [2, 3, 4, 30, 36, 38, 45, 50, 52, 60, 67, 68, 70, 74, 79, 81, 93, 94], "first": [2, 4, 8, 15, 22, 23, 30, 34, 41, 45, 50, 51, 55, 58, 60, 62, 74, 75, 78, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "index": [2, 8, 22, 30, 36, 44, 45, 46, 51, 60, 62, 67, 70, 71, 74, 75, 76, 78, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "should": [2, 3, 4, 5, 8, 12, 20, 22, 27, 30, 31, 34, 35, 36, 38, 39, 41, 43, 44, 45, 49, 50, 51, 54, 55, 57, 58, 59, 60, 62, 63, 67, 68, 70, 71, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "correspond": [2, 3, 4, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 38, 39, 41, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "differ": [2, 4, 5, 8, 11, 13, 19, 22, 23, 25, 30, 31, 33, 34, 35, 36, 37, 41, 45, 46, 48, 50, 55, 57, 59, 62, 74, 75, 76, 78, 79, 82, 83, 85, 88, 89, 92], "sampl": [2, 3, 4, 6, 8, 14, 18, 36, 38, 41, 52, 55, 58, 60, 62, 63, 72, 73, 80, 81, 83, 84, 86, 87, 90, 91, 93, 94], "size": [2, 8, 27, 31, 34, 35, 36, 41, 52, 57, 58, 62, 64, 66, 78, 81, 82, 83, 85, 86, 89, 91, 93], "here": [2, 4, 5, 8, 12, 34, 36, 39, 49, 50, 51, 52, 54, 55, 58, 59, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "re": [2, 4, 31, 35, 44, 50, 62, 72, 74, 75, 78, 79, 81, 90, 91, 92, 93, 94], "weight": [2, 8, 31, 32, 35, 41, 50, 57, 60, 62, 74, 75, 76, 79, 88, 93], "loss": [2, 32, 49, 60, 62, 82], "while": [2, 3, 8, 31, 34, 35, 40, 41, 45, 55, 58, 62, 72, 81, 82, 83, 85, 90], "train": [2, 3, 4, 8, 14, 16, 31, 32, 35, 41, 45, 49, 50, 55, 58, 59, 62, 63, 73, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 89, 91, 94], "support": [2, 3, 4, 10, 34, 41, 45, 46, 59, 60, 70, 72, 73, 74, 75, 76, 81, 82], "your": [2, 3, 4, 7, 8, 14, 30, 31, 33, 34, 35, 36, 41, 45, 48, 49, 50, 51, 52, 54, 59, 60, 62, 63, 64, 66, 67, 73, 74, 78, 80, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "recommend": [2, 4, 8, 11, 14, 34, 36, 50, 75, 76, 81, 82, 89, 90], "furthermor": 2, "correctli": [2, 3, 8, 30, 31, 35, 36, 39, 46, 51, 52, 57, 58, 62, 64, 79, 81, 86, 87, 90, 91, 93], "clonabl": [2, 62], "via": [2, 4, 8, 11, 14, 16, 20, 30, 32, 34, 35, 41, 45, 50, 55, 58, 59, 60, 62, 63, 66, 70, 74, 75, 76, 78, 79, 80, 81, 82, 86, 87, 88, 89, 90, 91, 92, 93, 94], "base": [2, 3, 4, 5, 8, 10, 11, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 57, 59, 60, 62, 63, 66, 68, 70, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 94], "clone": [2, 62, 86], "intern": [2, 3, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 34, 38, 39, 40, 41, 42, 43, 44, 45, 46, 54, 58, 62, 68, 73, 75, 81, 83, 85, 86, 88, 94], "multipl": [2, 3, 4, 10, 11, 30, 36, 44, 50, 51, 52, 54, 57, 58, 62, 72, 75, 76, 81, 82, 84, 86, 87, 90], "g": [2, 3, 4, 8, 10, 20, 30, 31, 35, 36, 42, 45, 52, 53, 59, 60, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93], "manual": [2, 62, 74, 81, 88, 89, 90, 92, 93, 94], "pytorch": [2, 31, 32, 35, 62, 72, 74, 81, 84, 86, 91], "call": [2, 3, 4, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 41, 45, 49, 59, 62, 74, 75, 76, 79, 81, 83, 88, 89, 91, 93, 94], "__init__": [2, 32, 62, 82], "independ": [2, 3, 8, 51, 62, 79, 89, 94], "compat": [2, 31, 34, 35, 49, 62, 63, 66, 70, 72, 81, 89, 90, 92, 93], "neural": [2, 32, 49, 59, 62, 74, 81, 82, 86, 88], "network": [2, 31, 32, 35, 49, 59, 62, 74, 79, 81, 82, 86, 88, 93], "typic": [2, 31, 35, 59, 62, 74, 76, 78, 79, 82, 88, 89, 92, 93], "initi": [2, 3, 11, 16, 31, 35, 50, 62, 79, 81, 92], "insid": [2, 35, 62, 81, 83], "There": [2, 3, 72, 83, 85, 86], "two": [2, 3, 8, 16, 22, 30, 31, 34, 35, 42, 45, 55, 57, 58, 73, 75, 76, 78, 79, 81, 82, 83, 86, 90, 91, 93, 94], "new": [2, 5, 12, 20, 31, 34, 35, 40, 44, 45, 50, 62, 74, 75, 79, 80, 81, 88, 89, 93, 94], "notion": 2, "confid": [2, 3, 8, 20, 30, 34, 36, 39, 41, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 66, 70, 72, 78, 79, 82, 83, 85, 86, 87, 89, 91, 92, 94], "packag": [2, 4, 5, 7, 8, 9, 13, 29, 33, 36, 37, 45, 48, 55, 58, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "prune": [2, 3, 36, 52, 62, 73, 87], "everyth": [2, 58, 83], "els": [2, 58, 75, 80, 81, 82, 85, 86], "mathemat": [2, 3, 8, 39], "keep": [2, 11, 12, 45, 72, 75, 80, 81, 91], "belong": [2, 3, 8, 30, 36, 38, 39, 51, 52, 53, 54, 59, 60, 64, 68, 70, 71, 76, 78, 79, 82, 83, 86, 88, 91, 94], "2": [2, 3, 4, 5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 49, 51, 52, 54, 59, 60, 62, 63, 67, 68, 70, 71, 80, 81, 89], "error": [2, 3, 4, 8, 31, 35, 36, 38, 39, 45, 51, 52, 54, 55, 57, 58, 60, 62, 64, 66, 67, 70, 73, 74, 75, 76, 78, 79, 80, 84, 92], "erron": [2, 3, 30, 36, 39, 45, 51, 52, 60, 62, 63, 64, 88, 90], "import": [2, 3, 4, 5, 6, 8, 10, 11, 12, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 34, 41, 43, 44, 50, 54, 57, 62, 63, 68, 70, 71, 72, 78, 79, 81, 86, 87, 88, 90, 91, 92, 93, 94], "linear_model": [2, 4, 30, 45, 62, 72, 74, 75, 76, 79, 81, 83, 85, 88, 93], "logisticregress": [2, 3, 4, 30, 45, 72, 74, 75, 76, 79, 81, 83, 85, 88, 93], "logreg": 2, "cl": [2, 12, 26, 62, 72, 81, 83, 90, 92, 93], "pass": [2, 3, 4, 6, 8, 10, 11, 12, 14, 21, 26, 28, 31, 34, 35, 36, 40, 41, 45, 49, 50, 52, 59, 60, 62, 68, 72, 74, 75, 76, 79, 80, 81, 83, 85, 87, 88, 90, 93], "x_train": [2, 75, 76, 83, 85, 86, 90, 92], "labels_maybe_with_error": 2, "had": [2, 3, 62, 87], "issu": [2, 3, 4, 6, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 27, 28, 30, 31, 33, 34, 35, 36, 48, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 77, 84, 85, 89, 90, 93], "pred": [2, 36, 45, 89, 90, 92, 93], "x_test": [2, 75, 76, 83, 86, 90, 92], "might": [2, 50, 62, 67, 75, 76, 81, 82, 92, 93], "case": [2, 3, 11, 30, 41, 50, 62, 74, 75, 76, 78, 80, 81, 82, 83, 88, 90, 92, 93, 94], "standard": [2, 3, 4, 26, 30, 36, 49, 51, 52, 54, 60, 62, 72, 75, 76, 78, 80, 83, 92], "adapt": [2, 31, 33, 45, 48, 62, 88], "skorch": [2, 62, 72, 81], "kera": [2, 48, 62, 72, 81], "scikera": [2, 49, 62, 81], "open": [2, 34, 80, 87, 94], "doesn": [2, 62, 72], "t": [2, 3, 8, 15, 23, 31, 32, 34, 35, 36, 41, 43, 44, 54, 59, 60, 62, 68, 70, 71, 72, 75, 76, 78, 79, 80, 82, 83, 86, 87, 94], "alreadi": [2, 4, 8, 14, 31, 34, 35, 39, 49, 50, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 92, 93], "exist": [2, 4, 8, 10, 16, 31, 34, 35, 44, 49, 55, 57, 59, 62, 72, 73, 75, 76, 79, 85, 86, 93, 94], "made": [2, 4, 14, 62, 79, 81, 82, 85, 87, 89, 90, 92, 93], "easi": [2, 39, 62, 75, 76, 80, 81, 83, 86], "inherit": [2, 5, 32, 62], "baseestim": [2, 35, 62], "yourmodel": [2, 62], "def": [2, 5, 12, 31, 35, 49, 62, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 93, 94], "self": [2, 3, 4, 5, 8, 10, 11, 12, 14, 27, 31, 32, 34, 35, 36, 41, 59, 60, 62, 75, 80, 82, 86, 91, 92, 94], "refer": [2, 8, 14, 31, 35, 51, 52, 54, 55, 57, 58, 62, 66, 67, 75, 76, 78, 79, 81, 82, 83, 89, 90], "origin": [2, 4, 8, 35, 36, 44, 45, 49, 51, 52, 55, 58, 59, 62, 63, 66, 68, 70, 75, 78, 79, 81, 82, 83, 87, 88, 90, 92, 93, 94], "total": [2, 3, 30, 34, 45, 51, 71, 81, 82, 91], "state": [2, 3, 4, 31, 32, 35, 40, 62, 83, 86, 87, 94], "art": [2, 32, 83, 86], "northcutt": [2, 3, 30, 59, 60], "et": [2, 3, 30, 32, 59, 60], "al": [2, 3, 30, 32, 59, 60], "2021": [2, 3, 30, 59, 60], "weak": [2, 58], "supervis": [2, 8, 75, 76, 81, 85], "find": [2, 4, 8, 11, 12, 14, 17, 18, 20, 21, 22, 23, 24, 27, 30, 31, 33, 34, 35, 36, 40, 44, 45, 48, 55, 58, 59, 60, 62, 64, 68, 70, 73, 75, 84, 89], "uncertainti": [2, 8, 38, 59, 62, 81, 88, 90], "It": [2, 3, 4, 5, 8, 10, 11, 14, 20, 23, 26, 28, 31, 35, 36, 39, 41, 50, 57, 58, 62, 72, 75, 76, 81, 82, 83, 86, 89], "work": [2, 3, 4, 5, 8, 10, 26, 30, 31, 34, 35, 36, 39, 44, 45, 46, 49, 50, 60, 62, 72, 73, 75, 76, 80, 88, 90, 93], "includ": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 33, 34, 35, 44, 45, 48, 50, 51, 54, 55, 59, 60, 62, 66, 67, 68, 70, 72, 73, 75, 76, 78, 79, 81, 82, 83, 86, 87, 88, 94], "deep": [2, 33, 35, 48, 49, 62, 79], "see": [2, 3, 4, 11, 30, 31, 34, 35, 36, 41, 45, 49, 51, 52, 54, 55, 58, 59, 60, 62, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "subfield": 2, "theori": [2, 83], "machin": [2, 4, 12, 14, 28, 33, 48, 62, 75, 76, 80, 85], "across": [2, 3, 4, 5, 8, 11, 20, 30, 34, 41, 51, 58, 59, 75, 76, 78, 79, 80, 81, 82, 83, 87, 89], "varieti": [2, 81, 92, 93], "like": [2, 3, 4, 5, 8, 12, 28, 30, 31, 34, 35, 36, 39, 45, 49, 50, 51, 54, 55, 57, 60, 62, 63, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "pu": [2, 45], "input": [2, 3, 4, 8, 14, 22, 30, 31, 34, 35, 39, 41, 44, 45, 46, 49, 58, 62, 72, 73, 76, 79, 80, 81, 82, 83, 85, 86, 87, 90, 91, 93, 94], "discret": [2, 36, 39, 45, 59, 60, 64, 66, 67], "vector": [2, 3, 4, 8, 14, 36, 39, 41, 42, 45, 59, 60, 72, 74, 75, 76, 78, 79, 82, 83, 86, 87, 88, 91, 93, 94], "would": [2, 3, 4, 31, 34, 35, 36, 45, 52, 62, 72, 75, 81, 82, 83, 88, 90, 93, 94], "obtain": [2, 4, 6, 8, 14, 36, 50, 52, 55, 58, 60, 63, 74, 76, 79, 81, 85, 87, 89, 91, 94], "been": [2, 30, 36, 39, 44, 45, 50, 51, 55, 57, 59, 60, 62, 74, 75, 78, 81, 83, 85, 86, 87, 88, 91, 94], "dure": [2, 8, 14, 59, 62, 74, 78, 79, 81, 83, 86, 89, 90, 92, 93, 94], "denot": [2, 3, 39, 41, 45, 52, 59, 60, 70], "tild": 2, "paper": [2, 8, 50, 59, 68, 70, 80, 83, 85, 88, 90, 94], "cv_n_fold": [2, 3, 62, 93], "5": [2, 3, 4, 6, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 35, 36, 38, 40, 41, 45, 50, 51, 54, 55, 58, 62, 63, 70, 75, 79, 80, 81, 86, 87, 88, 89, 91, 93, 94], "converge_latent_estim": [2, 3], "pulearn": [2, 45], "find_label_issues_kwarg": [2, 8, 62, 73, 81, 83], "label_quality_scores_kwarg": [2, 8], "low_memori": [2, 52, 68, 81], "clean": [2, 57, 60, 62, 63, 72, 75, 76, 80, 90, 92, 93], "even": [2, 3, 30, 34, 38, 39, 45, 62, 74, 81, 83, 85, 86, 87], "messi": [2, 62, 83], "ridden": [2, 62], "autom": [2, 62, 72, 76, 80, 81], "robust": [2, 39, 62, 76, 81], "prone": [2, 62], "out": [2, 3, 4, 8, 14, 24, 31, 35, 36, 41, 49, 52, 53, 55, 58, 59, 60, 62, 63, 71, 72, 73, 80, 81, 83, 84, 86, 87, 88, 90, 91, 93, 94], "current": [2, 3, 5, 8, 11, 12, 20, 31, 35, 36, 41, 50, 57, 62, 75, 76, 81, 85], "intend": [2, 11, 12, 13, 14, 28, 37, 50, 66, 70, 74, 75, 76, 79, 83], "A": [2, 3, 4, 5, 8, 10, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 35, 36, 39, 40, 41, 42, 43, 44, 45, 49, 50, 51, 54, 57, 58, 59, 60, 62, 64, 66, 67, 71, 73, 74, 75, 78, 79, 80, 81, 82, 83, 85, 87, 89, 92, 93, 94], "follow": [2, 3, 8, 12, 26, 30, 31, 34, 35, 41, 43, 50, 51, 55, 57, 58, 59, 62, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "experiment": [2, 31, 32, 34, 35, 52, 73, 81], "wrapper": [2, 4, 49, 74, 90, 92, 93], "around": [2, 4, 57, 75, 76, 87, 88, 94], "fasttext": [2, 48], "store": [2, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 59, 62, 78, 79, 80, 81, 91, 92, 93, 94], "along": [2, 41, 52, 70, 75, 76, 81, 82, 88], "dimens": [2, 45, 64, 67, 81, 82, 88, 91], "select": [2, 7, 8, 22, 50, 60, 82, 85, 88], "split": [2, 3, 4, 8, 10, 34, 41, 44, 45, 62, 74, 75, 76, 78, 79, 80, 82, 83, 86, 89, 92, 94], "cross": [2, 3, 8, 30, 36, 39, 40, 41, 52, 55, 58, 60, 62, 63, 73, 74, 75, 76, 78, 79, 80, 81, 83, 84, 86, 87, 90, 91, 92, 93, 94], "fold": [2, 3, 30, 36, 39, 62, 74, 78, 80, 81, 87, 91, 92], "By": [2, 4, 30, 51, 52, 62, 75, 81, 91], "need": [2, 3, 8, 30, 31, 34, 35, 36, 51, 52, 54, 59, 62, 72, 74, 75, 76, 79, 81, 83, 85, 86, 87, 91, 93], "holdout": [2, 3, 62], "comput": [2, 3, 4, 5, 6, 8, 17, 18, 20, 21, 22, 23, 24, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 45, 50, 51, 52, 54, 57, 58, 59, 60, 62, 63, 64, 66, 72, 73, 75, 76, 80, 83, 84, 86, 87, 88, 90, 91, 93], "them": [2, 3, 4, 5, 7, 8, 9, 10, 23, 29, 31, 33, 34, 35, 36, 48, 50, 59, 62, 73, 75, 76, 78, 79, 81, 82, 85, 86, 88, 90, 91, 92, 93, 94], "numer": [2, 3, 4, 8, 11, 20, 26, 41, 57, 59, 62, 67, 72, 73, 74, 75, 76, 77, 79, 82, 83, 85, 88, 90, 92, 93], "consist": [2, 3, 31, 35, 45, 50, 91, 94], "latent": [2, 3, 39], "thei": [2, 3, 4, 13, 19, 22, 25, 31, 32, 33, 35, 36, 37, 43, 45, 49, 52, 57, 60, 62, 63, 66, 70, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 88, 90, 93, 94], "relat": [2, 3, 11, 17, 18, 22, 23, 24, 27, 39, 45, 51, 62, 76, 79], "close": [2, 3, 8, 34, 39, 59, 74, 75, 76, 78, 79, 81, 82, 83, 87], "form": [2, 3, 8, 31, 32, 35, 39, 44, 45, 60, 62, 81], "equival": [2, 3, 31, 35, 39, 59, 88], "iter": [2, 3, 30, 31, 35, 36, 45, 51, 52, 62, 81, 85, 91], "enforc": [2, 31, 35, 45], "perfectli": [2, 30, 51, 83], "certain": [2, 3, 4, 31, 35, 49, 58, 62, 75, 76, 80, 88], "dict": [2, 3, 4, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 40, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 70, 75, 76, 81, 82, 94], "keyword": [2, 3, 4, 8, 14, 21, 23, 26, 31, 34, 35, 36, 38, 41, 44, 49, 50, 52, 59, 60, 62, 68, 70, 75], "filter": [2, 3, 8, 34, 44, 51, 53, 54, 56, 58, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 79, 80, 81, 82, 86, 87, 90, 91, 92, 93, 94], "find_label_issu": [2, 3, 8, 26, 34, 36, 51, 52, 54, 55, 57, 58, 62, 64, 66, 67, 68, 70, 71, 72, 73, 81, 86, 87, 90, 91, 92, 93, 94], "particularli": [2, 72, 85, 88], "filter_bi": [2, 3, 34, 36, 52, 73, 81], "frac_nois": [2, 36, 52, 68, 81], "min_examples_per_class": [2, 36, 52, 76, 81, 83], "impact": [2, 8, 75, 76, 82], "ml": [2, 4, 8, 13, 62, 72, 75, 76, 78, 79, 82, 85, 92, 93], "accuraci": [2, 32, 60, 74, 81, 82, 83, 85, 88, 90, 91, 92, 93], "n_job": [2, 34, 36, 52, 64, 66, 68, 81, 88, 91], "disabl": [2, 31, 35, 36, 88], "process": [2, 3, 5, 11, 14, 34, 36, 44, 50, 52, 58, 64, 66, 68, 74, 75, 81, 85, 89, 93], "caus": [2, 36, 41, 75, 76, 81], "rank": [2, 3, 8, 30, 34, 36, 41, 51, 52, 53, 55, 56, 58, 59, 61, 65, 67, 68, 69, 71, 72, 73, 75, 76, 80, 81, 86, 87, 88, 90, 91, 92, 93, 94], "get_label_quality_scor": [2, 34, 36, 41, 50, 52, 54, 55, 57, 60, 63, 66, 68, 70, 73, 83, 86, 87, 90, 91, 94], "adjust_pred_prob": [2, 8, 54, 59, 60, 83], "control": [2, 4, 7, 8, 14, 34, 36, 50, 58, 59, 62, 68, 70, 75, 76, 80, 81], "how": [2, 3, 4, 8, 11, 12, 14, 20, 30, 31, 32, 34, 35, 39, 45, 50, 51, 54, 55, 57, 59, 60, 62, 66, 70, 72, 75, 76, 78, 79, 80, 82, 87, 88, 89, 90, 91, 92, 93], "much": [2, 8, 30, 34, 36, 62, 81, 83, 85, 88], "output": [2, 3, 4, 8, 14, 31, 32, 35, 39, 45, 49, 50, 51, 55, 57, 58, 59, 62, 66, 67, 70, 71, 72, 73, 74, 75, 79, 80, 81, 82, 87, 88, 89, 90, 93], "print": [2, 4, 5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 45, 50, 51, 52, 57, 59, 60, 62, 64, 66, 67, 71, 73, 74, 76, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "suppress": [2, 34, 50, 57, 59, 60, 62, 64, 66, 67, 91, 94], "statement": [2, 34, 50, 57, 59, 60, 62, 64, 66, 67], "big": [2, 34, 52, 58, 62, 83], "limit": [2, 4, 14, 34, 52, 87, 91, 94], "memori": [2, 31, 34, 35, 52, 58, 64, 66, 75, 91], "label_issues_batch": [2, 33, 52, 81], "find_label_issues_batch": [2, 34, 52, 81], "pred_prob": [2, 3, 4, 6, 8, 14, 21, 22, 24, 27, 30, 34, 36, 38, 39, 40, 41, 42, 45, 46, 50, 51, 52, 54, 55, 58, 59, 60, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 92, 93], "threshold": [2, 3, 5, 8, 16, 17, 18, 20, 24, 26, 27, 34, 57, 58, 59, 60, 66, 70, 75, 87, 88, 91, 94], "inverse_noise_matrix": [2, 3, 8, 39, 45, 73, 83], "label_issu": [2, 34, 36, 52, 55, 62, 64, 73, 74, 79, 81, 82, 83, 90, 92, 93], "clf_kwarg": [2, 3, 8, 62], "clf_final_kwarg": [2, 62], "validation_func": [2, 3, 8], "correct": [2, 4, 8, 30, 34, 36, 38, 50, 51, 52, 54, 55, 57, 58, 60, 62, 63, 66, 70, 72, 74, 78, 79, 82, 83, 85, 87, 89, 90], "result": [2, 3, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 34, 35, 36, 38, 43, 45, 52, 54, 55, 58, 60, 62, 63, 64, 66, 70, 74, 75, 76, 78, 79, 81, 82, 83, 85, 90, 91, 92, 93, 94], "identifi": [2, 3, 4, 5, 8, 10, 14, 23, 28, 30, 34, 36, 52, 55, 58, 60, 62, 63, 64, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 86, 88, 90, 91, 92, 93, 94], "final": [2, 8, 62, 78, 87, 89, 90, 92], "remain": [2, 62, 73, 82, 90, 92, 93, 94], "datasetlik": [2, 45, 62], "beyond": [2, 4, 5, 7, 9, 29, 72, 91], "pd": [2, 3, 4, 5, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 40, 49, 50, 51, 62, 70, 74, 75, 76, 78, 79, 81, 83, 85, 90, 92, 93, 94], "datafram": [2, 3, 4, 5, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 34, 40, 45, 46, 49, 50, 51, 62, 67, 71, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 90, 91, 93, 94], "scipi": [2, 4, 11, 45], "spars": [2, 4, 8, 11, 14, 16, 27, 45, 46, 78], "csr_matrix": [2, 4, 11, 14, 16, 27], "torch": [2, 31, 32, 35, 74, 79, 80, 82, 88, 93], "util": [2, 4, 8, 14, 28, 31, 32, 35, 37, 50, 62, 72, 73, 74, 75, 76, 81, 82, 83, 88], "tensorflow": [2, 45, 49, 72, 74, 81], "object": [2, 4, 8, 10, 11, 14, 28, 31, 32, 34, 35, 41, 45, 46, 49, 52, 55, 56, 57, 58, 59, 62, 70, 72, 74, 76, 78, 82, 83, 84, 90, 93], "list": [2, 3, 4, 10, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 42, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 66, 67, 68, 70, 71, 73, 74, 75, 76, 80, 81, 82, 83, 86, 87, 90, 93, 94], "index_list": 2, "subset": [2, 3, 4, 14, 30, 34, 36, 45, 60, 67, 71, 74, 78, 79, 81, 82, 86, 87, 88, 89, 90, 92, 93, 94], "wa": [2, 3, 10, 12, 34, 45, 50, 51, 57, 59, 71, 74, 75, 76, 78, 79, 81, 83, 86, 87, 89, 91, 92, 93, 94], "abl": [2, 3, 8, 62, 74, 81, 83, 85, 86], "format": [2, 3, 4, 8, 10, 31, 34, 35, 36, 39, 40, 41, 42, 45, 46, 49, 50, 51, 52, 55, 58, 59, 60, 62, 64, 66, 67, 70, 71, 75, 76, 78, 80, 82, 85, 90, 91, 92, 94], "make": [2, 3, 16, 31, 34, 35, 41, 49, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "sure": [2, 34, 36, 41, 74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 92, 93], "shuffl": [2, 8, 45, 74, 79, 82, 86, 88], "ha": [2, 3, 4, 8, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 31, 35, 39, 41, 44, 45, 50, 55, 57, 62, 68, 70, 71, 72, 74, 75, 76, 78, 79, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "batch": [2, 34, 45, 49, 50, 64, 66, 81, 82, 88], "order": [2, 4, 8, 30, 31, 35, 36, 39, 40, 41, 45, 50, 51, 52, 55, 58, 59, 60, 64, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 87, 90, 91, 93, 94], "destroi": [2, 45], "oper": [2, 31, 34, 35, 45, 49, 60, 72, 79, 88, 92, 93], "eg": [2, 8, 45, 55, 58, 75, 76, 81], "repeat": [2, 45, 50, 85, 88], "appli": [2, 31, 35, 36, 41, 42, 44, 45, 54, 59, 68, 74, 75, 76, 78, 81, 82, 85, 86, 88, 89, 90, 91, 92, 93], "array_lik": [2, 3, 30, 36, 45, 52, 59, 63], "some": [2, 3, 4, 8, 12, 20, 30, 31, 33, 35, 36, 39, 44, 45, 48, 50, 51, 52, 54, 55, 58, 59, 60, 62, 64, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "seri": [2, 3, 34, 45, 46, 62, 70, 81], "row": [2, 3, 4, 8, 11, 23, 30, 34, 36, 38, 39, 45, 50, 51, 52, 54, 59, 60, 62, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 88, 92, 94], "rather": [2, 3, 22, 30, 45, 49, 50, 57, 66, 70, 85, 89, 91, 93, 94], "leav": [2, 36], "per": [2, 3, 11, 30, 34, 36, 41, 44, 50, 51, 52, 54, 57, 58, 60, 63, 64, 66, 70, 76, 81, 87, 94], "determin": [2, 3, 8, 14, 20, 22, 26, 30, 34, 36, 41, 45, 50, 52, 55, 57, 60, 66, 70, 75, 81, 85, 88, 90], "cutoff": [2, 3, 88], "consid": [2, 3, 4, 8, 11, 14, 21, 22, 24, 27, 30, 31, 35, 36, 45, 50, 57, 59, 60, 63, 66, 70, 74, 76, 78, 79, 81, 82, 83, 87, 88, 89, 90, 91, 92, 93], "section": [2, 3, 5, 8, 73, 78, 82], "3": [2, 3, 4, 5, 8, 30, 31, 35, 36, 39, 40, 41, 42, 43, 44, 45, 49, 52, 59, 60, 62, 63, 68, 70, 80, 81, 89], "equat": [2, 3, 39], "advanc": [2, 3, 4, 7, 8, 14, 57, 59, 70, 73, 76, 77, 83], "user": [2, 3, 4, 8, 12, 14, 23, 28, 31, 35, 36, 57, 59, 60, 62, 66, 70, 83], "specifi": [2, 3, 4, 6, 8, 11, 12, 14, 16, 27, 28, 31, 34, 35, 36, 41, 44, 50, 51, 52, 55, 57, 59, 60, 62, 63, 71, 73, 74, 76, 79, 82, 85, 87, 90, 93], "automat": [2, 3, 4, 22, 30, 72, 78, 79, 80, 81, 82, 85, 87, 90, 91, 92, 93, 94], "greater": [2, 3, 4, 7, 8, 24, 34, 45, 57, 76, 80, 81, 94], "count": [2, 20, 22, 30, 34, 36, 39, 45, 51, 52, 58, 73, 81, 82], "observ": [2, 3, 39, 74, 75, 76, 85, 88, 90], "mislabel": [2, 8, 30, 34, 36, 39, 50, 51, 52, 55, 57, 60, 66, 68, 70, 72, 74, 78, 79, 81, 82, 83, 86, 87, 90, 92, 93], "one": [2, 3, 4, 8, 22, 30, 31, 34, 35, 36, 41, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 82, 85, 88, 89, 90, 92, 93, 94], "get_label_issu": [2, 34, 62, 83, 90, 92, 93], "either": [2, 3, 5, 8, 31, 34, 35, 36, 50, 52, 57, 59, 60, 64, 66, 76, 86, 87], "boolean": [2, 5, 8, 20, 34, 36, 44, 50, 52, 55, 60, 62, 64, 66, 67, 72, 74, 76, 79, 81, 82, 87, 90, 91, 93], "label_issues_mask": [2, 36, 60, 62, 73], "indic": [2, 3, 4, 5, 8, 11, 20, 30, 34, 35, 36, 38, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 68, 70, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "its": [2, 4, 7, 8, 14, 31, 34, 35, 36, 43, 44, 52, 55, 58, 59, 60, 62, 64, 68, 70, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 93, 94], "return_indices_ranked_bi": [2, 34, 36, 52, 68, 73, 81, 83, 86, 92, 93], "significantli": [2, 82, 83, 85, 89], "reduc": [2, 34, 36, 45, 74, 81], "time": [2, 8, 31, 34, 35, 45, 50, 73, 75, 80, 81, 82, 83, 87, 88, 90, 91, 92, 93, 94], "take": [2, 4, 8, 30, 31, 35, 40, 41, 45, 49, 60, 78, 82, 85, 92, 94], "run": [2, 4, 5, 7, 9, 12, 14, 22, 23, 29, 31, 34, 35, 62, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "skip": [2, 8, 31, 35, 62, 74, 81, 86, 94], "slow": [2, 3], "step": [2, 5, 22, 41, 58, 81, 82, 83, 85, 89], "caution": [2, 4, 81], "previous": [2, 4, 11, 45, 59, 62, 73, 74, 75, 78, 79, 85, 89, 92], "assign": [2, 5, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 40, 41, 45, 62, 75, 78, 81, 82, 90, 91, 92, 94], "individu": [2, 8, 11, 22, 31, 35, 50, 54, 57, 60, 62, 68, 70, 73, 76, 78, 81, 85, 86, 87, 92, 94], "still": [2, 34, 35, 45, 59, 81, 82, 88, 92], "extra": [2, 31, 35, 45, 49, 50, 51, 62, 79, 81, 82, 85, 88], "receiv": [2, 8, 31, 35, 51, 54, 55, 62, 64, 68, 76, 87], "overwritten": [2, 62], "callabl": [2, 3, 41, 44, 49, 54, 81], "x_val": 2, "y_val": 2, "map": [2, 3, 10, 34, 35, 40, 44, 45, 58, 60, 62, 67, 74, 75, 76, 81, 82, 83, 86, 94], "appropri": [2, 8, 14, 52, 60, 75, 78, 86, 87], "earli": [2, 82], "stop": [2, 82], "x_valid": 2, "y_valid": 2, "could": [2, 8, 20, 30, 45, 59, 75, 78, 82, 86, 90, 92, 94], "f": [2, 5, 74, 75, 78, 79, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93], "ignor": [2, 31, 35, 44, 49, 62, 67, 71, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "allow": [2, 30, 31, 34, 35, 38, 45, 50, 58, 59, 62, 64, 66, 74, 81, 82, 89, 91, 93], "access": [2, 8, 11, 31, 35, 62, 76, 82, 86], "hyperparamet": [2, 54, 59, 82], "purpos": [2, 75, 76, 81, 86, 90], "want": [2, 4, 8, 30, 34, 46, 50, 52, 62, 75, 79, 80, 82, 85, 87, 88, 89, 91, 93, 94], "explicitli": [2, 6, 8, 35, 62], "yourself": [2, 4, 34, 76], "altern": [2, 5, 8, 41, 45, 49, 50, 60, 73, 74, 78, 79, 81, 82, 83, 85, 86, 88, 90, 93], "same": [2, 3, 4, 5, 8, 10, 12, 14, 22, 26, 31, 34, 35, 36, 45, 49, 50, 52, 59, 60, 62, 66, 67, 70, 71, 72, 75, 76, 78, 79, 81, 82, 87, 88, 89, 90, 91, 92, 93], "effect": [2, 8, 23, 31, 35, 50, 59, 62, 78, 79, 81, 82, 88], "offer": [2, 4, 74, 75, 76, 79, 81, 83, 86, 93], "after": [2, 3, 4, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 45, 50, 62, 75, 79, 81, 82, 83, 85, 87, 88, 89, 90, 91, 93], "attribut": [2, 4, 5, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 41, 59, 62, 75, 92], "label_issues_df": [2, 62, 82], "similar": [2, 8, 30, 31, 35, 45, 50, 54, 55, 57, 59, 62, 66, 70, 75, 76, 78, 79, 81, 82, 83, 87, 88, 91], "document": [2, 3, 4, 8, 12, 14, 30, 31, 34, 35, 36, 41, 44, 49, 51, 52, 54, 57, 58, 59, 62, 66, 67, 68, 70, 73, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "descript": [2, 4, 5, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 45, 55, 62, 75, 76], "were": [2, 3, 4, 30, 35, 51, 57, 70, 74, 78, 81, 83, 85, 87, 89, 91, 92], "present": [2, 3, 4, 8, 10, 11, 18, 30, 45, 59, 67, 72, 78, 81, 82, 88], "actual": [2, 3, 4, 30, 50, 51, 60, 76, 81, 83, 94], "num_class": [2, 30, 34, 45, 49, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 92, 93], "uniqu": [2, 27, 45, 67, 75, 81, 86, 88], "given_label": [2, 4, 26, 30, 39, 62, 67, 71, 74, 75, 76, 78, 79, 82, 83, 90, 91, 93, 94], "normal": [2, 3, 16, 22, 27, 36, 38, 41, 43, 44, 45, 60, 81, 83, 88], "trick": [2, 81], "distribut": [2, 3, 4, 8, 22, 24, 30, 35, 36, 40, 43, 50, 58, 59, 60, 72, 75, 76, 78, 79, 82, 88], "account": [2, 30, 50, 54, 59, 60, 79, 81, 83, 85, 86, 88, 90, 93], "word": [2, 3, 44, 70, 71, 81], "remov": [2, 8, 27, 30, 31, 35, 36, 62, 72, 79, 80, 81, 82, 88, 90, 92, 93], "so": [2, 3, 4, 5, 8, 12, 22, 30, 31, 34, 35, 36, 45, 50, 51, 57, 60, 62, 66, 70, 74, 75, 76, 79, 82, 83, 88, 91], "proportion": [2, 8, 36], "just": [2, 3, 4, 8, 11, 30, 32, 34, 45, 49, 60, 62, 64, 72, 73, 74, 76, 78, 79, 81, 82, 83, 86, 87, 88, 89, 91, 92, 93], "procedur": 2, "get": [2, 3, 4, 6, 11, 27, 31, 32, 35, 36, 41, 44, 45, 50, 52, 54, 59, 60, 62, 63, 64, 72, 74, 79, 80, 81, 82, 83, 88, 89, 90, 92, 93], "detect": [2, 4, 5, 7, 11, 12, 14, 16, 20, 24, 43, 53, 55, 56, 57, 58, 59, 60, 61, 62, 65, 69, 72, 75, 77, 82, 84, 86, 90, 91, 92, 93, 94], "arg": [2, 10, 20, 23, 27, 31, 32, 35, 41, 45, 60, 62], "kwarg": [2, 5, 8, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 41, 49, 62, 64, 66, 68, 81], "test": [2, 8, 22, 35, 41, 49, 62, 72, 75, 76, 78, 79, 82, 89, 90, 92, 93, 94], "expect": [2, 3, 31, 35, 36, 41, 50, 59, 60, 62, 81, 83, 85, 86, 87, 90, 92, 93, 94], "class_predict": 2, "evalu": [2, 8, 31, 32, 34, 35, 58, 62, 74, 75, 76, 81, 82, 83, 85, 89, 90, 91, 92, 93], "simpli": [2, 30, 60, 75, 76, 78, 79, 81, 83, 90, 91, 93, 94], "quantifi": [2, 4, 5, 8, 11, 36, 54, 59, 62, 72, 76, 78, 79, 82, 83, 87], "save_spac": [2, 8, 62], "potenti": [2, 8, 30, 36, 44, 52, 55, 58, 60, 62, 64, 66, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 91, 92, 94], "cach": [2, 79, 88, 93], "panda": [2, 4, 5, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 45, 46, 49, 50, 51, 73, 74, 75, 76, 78, 79, 80, 81, 83, 85, 90, 91, 92, 93], "unlik": [2, 8, 36, 38, 41, 49, 51, 52, 54, 70, 75, 85, 86, 88, 90], "both": [2, 4, 8, 14, 22, 30, 31, 35, 36, 45, 50, 52, 60, 64, 66, 71, 72, 75, 81, 82, 83, 85, 94], "mask": [2, 34, 36, 44, 45, 52, 55, 60, 62, 64, 66, 67, 72, 80, 81, 85, 87, 91, 94], "prefer": [2, 60, 68], "plan": 2, "subsequ": [2, 3, 31, 35, 79, 81, 83, 87, 93], "invok": [2, 31, 35, 83, 89], "scratch": [2, 62], "To": [2, 4, 5, 7, 8, 9, 11, 14, 22, 29, 31, 34, 35, 36, 49, 50, 52, 54, 58, 59, 60, 62, 63, 64, 66, 72, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "share": [2, 60, 62], "mostli": [2, 45, 57, 62], "longer": [2, 40, 44, 62, 73, 79, 81, 87, 93], "info": [2, 4, 5, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 62, 70, 75, 76, 80, 81, 94], "about": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 32, 34, 38, 50, 51, 54, 58, 62, 67, 70, 74, 75, 78, 79, 80, 81, 82, 83, 85, 88], "docstr": [2, 30, 31, 35, 45, 62, 80, 83], "unless": [2, 31, 35, 62, 81], "our": [2, 3, 8, 49, 50, 60, 62, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "is_label_issu": [2, 26, 62, 74, 75, 76, 78, 79, 82, 83, 90, 93], "entir": [2, 8, 22, 34, 36, 39, 51, 52, 57, 60, 62, 64, 66, 67, 72, 75, 76, 79, 81, 82, 83, 87, 88, 89, 91, 94], "accur": [2, 3, 4, 8, 14, 30, 34, 36, 50, 51, 52, 55, 58, 60, 62, 63, 64, 66, 67, 73, 76, 78, 79, 81, 82, 85, 90], "label_qu": [2, 50, 62, 83, 85, 90, 93], "measur": [2, 30, 50, 51, 62, 72, 80, 81, 83, 85, 86, 91, 92, 94], "qualiti": [2, 3, 4, 5, 8, 11, 26, 27, 30, 34, 36, 38, 41, 50, 51, 52, 54, 55, 57, 60, 62, 63, 66, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 84, 90, 92, 93], "lower": [2, 4, 5, 8, 11, 24, 34, 41, 43, 50, 51, 54, 57, 58, 60, 62, 63, 66, 70, 74, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 93, 94], "eas": 2, "comparison": [2, 31, 35, 58, 83, 85, 90], "against": [2, 31, 35, 75, 78, 81, 85, 86], "predicted_label": [2, 4, 26, 62, 67, 71, 74, 75, 76, 78, 79, 82, 83, 90, 91, 93], "ad": [2, 31, 35, 76, 85, 90], "precis": [2, 52, 55, 58, 81, 83, 91, 94], "definit": [2, 5, 62, 78, 92], "accessor": [2, 62], "describ": [2, 8, 16, 50, 59, 60, 62, 68, 70, 83, 85, 86, 87, 89, 94], "precomput": [2, 4, 39, 62, 80], "clear": [2, 62, 79, 90, 93], "save": [2, 4, 14, 31, 34, 35, 58, 62, 81, 87, 91, 94], "space": [2, 8, 59, 62, 78, 80, 82], "place": [2, 31, 35, 45, 62, 85, 92], "larg": [2, 34, 62, 78, 79, 81, 82, 88, 91, 94], "deploi": [2, 62, 78, 79, 81, 82], "care": [2, 8, 31, 35, 62, 79, 81, 83], "avail": [2, 4, 5, 10, 12, 28, 35, 62, 81, 83, 85, 87, 90], "cannot": [2, 4, 10, 12, 45, 89, 94], "anymor": 2, "classmethod": [2, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 35, 41, 62], "__init_subclass__": [2, 35, 62], "set_": [2, 35, 62], "_request": [2, 35, 62], "pep": [2, 35, 62], "487": [2, 35, 62], "look": [2, 4, 5, 14, 31, 35, 45, 62, 67, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 91, 92, 94], "inform": [2, 4, 5, 8, 11, 14, 28, 31, 35, 45, 50, 51, 55, 58, 62, 67, 70, 71, 72, 74, 75, 78, 79, 83, 85, 88, 91, 94], "__metadata_request__": [2, 35, 62], "infer": [2, 35, 45, 62, 67, 71, 82, 85, 86, 90, 92, 93], "signatur": [2, 31, 35, 62], "accept": [2, 31, 35, 60, 62, 75, 76], "metadata": [2, 35, 62, 78, 79, 82, 94], "through": [2, 4, 5, 35, 62, 74, 76, 79, 80, 81, 85, 88, 90, 93], "develop": [2, 7, 35, 62, 81, 83, 94], "request": [2, 35, 62, 76, 79, 80, 86, 92, 93, 94], "those": [2, 3, 8, 34, 35, 36, 49, 50, 52, 58, 62, 66, 70, 71, 72, 74, 81, 82, 87, 91], "http": [2, 4, 5, 7, 8, 9, 16, 29, 31, 32, 34, 35, 38, 45, 59, 62, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "www": [2, 35, 62, 88], "org": [2, 16, 31, 32, 35, 45, 59, 62, 81, 83, 94], "dev": [2, 35, 62], "0487": [2, 35, 62], "get_metadata_rout": [2, 35, 62], "rout": [2, 35, 62], "pleas": [2, 31, 35, 49, 62, 72, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 90, 93, 94], "guid": [2, 5, 35, 62, 73, 82], "mechan": [2, 31, 35, 62], "metadatarequest": [2, 35, 62], "encapsul": [2, 14, 35, 57, 62], "get_param": [2, 35, 49, 62], "subobject": [2, 35, 62], "param": [2, 8, 31, 35, 49, 59, 62, 81], "name": [2, 4, 5, 8, 10, 11, 30, 31, 35, 40, 41, 45, 49, 50, 51, 58, 62, 67, 71, 74, 76, 79, 80, 81, 82, 83, 86, 91, 93, 94], "set_fit_request": [2, 35, 62], "union": [2, 3, 4, 10, 34, 35, 41, 45, 46, 52, 58, 62, 66, 70, 81], "str": [2, 3, 4, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 39, 41, 44, 45, 49, 50, 51, 55, 57, 58, 60, 62, 67, 71, 74, 75, 81, 85, 86, 94], "unchang": [2, 31, 35, 62, 94], "relev": [2, 14, 22, 35, 62, 82], "enable_metadata_rout": [2, 35, 62], "set_config": [2, 35, 62], "meta": [2, 35, 62], "rais": [2, 4, 10, 11, 31, 35, 38, 41, 62, 81], "alia": [2, 31, 35, 62], "metadata_rout": [2, 35, 62], "retain": [2, 35, 45, 62], "chang": [2, 31, 34, 35, 38, 62, 70, 74, 75, 79, 81, 87, 88, 93, 94], "version": [2, 4, 5, 7, 8, 9, 13, 19, 25, 29, 31, 33, 35, 37, 38, 45, 48, 49, 60, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "sub": [2, 35, 57, 62], "pipelin": [2, 35, 62], "otherwis": [2, 8, 30, 31, 34, 35, 36, 42, 44, 45, 52, 59, 62, 64, 66, 67, 71, 79, 81, 93], "updat": [2, 11, 31, 34, 35, 62, 73, 75, 82], "set_param": [2, 35, 49, 62], "simpl": [2, 31, 35, 36, 50, 60, 62, 75, 76, 78, 79, 82, 85, 88, 90, 92, 93], "well": [2, 3, 8, 31, 35, 38, 39, 50, 52, 58, 60, 62, 67, 70, 71, 73, 75, 76, 78, 79, 81, 82, 83, 85, 87, 88], "nest": [2, 31, 35, 62, 68, 70, 71, 94], "latter": [2, 31, 35, 62, 88], "compon": [2, 35, 62], "__": [2, 35, 62], "set_score_request": [2, 62], "structur": [3, 59, 78, 92], "unobserv": 3, "less": [3, 4, 8, 27, 34, 41, 50, 59, 60, 64, 66, 70, 76, 78, 80, 81, 82, 83, 87, 94], "channel": [3, 74, 83], "character": 3, "flip": 3, "nm": 3, "invers": [3, 8, 30, 39, 45, 51, 76, 80, 93], "inv": 3, "confident_joint": [3, 20, 30, 36, 45, 51, 52, 73, 81, 83], "un": 3, "under": [3, 8, 31, 35, 51, 58, 59, 76, 78, 79, 82, 83, 88], "joint": [3, 30, 36, 39, 45, 51, 52, 80], "num_label_issu": [3, 34, 36, 52, 67, 71, 73], "estimation_method": [3, 34], "off_diagon": 3, "multi_label": [3, 30, 36, 45, 46, 52, 86], "don": [3, 72, 76, 78, 79, 82, 83, 87], "statis": 3, "compute_confident_joint": [3, 30, 36, 45, 52, 83], "off": [3, 36, 45, 57, 82, 83, 87, 88], "j": [3, 4, 30, 31, 35, 36, 52, 55, 58, 59, 68, 70, 71, 75, 76, 83, 91, 94], "confident_learn": [3, 36, 52, 83], "off_diagonal_calibr": 3, "calibr": [3, 36, 45, 50, 85], "cj": [3, 39, 45], "axi": [3, 27, 39, 41, 43, 64, 67, 74, 75, 76, 81, 82, 83, 85, 86, 88, 90, 91], "bincount": [3, 75, 76, 83, 85, 86], "alwai": [3, 8, 31, 35, 45, 74, 83, 90, 92, 93], "estimate_issu": 3, "over": [3, 8, 31, 34, 35, 57, 58, 64, 66, 76, 78, 80, 81, 82, 83, 88, 90, 92], "As": [3, 5, 72, 75, 76, 79, 83, 90, 94], "add": [3, 4, 5, 11, 31, 35, 49, 58, 74, 75, 76, 79, 81, 82, 83, 86, 93], "approach": [3, 30, 34, 36, 78, 83, 86, 88, 90, 92], "custom": [3, 5, 8, 9, 26, 31, 34, 35, 41, 44, 60, 76, 79, 83, 93], "know": [3, 75, 76, 78, 79, 81, 82, 83, 85], "cut": [3, 57, 72, 83], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 88, 94], "underestim": 3, "few": [3, 58, 72, 76, 81, 85, 86, 87, 88, 94], "4": [3, 4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 40, 41, 44, 54, 55, 57, 58, 60, 63, 70, 80, 81, 86, 91, 94], "detail": [3, 4, 8, 12, 14, 30, 31, 35, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 66, 67, 68, 72, 73, 74, 86, 88, 94], "num_issu": [3, 5, 34, 74, 75, 76, 78, 79, 82, 83], "calibrate_confident_joint": 3, "up": [3, 8, 15, 22, 23, 26, 36, 41, 50, 80, 81, 87, 90, 93, 94], "p_": [3, 30, 36], "pair": [3, 4, 8, 30, 36, 83], "v": [3, 8, 34, 51, 52, 54, 60, 75, 76, 86, 88, 89], "rest": [3, 4, 5, 7, 8, 9, 29, 51, 52, 54, 62, 75, 76, 78, 79, 81, 82, 83, 85, 90, 92, 93], "fashion": [3, 4, 64, 92], "2x2": 3, "incorrectli": [3, 30, 51, 52, 55, 78, 94], "calibrated_cj": 3, "c": [3, 8, 44, 52, 60, 72, 74, 75, 76, 78, 79, 81, 83, 86, 88, 89, 90, 92], "whose": [3, 4, 8, 24, 31, 35, 39, 44, 50, 54, 57, 63, 66, 70, 71, 74, 75, 76, 78, 79, 81, 82, 83, 86, 87, 88, 91, 94], "truli": [3, 88, 91], "estimate_joint": [3, 30, 83], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 52, 58, 83, 87, 89, 91, 94], "return_indices_of_off_diagon": 3, "frequenc": [3, 22, 50, 51, 58, 67, 88], "done": [3, 8, 62, 75, 81, 83, 86, 88, 89], "overfit": [3, 8, 55, 58, 74, 75, 76, 78, 79, 82, 89, 92], "classifict": 3, "singl": [3, 4, 22, 30, 31, 35, 41, 42, 45, 50, 51, 57, 58, 59, 60, 70, 74, 75, 81, 83, 86, 87, 92], "baselin": [3, 31, 36, 88, 90, 93], "proxi": 3, "tupl": [3, 27, 31, 35, 39, 40, 42, 44, 45, 50, 52, 58, 66, 68, 70, 71, 74, 94], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 8, 34, 39, 50, 64, 66, 72, 81, 82, 91, 93], "practic": [3, 76, 82, 83, 88, 90, 92, 93], "complet": [3, 74, 75, 76, 78, 79, 81, 82, 83, 87], "gist": 3, "cj_ish": 3, "guess": [3, 39, 83, 85], "8": [3, 4, 5, 6, 40, 41, 42, 44, 54, 68, 70, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "parallel": [3, 36, 58, 68, 80], "again": [3, 49, 81, 88, 92], "simplifi": [3, 12], "understand": [3, 7, 30, 51, 58, 76, 83, 90, 91, 94], "100": [3, 31, 35, 60, 75, 76, 78, 80, 81, 82, 83, 86, 91, 92, 93, 94], "optim": [3, 31, 32, 35, 49, 82, 85], "speed": [3, 36, 80, 81, 90, 93], "dtype": [3, 21, 22, 27, 31, 35, 44, 45, 54, 70, 74, 87], "enumer": [3, 31, 35, 74, 75, 76, 82, 94], "s_label": 3, "confident_bin": 3, "6": [3, 4, 35, 41, 45, 70, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "num_confident_bin": 3, "argmax": [3, 36, 60, 64, 67, 74, 81, 83, 88, 91], "elif": 3, "estimate_lat": 3, "py_method": [3, 39], "cnt": [3, 39], "1d": [3, 4, 14, 34, 36, 41, 42, 45, 46, 54, 63, 74, 92], "eqn": [3, 39], "margin": [3, 36, 39, 41, 60], "marginal_p": [3, 39], "shorthand": [3, 11], "proport": [3, 8, 30, 51, 83, 89], "poorli": [3, 39, 92], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 83], "variabl": [3, 5, 12, 23, 45, 62, 63, 74, 75, 78, 83, 86, 90], "exact": [3, 39, 75, 76, 78, 82, 92], "within": [3, 4, 8, 13, 31, 32, 35, 37, 52, 57, 66, 68, 70, 75, 76, 81, 82, 87, 91], "percent": 3, "often": [3, 30, 39, 51, 81, 83, 89, 91], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 8, 45, 46, 58, 74, 75, 78, 79, 81, 82, 87, 88, 93], "wai": [3, 4, 49, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 89, 92, 93], "pro": 3, "con": 3, "pred_proba": [3, 89], "combin": [3, 30, 75, 80, 81, 82, 83, 89, 90], "becaus": [3, 39, 45, 57, 79, 81, 83, 85, 87], "littl": [3, 34, 80, 87, 94], "uniform": [3, 60, 80, 81, 83], "20": [3, 5, 71, 74, 76, 79, 80, 81, 82, 83, 91, 94], "Such": [3, 82, 88], "bound": [3, 21, 31, 35, 55, 57, 58, 87], "reason": [3, 20, 31, 35], "comment": [3, 44, 94], "end": [3, 4, 31, 35, 58, 82, 91, 94], "file": [3, 4, 10, 33, 34, 48, 58, 74, 75, 78, 79, 80, 81, 87, 88, 91, 92, 94], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 83], "handl": [3, 4, 5, 8, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 73, 75, 76, 78, 79, 82, 83, 91, 92, 94], "five": [3, 55, 58, 83, 87], "estimate_cv_predicted_prob": [3, 83], "estimate_noise_matric": 3, "get_confident_threshold": [3, 34], "amongst": [3, 8], "confident_threshold": [3, 8, 20, 34, 59], "unifi": 4, "audit": [4, 7, 10, 11, 14, 74, 77, 78, 79, 81, 82, 83, 87], "kind": [4, 5, 74, 75, 78, 79, 80, 82, 83], "addit": [4, 5, 7, 8, 9, 11, 28, 29, 31, 35, 41, 46, 50, 58, 68, 74, 75, 78, 79, 82, 83, 85, 88, 89, 92, 93], "depend": [4, 5, 7, 8, 9, 10, 11, 29, 33, 36, 38, 45, 48, 52, 59, 62, 63, 72], "instal": [4, 5, 7, 8, 9, 29, 31, 33, 34, 35, 36, 48, 49, 64, 66], "pip": [4, 5, 7, 9, 29, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "development": [4, 5, 7, 9, 29], "git": [4, 5, 7, 9, 29, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "github": [4, 5, 7, 9, 29, 31, 32, 45, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "com": [4, 5, 7, 9, 29, 31, 32, 34, 38, 45, 59, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "egg": [4, 5, 7, 9, 29, 72, 80], "label_nam": [4, 5, 6, 8, 10, 16, 27, 72, 74, 75, 76, 78, 79, 81, 82, 83], "image_kei": [4, 82], "interfac": [4, 72, 81, 83], "librari": [4, 8, 35, 55, 58, 59, 72, 75, 79, 80, 81, 93], "goal": 4, "track": [4, 11, 12, 72, 75, 80, 81, 83], "intermedi": [4, 7, 76], "statist": [4, 8, 11, 20, 22, 30, 50, 51, 58, 76, 78, 79, 82, 83], "convert": [4, 10, 31, 35, 42, 43, 46, 50, 57, 66, 70, 73, 74, 79, 80, 81, 82, 85, 86, 87, 93], "hug": [4, 10, 82], "face": [4, 10, 14, 80, 82, 86], "kei": [4, 5, 8, 10, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 35, 41, 50, 51, 57, 59, 75, 76, 79, 81, 82, 83, 85, 87], "string": [4, 8, 10, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 35, 45, 50, 51, 63, 67, 70, 71, 78, 79, 81, 85, 86, 93, 94], "dictionari": [4, 5, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 35, 40, 45, 50, 51, 54, 55, 57, 58, 75, 76, 78, 79, 83, 85, 86, 87], "path": [4, 10, 31, 34, 35, 58, 74, 75, 81, 87], "local": [4, 10, 31, 32, 35, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "text": [4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 41, 59, 68, 70, 71, 72, 75, 76, 77, 80, 81, 83, 84, 85, 88], "txt": [4, 10, 94], "csv": [4, 10, 78, 79, 90, 92, 93], "json": [4, 10], "hub": [4, 10, 88], "regress": [4, 5, 10, 12, 14, 19, 26, 28, 75, 76, 79, 84, 85, 88, 93], "imag": [4, 7, 30, 35, 55, 57, 58, 59, 64, 66, 67, 72, 75, 76, 80, 81, 84, 85, 86, 87, 89, 91], "point": [4, 5, 8, 16, 22, 31, 35, 75, 76, 78, 79, 81, 82, 83, 85], "field": [4, 8, 31, 35], "themselv": [4, 90, 92, 93], "cleanvis": [4, 8], "level": [4, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 44, 68, 70, 76, 82, 84, 91], "load_dataset": [4, 10, 82], "glue": 4, "sst2": 4, "properti": [4, 10, 11], "has_label": [4, 10], "class_nam": [4, 10, 18, 30, 51, 58, 67, 71, 72, 80, 83, 87, 91, 94], "empti": [4, 10, 39, 50, 76, 81, 86], "find_issu": [4, 5, 6, 8, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 72, 74, 75, 76, 78, 79, 81, 82, 83], "knn_graph": [4, 8, 14, 16, 17, 22, 24, 27, 78], "issue_typ": [4, 5, 6, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 74, 75, 76, 78, 79, 81, 82, 83], "sort": [4, 14, 34, 36, 41, 50, 52, 55, 57, 58, 60, 66, 68, 70, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 90, 91, 92, 93, 94], "common": [4, 11, 14, 76, 77, 80, 81, 83, 86, 87, 91], "real": [4, 14, 72, 75, 76, 81, 83, 85, 86, 90, 91], "world": [4, 14, 72, 75, 76, 81, 83, 85, 90, 91], "interact": [4, 14, 79, 81], "embed": [4, 8, 14, 59, 72, 74, 75, 76, 78, 79, 83, 93], "thereof": [4, 14], "insight": [4, 14, 58, 85], "act": [4, 8, 57, 75], "issuefind": [4, 14, 28], "logic": [4, 12, 34, 36, 64, 66, 91], "best": [4, 14, 40, 50, 60, 75, 76, 78, 79, 81, 82, 85, 86, 88, 90, 92, 93, 94], "2d": [4, 14, 34, 41, 42, 44, 45, 50, 74, 86, 92], "num_exampl": [4, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 51, 74, 75, 76, 78, 79, 82, 83], "represent": [4, 8, 14, 31, 35, 42, 52, 72, 74, 75, 76, 79, 81, 82, 83, 88, 93], "num_featur": [4, 14, 31, 35, 49], "distanc": [4, 8, 14, 16, 22, 24, 27, 43, 57, 59, 78, 88], "nearest": [4, 8, 14, 21, 22, 24, 43, 59, 76, 79, 88], "neighbor": [4, 8, 14, 16, 21, 22, 24, 43, 59, 75, 76, 78, 79, 81, 82, 88], "graph": [4, 8, 11, 14, 16, 22, 27], "squar": [4, 45, 62, 80, 90], "csr": 4, "evenli": 4, "omit": [4, 57, 58, 82, 87], "itself": [4, 31, 35, 87], "three": [4, 8, 30, 50, 51, 62, 67, 74, 75, 76, 78, 80, 83, 85, 89, 90, 91, 92, 94], "indptr": 4, "wise": 4, "start": [4, 5, 8, 31, 32, 35, 72, 78, 86, 94], "th": [4, 40, 44, 45, 50, 52, 55, 57, 58, 59, 68, 70, 71, 79, 86, 87, 94], "ascend": [4, 30, 51, 82, 83], "segment": [4, 64, 66, 67, 84], "reflect": [4, 78, 79, 85, 87, 88, 90, 92, 93], "maintain": 4, "posit": [4, 31, 35, 43, 45, 58, 80, 88], "nearestneighbor": [4, 8, 16, 59, 78, 88], "kneighbors_graph": [4, 16, 78], "illustr": 4, "todens": 4, "second": [4, 41, 45, 58, 60, 75, 81, 83, 94], "duplic": [4, 7, 19, 20, 31, 35, 72, 75, 83], "explicit": 4, "precend": 4, "construct": [4, 5, 8, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 35, 41, 49], "neither": [4, 8, 12, 87], "nor": [4, 8, 12], "collect": [4, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 50, 81, 85, 94], "unspecifi": [4, 14, 36, 52], "interest": [4, 14, 20, 67, 71, 79, 83, 91, 92, 93, 94], "constructor": [4, 8, 14, 21, 26], "issuemanag": [4, 7, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28], "respons": [4, 14, 20, 62, 63, 80, 90, 94], "random_st": [4, 74, 75, 76, 82, 83, 86, 88, 92], "lab": [4, 6, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 34, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86], "comprehens": [4, 72, 82], "nbr": 4, "n_neighbor": [4, 8, 16, 59], "metric": [4, 8, 17, 22, 27, 45, 49, 58, 59, 74, 78, 79, 82, 83, 90, 92, 93], "euclidean": [4, 8, 57, 59, 78], "mode": [4, 16, 31, 34, 35, 88], "4x4": 4, "float64": [4, 22, 31, 35, 70], "compress": [4, 8, 45, 64, 66], "toarrai": 4, "NOT": [4, 34, 79], "23606798": 4, "41421356": 4, "configur": [4, 14, 41, 76], "suppos": [4, 8, 55, 88, 90, 92, 93], "who": [4, 57, 78, 83, 92, 94], "manag": [4, 6, 7, 8, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "clean_learning_kwarg": [4, 8, 21, 26], "labelissuemanag": [4, 8, 21], "prune_method": [4, 73], "prune_by_noise_r": [4, 36, 52, 83], "report": [4, 5, 9, 13, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 71, 72, 74, 75, 76, 78, 79, 83, 94], "include_descript": [4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28], "show_summary_scor": [4, 28], "summari": [4, 5, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 49, 51, 56, 65, 66, 68, 69, 70, 73, 74, 75, 76, 78, 79, 80, 82, 83, 87, 91, 94], "show": [4, 22, 31, 35, 40, 45, 58, 67, 71, 76, 78, 79, 80, 81, 82, 83, 85, 88, 90, 91, 92, 94], "top": [4, 8, 30, 34, 36, 45, 52, 55, 58, 60, 67, 71, 72, 74, 75, 76, 78, 79, 80, 81, 83, 87, 88, 90, 93, 94], "suffer": [4, 8, 11, 20, 52, 60, 71, 94], "onc": [4, 20, 30, 31, 35, 75, 81, 83, 86, 87, 92], "familiar": 4, "usag": [4, 34, 49], "found": [4, 5, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 45, 72, 74, 75, 76, 78, 79, 81, 82, 88, 90, 92, 93, 94], "issue_summari": [4, 8, 11, 75], "overal": [4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 41, 50, 51, 54, 57, 58, 62, 66, 67, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 87, 94], "sever": [4, 5, 8, 10, 11, 20, 31, 34, 35, 36, 54, 57, 59, 60, 66, 70, 72, 74, 75, 76, 78, 79, 80, 81, 83, 87, 88, 92, 93, 94], "dataissu": [4, 11, 14, 28], "outlier": [4, 7, 12, 19, 20, 27, 37, 60, 72, 75, 76, 83, 84], "someth": [4, 5, 31, 35, 60], "123": [4, 75, 76], "456": [4, 74, 79, 92, 93], "nearest_neighbor": 4, "7": [4, 41, 42, 49, 68, 70, 74, 75, 76, 78, 79, 80, 81, 85, 86, 87, 88, 90, 91, 92, 93, 94], "9": [4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 41, 42, 54, 68, 70, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "distance_to_nearest_neighbor": [4, 75, 76, 78, 79, 82, 83], "789": 4, "get_issu": [4, 8, 11, 74, 76, 78, 79, 81, 82], "issue_nam": [4, 5, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75, 76], "focu": [4, 11, 79, 91, 94], "full": [4, 8, 11, 34, 58, 82, 94], "summar": [4, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 67, 71, 72, 91], "valueerror": [4, 10, 11, 38, 41, 81], "specific_issu": [4, 11], "exhibit": [4, 8, 11, 67, 76, 78, 79, 82, 83, 87], "lie": [4, 8, 59, 60, 74, 75, 76, 78, 79, 82, 83, 93], "directli": [4, 12, 14, 28, 34, 49, 50, 76, 79, 86, 87, 90, 93], "compar": [4, 50, 59, 70, 75, 76, 78, 83], "get_issue_summari": [4, 11, 76], "get_info": [4, 11, 76, 79], "yet": [4, 15, 19, 23, 80, 85], "list_possible_issue_typ": [4, 12], "regist": [4, 5, 12, 13, 15, 23, 31, 35, 75], "registri": [4, 12], "list_default_issue_typ": [4, 12], "folder": [4, 74, 75, 82], "load": [4, 10, 34, 58, 80, 81, 82, 83, 87, 88, 91, 94], "futur": [4, 8, 20, 31, 35, 50, 72, 75, 79], "overwrit": [4, 75], "separ": [4, 30, 41, 54, 75, 76, 81, 82, 87, 89], "static": 4, "rememb": [4, 79, 81, 83], "part": [4, 8, 31, 35, 36, 55, 57, 58, 74, 75, 80, 91, 94], "ident": [4, 8, 20, 45, 79], "walk": 5, "alongsid": [5, 31, 35, 75, 81], "pre": [5, 6, 8, 31, 35, 75, 76, 82, 91, 94], "runtim": [5, 31, 34, 35, 62, 64, 66, 74, 81, 82], "issue_manager_factori": [5, 12, 75], "myissuemanag": [5, 12], "myissuemanagerforregress": 5, "decor": [5, 12], "ll": [5, 41, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "thing": [5, 35, 83, 90, 93], "next": [5, 50, 72, 74, 78, 79, 81, 85, 87, 90, 92, 93, 94], "dummi": 5, "randint": [5, 27, 41, 75, 76, 81], "mark": [5, 8, 73, 87, 88, 90], "regard": [5, 76, 83], "rand": [5, 41, 75, 76], "is_": [5, 8, 75], "_issu": [5, 8, 75], "issue_score_kei": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "whole": [5, 22, 31, 35, 76], "make_summari": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "popul": [5, 76, 79], "verbosity_level": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "std": 5, "raw_scor": 5, "bit": 5, "involv": [5, 34, 67, 71, 81, 86], "intermediate_arg": 5, "min": [5, 41, 57, 70, 75, 81, 88], "sin_filt": 5, "sin": 5, "arang": 5, "kernel": 5, "wip": 5, "progress": 5, "issue_manag": [5, 8, 9, 11, 13, 16, 17, 18, 21, 22, 23, 24, 26, 27, 75], "instanti": [5, 14, 34, 49, 59, 74, 76, 78, 93], "477762": 5, "286455": 5, "term": [5, 8, 39, 45, 58, 74, 75, 76, 78, 79, 82, 83], "4778": 5, "is_basic_issu": 5, "basic_scor": 5, "13": [5, 17, 24, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "003042": 5, "058117": 5, "11": [5, 49, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "121908": 5, "15": [5, 43, 62, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "169312": 5, "17": [5, 74, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "229044": 5, "2865": 5, "is_intermediate_issu": 5, "intermediate_scor": 5, "000000": [5, 75, 76, 80, 83], "007059": 5, "009967": 5, "010995": 5, "087332": 5, "016296": 5, "03947": 5, "019459": 5, "794251": 5, "underperform": [6, 7, 27], "group": [6, 7, 22, 27, 80, 87, 94], "dbscan": [6, 8, 27, 81], "hdbscan": [6, 81], "etc": [6, 8, 20, 31, 35, 39, 49, 50, 68, 72, 75, 76, 78, 79, 81, 82, 83], "sensit": [6, 8, 43], "ep": [6, 27, 58], "radiu": 6, "min_sampl": [6, 27], "datalab": [6, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 72, 74, 81, 82, 85, 92, 93], "kmean": [6, 81], "your_data": 6, "get_pred_prob": 6, "n_cluster": [6, 27, 81], "cluster_id": [6, 8, 27, 81], "labels_": 6, "underperforming_group": [6, 8, 19, 81], "search": [7, 8, 18, 22, 23, 44, 62, 81, 89], "nondefault": 7, "Near": [7, 81], "iid": [7, 22, 76, 78, 82, 83], "imbal": [7, 19, 54, 59, 60, 76], "null": [7, 19, 76, 79, 82, 83], "valuat": [7, 16], "togeth": [7, 8, 39, 75, 76, 78, 79, 82, 83, 90, 93, 94], "built": [7, 41], "own": [7, 31, 33, 35, 48, 54, 55, 58, 64, 68, 74, 76, 78, 79, 81, 82, 85, 86, 90, 91, 92, 93, 94], "prerequisit": 7, "basic": [7, 35, 49, 78, 79, 88], "page": [8, 76, 81, 83], "variou": [8, 11, 26, 33, 46, 48, 72, 75, 76, 78, 79, 80, 83, 85, 87, 92], "sai": [8, 31, 35, 86, 91], "why": [8, 79], "matter": [8, 30, 51], "_score": 8, "flag": [8, 20, 22, 36, 41, 51, 52, 55, 62, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 90, 91, 93], "badli": [8, 57, 94], "code": [8, 31, 35, 39, 45, 49, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "issue_scor": 8, "outlier_scor": [8, 24, 75, 76, 78, 79, 82, 83, 88], "atyp": [8, 59, 75, 76, 78, 79, 82, 83, 88], "datapoint": [8, 27, 36, 41, 45, 60, 63, 72, 74, 75, 76, 78, 79, 81, 89, 90, 92, 93], "is_issu": [8, 20], "is_outlier_issu": [8, 75, 76, 78, 79, 82, 83], "annot": [8, 30, 40, 50, 51, 52, 54, 55, 57, 58, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 84, 87, 91], "transform": [8, 41, 43, 45, 59, 60, 76, 79, 82, 88, 92, 93, 94], "dissimilar": [8, 78, 79], "preced": 8, "cosin": [8, 59, 88], "incorrect": [8, 57, 60, 63, 74, 75, 76, 78, 79, 82, 83, 87, 90, 92], "due": [8, 34, 36, 60, 64, 66, 74, 75, 76, 78, 79, 82, 83], "appear": [8, 30, 40, 51, 52, 55, 63, 76, 78, 79, 82, 90, 91], "likelihood": [8, 34, 36, 52, 57, 59, 60, 64, 68], "now": [8, 34, 73, 74, 76, 85, 87, 88, 90, 92, 93, 94], "u": [8, 74, 75, 78, 81, 82, 83, 85, 86, 89, 90, 91, 92, 93, 94], "token": [8, 44, 66, 67, 68, 69, 70, 71, 81, 83, 84], "calcul": [8, 16, 22, 34, 41, 50, 54, 55, 57, 58, 59, 62, 66, 80, 82], "hamper": [8, 80, 82], "analyt": [8, 72, 81, 85], "lead": [8, 57, 60, 82, 87], "draw": [8, 75, 76], "conclus": [8, 79], "try": [8, 34, 36, 49, 50, 64, 66, 72, 76, 78, 79, 81, 82, 83, 91], "veri": [8, 30, 51, 55, 57, 75, 76, 78, 79, 81, 82, 83, 85, 88, 90, 93], "rare": [8, 36, 58, 75, 76, 78, 79, 81, 82, 83], "anomal": [8, 60, 75, 76, 78, 79, 82, 83], "articl": [8, 34, 81], "ai": [8, 72, 74, 75, 76, 78, 79, 80, 81, 82, 84, 85, 86, 88, 90, 92, 93, 94], "blog": 8, "unexpect": [8, 31, 35, 79], "consequ": 8, "inspect": [8, 74, 76, 82, 83, 87, 90, 93], "neg": [8, 57, 58, 75, 76, 80], "affect": [8, 31, 35, 64, 70, 79, 81], "extrem": [8, 75, 76, 78, 79, 81, 82, 83], "rel": [8, 30, 50, 51, 59, 75, 76, 78, 79, 82, 83, 88], "record": [8, 31, 35, 74, 78, 90], "abbrevi": 8, "misspel": 8, "typo": [8, 71], "resolut": 8, "video": [8, 80], "audio": [8, 75, 76, 81, 84], "minor": [8, 44], "variat": 8, "translat": 8, "d": [8, 43, 78, 79, 83, 86, 92, 94], "constant": [8, 27, 62], "median": [8, 26, 43], "question": [8, 20, 72, 83], "nearli": [8, 20, 76, 78, 79, 82], "awar": [8, 73, 83], "presenc": [8, 83], "signific": [8, 76, 78, 79, 82, 83], "violat": [8, 76, 78, 79, 82, 83], "assumpt": [8, 76, 78, 79, 82, 83], "changepoint": [8, 76, 78, 79, 82, 83], "shift": [8, 76, 78, 79, 82, 83], "drift": [8, 76, 78, 82, 83], "autocorrel": [8, 76, 78, 79, 82, 83], "almost": [8, 76, 78, 79, 82, 83], "adjac": [8, 76, 78, 79, 82, 83], "tend": [8, 30, 39, 76, 78, 79, 82, 83, 91, 94], "sequenti": [8, 31, 35, 49, 82], "gap": 8, "b": [8, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 44, 45, 70, 78, 79, 80, 83, 89, 92, 94], "x1": [8, 55, 58, 87], "x2": [8, 55, 58, 87], "10th": 8, "100th": 8, "90": [8, 70, 78, 83, 89, 91, 92], "similarli": [8, 31, 35, 75, 78, 81, 82, 87], "math": [8, 82], "behind": [8, 59, 83], "fundament": 8, "proper": [8, 45, 50, 55, 58, 79, 82, 85, 87, 92], "closer": [8, 57, 87], "scenario": [8, 60, 75, 76], "underli": [8, 59, 68, 70, 94], "stem": [8, 59, 88], "evolv": 8, "influenc": 8, "accordingli": 8, "emploi": [8, 86, 88], "partit": [8, 89], "ahead": 8, "good": [8, 31, 35, 43, 49, 51, 57, 60, 64, 66, 67, 72, 78, 79, 82], "fix": [8, 50, 79, 83, 90, 93], "problem": [8, 34, 41, 67, 72, 75, 76, 79, 81, 82], "deploy": [8, 83, 90, 92, 93], "overlook": [8, 57, 87], "fact": 8, "thu": [8, 30, 35, 51, 74, 78, 79, 83, 89, 92, 94], "diagnos": [8, 76, 81], "rarest": [8, 76, 78, 79, 82, 83], "q": [8, 87], "fall": [8, 57, 66, 70, 83, 88], "subpar": 8, "special": [8, 44], "techniqu": 8, "smote": 8, "asymmetr": [8, 30], "properli": [8, 34, 40, 45, 46, 64, 81, 86, 88, 90, 91], "too": [8, 36, 41, 59, 76, 81, 82, 87], "dark": [8, 91], "bright": [8, 94], "blurri": [8, 82], "abnorm": [8, 58, 82], "cluster": [8, 16, 27], "slice": 8, "poor": 8, "subpopul": 8, "lowest": [8, 50, 58, 76, 81, 82, 85, 86, 87, 91], "get_self_confidence_for_each_label": [8, 41, 60], "power": [8, 78, 79, 80, 82, 83, 94], "r": [8, 34, 62, 75, 76, 90, 91], "tabular": [8, 72, 75, 76, 77, 81, 84, 85], "categor": [8, 59, 75, 76, 77, 81, 90, 92], "encod": [8, 42, 58, 64, 67, 78, 79, 81, 90, 91, 92, 93], "miss": [8, 23, 31, 35, 45, 55, 57, 76, 78, 79, 81, 82, 83, 87, 90], "pattern": 8, "contribut": [8, 16, 87], "isn": [8, 15, 23], "approxim": [8, 16, 34, 59, 85], "shaplei": [8, 16], "knn": [8, 11, 16, 22, 27, 59, 78, 88], "scalabl": 8, "sacrific": 8, "One": [8, 45, 59, 81], "quantif": 8, "exert": [8, 76], "possible_issue_typ": 8, "label_kwarg": 8, "outlier_kwarg": 8, "near_dupl": [8, 12, 17, 75, 76, 78, 79, 81, 82, 83], "near_duplicate_kwarg": 8, "non_iid": [8, 12, 22, 76, 78, 79, 82, 83], "non_iid_kwarg": 8, "class_imbal": [8, 18, 76, 78, 79, 82, 83], "class_imbalance_kwarg": 8, "underperforming_group_kwarg": 8, "null_kwarg": 8, "health_summary_paramet": [8, 21, 26], "health_summari": [8, 21, 30, 72, 80], "health_summary_kwarg": 8, "tandem": [8, 80], "view": [8, 31, 35, 36, 66, 68, 70, 72, 74, 75, 76, 78, 79, 80, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "ood_kwarg": 8, "outofdistribut": [8, 24, 59, 88], "outsid": 8, "outlierissuemanag": [8, 12, 24, 75], "nearduplicateissuemanag": [8, 12, 17], "noniidissuemanag": [8, 12, 22], "num_permut": [8, 22], "permut": [8, 22], "significance_threshold": [8, 22], "signic": 8, "noniid": [8, 19], "classimbalanceissuemanag": [8, 18], "underperforminggroupissuemanag": [8, 27], "determinin": 8, "neighbour": 8, "min_cluster_sampl": [8, 27], "filter_cluster_id": [8, 27], "clustering_kwarg": [8, 27], "faq": [8, 72, 76, 78, 79, 82, 84], "nullissuemanag": [8, 23], "data_valuation_kwarg": 8, "data_valu": [8, 19], "datavaluationissuemanag": [8, 16], "codeblock": 8, "demonstr": [8, 34, 75, 76, 79, 81, 82, 83, 85, 86, 87, 90, 91], "howev": [8, 31, 35, 45, 74, 78, 79, 82, 85, 89, 91, 92, 93], "mandatori": 8, "image_issue_types_kwarg": 8, "32": [8, 75, 80, 82, 85, 87, 91], "fewer": [8, 36, 45, 87], "vice": [8, 51], "versa": [8, 51], "light": [8, 80, 82, 87, 91], "29": [8, 80, 82, 85, 86, 87, 91, 94], "low_inform": [8, 82], "odd_aspect_ratio": [8, 82], "35": [8, 75, 80, 85, 86, 87, 91, 94], "odd_siz": [8, 82], "10": [8, 16, 17, 21, 22, 27, 31, 32, 58, 59, 60, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "doc": [8, 31, 35, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "data_issu": [9, 13, 14, 28, 75], "issue_find": [9, 13], "factori": [9, 13, 14], "except": [10, 49, 60, 75, 76, 82, 85], "dataformaterror": 10, "with_traceback": 10, "tb": 10, "__traceback__": 10, "datasetdicterror": 10, "datasetdict": 10, "usual": [10, 28, 82, 85, 90], "datasetloaderror": 10, "dataset_typ": 10, "fail": 10, "map_to_int": 10, "hold": 10, "is_avail": [10, 82], "serv": [11, 14, 85], "central": [11, 94], "repositori": 11, "strategi": [11, 41, 81], "being": [11, 30, 31, 35, 36, 41, 44, 45, 60, 78, 81, 83, 90, 91, 92], "_infostrategi": 11, "basi": 11, "collect_statist": 11, "reus": [11, 20], "avoid": [11, 31, 34, 35, 36, 45, 52, 55, 58, 62, 64, 66, 75, 76, 81], "recomput": [11, 93], "weighted_knn_graph": 11, "issue_manager_that_computes_knn_graph": 11, "collect_issues_from_issue_manag": 11, "collect_issues_from_imagelab": 11, "imagelab": 11, "set_health_scor": 11, "health": [11, 21, 30, 51, 72], "get_data_statist": 11, "concret": 12, "subclass": [12, 31, 35, 59, 75], "my_issu": 12, "stabl": [13, 19, 25, 33, 37, 45, 48, 59, 73], "unregist": 13, "instati": 14, "public": [14, 83, 87, 91, 94], "creation": [14, 35], "execut": [14, 31, 35, 75, 81, 87], "coordin": [14, 55, 57, 58, 87, 94], "behavior": [14, 30, 31, 35, 58], "At": [14, 58, 81], "associ": [14, 31, 35, 58, 85], "get_available_issue_typ": 14, "direct": [15, 23, 31, 35], "valuabl": 16, "vstack": [16, 45, 80, 81, 82, 83, 85, 86], "25": [16, 22, 31, 41, 43, 76, 80, 82, 83, 85, 86, 87, 91, 94], "classvar": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "short": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 44, 45], "data_valuation_scor": 16, "item": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 45, 75, 76, 81, 82, 83, 85, 86], "some_info_kei": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "additional_info_kei": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "default_threshold": [16, 24], "arxiv": [16, 83], "ab": [16, 83], "1911": 16, "07128": 16, "larger": [16, 62, 64, 66, 79, 80, 81, 82], "collect_info": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "info_to_omit": [16, 17, 18, 20, 21, 22, 24, 26, 27], "compos": [16, 17, 18, 20, 21, 22, 24, 26, 27, 31, 35, 79, 88, 93], "is_x_issu": [16, 17, 18, 20, 21, 22, 24, 26, 27], "x_score": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_a": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_b1": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_b2": [16, 17, 18, 20, 21, 22, 24, 26, 27], "report_str": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28], "_": [17, 20, 21, 22, 23, 26, 27, 41, 44, 45, 74, 75, 80, 82, 83, 86, 92], "near_duplicate_set": [17, 75, 76, 78, 79, 81, 82, 83], "occurr": [17, 18, 20, 22, 23, 24, 27, 44], "median_nn_dist": 17, "near_duplicate_scor": [17, 75, 76, 78, 79, 81, 82, 83], "class_imbalance_scor": [18, 76, 78, 79, 82, 83], "bleed": [19, 25, 33], "edg": [19, 25, 33, 57, 72, 83, 94], "sharp": [19, 25, 33], "abc": 20, "believ": [20, 91], "priori": [20, 83], "global": 20, "anoth": [20, 30, 34, 44, 57, 60, 78, 79, 81, 83, 85, 88, 93], "abstract": 20, "applic": [21, 50, 81, 83, 85, 86, 94], "typevar": [21, 31, 35, 57, 58], "_scalartype_co": 21, "covari": [21, 62, 90], "get_health_summari": 21, "summary_dict": 21, "label_scor": [21, 26, 74, 75, 76, 78, 79, 82, 83], "simplified_kolmogorov_smirnov_test": 22, "neighbor_histogram": 22, "non_neighbor_histogram": 22, "kolmogorov": 22, "smirnov": 22, "largest": [22, 34, 41, 60, 64, 66, 91], "empir": [22, 40, 50], "cumul": 22, "ecdf": 22, "histogram": [22, 78, 90], "absolut": [22, 26], "dimension": [22, 45, 74, 83, 88], "trial": 22, "non_iid_scor": [22, 76, 78, 79, 82, 83], "null_track": 23, "extend": [23, 42, 82, 88, 94], "superclass": 23, "arbitrari": [23, 30, 66, 70, 75, 88, 90], "prompt": 23, "address": [23, 75, 76, 79, 81, 93], "enabl": [23, 35], "null_scor": [23, 76, 79, 82, 83], "37037": 24, "q3_avg_dist": 24, "iqr_avg_dist": 24, "median_outlier_scor": 24, "ood": [24, 59, 60, 75, 76, 79, 82, 83, 88], "regressionlabelissuemanag": 26, "multipli": 26, "find_issues_with_predict": 26, "find_issues_with_featur": 26, "deleg": 26, "confus": [27, 30, 31, 35, 36, 45, 58, 93, 94], "50": [27, 35, 81, 82, 83, 85, 87, 88, 91], "keepdim": [27, 81], "outlier_cluster_label": 27, "no_underperforming_cluster_id": 27, "signifi": 27, "absenc": 27, "set_knn_graph": 27, "find_issues_kwarg": 27, "perform_clust": 27, "npt": 27, "int_": 27, "id": [27, 50, 75, 81, 82, 85], "int64": [27, 74, 85], "unique_cluster_id": 27, "get_worst_clust": 27, "_description_": 27, "performed_clust": 27, "worst_cluster_id": 27, "underperforming_group_scor": 27, "exclud": [28, 67, 71, 75, 94], "get_report": 28, "overview": [30, 74, 76, 78, 79, 82, 85, 87, 88, 90, 92, 93, 94], "modifi": [30, 31, 34, 35, 45, 81, 83], "help": [30, 31, 35, 58, 72, 73, 74, 75, 78, 79, 80, 81, 82, 85, 86, 90, 91, 92, 93, 94], "rank_classes_by_label_qu": [30, 76], "merg": [30, 44, 72, 80, 81, 94], "find_overlapping_class": [30, 81, 83], "problemat": [30, 51, 67, 71, 74, 87, 94], "unnorm": [30, 51, 83], "abov": [30, 31, 34, 35, 45, 50, 57, 58, 60, 66, 70, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "model_select": [30, 41, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 90, 92, 93], "cross_val_predict": [30, 35, 74, 75, 76, 78, 79, 81, 83, 85, 89, 90, 92, 93], "get_data_labels_from_dataset": 30, "yourfavoritemodel": [30, 83], "cv": [30, 41, 74, 75, 76, 78, 83, 85, 92], "df": [30, 45, 71, 74, 81], "overall_label_qu": [30, 51], "col": 30, "prob": [30, 44, 83, 89], "divid": [30, 51, 60], "label_nois": [30, 51], "human": [30, 80, 91, 94], "clearli": [30, 60, 82, 87, 91], "num": [30, 51, 80, 83], "overlap": [30, 72, 80, 81, 83], "ontolog": 30, "publish": [30, 94], "therefor": [30, 60], "vehicl": [30, 80], "truck": [30, 80, 88, 91], "intuit": [30, 51], "car": [30, 80, 87, 91], "frequent": [30, 50, 78, 81, 90], "characterist": 30, "l": [30, 31, 35, 55, 57, 58], "class1": 30, "class2": 30, "relationship": 30, "match": [30, 31, 35, 36, 50, 51, 60, 75, 76, 80, 82, 87, 89, 91], "dog": [30, 45, 51, 53, 67, 80, 81, 88, 89, 94], "cat": [30, 45, 51, 53, 80, 81, 88, 89], "captur": [30, 74, 87, 88, 91], "co": [30, 31, 32], "noisy_label": [30, 75, 76, 86], "overlapping_class": 30, "descend": [30, 31, 35, 41, 51, 58], "overall_label_health_scor": [30, 51, 83], "suggest": [30, 50, 51, 57, 79, 81, 82, 90, 93], "half": [30, 31, 35, 51, 80, 94], "health_scor": [30, 51], "classes_by_label_qu": [30, 76], "cnn": [31, 35, 82], "cifar": [31, 32, 80, 88], "teach": [31, 32], "bhanml": 31, "blob": 31, "master": [31, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "call_bn": 31, "bn": 31, "input_channel": 31, "n_output": 31, "dropout_r": 31, "top_bn": 31, "architectur": [31, 35], "shown": [31, 58, 75, 81, 85, 88, 89, 91, 94], "forward": [31, 32, 35, 82, 85], "overridden": [31, 35], "although": [31, 35, 59, 78, 92], "recip": [31, 35], "afterward": [31, 35], "sinc": [31, 35, 38, 46, 51, 58, 66, 70, 81, 85, 86, 87, 89, 94], "former": [31, 35], "hook": [31, 35, 80], "silent": [31, 34, 35], "t_destin": [31, 35], "__call__": [31, 35, 41], "add_modul": [31, 35], "child": [31, 35], "fn": [31, 35, 58], "recurs": [31, 35, 41], "submodul": [31, 35], "children": [31, 35, 94], "nn": [31, 32, 35, 82], "init": [31, 35, 83], "no_grad": [31, 35, 82, 88], "init_weight": [31, 35], "linear": [31, 35, 79, 82, 93], "fill_": [31, 35], "net": [31, 35, 74, 80, 82], "in_featur": [31, 35], "out_featur": [31, 35], "bia": [31, 35, 82], "tensor": [31, 32, 35, 74, 82, 88], "requires_grad": [31, 35], "bfloat16": [31, 35], "cast": [31, 35, 74], "buffer": [31, 35], "datatyp": [31, 35], "member": [31, 35, 75, 76], "xdoctest": [31, 35], "undefin": [31, 35], "var": [31, 35], "buf": [31, 35], "20l": [31, 35], "1l": [31, 35], "5l": [31, 35], "immedi": [31, 35, 88], "cpu": [31, 35, 36, 74, 82], "move": [31, 35, 41, 73, 80], "cuda": [31, 35, 74, 82], "devic": [31, 35, 74, 82], "gpu": [31, 35, 74, 79, 93], "live": [31, 35], "copi": [31, 35, 62, 74, 75, 76, 78, 81, 86, 89, 90, 92], "doubl": [31, 35], "dump_patch": [31, 35], "eval": [31, 35, 82, 86, 88], "dropout": [31, 35], "batchnorm": [31, 35], "grad": [31, 35], "extra_repr": [31, 35], "line": [31, 35, 72, 75, 80, 85, 88, 94], "get_buff": [31, 35], "target": [31, 32, 35, 62, 63, 88, 90], "throw": [31, 35], "get_submodul": [31, 35], "explan": [31, 35], "fulli": [31, 35, 49, 81], "qualifi": [31, 35], "referenc": [31, 35], "attributeerror": [31, 35], "invalid": [31, 35, 79], "resolv": [31, 35, 94], "get_extra_st": [31, 35], "state_dict": [31, 35], "set_extra_st": [31, 35], "build": [31, 35, 82, 91], "pickleabl": [31, 35], "serial": [31, 35], "backward": [31, 35, 82], "break": [31, 35, 82], "pickl": [31, 35, 87], "get_paramet": [31, 35], "let": [31, 35, 59, 60, 74, 76, 78, 79, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "net_b": [31, 35], "net_c": [31, 35], "conv": [31, 35], "conv2d": [31, 35, 82], "16": [31, 35, 41, 66, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "33": [31, 35, 80, 82, 87, 91], "kernel_s": [31, 35], "stride": [31, 35], "200": [31, 35, 60, 80, 87, 94], "diagram": [31, 35, 89], "degre": [31, 35, 90], "queri": [31, 35, 76, 81, 82], "named_modul": [31, 35], "o": [31, 35, 43, 44, 74, 75, 76, 80, 81, 83, 86, 87, 94], "transit": [31, 35], "ipu": [31, 35], "load_state_dict": [31, 35], "strict": [31, 35, 41], "persist": [31, 35], "strictli": [31, 35], "namedtupl": [31, 35], "missing_kei": [31, 35], "unexpected_kei": [31, 35], "runtimeerror": [31, 35], "idx": [31, 35, 45, 46, 58, 75, 81, 82, 83, 85, 87, 88], "named_buff": [31, 35], "prefix": [31, 35, 74, 94], "prepend": [31, 35], "running_var": [31, 35], "named_children": [31, 35], "conv4": [31, 35], "conv5": [31, 35], "memo": [31, 35], "remove_dupl": [31, 35], "named_paramet": [31, 35], "register_backward_hook": [31, 35], "deprec": [31, 35, 38], "favor": [31, 35], "register_full_backward_hook": [31, 35], "removablehandl": [31, 35], "register_buff": [31, 35], "running_mean": [31, 35], "register_forward_hook": [31, 35], "won": [31, 35, 75, 76, 81, 86], "inplac": [31, 35, 85], "register_forward_pre_hook": [31, 35], "gradient": [31, 35, 78, 82, 90], "respect": [31, 35, 58, 83], "grad_input": [31, 35], "grad_output": [31, 35], "technic": [31, 35], "caller": [31, 35], "register_load_state_dict_post_hook": [31, 35], "post": [31, 35], "incompatible_kei": [31, 35], "modif": [31, 35], "thrown": [31, 35], "clearn": [31, 35], "register_modul": [31, 35], "register_paramet": [31, 35], "requires_grad_": [31, 35], "autograd": [31, 35], "freez": [31, 35, 74, 79, 93], "finetun": [31, 35], "gan": [31, 35], "share_memori": [31, 35], "share_memory_": [31, 35], "destin": [31, 35], "keep_var": [31, 35], "shallow": [31, 35], "releas": [31, 35, 73, 81, 88], "design": [31, 35], "ordereddict": [31, 35], "detach": [31, 35, 82], "non_block": [31, 35], "memory_format": [31, 35], "channels_last": [31, 35], "Its": [31, 35, 41, 51, 57], "complex": [31, 35], "integr": [31, 35, 72], "asynchron": [31, 35], "host": [31, 35], "pin": [31, 35, 79, 80, 93], "desir": [31, 35, 44, 58], "4d": [31, 35], "ignore_w": [31, 35], "determinist": [31, 35, 74], "1913": [31, 35], "3420": [31, 35], "5113": [31, 35], "2325": [31, 35], "env": [31, 35], "torch_doctest_cuda1": [31, 35], "gpu1": [31, 35], "1914": [31, 35], "5112": [31, 35], "2324": [31, 35], "float16": [31, 35], "cdoubl": [31, 35], "3741": [31, 35], "2382": [31, 35], "5593": [31, 35], "4443": [31, 35], "complex128": [31, 35], "6122": [31, 35], "1150": [31, 35], "to_empti": [31, 35], "storag": [31, 35], "dst_type": [31, 35], "xpu": [31, 35], "zero_grad": [31, 35, 82], "set_to_non": [31, 35], "context": [31, 35, 87], "noisili": [32, 83], "han": 32, "2018": 32, "cifar_cnn": [32, 33], "loss_coteach": 32, "y_1": 32, "y_2": 32, "forget_r": 32, "class_weight": 32, "logit": [32, 49, 82], "decim": [32, 45], "quickli": [32, 74, 78, 79, 81, 82, 86, 88, 91, 92, 94], "forget": [32, 41, 94], "rate_schedul": 32, "epoch": [32, 35, 81, 82], "initialize_lr_schedul": 32, "lr": [32, 35], "001": [32, 60, 81], "250": [32, 75, 76, 83, 87], "epoch_decay_start": 32, "80": [32, 78, 82, 86, 90, 91, 92], "schedul": 32, "adjust": [32, 36, 54, 59, 60, 72, 83], "beta": 32, "adam": 32, "adjust_learning_r": 32, "alpha_plan": 32, "beta1_plan": 32, "forget_rate_schedul": 32, "num_gradu": 32, "expon": 32, "tell": [32, 79, 82, 83, 93], "train_load": [32, 35], "model1": [32, 83], "optimizer1": 32, "model2": [32, 83], "optimizer2": 32, "dataload": [32, 82, 88], "parser": 32, "parse_arg": 32, "num_iter_per_epoch": 32, "print_freq": 32, "topk": 32, "top1": 32, "top5": 32, "test_load": 32, "offici": [33, 48, 94], "wish": [33, 48, 88, 91, 94], "mnist_pytorch": 33, "coteach": [33, 73], "mini": [34, 64, 66, 81], "With": [34, 79, 83, 85, 90, 91, 93, 94], "low_self_confid": [34, 36, 52], "self_confid": [34, 36, 41, 52, 54, 60, 68, 70, 81, 83, 86, 92, 93], "conveni": [34, 74, 79, 93], "script": 34, "labelinspector": [34, 81], "adj_confident_thresholds_shar": 34, "labels_shar": 34, "pred_probs_shar": 34, "labels_fil": [34, 81], "pred_probs_fil": [34, 81], "batch_siz": [34, 35, 64, 66, 81, 82, 88, 91], "quality_score_kwarg": 34, "num_issue_kwarg": 34, "return_mask": 34, "variant": [34, 50, 91], "read": [34, 38, 76, 81, 83, 88, 94], "zarr": [34, 81], "memmap": [34, 91], "pythonspe": 34, "mmap": [34, 81], "hdf5": 34, "further": [34, 51, 52, 54, 57, 58, 66, 67, 74, 81], "yourfil": 34, "npy": [34, 80, 81, 91], "mmap_mod": [34, 91], "tip": [34, 36, 49, 81], "save_arrai": 34, "your_arrai": 34, "disk": [34, 80, 81], "npz": [34, 94], "maxim": [34, 50, 64, 66, 91], "multiprocess": [34, 36, 52, 64, 66, 81, 82, 91], "linux": [34, 64, 66], "physic": [34, 36, 64, 66, 87, 91], "psutil": [34, 36, 64, 66, 91], "labels_arrai": [34, 46], "predprob": 34, "pred_probs_arrai": 34, "back": [34, 58, 75, 81, 87, 88], "store_result": 34, "becom": [34, 88], "verifi": [34, 81, 85, 88], "long": [34, 50, 59, 85], "enough": [34, 45, 81], "chunk": [34, 89], "ram": [34, 80], "faster": [34, 59, 62, 64, 66, 81, 83], "end_index": 34, "labels_batch": 34, "pred_probs_batch": 34, "update_confident_threshold": 34, "batch_result": 34, "score_label_qu": 34, "indices_of_examples_with_issu": [34, 81], "shortcut": 34, "encount": [34, 36, 64], "1000": [34, 74, 79, 81, 82, 88], "aggreg": [34, 41, 50, 54, 57, 60, 70, 81, 83, 85], "get_num_issu": 34, "fetch": [34, 74, 76], "seen": [34, 81, 88, 94], "far": [34, 50], "get_quality_scor": 34, "label_quality_scor": [34, 54, 57, 60, 63, 83, 87, 90], "method1": 34, "method2": 34, "normalized_margin": [34, 36, 41, 52, 54, 60, 68, 70], "low_normalized_margin": [34, 36, 52], "issue_indic": [34, 57, 82], "update_num_issu": 34, "split_arr": 34, "arr": [34, 81], "chunksiz": 34, "convnet": 35, "bespok": [35, 49], "get_mnist_dataset": 35, "loader": [35, 82], "download": [35, 74, 81, 88], "mnist": [35, 72, 74, 80], "get_sklearn_digits_dataset": 35, "handwritten": 35, "digit": [35, 74, 80], "last": [35, 41, 55, 58, 75, 76, 81, 85, 94], "sklearn_digits_test_s": 35, "hard": [35, 80, 88], "simplenet": 35, "64": [35, 78, 82, 83, 87, 91, 92], "log_interv": 35, "01": [35, 60, 62, 74, 82, 83, 86, 87, 91, 94], "momentum": 35, "no_cuda": 35, "test_batch_s": [35, 82], "templat": 35, "flexibli": 35, "among": [35, 50, 83], "test_set": 35, "Be": 35, "overrid": 35, "train_idx": [35, 45, 88], "train_label": [35, 88, 93], "scikit": [35, 45, 59, 72, 74, 75, 76, 78, 79, 81, 84, 90, 93], "set_predict_proba_request": 35, "set_predict_request": 35, "encourag": [36, 52, 60, 63], "multilabel_classif": [36, 51, 52, 54, 60, 81, 86], "pred_probs_by_class": 36, "prune_count_matrix_col": 36, "rank_by_kwarg": [36, 52, 60, 83], "num_to_remove_per_class": [36, 52], "bad": [36, 52, 57, 60, 79, 81, 93], "seem": [36, 83, 86], "aren": 36, "confidence_weighted_entropi": [36, 41, 52, 54, 60, 68, 70], "label_issues_idx": [36, 60], "entropi": [36, 38, 40, 41, 59, 60], "prune_by_class": [36, 52, 83], "predicted_neq_given": [36, 52, 83], "prune_counts_matrix": 36, "smallest": [36, 60], "unus": 36, "number_of_mislabeled_examples_in_class_k": 36, "delet": [36, 72, 81, 93], "thread": [36, 52], "window": [36, 80], "shorter": [36, 55], "find_predicted_neq_given": 36, "find_label_issues_using_argmax_confusion_matrix": 36, "latent_algebra": [37, 73], "label_quality_util": 37, "multilabel_util": [37, 86], "multilabel_scor": [37, 54], "token_classification_util": [37, 94], "get_normalized_entropi": 38, "min_allowed_prob": 38, "wikipedia": 38, "activ": [38, 40, 50, 72, 85], "towardsdatasci": 38, "cheatsheet": 38, "ec57bc067c0b": 38, "clip": [38, 45, 74], "behav": 38, "unnecessari": [38, 81], "slightli": [38, 92, 93], "interv": [38, 41, 88], "herein": 39, "inexact": 39, "cours": 39, "propag": 39, "throughout": [39, 45, 62, 74, 85, 91, 94], "compute_ps_py_inv_noise_matrix": 39, "compute_py_inv_noise_matrix": 39, "compute_inv_noise_matrix": 39, "easili": [39, 73, 74, 76, 78, 79, 83, 85, 86, 88, 89, 90, 91, 92, 93], "increas": [39, 57, 59, 60, 74, 75, 81, 85, 86, 94], "dot": [39, 70, 81], "compute_noise_matrix_from_invers": 39, "compute_pi": 39, "true_labels_class_count": 39, "compute_pyx": 39, "pyx": 39, "multiannot": 40, "assert_valid_inputs_multiannot": 40, "labels_multiannot": [40, 50], "ensembl": [40, 41, 50, 60, 78, 81, 86, 88, 90, 92], "allow_single_label": 40, "annotator_id": 40, "assert_valid_pred_prob": 40, "pred_probs_unlabel": [40, 50], "format_multiannotator_label": [40, 50, 85], "lexicograph": [40, 45], "formatted_label": [40, 45], "old": [40, 45, 73, 80], "check_consensus_label_class": 40, "consensus_label": [40, 50, 85], "consensus_method": [40, 50], "consensu": [40, 50, 72, 84, 94], "establish": [40, 90, 93], "compute_soft_cross_entropi": 40, "soft": [40, 80], "find_best_temp_scal": 40, "coarse_search_rang": [40, 62, 81], "fine_search_s": [40, 62, 81], "temperatur": [40, 41, 57, 66, 70], "scale": [40, 43, 80, 81, 88, 91, 92], "factor": [40, 41, 43, 64, 66], "minim": [40, 57, 88], "temp_scale_pred_prob": 40, "temp": 40, "sharpen": [40, 80], "smoothen": 40, "classlabelscor": 41, "enum": 41, "get_normalized_margin_for_each_label": [41, 60], "get_confidence_weighted_entropy_for_each_label": [41, 60], "75": [41, 75, 76, 80, 85, 86, 87, 90, 91, 94], "from_str": 41, "scorer": 41, "exponential_moving_averag": [41, 54], "alpha": [41, 54, 57, 75, 76, 83, 86, 90], "exponenti": 41, "ema": 41, "s_1": 41, "s_k": 41, "ema_k": 41, "accord": [41, 52, 78, 79, 83, 94], "formula": [41, 43], "_t": 41, "cdot": 41, "s_t": 41, "qquad": 41, "leq": 41, "_1": 41, "give": [41, 60, 83, 85, 91], "recent": [41, 94], "success": 41, "previou": [41, 81, 82, 87], "discount": 41, "s_ema": 41, "175": [41, 83, 87], "softmin": [41, 54, 57, 66, 70], "underflow": 41, "nan": [41, 50, 78, 85, 90, 92], "possible_method": 41, "aggregated_scor": 41, "multilabelscor": 41, "base_scor": 41, "base_scorer_kwarg": 41, "aggregator_kwarg": [41, 54], "n_sampl": 41, "n_label": 41, "binari": [41, 45, 52, 54, 83, 94], "worst": [41, 85], "class_label_quality_scor": 41, "get_class_label_quality_scor": 41, "42": [41, 80, 82, 87, 91, 94], "452": [41, 79], "new_scor": 41, "575": 41, "get_label_quality_scores_per_class": [41, 54], "ml_scorer": 41, "multilabel_pi": 41, "binar": [41, 42], "get_cross_validated_multilabel_pred_prob": 41, "reformat": [41, 74], "wider": 41, "splitter": 41, "kfold": [41, 82], "multiclass": [41, 45, 50, 86], "onevsrestclassifi": [41, 86], "randomforestclassifi": [41, 83, 86], "n_split": [41, 76, 82, 86], "stack_compl": 42, "pred_prob_slic": 42, "get_onehot_num_class": 42, "onehot": 42, "multilabel": [42, 86], "int2onehot": [42, 86], "hot": [42, 52, 58, 64, 67, 78, 80, 81, 90, 91, 92], "onehot2int": [42, 86], "onehot_matrix": 42, "transform_distances_to_scor": 43, "avg_dist": 43, "scaling_factor": 43, "exp": [43, 59, 60, 75], "dt": 43, "right": [43, 55, 58, 79, 86, 87, 88, 93], "strength": [43, 58], "pronounc": 43, "differenti": 43, "ly": 43, "rule": [43, 44, 80], "thumb": 43, "ood_features_scor": [43, 59, 88], "88988177": 43, "80519832": 43, "token_classif": [44, 68, 70, 71, 81], "get_sent": [44, 94], "sentenc": [44, 68, 70, 71, 79, 93], "readabl": 44, "filter_sent": [44, 94], "lambda": [44, 74, 75, 81, 85], "long_sent": 44, "headlin": 44, "process_token": 44, "charact": [44, 45], "s1": 44, "s2": 44, "processed_token": 44, "alecnlcb": 44, "entiti": [44, 72, 81, 94], "mapped_ent": 44, "unique_ident": 44, "loc": [44, 75, 76, 82, 94], "merge_prob": 44, "probs_merg": 44, "55": [44, 80, 87, 90, 91], "0125": [44, 70], "0375": 44, "075": 44, "025": 44, "color_sent": 44, "color": [44, 67, 75, 76, 78, 83, 86, 88, 90, 91], "red": [44, 58, 75, 76, 80, 83, 86, 87, 88, 91], "colored_sent": 44, "termcolor": 44, "31msentenc": 44, "0m": 44, "ancillari": 45, "remove_noise_from_class": 45, "class_without_nois": 45, "any_other_class": 45, "choos": [45, 60, 78, 81, 83, 90, 92], "tradition": 45, "clip_noise_r": 45, "clip_valu": 45, "new_sum": 45, "preserv": 45, "value_count": [45, 81], "fill": 45, "wherea": [45, 52, 89], "come": [45, 75, 76, 81, 82, 91], "major": [45, 50, 73, 82, 88], "versu": [45, 83], "value_counts_fill_missing_class": 45, "get_missing_class": 45, "round_preserving_sum": 45, "obviou": 45, "cgdeboer": 45, "iteround": 45, "round_preserving_row_tot": 45, "reach": 45, "estimate_pu_f1": 45, "prob_s_eq_1": 45, "claesen": 45, "f1": [45, 58, 79, 83], "confusion_matrix": 45, "BE": 45, "print_square_matrix": 45, "left_nam": 45, "top_nam": 45, "titl": [45, 75, 76, 83, 86, 88], "short_titl": 45, "round_plac": 45, "pretti": [45, 83], "print_noise_matrix": [45, 83], "print_inverse_noise_matrix": 45, "print_joint_matrix": [45, 83], "joint_matrix": 45, "compress_int_arrai": 45, "num_possible_valu": 45, "train_val_split": 45, "holdout_idx": 45, "subset_x_i": 45, "extract": [45, 59, 74, 79, 85, 88, 91, 93], "subset_label": 45, "subset_data": 45, "extract_indices_tf": 45, "allow_shuffl": 45, "turn": [45, 72, 87], "unshuffle_tensorflow_dataset": 45, "shuffledataset": 45, "histori": 45, "pre_x": 45, "buffer_s": 45, "is_torch_dataset": 45, "is_tensorflow_dataset": 45, "csr_vstack": 45, "csr_matric": 45, "append": [45, 74, 80, 81, 82, 83, 85, 86, 88, 94], "bottom": [45, 55, 58, 87], "append_extra_datapoint": 45, "to_data": 45, "from_data": 45, "taken": 45, "get_num_class": 45, "label_matrix": 45, "canon": 45, "num_unique_class": 45, "get_unique_class": 45, "format_label": 45, "smart_display_datafram": 45, "displai": [45, 58, 67, 71, 74, 79, 83, 93, 94], "jupyt": [45, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "notebook": [45, 50, 74, 76, 80, 81, 83, 85, 86, 87, 91, 94], "consol": 45, "force_two_dimens": 45, "html": [45, 59, 78, 81, 83], "assert_valid_input": 46, "allow_missing_class": 46, "allow_one_class": 46, "assert_valid_class_label": 46, "assert_nonempty_input": 46, "assert_indexing_work": 46, "length_x": 46, "labels_to_arrai": 46, "labellik": 46, "keraswrappermodel": [49, 72], "keraswrappersequenti": 49, "tf": [49, 74], "legaci": 49, "lack": 49, "keraswrapp": 49, "huggingface_keras_imdb": 49, "unit": [49, 94], "model_kwarg": [49, 62], "compile_kwarg": 49, "sparsecategoricalcrossentropi": 49, "layer": [49, 74, 79, 88, 93], "dens": 49, "my_keras_model": 49, "from_logit": 49, "compil": 49, "declar": 49, "apply_softmax": 49, "analysi": 50, "analyz": [50, 72, 83, 85, 86], "get_label_quality_multiannot": [50, 85], "vote": 50, "crowdsourc": [50, 72, 85], "dawid": [50, 85], "skene": [50, 85], "analog": [50, 80, 85], "chosen": [50, 60, 81, 85], "crowdlab": [50, 85], "unlabel": [50, 78, 79, 82, 85, 88, 91], "decid": [50, 79, 80, 85, 90, 93, 94], "get_active_learning_scor": [50, 85], "activelab": [50, 85], "priorit": [50, 57, 87, 91, 94], "showcas": 50, "main": 50, "best_qual": 50, "quality_method": 50, "calibrate_prob": 50, "return_detailed_qu": 50, "return_annotator_stat": 50, "return_weight": 50, "label_quality_score_kwarg": 50, "necessarili": [50, 58, 79, 83], "did": [50, 51, 74, 78, 83, 85, 90, 92, 93], "majority_vot": 50, "ti": 50, "broken": [50, 58, 80], "highest": [50, 58, 75, 82, 89], "0th": 50, "consensus_quality_scor": [50, 85], "annotator_agr": [50, 85], "reman": 50, "1st": 50, "2nd": [50, 64], "3rd": 50, "consensus_label_suffix": 50, "consensus_quality_score_suffix": 50, "suffix": 50, "emsembl": 50, "weigh": [50, 80], "agreement": [50, 85], "agre": 50, "prevent": [50, 81], "overconfid": [50, 89], "wrong": [50, 55, 57, 73, 75, 76, 79, 81, 83, 87, 93], "detailed_label_qu": [50, 85], "annotator_stat": [50, 85], "model_weight": 50, "annotator_weight": 50, "warn": [50, 75, 76], "labels_info": 50, "num_annot": [50, 85], "deriv": [50, 85], "quality_annotator_1": 50, "quality_annotator_2": 50, "quality_annotator_m": 50, "annotator_qu": [50, 85], "num_examples_label": [50, 85], "agreement_with_consensu": [50, 85], "worst_class": [50, 85], "trustworthi": [50, 85, 90], "get_label_quality_multiannotator_ensembl": 50, "weigtht": 50, "budget": 50, "retrain": [50, 90, 93], "active_learning_scor": 50, "improv": [50, 76, 80, 81, 82, 83, 90, 91, 92, 93], "active_learning_scores_unlabel": 50, "get_active_learning_scores_ensembl": 50, "henc": [50, 74, 75, 85], "get_majority_vote_label": [50, 85], "event": 50, "lastli": [50, 78], "convert_long_to_wide_dataset": 50, "labels_multiannotator_long": 50, "wide": [50, 74, 92, 93], "suitabl": [50, 78, 92], "labels_multiannotator_wid": 50, "common_multilabel_issu": 51, "mutual": [51, 86], "exclus": [51, 86], "rank_classes_by_multilabel_qu": 51, "overall_multilabel_health_scor": 51, "multilabel_health_summari": 51, "classes_by_multilabel_qu": 51, "inner": [52, 66], "find_multilabel_issues_per_class": 52, "per_class_label_issu": 52, "label_issues_list": 52, "labels_list": 52, "pred_probs_list": [52, 60, 82, 83], "anim": [53, 88], "rat": 53, "predat": 53, "pet": 53, "reptil": 53, "manner": [54, 85, 90, 92, 93], "box": [55, 57, 58, 80, 87], "object_detect": [55, 57, 58, 87], "return_indices_ranked_by_scor": [55, 87], "overlapping_label_check": [55, 57], "suboptim": [55, 57], "locat": [55, 57, 87, 91, 94], "bbox": [55, 58, 87], "image_nam": [55, 58], "y1": [55, 58, 87], "y2": [55, 58, 87], "later": [55, 58, 59, 93, 94], "mmdetect": [55, 58, 87], "corner": [55, 58, 87], "swap": [55, 57, 67, 71], "penal": [55, 57], "concern": [55, 57, 72, 76], "aggregation_weight": 57, "imperfect": [57, 81], "chose": [57, 85, 87], "imperfectli": [57, 87], "dirti": [57, 60, 63, 90], "subtyp": 57, "badloc": 57, "nonneg": 57, "issues_from_scor": [57, 66, 67, 70, 71, 87, 91, 94], "compute_overlooked_box_scor": 57, "high_probability_threshold": 57, "auxiliary_input": [57, 58], "vari": [57, 76], "iou": [57, 58], "heavili": 57, "auxiliarytypesdict": 57, "pred_label": [57, 93], "pred_label_prob": 57, "pred_bbox": 57, "lab_label": 57, "lab_bbox": 57, "similarity_matrix": 57, "min_possible_similar": 57, "scores_overlook": 57, "compute_badloc_box_scor": 57, "low_probability_threshold": 57, "scores_badloc": 57, "compute_swap_box_scor": 57, "accident": [57, 78, 79, 81, 93], "scores_swap": 57, "pool_box_scores_per_imag": 57, "box_scor": 57, "image_scor": [57, 66, 91], "object_counts_per_imag": 58, "discov": [58, 76, 94], "auxiliari": [58, 88, 91], "_get_valid_inputs_for_compute_scor": 58, "object_count": 58, "bounding_box_size_distribut": 58, "down": 58, "bbox_siz": 58, "class_label_distribut": 58, "class_distribut": 58, "get_sorted_bbox_count_idx": 58, "plot": [58, 75, 76, 83, 86, 88, 90, 91], "sorted_idx": [58, 88], "plot_class_size_distribut": 58, "class_to_show": 58, "hidden": [58, 88], "max_class_to_show": 58, "plot_class_distribut": 58, "visual": [58, 75, 76, 82, 90, 92, 94], "prediction_threshold": 58, "overlai": [58, 87], "figsiz": [58, 75, 76, 82, 83, 86, 88], "save_path": [58, 87], "blue": [58, 80, 83, 87], "overlaid": 58, "side": [58, 80, 87], "figur": [58, 83, 86, 88, 90], "extens": [58, 83, 85], "png": [58, 87], "pdf": [58, 59], "svg": 58, "matplotlib": [58, 75, 76, 82, 83, 86, 87, 88, 90], "get_average_per_class_confusion_matrix": 58, "num_proc": [58, 82], "intersect": [58, 81], "tp": 58, "fp": 58, "ground": [58, 80, 83, 85, 90], "truth": [58, 83, 85, 90], "bias": 58, "avg_metr": 58, "distionari": 58, "95": [58, 68, 70, 76, 78, 80, 83, 90, 91], "calculate_per_class_metr": 58, "per_class_metr": 58, "Of": 59, "li": 59, "smaller": [59, 86, 87], "find_top_issu": [59, 60, 88], "reli": [59, 74, 75, 76, 79, 87, 88, 93], "dist_metr": 59, "dim": [59, 82, 91], "subtract": [59, 60], "renorm": [59, 60, 81], "least_confid": 59, "sum_": 59, "log": [59, 60, 73], "softmax": [59, 66, 70, 82], "literatur": 59, "gen": 59, "liu": 59, "lochman": 59, "zach": 59, "openaccess": 59, "thecvf": 59, "content": [59, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "cvpr2023": 59, "liu_gen_pushing_the_limits_of_softmax": 59, "based_out": 59, "distribution_detection_cvpr_2023_pap": 59, "fit_scor": [59, 88], "ood_predictions_scor": 59, "pretrain": [59, 74, 79, 88, 93], "adjust_confident_threshold": 59, "probabilist": [59, 74, 75, 76, 78, 79, 88, 89, 92], "order_label_issu": [60, 73], "whichev": [60, 89], "argsort": [60, 79, 82, 83, 88, 90, 93], "max_": 60, "get_label_quality_ensemble_scor": [60, 81, 83], "weight_ensemble_members_bi": 60, "custom_weight": 60, "log_loss_search_t_valu": 60, "0001": [60, 80], "scheme": 60, "log_loss_search": 60, "log_loss": [60, 79], "1e0": 60, "1e1": 60, "1e2": 60, "2e2": 60, "quality_scor": [60, 88], "forth": 60, "top_issue_indic": 60, "rank_bi": [60, 73], "weird": [60, 71], "minu": 60, "prob_label": 60, "max_prob_not_label": 60, "idea": 60, "AND": [60, 79], "corrupt": [62, 90], "linearregress": [62, 81, 90], "y_with_nois": 62, "n_boot": [62, 81], "include_aleatoric_uncertainti": [62, 81], "sole": [62, 75, 85, 88, 92], "bootstrap": [62, 81, 90], "resampl": [62, 74, 81], "epistem": [62, 81, 88, 90], "aleator": [62, 81, 90], "model_final_kwarg": 62, "coars": 62, "thorough": [62, 81], "fine": [62, 74, 79, 88, 93], "grain": 62, "grid": 62, "get_epistemic_uncertainti": 62, "varianc": [62, 83], "epistemic_uncertainti": 62, "get_aleatoric_uncertainti": 62, "residu": [62, 63, 81], "deviat": [62, 90], "ie": 62, "aleatoric_uncertainti": 62, "outr": 63, "contin": 63, "raw": [63, 72, 73, 76, 80, 82, 85, 87, 88], "aka": [63, 74, 83, 94], "00323821": 63, "33692597": 63, "00191686": 63, "semant": [64, 66, 67, 84], "pixel": [64, 66, 67, 88, 91], "h": [64, 66, 67, 91], "height": [64, 66, 67, 91], "w": [64, 66, 67, 91], "width": [64, 66, 67, 91], "labels_one_hot": [64, 67, 91], "stream": [64, 88, 94], "downsampl": [64, 66, 91], "shrink": [64, 66], "divis": [64, 66, 75], "segmant": [66, 67], "num_pixel_issu": [66, 91], "product": [66, 81, 82], "pixel_scor": [66, 91], "display_issu": [66, 67, 68, 70, 71, 91, 94], "highlight": [67, 71, 75, 76, 78, 91], "enter": 67, "legend": [67, 75, 76, 86, 87, 90, 91], "colormap": 67, "background": 67, "person": [67, 81, 87, 91, 94], "common_label_issu": [67, 71, 91, 94], "ambigu": [67, 71, 74, 79, 80, 83, 93, 94], "systemat": [67, 71, 85], "misunderstood": [67, 71], "issues_df": [67, 82], "filter_by_class": [67, 91], "class_index": 67, "issues_subset": [67, 71], "token_score_method": 70, "sentence_score_method": 70, "sentence_score_kwarg": 70, "compris": [70, 71], "token_scor": [70, 94], "converg": 70, "toward": 70, "_softmin_sentence_scor": 70, "sentence_scor": [70, 94], "token_info": 70, "70": [70, 78, 90, 91], "02": [70, 75, 76, 82, 83, 87, 91], "03": [70, 78, 80, 83, 87, 91, 94], "04": [70, 78, 82, 87, 91], "08": [70, 79, 83, 87, 91, 94], "commonli": [71, 73, 75, 76, 86, 94], "filter_by_token": [71, 94], "But": [71, 79, 83, 94], "restrict": [71, 81], "reliabl": [72, 74, 81, 85, 91, 92], "thousand": 72, "imagenet": [72, 80], "popular": [72, 85, 87], "centric": [72, 78, 79, 82, 84], "capabl": 72, "minut": [72, 74, 78, 79, 80, 85, 86, 87, 90, 91, 92, 93, 94], "conda": 72, "feature_embed": [72, 88], "Then": [72, 81, 82, 90, 92, 93], "your_dataset": [72, 74, 75, 76, 78, 79, 81, 82], "column_name_of_label": [72, 74, 75, 76, 78, 79, 82], "plagu": [72, 76], "untrain": 72, "\u30c4": 72, "label_issues_info": [72, 76], "sklearn_compatible_model": 72, "framework": [72, 86, 87], "complianc": 72, "tag": [72, 86, 94], "sequenc": 72, "recognit": [72, 74, 81, 94], "train_data": [72, 88, 90, 92, 93], "gotten": 72, "test_data": [72, 83, 86, 88, 90, 92, 93], "deal": [72, 76], "tutori": [72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "feel": [72, 74, 76, 81], "free": [72, 74, 76, 78, 79, 81, 82, 83], "ask": [72, 81], "slack": [72, 81], "project": [72, 90], "welcom": 72, "commun": [72, 81], "guidelin": [72, 87], "piec": 72, "studio": [72, 76, 78, 79, 81, 82], "platform": [72, 78, 79, 81, 82], "automl": [72, 81], "foundat": 72, "smart": [72, 78, 79, 81, 82], "edit": [72, 81], "easier": [72, 83], "unreli": [72, 74, 78, 79, 92], "older": 73, "outlin": 73, "substitut": 73, "v2": [73, 78, 92], "get_noise_indic": 73, "psx": 73, "sorted_index_method": 73, "order_label_error": 73, "label_errors_bool": 73, "latent_estim": 73, "num_label_error": 73, "learningwithnoisylabel": 73, "neatli": 73, "organ": [73, 78, 80, 92, 94], "reorgan": 73, "baseline_method": 73, "incorpor": [73, 83], "research": [73, 83], "polyplex": 73, "terminologi": 73, "label_error": 73, "quickstart": [74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "spoken": 74, "500": [74, 88, 94], "english": [74, 80], "pronunci": 74, "wav": 74, "huggingfac": [74, 75, 76, 82], "voxceleb": 74, "speech": [74, 94], "your_pred_prob": [74, 75, 76, 78, 79], "tensorflow_io": 74, "26": [74, 75, 80, 82, 83, 85, 87, 91], "huggingface_hub": 74, "12": [74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "branch": [74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "reproduc": [74, 78, 83, 85], "command": 74, "wget": [74, 87, 91, 94], "navig": 74, "link": [74, 80, 87], "browser": 74, "jakobovski": 74, "archiv": [74, 94], "v1": 74, "tar": [74, 88], "gz": [74, 88], "mkdir": [74, 94], "spoken_digit": 74, "xf": 74, "6_nicolas_32": 74, "data_path": 74, "listdir": 74, "nondeterminist": 74, "file_nam": 74, "endswith": 74, "file_path": 74, "join": [74, 81, 82], "39": [74, 75, 79, 80, 81, 82, 87, 90, 91, 93, 94], "7_george_26": 74, "0_nicolas_24": 74, "0_nicolas_6": 74, "listen": 74, "display_exampl": 74, "click": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "expand": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "pulldown": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "colab": [74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "tfio": 74, "pathlib": 74, "ipython": 74, "load_wav_16k_mono": 74, "filenam": 74, "khz": 74, "file_cont": 74, "io": [74, 80], "read_fil": 74, "sample_r": 74, "decode_wav": 74, "desired_channel": 74, "squeez": 74, "rate_in": 74, "rate_out": 74, "16000": 74, "wav_file_nam": 74, "audio_r": 74, "wav_file_exampl": 74, "plai": [74, 80, 81], "button": 74, "wav_file_name_exampl": 74, "7_jackson_43": 74, "hear": 74, "extractor": 74, "encoderclassifi": 74, "spkrec": 74, "xvect": 74, "feature_extractor": 74, "from_hparam": 74, "run_opt": 74, "uncom": 74, "wav_audio_file_path": 74, "head": [74, 76, 78, 79, 80, 82, 83, 85, 90, 92, 93], "torchaudio": 74, "extract_audio_embed": 74, "emb": [74, 82], "signal": 74, "encode_batch": 74, "embeddings_list": [74, 82], "embeddings_arrai": 74, "512": [74, 82], "14": [74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "196315": 74, "3194594": 74, "478977": 74, "2890828": 74, "8170278": 74, "892647": 74, "24": [74, 80, 82, 83, 85, 87, 91], "898054": 74, "256194": 74, "559642": 74, "559715": 74, "620667": 74, "285246": 74, "21": [74, 75, 80, 81, 83, 87, 91, 94], "709623": 74, "5033712": 74, "913803": 74, "8198366": 74, "1831512": 74, "208761": 74, "08426": 74, "3210406": 74, "005453": 74, "2161605": 74, "478239": 74, "682179": 74, "0538025": 74, "242471": 74, "0914207": 74, "7833488": 74, "039538": 74, "23": [74, 80, 82, 83, 87, 91], "56918": 74, "19": [74, 79, 80, 81, 82, 83, 88, 90, 91, 93], "761095": 74, "1258287": 74, "753235": 74, "3508894": 74, "598273": 74, "237122": 74, "2500": 74, "leverag": [74, 79, 81, 83, 85, 93], "tune": [74, 79, 80, 88, 93], "computation": [74, 79, 93], "intens": [74, 79, 93], "held": [74, 78, 79, 80, 87, 88, 89, 92], "straightforward": [74, 78, 92], "benefit": [74, 89, 91, 92], "tol": 74, "num_crossval_fold": [74, 78, 85, 92], "decreas": [74, 81], "never": [74, 83, 86, 88, 89], "accuracy_scor": [74, 79, 83, 92, 93], "cv_accuraci": 74, "9772": 74, "probabilit": [74, 93], "9980": 74, "176": [74, 80, 83, 86], "006488": 74, "2318": 74, "008269": 74, "986": 74, "010354": 74, "469": 74, "013459": 74, "516": 74, "013478": 74, "investig": 74, "100541": 74, "998729": 74, "998768": 74, "980980": 74, "998217": 74, "18": [74, 79, 80, 81, 82, 83, 87, 88, 90, 91, 93, 94], "identified_label_issu": [74, 79], "lowest_quality_label": [74, 79, 83, 90, 93], "sort_valu": [74, 76, 78, 79, 81, 82, 83, 85], "1946": 74, "1871": 74, "1955": 74, "2132": 74, "worth": [74, 83], "iloc": [74, 78, 79, 90, 92, 93], "6_yweweler_35": 74, "6_yweweler_36": 74, "6_yweweler_14": 74, "6_theo_27": 74, "4_george_31": 74, "6_nicolas_8": 74, "sound": 74, "quit": [74, 88], "22": [74, 75, 80, 82, 83, 86, 87, 91, 94], "blindli": [74, 81, 90, 92, 93], "trust": [74, 81, 83, 85, 89, 90, 92, 93], "underneath": 75, "hood": 75, "alert": 75, "introduct": 75, "mayb": [75, 76, 79], "examin": [75, 76, 78, 92], "your_feature_matrix": [75, 76], "toi": [75, 76, 80, 82, 83, 85], "train_test_split": [75, 76, 88, 92, 93], "inf": [75, 76], "mid": [75, 76], "bins_map": [75, 76], "create_data": [75, 76], "y_bin": [75, 76], "y_i": [75, 76], "y_bin_idx": [75, 76], "y_train": [75, 76, 83, 90], "y_test": [75, 76, 83, 90], "y_train_idx": [75, 76], "y_test_idx": [75, 76], "test_siz": [75, 76, 92, 93], "slide": [75, 76, 80], "decis": [75, 76, 92], "boundari": [75, 76], "frame": [75, 76], "x_out": [75, 76], "tini": [75, 76], "concaten": [75, 76, 81, 89], "y_out": [75, 76], "y_out_bin": [75, 76], "y_out_bin_idx": [75, 76], "exact_duplicate_idx": [75, 76], "x_duplic": [75, 76], "y_duplic": [75, 76], "y_duplicate_idx": [75, 76], "noisy_labels_idx": [75, 76, 86], "scatter": [75, 76, 83, 86, 90], "black": [75, 76, 80, 90], "cyan": [75, 76], "pyplot": [75, 76, 82, 83, 86, 88, 90], "plt": [75, 76, 82, 83, 86, 88, 90], "plot_data": [75, 76, 83, 86, 90], "fig": [75, 76, 80, 82, 88, 90], "ax": [75, 76, 82, 88, 90], "subplot": [75, 76, 82, 88], "set_titl": [75, 76, 82, 88], "set_xlabel": [75, 76], "x_1": [75, 76], "fontsiz": [75, 76, 82, 83, 86], "set_ylabel": [75, 76], "x_2": [75, 76], "set_xlim": [75, 76], "set_ylim": [75, 76], "linestyl": [75, 76], "circl": [75, 76, 83, 86], "misclassifi": [75, 76], "zip": [75, 76, 82, 87, 94], "label_err": [75, 76], "180": [75, 76, 87], "marker": [75, 76], "facecolor": [75, 76], "edgecolor": [75, 76], "linewidth": [75, 76, 88], "dup": [75, 76], "first_legend": [75, 76], "align": [75, 76], "title_fontproperti": [75, 76], "semibold": [75, 76], "second_legend": [75, 76], "45": [75, 76, 80, 82, 83, 87, 91], "gca": [75, 76], "add_artist": [75, 76], "tight_layout": [75, 76], "ideal": [75, 76], "logist": [75, 76, 79, 85, 88, 93], "remaind": 75, "modal": [75, 76, 81, 85], "regardless": [75, 76], "132": [75, 76, 83, 87], "9318": 75, "77": [75, 76, 78, 87, 91, 92], "006939": 75, "007830": 75, "40": [75, 76, 79, 80, 82, 91], "014826": 75, "107": [75, 76, 83, 86], "021220": 75, "120": [75, 76, 92], "026403": 75, "notic": [75, 83, 85, 87], "3558": [75, 76], "126": [75, 76, 83, 87], "006636": [75, 76], "130": [75, 76], "012571": [75, 76], "129": [75, 76], "127": [75, 76], "014909": [75, 76], "128": [75, 76, 82], "017443": [75, 76], "6160": [75, 76], "is_near_duplicate_issu": [75, 76, 78, 79, 81, 82, 83], "131": [75, 76, 91], "000000e": [75, 76], "00": [75, 76, 78, 80, 82, 91, 92], "000002": [75, 76], "463180e": [75, 76], "07": [75, 76, 78, 82, 83, 87, 91, 94], "51": [75, 76, 78, 80, 83, 87, 91], "161148": [75, 76], "859087e": [75, 76], "30": [75, 76, 80, 81, 82, 86, 91, 94], "3453": 75, "029542": 75, "031182": 75, "057961": 75, "058244": 75, "home": [75, 76, 79, 80, 88, 93], "runner": [75, 76, 79, 88, 93], "300": [75, 85, 94], "userwarn": [75, 76], "330": [75, 82, 87], "309": 75, "34": [75, 80, 82, 83, 85, 87, 88, 91, 94], "54": [75, 80, 82, 83, 87, 91], "039117": 75, "53": [75, 76, 78, 80, 86, 87, 91, 92], "044594": 75, "105": 75, "105121": 75, "133588": 75, "43": [75, 80, 82, 83, 87, 91, 93], "168035": 75, "125": 75, "101107": 75, "37": [75, 80, 91], "183382": 75, "109": [75, 80, 87], "209259": 75, "211042": 75, "221316": 75, "average_ood_scor": 75, "34530442089193386": 75, "52": [75, 80, 82, 87, 91, 94], "169820": 75, "087324e": 75, "89": [75, 78, 87, 90, 91], "92": [75, 83, 87, 91, 92], "259024": 75, "583757e": 75, "91": [75, 87, 91, 93], "346458": 75, "341292e": 75, "specfi": 75, "new_lab": 75, "scoring_funct": 75, "div": 75, "rem": 75, "inv_scal": 75, "49": [75, 80, 83, 87, 91], "superstitionissuemanag": 75, "unlucki": 75, "superstit": 75, "to_seri": 75, "issues_mask": 75, "summary_scor": 75, "9242": 75, "is_superstition_issu": 75, "superstition_scor": 75, "047581": 75, "090635": 75, "129591": 75, "65": [75, 82, 87, 91, 92], "164840": 75, "demo": [76, 78, 86, 92], "lurk": [76, 82, 83], "opt": 76, "hostedtoolcach": 76, "x64": 76, "lib": 76, "python3": 76, "site": 76, "_split": 76, "737": 76, "thoroughli": 76, "preprocess": [76, 78, 88, 90, 92, 93], "904": 76, "review": [76, 78, 79, 80, 81, 83, 87, 90, 91, 92, 93, 94], "8561": 76, "001894": 76, "58": [76, 78, 80, 83, 87, 91, 92, 94], "003565": 76, "007326": 76, "008974": 76, "009699": 76, "0227": 76, "is_class_imbalance_issu": [76, 78, 79, 82, 83], "022727": 76, "86": [76, 78, 82, 83, 87, 90, 91, 92], "87": [76, 82, 87, 90, 91, 93], "0000": [76, 79, 80, 82, 83], "is_null_issu": [76, 79, 82, 83], "96": [76, 78, 80, 83, 86, 87, 90, 91, 94], "94": [76, 78, 80, 83, 87, 90, 91, 92], "93": [76, 80, 87, 90, 91, 92], "8218": 76, "is_non_iid_issu": [76, 78, 79, 82, 83], "810274": 76, "826147": 76, "849587": 76, "855359": 76, "855485": 76, "821750488732925": 76, "auto": [76, 80, 81, 90, 92, 93], "conceptu": 76, "856061": 76, "355772": 76, "616034": 76, "821750": 76, "betweeen": 76, "859109": 76, "417707": 76, "664083": 76, "970324": 76, "816965": 76, "375317": 76, "641516": 76, "890575": 76, "530924": 76, "460593": 76, "601188": 76, "752776": 76, "321635": 76, "562539": 76, "948362": 76, "090224": 76, "472909": 76, "746763": 76, "878267": 76, "examples_w_issu": [76, 81], "013444": 76, "025173": 76, "026416": 76, "inde": [76, 79], "miscellan": [76, 94], "428571": 76, "111111": 76, "571429": 76, "407407": 76, "592593": 76, "337838": 76, "092593": 76, "662162": 76, "333333": [76, 80], "952381": 76, "666667": 76, "portion": 76, "huge": [76, 83], "worri": [76, 79], "critic": 76, "highli": [76, 82], "sql": [78, 92], "databas": [78, 92], "excel": [78, 92], "parquet": [78, 92], "student": [78, 90, 92, 94], "grade": [78, 90, 92], "900": [78, 90, 92], "exam": [78, 90, 92], "letter": [78, 92, 94], "hundr": [78, 92], "histgradientboostingclassifi": 78, "standardscal": [78, 88, 92], "possibli": [78, 92], "grades_data": [78, 92], "read_csv": [78, 79, 90, 92, 93], "stud_id": [78, 92], "exam_1": [78, 90, 92], "exam_2": [78, 90, 92], "exam_3": [78, 90, 92], "letter_grad": [78, 92], "f48f73": [78, 92], "0bd4e7": [78, 92], "81": [78, 79, 87, 90, 91, 92, 94], "great": [78, 80, 92], "particip": [78, 92], "cb9d7a": [78, 92], "61": [78, 82, 83, 87, 91, 92], "78": [78, 80, 82, 83, 87, 90, 91, 92], "9acca4": [78, 92], "48": [78, 80, 83, 87, 91, 92], "x_raw": [78, 92], "cat_featur": 78, "x_encod": [78, 92], "get_dummi": [78, 90, 92], "drop_first": [78, 92], "numeric_featur": [78, 92], "scaler": [78, 88, 92], "x_process": [78, 92], "fit_transform": [78, 92], "bring": [78, 79, 82, 85, 90, 92, 93], "byod": [78, 79, 82, 85, 90, 92, 93], "boost": [78, 81, 85, 90], "xgboost": [78, 81, 90], "think": [78, 79, 81, 86, 91, 94], "carefulli": [78, 79, 82, 92], "nonzero": 78, "suspici": [78, 92], "tabl": [78, 80, 85, 92], "358": 78, "294": [78, 87], "46": [78, 80, 83, 87, 91], "941": 78, "7109": 78, "000005": [78, 79, 82], "886": 78, "000059": 78, "709": 78, "000104": 78, "723": 78, "000169": 78, "689": 78, "000181": 78, "3590": 78, "051882e": 78, "683133e": 78, "536582e": 78, "406589e": 78, "324246e": 78, "6165": 78, "582": 78, "185": [78, 80, 87], "187": [78, 80], "27": [78, 80, 83, 87, 91, 94], "898": 78, "637": [78, 92], "0014": [78, 80], "595": 78, "702427": 78, "147": [78, 83, 87], "711186": 78, "157": [78, 83], "721394": 78, "771": 78, "731979": 78, "740335": 78, "0014153602099278074": 78, "1562": 78, "393": 78, "156217": 78, "391": 78, "806": 78, "805": 78, "156": [78, 83], "na": [78, 79, 82, 83, 85], "issue_result": 78, "000842": 78, "555944": 78, "004374": 78, "sorted_issu": 78, "73": [78, 80, 86, 87, 90, 91, 94], "deserv": 78, "outlier_result": 78, "sorted_outli": 78, "56": [78, 80, 82, 90, 91, 94], "lt": [78, 79, 80, 82, 85, 91], "style": [78, 91], "font": 78, "18px": 78, "ff00ff": 78, "bac": 78, "unintend": [78, 79], "mistak": [78, 79, 82, 92, 93], "duplicate_result": 78, "690": 78, "246": [78, 87], "perhap": [78, 83, 85], "twice": 78, "67": [78, 80, 82, 87, 90, 91], "wari": [78, 79, 81], "super": [78, 79, 82], "system": [78, 79, 82, 91], "intent": [79, 93], "servic": [79, 81, 93], "onlin": [79, 93], "bank": [79, 80, 93], "banking77": [79, 93], "oo": [79, 93], "000": [79, 80, 82, 93, 94], "categori": [79, 82, 93], "scope": [79, 93], "dive": 79, "your_featur": 79, "sentence_transform": [79, 93], "sentencetransform": [79, 93], "payment": [79, 93], "cancel_transf": [79, 93], "transfer": [79, 93], "fund": [79, 93], "cancel": [79, 93], "transact": [79, 93], "my": [79, 93], "revert": [79, 93], "morn": [79, 93], "realis": [79, 93], "yesterdai": [79, 93], "rent": [79, 93], "realli": [79, 85, 91, 93], "tomorrow": [79, 93], "raw_text": [79, 93], "apple_pay_or_google_pai": [79, 93], "getting_spare_card": [79, 93], "visa_or_mastercard": [79, 93], "beneficiary_not_allow": [79, 93], "card_about_to_expir": [79, 93], "lost_or_stolen_phon": [79, 93], "supported_cards_and_curr": [79, 93], "card_payment_fee_charg": [79, 93], "change_pin": [79, 93], "utter": [79, 93], "continu": [79, 81, 82, 85, 90, 92, 93, 94], "suit": [79, 80, 81, 93], "electra": [79, 93], "discrimin": [79, 93], "googl": [79, 93], "text_embed": 79, "No": [79, 81, 93], "google_electra": [79, 93], "pool": [79, 81, 88, 93], "400": [79, 93], "data_dict": [79, 83, 85], "84": [79, 87, 91], "41": [79, 80, 87, 90, 91, 94], "38": [79, 80, 82, 87, 91], "9720": 79, "981": 79, "974": 79, "000150": 79, "982": [79, 80], "000218": 79, "971": 79, "000512": 79, "980": [79, 80], "000947": 79, "3584": 79, "994": 79, "009642": 79, "999": 79, "013067": 79, "013841": 79, "433": 79, "014722": 79, "989": 79, "018224": 79, "6070": 79, "160": [79, 83], "095724": 79, "148": 79, "006237": 79, "546": 79, "099340": 79, "514": 79, "006485": 79, "481": 79, "123416": 79, "008165": 79, "313": [79, 87], "564102": 79, "572258": 79, "28": [79, 80, 82, 83, 85, 91, 94], "574915": 79, "31": [79, 80, 82, 83, 85, 87, 91], "575507": 79, "575874": 79, "658": 79, "659": [79, 90], "660": 79, "661": 79, "0800": 79, "454": 79, "453": 79, "455": 79, "791961": 79, "258508": 79, "699010": 79, "183136": 79, "771112": 79, "to_numpi": [79, 81, 90, 93], "data_with_suggested_label": 79, "suggested_label": 79, "charg": [79, 93], "cash": [79, 93], "holidai": [79, 93], "sent": [79, 93, 94], "card": [79, 80, 93], "mine": [79, 93], "expir": [79, 93], "me": [79, 93], "withdraw": 79, "monei": 79, "whoever": [79, 93], "outlier_issu": [79, 82], "lowest_quality_outli": 79, "OR": 79, "636c65616e6c616220697320617765736f6d6521": 79, "phone": [79, 80], "gone": 79, "gt": [79, 85, 94], "samp": 79, "br": 79, "press": [79, 94], "nonsens": 79, "sens": 79, "detriment": 79, "duplicate_issu": 79, "fee": 79, "pai": 79, "go": [79, 80, 83], "strongli": 79, "p_valu": 79, "benign": 79, "shortlist": [79, 90, 93], "curat": [79, 84], "mnist_test_set": 80, "imagenet_val_set": 80, "tench": 80, "goldfish": 80, "white": [80, 94], "shark": 80, "tiger": 80, "hammerhead": 80, "electr": 80, "rai": 80, "stingrai": 80, "cock": 80, "hen": 80, "ostrich": 80, "brambl": 80, "goldfinch": 80, "hous": 80, "finch": 80, "junco": 80, "indigo": 80, "bunt": 80, "american": [80, 94], "robin": 80, "bulbul": 80, "jai": 80, "magpi": 80, "chickade": 80, "dipper": 80, "kite": 80, "bald": 80, "eagl": 80, "vultur": 80, "grei": 80, "owl": 80, "fire": 80, "salamand": 80, "smooth": 80, "newt": 80, "spot": [80, 87], "axolotl": 80, "bullfrog": 80, "tree": 80, "frog": [80, 88], "tail": 80, "loggerhead": 80, "sea": 80, "turtl": 80, "leatherback": 80, "mud": 80, "terrapin": 80, "band": 80, "gecko": 80, "green": [80, 94], "iguana": 80, "carolina": 80, "anol": 80, "desert": 80, "grassland": 80, "whiptail": 80, "lizard": 80, "agama": 80, "frill": 80, "neck": 80, "allig": 80, "gila": 80, "monster": 80, "european": 80, "chameleon": 80, "komodo": 80, "dragon": 80, "nile": 80, "crocodil": 80, "triceratop": 80, "worm": 80, "snake": 80, "ring": 80, "eastern": 80, "hog": 80, "nose": 80, "kingsnak": 80, "garter": 80, "water": 80, "vine": 80, "night": 80, "boa": 80, "constrictor": 80, "african": 80, "rock": 80, "indian": 80, "cobra": 80, "mamba": 80, "saharan": 80, "horn": 80, "viper": 80, "diamondback": 80, "rattlesnak": 80, "sidewind": 80, "trilobit": 80, "harvestman": 80, "scorpion": 80, "yellow": 80, "garden": 80, "spider": 80, "barn": 80, "southern": 80, "widow": 80, "tarantula": 80, "wolf": 80, "tick": 80, "centiped": 80, "grous": 80, "ptarmigan": 80, "ruf": 80, "prairi": 80, "peacock": 80, "quail": 80, "partridg": 80, "parrot": 80, "macaw": 80, "sulphur": 80, "crest": 80, "cockatoo": 80, "lorikeet": 80, "coucal": 80, "bee": 80, "eater": 80, "hornbil": 80, "hummingbird": 80, "jacamar": 80, "toucan": 80, "duck": [80, 93], "breast": 80, "mergans": 80, "goos": 80, "swan": 80, "tusker": 80, "echidna": 80, "platypu": 80, "wallabi": 80, "koala": 80, "wombat": 80, "jellyfish": 80, "anemon": 80, "brain": 80, "coral": 80, "flatworm": 80, "nematod": 80, "conch": 80, "snail": 80, "slug": 80, "chiton": 80, "chamber": 80, "nautilu": 80, "dung": 80, "crab": 80, "fiddler": 80, "king": 80, "lobster": 80, "spini": 80, "crayfish": 80, "hermit": 80, "isopod": 80, "stork": 80, "spoonbil": 80, "flamingo": 80, "heron": 80, "egret": 80, "bittern": 80, "crane": 80, "bird": [80, 88], "limpkin": 80, "gallinul": 80, "coot": 80, "bustard": 80, "ruddi": 80, "turnston": 80, "dunlin": 80, "redshank": 80, "dowitch": 80, "oystercatch": 80, "pelican": 80, "penguin": 80, "albatross": 80, "whale": 80, "killer": 80, "dugong": 80, "lion": 80, "chihuahua": 80, "japanes": 80, "chin": 80, "maltes": 80, "pekinges": 80, "shih": 80, "tzu": 80, "charl": 80, "spaniel": 80, "papillon": 80, "terrier": 80, "rhodesian": 80, "ridgeback": 80, "afghan": [80, 94], "hound": 80, "basset": 80, "beagl": 80, "bloodhound": 80, "bluetick": 80, "coonhound": 80, "tan": 80, "walker": 80, "foxhound": 80, "redbon": 80, "borzoi": 80, "irish": 80, "wolfhound": 80, "italian": 80, "greyhound": 80, "whippet": 80, "ibizan": 80, "norwegian": 80, "elkhound": 80, "otterhound": 80, "saluki": 80, "scottish": 80, "deerhound": 80, "weimaran": 80, "staffordshir": 80, "bull": 80, "bedlington": 80, "border": 80, "kerri": 80, "norfolk": 80, "norwich": 80, "yorkshir": 80, "wire": 80, "fox": 80, "lakeland": 80, "sealyham": 80, "airedal": 80, "cairn": 80, "australian": 80, "dandi": 80, "dinmont": 80, "boston": 80, "miniatur": 80, "schnauzer": 80, "giant": 80, "tibetan": 80, "silki": 80, "coat": [80, 82], "wheaten": 80, "west": 80, "highland": 80, "lhasa": 80, "apso": 80, "flat": 80, "retriev": 80, "curli": 80, "golden": 80, "labrador": 80, "chesapeak": 80, "bai": 80, "german": [80, 94], "shorthair": 80, "pointer": 80, "vizsla": 80, "setter": 80, "gordon": 80, "brittani": 80, "clumber": 80, "springer": 80, "welsh": 80, "cocker": 80, "sussex": 80, "kuvasz": 80, "schipperk": 80, "groenendael": 80, "malinoi": 80, "briard": 80, "kelpi": 80, "komondor": 80, "sheepdog": 80, "shetland": 80, "colli": 80, "bouvier": 80, "de": 80, "flandr": 80, "rottweil": 80, "shepherd": 80, "dobermann": 80, "pinscher": 80, "swiss": [80, 94], "mountain": 80, "bernes": 80, "appenzel": 80, "sennenhund": 80, "entlebuch": 80, "boxer": 80, "bullmastiff": 80, "mastiff": 80, "french": 80, "bulldog": 80, "dane": 80, "st": 80, "bernard": 80, "huski": 80, "alaskan": 80, "malamut": 80, "siberian": 80, "dalmatian": 80, "affenpinsch": 80, "basenji": 80, "pug": 80, "leonberg": 80, "newfoundland": 80, "pyrenean": 80, "samoi": 80, "pomeranian": 80, "chow": 80, "keeshond": 80, "griffon": 80, "bruxelloi": 80, "pembrok": 80, "corgi": 80, "cardigan": 80, "poodl": 80, "mexican": 80, "hairless": 80, "tundra": 80, "coyot": 80, "dingo": 80, "dhole": 80, "wild": 80, "hyena": 80, "kit": 80, "arctic": 80, "tabbi": 80, "persian": 80, "siames": 80, "egyptian": 80, "mau": 80, "cougar": 80, "lynx": 80, "leopard": 80, "snow": 80, "jaguar": 80, "cheetah": 80, "brown": [80, 91], "bear": 80, "polar": 80, "sloth": 80, "mongoos": 80, "meerkat": 80, "beetl": 80, "ladybug": 80, "longhorn": 80, "leaf": 80, "rhinocero": 80, "weevil": 80, "fly": 80, "ant": 80, "grasshopp": 80, "cricket": 80, "stick": 80, "insect": 80, "cockroach": 80, "manti": 80, "cicada": 80, "leafhopp": 80, "lacew": 80, "dragonfli": 80, "damselfli": 80, "admir": 80, "ringlet": 80, "monarch": 80, "butterfli": 80, "gossam": 80, "wing": 80, "starfish": 80, "urchin": 80, "cucumb": 80, "cottontail": 80, "rabbit": 80, "hare": 80, "angora": 80, "hamster": 80, "porcupin": 80, "squirrel": 80, "marmot": 80, "beaver": 80, "guinea": 80, "pig": 80, "sorrel": 80, "zebra": 80, "boar": 80, "warthog": 80, "hippopotamu": 80, "ox": 80, "buffalo": 80, "bison": 80, "bighorn": 80, "sheep": 80, "alpin": 80, "ibex": 80, "hartebeest": 80, "impala": 80, "gazel": 80, "dromedari": 80, "llama": 80, "weasel": 80, "mink": 80, "polecat": 80, "foot": 80, "ferret": 80, "otter": 80, "skunk": 80, "badger": 80, "armadillo": 80, "toed": 80, "orangutan": 80, "gorilla": 80, "chimpanze": 80, "gibbon": 80, "siamang": 80, "guenon": 80, "pata": 80, "monkei": 80, "baboon": 80, "macaqu": 80, "langur": 80, "colobu": 80, "probosci": 80, "marmoset": 80, "capuchin": 80, "howler": 80, "titi": 80, "geoffroi": 80, "lemur": 80, "indri": 80, "asian": 80, "eleph": 80, "bush": 80, "snoek": 80, "eel": 80, "coho": 80, "salmon": 80, "beauti": 80, "clownfish": 80, "sturgeon": 80, "garfish": 80, "lionfish": 80, "pufferfish": 80, "abacu": 80, "abaya": 80, "academ": 80, "gown": 80, "accordion": 80, "acoust": 80, "guitar": 80, "aircraft": 80, "carrier": 80, "airlin": 80, "airship": 80, "altar": 80, "ambul": 80, "amphibi": 80, "clock": [80, 94], "apiari": 80, "apron": 80, "wast": 80, "assault": 80, "rifl": 80, "backpack": 80, "bakeri": 80, "balanc": 80, "beam": 80, "balloon": 80, "ballpoint": 80, "pen": 80, "aid": 80, "banjo": 80, "balust": 80, "barbel": 80, "barber": 80, "chair": [80, 87], "barbershop": 80, "baromet": 80, "barrel": 80, "wheelbarrow": 80, "basebal": 80, "basketbal": 80, "bassinet": 80, "bassoon": 80, "swim": 80, "cap": 80, "bath": 80, "towel": 80, "bathtub": 80, "station": 80, "wagon": 80, "lighthous": 80, "beaker": 80, "militari": 80, "beer": 80, "bottl": 80, "glass": 80, "bell": 80, "cot": 80, "bib": 80, "bicycl": [80, 91], "bikini": 80, "binder": 80, "binocular": 80, "birdhous": 80, "boathous": 80, "bobsleigh": 80, "bolo": 80, "tie": 80, "poke": 80, "bonnet": 80, "bookcas": 80, "bookstor": 80, "bow": 80, "brass": 80, "bra": 80, "breakwat": 80, "breastplat": 80, "broom": 80, "bucket": 80, "buckl": 80, "bulletproof": 80, "vest": 80, "butcher": 80, "shop": 80, "taxicab": 80, "cauldron": 80, "candl": 80, "cannon": 80, "cano": 80, "mirror": [80, 87], "carousel": 80, "tool": [80, 83, 85], "carton": 80, "wheel": 80, "teller": 80, "cassett": 80, "player": 80, "castl": 80, "catamaran": 80, "cd": 80, "cello": 80, "mobil": [80, 94], "chain": 80, "fenc": [80, 91], "mail": 80, "chainsaw": 80, "chest": 80, "chiffoni": 80, "chime": 80, "china": 80, "cabinet": 80, "christma": 80, "stock": 80, "church": 80, "movi": 80, "theater": 80, "cleaver": 80, "cliff": 80, "dwell": 80, "cloak": 80, "clog": 80, "cocktail": 80, "shaker": 80, "coffe": 80, "mug": 80, "coffeemak": 80, "coil": 80, "lock": 80, "keyboard": 80, "confectioneri": 80, "ship": [80, 88], "corkscrew": 80, "cornet": 80, "cowboi": 80, "boot": 80, "hat": 80, "cradl": 80, "crash": 80, "helmet": 80, "crate": 80, "infant": 80, "bed": 80, "crock": 80, "pot": 80, "croquet": 80, "crutch": 80, "cuirass": 80, "dam": 80, "desk": 80, "desktop": 80, "rotari": 80, "dial": 80, "telephon": 80, "diaper": 80, "watch": 80, "dine": 80, "dishcloth": 80, "dishwash": 80, "disc": 80, "brake": 80, "dock": 80, "sled": 80, "dome": 80, "doormat": 80, "drill": 80, "rig": 80, "drum": 80, "drumstick": 80, "dumbbel": 80, "dutch": 80, "oven": 80, "fan": 80, "locomot": 80, "entertain": 80, "center": 80, "envelop": 80, "espresso": 80, "powder": 80, "feather": 80, "fireboat": 80, "engin": [80, 91], "screen": 80, "sheet": 80, "flagpol": 80, "flute": 80, "footbal": 80, "forklift": 80, "fountain": 80, "poster": 80, "freight": 80, "fry": 80, "pan": 80, "fur": 80, "garbag": 80, "ga": 80, "pump": 80, "goblet": 80, "kart": 80, "golf": 80, "cart": 80, "gondola": 80, "gong": 80, "grand": 80, "piano": 80, "greenhous": 80, "grill": 80, "groceri": 80, "guillotin": 80, "barrett": 80, "hair": 80, "sprai": 80, "hammer": 80, "dryer": 80, "hand": [80, 83], "handkerchief": 80, "drive": 80, "harmonica": 80, "harp": 80, "harvest": 80, "hatchet": 80, "holster": 80, "honeycomb": 80, "hoop": 80, "skirt": 80, "horizont": 80, "bar": 80, "hors": [80, 88, 93], "drawn": 80, "hourglass": 80, "ipod": 80, "cloth": 80, "iron": 80, "jack": 80, "lantern": 80, "jean": 80, "jeep": 80, "shirt": [80, 82], "jigsaw": 80, "puzzl": 80, "pull": 80, "rickshaw": 80, "joystick": 80, "kimono": 80, "knee": 80, "pad": 80, "knot": 80, "ladl": 80, "lampshad": 80, "laptop": 80, "lawn": 80, "mower": 80, "knife": 80, "lifeboat": 80, "lighter": 80, "limousin": 80, "ocean": 80, "liner": 80, "lipstick": 80, "slip": 80, "shoe": 80, "lotion": 80, "speaker": 80, "loup": 80, "sawmil": 80, "magnet": 80, "compass": 80, "bag": [80, 82, 88, 89], "mailbox": 80, "tight": 80, "tank": 80, "manhol": 80, "maraca": 80, "marimba": 80, "maypol": 80, "maze": 80, "cup": [80, 87], "medicin": 80, "megalith": 80, "microphon": 80, "microwav": 80, "milk": 80, "minibu": 80, "miniskirt": 80, "minivan": 80, "missil": 80, "mitten": 80, "mix": 80, "bowl": 80, "modem": 80, "monasteri": 80, "monitor": 80, "mope": 80, "mortar": 80, "mosqu": 80, "mosquito": 80, "scooter": 80, "bike": 80, "tent": 80, "mous": [80, 81], "mousetrap": 80, "van": 80, "muzzl": 80, "nail": 80, "brace": 80, "necklac": 80, "nippl": 80, "obelisk": 80, "obo": 80, "ocarina": 80, "odomet": 80, "oil": 80, "oscilloscop": 80, "overskirt": 80, "bullock": 80, "oxygen": 80, "packet": 80, "paddl": 80, "padlock": 80, "paintbrush": 80, "pajama": 80, "palac": [80, 94], "parachut": 80, "park": 80, "bench": 80, "meter": 80, "passeng": 80, "patio": 80, "payphon": 80, "pedest": 80, "pencil": 80, "perfum": 80, "petri": 80, "dish": 80, "photocopi": 80, "plectrum": 80, "pickelhaub": 80, "picket": 80, "pickup": 80, "pier": 80, "piggi": 80, "pill": 80, "pillow": 80, "ping": 80, "pong": 80, "pinwheel": 80, "pirat": 80, "pitcher": 80, "plane": 80, "planetarium": 80, "plastic": 80, "plate": 80, "rack": 80, "plow": 80, "plunger": 80, "polaroid": 80, "camera": 80, "pole": [80, 91], "polic": 80, "poncho": 80, "billiard": 80, "soda": 80, "potter": 80, "prayer": 80, "rug": 80, "printer": 80, "prison": 80, "projectil": 80, "projector": 80, "hockei": 80, "puck": 80, "punch": 80, "purs": 80, "quill": 80, "quilt": 80, "race": 80, "racket": 80, "radiat": 80, "radio": 80, "telescop": 80, "rain": 80, "recreat": 80, "reel": 80, "reflex": 80, "refriger": 80, "remot": 80, "restaur": 80, "revolv": 80, "rotisseri": 80, "eras": 80, "rugbi": 80, "ruler": 80, "safe": 80, "safeti": 80, "salt": 80, "sandal": [80, 82], "sarong": 80, "saxophon": 80, "scabbard": 80, "school": 80, "bu": [80, 91], "schooner": 80, "scoreboard": 80, "crt": 80, "screw": 80, "screwdriv": 80, "seat": 80, "belt": 80, "sew": 80, "shield": 80, "shoji": 80, "basket": 80, "shovel": 80, "shower": 80, "curtain": 80, "ski": 80, "sleep": 80, "door": 80, "slot": 80, "snorkel": 80, "snowmobil": 80, "snowplow": 80, "soap": 80, "dispens": 80, "soccer": [80, 94], "sock": 80, "solar": 80, "thermal": 80, "collector": 80, "sombrero": 80, "soup": 80, "heater": 80, "shuttl": 80, "spatula": 80, "motorboat": 80, "web": 80, "spindl": 80, "sport": [80, 94], "spotlight": 80, "stage": 80, "steam": 80, "arch": 80, "bridg": 80, "steel": 80, "stethoscop": 80, "scarf": 80, "stone": 80, "wall": [80, 91], "stopwatch": 80, "stove": 80, "strainer": 80, "tram": 80, "stretcher": 80, "couch": 80, "stupa": 80, "submarin": 80, "sundial": 80, "sunglass": 80, "sunscreen": 80, "suspens": 80, "mop": 80, "sweatshirt": 80, "swimsuit": 80, "swing": 80, "switch": 80, "syring": 80, "lamp": 80, "tape": 80, "teapot": 80, "teddi": 80, "televis": [80, 94], "tenni": 80, "thatch": 80, "roof": 80, "front": 80, "thimbl": 80, "thresh": 80, "throne": 80, "tile": 80, "toaster": 80, "tobacco": 80, "toilet": 80, "totem": 80, "tow": 80, "tractor": 80, "semi": 80, "trailer": 80, "trai": 80, "trench": 80, "tricycl": 80, "trimaran": 80, "tripod": 80, "triumphal": 80, "trolleybu": 80, "trombon": 80, "tub": 80, "turnstil": 80, "typewrit": 80, "umbrella": 80, "unicycl": 80, "upright": 80, "vacuum": 80, "cleaner": 80, "vase": 80, "vault": 80, "velvet": 80, "vend": 80, "vestment": 80, "viaduct": 80, "violin": 80, "volleybal": 80, "waffl": 80, "wallet": 80, "wardrob": 80, "sink": 80, "wash": 80, "jug": 80, "tower": 80, "whiskei": 80, "whistl": 80, "wig": 80, "shade": [80, 91], "windsor": 80, "wine": 80, "wok": 80, "wooden": 80, "spoon": 80, "wool": 80, "rail": 80, "shipwreck": 80, "yawl": 80, "yurt": 80, "websit": 80, "comic": 80, "book": 80, "crossword": 80, "traffic": [80, 87, 91], "sign": [80, 91, 94], "dust": 80, "jacket": [80, 87], "menu": 80, "guacamol": 80, "consomm": 80, "trifl": 80, "ic": 80, "cream": 80, "pop": 80, "baguett": 80, "bagel": 80, "pretzel": 80, "cheeseburg": 80, "mash": 80, "potato": 80, "cabbag": 80, "broccoli": 80, "cauliflow": 80, "zucchini": 80, "spaghetti": 80, "squash": 80, "acorn": 80, "butternut": 80, "artichok": 80, "pepper": 80, "cardoon": 80, "mushroom": 80, "granni": 80, "smith": 80, "strawberri": 80, "orang": 80, "lemon": 80, "pineappl": 80, "banana": 80, "jackfruit": 80, "custard": 80, "appl": 80, "pomegran": 80, "hai": 80, "carbonara": 80, "chocol": 80, "syrup": 80, "dough": 80, "meatloaf": 80, "pizza": 80, "pie": 80, "burrito": 80, "eggnog": 80, "alp": 80, "bubbl": 80, "reef": 80, "geyser": 80, "lakeshor": 80, "promontori": 80, "shoal": 80, "seashor": 80, "vallei": 80, "volcano": 80, "bridegroom": 80, "scuba": 80, "diver": 80, "rapese": 80, "daisi": 80, "ladi": 80, "slipper": 80, "corn": 80, "rose": 80, "hip": 80, "chestnut": 80, "fungu": 80, "agar": 80, "gyromitra": 80, "stinkhorn": 80, "earth": 80, "star": 80, "wood": 80, "bolet": 80, "ear": 80, "cifar10_test_set": 80, "airplan": [80, 88], "automobil": [80, 88], "deer": [80, 88], "cifar100_test_set": 80, "aquarium_fish": 80, "babi": 80, "boi": 80, "camel": 80, "caterpillar": 80, "cattl": [80, 94], "cloud": 80, "dinosaur": 80, "dolphin": 80, "flatfish": 80, "forest": 80, "girl": 80, "kangaroo": 80, "lawn_mow": 80, "man": 80, "maple_tre": 80, "motorcycl": [80, 91], "oak_tre": 80, "orchid": 80, "palm_tre": 80, "pear": 80, "pickup_truck": 80, "pine_tre": 80, "plain": 80, "poppi": 80, "possum": 80, "raccoon": 80, "road": [80, 91], "rocket": 80, "seal": 80, "shrew": 80, "skyscrap": 80, "streetcar": 80, "sunflow": 80, "sweet_pepp": 80, "trout": 80, "tulip": 80, "willow_tre": 80, "woman": [80, 87], "caltech256": 80, "ak47": 80, "bat": 80, "glove": 80, "birdbath": 80, "blimp": 80, "bonsai": 80, "boom": 80, "breadmak": 80, "buddha": 80, "bulldoz": 80, "cactu": 80, "cake": 80, "tire": 80, "cartman": 80, "cereal": 80, "chandeli": 80, "chess": 80, "board": 80, "chimp": 80, "chopstick": 80, "coffin": 80, "coin": 80, "comet": 80, "cormor": 80, "globe": 80, "diamond": 80, "dice": 80, "doorknob": 80, "drink": 80, "straw": 80, "dumb": 80, "eiffel": 80, "elk": 80, "ewer": 80, "eyeglass": 80, "fern": 80, "fighter": 80, "jet": [80, 90], "extinguish": 80, "hydrant": 80, "firework": 80, "flashlight": 80, "floppi": 80, "fri": 80, "frisbe": 80, "galaxi": 80, "giraff": 80, "goat": 80, "gate": 80, "grape": 80, "pick": [80, 81], "hamburg": 80, "hammock": 80, "harpsichord": 80, "hawksbil": 80, "helicopt": 80, "hibiscu": 80, "homer": 80, "simpson": 80, "horsesho": 80, "air": 80, "skeleton": 80, "ibi": 80, "cone": 80, "iri": 80, "jesu": 80, "christ": 80, "joi": 80, "kayak": 80, "ketch": 80, "ladder": 80, "lath": 80, "licens": 80, "lightbulb": 80, "lightn": 80, "mandolin": 80, "mar": 80, "mattress": 80, "megaphon": 80, "menorah": 80, "microscop": 80, "minaret": 80, "minotaur": 80, "motorbik": 80, "mussel": 80, "neckti": 80, "octopu": 80, "palm": 80, "pilot": 80, "paperclip": 80, "shredder": 80, "pci": 80, "peopl": [80, 87], "pez": 80, "picnic": 80, "pram": 80, "prai": 80, "pyramid": 80, "rainbow": 80, "roulett": 80, "saddl": 80, "saturn": 80, "segwai": 80, "propel": 80, "sextant": 80, "music": 80, "skateboard": 80, "smokestack": 80, "sneaker": 80, "boat": 80, "stain": 80, "steer": 80, "stirrup": 80, "superman": 80, "sushi": 80, "armi": [80, 94], "sword": 80, "tambourin": 80, "teepe": 80, "court": 80, "theodolit": 80, "tomato": 80, "tombston": 80, "tour": 80, "pisa": 80, "treadmil": 80, "fork": 80, "tweezer": 80, "unicorn": 80, "vcr": 80, "waterfal": 80, "watermelon": 80, "weld": 80, "windmil": 80, "xylophon": 80, "yarmulk": 80, "yo": 80, "toad": 80, "twenty_news_test_set": 80, "alt": 80, "atheism": 80, "comp": 80, "graphic": [80, 91], "misc": [80, 94], "sy": 80, "ibm": 80, "pc": 80, "hardwar": 80, "mac": 80, "forsal": 80, "rec": 80, "sci": 80, "crypt": 80, "electron": 80, "med": 80, "soc": 80, "religion": 80, "christian": [80, 94], "talk": [80, 94], "polit": 80, "gun": 80, "mideast": 80, "amazon": 80, "neutral": 80, "imdb_test_set": 80, "all_class": 80, "20news_test_set": 80, "_load_classes_predprobs_label": 80, "dataset_nam": 80, "labelerror": 80, "url_bas": 80, "5392f6c71473055060be3044becdde1cbc18284d": 80, "url_label": 80, "original_test_label": 80, "_original_label": 80, "url_prob": 80, "cross_validated_predicted_prob": 80, "_pyx": 80, "num_part": 80, "datatset": 80, "bytesio": 80, "allow_pickl": 80, "pred_probs_part": 80, "url": 80, "_of_": 80, "nload": 80, "imdb": 80, "ve": [80, 81, 83, 85, 87], "interpret": [80, 81, 83], "capit": 80, "29780": 80, "256": [80, 81, 87], "780": 80, "medic": [80, 94], "doctor": 80, "254": [80, 87], "359223": 80, "640777": 80, "184": [80, 83], "258427": 80, "341176": 80, "263158": 80, "658824": 80, "337349": 80, "246575": 80, "662651": 80, "248": 80, "330000": 80, "355769": 80, "670000": 80, "251": [80, 87], "167": [80, 83, 87], "252": 80, "112": 80, "253": [80, 87], "022989": 80, "255": [80, 82], "049505": 80, "190": [80, 83, 87], "66": [80, 91], "002216": 80, "000974": 80, "59": [80, 82, 87, 91], "88": [80, 82, 83, 86, 87, 90, 91], "000873": 80, "000739": 80, "79": [80, 87, 91, 92], "32635": 80, "32636": 80, "47": [80, 82, 87, 91], "32637": 80, "32638": 80, "32639": 80, "32640": 80, "051": 80, "002242": 80, "997758": 80, "002088": 80, "001045": 80, "997912": 80, "002053": 80, "997947": 80, "001980": 80, "000991": 80, "998020": 80, "001946": 80, "002915": 80, "998054": 80, "001938": 80, "002904": 80, "998062": 80, "001020": 80, "998980": 80, "001018": 80, "002035": 80, "998982": 80, "999009": 80, "0003": 80, "0002": 80, "36": [80, 91, 94], "44": [80, 82, 86, 87, 91], "71": [80, 82, 83, 87, 91], "071": 80, "067269": 80, "929": 80, "046": 80, "058243": 80, "954": 80, "035": 80, "032096": 80, "965": 80, "031": 80, "012232": 80, "969": 80, "022": 80, "025896": 80, "978": 80, "020": [80, 83], "013092": 80, "018": 80, "013065": 80, "016": 80, "030542": 80, "984": 80, "013": 80, "020833": 80, "987": 80, "012": 80, "010020": 80, "988": 80, "0073": 80, "0020": 80, "0016": 80, "0015": 80, "0013": 80, "0012": 80, "0010": 80, "0008": 80, "0007": 80, "0006": 80, "0005": 80, "0004": 80, "244": [80, 87], "98": [80, 81, 82, 90, 91], "452381": 80, "459770": 80, "72": [80, 83, 86, 90, 91], "523364": 80, "460784": 80, "446602": 80, "57": [80, 82, 83, 91], "68": [80, 82, 83, 87, 91, 92], "103774": 80, "030612": 80, "97": [80, 81, 83, 87, 90, 91, 92, 94], "110092": 80, "049020": 80, "99": [80, 83, 91, 92, 94], "0034": 80, "0032": 80, "0026": 80, "0025": 80, "4945": 80, "4946": 80, "4947": 80, "4948": 80, "4949": 80, "4950": 80, "846": 80, "82": [80, 82, 83, 87, 91], "7532": 80, "532": 80, "034483": 80, "009646": 80, "965517": 80, "030457": 80, "020513": 80, "969543": 80, "028061": 80, "035443": 80, "971939": 80, "025316": 80, "005168": 80, "974684": 80, "049751": 80, "979487": 80, "019920": 80, "042802": 80, "980080": 80, "017677": 80, "005115": 80, "982323": 80, "012987": 80, "005236": 80, "987013": 80, "012723": 80, "025126": 80, "987277": 80, "010989": 80, "008264": 80, "989011": 80, "010283": 80, "027778": 80, "989717": 80, "009677": 80, "990323": 80, "007614": 80, "010127": 80, "992386": 80, "005051": 80, "994949": 80, "005025": 80, "994975": 80, "005013": 80, "994987": 80, "001859": 80, "001328": 80, "000929": 80, "000664": 80, "186": [80, 83], "188": [80, 83, 86], "189": [80, 83], "snippet": 81, "nlp": [81, 94], "mind": [81, 83], "number_of_class": 81, "total_number_of_data_point": 81, "drop": [81, 85, 90, 93], "feed": 81, "alphabet": 81, "labels_proper_format": 81, "your_classifi": 81, "issues_datafram": 81, "class_predicted_for_flagged_exampl": 81, "class_predicted_for_all_exampl": 81, "grant": 81, "datataset": 81, "fair": [81, 83], "game": 81, "speedup": [81, 88], "flexibl": 81, "tempfil": 81, "mkdtemp": 81, "sped": 81, "anywai": 81, "pred_probs_merg": 81, "merge_rare_class": 81, "count_threshold": 81, "class_mapping_orig2new": 81, "heath_summari": 81, "num_examples_per_class": 81, "rare_class": 81, "num_classes_merg": 81, "other_class": 81, "labels_merg": 81, "new_c": 81, "merged_prob": 81, "hstack": [81, 82, 83, 85], "new_class": 81, "original_class": 81, "num_check": 81, "ones_array_ref": 81, "isclos": 81, "though": [81, 83, 94], "successfulli": 81, "meaning": [81, 88], "virtuou": [81, 85], "cycl": [81, 85], "jointli": 81, "junk": 81, "clutter": 81, "unknown": 81, "caltech": 81, "combined_boolean_mask": 81, "mask1": 81, "mask2": 81, "gradientboostingclassifi": [81, 83], "true_error": [81, 83, 86], "101": [81, 87], "102": [81, 86, 87], "104": [81, 83, 87], "model_to_find_error": 81, "model_to_return": 81, "cl0": 81, "randomizedsearchcv": 81, "expens": 81, "param_distribut": 81, "learning_r": [81, 83], "max_depth": [81, 83], "magnitud": 81, "coeffici": [81, 90], "optin": 81, "environ": [81, 83], "rerun": [81, 83], "cell": [81, 83], "On": [81, 83, 87], "unabl": [81, 83], "render": [81, 83], "nbviewer": [81, 83], "cleanlearningcleanlearn": [81, 83], "linearregressionlinearregress": 81, "n_init": 81, "fit_predict": 81, "continuous_column": 81, "categorical_column": 81, "data_df": 81, "feature_a": 81, "feature_b": 81, "unexpectedli": 81, "emphas": 81, "especi": [81, 82, 90, 92, 93], "crucial": 81, "merge_duplicate_set": 81, "merge_kei": 81, "construct_group_kei": 81, "merged_set": 81, "consolidate_set": 81, "tolist": [81, 86], "issubset": 81, "frozenset": 81, "sets_list": 81, "mutabl": 81, "new_set": 81, "current_set": 81, "intersecting_set": 81, "lowest_score_strategi": 81, "sub_df": 81, "idxmin": 81, "filter_near_dupl": 81, "strategy_fn": 81, "strategy_kwarg": 81, "duplicate_row": 81, "group_kei": 81, "to_keep_indic": 81, "groupbi": 81, "explod": 81, "to_remov": 81, "isin": [81, 88], "kept": 81, "near_duplicate_issu": [81, 82], "ids_to_remove_seri": 81, "assist": 81, "streamlin": 81, "ux": 81, "agpl": 81, "compani": 81, "commerci": 81, "alter": 81, "email": 81, "discuss": 81, "anywher": 81, "profession": 81, "expert": 81, "60": [82, 83, 91], "excess": 82, "torchvis": [82, 88], "tensordataset": 82, "stratifiedkfold": [82, 86], "tqdm": 82, "fashion_mnist": 82, "num_row": 82, "60000": 82, "pil": 82, "transformed_dataset": 82, "with_format": 82, "unsqueez": 82, "cpu_count": 82, "torch_dataset": 82, "quick": [82, 86], "relu": 82, "batchnorm2d": 82, "maxpool2d": 82, "lazylinear": 82, "flatten": 82, "get_test_accuraci": 82, "testload": [82, 88], "energi": 82, "trainload": [82, 88], "n_epoch": 82, "patienc": 82, "criterion": 82, "crossentropyloss": 82, "adamw": 82, "best_test_accuraci": 82, "start_epoch": 82, "running_loss": 82, "best_epoch": 82, "end_epoch": 82, "3f": [82, 90], "acc": [82, 83], "time_taken": 82, "compute_embed": 82, "compute_pred_prob": 82, "train_batch_s": 82, "num_work": 82, "worker": [82, 94], "train_id_list": 82, "test_id_list": 82, "train_id": 82, "test_id": 82, "embeddings_model": 82, "ntrain": 82, "trainset": 82, "testset": 82, "pin_memori": 82, "fold_embed": 82, "fold_pred_prob": 82, "finish": 82, "483": 82, "835": 82, "896": 82, "331": 82, "310": 82, "667": 82, "stderr": [82, 91], "sphinxverbatim": [82, 91, 94], "04it": [82, 91], "57it": [82, 91], "36it": [82, 91], "11it": [82, 91], "31it": [82, 91], "62": [82, 83, 87, 90, 91], "78it": [82, 91], "18it": [82, 91], "54it": [82, 91], "63": [82, 83, 87, 91], "06it": [82, 91], "34it": [82, 91], "85": [82, 87, 90, 91], "92it": [82, 91], "99it": [82, 91], "492": 82, "085": 82, "738": 82, "290": [82, 87], "632": 82, "17it": [82, 91], "23it": [82, 91], "67it": [82, 91], "28it": [82, 91], "98it": [82, 91], "09it": [82, 91], "50it": [82, 91], "21it": [82, 91], "82it": [82, 91], "22it": [82, 91], "30it": [82, 91], "476": 82, "305": [82, 90], "767": 82, "328": [82, 87], "335": 82, "551": 82, "49it": [82, 91], "44it": [82, 91], "96it": [82, 91], "69it": [82, 91], "32it": [82, 91], "55it": [82, 91], "25it": [82, 91], "12it": [82, 91], "69": [82, 83, 90, 91, 94], "60it": [82, 91], "reorder": 82, "vision": 82, "grayscal": 82, "exce": 82, "max_preval": 82, "7620": 82, "3692": 82, "3521": 82, "225": [82, 86], "166": 82, "3691": 82, "40378": 82, "943831e": 82, "54473": 82, "066211e": 82, "06": [82, 83, 87, 91, 94], "29412": 82, "899069e": 82, "25316": 82, "984817e": 82, "52247": 82, "245879e": 82, "9581": 82, "19228": 82, "dress": 82, "54078": 82, "000010": 82, "pullov": 82, "32657": 82, "21282": 82, "000011": 82, "11262": 82, "000014": 82, "6294": 82, "30659": 82, "000798": 82, "30968": 82, "000015": 82, "258": 82, "000907": 82, "9762": 82, "54565": 82, "47139": 82, "000017": 82, "001423": 82, "000026": 82, "39992": 82, "39993": 82, "39994": 82, "39995": 82, "7834": 82, "42819": 82, "629362": 82, "51431": 82, "654330": 82, "55548": 82, "658364": 82, "51191": 82, "668572": 82, "50081": 82, "669703": 82, "7834321613629787": 82, "13732": 82, "13733": 82, "13734": 82, "47635": 82, "110901": 82, "974390": 82, "998733": 82, "937117": 82, "998755": 82, "53564": 82, "5473": 82, "trouser": 82, "plot_label_issue_exampl": 82, "ncol": [82, 88], "nrow": [82, 88], "ceil": 82, "axes_list": 82, "label_issue_indic": 82, "gl": 82, "sl": 82, "fontdict": 82, "imshow": [82, 88], "cmap": [82, 90], "grai": 82, "subplots_adjust": 82, "hspace": 82, "outsiz": 82, "outlier_issues_df": 82, "depict": [82, 86, 87, 88, 89, 91], "plot_outlier_issues_exampl": 82, "n_comparison_imag": 82, "sample_from_class": 82, "number_of_sampl": 82, "non_outlier_indic": 82, "isnul": 82, "non_outlier_indices_excluding_curr": 82, "sampled_indic": 82, "label_scores_of_sampl": 82, "top_score_indic": 82, "top_label_indic": 82, "sampled_imag": 82, "get_image_given_label_and_sampl": 82, "image_from_dataset": 82, "corresponding_label": 82, "comparison_imag": 82, "images_to_plot": 82, "idlist": 82, "iterrow": 82, "closest": 82, "counterpart": 82, "near_duplicate_issues_df": 82, "plot_near_duplicate_issue_exampl": 82, "seen_id_pair": 82, "get_image_and_given_label_and_predicted_label": 82, "duplicate_imag": 82, "nd_set": 82, "challeng": 82, "dark_issu": 82, "reveal": [82, 91], "dark_scor": 82, "dark_issues_df": 82, "is_dark_issu": 82, "34848": 82, "203922": 82, "50270": 82, "204588": 82, "3936": 82, "213098": 82, "733": 82, "217686": 82, "8094": 82, "230118": 82, "plot_image_issue_exampl": 82, "difficult": 82, "disproportion": 82, "lowinfo_issu": 82, "low_information_scor": 82, "lowinfo_issues_df": 82, "is_low_information_issu": 82, "53050": 82, "067975": 82, "40875": 82, "089929": 82, "9594": 82, "092601": 82, "34825": 82, "107744": 82, "37530": 82, "108516": 82, "lot": 82, "depth": 83, "survei": [83, 94], "focus": [83, 85], "scienc": 83, "multivariate_norm": [83, 85, 86], "make_data": [83, 85], "cov": [83, 85, 86], "avg_trac": [83, 86], "test_label": [83, 86, 88, 93], "py_tru": 83, "noise_matrix_tru": 83, "noise_marix": 83, "s_test": 83, "noisy_test_label": 83, "purpl": 83, "val": 83, "namespac": 83, "exec": 83, "markerfacecolor": [83, 86], "markeredgecolor": [83, 86, 90], "markers": [83, 86, 90], "markeredgewidth": [83, 86, 90], "realist": 83, "7560": 83, "638483e": 83, "897052e": 83, "548986e": 83, "924634e": 83, "374580e": 83, "3454": 83, "014051": 83, "020451": 83, "249": [83, 87], "042594": 83, "043859": 83, "045954": 83, "6120": 83, "023714": 83, "007136": 83, "119": [83, 87], "107266": 83, "103": [83, 87], "033738": 83, "238": [83, 87], "119505": 83, "236": [83, 87, 94], "037843": 83, "222": 83, "614915": 83, "122": [83, 87], "624422": 83, "625965": 83, "626079": 83, "118": 83, "627675": 83, "158": 83, "159": [83, 86, 87], "161": 83, "1960": 83, "196": [83, 87], "223": [83, 87], "221": 83, "219": [83, 87], "695174": 83, "323529": 83, "522929": 83, "013722": 83, "675606": 83, "646438": 83, "anyth": 83, "enhanc": [83, 85, 87], "magic": 83, "83": [83, 87, 90, 91, 92, 94], "liter": 83, "identif": 83, "x27": 83, "logisticregressionlogisticregress": 83, "ever": 83, "092": 83, "040": 83, "024": 83, "004": 83, "surpris": 83, "1705": 83, "01936": 83, "ton": 83, "yourfavoritemodel1": 83, "merged_label": 83, "merged_test_label": 83, "newli": [83, 85], "yourfavoritemodel2": 83, "yourfavoritemodel3": 83, "cl3": 83, "takeawai": 83, "That": [83, 86], "randomli": 83, "my_test_pred_prob": 83, "my_test_pr": 83, "issues_test": 83, "corrected_test_label": 83, "pretend": 83, "cl_test_pr": 83, "fairli": 83, "label_acc": 83, "percentag": 83, "offset": 83, "nquestion": 83, "overestim": 83, "answer": 83, "experienc": 83, "76": [83, 86, 87, 90, 91, 92], "knowledg": 83, "quantiti": [83, 90], "prioiri": 83, "known": 83, "versatil": 83, "label_issues_indic": 83, "213": [83, 87], "212": [83, 92], "218": [83, 87], "152": 83, "197": [83, 87], "170": 83, "214": 83, "164": [83, 86], "198": [83, 87], "191": [83, 87], "121": [83, 93], "117": [83, 90], "206": [83, 87], "115": [83, 87], "193": 83, "194": 83, "201": [83, 87], "174": 83, "163": 83, "150": [83, 85, 87, 94], "169": [83, 94], "151": [83, 87], "168": 83, "precision_scor": 83, "recall_scor": 83, "f1_score": 83, "true_label_issu": 83, "filter_by_list": 83, "718750": [83, 85], "807018": 83, "912": 83, "733333": 83, "800000": 83, "721311": 83, "792793": 83, "908": 83, "676923": 83, "765217": 83, "892": 83, "567901": 83, "702290": 83, "844": 83, "gaug": 83, "label_issues_count": 83, "155": [83, 87], "172": [83, 86], "easiest": 83, "modular": 83, "penalti": 83, "l2": 83, "model3": 83, "n_estim": 83, "cv_pred_probs_1": 83, "cv_pred_probs_2": 83, "cv_pred_probs_3": 83, "label_quality_scores_best": 83, "cv_pred_probs_ensembl": 83, "label_quality_scores_bett": 83, "superior": [83, 89], "workflow": [84, 90], "speechbrain": 84, "timm": 84, "glad": 85, "multiannotator_label": 85, "noisier": 85, "111": [85, 90], "local_data": [85, 86], "true_labels_train": [85, 86], "noise_matrix_bett": 85, "noise_matrix_wors": 85, "transpos": [85, 88], "dropna": 85, "zfill": 85, "row_na_check": 85, "notna": 85, "reset_index": 85, "a0001": 85, "a0002": 85, "a0003": 85, "a0004": 85, "a0005": 85, "a0006": 85, "a0007": 85, "a0008": 85, "a0009": 85, "a0010": 85, "a0041": 85, "a0042": 85, "a0043": 85, "a0044": 85, "a0045": 85, "a0046": 85, "a0047": 85, "a0048": 85, "a0049": 85, "a0050": 85, "60856743": 85, "41693214": 85, "40908785": 85, "87147629": 85, "64941785": 85, "10774851": 85, "0524466": 85, "71853246": 85, "37169848": 85, "66031048": 85, "multiannotator_util": 85, "crude": 85, "straight": 85, "majority_vote_label": 85, "736157": 85, "757738": 85, "782255": 85, "715585": 85, "824273": 85, "quality_annotator_a0001": 85, "quality_annotator_a0002": 85, "quality_annotator_a0003": 85, "quality_annotator_a0004": 85, "quality_annotator_a0005": 85, "quality_annotator_a0006": 85, "quality_annotator_a0007": 85, "quality_annotator_a0008": 85, "quality_annotator_a0009": 85, "quality_annotator_a0010": 85, "quality_annotator_a0041": 85, "quality_annotator_a0042": 85, "quality_annotator_a0043": 85, "quality_annotator_a0044": 85, "quality_annotator_a0045": 85, "quality_annotator_a0046": 85, "quality_annotator_a0047": 85, "quality_annotator_a0048": 85, "quality_annotator_a0049": 85, "quality_annotator_a0050": 85, "070551": 85, "216064": 85, "119178": 85, "alongisd": 85, "244982": 85, "208333": 85, "295978": 85, "294118": 85, "324194": 85, "310345": 85, "355315": 85, "346154": 85, "439728": 85, "480000": 85, "a0031": 85, "523205": 85, "580645": 85, "a0034": 85, "535313": 85, "607143": 85, "a0021": 85, "607002": 85, "a0015": 85, "609527": 85, "678571": 85, "a0011": 85, "621101": 85, "692308": 85, "wors": 85, "improved_consensus_label": 85, "majority_vote_accuraci": 85, "cleanlab_label_accuraci": 85, "8581081081081081": 85, "9797297297297297": 85, "besid": 85, "sorted_consensus_quality_scor": 85, "worst_qual": 85, "better_qu": 85, "worst_quality_accuraci": 85, "better_quality_accuraci": 85, "9893238434163701": 85, "improved_pred_prob": 85, "treat": [85, 86, 90, 94], "analzi": 85, "copyright": 86, "advertis": 86, "violenc": 86, "nsfw": 86, "ranked_label_issu": [86, 92, 93], "multioutput": 86, "multioutputclassifi": 86, "celeba": 86, "make_multilabel_data": 86, "boxes_coordin": 86, "box_multilabel": 86, "make_multi": 86, "bx1": 86, "by1": 86, "bx2": 86, "by2": 86, "label_list": 86, "ur": 86, "upper": 86, "inidx": 86, "logical_and": 86, "inv_d": 86, "labels_idx": 86, "true_labels_test": 86, "dict_unique_label": 86, "get_color_arrai": 86, "dcolor": 86, "aa4400": 86, "55227f": 86, "55a100": 86, "00ff00": 86, "007f7f": 86, "386b55": 86, "0000ff": 86, "simplic": 86, "advis": 86, "y_onehot": 86, "single_class_label": 86, "stratifi": [86, 89], "kf": 86, "train_index": 86, "test_index": 86, "clf_cv": 86, "x_train_cv": 86, "x_test_cv": 86, "y_train_cv": 86, "y_test_cv": 86, "y_pred_cv": 86, "saw": 86, "num_to_displai": 86, "09": [86, 87, 91], "275": 86, "267": 86, "171": 86, "234": 86, "165": 86, "227": [86, 87], "262": [86, 87], "263": [86, 87], "266": [86, 87], "139": 86, "143": [86, 87], "216": [86, 87, 94], "265": 86, "despit": [86, 94], "suspect": 86, "888": 86, "8224": 86, "9632": 86, "968": 86, "6512": 86, "0444": 86, "774": 86, "labels_binary_format": 86, "labels_list_format": 86, "surround": 87, "scene": 87, "coco": 87, "everydai": 87, "has_label_issu": 87, "insal": 87, "nc": [87, 91, 94], "s3": [87, 91, 94], "amazonaw": [87, 91, 94], "objectdetectionbenchmark": 87, "tutorial_obj": 87, "pkl": 87, "example_imag": 87, "unzip": [87, 94], "begin": 87, "detectron2": 87, "image_path": 87, "rb": 87, "image_to_visu": 87, "seg_map": 87, "334": 87, "float32": 87, "bboxes_ignor": 87, "286": 87, "285": 87, "224": 87, "231": 87, "293": 87, "235": 87, "289": [87, 90], "282": 87, "74": [87, 90, 91, 92], "281": 87, "271": 87, "280": 87, "277": 87, "279": 87, "287": 87, "299": 87, "276": 87, "307": 87, "321": 87, "326": 87, "333": 87, "261": 87, "319": 87, "257": 87, "295": 87, "283": 87, "243": 87, "303": 87, "316": 87, "247": 87, "323": 87, "327": 87, "226": 87, "228": 87, "232": 87, "239": 87, "240": 87, "209": 87, "242": 87, "202": 87, "230": 87, "215": 87, "220": 87, "229": 87, "217": 87, "237": 87, "207": 87, "204": 87, "205": 87, "153": 87, "149": 87, "140": 87, "124": 87, "268": 87, "273": 87, "108": 87, "284": 87, "110": 87, "136": 87, "145": 87, "173": 87, "297": 87, "317": 87, "192": 87, "329": 87, "332": 87, "324": 87, "203": 87, "320": 87, "314": 87, "199": 87, "291": 87, "000000481413": 87, "jpg": 87, "42398": 87, "44503": 87, "337": [87, 93], "29968": 87, "336": 87, "21005": 87, "9978472": 87, "forgot": 87, "drew": 87, "label_issue_idx": 87, "num_examples_to_show": 87, "113": [87, 90], "candid": 87, "97489622": 87, "70610878": 87, "98764951": 87, "88899237": 87, "99085805": 87, "issue_idx": 87, "95569726e": 87, "03354841e": 87, "57510169e": 87, "58447666e": 87, "39755858e": 87, "suppli": 87, "issue_to_visu": 87, "000000009483": 87, "95569726168054e": 87, "addition": [87, 91], "visibl": 87, "missmatch": 87, "likelei": 87, "agnost": 87, "vaidat": 87, "inconsist": 87, "000000395701": 87, "033548411774308e": 87, "armchair": 87, "tv": 87, "000000154004": 87, "38300759625496356": 87, "foreground": 87, "000000448410": 87, "0008575101690203273": 87, "crowd": 87, "alon": 87, "explor": [87, 88], "resembl": [87, 88], "000000499768": 87, "9748962231208227": 87, "000000521141": 87, "8889923658893665": 87, "000000143931": 87, "9876495074395956": 87, "train_feature_embed": 88, "ood_train_feature_scor": 88, "test_feature_embed": 88, "ood_test_feature_scor": 88, "ood_train_predictions_scor": 88, "train_pred_prob": 88, "ood_test_predictions_scor": 88, "test_pred_prob": 88, "pylab": 88, "rcparam": 88, "baggingclassifi": 88, "therebi": 88, "rescal": 88, "transform_norm": 88, "totensor": 88, "root": 88, "animal_class": 88, "non_animal_class": 88, "animal_idx": 88, "test_idx": 88, "toronto": 88, "edu": 88, "kriz": 88, "5000": 88, "plot_imag": 88, "visualize_outli": 88, "txt_class": 88, "img": [88, 90], "npimg": 88, "show_label": 88, "data_subset": 88, "resnet50": 88, "corpu": 88, "2048": 88, "embed_imag": 88, "create_model": 88, "rwightman": 88, "v0": 88, "rsb": 88, "resnet50_a1_0": 88, "14fe96d1": 88, "pth": 88, "checkpoint": 88, "strang": 88, "odd": 88, "train_ood_features_scor": 88, "top_train_ood_features_idx": 88, "fun": 88, "negat": 88, "homogen": 88, "bottom_train_ood_features_idx": 88, "test_ood_features_scor": 88, "top_ood_features_idx": 88, "inevit": 88, "trade": 88, "5th": 88, "percentil": 88, "fifth_percentil": 88, "plt_rang": 88, "hist": 88, "train_outlier_scor": 88, "ylabel": 88, "axvlin": 88, "test_outlier_scor": 88, "ood_features_indic": 88, "revisit": 88, "unusu": 88, "return_invers": 88, "train_feature_embeddings_sc": 88, "test_feature_embeddings_sc": 88, "train_pred_label": 88, "9702": 88, "train_ood_predictions_scor": 88, "test_ood_predictions_scor": 88, "mainli": [88, 94], "lost": 88, "unsuit": 89, "ok": [89, 94], "convention": 89, "aforement": 89, "hypothet": 89, "contrast": 89, "tradit": 89, "disjoint": 89, "out_of_sample_pred_probs_for_a": 89, "out_of_sample_pred_probs_for_b": 89, "out_of_sample_pred_probs_for_c": 89, "out_of_sample_pred_prob": 89, "price": 90, "incom": 90, "ag": 90, "histgradientboostingregressor": 90, "r2_score": 90, "student_grades_r": 90, "final_scor": 90, "true_final_scor": 90, "homework": 90, "3d": 90, "hue": 90, "mpl_toolkit": 90, "mplot3d": 90, "axes3d": 90, "errors_idx": 90, "add_subplot": 90, "z": 90, "colorbar": 90, "errors_mask": 90, "feature_column": 90, "predicted_column": 90, "x_train_raw": 90, "x_test_raw": 90, "categorical_featur": [90, 92], "randomforestregressor": 90, "629763": 90, "521450": 90, "954607": 90, "547234": 90, "338296": 90, "754531": 90, "619090": 90, "312295": 90, "806626": 90, "784048": 90, "identified_issu": [90, 93], "367": 90, "560": 90, "318": 90, "688": 90, "657": 90, "view_datapoint": 90, "concat": 90, "consum": [90, 93], "baseline_model": [90, 93], "preds_og": 90, "r2_og": 90, "838": 90, "robustli": [90, 92, 93], "acceler": [90, 93], "found_label_issu": 90, "preds_cl": 90, "r2_cl": 90, "925": 90, "effort": [90, 92, 93], "favorit": 90, "13091885": 90, "48412548": 90, "00695165": 90, "44421119": 90, "43029854": 90, "synthia": 91, "imagesegment": 91, "given_mask": 91, "predicted_mask": 91, "set_printopt": [91, 94], "sky": 91, "sidewalk": 91, "veget": 91, "terrain": 91, "rider": 91, "pred_probs_filepath": 91, "1088": 91, "1920": 91, "label_filepath": 91, "synthia_class": 91, "maunal": 91, "100000": 91, "244800": 91, "leftmost": 91, "area": 91, "middl": [91, 94], "infact": 91, "rightmost": 91, "discrep": 91, "4997817": 91, "17362": 91, "173608": 91, "58it": 91, "34895": 91, "174610": 91, "72it": 91, "52475": 91, "175150": 91, "77it": 91, "69991": 91, "173938": 91, "91it": 91, "87813": 91, "175471": 91, "95it": 91, "105688": 91, "176578": 91, "81it": 91, "123543": 91, "177216": 91, "89it": 91, "141289": 91, "177290": 91, "20it": 91, "159076": 91, "177466": 91, "176918": 91, "177754": 91, "194876": 91, "178308": 91, "24it": 91, "212708": 91, "178223": 91, "48it": 91, "230531": 91, "178136": 91, "248505": 91, "178619": 91, "00it": 91, "266377": 91, "178647": 91, "43it": 91, "284425": 91, "179193": 91, "302378": 91, "179290": 91, "320347": 91, "179407": 91, "53it": 91, "338297": 91, "179431": 91, "356241": 91, "179348": 91, "374208": 91, "179441": 91, "392153": 91, "175413": 91, "410009": 91, "176338": 91, "427853": 91, "176958": 91, "47it": 91, "445826": 91, "177779": 91, "463613": 91, "177528": 91, "481507": 91, "177946": 91, "35it": 91, "499307": 91, "177661": 91, "517142": 91, "177861": 91, "59it": 91, "535037": 91, "178184": 91, "552923": 91, "178382": 91, "570763": 91, "171086": 91, "588402": 91, "172627": 91, "606160": 91, "174079": 91, "623955": 91, "175220": 91, "83it": 91, "641860": 91, "176352": 91, "97it": 91, "659728": 91, "177041": 91, "677498": 91, "177235": 91, "695298": 91, "177460": 91, "713108": 91, "177648": 91, "731013": 91, "178065": 91, "71it": 91, "748824": 91, "177652": 91, "38it": 91, "766599": 91, "177677": 91, "08it": 91, "784420": 91, "177831": 91, "64it": 91, "802279": 91, "178056": 91, "61it": 91, "820264": 91, "178591": 91, "838197": 91, "178808": 91, "856111": 91, "178905": 91, "874002": 91, "178854": 91, "94it": 91, "891888": 91, "178769": 91, "86it": 91, "909766": 91, "177999": 91, "88it": 91, "927567": 91, "175332": 91, "945109": 91, "172027": 91, "962858": 91, "173626": 91, "980440": 91, "174271": 91, "998071": 91, "174873": 91, "76it": 91, "1015609": 91, "175019": 91, "90it": 91, "1033146": 91, "175119": 91, "66it": 91, "1050707": 91, "175263": 91, "37it": 91, "1068310": 91, "175489": 91, "1086006": 91, "175925": 91, "40it": 91, "1103856": 91, "176693": 91, "74it": 91, "1121527": 91, "176653": 91, "73it": 91, "1139194": 91, "175610": 91, "1156758": 91, "175340": 91, "14it": 91, "1174423": 91, "175726": 91, "52it": 91, "1192038": 91, "175849": 91, "70it": 91, "1209681": 91, "176020": 91, "1227284": 91, "175879": 91, "1244924": 91, "176033": 91, "1262528": 91, "175800": 91, "45it": 91, "1280128": 91, "175857": 91, "1297844": 91, "176243": 91, "1315598": 91, "176628": 91, "10it": 91, "1333326": 91, "176821": 91, "1351011": 91, "176827": 91, "1368694": 91, "176802": 91, "46it": 91, "1386390": 91, "176847": 91, "1404075": 91, "176737": 91, "1421749": 91, "176144": 91, "1439364": 91, "175538": 91, "1456919": 91, "175153": 91, "1474435": 91, "170969": 91, "1491823": 91, "171820": 91, "1509255": 91, "172556": 91, "1526644": 91, "172949": 91, "51it": 91, "1544068": 91, "173330": 91, "1561511": 91, "173656": 91, "01it": 91, "1578882": 91, "173600": 91, "68it": 91, "1596246": 91, "173421": 91, "1613759": 91, "173930": 91, "1631217": 91, "174122": 91, "33it": 91, "1648631": 91, "170877": 91, "1666045": 91, "171838": 91, "1683650": 91, "173084": 91, "1701219": 91, "173858": 91, "1718873": 91, "174654": 91, "1736344": 91, "174622": 91, "1753942": 91, "175023": 91, "1771448": 91, "174993": 91, "1788950": 91, "174751": 91, "1806535": 91, "175076": 91, "1824044": 91, "174947": 91, "1841882": 91, "175973": 91, "1859592": 91, "176306": 91, "1877279": 91, "176473": 91, "1894943": 91, "176520": 91, "1912596": 91, "176219": 91, "1930263": 91, "1947942": 91, "176481": 91, "1965591": 91, "175961": 91, "1983188": 91, "175748": 91, "07it": 91, "2000787": 91, "175815": 91, "2018369": 91, "168730": 91, "26it": 91, "2035840": 91, "170467": 91, "93it": 91, "2053380": 91, "171912": 91, "16it": 91, "2070874": 91, "172804": 91, "2088397": 91, "173519": 91, "2105931": 91, "174057": 91, "2123526": 91, "174619": 91, "2140999": 91, "174636": 91, "87it": 91, "2158470": 91, "174638": 91, "2175968": 91, "174737": 91, "2193446": 91, "174633": 91, "2211104": 91, "175212": 91, "2228891": 91, "176005": 91, "2246705": 91, "176642": 91, "2264371": 91, "176477": 91, "2282020": 91, "176270": 91, "2299648": 91, "176268": 91, "65it": 91, "2317443": 91, "176769": 91, "2335287": 91, "177266": 91, "2353014": 91, "177237": 91, "2370811": 91, "177454": 91, "13it": 91, "2388644": 91, "177713": 91, "2406515": 91, "178008": 91, "2424382": 91, "178202": 91, "2442203": 91, "176953": 91, "39it": 91, "2460064": 91, "177445": 91, "2478011": 91, "178046": 91, "2495859": 91, "178170": 91, "2513719": 91, "178294": 91, "2531550": 91, "177984": 91, "2549350": 91, "177765": 91, "2567127": 91, "177467": 91, "75it": 91, "2584875": 91, "177135": 91, "41it": 91, "2602589": 91, "176761": 91, "2620266": 91, "176400": 91, "2637907": 91, "176353": 91, "2655543": 91, "175946": 91, "2673138": 91, "175874": 91, "2690726": 91, "175848": 91, "2708319": 91, "175869": 91, "29it": 91, "2725906": 91, "170165": 91, "2743530": 91, "171941": 91, "2761055": 91, "172913": 91, "2778599": 91, "173657": 91, "2796132": 91, "174152": 91, "2813713": 91, "174642": 91, "2831215": 91, "85it": 91, "2848781": 91, "175020": 91, "2866288": 91, "174922": 91, "2883784": 91, "174867": 91, "2901274": 91, "174518": 91, "2918784": 91, "174687": 91, "2936508": 91, "175446": 91, "2954190": 91, "175855": 91, "2971867": 91, "176126": 91, "05it": 91, "2989607": 91, "176504": 91, "3007465": 91, "177122": 91, "3025371": 91, "177699": 91, "3043142": 91, "177526": 91, "3060895": 91, "177261": 91, "3078730": 91, "177583": 91, "27it": 91, "3096489": 91, "173439": 91, "3114319": 91, "174869": 91, "3132167": 91, "175934": 91, "19it": 91, "3150036": 91, "176751": 91, "3167884": 91, "177263": 91, "84it": 91, "3185698": 91, "177523": 91, "3203568": 91, "177872": 91, "3221360": 91, "177711": 91, "3239134": 91, "177577": 91, "15it": 91, "3256894": 91, "176368": 91, "3274697": 91, "176860": 91, "3292438": 91, "177021": 91, "3310162": 91, "177081": 91, "3327911": 91, "177201": 91, "3345632": 91, "177070": 91, "3363340": 91, "176560": 91, "3380997": 91, "176297": 91, "3398628": 91, "175923": 91, "3416444": 91, "176583": 91, "3434103": 91, "176134": 91, "3451738": 91, "176195": 91, "3469358": 91, "176027": 91, "3486962": 91, "175916": 91, "3504554": 91, "175282": 91, "3522083": 91, "175175": 91, "3539798": 91, "175762": 91, "3557423": 91, "175905": 91, "3575120": 91, "3592743": 91, "176124": 91, "3610402": 91, "176260": 91, "3628029": 91, "176148": 91, "3645692": 91, "176289": 91, "3663322": 91, "176222": 91, "3680980": 91, "176324": 91, "3698735": 91, "176687": 91, "3716404": 91, "3734091": 91, "176697": 91, "3751761": 91, "176640": 91, "3769457": 91, "176732": 91, "3787209": 91, "176965": 91, "3804906": 91, "176115": 91, "3822589": 91, "3840267": 91, "176456": 91, "3857949": 91, "176561": 91, "3875608": 91, "176566": 91, "3893265": 91, "176410": 91, "3910907": 91, "176281": 91, "3928536": 91, "176164": 91, "3946173": 91, "176221": 91, "3963796": 91, "175821": 91, "79it": 91, "3981379": 91, "175679": 91, "3999029": 91, "175919": 91, "4016746": 91, "176291": 91, "4034376": 91, "176165": 91, "4051993": 91, "176084": 91, "4069602": 91, "175885": 91, "63it": 91, "4087191": 91, "175394": 91, "4104731": 91, "175012": 91, "4122236": 91, "4139739": 91, "174861": 91, "56it": 91, "4157233": 91, "174879": 91, "4174722": 91, "167593": 91, "4192083": 91, "169341": 91, "4209474": 91, "170677": 91, "4226976": 91, "171956": 91, "4244411": 91, "172661": 91, "4261828": 91, "173107": 91, "4279356": 91, "173752": 91, "4296896": 91, "174242": 91, "4314328": 91, "173327": 91, "4331919": 91, "174094": 91, "4349427": 91, "174384": 91, "4367051": 91, "174935": 91, "4384595": 91, "175083": 91, "4402158": 91, "175244": 91, "4419786": 91, "175552": 91, "4437371": 91, "175636": 91, "4455003": 91, "175837": 91, "4472798": 91, "176468": 91, "4490560": 91, "176808": 91, "4508294": 91, "4525991": 91, "176542": 91, "4543710": 91, "4561463": 91, "176966": 91, "4579245": 91, "177220": 91, "4597064": 91, "177506": 91, "4614841": 91, "177581": 91, "4632600": 91, "177414": 91, "4650342": 91, "176810": 91, "4668024": 91, "163789": 91, "4685720": 91, "167519": 91, "4703228": 91, "169696": 91, "4720997": 91, "172024": 91, "4738747": 91, "173631": 91, "4756401": 91, "174489": 91, "4774026": 91, "175007": 91, "4791625": 91, "175295": 91, "4809276": 91, "175653": 91, "4827007": 91, "176143": 91, "4844714": 91, "176418": 91, "4862493": 91, "176823": 91, "4880182": 91, "4897903": 91, "176884": 91, "4915661": 91, "177087": 91, "4933428": 91, "177258": 91, "4951156": 91, "177082": 91, "4968913": 91, "177225": 91, "4986697": 91, "177405": 91, "175774": 91, "3263230": 91, "783379": 91, "275110": 91, "255792": 91, "78225": 91, "55990": 91, "54427": 91, "33591": 91, "24645": 91, "21308": 91, "15045": 91, "14171": 91, "13832": 91, "13498": 91, "11490": 91, "9164": 91, "8769": 91, "6999": 91, "6031": 91, "5011": 91, "mistakenli": 91, "class_issu": 91, "aim": [91, 94], "domin": 91, "extratreesclassifi": 92, "extratre": 92, "labelencod": [92, 93], "labels_raw": 92, "interg": [92, 93], "tress": 92, "827": 92, "cheat": 92, "0pt": 92, "233": 92, "labels_train": 92, "labels_test": 92, "acc_og": [92, 93], "783068783068783": 92, "acc_cl": [92, 93], "8095238095238095": 92, "earlier": [93, 94], "raw_label": 93, "raw_train_text": 93, "raw_test_text": 93, "raw_train_label": 93, "raw_test_label": 93, "encond": 93, "train_text": 93, "test_text": 93, "858050": 93, "545854": 93, "826194": 93, "965814": 93, "791923": 93, "646": 93, "390": 93, "628": 93, "702": 93, "863": 93, "135": 93, "735": 93, "print_as_df": 93, "inverse_transform": 93, "fight": 93, "bunch": 94, "conll": 94, "2003": 94, "love": 94, "n_i": 94, "optional_list_of_ordered_class_nam": 94, "deepai": 94, "conll2003": 94, "rm": 94, "tokenclassif": 94, "2024": 94, "2400": 94, "52e0": 94, "1a00": 94, "718": 94, "connect": 94, "443": 94, "await": 94, "982975": 94, "960k": 94, "kb": 94, "959": 94, "94k": 94, "mb": 94, "directori": 94, "inflat": 94, "182": 94, "134": 94, "17045998": 94, "16m": 94, "octet": 94, "71m": 94, "8mb": 94, "26m": 94, "4mb": 94, "bert": 94, "read_npz": 94, "filepath": 94, "corrsespond": 94, "iob2": 94, "given_ent": 94, "entity_map": 94, "readfil": 94, "sep": 94, "startswith": 94, "docstart": 94, "isalpha": 94, "isupp": 94, "indices_to_preview": 94, "nsentenc": 94, "eu": 94, "reject": 94, "boycott": 94, "british": 94, "lamb": 94, "00030412": 94, "00023826": 94, "99936208": 94, "00007009": 94, "00002545": 94, "99998795": 94, "00000401": 94, "00000218": 94, "00000455": 94, "00000131": 94, "00000749": 94, "99996115": 94, "00001371": 94, "0000087": 94, "00000895": 94, "99998936": 94, "00000382": 94, "00000178": 94, "00000366": 94, "00000137": 94, "99999101": 94, "00000266": 94, "00000174": 94, "0000035": 94, "00000109": 94, "99998768": 94, "00000482": 94, "00000202": 94, "00000438": 94, "0000011": 94, "00000465": 94, "99996392": 94, "00001105": 94, "0000116": 94, "00000878": 94, "99998671": 94, "00000364": 94, "00000213": 94, "00000472": 94, "00000281": 94, "99999073": 94, "00000211": 94, "00000159": 94, "00000442": 94, "00000115": 94, "peter": 94, "blackburn": 94, "00000358": 94, "00000529": 94, "99995623": 94, "000022": 94, "0000129": 94, "0000024": 94, "00001812": 94, "99994141": 94, "00001645": 94, "00002162": 94, "brussel": 94, "1996": 94, "00001172": 94, "00000821": 94, "00004661": 94, "0000618": 94, "99987167": 94, "99999061": 94, "00000201": 94, "00000195": 94, "00000408": 94, "00000135": 94, "2254": 94, "2907": 94, "19392": 94, "9962": 94, "8904": 94, "19303": 94, "12918": 94, "9256": 94, "11855": 94, "18392": 94, "20426": 94, "19402": 94, "14744": 94, "19371": 94, "4645": 94, "10331": 94, "9430": 94, "6143": 94, "18367": 94, "12914": 94, "todai": 94, "weather": 94, "march": 94, "scalfaro": 94, "northern": 94, "himself": 94, "said": 94, "germani": 94, "nastja": 94, "rysich": 94, "north": 94, "spla": 94, "fought": 94, "khartoum": 94, "govern": 94, "south": 94, "1983": 94, "autonomi": 94, "animist": 94, "region": 94, "moslem": 94, "arabis": 94, "mayor": 94, "antonio": 94, "gonzalez": 94, "garcia": 94, "revolutionari": 94, "parti": 94, "wednesdai": 94, "troop": 94, "raid": 94, "farm": 94, "stole": 94, "rape": 94, "women": 94, "spring": 94, "chg": 94, "hrw": 94, "12pct": 94, "princ": 94, "photo": 94, "moment": 94, "spokeswoman": 94, "rainier": 94, "told": 94, "reuter": 94, "danila": 94, "carib": 94, "w224": 94, "equip": 94, "radiomet": 94, "earn": 94, "19996": 94, "london": 94, "denom": 94, "sale": 94, "uk": 94, "jp": 94, "fr": 94, "maccabi": 94, "hapoel": 94, "haifa": 94, "tel": 94, "aviv": 94, "hospit": 94, "rever": 94, "roman": 94, "cathol": 94, "nun": 94, "admit": 94, "calcutta": 94, "week": 94, "ago": 94, "fever": 94, "vomit": 94, "allianc": 94, "embattl": 94, "kabul": 94, "salang": 94, "highwai": 94, "mondai": 94, "tuesdai": 94, "suprem": 94, "council": 94, "led": 94, "jumbish": 94, "milli": 94, "movement": 94, "warlord": 94, "abdul": 94, "rashid": 94, "dostum": 94, "dollar": 94, "exchang": 94, "3570": 94, "12049": 94, "born": 94, "1937": 94, "provinc": 94, "anhui": 94, "dai": 94, "came": 94, "shanghai": 94, "citi": 94, "prolif": 94, "author": 94, "teacher": 94, "chines": 94, "16764": 94, "1990": 94, "historian": 94, "alan": 94, "john": 94, "percival": 94, "taylor": 94, "di": 94, "20446": 94, "pace": 94, "bowler": 94, "ian": 94, "harvei": 94, "claim": 94, "victoria": 94, "15514": 94, "cotti": 94, "osc": 94, "foreign": 94, "minist": 94, "7525": 94, "sultan": 94, "specter": 94, "met": 94, "crown": 94, "abdullah": 94, "defenc": 94, "aviat": 94, "jeddah": 94, "saudi": 94, "agenc": 94, "2288": 94, "hi": 94, "customari": 94, "outfit": 94, "champion": 94, "damp": 94, "scalp": 94, "canada": 94, "reign": 94, "olymp": 94, "donovan": 94, "bailei": 94, "1992": 94, "linford": 94, "christi": 94, "britain": 94, "1984": 94, "1988": 94, "carl": 94, "lewi": 94, "ambigi": 94, "punctuat": 94, "chicago": 94, "digest": 94, "philadelphia": 94, "usda": 94, "york": 94, "token_issu": 94, "471": 94, "kean": 94, "year": 94, "contract": 94, "manchest": 94, "19072": 94, "societi": 94, "million": 94, "bite": 94, "deliv": 94, "19910": 94, "father": 94, "clarenc": 94, "woolmer": 94, "renam": 94, "uttar": 94, "pradesh": 94, "india": 94, "ranji": 94, "trophi": 94, "nation": 94, "championship": 94, "captain": 94, "1949": 94, "15658": 94, "19879": 94, "iii": 94, "brian": 94, "shimer": 94, "randi": 94, "jone": 94, "19104": 94}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [9, 0, 0, "-", "datalab"], [30, 0, 0, "-", "dataset"], [33, 0, 0, "-", "experimental"], [36, 0, 0, "-", "filter"], [37, 0, 0, "-", "internal"], [48, 0, 0, "-", "models"], [50, 0, 0, "-", "multiannotator"], [53, 0, 0, "-", "multilabel_classification"], [56, 0, 0, "-", "object_detection"], [59, 0, 0, "-", "outlier"], [60, 0, 0, "-", "rank"], [61, 0, 0, "-", "regression"], [65, 0, 0, "-", "segmentation"], [69, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.datalab": [[4, 0, 0, "-", "datalab"], [13, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[4, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[4, 4, 1, "", "class_names"], [4, 3, 1, "", "find_issues"], [4, 3, 1, "", "get_info"], [4, 3, 1, "", "get_issue_summary"], [4, 3, 1, "", "get_issues"], [4, 4, 1, "", "has_labels"], [4, 4, 1, "", "info"], [4, 4, 1, "", "issue_summary"], [4, 4, 1, "", "issues"], [4, 4, 1, "", "labels"], [4, 3, 1, "", "list_default_issue_types"], [4, 3, 1, "", "list_possible_issue_types"], [4, 3, 1, "", "load"], [4, 3, 1, "", "report"], [4, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[10, 0, 0, "-", "data"], [11, 0, 0, "-", "data_issues"], [14, 0, 0, "-", "issue_finder"], [12, 0, 0, "-", "issue_manager_factory"], [28, 0, 0, "-", "report"]], "cleanlab.datalab.internal.data": [[10, 2, 1, "", "Data"], [10, 5, 1, "", "DataFormatError"], [10, 5, 1, "", "DatasetDictError"], [10, 5, 1, "", "DatasetLoadError"], [10, 2, 1, "", "Label"]], "cleanlab.datalab.internal.data.Data": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[11, 2, 1, "", "DataIssues"], [11, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[11, 3, 1, "", "collect_issues_from_imagelab"], [11, 3, 1, "", "collect_issues_from_issue_manager"], [11, 3, 1, "", "collect_statistics"], [11, 3, 1, "", "get_info"], [11, 3, 1, "", "get_issue_summary"], [11, 3, 1, "", "get_issues"], [11, 6, 1, "", "info"], [11, 6, 1, "", "issue_summary"], [11, 6, 1, "", "issues"], [11, 3, 1, "", "set_health_score"], [11, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[14, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[14, 3, 1, "", "find_issues"], [14, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[16, 0, 0, "-", "data_valuation"], [17, 0, 0, "-", "duplicate"], [18, 0, 0, "-", "imbalance"], [20, 0, 0, "-", "issue_manager"], [21, 0, 0, "-", "label"], [22, 0, 0, "-", "noniid"], [23, 0, 0, "-", "null"], [24, 0, 0, "-", "outlier"], [27, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[16, 6, 1, "", "DEFAULT_THRESHOLD"], [16, 3, 1, "", "collect_info"], [16, 6, 1, "", "description"], [16, 3, 1, "", "find_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_name"], [16, 6, 1, "", "issue_score_key"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "make_summary"], [16, 3, 1, "", "report"], [16, 6, 1, "", "summary"], [16, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[17, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[17, 3, 1, "", "collect_info"], [17, 6, 1, "", "description"], [17, 3, 1, "", "find_issues"], [17, 6, 1, "", "info"], [17, 6, 1, "", "issue_name"], [17, 6, 1, "", "issue_score_key"], [17, 6, 1, "", "issues"], [17, 3, 1, "", "make_summary"], [17, 6, 1, "", "near_duplicate_sets"], [17, 3, 1, "", "report"], [17, 6, 1, "", "summary"], [17, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[18, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[18, 3, 1, "", "collect_info"], [18, 6, 1, "", "description"], [18, 3, 1, "", "find_issues"], [18, 6, 1, "", "info"], [18, 6, 1, "", "issue_name"], [18, 6, 1, "", "issue_score_key"], [18, 6, 1, "", "issues"], [18, 3, 1, "", "make_summary"], [18, 3, 1, "", "report"], [18, 6, 1, "", "summary"], [18, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[20, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[21, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 3, 1, "", "get_health_summary"], [21, 6, 1, "", "health_summary_parameters"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[22, 2, 1, "", "NonIIDIssueManager"], [22, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[23, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[24, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[24, 6, 1, "", "DEFAULT_THRESHOLDS"], [24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 6, 1, "", "ood"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[26, 2, 1, "", "RegressionLabelIssueManager"], [26, 1, 1, "", "find_issues_with_features"], [26, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[27, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[27, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [27, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "filter_cluster_ids"], [27, 3, 1, "", "find_issues"], [27, 3, 1, "", "get_worst_cluster"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "perform_clustering"], [27, 3, 1, "", "report"], [27, 3, 1, "", "set_knn_graph"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, 7, 1, "", "REGISTRY"], [12, 1, 1, "", "list_default_issue_types"], [12, 1, 1, "", "list_possible_issue_types"], [12, 1, 1, "", "register"]], "cleanlab.datalab.internal.report": [[28, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[28, 3, 1, "", "get_report"], [28, 3, 1, "", "report"]], "cleanlab.dataset": [[30, 1, 1, "", "find_overlapping_classes"], [30, 1, 1, "", "health_summary"], [30, 1, 1, "", "overall_label_health_score"], [30, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[31, 0, 0, "-", "cifar_cnn"], [32, 0, 0, "-", "coteaching"], [34, 0, 0, "-", "label_issues_batched"], [35, 0, 0, "-", "mnist_pytorch"]], "cleanlab.experimental.cifar_cnn": [[31, 2, 1, "", "CNN"], [31, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[31, 6, 1, "", "T_destination"], [31, 3, 1, "", "__call__"], [31, 3, 1, "", "add_module"], [31, 3, 1, "", "apply"], [31, 3, 1, "", "bfloat16"], [31, 3, 1, "", "buffers"], [31, 3, 1, "", "children"], [31, 3, 1, "", "cpu"], [31, 3, 1, "", "cuda"], [31, 3, 1, "", "double"], [31, 6, 1, "", "dump_patches"], [31, 3, 1, "", "eval"], [31, 3, 1, "", "extra_repr"], [31, 3, 1, "", "float"], [31, 3, 1, "id0", "forward"], [31, 3, 1, "", "get_buffer"], [31, 3, 1, "", "get_extra_state"], [31, 3, 1, "", "get_parameter"], [31, 3, 1, "", "get_submodule"], [31, 3, 1, "", "half"], [31, 3, 1, "", "ipu"], [31, 3, 1, "", "load_state_dict"], [31, 3, 1, "", "modules"], [31, 3, 1, "", "named_buffers"], [31, 3, 1, "", "named_children"], [31, 3, 1, "", "named_modules"], [31, 3, 1, "", "named_parameters"], [31, 3, 1, "", "parameters"], [31, 3, 1, "", "register_backward_hook"], [31, 3, 1, "", "register_buffer"], [31, 3, 1, "", "register_forward_hook"], [31, 3, 1, "", "register_forward_pre_hook"], [31, 3, 1, "", "register_full_backward_hook"], [31, 3, 1, "", "register_load_state_dict_post_hook"], [31, 3, 1, "", "register_module"], [31, 3, 1, "", "register_parameter"], [31, 3, 1, "", "requires_grad_"], [31, 3, 1, "", "set_extra_state"], [31, 3, 1, "", "share_memory"], [31, 3, 1, "", "state_dict"], [31, 3, 1, "", "to"], [31, 3, 1, "", "to_empty"], [31, 3, 1, "", "train"], [31, 6, 1, "", "training"], [31, 3, 1, "", "type"], [31, 3, 1, "", "xpu"], [31, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[32, 1, 1, "", "adjust_learning_rate"], [32, 1, 1, "", "evaluate"], [32, 1, 1, "", "forget_rate_scheduler"], [32, 1, 1, "", "initialize_lr_scheduler"], [32, 1, 1, "", "loss_coteaching"], [32, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[34, 2, 1, "", "LabelInspector"], [34, 7, 1, "", "adj_confident_thresholds_shared"], [34, 1, 1, "", "find_label_issues_batched"], [34, 7, 1, "", "labels_shared"], [34, 7, 1, "", "pred_probs_shared"], [34, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[34, 3, 1, "", "get_confident_thresholds"], [34, 3, 1, "", "get_label_issues"], [34, 3, 1, "", "get_num_issues"], [34, 3, 1, "", "get_quality_scores"], [34, 3, 1, "", "score_label_quality"], [34, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[35, 2, 1, "", "CNN"], [35, 2, 1, "", "SimpleNet"], [35, 1, 1, "", "get_mnist_dataset"], [35, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[35, 3, 1, "", "__init_subclass__"], [35, 6, 1, "", "batch_size"], [35, 6, 1, "", "dataset"], [35, 6, 1, "", "epochs"], [35, 3, 1, "id0", "fit"], [35, 3, 1, "", "get_metadata_routing"], [35, 3, 1, "", "get_params"], [35, 6, 1, "", "loader"], [35, 6, 1, "", "log_interval"], [35, 6, 1, "", "lr"], [35, 6, 1, "", "momentum"], [35, 6, 1, "", "no_cuda"], [35, 3, 1, "id1", "predict"], [35, 3, 1, "id4", "predict_proba"], [35, 6, 1, "", "seed"], [35, 3, 1, "", "set_fit_request"], [35, 3, 1, "", "set_params"], [35, 3, 1, "", "set_predict_proba_request"], [35, 3, 1, "", "set_predict_request"], [35, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[35, 6, 1, "", "T_destination"], [35, 3, 1, "", "__call__"], [35, 3, 1, "", "add_module"], [35, 3, 1, "", "apply"], [35, 3, 1, "", "bfloat16"], [35, 3, 1, "", "buffers"], [35, 3, 1, "", "children"], [35, 3, 1, "", "cpu"], [35, 3, 1, "", "cuda"], [35, 3, 1, "", "double"], [35, 6, 1, "", "dump_patches"], [35, 3, 1, "", "eval"], [35, 3, 1, "", "extra_repr"], [35, 3, 1, "", "float"], [35, 3, 1, "", "forward"], [35, 3, 1, "", "get_buffer"], [35, 3, 1, "", "get_extra_state"], [35, 3, 1, "", "get_parameter"], [35, 3, 1, "", "get_submodule"], [35, 3, 1, "", "half"], [35, 3, 1, "", "ipu"], [35, 3, 1, "", "load_state_dict"], [35, 3, 1, "", "modules"], [35, 3, 1, "", "named_buffers"], [35, 3, 1, "", "named_children"], [35, 3, 1, "", "named_modules"], [35, 3, 1, "", "named_parameters"], [35, 3, 1, "", "parameters"], [35, 3, 1, "", "register_backward_hook"], [35, 3, 1, "", "register_buffer"], [35, 3, 1, "", "register_forward_hook"], [35, 3, 1, "", "register_forward_pre_hook"], [35, 3, 1, "", "register_full_backward_hook"], [35, 3, 1, "", "register_load_state_dict_post_hook"], [35, 3, 1, "", "register_module"], [35, 3, 1, "", "register_parameter"], [35, 3, 1, "", "requires_grad_"], [35, 3, 1, "", "set_extra_state"], [35, 3, 1, "", "share_memory"], [35, 3, 1, "", "state_dict"], [35, 3, 1, "", "to"], [35, 3, 1, "", "to_empty"], [35, 3, 1, "", "train"], [35, 6, 1, "", "training"], [35, 3, 1, "", "type"], [35, 3, 1, "", "xpu"], [35, 3, 1, "", "zero_grad"]], "cleanlab.filter": [[36, 1, 1, "", "find_label_issues"], [36, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [36, 1, 1, "", "find_predicted_neq_given"], [36, 7, 1, "", "pred_probs_by_class"], [36, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[38, 0, 0, "-", "label_quality_utils"], [39, 0, 0, "-", "latent_algebra"], [40, 0, 0, "-", "multiannotator_utils"], [41, 0, 0, "-", "multilabel_scorer"], [42, 0, 0, "-", "multilabel_utils"], [43, 0, 0, "-", "outlier"], [44, 0, 0, "-", "token_classification_utils"], [45, 0, 0, "-", "util"], [46, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[38, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[39, 1, 1, "", "compute_inv_noise_matrix"], [39, 1, 1, "", "compute_noise_matrix_from_inverse"], [39, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [39, 1, 1, "", "compute_py"], [39, 1, 1, "", "compute_py_inv_noise_matrix"], [39, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[40, 1, 1, "", "assert_valid_inputs_multiannotator"], [40, 1, 1, "", "assert_valid_pred_probs"], [40, 1, 1, "", "check_consensus_label_classes"], [40, 1, 1, "", "compute_soft_cross_entropy"], [40, 1, 1, "", "find_best_temp_scaler"], [40, 1, 1, "", "format_multiannotator_labels"], [40, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[41, 2, 1, "", "Aggregator"], [41, 2, 1, "", "ClassLabelScorer"], [41, 2, 1, "", "MultilabelScorer"], [41, 1, 1, "", "exponential_moving_average"], [41, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [41, 1, 1, "", "get_label_quality_scores"], [41, 1, 1, "", "multilabel_py"], [41, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[41, 3, 1, "", "__call__"], [41, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[41, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [41, 6, 1, "", "NORMALIZED_MARGIN"], [41, 6, 1, "", "SELF_CONFIDENCE"], [41, 3, 1, "", "__call__"], [41, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[41, 3, 1, "", "__call__"], [41, 3, 1, "", "aggregate"], [41, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[42, 1, 1, "", "get_onehot_num_classes"], [42, 1, 1, "", "int2onehot"], [42, 1, 1, "", "onehot2int"], [42, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[43, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[44, 1, 1, "", "color_sentence"], [44, 1, 1, "", "filter_sentence"], [44, 1, 1, "", "get_sentence"], [44, 1, 1, "", "mapping"], [44, 1, 1, "", "merge_probs"], [44, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[45, 1, 1, "", "append_extra_datapoint"], [45, 1, 1, "", "clip_noise_rates"], [45, 1, 1, "", "clip_values"], [45, 1, 1, "", "compress_int_array"], [45, 1, 1, "", "confusion_matrix"], [45, 1, 1, "", "csr_vstack"], [45, 1, 1, "", "estimate_pu_f1"], [45, 1, 1, "", "extract_indices_tf"], [45, 1, 1, "", "force_two_dimensions"], [45, 1, 1, "", "format_labels"], [45, 1, 1, "", "get_missing_classes"], [45, 1, 1, "", "get_num_classes"], [45, 1, 1, "", "get_unique_classes"], [45, 1, 1, "", "is_tensorflow_dataset"], [45, 1, 1, "", "is_torch_dataset"], [45, 1, 1, "", "num_unique_classes"], [45, 1, 1, "", "print_inverse_noise_matrix"], [45, 1, 1, "", "print_joint_matrix"], [45, 1, 1, "", "print_noise_matrix"], [45, 1, 1, "", "print_square_matrix"], [45, 1, 1, "", "remove_noise_from_class"], [45, 1, 1, "", "round_preserving_row_totals"], [45, 1, 1, "", "round_preserving_sum"], [45, 1, 1, "", "smart_display_dataframe"], [45, 1, 1, "", "subset_X_y"], [45, 1, 1, "", "subset_data"], [45, 1, 1, "", "subset_labels"], [45, 1, 1, "", "train_val_split"], [45, 1, 1, "", "unshuffle_tensorflow_dataset"], [45, 1, 1, "", "value_counts"], [45, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[46, 1, 1, "", "assert_indexing_works"], [46, 1, 1, "", "assert_nonempty_input"], [46, 1, 1, "", "assert_valid_class_labels"], [46, 1, 1, "", "assert_valid_inputs"], [46, 1, 1, "", "labels_to_array"]], "cleanlab.models": [[49, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[49, 2, 1, "", "KerasWrapperModel"], [49, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[49, 3, 1, "", "fit"], [49, 3, 1, "", "get_params"], [49, 3, 1, "", "predict"], [49, 3, 1, "", "predict_proba"], [49, 3, 1, "", "set_params"], [49, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[49, 3, 1, "", "fit"], [49, 3, 1, "", "get_params"], [49, 3, 1, "", "predict"], [49, 3, 1, "", "predict_proba"], [49, 3, 1, "", "set_params"], [49, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[50, 1, 1, "", "convert_long_to_wide_dataset"], [50, 1, 1, "", "get_active_learning_scores"], [50, 1, 1, "", "get_active_learning_scores_ensemble"], [50, 1, 1, "", "get_label_quality_multiannotator"], [50, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [50, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[51, 0, 0, "-", "dataset"], [52, 0, 0, "-", "filter"], [54, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[51, 1, 1, "", "common_multilabel_issues"], [51, 1, 1, "", "multilabel_health_summary"], [51, 1, 1, "", "overall_multilabel_health_score"], [51, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[52, 1, 1, "", "find_label_issues"], [52, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[54, 1, 1, "", "get_label_quality_scores"], [54, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[55, 0, 0, "-", "filter"], [57, 0, 0, "-", "rank"], [58, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[55, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[57, 1, 1, "", "compute_badloc_box_scores"], [57, 1, 1, "", "compute_overlooked_box_scores"], [57, 1, 1, "", "compute_swap_box_scores"], [57, 1, 1, "", "get_label_quality_scores"], [57, 1, 1, "", "issues_from_scores"], [57, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[58, 1, 1, "", "bounding_box_size_distribution"], [58, 1, 1, "", "calculate_per_class_metrics"], [58, 1, 1, "", "class_label_distribution"], [58, 1, 1, "", "get_average_per_class_confusion_matrix"], [58, 1, 1, "", "get_sorted_bbox_count_idxs"], [58, 1, 1, "", "object_counts_per_image"], [58, 1, 1, "", "plot_class_distribution"], [58, 1, 1, "", "plot_class_size_distributions"], [58, 1, 1, "", "visualize"]], "cleanlab.outlier": [[59, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[59, 3, 1, "", "fit"], [59, 3, 1, "", "fit_score"], [59, 3, 1, "", "score"]], "cleanlab.rank": [[60, 1, 1, "", "find_top_issues"], [60, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [60, 1, 1, "", "get_label_quality_ensemble_scores"], [60, 1, 1, "", "get_label_quality_scores"], [60, 1, 1, "", "get_normalized_margin_for_each_label"], [60, 1, 1, "", "get_self_confidence_for_each_label"], [60, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[62, 0, 0, "-", "learn"], [63, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[62, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[62, 3, 1, "", "__init_subclass__"], [62, 3, 1, "", "find_label_issues"], [62, 3, 1, "", "fit"], [62, 3, 1, "", "get_aleatoric_uncertainty"], [62, 3, 1, "", "get_epistemic_uncertainty"], [62, 3, 1, "", "get_label_issues"], [62, 3, 1, "", "get_metadata_routing"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "save_space"], [62, 3, 1, "", "score"], [62, 3, 1, "", "set_fit_request"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[63, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"], [67, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[64, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[67, 1, 1, "", "common_label_issues"], [67, 1, 1, "", "display_issues"], [67, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[68, 0, 0, "-", "filter"], [70, 0, 0, "-", "rank"], [71, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[68, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[70, 1, 1, "", "get_label_quality_scores"], [70, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[71, 1, 1, "", "common_label_issues"], [71, 1, 1, "", "display_issues"], [71, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 74, 78, 79, 81, 82, 83, 86, 92, 93, 94], "count": [3, 83], "datalab": [4, 5, 7, 8, 9, 75, 76, 77, 78, 79, 83], "creat": [5, 75, 76, 83, 85], "your": [5, 72, 75, 76, 79, 81, 83], "own": 5, "issu": [5, 7, 8, 19, 26, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 91, 92, 94], "manag": [5, 19], "prerequisit": 5, "implement": 5, "issuemanag": [5, 75], "basic": 5, "check": 5, "intermedi": 5, "advanc": [5, 75], "us": [5, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "gener": 6, "cluster": [6, 81], "id": 6, "guid": [7, 9], "type": [7, 8, 83], "custom": [7, 75], "can": [8, 76, 80, 81, 83, 85], "detect": [8, 76, 78, 79, 81, 83, 87, 88], "estim": [8, 83, 85], "each": 8, "label": [8, 21, 26, 72, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 90, 91, 92, 93, 94], "outlier": [8, 24, 43, 59, 78, 79, 82, 88], "Near": [8, 76, 78, 79, 82], "duplic": [8, 17, 76, 78, 79, 81, 82], "non": [8, 79], "iid": [8, 79], "class": [8, 73, 83, 91], "imbal": [8, 18], "imag": [8, 82, 88], "specif": [8, 19, 91], "underperform": [8, 81], "group": [8, 81], "null": [8, 23], "data": [8, 10, 72, 74, 75, 76, 78, 79, 80, 81, 83, 85, 86, 87, 88, 90, 91, 92, 94], "valuat": 8, "option": 8, "paramet": [8, 83], "get": [9, 75, 76, 85, 86, 87, 91, 94], "start": [9, 80], "api": 9, "refer": 9, "data_issu": 11, "factori": 12, "intern": [13, 37], "issue_find": 14, "data_valu": 16, "issue_manag": [19, 20], "regist": 19, "unregist": 19, "ml": [19, 81, 83], "task": 19, "noniid": 22, "regress": [25, 61, 62, 63, 81, 90], "prioriti": 26, "order": 26, "find": [26, 72, 74, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "underperforming_group": 27, "report": [28, 82], "dataset": [30, 51, 72, 76, 79, 80, 81, 82, 83, 86, 87, 88, 90, 91, 93, 94], "cifar_cnn": 31, "coteach": 32, "experiment": 33, "label_issues_batch": 34, "mnist_pytorch": 35, "filter": [36, 52, 55, 64, 68, 83], "label_quality_util": 38, "latent_algebra": 39, "multiannotator_util": 40, "multilabel_scor": 41, "multilabel_util": 42, "token_classification_util": 44, "util": 45, "valid": [46, 82, 89], "fasttext": 47, "model": [48, 72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "kera": 49, "multiannot": [50, 85], "multilabel_classif": 53, "rank": [54, 57, 60, 63, 66, 70, 83], "object_detect": 56, "summari": [58, 67, 71], "learn": [62, 76, 81, 83, 92], "segment": [65, 91], "token_classif": [69, 94], "cleanlab": [72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "open": [72, 81], "sourc": [72, 81], "document": 72, "quickstart": 72, "1": [72, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "instal": [72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "2": [72, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "common": [72, 73, 94], "3": [72, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "handl": [72, 81], "error": [72, 81, 82, 83, 85, 86, 87, 90, 91, 93, 94], "train": [72, 74, 81, 88, 90, 92, 93], "robust": [72, 83, 90, 92, 93], "noisi": [72, 83, 90, 92, 93], "4": [72, 74, 75, 76, 78, 79, 82, 83, 85, 87, 88, 90, 92, 93], "curat": [72, 80], "fix": [72, 81], "level": [72, 80, 83, 94], "5": [72, 74, 76, 78, 82, 83, 85, 90, 92], "improv": [72, 85], "via": [72, 83, 85], "mani": [72, 83], "other": [72, 85, 87, 90], "techniqu": 72, "contribut": 72, "easi": [72, 78, 79, 82], "mode": [72, 78, 79, 82], "how": [73, 81, 83, 85, 86, 94], "migrat": 73, "version": 73, "0": 73, "from": [73, 75, 76, 83, 90, 92, 93], "pre": [73, 74, 81, 88], "function": [73, 75], "name": 73, "chang": 73, "modul": [73, 83], "new": 73, "remov": 73, "argument": [73, 75], "variabl": 73, "audio": 74, "speechbrain": 74, "depend": [74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "import": [74, 75, 76, 80, 82, 83, 85], "them": [74, 80, 83], "load": [74, 75, 76, 78, 79, 90, 92, 93], "featur": [74, 82, 88], "fit": 74, "linear": 74, "comput": [74, 78, 79, 81, 82, 85, 89, 92], "out": [74, 75, 76, 78, 79, 82, 85, 89, 92], "sampl": [74, 75, 76, 78, 79, 82, 85, 89, 92], "predict": [74, 75, 76, 78, 79, 82, 85, 86, 87, 89, 92], "probabl": [74, 75, 76, 78, 79, 82, 85, 89, 92], "workflow": [75, 83], "audit": [75, 76], "requir": [75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "classifi": [75, 76], "instanti": 75, "object": [75, 87], "increment": 75, "search": 75, "specifi": [75, 81], "nondefault": 75, "save": 75, "ad": 75, "A": 76, "unifi": 76, "all": [76, 83], "kind": [76, 87], "skip": [76, 80, 83, 85], "detail": [76, 80, 83, 85], "more": [76, 83, 90, 92, 93], "about": 76, "addit": 76, "inform": [76, 82], "tutori": [77, 80, 84], "tabular": [78, 92], "numer": 78, "categor": 78, "column": 78, "process": [78, 88, 90, 92], "select": [78, 92], "construct": 78, "k": [78, 82, 89], "nearest": 78, "neighbour": 78, "graph": 78, "text": [79, 93, 94], "format": [79, 81, 86, 87, 93], "defin": [79, 82, 90, 93], "drift": 79, "fetch": [80, 82], "evalu": 80, "health": [80, 83], "8": [80, 83], "popular": 80, "faq": 81, "what": [81, 83, 89], "do": [81, 83], "i": [81, 83, 89], "infer": 81, "correct": 81, "exampl": [81, 82, 83, 88], "ha": 81, "flag": 81, "should": 81, "v": 81, "test": [81, 83, 88], "big": 81, "limit": 81, "memori": 81, "why": 81, "isn": 81, "t": 81, "cleanlearn": [81, 83], "work": [81, 83, 85, 94], "me": 81, "differ": [81, 87], "clean": [81, 83], "final": 81, "hyperparamet": 81, "tune": 81, "onli": 81, "one": [81, 83, 86, 91], "doe": [81, 85, 94], "take": 81, "so": 81, "long": 81, "slice": 81, "when": [81, 83], "identifi": [81, 87], "run": 81, "licens": 81, "under": 81, "an": 81, "answer": 81, "question": 81, "pytorch": [82, 88], "normal": 82, "fashion": 82, "mnist": 82, "prepar": 82, "fold": [82, 89], "cross": [82, 89], "embed": [82, 88], "7": [82, 83], "view": 82, "most": [82, 94], "like": 82, "sever": 82, "set": [82, 83], "dark": 82, "top": [82, 91], "low": 82, "The": 83, "centric": 83, "ai": 83, "machin": 83, "find_label_issu": 83, "line": 83, "code": 83, "visual": [83, 87, 88, 91], "twenti": 83, "lowest": 83, "qualiti": [83, 85, 86, 87, 91, 94], "see": 83, "now": 83, "let": 83, "": 83, "happen": 83, "we": 83, "merg": 83, "seafoam": 83, "green": 83, "yellow": 83, "too": 83, "you": 83, "re": 83, "6": 83, "One": 83, "score": [83, 85, 86, 87, 91, 94], "rule": 83, "overal": [83, 91], "accur": 83, "thi": 83, "directli": 83, "fulli": 83, "character": 83, "nois": 83, "matrix": [83, 86], "joint": 83, "prior": 83, "true": 83, "distribut": 83, "flip": 83, "rate": 83, "ani": 83, "again": 83, "support": 83, "lot": 83, "method": 83, "filter_bi": 83, "automat": 83, "everi": 83, "uniqu": 83, "num_label_issu": 83, "threshold": 83, "found": 83, "Not": 83, "sure": 83, "ensembl": 83, "multipl": [83, 85], "predictor": 83, "consensu": 85, "annot": 85, "initi": 85, "major": 85, "vote": 85, "better": 85, "statist": 85, "compar": 85, "inspect": 85, "potenti": [85, 90, 93], "retrain": 85, "further": 85, "multi": 86, "given": 86, "hot": 86, "binari": 86, "download": [87, 91, 94], "objectlab": 87, "timm": 88, "cifar10": 88, "some": 88, "pred_prob": [88, 91, 94], "wai": 90, "semant": 91, "which": 91, "ar": 91, "commonli": 91, "mislabel": [91, 94], "focus": 91, "scikit": 92, "token": 94, "word": 94, "sentenc": 94, "contain": 94, "particular": 94}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 56}}) \ No newline at end of file +Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/report", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/dataset_health", "tutorials/faq", "tutorials/image", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/tabular", "tutorials/text", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/fasttext.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/image.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/tabular.ipynb", "tutorials/text.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "datalab", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "report", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "outlier", "token_classification_utils", "util", "validation", "fasttext", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "Audio Classification with SpeechBrain and Cleanlab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Find Dataset-level Issues for Dataset Curation", "FAQ", "Image Classification with PyTorch and Cleanlab", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Classification with Tabular Data using Scikit-Learn and Cleanlab", "Text Classification with Noisy Labels", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 73, 75, 76, 83, 85, 86], "helper": [1, 14, 34, 38, 40, 41, 42, 43, 44, 45, 57, 80, 82, 94], "method": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "ar": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 25, 26, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "us": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 72, 73, 75, 80, 84, 89], "benchmark": [1, 31, 72, 73, 75, 76, 83, 85, 86], "cleanlab": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 75, 76, 80, 84, 89], "": [1, 2, 3, 8, 16, 30, 31, 35, 38, 41, 43, 45, 50, 51, 55, 57, 58, 59, 60, 62, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "core": [1, 4, 34, 36, 64, 66, 91], "algorithm": [1, 2, 6, 8, 27, 32, 45, 50, 59, 68, 70, 72, 81, 83, 85, 94], "These": [1, 2, 3, 6, 8, 19, 33, 36, 37, 48, 50, 51, 54, 58, 59, 63, 67, 68, 70, 71, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "introduc": [1, 74, 81, 83], "synthet": [1, 85, 86, 91], "nois": [1, 2, 3, 30, 36, 39, 45, 51, 75, 76, 80, 85], "label": [1, 2, 3, 4, 5, 6, 7, 10, 14, 18, 19, 20, 25, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 45, 46, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 75, 80, 84, 88, 89], "classif": [1, 3, 4, 5, 8, 12, 14, 28, 30, 34, 36, 39, 41, 42, 45, 50, 51, 52, 53, 54, 59, 60, 68, 69, 70, 71, 72, 73, 75, 76, 84, 85, 88, 89, 90, 91], "dataset": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 22, 23, 24, 26, 27, 34, 35, 36, 39, 41, 45, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 77, 78, 84, 85, 89, 92], "specif": [1, 3, 4, 7, 12, 13, 14, 23, 28, 33, 48, 52, 55, 58, 67, 71, 76, 78, 79, 82, 83, 94], "thi": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 38, 39, 41, 42, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "modul": [1, 3, 11, 12, 13, 14, 19, 25, 28, 30, 31, 32, 33, 34, 35, 36, 43, 45, 48, 50, 59, 60, 72, 81, 82, 86], "provid": [1, 2, 3, 4, 5, 6, 8, 12, 14, 16, 21, 26, 30, 31, 32, 34, 35, 36, 39, 45, 49, 50, 51, 52, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 88, 89, 90, 91, 92, 93, 94], "gener": [1, 2, 3, 5, 8, 16, 21, 28, 30, 41, 45, 46, 59, 60, 62, 67, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 89, 90, 91, 93, 94], "valid": [1, 2, 3, 4, 8, 10, 30, 36, 37, 39, 40, 41, 43, 45, 50, 52, 55, 58, 60, 62, 63, 71, 73, 74, 75, 76, 78, 79, 80, 81, 83, 84, 86, 87, 90, 91, 92, 93, 94], "matric": [1, 3, 39, 81], "which": [1, 2, 3, 4, 8, 10, 11, 12, 14, 16, 20, 22, 28, 30, 31, 35, 36, 39, 41, 44, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 66, 67, 68, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "learn": [1, 2, 3, 4, 8, 12, 14, 20, 26, 28, 32, 33, 34, 35, 36, 38, 40, 45, 48, 50, 52, 59, 61, 63, 66, 70, 72, 74, 75, 78, 79, 80, 82, 84, 85, 90, 93], "i": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "possibl": [1, 2, 3, 8, 30, 31, 35, 36, 38, 39, 41, 52, 53, 54, 55, 57, 58, 59, 60, 62, 68, 70, 71, 76, 81, 83, 85, 86, 87, 90, 91, 94], "noisi": [1, 2, 3, 8, 30, 32, 35, 36, 39, 45, 51, 52, 54, 60, 62, 63, 64, 66, 67, 73, 75, 76, 78, 79, 81, 84, 85], "given": [1, 2, 3, 8, 26, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 44, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "matrix": [1, 2, 3, 4, 8, 14, 16, 27, 30, 36, 38, 39, 42, 45, 46, 52, 57, 58, 59, 60, 78, 88], "trace": [1, 75, 76, 83, 85, 86], "valu": [1, 2, 3, 4, 8, 10, 11, 14, 16, 20, 22, 23, 30, 31, 32, 34, 35, 36, 38, 39, 41, 43, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 71, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "more": [1, 2, 3, 4, 5, 8, 11, 14, 16, 22, 30, 31, 34, 35, 38, 41, 43, 45, 50, 51, 52, 53, 54, 55, 57, 58, 60, 62, 63, 66, 67, 68, 70, 72, 74, 75, 78, 79, 80, 81, 82, 85, 86, 87, 88, 91, 94], "function": [1, 2, 3, 4, 5, 11, 12, 14, 21, 22, 26, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 76, 80, 81, 83, 85, 86, 87, 91, 92, 93, 94], "noise_matrix_is_valid": 1, "noise_matrix": [1, 2, 3, 8, 39, 45, 75, 76, 83, 85, 86], "py": [1, 3, 28, 31, 32, 36, 39, 41, 75, 76, 83, 85, 86], "verbos": [1, 2, 4, 5, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 34, 36, 50, 51, 52, 57, 59, 60, 62, 64, 66, 67, 71, 75, 83, 85], "fals": [1, 2, 3, 4, 5, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 34, 35, 36, 40, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 74, 75, 76, 78, 79, 81, 82, 83, 85, 87, 88, 90, 91, 93], "sourc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71], "prior": [1, 2, 3, 30, 36, 39, 41], "repres": [1, 2, 3, 5, 8, 10, 14, 16, 22, 30, 34, 36, 39, 42, 43, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "p": [1, 2, 3, 8, 30, 36, 38, 39, 45, 50, 58, 59, 60, 64, 76, 78, 79, 82, 83, 85, 94], "true_label": [1, 2, 3, 30, 39, 45, 83, 85], "k": [1, 2, 3, 4, 6, 8, 10, 14, 16, 17, 21, 22, 24, 27, 30, 34, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 53, 54, 55, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 74, 75, 76, 81, 83, 85, 86, 87, 88, 91, 92, 94], "check": [1, 2, 4, 7, 8, 10, 14, 23, 31, 34, 35, 40, 46, 49, 55, 58, 62, 72, 74, 75, 76, 81, 82, 83, 85, 86, 90, 92, 93], "learnabl": 1, "mean": [1, 2, 5, 6, 10, 11, 20, 22, 32, 35, 39, 41, 43, 57, 62, 76, 79, 81, 83, 85, 86, 88, 90, 93], "achiev": [1, 2, 31, 32, 35, 62, 81, 85, 94], "better": [1, 4, 36, 50, 52, 60, 62, 63, 72, 74, 76, 78, 79, 81, 83, 86, 87, 88, 93, 94], "than": [1, 2, 3, 5, 8, 22, 24, 27, 30, 36, 45, 49, 50, 55, 57, 59, 60, 62, 66, 70, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 94], "random": [1, 2, 3, 5, 8, 16, 27, 34, 41, 50, 60, 62, 74, 75, 76, 78, 81, 82, 83, 85, 86, 88, 92], "perform": [1, 2, 5, 8, 22, 24, 27, 31, 35, 41, 58, 62, 72, 75, 81, 83, 85, 86, 89, 90, 92, 93], "averag": [1, 3, 8, 20, 24, 30, 31, 35, 41, 43, 50, 51, 58, 59, 60, 81, 85, 88], "amount": [1, 3, 82], "paramet": [1, 2, 3, 4, 7, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 76, 79, 82, 92, 93], "np": [1, 2, 3, 4, 5, 14, 16, 27, 30, 32, 34, 36, 38, 39, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 70, 71, 74, 75, 76, 78, 80, 81, 82, 83, 85, 86, 88, 90, 91, 92, 93, 94], "ndarrai": [1, 2, 3, 4, 14, 21, 22, 26, 27, 30, 32, 34, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 94], "an": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 43, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "arrai": [1, 2, 3, 4, 5, 8, 10, 14, 16, 22, 30, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 75, 76, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "shape": [1, 2, 3, 4, 14, 16, 30, 32, 34, 36, 38, 39, 40, 41, 43, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 80, 81, 83, 86, 87, 88, 91, 94], "condit": [1, 2, 3, 39, 44, 45, 60, 82, 83, 94], "probabl": [1, 2, 3, 4, 6, 8, 14, 21, 24, 30, 34, 35, 36, 38, 39, 41, 42, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 73, 80, 81, 83, 84, 86, 87, 88, 91, 94], "k_": [1, 2, 3, 39, 45], "k_y": [1, 2, 3, 39, 45], "contain": [1, 2, 3, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93], "fraction": [1, 2, 3, 8, 18, 32, 39, 45, 50, 62, 78, 81], "exampl": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 44, 45, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 85, 86, 87, 89, 90, 91, 92, 93, 94], "everi": [1, 2, 3, 4, 14, 31, 35, 36, 39, 44, 45, 52, 60, 62, 63, 74, 75, 76, 78, 79, 81, 82, 85, 87, 89, 91, 92, 94], "class": [1, 2, 3, 4, 5, 7, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 44, 45, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 92, 93, 94], "other": [1, 2, 3, 4, 8, 14, 20, 23, 30, 31, 33, 34, 35, 36, 39, 42, 45, 46, 48, 50, 51, 54, 58, 59, 60, 62, 67, 74, 75, 76, 78, 79, 81, 82, 83, 86, 88, 91, 94], "assum": [1, 2, 3, 10, 36, 39, 44, 45, 60, 64, 67, 81, 88, 91, 94], "column": [1, 2, 3, 4, 8, 10, 11, 26, 30, 34, 36, 39, 41, 42, 44, 45, 50, 51, 52, 54, 55, 58, 59, 60, 62, 67, 68, 70, 71, 74, 75, 76, 79, 80, 81, 82, 83, 85, 87, 90, 91, 92, 93, 94], "sum": [1, 2, 3, 22, 27, 30, 39, 41, 45, 51, 52, 54, 57, 62, 75, 76, 81, 82, 83, 85, 86, 91, 94], "1": [1, 2, 3, 4, 5, 8, 10, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 80, 81, 89], "each": [1, 2, 3, 4, 5, 6, 7, 11, 12, 14, 18, 20, 21, 22, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 45, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "true": [1, 2, 3, 4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 39, 41, 44, 45, 46, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "return": [1, 2, 3, 4, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "type": [1, 2, 3, 4, 5, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 86, 87, 91, 92, 94], "bool": [1, 2, 3, 4, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 34, 35, 36, 41, 44, 45, 50, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71], "is_valid": 1, "whether": [1, 3, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 31, 34, 35, 36, 45, 50, 51, 52, 54, 55, 71, 74, 76, 78, 79, 80, 81, 82, 83, 90, 93, 94], "generate_noisy_label": [1, 75, 76, 83, 85, 86], "from": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 21, 23, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 39, 41, 42, 43, 44, 45, 50, 52, 54, 57, 58, 59, 60, 62, 63, 68, 70, 71, 72, 74, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 91, 94], "perfect": [1, 2, 30, 62, 83, 87], "exactli": [1, 3, 8, 30, 31, 35, 36, 53, 59, 75, 76, 78, 79, 82, 83], "yield": [1, 31, 35], "between": [1, 4, 8, 13, 14, 19, 20, 22, 25, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 43, 48, 50, 51, 54, 57, 59, 60, 62, 63, 66, 70, 71, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "below": [1, 3, 4, 8, 30, 31, 34, 35, 36, 38, 41, 50, 51, 52, 57, 58, 66, 70, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "we": [1, 2, 3, 4, 5, 8, 11, 20, 31, 34, 35, 36, 41, 45, 46, 50, 57, 58, 60, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "loop": [1, 3, 39, 45, 82], "implement": [1, 2, 3, 4, 7, 12, 20, 31, 32, 34, 35, 39, 45, 62, 72, 74, 75, 78, 88, 89, 92], "what": [1, 4, 7, 8, 14, 28, 30, 32, 34, 36, 50, 51, 55, 57, 74, 75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "doe": [1, 2, 3, 8, 34, 35, 36, 41, 46, 57, 58, 62, 64, 66, 70, 74, 75, 76, 78, 79, 82, 86, 90, 91, 93], "do": [1, 2, 4, 8, 30, 34, 35, 45, 46, 59, 60, 64, 74, 75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "fast": 1, "explain": [1, 8], "python": [1, 2, 35, 49, 62, 75, 76, 80, 88], "pseudocod": [1, 89], "happen": [1, 8, 36, 52, 79, 85, 91], "n": [1, 2, 3, 4, 5, 30, 31, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 74, 79, 80, 81, 82, 85, 86, 90, 91, 92, 93, 94], "without": [1, 2, 4, 8, 10, 12, 18, 31, 35, 54, 62, 72, 74, 79, 83, 87, 88, 93], "ani": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 38, 40, 43, 44, 45, 49, 50, 52, 54, 55, 57, 58, 60, 62, 64, 66, 67, 72, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93], "distinct": [1, 16, 45, 94], "natur": [1, 8, 85, 88], "number": [1, 2, 3, 4, 5, 6, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 91, 94], "0": [1, 2, 3, 4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "count_joint": 1, "len": [1, 2, 3, 5, 30, 34, 39, 44, 45, 46, 59, 60, 62, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93, 94], "y": [1, 2, 3, 4, 6, 16, 26, 27, 35, 39, 41, 45, 46, 49, 58, 62, 63, 74, 75, 76, 78, 81, 83, 85, 86, 88, 90, 93], "round": [1, 34, 36, 45, 62, 81, 90], "astyp": [1, 85], "int": [1, 2, 3, 4, 5, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 41, 42, 43, 44, 45, 51, 52, 54, 58, 59, 60, 62, 64, 66, 67, 68, 71, 74, 75, 82, 88], "rang": [1, 3, 4, 5, 10, 39, 41, 43, 45, 58, 62, 63, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 94], "idx_flip": 1, "where": [1, 2, 3, 4, 5, 8, 10, 11, 14, 20, 30, 34, 36, 39, 40, 41, 42, 43, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94], "pragma": 1, "cover": [1, 3, 73, 80], "choic": [1, 6, 36, 43, 81, 82, 86, 88], "replac": [1, 44, 49, 60, 75, 76, 79, 80, 81, 82, 85, 88, 92, 93], "generate_noise_matrix_from_trac": [1, 75, 76, 83, 85, 86], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 60, 74, 75, 76], "05": [1, 8, 22, 26, 44, 58, 62, 68, 70, 78, 80, 81, 83, 87, 91], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 75, 76, 83, 85, 86], "none": [1, 2, 3, 4, 5, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 75, 76, 81, 82, 83, 85, 86, 91], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 8, 22, 35, 41, 62, 74, 75, 76, 78, 80, 83, 85, 86, 92], "max_it": [1, 74, 79, 88, 93], "10000": [1, 34, 80, 81], "x": [1, 2, 3, 4, 8, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 31, 32, 35, 36, 38, 39, 41, 44, 45, 46, 49, 50, 52, 58, 59, 60, 62, 64, 74, 75, 76, 78, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93], "diagon": [1, 3, 4, 36, 39, 45], "equal": [1, 3, 8, 10, 52, 57, 67, 89], "creat": [1, 2, 7, 14, 16, 31, 34, 35, 36, 45, 62, 72, 74, 78, 79, 81, 82, 91, 93, 94], "impli": [1, 8, 30, 51, 58], "float": [1, 2, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 40, 41, 43, 44, 45, 50, 51, 52, 54, 57, 58, 62, 66, 70, 74, 75, 76, 83, 85, 86], "entri": [1, 3, 4, 30, 31, 35, 36, 38, 42, 43, 45, 50, 51, 52, 55, 78, 79, 83, 86, 87, 92, 93], "maximum": [1, 8, 59, 67, 71, 91], "minimum": [1, 6, 8, 18, 36, 38, 52, 57, 70], "noise_r": 1, "non": [1, 2, 3, 4, 7, 14, 22, 31, 35, 36, 57, 62, 75, 81, 83, 85, 87, 88], "default": [1, 2, 3, 4, 5, 8, 12, 14, 24, 26, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 75, 81, 82, 91], "If": [1, 2, 3, 4, 8, 10, 11, 14, 22, 24, 30, 31, 34, 35, 36, 38, 39, 41, 44, 45, 49, 50, 51, 52, 55, 57, 58, 59, 62, 63, 64, 66, 67, 70, 71, 72, 73, 74, 75, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "have": [1, 2, 3, 4, 8, 14, 19, 22, 25, 30, 31, 33, 34, 35, 36, 39, 41, 45, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 67, 71, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "all": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 20, 28, 30, 31, 34, 35, 36, 39, 41, 42, 44, 45, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "necessari": [1, 2, 3, 5, 8, 10, 44, 75], "In": [1, 2, 3, 8, 30, 31, 34, 35, 50, 51, 53, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 88, 89, 90, 91, 92, 93, 94], "particular": [1, 4, 8, 11, 12, 14, 17, 18, 20, 22, 23, 24, 27, 31, 35, 45, 50, 54, 58, 62, 67, 71, 72, 74, 76, 79, 81, 85, 86, 88, 90, 92, 93], "satisfi": [1, 3, 30], "requir": [1, 2, 4, 5, 6, 7, 8, 9, 10, 26, 29, 31, 32, 33, 34, 35, 36, 39, 45, 48, 49, 52, 59, 60, 62, 64, 72, 73, 74, 80, 81, 83, 89], "argument": [1, 2, 3, 4, 8, 14, 21, 23, 26, 27, 31, 34, 35, 36, 41, 46, 49, 50, 51, 52, 54, 57, 58, 59, 60, 62, 66, 67, 68, 70, 76, 79, 80, 81, 82, 87, 90, 93, 94], "when": [1, 2, 3, 4, 8, 10, 12, 21, 22, 31, 35, 36, 39, 41, 45, 49, 52, 54, 55, 57, 59, 60, 62, 63, 75, 76, 78, 79, 82, 85, 89, 90, 91, 92, 93, 94], "The": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 38, 39, 40, 41, 42, 43, 45, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 67, 68, 70, 72, 74, 75, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "rate": [1, 2, 3, 8, 32, 45, 74, 94], "set": [1, 2, 3, 4, 7, 8, 10, 11, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 40, 41, 43, 45, 49, 50, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 75, 76, 78, 79, 81, 85, 86, 88, 89, 90, 91, 92, 93, 94], "note": [1, 2, 3, 5, 6, 8, 23, 27, 31, 34, 35, 36, 41, 45, 50, 55, 57, 58, 59, 60, 62, 63, 67, 73, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "you": [1, 2, 3, 4, 5, 8, 12, 14, 30, 31, 33, 34, 35, 36, 41, 48, 49, 50, 52, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "high": [1, 2, 14, 34, 36, 45, 57, 60, 62, 75, 76, 80, 82, 83, 87, 90, 91, 92, 93, 94], "mai": [1, 2, 3, 4, 8, 11, 19, 20, 25, 30, 31, 33, 34, 35, 36, 39, 41, 45, 50, 51, 55, 57, 58, 59, 60, 62, 64, 67, 71, 73, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 89, 90, 91, 93, 94], "imposs": [1, 8, 83], "also": [1, 2, 3, 4, 5, 8, 20, 30, 31, 34, 35, 36, 44, 49, 50, 59, 62, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "low": [1, 8, 45, 50, 72, 75, 76, 79, 83, 87, 91], "zero": [1, 3, 4, 31, 35, 38, 45, 46, 75, 82, 86, 87, 88], "forc": [1, 2, 3, 4, 35, 75, 94], "instead": [1, 2, 3, 8, 11, 14, 28, 30, 31, 34, 35, 36, 39, 45, 49, 50, 52, 54, 58, 59, 60, 62, 63, 66, 68, 70, 73, 74, 78, 79, 81, 82, 83, 86, 87, 88, 90, 91, 92, 93, 94], "onli": [1, 2, 3, 4, 5, 8, 14, 21, 22, 26, 30, 31, 34, 35, 36, 38, 39, 44, 45, 49, 50, 59, 60, 62, 64, 66, 70, 71, 72, 74, 75, 76, 79, 82, 85, 86, 87, 88, 89, 90, 91, 93, 94], "guarante": [1, 3, 4, 13, 19, 25, 31, 33, 35, 37, 39, 48, 73], "produc": [1, 2, 4, 8, 14, 41, 50, 60, 62, 64, 66, 72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 93, 94], "higher": [1, 4, 8, 30, 36, 38, 39, 41, 43, 50, 51, 62, 76, 79, 81, 87], "opposit": [1, 94], "occur": [1, 3, 8, 30, 44, 57, 75, 76, 81, 82, 88], "small": [1, 3, 8, 30, 34, 41, 45, 51, 58, 79, 80, 82, 86, 88, 93], "numpi": [1, 3, 4, 5, 8, 10, 16, 27, 34, 35, 41, 43, 44, 46, 49, 54, 57, 62, 63, 68, 70, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "max": [1, 36, 59, 60, 76, 82, 88], "tri": [1, 31, 35, 89], "befor": [1, 2, 3, 31, 35, 43, 45, 59, 62, 67, 79, 81, 83, 85, 88, 90, 92, 93], "option": [1, 2, 3, 4, 5, 6, 7, 10, 11, 14, 21, 22, 26, 30, 31, 34, 35, 36, 39, 41, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 72, 74, 75, 76, 78, 81, 82, 83, 90, 91, 92], "left": [1, 2, 36, 38, 43, 45, 52, 55, 58, 75, 76, 86, 87, 88, 91], "stochast": 1, "exceed": 1, "generate_n_rand_probabilities_that_sum_to_m": 1, "m": [1, 4, 31, 35, 40, 41, 50, 55, 57, 58, 59, 75, 76, 80, 85, 86, 87, 94], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 31, 35, 49, 81, 83, 91], "length": [1, 4, 10, 22, 23, 30, 32, 36, 45, 52, 55, 59, 60, 62, 64, 67, 71, 74, 86, 88, 91, 92, 94], "must": [1, 2, 3, 4, 14, 30, 31, 32, 33, 35, 36, 39, 41, 42, 45, 48, 49, 50, 51, 52, 59, 60, 62, 64, 66, 67, 68, 70, 71, 74, 85, 89, 91, 94], "randomly_distribute_n_balls_into_k_bin": 1, "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 8, 10, 30, 34, 36, 42, 45, 46, 50, 52, 58, 64, 66, 67, 68, 70, 71, 74, 81, 85, 86, 87, 91, 92, 93, 94], "ball": [1, 80], "bin": [1, 3, 52, 75, 76, 88], "ensur": [1, 2, 8, 31, 35, 45, 46, 57, 60, 62, 74, 75, 76, 79, 81, 82, 83, 88, 89, 90, 92, 93], "most": [1, 3, 4, 5, 8, 14, 30, 34, 36, 41, 49, 50, 51, 52, 55, 57, 58, 59, 60, 63, 66, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93], "least": [1, 8, 16, 27, 30, 34, 50, 51, 57, 60, 70, 76, 81, 82, 85, 88, 91], "int_arrai": [1, 45], "can": [2, 3, 4, 5, 6, 7, 11, 12, 14, 28, 30, 31, 32, 33, 34, 35, 36, 40, 41, 42, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 71, 72, 73, 74, 75, 78, 79, 82, 86, 87, 88, 89, 90, 91, 92, 93, 94], "model": [2, 3, 4, 8, 14, 16, 26, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 44, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 75, 76, 80, 84, 89, 91, 94], "For": [2, 3, 4, 5, 7, 8, 9, 14, 20, 29, 30, 31, 34, 35, 36, 39, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 68, 70, 71, 72, 74, 76, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 93, 94], "regular": [2, 3, 34, 49], "multi": [2, 3, 8, 30, 31, 34, 35, 36, 40, 41, 42, 45, 46, 51, 52, 53, 54, 59, 60, 72, 81, 83, 84], "task": [2, 4, 5, 8, 10, 12, 13, 14, 26, 28, 30, 34, 39, 41, 42, 43, 45, 50, 52, 60, 62, 72, 74, 79, 80, 81, 83, 86, 88, 91, 93, 94], "cleanlearn": [2, 3, 8, 21, 26, 31, 45, 49, 62, 63, 72, 73, 90, 92, 93], "wrap": [2, 31, 35, 49, 59, 62, 72, 75, 76, 78, 79, 83, 90, 92, 93], "instanc": [2, 3, 4, 5, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 35, 41, 49, 58, 59, 62, 67, 74, 75, 76, 78, 79, 82, 83, 92], "sklearn": [2, 3, 4, 6, 8, 16, 27, 30, 35, 41, 45, 49, 59, 62, 63, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 89, 90, 92, 93], "classifi": [2, 3, 35, 41, 45, 50, 53, 59, 60, 72, 73, 74, 78, 79, 81, 85, 86, 88, 89, 91, 92, 93, 94], "adher": [2, 35, 62], "estim": [2, 3, 4, 7, 11, 20, 30, 34, 35, 36, 39, 45, 50, 51, 52, 57, 59, 62, 64, 66, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 84, 86, 87, 88, 89, 90, 91, 94], "api": [2, 3, 12, 49, 59, 62, 73, 81, 90], "defin": [2, 3, 4, 5, 8, 12, 20, 30, 31, 32, 34, 35, 36, 60, 62, 64, 75, 76, 78, 81, 85, 88, 94], "four": [2, 8, 80, 83, 94], "clf": [2, 3, 4, 41, 62, 72, 78, 81, 83, 86, 92], "fit": [2, 3, 4, 6, 8, 16, 35, 49, 59, 62, 72, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 89, 90, 92, 93, 94], "sample_weight": [2, 35, 62, 83], "predict_proba": [2, 4, 30, 35, 41, 49, 74, 75, 76, 78, 79, 81, 83, 85, 86, 88, 92], "predict": [2, 3, 4, 6, 8, 14, 20, 21, 24, 26, 30, 34, 35, 36, 38, 39, 41, 42, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 80, 81, 83, 84, 88, 90, 91, 93, 94], "score": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 34, 36, 38, 41, 43, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 88, 90, 92, 93], "data": [2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 32, 33, 34, 35, 36, 41, 42, 45, 48, 49, 50, 51, 52, 53, 57, 59, 60, 61, 62, 67, 68, 69, 70, 71, 73, 77, 82, 84, 89, 93], "e": [2, 3, 4, 8, 10, 20, 30, 31, 34, 35, 36, 39, 41, 42, 45, 46, 50, 51, 52, 53, 59, 60, 62, 64, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93], "featur": [2, 3, 4, 6, 8, 14, 17, 21, 22, 23, 24, 26, 27, 41, 45, 59, 62, 72, 75, 76, 78, 79, 81, 83, 85, 90, 92], "element": [2, 3, 4, 30, 36, 38, 45, 50, 52, 60, 67, 68, 70, 74, 79, 81, 93, 94], "first": [2, 4, 8, 15, 22, 23, 30, 34, 41, 45, 50, 51, 55, 58, 60, 62, 74, 75, 78, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "index": [2, 8, 22, 30, 36, 44, 45, 46, 51, 60, 62, 67, 70, 71, 74, 75, 76, 78, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "should": [2, 3, 4, 5, 8, 12, 20, 22, 27, 30, 31, 34, 35, 36, 38, 39, 41, 43, 44, 45, 49, 50, 51, 54, 55, 57, 58, 59, 60, 62, 63, 67, 68, 70, 71, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "correspond": [2, 3, 4, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 38, 39, 41, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "differ": [2, 4, 5, 8, 11, 13, 19, 22, 23, 25, 30, 31, 33, 34, 35, 36, 37, 41, 45, 46, 48, 50, 55, 57, 59, 62, 74, 75, 76, 78, 79, 82, 83, 85, 88, 89, 92], "sampl": [2, 3, 4, 6, 8, 14, 18, 36, 38, 41, 52, 55, 58, 60, 62, 63, 72, 73, 80, 81, 83, 84, 86, 87, 90, 91, 93, 94], "size": [2, 8, 27, 31, 34, 35, 36, 41, 52, 57, 58, 62, 64, 66, 78, 81, 82, 83, 85, 86, 89, 91, 93], "here": [2, 4, 5, 8, 12, 34, 36, 39, 49, 50, 51, 52, 54, 55, 58, 59, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "re": [2, 4, 31, 35, 44, 50, 62, 72, 74, 75, 78, 79, 81, 90, 91, 92, 93, 94], "weight": [2, 8, 31, 32, 35, 41, 50, 57, 60, 62, 74, 75, 76, 79, 88, 93], "loss": [2, 32, 49, 60, 62, 82], "while": [2, 3, 8, 31, 34, 35, 40, 41, 45, 55, 58, 62, 72, 81, 82, 83, 85, 90], "train": [2, 3, 4, 8, 14, 16, 31, 32, 35, 41, 45, 49, 50, 55, 58, 59, 62, 63, 73, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 89, 91, 94], "support": [2, 3, 4, 10, 34, 41, 45, 46, 59, 60, 70, 72, 73, 74, 75, 76, 81, 82], "your": [2, 3, 4, 7, 8, 14, 30, 31, 33, 34, 35, 36, 41, 45, 48, 49, 50, 51, 52, 54, 59, 60, 62, 63, 64, 66, 67, 73, 74, 78, 80, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "recommend": [2, 4, 8, 11, 14, 34, 36, 50, 75, 76, 81, 82, 89, 90], "furthermor": 2, "correctli": [2, 3, 8, 30, 31, 35, 36, 39, 46, 51, 52, 57, 58, 62, 64, 79, 81, 86, 87, 90, 91, 93], "clonabl": [2, 62], "via": [2, 4, 8, 11, 14, 16, 20, 30, 32, 34, 35, 41, 45, 50, 55, 58, 59, 60, 62, 63, 66, 70, 74, 75, 76, 78, 79, 80, 81, 82, 86, 87, 88, 89, 90, 91, 92, 93, 94], "base": [2, 3, 4, 5, 8, 10, 11, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 57, 59, 60, 62, 63, 66, 68, 70, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 94], "clone": [2, 62, 86], "intern": [2, 3, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 34, 38, 39, 40, 41, 42, 43, 44, 45, 46, 54, 58, 62, 68, 73, 75, 81, 83, 85, 86, 88, 94], "multipl": [2, 3, 4, 10, 11, 30, 36, 44, 50, 51, 52, 54, 57, 58, 62, 72, 75, 76, 81, 82, 84, 86, 87, 90], "g": [2, 3, 4, 8, 10, 20, 30, 31, 35, 36, 42, 45, 52, 53, 59, 60, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93], "manual": [2, 62, 74, 81, 88, 89, 90, 92, 93, 94], "pytorch": [2, 31, 32, 35, 62, 72, 74, 81, 84, 86, 91], "call": [2, 3, 4, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 41, 45, 49, 59, 62, 74, 75, 76, 79, 81, 83, 88, 89, 91, 93, 94], "__init__": [2, 32, 62, 82], "independ": [2, 3, 8, 51, 62, 79, 89, 94], "compat": [2, 31, 34, 35, 49, 62, 63, 66, 70, 72, 81, 89, 90, 92, 93], "neural": [2, 32, 49, 59, 62, 74, 81, 82, 86, 88], "network": [2, 31, 32, 35, 49, 59, 62, 74, 79, 81, 82, 86, 88, 93], "typic": [2, 31, 35, 59, 62, 74, 76, 78, 79, 82, 88, 89, 92, 93], "initi": [2, 3, 11, 16, 31, 35, 50, 62, 79, 81, 92], "insid": [2, 35, 62, 81, 83], "There": [2, 3, 72, 83, 85, 86], "two": [2, 3, 8, 16, 22, 30, 31, 34, 35, 42, 45, 55, 57, 58, 73, 75, 76, 78, 79, 81, 82, 83, 86, 90, 91, 93, 94], "new": [2, 5, 12, 20, 31, 34, 35, 40, 44, 45, 50, 62, 74, 75, 79, 80, 81, 88, 89, 93, 94], "notion": 2, "confid": [2, 3, 8, 20, 30, 34, 36, 39, 41, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 66, 70, 72, 78, 79, 82, 83, 85, 86, 87, 89, 91, 92, 94], "packag": [2, 4, 5, 7, 8, 9, 13, 29, 33, 36, 37, 45, 48, 55, 58, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "prune": [2, 3, 36, 52, 62, 73, 87], "everyth": [2, 58, 83], "els": [2, 58, 75, 80, 81, 82, 85, 86], "mathemat": [2, 3, 8, 39], "keep": [2, 11, 12, 45, 72, 75, 80, 81, 91], "belong": [2, 3, 8, 30, 36, 38, 39, 51, 52, 53, 54, 59, 60, 64, 68, 70, 71, 76, 78, 79, 82, 83, 86, 88, 91, 94], "2": [2, 3, 4, 5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 49, 51, 52, 54, 59, 60, 62, 63, 67, 68, 70, 71, 80, 81, 89], "error": [2, 3, 4, 8, 31, 35, 36, 38, 39, 45, 51, 52, 54, 55, 57, 58, 60, 62, 64, 66, 67, 70, 73, 74, 75, 76, 78, 79, 80, 84, 92], "erron": [2, 3, 30, 36, 39, 45, 51, 52, 60, 62, 63, 64, 88, 90], "import": [2, 3, 4, 5, 6, 8, 10, 11, 12, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 34, 41, 43, 44, 50, 54, 57, 62, 63, 68, 70, 71, 72, 78, 79, 81, 86, 87, 88, 90, 91, 92, 93, 94], "linear_model": [2, 4, 30, 45, 62, 72, 74, 75, 76, 79, 81, 83, 85, 88, 93], "logisticregress": [2, 3, 4, 30, 45, 72, 74, 75, 76, 79, 81, 83, 85, 88, 93], "logreg": 2, "cl": [2, 12, 26, 62, 72, 81, 83, 90, 92, 93], "pass": [2, 3, 4, 6, 8, 10, 11, 12, 14, 21, 26, 28, 31, 34, 35, 36, 40, 41, 45, 49, 50, 52, 59, 60, 62, 68, 72, 74, 75, 76, 79, 80, 81, 83, 85, 87, 88, 90, 93], "x_train": [2, 75, 76, 83, 85, 86, 90, 92], "labels_maybe_with_error": 2, "had": [2, 3, 62, 87], "issu": [2, 3, 4, 6, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 27, 28, 30, 31, 33, 34, 35, 36, 48, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 77, 84, 85, 89, 90, 93], "pred": [2, 36, 45, 89, 90, 92, 93], "x_test": [2, 75, 76, 83, 86, 90, 92], "might": [2, 50, 62, 67, 75, 76, 81, 82, 92, 93], "case": [2, 3, 11, 30, 41, 50, 62, 74, 75, 76, 78, 80, 81, 82, 83, 88, 90, 92, 93, 94], "standard": [2, 3, 4, 26, 30, 36, 49, 51, 52, 54, 60, 62, 72, 75, 76, 78, 80, 83, 92], "adapt": [2, 31, 33, 45, 48, 62, 88], "skorch": [2, 62, 72, 81], "kera": [2, 48, 62, 72, 81], "scikera": [2, 49, 62, 81], "open": [2, 34, 80, 87, 94], "doesn": [2, 62, 72], "t": [2, 3, 8, 15, 23, 31, 32, 34, 35, 36, 41, 43, 44, 54, 59, 60, 62, 68, 70, 71, 72, 75, 76, 78, 79, 80, 82, 83, 86, 87, 94], "alreadi": [2, 4, 8, 14, 31, 34, 35, 39, 49, 50, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 92, 93], "exist": [2, 4, 8, 10, 16, 31, 34, 35, 44, 49, 55, 57, 59, 62, 72, 73, 75, 76, 79, 85, 86, 93, 94], "made": [2, 4, 14, 62, 79, 81, 82, 85, 87, 89, 90, 92, 93], "easi": [2, 39, 62, 75, 76, 80, 81, 83, 86], "inherit": [2, 5, 32, 62], "baseestim": [2, 35, 62], "yourmodel": [2, 62], "def": [2, 5, 12, 31, 35, 49, 62, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 93, 94], "self": [2, 3, 4, 5, 8, 10, 11, 12, 14, 27, 31, 32, 34, 35, 36, 41, 59, 60, 62, 75, 80, 82, 86, 91, 92, 94], "refer": [2, 8, 14, 31, 35, 51, 52, 54, 55, 57, 58, 62, 66, 67, 75, 76, 78, 79, 81, 82, 83, 89, 90], "origin": [2, 4, 8, 35, 36, 44, 45, 49, 51, 52, 55, 58, 59, 62, 63, 66, 68, 70, 75, 78, 79, 81, 82, 83, 87, 88, 90, 92, 93, 94], "total": [2, 3, 30, 34, 45, 51, 71, 81, 82, 91], "state": [2, 3, 4, 31, 32, 35, 40, 62, 83, 86, 87, 94], "art": [2, 32, 83, 86], "northcutt": [2, 3, 30, 59, 60], "et": [2, 3, 30, 32, 59, 60], "al": [2, 3, 30, 32, 59, 60], "2021": [2, 3, 30, 59, 60], "weak": [2, 58], "supervis": [2, 8, 75, 76, 81, 85], "find": [2, 4, 8, 11, 12, 14, 17, 18, 20, 21, 22, 23, 24, 27, 30, 31, 33, 34, 35, 36, 40, 44, 45, 48, 55, 58, 59, 60, 62, 64, 68, 70, 73, 75, 84, 89], "uncertainti": [2, 8, 38, 59, 62, 81, 88, 90], "It": [2, 3, 4, 5, 8, 10, 11, 14, 20, 23, 26, 28, 31, 35, 36, 39, 41, 50, 57, 58, 62, 72, 75, 76, 81, 82, 83, 86, 89], "work": [2, 3, 4, 5, 8, 10, 26, 30, 31, 34, 35, 36, 39, 44, 45, 46, 49, 50, 60, 62, 72, 73, 75, 76, 80, 88, 90, 93], "includ": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 33, 34, 35, 44, 45, 48, 50, 51, 54, 55, 59, 60, 62, 66, 67, 68, 70, 72, 73, 75, 76, 78, 79, 81, 82, 83, 86, 87, 88, 94], "deep": [2, 33, 35, 48, 49, 62, 79], "see": [2, 3, 4, 11, 30, 31, 34, 35, 36, 41, 45, 49, 51, 52, 54, 55, 58, 59, 60, 62, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "subfield": 2, "theori": [2, 83], "machin": [2, 4, 12, 14, 28, 33, 48, 62, 75, 76, 80, 85], "across": [2, 3, 4, 5, 8, 11, 20, 30, 34, 41, 51, 58, 59, 75, 76, 78, 79, 80, 81, 82, 83, 87, 89], "varieti": [2, 81, 92, 93], "like": [2, 3, 4, 5, 8, 12, 28, 30, 31, 34, 35, 36, 39, 45, 49, 50, 51, 54, 55, 57, 60, 62, 63, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "pu": [2, 45], "input": [2, 3, 4, 8, 14, 22, 30, 31, 34, 35, 39, 41, 44, 45, 46, 49, 58, 62, 72, 73, 76, 79, 80, 81, 82, 83, 85, 86, 87, 90, 91, 93, 94], "discret": [2, 36, 39, 45, 59, 60, 64, 66, 67], "vector": [2, 3, 4, 8, 14, 36, 39, 41, 42, 45, 59, 60, 72, 74, 75, 76, 78, 79, 82, 83, 86, 87, 88, 91, 93, 94], "would": [2, 3, 4, 31, 34, 35, 36, 45, 52, 62, 72, 75, 81, 82, 83, 88, 90, 93, 94], "obtain": [2, 4, 6, 8, 14, 36, 50, 52, 55, 58, 60, 63, 74, 76, 79, 81, 85, 87, 89, 91, 94], "been": [2, 30, 36, 39, 44, 45, 50, 51, 55, 57, 59, 60, 62, 74, 75, 78, 81, 83, 85, 86, 87, 88, 91, 94], "dure": [2, 8, 14, 59, 62, 74, 78, 79, 81, 83, 86, 89, 90, 92, 93, 94], "denot": [2, 3, 39, 41, 45, 52, 59, 60, 70], "tild": 2, "paper": [2, 8, 50, 59, 68, 70, 80, 83, 85, 88, 90, 94], "cv_n_fold": [2, 3, 62, 93], "5": [2, 3, 4, 6, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 35, 36, 38, 40, 41, 45, 50, 51, 54, 55, 58, 62, 63, 70, 75, 79, 80, 81, 86, 87, 88, 89, 91, 93, 94], "converge_latent_estim": [2, 3], "pulearn": [2, 45], "find_label_issues_kwarg": [2, 8, 62, 73, 81, 83], "label_quality_scores_kwarg": [2, 8], "low_memori": [2, 52, 68, 81], "clean": [2, 57, 60, 62, 63, 72, 75, 76, 80, 90, 92, 93], "even": [2, 3, 30, 34, 38, 39, 45, 62, 74, 81, 83, 85, 86, 87], "messi": [2, 62, 83], "ridden": [2, 62], "autom": [2, 62, 72, 76, 80, 81], "robust": [2, 39, 62, 76, 81], "prone": [2, 62], "out": [2, 3, 4, 8, 14, 24, 31, 35, 36, 41, 49, 52, 53, 55, 58, 59, 60, 62, 63, 71, 72, 73, 80, 81, 83, 84, 86, 87, 88, 90, 91, 93, 94], "current": [2, 3, 5, 8, 11, 12, 20, 31, 35, 36, 41, 50, 57, 62, 75, 76, 81, 85], "intend": [2, 11, 12, 13, 14, 28, 37, 50, 66, 70, 74, 75, 76, 79, 83], "A": [2, 3, 4, 5, 8, 10, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 35, 36, 39, 40, 41, 42, 43, 44, 45, 49, 50, 51, 54, 57, 58, 59, 60, 62, 64, 66, 67, 71, 73, 74, 75, 78, 79, 80, 81, 82, 83, 85, 87, 89, 92, 93, 94], "follow": [2, 3, 8, 12, 26, 30, 31, 34, 35, 41, 43, 50, 51, 55, 57, 58, 59, 62, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "experiment": [2, 31, 32, 34, 35, 52, 73, 81], "wrapper": [2, 4, 49, 74, 90, 92, 93], "around": [2, 4, 57, 75, 76, 87, 88, 94], "fasttext": [2, 48], "store": [2, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 59, 62, 78, 79, 80, 81, 91, 92, 93, 94], "along": [2, 41, 52, 70, 75, 76, 81, 82, 88], "dimens": [2, 45, 64, 67, 81, 82, 88, 91], "select": [2, 7, 8, 22, 50, 60, 82, 85, 88], "split": [2, 3, 4, 8, 10, 34, 41, 44, 45, 62, 74, 75, 76, 78, 79, 80, 82, 83, 86, 89, 92, 94], "cross": [2, 3, 8, 30, 36, 39, 40, 41, 52, 55, 58, 60, 62, 63, 73, 74, 75, 76, 78, 79, 80, 81, 83, 84, 86, 87, 90, 91, 92, 93, 94], "fold": [2, 3, 30, 36, 39, 62, 74, 78, 80, 81, 87, 91, 92], "By": [2, 4, 30, 51, 52, 62, 75, 81, 91], "need": [2, 3, 8, 30, 31, 34, 35, 36, 51, 52, 54, 59, 62, 72, 74, 75, 76, 79, 81, 83, 85, 86, 87, 91, 93], "holdout": [2, 3, 62], "comput": [2, 3, 4, 5, 6, 8, 17, 18, 20, 21, 22, 23, 24, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 45, 50, 51, 52, 54, 57, 58, 59, 60, 62, 63, 64, 66, 72, 73, 75, 76, 80, 83, 84, 86, 87, 88, 90, 91, 93], "them": [2, 3, 4, 5, 7, 8, 9, 10, 23, 29, 31, 33, 34, 35, 36, 48, 50, 59, 62, 73, 75, 76, 78, 79, 81, 82, 85, 86, 88, 90, 91, 92, 93, 94], "numer": [2, 3, 4, 8, 11, 20, 26, 41, 57, 59, 62, 67, 72, 73, 74, 75, 76, 77, 79, 82, 83, 85, 88, 90, 92, 93], "consist": [2, 3, 31, 35, 45, 50, 91, 94], "latent": [2, 3, 39], "thei": [2, 3, 4, 13, 19, 22, 25, 31, 32, 33, 35, 36, 37, 43, 45, 49, 52, 57, 60, 62, 63, 66, 70, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 88, 90, 93, 94], "relat": [2, 3, 11, 17, 18, 22, 23, 24, 27, 39, 45, 51, 62, 76, 79], "close": [2, 3, 8, 34, 39, 59, 74, 75, 76, 78, 79, 81, 82, 83, 87], "form": [2, 3, 8, 31, 32, 35, 39, 44, 45, 60, 62, 81], "equival": [2, 3, 31, 35, 39, 59, 88], "iter": [2, 3, 30, 31, 35, 36, 45, 51, 52, 62, 81, 85, 91], "enforc": [2, 31, 35, 45], "perfectli": [2, 30, 51, 83], "certain": [2, 3, 4, 31, 35, 49, 58, 62, 75, 76, 80, 88], "dict": [2, 3, 4, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 40, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 70, 75, 76, 81, 82, 94], "keyword": [2, 3, 4, 8, 14, 21, 23, 26, 31, 34, 35, 36, 38, 41, 44, 49, 50, 52, 59, 60, 62, 68, 70, 75], "filter": [2, 3, 8, 34, 44, 51, 53, 54, 56, 58, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 79, 80, 81, 82, 86, 87, 90, 91, 92, 93, 94], "find_label_issu": [2, 3, 8, 26, 34, 36, 51, 52, 54, 55, 57, 58, 62, 64, 66, 67, 68, 70, 71, 72, 73, 81, 86, 87, 90, 91, 92, 93, 94], "particularli": [2, 72, 85, 88], "filter_bi": [2, 3, 34, 36, 52, 73, 81], "frac_nois": [2, 36, 52, 68, 81], "min_examples_per_class": [2, 36, 52, 76, 81, 83], "impact": [2, 8, 75, 76, 82], "ml": [2, 4, 8, 13, 62, 72, 75, 76, 78, 79, 82, 85, 92, 93], "accuraci": [2, 32, 60, 74, 81, 82, 83, 85, 88, 90, 91, 92, 93], "n_job": [2, 34, 36, 52, 64, 66, 68, 81, 88, 91], "disabl": [2, 31, 35, 36, 88], "process": [2, 3, 5, 11, 14, 34, 36, 44, 50, 52, 58, 64, 66, 68, 74, 75, 81, 85, 89, 93], "caus": [2, 36, 41, 75, 76, 81], "rank": [2, 3, 8, 30, 34, 36, 41, 51, 52, 53, 55, 56, 58, 59, 61, 65, 67, 68, 69, 71, 72, 73, 75, 76, 80, 81, 86, 87, 88, 90, 91, 92, 93, 94], "get_label_quality_scor": [2, 34, 36, 41, 50, 52, 54, 55, 57, 60, 63, 66, 68, 70, 73, 83, 86, 87, 90, 91, 94], "adjust_pred_prob": [2, 8, 54, 59, 60, 83], "control": [2, 4, 7, 8, 14, 34, 36, 50, 58, 59, 62, 68, 70, 75, 76, 80, 81], "how": [2, 3, 4, 8, 11, 12, 14, 20, 30, 31, 32, 34, 35, 39, 45, 50, 51, 54, 55, 57, 59, 60, 62, 66, 70, 72, 75, 76, 78, 79, 80, 82, 87, 88, 89, 90, 91, 92, 93], "much": [2, 8, 30, 34, 36, 62, 81, 83, 85, 88], "output": [2, 3, 4, 8, 14, 31, 32, 35, 39, 45, 49, 50, 51, 55, 57, 58, 59, 62, 66, 67, 70, 71, 72, 73, 74, 75, 79, 80, 81, 82, 87, 88, 89, 90, 93], "print": [2, 4, 5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 45, 50, 51, 52, 57, 59, 60, 62, 64, 66, 67, 71, 73, 74, 76, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "suppress": [2, 34, 50, 57, 59, 60, 62, 64, 66, 67, 91, 94], "statement": [2, 34, 50, 57, 59, 60, 62, 64, 66, 67], "big": [2, 34, 52, 58, 62, 83], "limit": [2, 4, 14, 34, 52, 87, 91, 94], "memori": [2, 31, 34, 35, 52, 58, 64, 66, 75, 91], "label_issues_batch": [2, 33, 52, 81], "find_label_issues_batch": [2, 34, 52, 81], "pred_prob": [2, 3, 4, 6, 8, 14, 21, 22, 24, 27, 30, 34, 36, 38, 39, 40, 41, 42, 45, 46, 50, 51, 52, 54, 55, 58, 59, 60, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 92, 93], "threshold": [2, 3, 5, 8, 16, 17, 18, 20, 24, 26, 27, 34, 57, 58, 59, 60, 66, 70, 75, 87, 88, 91, 94], "inverse_noise_matrix": [2, 3, 8, 39, 45, 73, 83], "label_issu": [2, 34, 36, 52, 55, 62, 64, 73, 74, 79, 81, 82, 83, 90, 92, 93], "clf_kwarg": [2, 3, 8, 62], "clf_final_kwarg": [2, 62], "validation_func": [2, 3, 8], "correct": [2, 4, 8, 30, 34, 36, 38, 50, 51, 52, 54, 55, 57, 58, 60, 62, 63, 66, 70, 72, 74, 78, 79, 82, 83, 85, 87, 89, 90], "result": [2, 3, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 34, 35, 36, 38, 43, 45, 52, 54, 55, 58, 60, 62, 63, 64, 66, 70, 74, 75, 76, 78, 79, 81, 82, 83, 85, 90, 91, 92, 93, 94], "identifi": [2, 3, 4, 5, 8, 10, 14, 23, 28, 30, 34, 36, 52, 55, 58, 60, 62, 63, 64, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 86, 88, 90, 91, 92, 93, 94], "final": [2, 8, 62, 78, 87, 89, 90, 92], "remain": [2, 62, 73, 82, 90, 92, 93, 94], "datasetlik": [2, 45, 62], "beyond": [2, 4, 5, 7, 9, 29, 72, 91], "pd": [2, 3, 4, 5, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 40, 49, 50, 51, 62, 70, 74, 75, 76, 78, 79, 81, 83, 85, 90, 92, 93, 94], "datafram": [2, 3, 4, 5, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 34, 40, 45, 46, 49, 50, 51, 62, 67, 71, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 90, 91, 93, 94], "scipi": [2, 4, 11, 45], "spars": [2, 4, 8, 11, 14, 16, 27, 45, 46, 78], "csr_matrix": [2, 4, 11, 14, 16, 27], "torch": [2, 31, 32, 35, 74, 79, 80, 82, 88, 93], "util": [2, 4, 8, 14, 28, 31, 32, 35, 37, 50, 62, 72, 73, 74, 75, 76, 81, 82, 83, 88], "tensorflow": [2, 45, 49, 72, 74, 81], "object": [2, 4, 8, 10, 11, 14, 28, 31, 32, 34, 35, 41, 45, 46, 49, 52, 55, 56, 57, 58, 59, 62, 70, 72, 74, 76, 78, 82, 83, 84, 90, 93], "list": [2, 3, 4, 10, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 42, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 66, 67, 68, 70, 71, 73, 74, 75, 76, 80, 81, 82, 83, 86, 87, 90, 93, 94], "index_list": 2, "subset": [2, 3, 4, 14, 30, 34, 36, 45, 60, 67, 71, 74, 78, 79, 81, 82, 86, 87, 88, 89, 90, 92, 93, 94], "wa": [2, 3, 10, 12, 34, 45, 50, 51, 57, 59, 71, 74, 75, 76, 78, 79, 81, 83, 86, 87, 89, 91, 92, 93, 94], "abl": [2, 3, 8, 62, 74, 81, 83, 85, 86], "format": [2, 3, 4, 8, 10, 31, 34, 35, 36, 39, 40, 41, 42, 45, 46, 49, 50, 51, 52, 55, 58, 59, 60, 62, 64, 66, 67, 70, 71, 75, 76, 78, 80, 82, 85, 90, 91, 92, 94], "make": [2, 3, 16, 31, 34, 35, 41, 49, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "sure": [2, 34, 36, 41, 74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 92, 93], "shuffl": [2, 8, 45, 74, 79, 82, 86, 88], "ha": [2, 3, 4, 8, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 31, 35, 39, 41, 44, 45, 50, 55, 57, 62, 68, 70, 71, 72, 74, 75, 76, 78, 79, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "batch": [2, 34, 45, 49, 50, 64, 66, 81, 82, 88], "order": [2, 4, 8, 30, 31, 35, 36, 39, 40, 41, 45, 50, 51, 52, 55, 58, 59, 60, 64, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 87, 90, 91, 93, 94], "destroi": [2, 45], "oper": [2, 31, 34, 35, 45, 49, 60, 72, 79, 88, 92, 93], "eg": [2, 8, 45, 55, 58, 75, 76, 81], "repeat": [2, 45, 50, 85, 88], "appli": [2, 31, 35, 36, 41, 42, 44, 45, 54, 59, 68, 74, 75, 76, 78, 81, 82, 85, 86, 88, 89, 90, 91, 92, 93], "array_lik": [2, 3, 30, 36, 45, 52, 59, 63], "some": [2, 3, 4, 8, 12, 20, 30, 31, 33, 35, 36, 39, 44, 45, 48, 50, 51, 52, 54, 55, 58, 59, 60, 62, 64, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "seri": [2, 3, 34, 45, 46, 62, 70, 81], "row": [2, 3, 4, 8, 11, 23, 30, 34, 36, 38, 39, 45, 50, 51, 52, 54, 59, 60, 62, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 88, 92, 94], "rather": [2, 3, 22, 30, 45, 49, 50, 57, 66, 70, 85, 89, 91, 93, 94], "leav": [2, 36], "per": [2, 3, 11, 30, 34, 36, 41, 44, 50, 51, 52, 54, 57, 58, 60, 63, 64, 66, 70, 76, 81, 87, 94], "determin": [2, 3, 8, 14, 20, 22, 26, 30, 34, 36, 41, 45, 50, 52, 55, 57, 60, 66, 70, 75, 81, 85, 88, 90], "cutoff": [2, 3, 88], "consid": [2, 3, 4, 8, 11, 14, 21, 22, 24, 27, 30, 31, 35, 36, 45, 50, 57, 59, 60, 63, 66, 70, 74, 76, 78, 79, 81, 82, 83, 87, 88, 89, 90, 91, 92, 93], "section": [2, 3, 5, 8, 73, 78, 82], "3": [2, 3, 4, 5, 8, 30, 31, 35, 36, 39, 40, 41, 42, 43, 44, 45, 49, 52, 59, 60, 62, 63, 68, 70, 80, 81, 89], "equat": [2, 3, 39], "advanc": [2, 3, 4, 7, 8, 14, 57, 59, 70, 73, 76, 77, 83], "user": [2, 3, 4, 8, 12, 14, 23, 28, 31, 35, 36, 57, 59, 60, 62, 66, 70, 83], "specifi": [2, 3, 4, 6, 8, 11, 12, 14, 16, 27, 28, 31, 34, 35, 36, 41, 44, 50, 51, 52, 55, 57, 59, 60, 62, 63, 71, 73, 74, 76, 79, 82, 85, 87, 90, 93], "automat": [2, 3, 4, 22, 30, 72, 78, 79, 80, 81, 82, 85, 87, 90, 91, 92, 93, 94], "greater": [2, 3, 4, 7, 8, 24, 34, 45, 57, 76, 80, 81, 94], "count": [2, 20, 22, 30, 34, 36, 39, 45, 51, 52, 58, 73, 81, 82], "observ": [2, 3, 39, 74, 75, 76, 85, 88, 90], "mislabel": [2, 8, 30, 34, 36, 39, 50, 51, 52, 55, 57, 60, 66, 68, 70, 72, 74, 78, 79, 81, 82, 83, 86, 87, 90, 92, 93], "one": [2, 3, 4, 8, 22, 30, 31, 34, 35, 36, 41, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 82, 85, 88, 89, 90, 92, 93, 94], "get_label_issu": [2, 34, 62, 83, 90, 92, 93], "either": [2, 3, 5, 8, 31, 34, 35, 36, 50, 52, 57, 59, 60, 64, 66, 76, 86, 87], "boolean": [2, 5, 8, 20, 34, 36, 44, 50, 52, 55, 60, 62, 64, 66, 67, 72, 74, 76, 79, 81, 82, 87, 90, 91, 93], "label_issues_mask": [2, 36, 60, 62, 73], "indic": [2, 3, 4, 5, 8, 11, 20, 30, 34, 35, 36, 38, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 68, 70, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "its": [2, 4, 7, 8, 14, 31, 34, 35, 36, 43, 44, 52, 55, 58, 59, 60, 62, 64, 68, 70, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 93, 94], "return_indices_ranked_bi": [2, 34, 36, 52, 68, 73, 81, 83, 86, 92, 93], "significantli": [2, 82, 83, 85, 89], "reduc": [2, 34, 36, 45, 74, 81], "time": [2, 8, 31, 34, 35, 45, 50, 73, 75, 80, 81, 82, 83, 87, 88, 90, 91, 92, 93, 94], "take": [2, 4, 8, 30, 31, 35, 40, 41, 45, 49, 60, 78, 82, 85, 92, 94], "run": [2, 4, 5, 7, 9, 12, 14, 22, 23, 29, 31, 34, 35, 62, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "skip": [2, 8, 31, 35, 62, 74, 81, 86, 94], "slow": [2, 3], "step": [2, 5, 22, 41, 58, 81, 82, 83, 85, 89], "caution": [2, 4, 81], "previous": [2, 4, 11, 45, 59, 62, 73, 74, 75, 78, 79, 85, 89, 92], "assign": [2, 5, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 40, 41, 45, 62, 75, 78, 81, 82, 90, 91, 92, 94], "individu": [2, 8, 11, 22, 31, 35, 50, 54, 57, 60, 62, 68, 70, 73, 76, 78, 81, 85, 86, 87, 92, 94], "still": [2, 34, 35, 45, 59, 81, 82, 88, 92], "extra": [2, 31, 35, 45, 49, 50, 51, 62, 79, 81, 82, 85, 88], "receiv": [2, 8, 31, 35, 51, 54, 55, 62, 64, 68, 76, 87], "overwritten": [2, 62], "callabl": [2, 3, 41, 44, 49, 54, 81], "x_val": 2, "y_val": 2, "map": [2, 3, 10, 34, 35, 40, 44, 45, 58, 60, 62, 67, 74, 75, 76, 81, 82, 83, 86, 94], "appropri": [2, 8, 14, 52, 60, 75, 78, 86, 87], "earli": [2, 82], "stop": [2, 82], "x_valid": 2, "y_valid": 2, "could": [2, 8, 20, 30, 45, 59, 75, 78, 82, 86, 90, 92, 94], "f": [2, 5, 74, 75, 78, 79, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93], "ignor": [2, 31, 35, 44, 49, 62, 67, 71, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "allow": [2, 30, 31, 34, 35, 38, 45, 50, 58, 59, 62, 64, 66, 74, 81, 82, 89, 91, 93], "access": [2, 8, 11, 31, 35, 62, 76, 82, 86], "hyperparamet": [2, 54, 59, 82], "purpos": [2, 75, 76, 81, 86, 90], "want": [2, 4, 8, 30, 34, 46, 50, 52, 62, 75, 79, 80, 82, 85, 87, 88, 89, 91, 93, 94], "explicitli": [2, 6, 8, 35, 62], "yourself": [2, 4, 34, 76], "altern": [2, 5, 8, 41, 45, 49, 50, 60, 73, 74, 78, 79, 81, 82, 83, 85, 86, 88, 90, 93], "same": [2, 3, 4, 5, 8, 10, 12, 14, 22, 26, 31, 34, 35, 36, 45, 49, 50, 52, 59, 60, 62, 66, 67, 70, 71, 72, 75, 76, 78, 79, 81, 82, 87, 88, 89, 90, 91, 92, 93], "effect": [2, 8, 23, 31, 35, 50, 59, 62, 78, 79, 81, 82, 88], "offer": [2, 4, 74, 75, 76, 79, 81, 83, 86, 93], "after": [2, 3, 4, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 45, 50, 62, 75, 79, 81, 82, 83, 85, 87, 88, 89, 90, 91, 93], "attribut": [2, 4, 5, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 41, 59, 62, 75, 92], "label_issues_df": [2, 62, 82], "similar": [2, 8, 30, 31, 35, 45, 50, 54, 55, 57, 59, 62, 66, 70, 75, 76, 78, 79, 81, 82, 83, 87, 88, 91], "document": [2, 3, 4, 8, 12, 14, 30, 31, 34, 35, 36, 41, 44, 49, 51, 52, 54, 57, 58, 59, 62, 66, 67, 68, 70, 73, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "descript": [2, 4, 5, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 45, 55, 62, 75, 76], "were": [2, 3, 4, 30, 35, 51, 57, 70, 74, 78, 81, 83, 85, 87, 89, 91, 92], "present": [2, 3, 4, 8, 10, 11, 18, 30, 45, 59, 67, 72, 78, 81, 82, 88], "actual": [2, 3, 4, 30, 50, 51, 60, 76, 81, 83, 94], "num_class": [2, 30, 34, 45, 49, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 92, 93], "uniqu": [2, 27, 45, 67, 75, 81, 86, 88], "given_label": [2, 4, 26, 30, 39, 62, 67, 71, 74, 75, 76, 78, 79, 82, 83, 90, 91, 93, 94], "normal": [2, 3, 16, 22, 27, 36, 38, 41, 43, 44, 45, 60, 81, 83, 88], "trick": [2, 81], "distribut": [2, 3, 4, 8, 22, 24, 30, 35, 36, 40, 43, 50, 58, 59, 60, 72, 75, 76, 78, 79, 82, 88], "account": [2, 30, 50, 54, 59, 60, 79, 81, 83, 85, 86, 88, 90, 93], "word": [2, 3, 44, 70, 71, 81], "remov": [2, 8, 27, 30, 31, 35, 36, 62, 72, 79, 80, 81, 82, 88, 90, 92, 93], "so": [2, 3, 4, 5, 8, 12, 22, 30, 31, 34, 35, 36, 45, 50, 51, 57, 60, 62, 66, 70, 74, 75, 76, 79, 82, 83, 88, 91], "proportion": [2, 8, 36], "just": [2, 3, 4, 8, 11, 30, 32, 34, 45, 49, 60, 62, 64, 72, 73, 74, 76, 78, 79, 81, 82, 83, 86, 87, 88, 89, 91, 92, 93], "procedur": 2, "get": [2, 3, 4, 6, 11, 27, 31, 32, 35, 36, 41, 44, 45, 50, 52, 54, 59, 60, 62, 63, 64, 72, 74, 79, 80, 81, 82, 83, 88, 89, 90, 92, 93], "detect": [2, 4, 5, 7, 11, 12, 14, 16, 20, 24, 43, 53, 55, 56, 57, 58, 59, 60, 61, 62, 65, 69, 72, 75, 77, 82, 84, 86, 90, 91, 92, 93, 94], "arg": [2, 10, 20, 23, 27, 31, 32, 35, 41, 45, 60, 62], "kwarg": [2, 5, 8, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 41, 49, 62, 64, 66, 68, 81], "test": [2, 8, 22, 35, 41, 49, 62, 72, 75, 76, 78, 79, 82, 89, 90, 92, 93, 94], "expect": [2, 3, 31, 35, 36, 41, 50, 59, 60, 62, 81, 83, 85, 86, 87, 90, 92, 93, 94], "class_predict": 2, "evalu": [2, 8, 31, 32, 34, 35, 58, 62, 74, 75, 76, 81, 82, 83, 85, 89, 90, 91, 92, 93], "simpli": [2, 30, 60, 75, 76, 78, 79, 81, 83, 90, 91, 93, 94], "quantifi": [2, 4, 5, 8, 11, 36, 54, 59, 62, 72, 76, 78, 79, 82, 83, 87], "save_spac": [2, 8, 62], "potenti": [2, 8, 30, 36, 44, 52, 55, 58, 60, 62, 64, 66, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 91, 92, 94], "cach": [2, 79, 88, 93], "panda": [2, 4, 5, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 45, 46, 49, 50, 51, 73, 74, 75, 76, 78, 79, 80, 81, 83, 85, 90, 91, 92, 93], "unlik": [2, 8, 36, 38, 41, 49, 51, 52, 54, 70, 75, 85, 86, 88, 90], "both": [2, 4, 8, 14, 22, 30, 31, 35, 36, 45, 50, 52, 60, 64, 66, 71, 72, 75, 81, 82, 83, 85, 94], "mask": [2, 34, 36, 44, 45, 52, 55, 60, 62, 64, 66, 67, 72, 80, 81, 85, 87, 91, 94], "prefer": [2, 60, 68], "plan": 2, "subsequ": [2, 3, 31, 35, 79, 81, 83, 87, 93], "invok": [2, 31, 35, 83, 89], "scratch": [2, 62], "To": [2, 4, 5, 7, 8, 9, 11, 14, 22, 29, 31, 34, 35, 36, 49, 50, 52, 54, 58, 59, 60, 62, 63, 64, 66, 72, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "share": [2, 60, 62], "mostli": [2, 45, 57, 62], "longer": [2, 40, 44, 62, 73, 79, 81, 87, 93], "info": [2, 4, 5, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 62, 70, 75, 76, 80, 81, 94], "about": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 32, 34, 38, 50, 51, 54, 58, 62, 67, 70, 74, 75, 78, 79, 80, 81, 82, 83, 85, 88], "docstr": [2, 30, 31, 35, 45, 62, 80, 83], "unless": [2, 31, 35, 62, 81], "our": [2, 3, 8, 49, 50, 60, 62, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "is_label_issu": [2, 26, 62, 74, 75, 76, 78, 79, 82, 83, 90, 93], "entir": [2, 8, 22, 34, 36, 39, 51, 52, 57, 60, 62, 64, 66, 67, 72, 75, 76, 79, 81, 82, 83, 87, 88, 89, 91, 94], "accur": [2, 3, 4, 8, 14, 30, 34, 36, 50, 51, 52, 55, 58, 60, 62, 63, 64, 66, 67, 73, 76, 78, 79, 81, 82, 85, 90], "label_qu": [2, 50, 62, 83, 85, 90, 93], "measur": [2, 30, 50, 51, 62, 72, 80, 81, 83, 85, 86, 91, 92, 94], "qualiti": [2, 3, 4, 5, 8, 11, 26, 27, 30, 34, 36, 38, 41, 50, 51, 52, 54, 55, 57, 60, 62, 63, 66, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 84, 90, 92, 93], "lower": [2, 4, 5, 8, 11, 24, 34, 41, 43, 50, 51, 54, 57, 58, 60, 62, 63, 66, 70, 74, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 93, 94], "eas": 2, "comparison": [2, 31, 35, 58, 83, 85, 90], "against": [2, 31, 35, 75, 78, 81, 85, 86], "predicted_label": [2, 4, 26, 62, 67, 71, 74, 75, 76, 78, 79, 82, 83, 90, 91, 93], "ad": [2, 31, 35, 76, 85, 90], "precis": [2, 52, 55, 58, 81, 83, 91, 94], "definit": [2, 5, 62, 78, 92], "accessor": [2, 62], "describ": [2, 8, 16, 50, 59, 60, 62, 68, 70, 83, 85, 86, 87, 89, 94], "precomput": [2, 4, 39, 62, 80], "clear": [2, 62, 79, 90, 93], "save": [2, 4, 14, 31, 34, 35, 58, 62, 81, 87, 91, 94], "space": [2, 8, 59, 62, 78, 80, 82], "place": [2, 31, 35, 45, 62, 85, 92], "larg": [2, 34, 62, 78, 79, 81, 82, 88, 91, 94], "deploi": [2, 62, 78, 79, 81, 82], "care": [2, 8, 31, 35, 62, 79, 81, 83], "avail": [2, 4, 5, 10, 12, 28, 35, 62, 81, 83, 85, 87, 90], "cannot": [2, 4, 10, 12, 45, 89, 94], "anymor": 2, "classmethod": [2, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 35, 41, 62], "__init_subclass__": [2, 35, 62], "set_": [2, 35, 62], "_request": [2, 35, 62], "pep": [2, 35, 62], "487": [2, 35, 62], "look": [2, 4, 5, 14, 31, 35, 45, 62, 67, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 91, 92, 94], "inform": [2, 4, 5, 8, 11, 14, 28, 31, 35, 45, 50, 51, 55, 58, 62, 67, 70, 71, 72, 74, 75, 78, 79, 83, 85, 88, 91, 94], "__metadata_request__": [2, 35, 62], "infer": [2, 35, 45, 62, 67, 71, 82, 85, 86, 90, 92, 93], "signatur": [2, 31, 35, 62], "accept": [2, 31, 35, 60, 62, 75, 76], "metadata": [2, 35, 62, 78, 79, 82, 94], "through": [2, 4, 5, 35, 62, 74, 76, 79, 80, 81, 85, 88, 90, 93], "develop": [2, 7, 35, 62, 81, 83, 94], "request": [2, 35, 62, 76, 79, 80, 86, 92, 93, 94], "those": [2, 3, 8, 34, 35, 36, 49, 50, 52, 58, 62, 66, 70, 71, 72, 74, 81, 82, 87, 91], "http": [2, 4, 5, 7, 8, 9, 16, 29, 31, 32, 34, 35, 38, 45, 59, 62, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "www": [2, 35, 62, 88], "org": [2, 16, 31, 32, 35, 45, 59, 62, 81, 83, 94], "dev": [2, 35, 62], "0487": [2, 35, 62], "get_metadata_rout": [2, 35, 62], "rout": [2, 35, 62], "pleas": [2, 31, 35, 49, 62, 72, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 90, 93, 94], "guid": [2, 5, 35, 62, 73, 82], "mechan": [2, 31, 35, 62], "metadatarequest": [2, 35, 62], "encapsul": [2, 14, 35, 57, 62], "get_param": [2, 35, 49, 62], "subobject": [2, 35, 62], "param": [2, 8, 31, 35, 49, 59, 62, 81], "name": [2, 4, 5, 8, 10, 11, 30, 31, 35, 40, 41, 45, 49, 50, 51, 58, 62, 67, 71, 74, 76, 79, 80, 81, 82, 83, 86, 91, 93, 94], "set_fit_request": [2, 35, 62], "union": [2, 3, 4, 10, 34, 35, 41, 45, 46, 52, 58, 62, 66, 70, 81], "str": [2, 3, 4, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 39, 41, 44, 45, 49, 50, 51, 55, 57, 58, 60, 62, 67, 71, 74, 75, 81, 85, 86, 94], "unchang": [2, 31, 35, 62, 94], "relev": [2, 14, 22, 35, 62, 82], "enable_metadata_rout": [2, 35, 62], "set_config": [2, 35, 62], "meta": [2, 35, 62], "rais": [2, 4, 10, 11, 31, 35, 38, 41, 62, 81], "alia": [2, 31, 35, 62], "metadata_rout": [2, 35, 62], "retain": [2, 35, 45, 62], "chang": [2, 31, 34, 35, 38, 62, 70, 74, 75, 79, 81, 87, 88, 93, 94], "version": [2, 4, 5, 7, 8, 9, 13, 19, 25, 29, 31, 33, 35, 37, 38, 45, 48, 49, 60, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "sub": [2, 35, 57, 62], "pipelin": [2, 35, 62], "otherwis": [2, 8, 30, 31, 34, 35, 36, 42, 44, 45, 52, 59, 62, 64, 66, 67, 71, 79, 81, 93], "updat": [2, 11, 31, 34, 35, 62, 73, 75, 82], "set_param": [2, 35, 49, 62], "simpl": [2, 31, 35, 36, 50, 60, 62, 75, 76, 78, 79, 82, 85, 88, 90, 92, 93], "well": [2, 3, 8, 31, 35, 38, 39, 50, 52, 58, 60, 62, 67, 70, 71, 73, 75, 76, 78, 79, 81, 82, 83, 85, 87, 88], "nest": [2, 31, 35, 62, 68, 70, 71, 94], "latter": [2, 31, 35, 62, 88], "compon": [2, 35, 62], "__": [2, 35, 62], "set_score_request": [2, 62], "structur": [3, 59, 78, 92], "unobserv": 3, "less": [3, 4, 8, 27, 34, 41, 50, 59, 60, 64, 66, 70, 76, 78, 80, 81, 82, 83, 87, 94], "channel": [3, 74, 83], "character": 3, "flip": 3, "nm": 3, "invers": [3, 8, 30, 39, 45, 51, 76, 80, 93], "inv": 3, "confident_joint": [3, 20, 30, 36, 45, 51, 52, 73, 81, 83], "un": 3, "under": [3, 8, 31, 35, 51, 58, 59, 76, 78, 79, 82, 83, 88], "joint": [3, 30, 36, 39, 45, 51, 52, 80], "num_label_issu": [3, 34, 36, 52, 67, 71, 73], "estimation_method": [3, 34], "off_diagon": 3, "multi_label": [3, 30, 36, 45, 46, 52, 86], "don": [3, 72, 76, 78, 79, 82, 83, 87], "statis": 3, "compute_confident_joint": [3, 30, 36, 45, 52, 83], "off": [3, 36, 45, 57, 82, 83, 87, 88], "j": [3, 4, 30, 31, 35, 36, 52, 55, 58, 59, 68, 70, 71, 75, 76, 83, 91, 94], "confident_learn": [3, 36, 52, 83], "off_diagonal_calibr": 3, "calibr": [3, 36, 45, 50, 85], "cj": [3, 39, 45], "axi": [3, 27, 39, 41, 43, 64, 67, 74, 75, 76, 81, 82, 83, 85, 86, 88, 90, 91], "bincount": [3, 75, 76, 83, 85, 86], "alwai": [3, 8, 31, 35, 45, 74, 83, 90, 92, 93], "estimate_issu": 3, "over": [3, 8, 31, 34, 35, 57, 58, 64, 66, 76, 78, 80, 81, 82, 83, 88, 90, 92], "As": [3, 5, 72, 75, 76, 79, 83, 90, 94], "add": [3, 4, 5, 11, 31, 35, 49, 58, 74, 75, 76, 79, 81, 82, 83, 86, 93], "approach": [3, 30, 34, 36, 78, 83, 86, 88, 90, 92], "custom": [3, 5, 8, 9, 26, 31, 34, 35, 41, 44, 60, 76, 79, 83, 93], "know": [3, 75, 76, 78, 79, 81, 82, 83, 85], "cut": [3, 57, 72, 83], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 88, 94], "underestim": 3, "few": [3, 58, 72, 76, 81, 85, 86, 87, 88, 94], "4": [3, 4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 40, 41, 44, 54, 55, 57, 58, 60, 63, 70, 80, 81, 86, 91, 94], "detail": [3, 4, 8, 12, 14, 30, 31, 35, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 66, 67, 68, 72, 73, 74, 86, 88, 94], "num_issu": [3, 5, 34, 74, 75, 76, 78, 79, 82, 83], "calibrate_confident_joint": 3, "up": [3, 8, 15, 22, 23, 26, 36, 41, 50, 80, 81, 87, 90, 93, 94], "p_": [3, 30, 36], "pair": [3, 4, 8, 30, 36, 83], "v": [3, 8, 34, 51, 52, 54, 60, 75, 76, 86, 88, 89], "rest": [3, 4, 5, 7, 8, 9, 29, 51, 52, 54, 62, 75, 76, 78, 79, 81, 82, 83, 85, 90, 92, 93], "fashion": [3, 4, 64, 92], "2x2": 3, "incorrectli": [3, 30, 51, 52, 55, 78, 94], "calibrated_cj": 3, "c": [3, 8, 44, 52, 60, 72, 74, 75, 76, 78, 79, 81, 83, 86, 88, 89, 90, 92], "whose": [3, 4, 8, 24, 31, 35, 39, 44, 50, 54, 57, 63, 66, 70, 71, 74, 75, 76, 78, 79, 81, 82, 83, 86, 87, 88, 91, 94], "truli": [3, 88, 91], "estimate_joint": [3, 30, 83], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 52, 58, 83, 87, 89, 91, 94], "return_indices_of_off_diagon": 3, "frequenc": [3, 22, 50, 51, 58, 67, 88], "done": [3, 8, 62, 75, 81, 83, 86, 88, 89], "overfit": [3, 8, 55, 58, 74, 75, 76, 78, 79, 82, 89, 92], "classifict": 3, "singl": [3, 4, 22, 30, 31, 35, 41, 42, 45, 50, 51, 57, 58, 59, 60, 70, 74, 75, 81, 83, 86, 87, 92], "baselin": [3, 31, 36, 88, 90, 93], "proxi": 3, "tupl": [3, 27, 31, 35, 39, 40, 42, 44, 45, 50, 52, 58, 66, 68, 70, 71, 74, 94], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 8, 34, 39, 50, 64, 66, 72, 81, 82, 91, 93], "practic": [3, 76, 82, 83, 88, 90, 92, 93], "complet": [3, 74, 75, 76, 78, 79, 81, 82, 83, 87], "gist": 3, "cj_ish": 3, "guess": [3, 39, 83, 85], "8": [3, 4, 5, 6, 40, 41, 42, 44, 54, 68, 70, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "parallel": [3, 36, 58, 68, 80], "again": [3, 49, 81, 88, 92], "simplifi": [3, 12], "understand": [3, 7, 30, 51, 58, 76, 83, 90, 91, 94], "100": [3, 31, 35, 60, 75, 76, 78, 80, 81, 82, 83, 86, 91, 92, 93, 94], "optim": [3, 31, 32, 35, 49, 82, 85], "speed": [3, 36, 80, 81, 90, 93], "dtype": [3, 21, 22, 27, 31, 35, 44, 45, 54, 70, 74, 87], "enumer": [3, 31, 35, 74, 75, 76, 82, 94], "s_label": 3, "confident_bin": 3, "6": [3, 4, 35, 41, 45, 70, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "num_confident_bin": 3, "argmax": [3, 36, 60, 64, 67, 74, 81, 83, 88, 91], "elif": 3, "estimate_lat": 3, "py_method": [3, 39], "cnt": [3, 39], "1d": [3, 4, 14, 34, 36, 41, 42, 45, 46, 54, 63, 74, 92], "eqn": [3, 39], "margin": [3, 36, 39, 41, 60], "marginal_p": [3, 39], "shorthand": [3, 11], "proport": [3, 8, 30, 51, 83, 89], "poorli": [3, 39, 92], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 83], "variabl": [3, 5, 12, 23, 45, 62, 63, 74, 75, 78, 83, 86, 90], "exact": [3, 39, 75, 76, 78, 82, 92], "within": [3, 4, 8, 13, 31, 32, 35, 37, 52, 57, 66, 68, 70, 75, 76, 81, 82, 87, 91], "percent": 3, "often": [3, 30, 39, 51, 81, 83, 89, 91], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 8, 45, 46, 58, 74, 75, 78, 79, 81, 82, 87, 88, 93], "wai": [3, 4, 49, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 89, 92, 93], "pro": 3, "con": 3, "pred_proba": [3, 89], "combin": [3, 30, 75, 80, 81, 82, 83, 89, 90], "becaus": [3, 39, 45, 57, 79, 81, 83, 85, 87], "littl": [3, 34, 80, 87, 94], "uniform": [3, 60, 80, 81, 83], "20": [3, 5, 71, 74, 76, 79, 80, 81, 82, 83, 91, 94], "Such": [3, 82, 88], "bound": [3, 21, 31, 35, 55, 57, 58, 87], "reason": [3, 20, 31, 35], "comment": [3, 44, 94], "end": [3, 4, 31, 35, 58, 82, 91, 94], "file": [3, 4, 10, 33, 34, 48, 58, 74, 75, 78, 79, 80, 81, 87, 88, 91, 92, 94], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 83], "handl": [3, 4, 5, 8, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 73, 75, 76, 78, 79, 82, 83, 91, 92, 94], "five": [3, 55, 58, 83, 87], "estimate_cv_predicted_prob": [3, 83], "estimate_noise_matric": 3, "get_confident_threshold": [3, 34], "amongst": [3, 8], "confident_threshold": [3, 8, 20, 34, 59], "unifi": 4, "audit": [4, 7, 10, 11, 14, 74, 77, 78, 79, 81, 82, 83, 87], "kind": [4, 5, 74, 75, 78, 79, 80, 82, 83], "addit": [4, 5, 7, 8, 9, 11, 28, 29, 31, 35, 41, 46, 50, 58, 68, 74, 75, 78, 79, 82, 83, 85, 88, 89, 92, 93], "depend": [4, 5, 7, 8, 9, 10, 11, 29, 33, 36, 38, 45, 48, 52, 59, 62, 63, 72], "instal": [4, 5, 7, 8, 9, 29, 31, 33, 34, 35, 36, 48, 49, 64, 66], "pip": [4, 5, 7, 9, 29, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "development": [4, 5, 7, 9, 29], "git": [4, 5, 7, 9, 29, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "github": [4, 5, 7, 9, 29, 31, 32, 45, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "com": [4, 5, 7, 9, 29, 31, 32, 34, 38, 45, 59, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "egg": [4, 5, 7, 9, 29, 72, 80], "label_nam": [4, 5, 6, 8, 10, 16, 27, 72, 74, 75, 76, 78, 79, 81, 82, 83], "image_kei": [4, 82], "interfac": [4, 72, 81, 83], "librari": [4, 8, 35, 55, 58, 59, 72, 75, 79, 80, 81, 93], "goal": 4, "track": [4, 11, 12, 72, 75, 80, 81, 83], "intermedi": [4, 7, 76], "statist": [4, 8, 11, 20, 22, 30, 50, 51, 58, 76, 78, 79, 82, 83], "convert": [4, 10, 31, 35, 42, 43, 46, 50, 57, 66, 70, 73, 74, 79, 80, 81, 82, 85, 86, 87, 93], "hug": [4, 10, 82], "face": [4, 10, 14, 80, 82, 86], "kei": [4, 5, 8, 10, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 35, 41, 50, 51, 57, 59, 75, 76, 79, 81, 82, 83, 85, 87], "string": [4, 8, 10, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 35, 45, 50, 51, 63, 67, 70, 71, 78, 79, 81, 85, 86, 93, 94], "dictionari": [4, 5, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 35, 40, 45, 50, 51, 54, 55, 57, 58, 75, 76, 78, 79, 83, 85, 86, 87], "path": [4, 10, 31, 34, 35, 58, 74, 75, 81, 87], "local": [4, 10, 31, 32, 35, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "text": [4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 41, 59, 68, 70, 71, 72, 75, 76, 77, 80, 81, 83, 84, 85, 88], "txt": [4, 10, 94], "csv": [4, 10, 78, 79, 90, 92, 93], "json": [4, 10], "hub": [4, 10, 88], "regress": [4, 5, 10, 12, 14, 19, 26, 28, 75, 76, 79, 84, 85, 88, 93], "imag": [4, 7, 30, 35, 55, 57, 58, 59, 64, 66, 67, 72, 75, 76, 80, 81, 84, 85, 86, 87, 89, 91], "point": [4, 5, 8, 16, 22, 31, 35, 75, 76, 78, 79, 81, 82, 83, 85], "field": [4, 8, 31, 35], "themselv": [4, 90, 92, 93], "cleanvis": [4, 8], "level": [4, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 44, 68, 70, 76, 82, 84, 91], "load_dataset": [4, 10, 82], "glue": 4, "sst2": 4, "properti": [4, 10, 11], "has_label": [4, 10], "class_nam": [4, 10, 18, 30, 51, 58, 67, 71, 72, 80, 83, 87, 91, 94], "empti": [4, 10, 39, 50, 76, 81, 86], "find_issu": [4, 5, 6, 8, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 72, 74, 75, 76, 78, 79, 81, 82, 83], "knn_graph": [4, 8, 14, 16, 17, 22, 24, 27, 78], "issue_typ": [4, 5, 6, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 74, 75, 76, 78, 79, 81, 82, 83], "sort": [4, 14, 34, 36, 41, 50, 52, 55, 57, 58, 60, 66, 68, 70, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 90, 91, 92, 93, 94], "common": [4, 11, 14, 76, 77, 80, 81, 83, 86, 87, 91], "real": [4, 14, 72, 75, 76, 81, 83, 85, 86, 90, 91], "world": [4, 14, 72, 75, 76, 81, 83, 85, 90, 91], "interact": [4, 14, 79, 81], "embed": [4, 8, 14, 59, 72, 74, 75, 76, 78, 79, 83, 93], "thereof": [4, 14], "insight": [4, 14, 58, 85], "act": [4, 8, 57, 75], "issuefind": [4, 14, 28], "logic": [4, 12, 34, 36, 64, 66, 91], "best": [4, 14, 40, 50, 60, 75, 76, 78, 79, 81, 82, 85, 86, 88, 90, 92, 93, 94], "2d": [4, 14, 34, 41, 42, 44, 45, 50, 74, 86, 92], "num_exampl": [4, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 51, 74, 75, 76, 78, 79, 82, 83], "represent": [4, 8, 14, 31, 35, 42, 52, 72, 74, 75, 76, 79, 81, 82, 83, 88, 93], "num_featur": [4, 14, 31, 35, 49], "distanc": [4, 8, 14, 16, 22, 24, 27, 43, 57, 59, 78, 88], "nearest": [4, 8, 14, 21, 22, 24, 43, 59, 76, 79, 88], "neighbor": [4, 8, 14, 16, 21, 22, 24, 43, 59, 75, 76, 78, 79, 81, 82, 88], "graph": [4, 8, 11, 14, 16, 22, 27], "squar": [4, 45, 62, 80, 90], "csr": 4, "evenli": 4, "omit": [4, 57, 58, 82, 87], "itself": [4, 31, 35, 87], "three": [4, 8, 30, 50, 51, 62, 67, 74, 75, 76, 78, 80, 83, 85, 89, 90, 91, 92, 94], "indptr": 4, "wise": 4, "start": [4, 5, 8, 31, 32, 35, 72, 78, 86, 94], "th": [4, 40, 44, 45, 50, 52, 55, 57, 58, 59, 68, 70, 71, 79, 86, 87, 94], "ascend": [4, 30, 51, 82, 83], "segment": [4, 64, 66, 67, 84], "reflect": [4, 78, 79, 85, 87, 88, 90, 92, 93], "maintain": 4, "posit": [4, 31, 35, 43, 45, 58, 80, 88], "nearestneighbor": [4, 8, 16, 59, 78, 88], "kneighbors_graph": [4, 16, 78], "illustr": 4, "todens": 4, "second": [4, 41, 45, 58, 60, 75, 81, 83, 94], "duplic": [4, 7, 19, 20, 31, 35, 72, 75, 83], "explicit": 4, "precend": 4, "construct": [4, 5, 8, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 35, 41, 49], "neither": [4, 8, 12, 87], "nor": [4, 8, 12], "collect": [4, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 50, 81, 85, 94], "unspecifi": [4, 14, 36, 52], "interest": [4, 14, 20, 67, 71, 79, 83, 91, 92, 93, 94], "constructor": [4, 8, 14, 21, 26], "issuemanag": [4, 7, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28], "respons": [4, 14, 20, 62, 63, 80, 90, 94], "random_st": [4, 74, 75, 76, 82, 83, 86, 88, 92], "lab": [4, 6, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 34, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86], "comprehens": [4, 72, 82], "nbr": 4, "n_neighbor": [4, 8, 16, 59], "metric": [4, 8, 17, 22, 27, 45, 49, 58, 59, 74, 78, 79, 82, 83, 90, 92, 93], "euclidean": [4, 8, 57, 59, 78], "mode": [4, 16, 31, 34, 35, 88], "4x4": 4, "float64": [4, 22, 31, 35, 70], "compress": [4, 8, 45, 64, 66], "toarrai": 4, "NOT": [4, 34, 79], "23606798": 4, "41421356": 4, "configur": [4, 14, 41, 76], "suppos": [4, 8, 55, 88, 90, 92, 93], "who": [4, 57, 78, 83, 92, 94], "manag": [4, 6, 7, 8, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "clean_learning_kwarg": [4, 8, 21, 26], "labelissuemanag": [4, 8, 21], "prune_method": [4, 73], "prune_by_noise_r": [4, 36, 52, 83], "report": [4, 5, 9, 13, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 71, 72, 74, 75, 76, 78, 79, 83, 94], "include_descript": [4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28], "show_summary_scor": [4, 28], "summari": [4, 5, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 49, 51, 56, 65, 66, 68, 69, 70, 73, 74, 75, 76, 78, 79, 80, 82, 83, 87, 91, 94], "show": [4, 22, 31, 35, 40, 45, 58, 67, 71, 76, 78, 79, 80, 81, 82, 83, 85, 88, 90, 91, 92, 94], "top": [4, 8, 30, 34, 36, 45, 52, 55, 58, 60, 67, 71, 72, 74, 75, 76, 78, 79, 80, 81, 83, 87, 88, 90, 93, 94], "suffer": [4, 8, 11, 20, 52, 60, 71, 94], "onc": [4, 20, 30, 31, 35, 75, 81, 83, 86, 87, 92], "familiar": 4, "usag": [4, 34, 49], "found": [4, 5, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 45, 72, 74, 75, 76, 78, 79, 81, 82, 88, 90, 92, 93, 94], "issue_summari": [4, 8, 11, 75], "overal": [4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 41, 50, 51, 54, 57, 58, 62, 66, 67, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 87, 94], "sever": [4, 5, 8, 10, 11, 20, 31, 34, 35, 36, 54, 57, 59, 60, 66, 70, 72, 74, 75, 76, 78, 79, 80, 81, 83, 87, 88, 92, 93, 94], "dataissu": [4, 11, 14, 28], "outlier": [4, 7, 12, 19, 20, 27, 37, 60, 72, 75, 76, 83, 84], "someth": [4, 5, 31, 35, 60], "123": [4, 75, 76], "456": [4, 74, 79, 92, 93], "nearest_neighbor": 4, "7": [4, 41, 42, 49, 68, 70, 74, 75, 76, 78, 79, 80, 81, 85, 86, 87, 88, 90, 91, 92, 93, 94], "9": [4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 41, 42, 54, 68, 70, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "distance_to_nearest_neighbor": [4, 75, 76, 78, 79, 82, 83], "789": 4, "get_issu": [4, 8, 11, 74, 76, 78, 79, 81, 82], "issue_nam": [4, 5, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75, 76], "focu": [4, 11, 79, 91, 94], "full": [4, 8, 11, 34, 58, 82, 94], "summar": [4, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 67, 71, 72, 91], "valueerror": [4, 10, 11, 38, 41, 81], "specific_issu": [4, 11], "exhibit": [4, 8, 11, 67, 76, 78, 79, 82, 83, 87], "lie": [4, 8, 59, 60, 74, 75, 76, 78, 79, 82, 83, 93], "directli": [4, 12, 14, 28, 34, 49, 50, 76, 79, 86, 87, 90, 93], "compar": [4, 50, 59, 70, 75, 76, 78, 83], "get_issue_summari": [4, 11, 76], "get_info": [4, 11, 76, 79], "yet": [4, 15, 19, 23, 80, 85], "list_possible_issue_typ": [4, 12], "regist": [4, 5, 12, 13, 15, 23, 31, 35, 75], "registri": [4, 12], "list_default_issue_typ": [4, 12], "folder": [4, 74, 75, 82], "load": [4, 10, 34, 58, 80, 81, 82, 83, 87, 88, 91, 94], "futur": [4, 8, 20, 31, 35, 50, 72, 75, 79], "overwrit": [4, 75], "separ": [4, 30, 41, 54, 75, 76, 81, 82, 87, 89], "static": 4, "rememb": [4, 79, 81, 83], "part": [4, 8, 31, 35, 36, 55, 57, 58, 74, 75, 80, 91, 94], "ident": [4, 8, 20, 45, 79], "walk": 5, "alongsid": [5, 31, 35, 75, 81], "pre": [5, 6, 8, 31, 35, 75, 76, 82, 91, 94], "runtim": [5, 31, 34, 35, 62, 64, 66, 74, 81, 82], "issue_manager_factori": [5, 12, 75], "myissuemanag": [5, 12], "myissuemanagerforregress": 5, "decor": [5, 12], "ll": [5, 41, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "thing": [5, 35, 83, 90, 93], "next": [5, 50, 72, 74, 78, 79, 81, 85, 87, 90, 92, 93, 94], "dummi": 5, "randint": [5, 27, 41, 75, 76, 81], "mark": [5, 8, 73, 87, 88, 90], "regard": [5, 76, 83], "rand": [5, 41, 75, 76], "is_": [5, 8, 75], "_issu": [5, 8, 75], "issue_score_kei": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "whole": [5, 22, 31, 35, 76], "make_summari": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "popul": [5, 76, 79], "verbosity_level": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "std": 5, "raw_scor": 5, "bit": 5, "involv": [5, 34, 67, 71, 81, 86], "intermediate_arg": 5, "min": [5, 41, 57, 70, 75, 81, 88], "sin_filt": 5, "sin": 5, "arang": 5, "kernel": 5, "wip": 5, "progress": 5, "issue_manag": [5, 8, 9, 11, 13, 16, 17, 18, 21, 22, 23, 24, 26, 27, 75], "instanti": [5, 14, 34, 49, 59, 74, 76, 78, 93], "477762": 5, "286455": 5, "term": [5, 8, 39, 45, 58, 74, 75, 76, 78, 79, 82, 83], "4778": 5, "is_basic_issu": 5, "basic_scor": 5, "13": [5, 17, 24, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "003042": 5, "058117": 5, "11": [5, 49, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "121908": 5, "15": [5, 43, 62, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "169312": 5, "17": [5, 74, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "229044": 5, "2865": 5, "is_intermediate_issu": 5, "intermediate_scor": 5, "000000": [5, 75, 76, 80, 83], "007059": 5, "009967": 5, "010995": 5, "087332": 5, "016296": 5, "03947": 5, "019459": 5, "794251": 5, "underperform": [6, 7, 27], "group": [6, 7, 22, 27, 80, 87, 94], "dbscan": [6, 8, 27, 81], "hdbscan": [6, 81], "etc": [6, 8, 20, 31, 35, 39, 49, 50, 68, 72, 75, 76, 78, 79, 81, 82, 83], "sensit": [6, 8, 43], "ep": [6, 27, 58], "radiu": 6, "min_sampl": [6, 27], "datalab": [6, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 72, 74, 81, 82, 85, 92, 93], "kmean": [6, 81], "your_data": 6, "get_pred_prob": 6, "n_cluster": [6, 27, 81], "cluster_id": [6, 8, 27, 81], "labels_": 6, "underperforming_group": [6, 8, 19, 81], "search": [7, 8, 18, 22, 23, 44, 62, 81, 89], "nondefault": 7, "Near": [7, 81], "iid": [7, 22, 76, 78, 82, 83], "imbal": [7, 19, 54, 59, 60, 76], "null": [7, 19, 76, 79, 82, 83], "valuat": [7, 16], "togeth": [7, 8, 39, 75, 76, 78, 79, 82, 83, 90, 93, 94], "built": [7, 41], "own": [7, 31, 33, 35, 48, 54, 55, 58, 64, 68, 74, 76, 78, 79, 81, 82, 85, 86, 90, 91, 92, 93, 94], "prerequisit": 7, "basic": [7, 35, 49, 78, 79, 88], "page": [8, 76, 81, 83], "variou": [8, 11, 26, 33, 46, 48, 72, 75, 76, 78, 79, 80, 83, 85, 87, 92], "sai": [8, 31, 35, 86, 91], "why": [8, 79], "matter": [8, 30, 51], "_score": 8, "flag": [8, 20, 22, 36, 41, 51, 52, 55, 62, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 90, 91, 93], "badli": [8, 57, 94], "code": [8, 31, 35, 39, 45, 49, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "issue_scor": 8, "outlier_scor": [8, 24, 75, 76, 78, 79, 82, 83, 88], "atyp": [8, 59, 75, 76, 78, 79, 82, 83, 88], "datapoint": [8, 27, 36, 41, 45, 60, 63, 72, 74, 75, 76, 78, 79, 81, 89, 90, 92, 93], "is_issu": [8, 20], "is_outlier_issu": [8, 75, 76, 78, 79, 82, 83], "annot": [8, 30, 40, 50, 51, 52, 54, 55, 57, 58, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 84, 87, 91], "transform": [8, 41, 43, 45, 59, 60, 76, 79, 82, 88, 92, 93, 94], "dissimilar": [8, 78, 79], "preced": 8, "cosin": [8, 59, 88], "incorrect": [8, 57, 60, 63, 74, 75, 76, 78, 79, 82, 83, 87, 90, 92], "due": [8, 34, 36, 60, 64, 66, 74, 75, 76, 78, 79, 82, 83], "appear": [8, 30, 40, 51, 52, 55, 63, 76, 78, 79, 82, 90, 91], "likelihood": [8, 34, 36, 52, 57, 59, 60, 64, 68], "now": [8, 34, 73, 74, 76, 85, 87, 88, 90, 92, 93, 94], "u": [8, 74, 75, 78, 81, 82, 83, 85, 86, 89, 90, 91, 92, 93, 94], "token": [8, 44, 66, 67, 68, 69, 70, 71, 81, 83, 84], "calcul": [8, 16, 22, 34, 41, 50, 54, 55, 57, 58, 59, 62, 66, 80, 82], "hamper": [8, 80, 82], "analyt": [8, 72, 81, 85], "lead": [8, 57, 60, 82, 87], "draw": [8, 75, 76], "conclus": [8, 79], "try": [8, 34, 36, 49, 50, 64, 66, 72, 76, 78, 79, 81, 82, 83, 91], "veri": [8, 30, 51, 55, 57, 75, 76, 78, 79, 81, 82, 83, 85, 88, 90, 93], "rare": [8, 36, 58, 75, 76, 78, 79, 81, 82, 83], "anomal": [8, 60, 75, 76, 78, 79, 82, 83], "articl": [8, 34, 81], "ai": [8, 72, 74, 75, 76, 78, 79, 80, 81, 82, 84, 85, 86, 88, 90, 92, 93, 94], "blog": 8, "unexpect": [8, 31, 35, 79], "consequ": 8, "inspect": [8, 74, 76, 82, 83, 87, 90, 93], "neg": [8, 57, 58, 75, 76, 80], "affect": [8, 31, 35, 64, 70, 79, 81], "extrem": [8, 75, 76, 78, 79, 81, 82, 83], "rel": [8, 30, 50, 51, 59, 75, 76, 78, 79, 82, 83, 88], "record": [8, 31, 35, 74, 78, 90], "abbrevi": 8, "misspel": 8, "typo": [8, 71], "resolut": 8, "video": [8, 80], "audio": [8, 75, 76, 81, 84], "minor": [8, 44], "variat": 8, "translat": 8, "d": [8, 43, 78, 79, 83, 86, 92, 94], "constant": [8, 27, 62], "median": [8, 26, 43], "question": [8, 20, 72, 83], "nearli": [8, 20, 76, 78, 79, 82], "awar": [8, 73, 83], "presenc": [8, 83], "signific": [8, 76, 78, 79, 82, 83], "violat": [8, 76, 78, 79, 82, 83], "assumpt": [8, 76, 78, 79, 82, 83], "changepoint": [8, 76, 78, 79, 82, 83], "shift": [8, 76, 78, 79, 82, 83], "drift": [8, 76, 78, 82, 83], "autocorrel": [8, 76, 78, 79, 82, 83], "almost": [8, 76, 78, 79, 82, 83], "adjac": [8, 76, 78, 79, 82, 83], "tend": [8, 30, 39, 76, 78, 79, 82, 83, 91, 94], "sequenti": [8, 31, 35, 49, 82], "gap": 8, "b": [8, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 44, 45, 70, 78, 79, 80, 83, 89, 92, 94], "x1": [8, 55, 58, 87], "x2": [8, 55, 58, 87], "10th": 8, "100th": 8, "90": [8, 70, 78, 82, 83, 89, 91, 92], "similarli": [8, 31, 35, 75, 78, 81, 82, 87], "math": [8, 82], "behind": [8, 59, 83], "fundament": 8, "proper": [8, 45, 50, 55, 58, 79, 82, 85, 87, 92], "closer": [8, 57, 87], "scenario": [8, 60, 75, 76], "underli": [8, 59, 68, 70, 94], "stem": [8, 59, 88], "evolv": 8, "influenc": 8, "accordingli": 8, "emploi": [8, 86, 88], "partit": [8, 89], "ahead": 8, "good": [8, 31, 35, 43, 49, 51, 57, 60, 64, 66, 67, 72, 78, 79, 82], "fix": [8, 50, 79, 83, 90, 93], "problem": [8, 34, 41, 67, 72, 75, 76, 79, 81, 82], "deploy": [8, 83, 90, 92, 93], "overlook": [8, 57, 87], "fact": 8, "thu": [8, 30, 35, 51, 74, 78, 79, 83, 89, 92, 94], "diagnos": [8, 76, 81], "rarest": [8, 76, 78, 79, 82, 83], "q": [8, 87], "fall": [8, 57, 66, 70, 83, 88], "subpar": 8, "special": [8, 44], "techniqu": 8, "smote": 8, "asymmetr": [8, 30], "properli": [8, 34, 40, 45, 46, 64, 81, 86, 88, 90, 91], "too": [8, 36, 41, 59, 76, 81, 82, 87], "dark": [8, 91], "bright": [8, 94], "blurri": [8, 82], "abnorm": [8, 58, 82], "cluster": [8, 16, 27], "slice": 8, "poor": 8, "subpopul": 8, "lowest": [8, 50, 58, 76, 81, 82, 85, 86, 87, 91], "get_self_confidence_for_each_label": [8, 41, 60], "power": [8, 78, 79, 80, 82, 83, 94], "r": [8, 34, 62, 75, 76, 90, 91], "tabular": [8, 72, 75, 76, 77, 81, 84, 85], "categor": [8, 59, 75, 76, 77, 81, 90, 92], "encod": [8, 42, 58, 64, 67, 78, 79, 81, 90, 91, 92, 93], "miss": [8, 23, 31, 35, 45, 55, 57, 76, 78, 79, 81, 82, 83, 87, 90], "pattern": 8, "contribut": [8, 16, 87], "isn": [8, 15, 23], "approxim": [8, 16, 34, 59, 85], "shaplei": [8, 16], "knn": [8, 11, 16, 22, 27, 59, 78, 88], "scalabl": 8, "sacrific": 8, "One": [8, 45, 59, 81], "quantif": 8, "exert": [8, 76], "possible_issue_typ": 8, "label_kwarg": 8, "outlier_kwarg": 8, "near_dupl": [8, 12, 17, 75, 76, 78, 79, 81, 82, 83], "near_duplicate_kwarg": 8, "non_iid": [8, 12, 22, 76, 78, 79, 82, 83], "non_iid_kwarg": 8, "class_imbal": [8, 18, 76, 78, 79, 82, 83], "class_imbalance_kwarg": 8, "underperforming_group_kwarg": 8, "null_kwarg": 8, "health_summary_paramet": [8, 21, 26], "health_summari": [8, 21, 30, 72, 80], "health_summary_kwarg": 8, "tandem": [8, 80], "view": [8, 31, 35, 36, 66, 68, 70, 72, 74, 75, 76, 78, 79, 80, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "ood_kwarg": 8, "outofdistribut": [8, 24, 59, 88], "outsid": 8, "outlierissuemanag": [8, 12, 24, 75], "nearduplicateissuemanag": [8, 12, 17], "noniidissuemanag": [8, 12, 22], "num_permut": [8, 22], "permut": [8, 22], "significance_threshold": [8, 22], "signic": 8, "noniid": [8, 19], "classimbalanceissuemanag": [8, 18], "underperforminggroupissuemanag": [8, 27], "determinin": 8, "neighbour": 8, "min_cluster_sampl": [8, 27], "filter_cluster_id": [8, 27], "clustering_kwarg": [8, 27], "faq": [8, 72, 76, 78, 79, 82, 84], "nullissuemanag": [8, 23], "data_valuation_kwarg": 8, "data_valu": [8, 19], "datavaluationissuemanag": [8, 16], "codeblock": 8, "demonstr": [8, 34, 75, 76, 79, 81, 82, 83, 85, 86, 87, 90, 91], "howev": [8, 31, 35, 45, 74, 78, 79, 82, 85, 89, 91, 92, 93], "mandatori": 8, "image_issue_types_kwarg": 8, "32": [8, 75, 80, 85, 87, 91], "fewer": [8, 36, 45, 87], "vice": [8, 51], "versa": [8, 51], "light": [8, 80, 82, 87, 91], "29": [8, 80, 82, 85, 86, 87, 91, 94], "low_inform": [8, 82], "odd_aspect_ratio": [8, 82], "35": [8, 75, 80, 82, 85, 86, 87, 91], "odd_siz": [8, 82], "10": [8, 16, 17, 21, 22, 27, 31, 32, 58, 59, 60, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "doc": [8, 31, 35, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "data_issu": [9, 13, 14, 28, 75], "issue_find": [9, 13], "factori": [9, 13, 14], "except": [10, 49, 60, 75, 76, 82, 85], "dataformaterror": 10, "with_traceback": 10, "tb": 10, "__traceback__": 10, "datasetdicterror": 10, "datasetdict": 10, "usual": [10, 28, 82, 85, 90], "datasetloaderror": 10, "dataset_typ": 10, "fail": 10, "map_to_int": 10, "hold": 10, "is_avail": [10, 82], "serv": [11, 14, 85], "central": [11, 94], "repositori": 11, "strategi": [11, 41, 81], "being": [11, 30, 31, 35, 36, 41, 44, 45, 60, 78, 81, 83, 90, 91, 92], "_infostrategi": 11, "basi": 11, "collect_statist": 11, "reus": [11, 20], "avoid": [11, 31, 34, 35, 36, 45, 52, 55, 58, 62, 64, 66, 75, 76, 81], "recomput": [11, 93], "weighted_knn_graph": 11, "issue_manager_that_computes_knn_graph": 11, "collect_issues_from_issue_manag": 11, "collect_issues_from_imagelab": 11, "imagelab": 11, "set_health_scor": 11, "health": [11, 21, 30, 51, 72], "get_data_statist": 11, "concret": 12, "subclass": [12, 31, 35, 59, 75], "my_issu": 12, "stabl": [13, 19, 25, 33, 37, 45, 48, 59, 73], "unregist": 13, "instati": 14, "public": [14, 83, 87, 91, 94], "creation": [14, 35], "execut": [14, 31, 35, 75, 81, 87], "coordin": [14, 55, 57, 58, 87, 94], "behavior": [14, 30, 31, 35, 58], "At": [14, 58, 81], "associ": [14, 31, 35, 58, 85], "get_available_issue_typ": 14, "direct": [15, 23, 31, 35], "valuabl": 16, "vstack": [16, 45, 80, 81, 82, 83, 85, 86], "25": [16, 22, 31, 41, 43, 76, 80, 82, 83, 85, 86, 87, 91, 94], "classvar": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "short": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 44, 45], "data_valuation_scor": 16, "item": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 45, 75, 76, 81, 82, 83, 85, 86], "some_info_kei": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "additional_info_kei": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "default_threshold": [16, 24], "arxiv": [16, 83], "ab": [16, 83], "1911": 16, "07128": 16, "larger": [16, 62, 64, 66, 79, 80, 81, 82], "collect_info": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "info_to_omit": [16, 17, 18, 20, 21, 22, 24, 26, 27], "compos": [16, 17, 18, 20, 21, 22, 24, 26, 27, 31, 35, 79, 88, 93], "is_x_issu": [16, 17, 18, 20, 21, 22, 24, 26, 27], "x_score": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_a": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_b1": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_b2": [16, 17, 18, 20, 21, 22, 24, 26, 27], "report_str": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28], "_": [17, 20, 21, 22, 23, 26, 27, 41, 44, 45, 74, 75, 80, 82, 83, 86, 92], "near_duplicate_set": [17, 75, 76, 78, 79, 81, 82, 83], "occurr": [17, 18, 20, 22, 23, 24, 27, 44], "median_nn_dist": 17, "near_duplicate_scor": [17, 75, 76, 78, 79, 81, 82, 83], "class_imbalance_scor": [18, 76, 78, 79, 82, 83], "bleed": [19, 25, 33], "edg": [19, 25, 33, 57, 72, 83, 94], "sharp": [19, 25, 33], "abc": 20, "believ": [20, 91], "priori": [20, 83], "global": 20, "anoth": [20, 30, 34, 44, 57, 60, 78, 79, 81, 83, 85, 88, 93], "abstract": 20, "applic": [21, 50, 81, 83, 85, 86, 94], "typevar": [21, 31, 35, 57, 58], "_scalartype_co": 21, "covari": [21, 62, 90], "get_health_summari": 21, "summary_dict": 21, "label_scor": [21, 26, 74, 75, 76, 78, 79, 82, 83], "simplified_kolmogorov_smirnov_test": 22, "neighbor_histogram": 22, "non_neighbor_histogram": 22, "kolmogorov": 22, "smirnov": 22, "largest": [22, 34, 41, 60, 64, 66, 91], "empir": [22, 40, 50], "cumul": 22, "ecdf": 22, "histogram": [22, 78, 90], "absolut": [22, 26], "dimension": [22, 45, 74, 83, 88], "trial": 22, "non_iid_scor": [22, 76, 78, 79, 82, 83], "null_track": 23, "extend": [23, 42, 82, 88, 94], "superclass": 23, "arbitrari": [23, 30, 66, 70, 75, 88, 90], "prompt": 23, "address": [23, 75, 76, 79, 81, 93], "enabl": [23, 35], "null_scor": [23, 76, 79, 82, 83], "37037": 24, "q3_avg_dist": 24, "iqr_avg_dist": 24, "median_outlier_scor": 24, "ood": [24, 59, 60, 75, 76, 79, 82, 83, 88], "regressionlabelissuemanag": 26, "multipli": 26, "find_issues_with_predict": 26, "find_issues_with_featur": 26, "deleg": 26, "confus": [27, 30, 31, 35, 36, 45, 58, 93, 94], "50": [27, 35, 81, 82, 83, 85, 87, 88, 91], "keepdim": [27, 81], "outlier_cluster_label": 27, "no_underperforming_cluster_id": 27, "signifi": 27, "absenc": 27, "set_knn_graph": 27, "find_issues_kwarg": 27, "perform_clust": 27, "npt": 27, "int_": 27, "id": [27, 50, 75, 81, 82, 85], "int64": [27, 74, 85], "unique_cluster_id": 27, "get_worst_clust": 27, "_description_": 27, "performed_clust": 27, "worst_cluster_id": 27, "underperforming_group_scor": 27, "exclud": [28, 67, 71, 75, 94], "get_report": 28, "overview": [30, 74, 76, 78, 79, 82, 85, 87, 88, 90, 92, 93, 94], "modifi": [30, 31, 34, 35, 45, 81, 83], "help": [30, 31, 35, 58, 72, 73, 74, 75, 78, 79, 80, 81, 82, 85, 86, 90, 91, 92, 93, 94], "rank_classes_by_label_qu": [30, 76], "merg": [30, 44, 72, 80, 81, 94], "find_overlapping_class": [30, 81, 83], "problemat": [30, 51, 67, 71, 74, 87, 94], "unnorm": [30, 51, 83], "abov": [30, 31, 34, 35, 45, 50, 57, 58, 60, 66, 70, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "model_select": [30, 41, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 90, 92, 93], "cross_val_predict": [30, 35, 74, 75, 76, 78, 79, 81, 83, 85, 89, 90, 92, 93], "get_data_labels_from_dataset": 30, "yourfavoritemodel": [30, 83], "cv": [30, 41, 74, 75, 76, 78, 83, 85, 92], "df": [30, 45, 71, 74, 81], "overall_label_qu": [30, 51], "col": 30, "prob": [30, 44, 83, 89], "divid": [30, 51, 60], "label_nois": [30, 51], "human": [30, 80, 91, 94], "clearli": [30, 60, 82, 87, 91], "num": [30, 51, 80, 83], "overlap": [30, 72, 80, 81, 83], "ontolog": 30, "publish": [30, 94], "therefor": [30, 60], "vehicl": [30, 80], "truck": [30, 80, 88, 91], "intuit": [30, 51], "car": [30, 80, 87, 91], "frequent": [30, 50, 78, 81, 90], "characterist": 30, "l": [30, 31, 35, 55, 57, 58], "class1": 30, "class2": 30, "relationship": 30, "match": [30, 31, 35, 36, 50, 51, 60, 75, 76, 80, 82, 87, 89, 91], "dog": [30, 45, 51, 53, 67, 80, 81, 88, 89, 94], "cat": [30, 45, 51, 53, 80, 81, 88, 89], "captur": [30, 74, 87, 88, 91], "co": [30, 31, 32], "noisy_label": [30, 75, 76, 86], "overlapping_class": 30, "descend": [30, 31, 35, 41, 51, 58], "overall_label_health_scor": [30, 51, 83], "suggest": [30, 50, 51, 57, 79, 81, 82, 90, 93], "half": [30, 31, 35, 51, 80, 94], "health_scor": [30, 51], "classes_by_label_qu": [30, 76], "cnn": [31, 35, 82], "cifar": [31, 32, 80, 88], "teach": [31, 32], "bhanml": 31, "blob": 31, "master": [31, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "call_bn": 31, "bn": 31, "input_channel": 31, "n_output": 31, "dropout_r": 31, "top_bn": 31, "architectur": [31, 35], "shown": [31, 58, 75, 81, 85, 88, 89, 91, 94], "forward": [31, 32, 35, 82, 85], "overridden": [31, 35], "although": [31, 35, 59, 78, 92], "recip": [31, 35], "afterward": [31, 35], "sinc": [31, 35, 38, 46, 51, 58, 66, 70, 81, 85, 86, 87, 89, 94], "former": [31, 35], "hook": [31, 35, 80], "silent": [31, 34, 35], "t_destin": [31, 35], "__call__": [31, 35, 41], "add_modul": [31, 35], "child": [31, 35], "fn": [31, 35, 58], "recurs": [31, 35, 41], "submodul": [31, 35], "children": [31, 35, 94], "nn": [31, 32, 35, 82], "init": [31, 35, 83], "no_grad": [31, 35, 82, 88], "init_weight": [31, 35], "linear": [31, 35, 79, 82, 93], "fill_": [31, 35], "net": [31, 35, 74, 80, 82], "in_featur": [31, 35], "out_featur": [31, 35], "bia": [31, 35, 82], "tensor": [31, 32, 35, 74, 82, 88], "requires_grad": [31, 35], "bfloat16": [31, 35], "cast": [31, 35, 74], "buffer": [31, 35], "datatyp": [31, 35], "member": [31, 35, 75, 76], "xdoctest": [31, 35], "undefin": [31, 35], "var": [31, 35], "buf": [31, 35], "20l": [31, 35], "1l": [31, 35], "5l": [31, 35], "immedi": [31, 35, 88], "cpu": [31, 35, 36, 74, 82], "move": [31, 35, 41, 73, 80], "cuda": [31, 35, 74, 82], "devic": [31, 35, 74, 82], "gpu": [31, 35, 74, 79, 93], "live": [31, 35], "copi": [31, 35, 62, 74, 75, 76, 78, 81, 86, 89, 90, 92], "doubl": [31, 35], "dump_patch": [31, 35], "eval": [31, 35, 82, 86, 88], "dropout": [31, 35], "batchnorm": [31, 35], "grad": [31, 35], "extra_repr": [31, 35], "line": [31, 35, 72, 75, 80, 85, 88, 94], "get_buff": [31, 35], "target": [31, 32, 35, 62, 63, 88, 90], "throw": [31, 35], "get_submodul": [31, 35], "explan": [31, 35], "fulli": [31, 35, 49, 81], "qualifi": [31, 35], "referenc": [31, 35], "attributeerror": [31, 35], "invalid": [31, 35, 79], "resolv": [31, 35, 94], "get_extra_st": [31, 35], "state_dict": [31, 35], "set_extra_st": [31, 35], "build": [31, 35, 82, 91], "pickleabl": [31, 35], "serial": [31, 35], "backward": [31, 35, 82], "break": [31, 35, 82], "pickl": [31, 35, 87], "get_paramet": [31, 35], "let": [31, 35, 59, 60, 74, 76, 78, 79, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "net_b": [31, 35], "net_c": [31, 35], "conv": [31, 35], "conv2d": [31, 35, 82], "16": [31, 35, 41, 66, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "33": [31, 35, 80, 82, 87, 91], "kernel_s": [31, 35], "stride": [31, 35], "200": [31, 35, 60, 80, 87, 94], "diagram": [31, 35, 89], "degre": [31, 35, 90], "queri": [31, 35, 76, 81, 82], "named_modul": [31, 35], "o": [31, 35, 43, 44, 74, 75, 76, 80, 81, 83, 86, 87, 94], "transit": [31, 35], "ipu": [31, 35], "load_state_dict": [31, 35], "strict": [31, 35, 41], "persist": [31, 35], "strictli": [31, 35], "namedtupl": [31, 35], "missing_kei": [31, 35], "unexpected_kei": [31, 35], "runtimeerror": [31, 35], "idx": [31, 35, 45, 46, 58, 75, 81, 82, 83, 85, 87, 88], "named_buff": [31, 35], "prefix": [31, 35, 74, 94], "prepend": [31, 35], "running_var": [31, 35], "named_children": [31, 35], "conv4": [31, 35], "conv5": [31, 35], "memo": [31, 35], "remove_dupl": [31, 35], "named_paramet": [31, 35], "register_backward_hook": [31, 35], "deprec": [31, 35, 38], "favor": [31, 35], "register_full_backward_hook": [31, 35], "removablehandl": [31, 35], "register_buff": [31, 35], "running_mean": [31, 35], "register_forward_hook": [31, 35], "won": [31, 35, 75, 76, 81, 86], "inplac": [31, 35, 85], "register_forward_pre_hook": [31, 35], "gradient": [31, 35, 78, 82, 90], "respect": [31, 35, 58, 83], "grad_input": [31, 35], "grad_output": [31, 35], "technic": [31, 35], "caller": [31, 35], "register_load_state_dict_post_hook": [31, 35], "post": [31, 35], "incompatible_kei": [31, 35], "modif": [31, 35], "thrown": [31, 35], "clearn": [31, 35], "register_modul": [31, 35], "register_paramet": [31, 35], "requires_grad_": [31, 35], "autograd": [31, 35], "freez": [31, 35, 74, 79, 93], "finetun": [31, 35], "gan": [31, 35], "share_memori": [31, 35], "share_memory_": [31, 35], "destin": [31, 35], "keep_var": [31, 35], "shallow": [31, 35], "releas": [31, 35, 73, 81, 88], "design": [31, 35], "ordereddict": [31, 35], "detach": [31, 35, 82], "non_block": [31, 35], "memory_format": [31, 35], "channels_last": [31, 35], "Its": [31, 35, 41, 51, 57], "complex": [31, 35], "integr": [31, 35, 72], "asynchron": [31, 35], "host": [31, 35], "pin": [31, 35, 79, 80, 93], "desir": [31, 35, 44, 58], "4d": [31, 35], "ignore_w": [31, 35], "determinist": [31, 35, 74], "1913": [31, 35], "3420": [31, 35], "5113": [31, 35], "2325": [31, 35], "env": [31, 35], "torch_doctest_cuda1": [31, 35], "gpu1": [31, 35], "1914": [31, 35], "5112": [31, 35], "2324": [31, 35], "float16": [31, 35], "cdoubl": [31, 35], "3741": [31, 35], "2382": [31, 35], "5593": [31, 35], "4443": [31, 35], "complex128": [31, 35], "6122": [31, 35], "1150": [31, 35], "to_empti": [31, 35], "storag": [31, 35], "dst_type": [31, 35], "xpu": [31, 35], "zero_grad": [31, 35, 82], "set_to_non": [31, 35], "context": [31, 35, 87], "noisili": [32, 83], "han": 32, "2018": 32, "cifar_cnn": [32, 33], "loss_coteach": 32, "y_1": 32, "y_2": 32, "forget_r": 32, "class_weight": 32, "logit": [32, 49, 82], "decim": [32, 45], "quickli": [32, 74, 78, 79, 81, 82, 86, 88, 91, 92, 94], "forget": [32, 41, 94], "rate_schedul": 32, "epoch": [32, 35, 81, 82], "initialize_lr_schedul": 32, "lr": [32, 35], "001": [32, 60, 81], "250": [32, 75, 76, 83, 87], "epoch_decay_start": 32, "80": [32, 78, 86, 90, 91, 92], "schedul": 32, "adjust": [32, 36, 54, 59, 60, 72, 83], "beta": 32, "adam": 32, "adjust_learning_r": 32, "alpha_plan": 32, "beta1_plan": 32, "forget_rate_schedul": 32, "num_gradu": 32, "expon": 32, "tell": [32, 79, 82, 83, 93], "train_load": [32, 35], "model1": [32, 83], "optimizer1": 32, "model2": [32, 83], "optimizer2": 32, "dataload": [32, 82, 88], "parser": 32, "parse_arg": 32, "num_iter_per_epoch": 32, "print_freq": 32, "topk": 32, "top1": 32, "top5": 32, "test_load": 32, "offici": [33, 48, 94], "wish": [33, 48, 88, 91, 94], "mnist_pytorch": 33, "coteach": [33, 73], "mini": [34, 64, 66, 81], "With": [34, 79, 83, 85, 90, 91, 93, 94], "low_self_confid": [34, 36, 52], "self_confid": [34, 36, 41, 52, 54, 60, 68, 70, 81, 83, 86, 92, 93], "conveni": [34, 74, 79, 93], "script": 34, "labelinspector": [34, 81], "adj_confident_thresholds_shar": 34, "labels_shar": 34, "pred_probs_shar": 34, "labels_fil": [34, 81], "pred_probs_fil": [34, 81], "batch_siz": [34, 35, 64, 66, 81, 82, 88, 91], "quality_score_kwarg": 34, "num_issue_kwarg": 34, "return_mask": 34, "variant": [34, 50, 91], "read": [34, 38, 76, 81, 83, 88, 94], "zarr": [34, 81], "memmap": [34, 91], "pythonspe": 34, "mmap": [34, 81], "hdf5": 34, "further": [34, 51, 52, 54, 57, 58, 66, 67, 74, 81], "yourfil": 34, "npy": [34, 80, 81, 91], "mmap_mod": [34, 91], "tip": [34, 36, 49, 81], "save_arrai": 34, "your_arrai": 34, "disk": [34, 80, 81], "npz": [34, 94], "maxim": [34, 50, 64, 66, 91], "multiprocess": [34, 36, 52, 64, 66, 81, 82, 91], "linux": [34, 64, 66], "physic": [34, 36, 64, 66, 87, 91], "psutil": [34, 36, 64, 66, 91], "labels_arrai": [34, 46], "predprob": 34, "pred_probs_arrai": 34, "back": [34, 58, 75, 81, 87, 88], "store_result": 34, "becom": [34, 88], "verifi": [34, 81, 85, 88], "long": [34, 50, 59, 85], "enough": [34, 45, 81], "chunk": [34, 89], "ram": [34, 80], "faster": [34, 59, 62, 64, 66, 81, 83], "end_index": 34, "labels_batch": 34, "pred_probs_batch": 34, "update_confident_threshold": 34, "batch_result": 34, "score_label_qu": 34, "indices_of_examples_with_issu": [34, 81], "shortcut": 34, "encount": [34, 36, 64], "1000": [34, 74, 79, 81, 82, 88], "aggreg": [34, 41, 50, 54, 57, 60, 70, 81, 83, 85], "get_num_issu": 34, "fetch": [34, 74, 76], "seen": [34, 81, 88, 94], "far": [34, 50], "get_quality_scor": 34, "label_quality_scor": [34, 54, 57, 60, 63, 83, 87, 90], "method1": 34, "method2": 34, "normalized_margin": [34, 36, 41, 52, 54, 60, 68, 70], "low_normalized_margin": [34, 36, 52], "issue_indic": [34, 57, 82], "update_num_issu": 34, "split_arr": 34, "arr": [34, 81], "chunksiz": 34, "convnet": 35, "bespok": [35, 49], "get_mnist_dataset": 35, "loader": [35, 82], "download": [35, 74, 81, 88], "mnist": [35, 72, 74, 80], "get_sklearn_digits_dataset": 35, "handwritten": 35, "digit": [35, 74, 80], "last": [35, 41, 55, 58, 75, 76, 81, 85, 94], "sklearn_digits_test_s": 35, "hard": [35, 80, 88], "simplenet": 35, "64": [35, 78, 82, 83, 87, 91, 92], "log_interv": 35, "01": [35, 60, 62, 74, 82, 83, 86, 87, 91, 94], "momentum": 35, "no_cuda": 35, "test_batch_s": [35, 82], "templat": 35, "flexibli": 35, "among": [35, 50, 83], "test_set": 35, "Be": 35, "overrid": 35, "train_idx": [35, 45, 88], "train_label": [35, 88, 93], "scikit": [35, 45, 59, 72, 74, 75, 76, 78, 79, 81, 84, 90, 93], "set_predict_proba_request": 35, "set_predict_request": 35, "encourag": [36, 52, 60, 63], "multilabel_classif": [36, 51, 52, 54, 60, 81, 86], "pred_probs_by_class": 36, "prune_count_matrix_col": 36, "rank_by_kwarg": [36, 52, 60, 83], "num_to_remove_per_class": [36, 52], "bad": [36, 52, 57, 60, 79, 81, 93], "seem": [36, 83, 86], "aren": 36, "confidence_weighted_entropi": [36, 41, 52, 54, 60, 68, 70], "label_issues_idx": [36, 60], "entropi": [36, 38, 40, 41, 59, 60], "prune_by_class": [36, 52, 83], "predicted_neq_given": [36, 52, 83], "prune_counts_matrix": 36, "smallest": [36, 60], "unus": 36, "number_of_mislabeled_examples_in_class_k": 36, "delet": [36, 72, 81, 93], "thread": [36, 52], "window": [36, 80], "shorter": [36, 55], "find_predicted_neq_given": 36, "find_label_issues_using_argmax_confusion_matrix": 36, "latent_algebra": [37, 73], "label_quality_util": 37, "multilabel_util": [37, 86], "multilabel_scor": [37, 54], "token_classification_util": [37, 94], "get_normalized_entropi": 38, "min_allowed_prob": 38, "wikipedia": 38, "activ": [38, 40, 50, 72, 85], "towardsdatasci": 38, "cheatsheet": 38, "ec57bc067c0b": 38, "clip": [38, 45, 74], "behav": 38, "unnecessari": [38, 81], "slightli": [38, 92, 93], "interv": [38, 41, 88], "herein": 39, "inexact": 39, "cours": 39, "propag": 39, "throughout": [39, 45, 62, 74, 85, 91, 94], "compute_ps_py_inv_noise_matrix": 39, "compute_py_inv_noise_matrix": 39, "compute_inv_noise_matrix": 39, "easili": [39, 73, 74, 76, 78, 79, 83, 85, 86, 88, 89, 90, 91, 92, 93], "increas": [39, 57, 59, 60, 74, 75, 81, 85, 86, 94], "dot": [39, 70, 81], "compute_noise_matrix_from_invers": 39, "compute_pi": 39, "true_labels_class_count": 39, "compute_pyx": 39, "pyx": 39, "multiannot": 40, "assert_valid_inputs_multiannot": 40, "labels_multiannot": [40, 50], "ensembl": [40, 41, 50, 60, 78, 81, 86, 88, 90, 92], "allow_single_label": 40, "annotator_id": 40, "assert_valid_pred_prob": 40, "pred_probs_unlabel": [40, 50], "format_multiannotator_label": [40, 50, 85], "lexicograph": [40, 45], "formatted_label": [40, 45], "old": [40, 45, 73, 80], "check_consensus_label_class": 40, "consensus_label": [40, 50, 85], "consensus_method": [40, 50], "consensu": [40, 50, 72, 84, 94], "establish": [40, 90, 93], "compute_soft_cross_entropi": 40, "soft": [40, 80], "find_best_temp_scal": 40, "coarse_search_rang": [40, 62, 81], "fine_search_s": [40, 62, 81], "temperatur": [40, 41, 57, 66, 70], "scale": [40, 43, 80, 81, 88, 91, 92], "factor": [40, 41, 43, 64, 66], "minim": [40, 57, 88], "temp_scale_pred_prob": 40, "temp": 40, "sharpen": [40, 80], "smoothen": 40, "classlabelscor": 41, "enum": 41, "get_normalized_margin_for_each_label": [41, 60], "get_confidence_weighted_entropy_for_each_label": [41, 60], "75": [41, 75, 76, 80, 85, 86, 87, 90, 91, 94], "from_str": 41, "scorer": 41, "exponential_moving_averag": [41, 54], "alpha": [41, 54, 57, 75, 76, 83, 86, 90], "exponenti": 41, "ema": 41, "s_1": 41, "s_k": 41, "ema_k": 41, "accord": [41, 52, 78, 79, 83, 94], "formula": [41, 43], "_t": 41, "cdot": 41, "s_t": 41, "qquad": 41, "leq": 41, "_1": 41, "give": [41, 60, 83, 85, 91], "recent": [41, 94], "success": 41, "previou": [41, 81, 82, 87], "discount": 41, "s_ema": 41, "175": [41, 83, 87], "softmin": [41, 54, 57, 66, 70], "underflow": 41, "nan": [41, 50, 78, 85, 90, 92], "possible_method": 41, "aggregated_scor": 41, "multilabelscor": 41, "base_scor": 41, "base_scorer_kwarg": 41, "aggregator_kwarg": [41, 54], "n_sampl": 41, "n_label": 41, "binari": [41, 45, 52, 54, 83, 94], "worst": [41, 85], "class_label_quality_scor": 41, "get_class_label_quality_scor": 41, "42": [41, 80, 82, 87, 91, 94], "452": [41, 79], "new_scor": 41, "575": 41, "get_label_quality_scores_per_class": [41, 54], "ml_scorer": 41, "multilabel_pi": 41, "binar": [41, 42], "get_cross_validated_multilabel_pred_prob": 41, "reformat": [41, 74], "wider": 41, "splitter": 41, "kfold": [41, 82], "multiclass": [41, 45, 50, 86], "onevsrestclassifi": [41, 86], "randomforestclassifi": [41, 83, 86], "n_split": [41, 76, 82, 86], "stack_compl": 42, "pred_prob_slic": 42, "get_onehot_num_class": 42, "onehot": 42, "multilabel": [42, 86], "int2onehot": [42, 86], "hot": [42, 52, 58, 64, 67, 78, 80, 81, 90, 91, 92], "onehot2int": [42, 86], "onehot_matrix": 42, "transform_distances_to_scor": 43, "avg_dist": 43, "scaling_factor": 43, "exp": [43, 59, 60, 75], "dt": 43, "right": [43, 55, 58, 79, 86, 87, 88, 93], "strength": [43, 58], "pronounc": 43, "differenti": 43, "ly": 43, "rule": [43, 44, 80], "thumb": 43, "ood_features_scor": [43, 59, 88], "88988177": 43, "80519832": 43, "token_classif": [44, 68, 70, 71, 81], "get_sent": [44, 94], "sentenc": [44, 68, 70, 71, 79, 93], "readabl": 44, "filter_sent": [44, 94], "lambda": [44, 74, 75, 81, 85], "long_sent": 44, "headlin": 44, "process_token": 44, "charact": [44, 45], "s1": 44, "s2": 44, "processed_token": 44, "alecnlcb": 44, "entiti": [44, 72, 81, 94], "mapped_ent": 44, "unique_ident": 44, "loc": [44, 75, 76, 82, 94], "merge_prob": 44, "probs_merg": 44, "55": [44, 80, 82, 87, 90, 91], "0125": [44, 70], "0375": 44, "075": 44, "025": 44, "color_sent": 44, "color": [44, 67, 75, 76, 78, 83, 86, 88, 90, 91], "red": [44, 58, 75, 76, 80, 83, 86, 87, 88, 91], "colored_sent": 44, "termcolor": 44, "31msentenc": 44, "0m": 44, "ancillari": 45, "remove_noise_from_class": 45, "class_without_nois": 45, "any_other_class": 45, "choos": [45, 60, 78, 81, 83, 90, 92], "tradition": 45, "clip_noise_r": 45, "clip_valu": 45, "new_sum": 45, "preserv": 45, "value_count": [45, 81], "fill": 45, "wherea": [45, 52, 89], "come": [45, 75, 76, 81, 82, 91], "major": [45, 50, 73, 82, 88], "versu": [45, 83], "value_counts_fill_missing_class": 45, "get_missing_class": 45, "round_preserving_sum": 45, "obviou": 45, "cgdeboer": 45, "iteround": 45, "round_preserving_row_tot": 45, "reach": 45, "estimate_pu_f1": 45, "prob_s_eq_1": 45, "claesen": 45, "f1": [45, 58, 79, 83], "confusion_matrix": 45, "BE": 45, "print_square_matrix": 45, "left_nam": 45, "top_nam": 45, "titl": [45, 75, 76, 83, 86, 88], "short_titl": 45, "round_plac": 45, "pretti": [45, 83], "print_noise_matrix": [45, 83], "print_inverse_noise_matrix": 45, "print_joint_matrix": [45, 83], "joint_matrix": 45, "compress_int_arrai": 45, "num_possible_valu": 45, "train_val_split": 45, "holdout_idx": 45, "subset_x_i": 45, "extract": [45, 59, 74, 79, 85, 88, 91, 93], "subset_label": 45, "subset_data": 45, "extract_indices_tf": 45, "allow_shuffl": 45, "turn": [45, 72, 87], "unshuffle_tensorflow_dataset": 45, "shuffledataset": 45, "histori": 45, "pre_x": 45, "buffer_s": 45, "is_torch_dataset": 45, "is_tensorflow_dataset": 45, "csr_vstack": 45, "csr_matric": 45, "append": [45, 74, 80, 81, 82, 83, 85, 86, 88, 94], "bottom": [45, 55, 58, 87], "append_extra_datapoint": 45, "to_data": 45, "from_data": 45, "taken": 45, "get_num_class": 45, "label_matrix": 45, "canon": 45, "num_unique_class": 45, "get_unique_class": 45, "format_label": 45, "smart_display_datafram": 45, "displai": [45, 58, 67, 71, 74, 79, 83, 93, 94], "jupyt": [45, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "notebook": [45, 50, 74, 76, 80, 81, 83, 85, 86, 87, 91, 94], "consol": 45, "force_two_dimens": 45, "html": [45, 59, 78, 81, 83], "assert_valid_input": 46, "allow_missing_class": 46, "allow_one_class": 46, "assert_valid_class_label": 46, "assert_nonempty_input": 46, "assert_indexing_work": 46, "length_x": 46, "labels_to_arrai": 46, "labellik": 46, "keraswrappermodel": [49, 72], "keraswrappersequenti": 49, "tf": [49, 74], "legaci": 49, "lack": 49, "keraswrapp": 49, "huggingface_keras_imdb": 49, "unit": [49, 94], "model_kwarg": [49, 62], "compile_kwarg": 49, "sparsecategoricalcrossentropi": 49, "layer": [49, 74, 79, 88, 93], "dens": 49, "my_keras_model": 49, "from_logit": 49, "compil": 49, "declar": 49, "apply_softmax": 49, "analysi": 50, "analyz": [50, 72, 83, 85, 86], "get_label_quality_multiannot": [50, 85], "vote": 50, "crowdsourc": [50, 72, 85], "dawid": [50, 85], "skene": [50, 85], "analog": [50, 80, 85], "chosen": [50, 60, 81, 85], "crowdlab": [50, 85], "unlabel": [50, 78, 79, 82, 85, 88, 91], "decid": [50, 79, 80, 85, 90, 93, 94], "get_active_learning_scor": [50, 85], "activelab": [50, 85], "priorit": [50, 57, 87, 91, 94], "showcas": 50, "main": 50, "best_qual": 50, "quality_method": 50, "calibrate_prob": 50, "return_detailed_qu": 50, "return_annotator_stat": 50, "return_weight": 50, "label_quality_score_kwarg": 50, "necessarili": [50, 58, 79, 83], "did": [50, 51, 74, 78, 83, 85, 90, 92, 93], "majority_vot": 50, "ti": 50, "broken": [50, 58, 80], "highest": [50, 58, 75, 82, 89], "0th": 50, "consensus_quality_scor": [50, 85], "annotator_agr": [50, 85], "reman": 50, "1st": 50, "2nd": [50, 64], "3rd": 50, "consensus_label_suffix": 50, "consensus_quality_score_suffix": 50, "suffix": 50, "emsembl": 50, "weigh": [50, 80], "agreement": [50, 85], "agre": 50, "prevent": [50, 81], "overconfid": [50, 89], "wrong": [50, 55, 57, 73, 75, 76, 79, 81, 83, 87, 93], "detailed_label_qu": [50, 85], "annotator_stat": [50, 85], "model_weight": 50, "annotator_weight": 50, "warn": [50, 75, 76], "labels_info": 50, "num_annot": [50, 85], "deriv": [50, 85], "quality_annotator_1": 50, "quality_annotator_2": 50, "quality_annotator_m": 50, "annotator_qu": [50, 85], "num_examples_label": [50, 85], "agreement_with_consensu": [50, 85], "worst_class": [50, 85], "trustworthi": [50, 85, 90], "get_label_quality_multiannotator_ensembl": 50, "weigtht": 50, "budget": 50, "retrain": [50, 90, 93], "active_learning_scor": 50, "improv": [50, 76, 80, 81, 82, 83, 90, 91, 92, 93], "active_learning_scores_unlabel": 50, "get_active_learning_scores_ensembl": 50, "henc": [50, 74, 75, 85], "get_majority_vote_label": [50, 85], "event": 50, "lastli": [50, 78], "convert_long_to_wide_dataset": 50, "labels_multiannotator_long": 50, "wide": [50, 74, 92, 93], "suitabl": [50, 78, 92], "labels_multiannotator_wid": 50, "common_multilabel_issu": 51, "mutual": [51, 86], "exclus": [51, 86], "rank_classes_by_multilabel_qu": 51, "overall_multilabel_health_scor": 51, "multilabel_health_summari": 51, "classes_by_multilabel_qu": 51, "inner": [52, 66], "find_multilabel_issues_per_class": 52, "per_class_label_issu": 52, "label_issues_list": 52, "labels_list": 52, "pred_probs_list": [52, 60, 82, 83], "anim": [53, 88], "rat": 53, "predat": 53, "pet": 53, "reptil": 53, "manner": [54, 85, 90, 92, 93], "box": [55, 57, 58, 80, 87], "object_detect": [55, 57, 58, 87], "return_indices_ranked_by_scor": [55, 87], "overlapping_label_check": [55, 57], "suboptim": [55, 57], "locat": [55, 57, 87, 91, 94], "bbox": [55, 58, 87], "image_nam": [55, 58], "y1": [55, 58, 87], "y2": [55, 58, 87], "later": [55, 58, 59, 93, 94], "mmdetect": [55, 58, 87], "corner": [55, 58, 87], "swap": [55, 57, 67, 71], "penal": [55, 57], "concern": [55, 57, 72, 76], "aggregation_weight": 57, "imperfect": [57, 81], "chose": [57, 85, 87], "imperfectli": [57, 87], "dirti": [57, 60, 63, 90], "subtyp": 57, "badloc": 57, "nonneg": 57, "issues_from_scor": [57, 66, 67, 70, 71, 87, 91, 94], "compute_overlooked_box_scor": 57, "high_probability_threshold": 57, "auxiliary_input": [57, 58], "vari": [57, 76], "iou": [57, 58], "heavili": 57, "auxiliarytypesdict": 57, "pred_label": [57, 93], "pred_label_prob": 57, "pred_bbox": 57, "lab_label": 57, "lab_bbox": 57, "similarity_matrix": 57, "min_possible_similar": 57, "scores_overlook": 57, "compute_badloc_box_scor": 57, "low_probability_threshold": 57, "scores_badloc": 57, "compute_swap_box_scor": 57, "accident": [57, 78, 79, 81, 93], "scores_swap": 57, "pool_box_scores_per_imag": 57, "box_scor": 57, "image_scor": [57, 66, 91], "object_counts_per_imag": 58, "discov": [58, 76, 94], "auxiliari": [58, 88, 91], "_get_valid_inputs_for_compute_scor": 58, "object_count": 58, "bounding_box_size_distribut": 58, "down": 58, "bbox_siz": 58, "class_label_distribut": 58, "class_distribut": 58, "get_sorted_bbox_count_idx": 58, "plot": [58, 75, 76, 83, 86, 88, 90, 91], "sorted_idx": [58, 88], "plot_class_size_distribut": 58, "class_to_show": 58, "hidden": [58, 88], "max_class_to_show": 58, "plot_class_distribut": 58, "visual": [58, 75, 76, 82, 90, 92, 94], "prediction_threshold": 58, "overlai": [58, 87], "figsiz": [58, 75, 76, 82, 83, 86, 88], "save_path": [58, 87], "blue": [58, 80, 83, 87], "overlaid": 58, "side": [58, 80, 87], "figur": [58, 83, 86, 88, 90], "extens": [58, 83, 85], "png": [58, 87], "pdf": [58, 59], "svg": 58, "matplotlib": [58, 75, 76, 82, 83, 86, 87, 88, 90], "get_average_per_class_confusion_matrix": 58, "num_proc": [58, 82], "intersect": [58, 81], "tp": 58, "fp": 58, "ground": [58, 80, 83, 85, 90], "truth": [58, 83, 85, 90], "bias": 58, "avg_metr": 58, "distionari": 58, "95": [58, 68, 70, 76, 78, 80, 83, 90, 91], "calculate_per_class_metr": 58, "per_class_metr": 58, "Of": 59, "li": 59, "smaller": [59, 86, 87], "find_top_issu": [59, 60, 88], "reli": [59, 74, 75, 76, 79, 87, 88, 93], "dist_metr": 59, "dim": [59, 82, 91], "subtract": [59, 60], "renorm": [59, 60, 81], "least_confid": 59, "sum_": 59, "log": [59, 60, 73], "softmax": [59, 66, 70, 82], "literatur": 59, "gen": 59, "liu": 59, "lochman": 59, "zach": 59, "openaccess": 59, "thecvf": 59, "content": [59, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "cvpr2023": 59, "liu_gen_pushing_the_limits_of_softmax": 59, "based_out": 59, "distribution_detection_cvpr_2023_pap": 59, "fit_scor": [59, 88], "ood_predictions_scor": 59, "pretrain": [59, 74, 79, 88, 93], "adjust_confident_threshold": 59, "probabilist": [59, 74, 75, 76, 78, 79, 88, 89, 92], "order_label_issu": [60, 73], "whichev": [60, 89], "argsort": [60, 79, 82, 83, 88, 90, 93], "max_": 60, "get_label_quality_ensemble_scor": [60, 81, 83], "weight_ensemble_members_bi": 60, "custom_weight": 60, "log_loss_search_t_valu": 60, "0001": [60, 80], "scheme": 60, "log_loss_search": 60, "log_loss": [60, 79], "1e0": 60, "1e1": 60, "1e2": 60, "2e2": 60, "quality_scor": [60, 88], "forth": 60, "top_issue_indic": 60, "rank_bi": [60, 73], "weird": [60, 71], "minu": 60, "prob_label": 60, "max_prob_not_label": 60, "idea": 60, "AND": [60, 79], "corrupt": [62, 90], "linearregress": [62, 81, 90], "y_with_nois": 62, "n_boot": [62, 81], "include_aleatoric_uncertainti": [62, 81], "sole": [62, 75, 85, 88, 92], "bootstrap": [62, 81, 90], "resampl": [62, 74, 81], "epistem": [62, 81, 88, 90], "aleator": [62, 81, 90], "model_final_kwarg": 62, "coars": 62, "thorough": [62, 81], "fine": [62, 74, 79, 88, 93], "grain": 62, "grid": 62, "get_epistemic_uncertainti": 62, "varianc": [62, 83], "epistemic_uncertainti": 62, "get_aleatoric_uncertainti": 62, "residu": [62, 63, 81], "deviat": [62, 90], "ie": 62, "aleatoric_uncertainti": 62, "outr": 63, "contin": 63, "raw": [63, 72, 73, 76, 80, 82, 85, 87, 88], "aka": [63, 74, 83, 94], "00323821": 63, "33692597": 63, "00191686": 63, "semant": [64, 66, 67, 84], "pixel": [64, 66, 67, 88, 91], "h": [64, 66, 67, 91], "height": [64, 66, 67, 91], "w": [64, 66, 67, 91], "width": [64, 66, 67, 91], "labels_one_hot": [64, 67, 91], "stream": [64, 88, 94], "downsampl": [64, 66, 91], "shrink": [64, 66], "divis": [64, 66, 75], "segmant": [66, 67], "num_pixel_issu": [66, 91], "product": [66, 81, 82], "pixel_scor": [66, 91], "display_issu": [66, 67, 68, 70, 71, 91, 94], "highlight": [67, 71, 75, 76, 78, 91], "enter": 67, "legend": [67, 75, 76, 86, 87, 90, 91], "colormap": 67, "background": 67, "person": [67, 81, 87, 91, 94], "common_label_issu": [67, 71, 91, 94], "ambigu": [67, 71, 74, 79, 80, 83, 93, 94], "systemat": [67, 71, 85], "misunderstood": [67, 71], "issues_df": [67, 82], "filter_by_class": [67, 91], "class_index": 67, "issues_subset": [67, 71], "token_score_method": 70, "sentence_score_method": 70, "sentence_score_kwarg": 70, "compris": [70, 71], "token_scor": [70, 94], "converg": 70, "toward": 70, "_softmin_sentence_scor": 70, "sentence_scor": [70, 94], "token_info": 70, "70": [70, 78, 90, 91], "02": [70, 75, 76, 82, 83, 87, 91], "03": [70, 78, 80, 82, 83, 87, 91, 94], "04": [70, 78, 87, 91], "08": [70, 79, 83, 87, 91, 94], "commonli": [71, 73, 75, 76, 86, 94], "filter_by_token": [71, 94], "But": [71, 79, 83, 94], "restrict": [71, 81], "reliabl": [72, 74, 81, 85, 91, 92], "thousand": 72, "imagenet": [72, 80], "popular": [72, 85, 87], "centric": [72, 78, 79, 82, 84], "capabl": 72, "minut": [72, 74, 78, 79, 80, 85, 86, 87, 90, 91, 92, 93, 94], "conda": 72, "feature_embed": [72, 88], "Then": [72, 81, 82, 90, 92, 93], "your_dataset": [72, 74, 75, 76, 78, 79, 81, 82], "column_name_of_label": [72, 74, 75, 76, 78, 79, 82], "plagu": [72, 76], "untrain": 72, "\u30c4": 72, "label_issues_info": [72, 76], "sklearn_compatible_model": 72, "framework": [72, 86, 87], "complianc": 72, "tag": [72, 86, 94], "sequenc": 72, "recognit": [72, 74, 81, 94], "train_data": [72, 88, 90, 92, 93], "gotten": 72, "test_data": [72, 83, 86, 88, 90, 92, 93], "deal": [72, 76], "tutori": [72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "feel": [72, 74, 76, 81], "free": [72, 74, 76, 78, 79, 81, 82, 83], "ask": [72, 81], "slack": [72, 81], "project": [72, 90], "welcom": 72, "commun": [72, 81], "guidelin": [72, 87], "piec": 72, "studio": [72, 76, 78, 79, 81, 82], "platform": [72, 78, 79, 81, 82], "automl": [72, 81], "foundat": 72, "smart": [72, 78, 79, 81, 82], "edit": [72, 81], "easier": [72, 83], "unreli": [72, 74, 78, 79, 92], "older": 73, "outlin": 73, "substitut": 73, "v2": [73, 78, 92], "get_noise_indic": 73, "psx": 73, "sorted_index_method": 73, "order_label_error": 73, "label_errors_bool": 73, "latent_estim": 73, "num_label_error": 73, "learningwithnoisylabel": 73, "neatli": 73, "organ": [73, 78, 80, 92, 94], "reorgan": 73, "baseline_method": 73, "incorpor": [73, 83], "research": [73, 83], "polyplex": 73, "terminologi": 73, "label_error": 73, "quickstart": [74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "spoken": 74, "500": [74, 88, 94], "english": [74, 80], "pronunci": 74, "wav": 74, "huggingfac": [74, 75, 76, 82], "voxceleb": 74, "speech": [74, 94], "your_pred_prob": [74, 75, 76, 78, 79], "tensorflow_io": 74, "26": [74, 75, 80, 82, 83, 85, 87, 91], "huggingface_hub": 74, "12": [74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "branch": [74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "reproduc": [74, 78, 83, 85], "command": 74, "wget": [74, 87, 91, 94], "navig": 74, "link": [74, 80, 87], "browser": 74, "jakobovski": 74, "archiv": [74, 94], "v1": 74, "tar": [74, 88], "gz": [74, 88], "mkdir": [74, 94], "spoken_digit": 74, "xf": 74, "6_nicolas_32": 74, "data_path": 74, "listdir": 74, "nondeterminist": 74, "file_nam": 74, "endswith": 74, "file_path": 74, "join": [74, 81, 82], "39": [74, 75, 79, 80, 81, 82, 87, 90, 91, 93, 94], "7_george_26": 74, "0_nicolas_24": 74, "0_nicolas_6": 74, "listen": 74, "display_exampl": 74, "click": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "expand": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "pulldown": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "colab": [74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "tfio": 74, "pathlib": 74, "ipython": 74, "load_wav_16k_mono": 74, "filenam": 74, "khz": 74, "file_cont": 74, "io": [74, 80], "read_fil": 74, "sample_r": 74, "decode_wav": 74, "desired_channel": 74, "squeez": 74, "rate_in": 74, "rate_out": 74, "16000": 74, "wav_file_nam": 74, "audio_r": 74, "wav_file_exampl": 74, "plai": [74, 80, 81], "button": 74, "wav_file_name_exampl": 74, "7_jackson_43": 74, "hear": 74, "extractor": 74, "encoderclassifi": 74, "spkrec": 74, "xvect": 74, "feature_extractor": 74, "from_hparam": 74, "run_opt": 74, "uncom": 74, "wav_audio_file_path": 74, "head": [74, 76, 78, 79, 80, 82, 83, 85, 90, 92, 93], "torchaudio": 74, "extract_audio_embed": 74, "emb": [74, 82], "signal": 74, "encode_batch": 74, "embeddings_list": [74, 82], "embeddings_arrai": 74, "512": [74, 82], "14": [74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "196315": 74, "3194594": 74, "478977": 74, "2890828": 74, "8170278": 74, "892647": 74, "24": [74, 80, 83, 85, 87, 91], "898054": 74, "256194": 74, "559642": 74, "559715": 74, "620667": 74, "285246": 74, "21": [74, 75, 80, 81, 83, 87, 91, 94], "709623": 74, "5033712": 74, "913803": 74, "8198366": 74, "1831512": 74, "208761": 74, "08426": 74, "3210406": 74, "005453": 74, "2161605": 74, "478239": 74, "682179": 74, "0538025": 74, "242471": 74, "0914207": 74, "7833488": 74, "039538": 74, "23": [74, 80, 82, 83, 87, 91, 94], "56918": 74, "19": [74, 79, 80, 81, 82, 83, 88, 90, 91, 93], "761095": 74, "1258287": 74, "753235": 74, "3508894": 74, "598273": 74, "237122": 74, "2500": 74, "leverag": [74, 79, 81, 83, 85, 93], "tune": [74, 79, 80, 88, 93], "computation": [74, 79, 93], "intens": [74, 79, 93], "held": [74, 78, 79, 80, 87, 88, 89, 92], "straightforward": [74, 78, 92], "benefit": [74, 89, 91, 92], "tol": 74, "num_crossval_fold": [74, 78, 85, 92], "decreas": [74, 81], "never": [74, 83, 86, 88, 89], "accuracy_scor": [74, 79, 83, 92, 93], "cv_accuraci": 74, "9772": 74, "probabilit": [74, 93], "9980": 74, "176": [74, 80, 83, 86], "006488": 74, "2318": 74, "008269": 74, "986": 74, "010354": 74, "469": 74, "013459": 74, "516": 74, "013478": 74, "investig": 74, "100541": 74, "998729": 74, "998768": 74, "980980": 74, "998217": 74, "18": [74, 79, 80, 81, 82, 83, 87, 88, 90, 91, 93, 94], "identified_label_issu": [74, 79], "lowest_quality_label": [74, 79, 83, 90, 93], "sort_valu": [74, 76, 78, 79, 81, 82, 83, 85], "1946": 74, "1871": 74, "1955": 74, "2132": 74, "worth": [74, 83], "iloc": [74, 78, 79, 90, 92, 93], "6_yweweler_35": 74, "6_yweweler_36": 74, "6_yweweler_14": 74, "6_theo_27": 74, "4_george_31": 74, "6_nicolas_8": 74, "sound": 74, "quit": [74, 88], "22": [74, 75, 80, 82, 83, 86, 87, 91, 94], "blindli": [74, 81, 90, 92, 93], "trust": [74, 81, 83, 85, 89, 90, 92, 93], "underneath": 75, "hood": 75, "alert": 75, "introduct": 75, "mayb": [75, 76, 79], "examin": [75, 76, 78, 92], "your_feature_matrix": [75, 76], "toi": [75, 76, 80, 82, 83, 85], "train_test_split": [75, 76, 88, 92, 93], "inf": [75, 76], "mid": [75, 76], "bins_map": [75, 76], "create_data": [75, 76], "y_bin": [75, 76], "y_i": [75, 76], "y_bin_idx": [75, 76], "y_train": [75, 76, 83, 90], "y_test": [75, 76, 83, 90], "y_train_idx": [75, 76], "y_test_idx": [75, 76], "test_siz": [75, 76, 92, 93], "slide": [75, 76, 80], "decis": [75, 76, 92], "boundari": [75, 76], "frame": [75, 76], "x_out": [75, 76], "tini": [75, 76], "concaten": [75, 76, 81, 89], "y_out": [75, 76], "y_out_bin": [75, 76], "y_out_bin_idx": [75, 76], "exact_duplicate_idx": [75, 76], "x_duplic": [75, 76], "y_duplic": [75, 76], "y_duplicate_idx": [75, 76], "noisy_labels_idx": [75, 76, 86], "scatter": [75, 76, 83, 86, 90], "black": [75, 76, 80, 90], "cyan": [75, 76], "pyplot": [75, 76, 82, 83, 86, 88, 90], "plt": [75, 76, 82, 83, 86, 88, 90], "plot_data": [75, 76, 83, 86, 90], "fig": [75, 76, 80, 82, 88, 90], "ax": [75, 76, 82, 88, 90], "subplot": [75, 76, 82, 88], "set_titl": [75, 76, 82, 88], "set_xlabel": [75, 76], "x_1": [75, 76], "fontsiz": [75, 76, 82, 83, 86], "set_ylabel": [75, 76], "x_2": [75, 76], "set_xlim": [75, 76], "set_ylim": [75, 76], "linestyl": [75, 76], "circl": [75, 76, 83, 86], "misclassifi": [75, 76], "zip": [75, 76, 82, 87, 94], "label_err": [75, 76], "180": [75, 76, 87], "marker": [75, 76], "facecolor": [75, 76], "edgecolor": [75, 76], "linewidth": [75, 76, 88], "dup": [75, 76], "first_legend": [75, 76], "align": [75, 76], "title_fontproperti": [75, 76], "semibold": [75, 76], "second_legend": [75, 76], "45": [75, 76, 80, 82, 83, 87, 91], "gca": [75, 76], "add_artist": [75, 76], "tight_layout": [75, 76], "ideal": [75, 76], "logist": [75, 76, 79, 85, 88, 93], "remaind": 75, "modal": [75, 76, 81, 85], "regardless": [75, 76], "132": [75, 76, 83, 87, 94], "9318": 75, "77": [75, 76, 78, 87, 91, 92], "006939": 75, "007830": 75, "40": [75, 76, 79, 80, 82, 91], "014826": 75, "107": [75, 76, 83, 86], "021220": 75, "120": [75, 76, 92], "026403": 75, "notic": [75, 83, 85, 87], "3558": [75, 76], "126": [75, 76, 83, 87], "006636": [75, 76], "130": [75, 76], "012571": [75, 76], "129": [75, 76], "127": [75, 76], "014909": [75, 76], "128": [75, 76, 82], "017443": [75, 76], "6160": [75, 76], "is_near_duplicate_issu": [75, 76, 78, 79, 81, 82, 83], "131": [75, 76, 91], "000000e": [75, 76], "00": [75, 76, 78, 80, 82, 91, 92], "000002": [75, 76], "463180e": [75, 76], "07": [75, 76, 78, 82, 83, 87, 91], "51": [75, 76, 78, 80, 83, 87, 91], "161148": [75, 76], "859087e": [75, 76], "30": [75, 76, 80, 81, 82, 86, 91, 94], "3453": 75, "029542": 75, "031182": 75, "057961": 75, "058244": 75, "home": [75, 76, 79, 80, 88, 93], "runner": [75, 76, 79, 88, 93], "300": [75, 85, 94], "userwarn": [75, 76], "330": [75, 82, 87], "309": 75, "34": [75, 80, 83, 85, 87, 88, 91, 94], "54": [75, 80, 83, 87, 91, 94], "039117": 75, "53": [75, 76, 78, 80, 82, 86, 87, 91, 92], "044594": 75, "105": 75, "105121": 75, "133588": 75, "43": [75, 80, 82, 83, 87, 91, 93], "168035": 75, "125": 75, "101107": 75, "37": [75, 80, 91], "183382": 75, "109": [75, 80, 87], "209259": 75, "211042": 75, "221316": 75, "average_ood_scor": 75, "34530442089193386": 75, "52": [75, 80, 82, 87, 91, 94], "169820": 75, "087324e": 75, "89": [75, 78, 87, 90, 91, 94], "92": [75, 83, 87, 91, 92], "259024": 75, "583757e": 75, "91": [75, 87, 91, 93], "346458": 75, "341292e": 75, "specfi": 75, "new_lab": 75, "scoring_funct": 75, "div": 75, "rem": 75, "inv_scal": 75, "49": [75, 80, 83, 87, 91, 94], "superstitionissuemanag": 75, "unlucki": 75, "superstit": 75, "to_seri": 75, "issues_mask": 75, "summary_scor": 75, "9242": 75, "is_superstition_issu": 75, "superstition_scor": 75, "047581": 75, "090635": 75, "129591": 75, "65": [75, 82, 87, 91, 92], "164840": 75, "demo": [76, 78, 86, 92], "lurk": [76, 82, 83], "opt": 76, "hostedtoolcach": 76, "x64": 76, "lib": 76, "python3": 76, "site": 76, "_split": 76, "737": 76, "thoroughli": 76, "preprocess": [76, 78, 88, 90, 92, 93], "904": 76, "review": [76, 78, 79, 80, 81, 83, 87, 90, 91, 92, 93, 94], "8561": 76, "001894": 76, "58": [76, 78, 80, 83, 87, 91, 92], "003565": 76, "007326": 76, "008974": 76, "009699": 76, "0227": 76, "is_class_imbalance_issu": [76, 78, 79, 82, 83], "022727": 76, "86": [76, 78, 82, 83, 87, 90, 91, 92], "87": [76, 82, 87, 90, 91, 93], "0000": [76, 79, 80, 82, 83], "is_null_issu": [76, 79, 82, 83], "96": [76, 78, 80, 83, 86, 87, 90, 91], "94": [76, 78, 80, 83, 87, 90, 91, 92], "93": [76, 80, 87, 90, 91, 92, 94], "8218": 76, "is_non_iid_issu": [76, 78, 79, 82, 83], "810274": 76, "826147": 76, "849587": 76, "855359": 76, "855485": 76, "821750488732925": 76, "auto": [76, 80, 81, 90, 92, 93], "conceptu": 76, "856061": 76, "355772": 76, "616034": 76, "821750": 76, "betweeen": 76, "859109": 76, "417707": 76, "664083": 76, "970324": 76, "816965": 76, "375317": 76, "641516": 76, "890575": 76, "530924": 76, "460593": 76, "601188": 76, "752776": 76, "321635": 76, "562539": 76, "948362": 76, "090224": 76, "472909": 76, "746763": 76, "878267": 76, "examples_w_issu": [76, 81], "013444": 76, "025173": 76, "026416": 76, "inde": [76, 79], "miscellan": [76, 94], "428571": 76, "111111": 76, "571429": 76, "407407": 76, "592593": 76, "337838": 76, "092593": 76, "662162": 76, "333333": [76, 80], "952381": 76, "666667": 76, "portion": 76, "huge": [76, 83], "worri": [76, 79], "critic": 76, "highli": [76, 82], "sql": [78, 92], "databas": [78, 92], "excel": [78, 92], "parquet": [78, 92], "student": [78, 90, 92, 94], "grade": [78, 90, 92], "900": [78, 90, 92], "exam": [78, 90, 92], "letter": [78, 92, 94], "hundr": [78, 92], "histgradientboostingclassifi": 78, "standardscal": [78, 88, 92], "possibli": [78, 92], "grades_data": [78, 92], "read_csv": [78, 79, 90, 92, 93], "stud_id": [78, 92], "exam_1": [78, 90, 92], "exam_2": [78, 90, 92], "exam_3": [78, 90, 92], "letter_grad": [78, 92], "f48f73": [78, 92], "0bd4e7": [78, 92], "81": [78, 79, 87, 90, 91, 92, 94], "great": [78, 80, 92], "particip": [78, 92], "cb9d7a": [78, 92], "61": [78, 83, 87, 91, 92], "78": [78, 80, 82, 83, 87, 90, 91, 92], "9acca4": [78, 92], "48": [78, 80, 82, 83, 87, 91, 92], "x_raw": [78, 92], "cat_featur": 78, "x_encod": [78, 92], "get_dummi": [78, 90, 92], "drop_first": [78, 92], "numeric_featur": [78, 92], "scaler": [78, 88, 92], "x_process": [78, 92], "fit_transform": [78, 92], "bring": [78, 79, 82, 85, 90, 92, 93], "byod": [78, 79, 82, 85, 90, 92, 93], "boost": [78, 81, 85, 90], "xgboost": [78, 81, 90], "think": [78, 79, 81, 86, 91, 94], "carefulli": [78, 79, 82, 92], "nonzero": 78, "suspici": [78, 92], "tabl": [78, 80, 85, 92], "358": 78, "294": [78, 87], "46": [78, 80, 83, 87, 91], "941": 78, "7109": 78, "000005": [78, 79, 82], "886": 78, "000059": 78, "709": 78, "000104": 78, "723": 78, "000169": 78, "689": 78, "000181": 78, "3590": 78, "051882e": 78, "683133e": 78, "536582e": 78, "406589e": 78, "324246e": 78, "6165": 78, "582": 78, "185": [78, 80, 87, 94], "187": [78, 80], "27": [78, 80, 82, 83, 87, 91, 94], "898": 78, "637": [78, 92], "0014": [78, 80], "595": 78, "702427": 78, "147": [78, 83, 87], "711186": 78, "157": [78, 83], "721394": 78, "771": 78, "731979": 78, "740335": 78, "0014153602099278074": 78, "1562": 78, "393": 78, "156217": 78, "391": 78, "806": 78, "805": 78, "156": [78, 83, 94], "na": [78, 79, 82, 83, 85], "issue_result": 78, "000842": 78, "555944": 78, "004374": 78, "sorted_issu": 78, "73": [78, 80, 82, 86, 87, 90, 91], "deserv": 78, "outlier_result": 78, "sorted_outli": 78, "56": [78, 80, 90, 91], "lt": [78, 79, 80, 82, 85, 91], "style": [78, 91], "font": 78, "18px": 78, "ff00ff": 78, "bac": 78, "unintend": [78, 79], "mistak": [78, 79, 82, 92, 93], "duplicate_result": 78, "690": 78, "246": [78, 87], "perhap": [78, 83, 85], "twice": 78, "67": [78, 80, 82, 87, 90, 91], "wari": [78, 79, 81], "super": [78, 79, 82], "system": [78, 79, 82, 91], "intent": [79, 93], "servic": [79, 81, 93], "onlin": [79, 93], "bank": [79, 80, 93], "banking77": [79, 93], "oo": [79, 93], "000": [79, 80, 82, 93, 94], "categori": [79, 82, 93], "scope": [79, 93], "dive": 79, "your_featur": 79, "sentence_transform": [79, 93], "sentencetransform": [79, 93], "payment": [79, 93], "cancel_transf": [79, 93], "transfer": [79, 93], "fund": [79, 93], "cancel": [79, 93], "transact": [79, 93], "my": [79, 93], "revert": [79, 93], "morn": [79, 93], "realis": [79, 93], "yesterdai": [79, 93], "rent": [79, 93], "realli": [79, 85, 91, 93], "tomorrow": [79, 93], "raw_text": [79, 93], "supported_cards_and_curr": [79, 93], "apple_pay_or_google_pai": [79, 93], "card_about_to_expir": [79, 93], "getting_spare_card": [79, 93], "lost_or_stolen_phon": [79, 93], "card_payment_fee_charg": [79, 93], "beneficiary_not_allow": [79, 93], "change_pin": [79, 93], "visa_or_mastercard": [79, 93], "utter": [79, 93], "continu": [79, 81, 82, 85, 90, 92, 93, 94], "suit": [79, 80, 81, 93], "electra": [79, 93], "discrimin": [79, 93], "googl": [79, 93], "text_embed": 79, "No": [79, 81, 93], "google_electra": [79, 93], "pool": [79, 81, 88, 93], "400": [79, 93], "data_dict": [79, 83, 85], "84": [79, 87, 91], "41": [79, 80, 87, 90, 91], "38": [79, 80, 87, 91], "9720": 79, "981": 79, "974": 79, "000150": 79, "982": [79, 80], "000218": 79, "971": 79, "000512": 79, "980": [79, 80], "000947": 79, "3584": 79, "994": 79, "009642": 79, "999": 79, "013067": 79, "013841": 79, "433": 79, "014722": 79, "989": 79, "018224": 79, "6070": 79, "160": [79, 83], "095724": 79, "148": 79, "006237": 79, "546": 79, "099340": 79, "514": 79, "006485": 79, "481": 79, "123416": 79, "008165": 79, "313": [79, 87], "564102": 79, "572258": 79, "28": [79, 80, 82, 83, 85, 91, 94], "574915": 79, "31": [79, 80, 82, 83, 85, 87, 91], "575507": 79, "575874": 79, "658": 79, "659": [79, 90], "660": 79, "661": 79, "0800": 79, "454": 79, "453": 79, "455": 79, "791961": 79, "258508": 79, "699010": 79, "183136": 79, "771112": 79, "to_numpi": [79, 81, 90, 93], "data_with_suggested_label": 79, "suggested_label": 79, "charg": [79, 93], "cash": [79, 93], "holidai": [79, 93], "sent": [79, 93, 94], "card": [79, 80, 93], "mine": [79, 93], "expir": [79, 93], "me": [79, 93], "withdraw": 79, "monei": 79, "whoever": [79, 93], "outlier_issu": [79, 82], "lowest_quality_outli": 79, "OR": 79, "636c65616e6c616220697320617765736f6d6521": 79, "phone": [79, 80], "gone": 79, "gt": [79, 85, 94], "samp": 79, "br": 79, "press": [79, 94], "nonsens": 79, "sens": 79, "detriment": 79, "duplicate_issu": 79, "fee": 79, "pai": 79, "go": [79, 80, 83], "strongli": 79, "p_valu": 79, "benign": 79, "shortlist": [79, 90, 93], "curat": [79, 84], "mnist_test_set": 80, "imagenet_val_set": 80, "tench": 80, "goldfish": 80, "white": [80, 94], "shark": 80, "tiger": 80, "hammerhead": 80, "electr": 80, "rai": 80, "stingrai": 80, "cock": 80, "hen": 80, "ostrich": 80, "brambl": 80, "goldfinch": 80, "hous": 80, "finch": 80, "junco": 80, "indigo": 80, "bunt": 80, "american": [80, 94], "robin": 80, "bulbul": 80, "jai": 80, "magpi": 80, "chickade": 80, "dipper": 80, "kite": 80, "bald": 80, "eagl": 80, "vultur": 80, "grei": 80, "owl": 80, "fire": 80, "salamand": 80, "smooth": 80, "newt": 80, "spot": [80, 87], "axolotl": 80, "bullfrog": 80, "tree": 80, "frog": [80, 88], "tail": 80, "loggerhead": 80, "sea": 80, "turtl": 80, "leatherback": 80, "mud": 80, "terrapin": 80, "band": 80, "gecko": 80, "green": [80, 94], "iguana": 80, "carolina": 80, "anol": 80, "desert": 80, "grassland": 80, "whiptail": 80, "lizard": 80, "agama": 80, "frill": 80, "neck": 80, "allig": 80, "gila": 80, "monster": 80, "european": 80, "chameleon": 80, "komodo": 80, "dragon": 80, "nile": 80, "crocodil": 80, "triceratop": 80, "worm": 80, "snake": 80, "ring": 80, "eastern": 80, "hog": 80, "nose": 80, "kingsnak": 80, "garter": 80, "water": 80, "vine": 80, "night": 80, "boa": 80, "constrictor": 80, "african": 80, "rock": 80, "indian": 80, "cobra": 80, "mamba": 80, "saharan": 80, "horn": 80, "viper": 80, "diamondback": 80, "rattlesnak": 80, "sidewind": 80, "trilobit": 80, "harvestman": 80, "scorpion": 80, "yellow": 80, "garden": 80, "spider": 80, "barn": 80, "southern": 80, "widow": 80, "tarantula": 80, "wolf": 80, "tick": 80, "centiped": 80, "grous": 80, "ptarmigan": 80, "ruf": 80, "prairi": 80, "peacock": 80, "quail": 80, "partridg": 80, "parrot": 80, "macaw": 80, "sulphur": 80, "crest": 80, "cockatoo": 80, "lorikeet": 80, "coucal": 80, "bee": 80, "eater": 80, "hornbil": 80, "hummingbird": 80, "jacamar": 80, "toucan": 80, "duck": [80, 93], "breast": 80, "mergans": 80, "goos": 80, "swan": 80, "tusker": 80, "echidna": 80, "platypu": 80, "wallabi": 80, "koala": 80, "wombat": 80, "jellyfish": 80, "anemon": 80, "brain": 80, "coral": 80, "flatworm": 80, "nematod": 80, "conch": 80, "snail": 80, "slug": 80, "chiton": 80, "chamber": 80, "nautilu": 80, "dung": 80, "crab": 80, "fiddler": 80, "king": 80, "lobster": 80, "spini": 80, "crayfish": 80, "hermit": 80, "isopod": 80, "stork": 80, "spoonbil": 80, "flamingo": 80, "heron": 80, "egret": 80, "bittern": 80, "crane": 80, "bird": [80, 88], "limpkin": 80, "gallinul": 80, "coot": 80, "bustard": 80, "ruddi": 80, "turnston": 80, "dunlin": 80, "redshank": 80, "dowitch": 80, "oystercatch": 80, "pelican": 80, "penguin": 80, "albatross": 80, "whale": 80, "killer": 80, "dugong": 80, "lion": 80, "chihuahua": 80, "japanes": 80, "chin": 80, "maltes": 80, "pekinges": 80, "shih": 80, "tzu": 80, "charl": 80, "spaniel": 80, "papillon": 80, "terrier": 80, "rhodesian": 80, "ridgeback": 80, "afghan": [80, 94], "hound": 80, "basset": 80, "beagl": 80, "bloodhound": 80, "bluetick": 80, "coonhound": 80, "tan": 80, "walker": 80, "foxhound": 80, "redbon": 80, "borzoi": 80, "irish": 80, "wolfhound": 80, "italian": 80, "greyhound": 80, "whippet": 80, "ibizan": 80, "norwegian": 80, "elkhound": 80, "otterhound": 80, "saluki": 80, "scottish": 80, "deerhound": 80, "weimaran": 80, "staffordshir": 80, "bull": 80, "bedlington": 80, "border": 80, "kerri": 80, "norfolk": 80, "norwich": 80, "yorkshir": 80, "wire": 80, "fox": 80, "lakeland": 80, "sealyham": 80, "airedal": 80, "cairn": 80, "australian": 80, "dandi": 80, "dinmont": 80, "boston": 80, "miniatur": 80, "schnauzer": 80, "giant": 80, "tibetan": 80, "silki": 80, "coat": [80, 82], "wheaten": 80, "west": 80, "highland": 80, "lhasa": 80, "apso": 80, "flat": 80, "retriev": 80, "curli": 80, "golden": 80, "labrador": 80, "chesapeak": 80, "bai": 80, "german": [80, 94], "shorthair": 80, "pointer": 80, "vizsla": 80, "setter": 80, "gordon": 80, "brittani": 80, "clumber": 80, "springer": 80, "welsh": 80, "cocker": 80, "sussex": 80, "kuvasz": 80, "schipperk": 80, "groenendael": 80, "malinoi": 80, "briard": 80, "kelpi": 80, "komondor": 80, "sheepdog": 80, "shetland": 80, "colli": 80, "bouvier": 80, "de": 80, "flandr": 80, "rottweil": 80, "shepherd": 80, "dobermann": 80, "pinscher": 80, "swiss": [80, 94], "mountain": 80, "bernes": 80, "appenzel": 80, "sennenhund": 80, "entlebuch": 80, "boxer": 80, "bullmastiff": 80, "mastiff": 80, "french": 80, "bulldog": 80, "dane": 80, "st": 80, "bernard": 80, "huski": 80, "alaskan": 80, "malamut": 80, "siberian": 80, "dalmatian": 80, "affenpinsch": 80, "basenji": 80, "pug": 80, "leonberg": 80, "newfoundland": 80, "pyrenean": 80, "samoi": 80, "pomeranian": 80, "chow": 80, "keeshond": 80, "griffon": 80, "bruxelloi": 80, "pembrok": 80, "corgi": 80, "cardigan": 80, "poodl": 80, "mexican": 80, "hairless": 80, "tundra": 80, "coyot": 80, "dingo": 80, "dhole": 80, "wild": 80, "hyena": 80, "kit": 80, "arctic": 80, "tabbi": 80, "persian": 80, "siames": 80, "egyptian": 80, "mau": 80, "cougar": 80, "lynx": 80, "leopard": 80, "snow": 80, "jaguar": 80, "cheetah": 80, "brown": [80, 91], "bear": 80, "polar": 80, "sloth": 80, "mongoos": 80, "meerkat": 80, "beetl": 80, "ladybug": 80, "longhorn": 80, "leaf": 80, "rhinocero": 80, "weevil": 80, "fly": 80, "ant": 80, "grasshopp": 80, "cricket": 80, "stick": 80, "insect": 80, "cockroach": 80, "manti": 80, "cicada": 80, "leafhopp": 80, "lacew": 80, "dragonfli": 80, "damselfli": 80, "admir": 80, "ringlet": 80, "monarch": 80, "butterfli": 80, "gossam": 80, "wing": 80, "starfish": 80, "urchin": 80, "cucumb": 80, "cottontail": 80, "rabbit": 80, "hare": 80, "angora": 80, "hamster": 80, "porcupin": 80, "squirrel": 80, "marmot": 80, "beaver": 80, "guinea": 80, "pig": 80, "sorrel": 80, "zebra": 80, "boar": 80, "warthog": 80, "hippopotamu": 80, "ox": 80, "buffalo": 80, "bison": 80, "bighorn": 80, "sheep": 80, "alpin": 80, "ibex": 80, "hartebeest": 80, "impala": 80, "gazel": 80, "dromedari": 80, "llama": 80, "weasel": 80, "mink": 80, "polecat": 80, "foot": 80, "ferret": 80, "otter": 80, "skunk": 80, "badger": 80, "armadillo": 80, "toed": 80, "orangutan": 80, "gorilla": 80, "chimpanze": 80, "gibbon": 80, "siamang": 80, "guenon": 80, "pata": 80, "monkei": 80, "baboon": 80, "macaqu": 80, "langur": 80, "colobu": 80, "probosci": 80, "marmoset": 80, "capuchin": 80, "howler": 80, "titi": 80, "geoffroi": 80, "lemur": 80, "indri": 80, "asian": 80, "eleph": 80, "bush": 80, "snoek": 80, "eel": 80, "coho": 80, "salmon": 80, "beauti": 80, "clownfish": 80, "sturgeon": 80, "garfish": 80, "lionfish": 80, "pufferfish": 80, "abacu": 80, "abaya": 80, "academ": 80, "gown": 80, "accordion": 80, "acoust": 80, "guitar": 80, "aircraft": 80, "carrier": 80, "airlin": 80, "airship": 80, "altar": 80, "ambul": 80, "amphibi": 80, "clock": [80, 94], "apiari": 80, "apron": 80, "wast": 80, "assault": 80, "rifl": 80, "backpack": 80, "bakeri": 80, "balanc": 80, "beam": 80, "balloon": 80, "ballpoint": 80, "pen": 80, "aid": 80, "banjo": 80, "balust": 80, "barbel": 80, "barber": 80, "chair": [80, 87], "barbershop": 80, "baromet": 80, "barrel": 80, "wheelbarrow": 80, "basebal": 80, "basketbal": 80, "bassinet": 80, "bassoon": 80, "swim": 80, "cap": 80, "bath": 80, "towel": 80, "bathtub": 80, "station": 80, "wagon": 80, "lighthous": 80, "beaker": 80, "militari": 80, "beer": 80, "bottl": 80, "glass": 80, "bell": 80, "cot": 80, "bib": 80, "bicycl": [80, 91], "bikini": 80, "binder": 80, "binocular": 80, "birdhous": 80, "boathous": 80, "bobsleigh": 80, "bolo": 80, "tie": 80, "poke": 80, "bonnet": 80, "bookcas": 80, "bookstor": 80, "bow": 80, "brass": 80, "bra": 80, "breakwat": 80, "breastplat": 80, "broom": 80, "bucket": 80, "buckl": 80, "bulletproof": 80, "vest": 80, "butcher": 80, "shop": 80, "taxicab": 80, "cauldron": 80, "candl": 80, "cannon": 80, "cano": 80, "mirror": [80, 87], "carousel": 80, "tool": [80, 83, 85], "carton": 80, "wheel": 80, "teller": 80, "cassett": 80, "player": 80, "castl": 80, "catamaran": 80, "cd": 80, "cello": 80, "mobil": [80, 94], "chain": 80, "fenc": [80, 91], "mail": 80, "chainsaw": 80, "chest": 80, "chiffoni": 80, "chime": 80, "china": 80, "cabinet": 80, "christma": 80, "stock": 80, "church": 80, "movi": 80, "theater": 80, "cleaver": 80, "cliff": 80, "dwell": 80, "cloak": 80, "clog": 80, "cocktail": 80, "shaker": 80, "coffe": 80, "mug": 80, "coffeemak": 80, "coil": 80, "lock": 80, "keyboard": 80, "confectioneri": 80, "ship": [80, 88], "corkscrew": 80, "cornet": 80, "cowboi": 80, "boot": 80, "hat": 80, "cradl": 80, "crash": 80, "helmet": 80, "crate": 80, "infant": 80, "bed": 80, "crock": 80, "pot": 80, "croquet": 80, "crutch": 80, "cuirass": 80, "dam": 80, "desk": 80, "desktop": 80, "rotari": 80, "dial": 80, "telephon": 80, "diaper": 80, "watch": 80, "dine": 80, "dishcloth": 80, "dishwash": 80, "disc": 80, "brake": 80, "dock": 80, "sled": 80, "dome": 80, "doormat": 80, "drill": 80, "rig": 80, "drum": 80, "drumstick": 80, "dumbbel": 80, "dutch": 80, "oven": 80, "fan": 80, "locomot": 80, "entertain": 80, "center": 80, "envelop": 80, "espresso": 80, "powder": 80, "feather": 80, "fireboat": 80, "engin": [80, 91], "screen": 80, "sheet": 80, "flagpol": 80, "flute": 80, "footbal": 80, "forklift": 80, "fountain": 80, "poster": 80, "freight": 80, "fry": 80, "pan": 80, "fur": 80, "garbag": 80, "ga": 80, "pump": 80, "goblet": 80, "kart": 80, "golf": 80, "cart": 80, "gondola": 80, "gong": 80, "grand": 80, "piano": 80, "greenhous": 80, "grill": 80, "groceri": 80, "guillotin": 80, "barrett": 80, "hair": 80, "sprai": 80, "hammer": 80, "dryer": 80, "hand": [80, 83], "handkerchief": 80, "drive": 80, "harmonica": 80, "harp": 80, "harvest": 80, "hatchet": 80, "holster": 80, "honeycomb": 80, "hoop": 80, "skirt": 80, "horizont": 80, "bar": 80, "hors": [80, 88, 93], "drawn": 80, "hourglass": 80, "ipod": 80, "cloth": 80, "iron": 80, "jack": 80, "lantern": 80, "jean": 80, "jeep": 80, "shirt": [80, 82], "jigsaw": 80, "puzzl": 80, "pull": 80, "rickshaw": 80, "joystick": 80, "kimono": 80, "knee": 80, "pad": 80, "knot": 80, "ladl": 80, "lampshad": 80, "laptop": 80, "lawn": 80, "mower": 80, "knife": 80, "lifeboat": 80, "lighter": 80, "limousin": 80, "ocean": 80, "liner": 80, "lipstick": 80, "slip": 80, "shoe": 80, "lotion": 80, "speaker": 80, "loup": 80, "sawmil": 80, "magnet": 80, "compass": 80, "bag": [80, 82, 88, 89], "mailbox": 80, "tight": 80, "tank": 80, "manhol": 80, "maraca": 80, "marimba": 80, "maypol": 80, "maze": 80, "cup": [80, 87], "medicin": 80, "megalith": 80, "microphon": 80, "microwav": 80, "milk": 80, "minibu": 80, "miniskirt": 80, "minivan": 80, "missil": 80, "mitten": 80, "mix": 80, "bowl": 80, "modem": 80, "monasteri": 80, "monitor": 80, "mope": 80, "mortar": 80, "mosqu": 80, "mosquito": 80, "scooter": 80, "bike": 80, "tent": 80, "mous": [80, 81], "mousetrap": 80, "van": 80, "muzzl": 80, "nail": 80, "brace": 80, "necklac": 80, "nippl": 80, "obelisk": 80, "obo": 80, "ocarina": 80, "odomet": 80, "oil": 80, "oscilloscop": 80, "overskirt": 80, "bullock": 80, "oxygen": 80, "packet": 80, "paddl": 80, "padlock": 80, "paintbrush": 80, "pajama": 80, "palac": [80, 94], "parachut": 80, "park": 80, "bench": 80, "meter": 80, "passeng": 80, "patio": 80, "payphon": 80, "pedest": 80, "pencil": 80, "perfum": 80, "petri": 80, "dish": 80, "photocopi": 80, "plectrum": 80, "pickelhaub": 80, "picket": 80, "pickup": 80, "pier": 80, "piggi": 80, "pill": 80, "pillow": 80, "ping": 80, "pong": 80, "pinwheel": 80, "pirat": 80, "pitcher": 80, "plane": 80, "planetarium": 80, "plastic": 80, "plate": 80, "rack": 80, "plow": 80, "plunger": 80, "polaroid": 80, "camera": 80, "pole": [80, 91], "polic": 80, "poncho": 80, "billiard": 80, "soda": 80, "potter": 80, "prayer": 80, "rug": 80, "printer": 80, "prison": 80, "projectil": 80, "projector": 80, "hockei": 80, "puck": 80, "punch": 80, "purs": 80, "quill": 80, "quilt": 80, "race": 80, "racket": 80, "radiat": 80, "radio": 80, "telescop": 80, "rain": 80, "recreat": 80, "reel": 80, "reflex": 80, "refriger": 80, "remot": 80, "restaur": 80, "revolv": 80, "rotisseri": 80, "eras": 80, "rugbi": 80, "ruler": 80, "safe": 80, "safeti": 80, "salt": 80, "sandal": [80, 82], "sarong": 80, "saxophon": 80, "scabbard": 80, "school": 80, "bu": [80, 91], "schooner": 80, "scoreboard": 80, "crt": 80, "screw": 80, "screwdriv": 80, "seat": 80, "belt": 80, "sew": 80, "shield": 80, "shoji": 80, "basket": 80, "shovel": 80, "shower": 80, "curtain": 80, "ski": 80, "sleep": 80, "door": 80, "slot": 80, "snorkel": 80, "snowmobil": 80, "snowplow": 80, "soap": 80, "dispens": 80, "soccer": [80, 94], "sock": 80, "solar": 80, "thermal": 80, "collector": 80, "sombrero": 80, "soup": 80, "heater": 80, "shuttl": 80, "spatula": 80, "motorboat": 80, "web": 80, "spindl": 80, "sport": [80, 94], "spotlight": 80, "stage": 80, "steam": 80, "arch": 80, "bridg": 80, "steel": 80, "stethoscop": 80, "scarf": 80, "stone": 80, "wall": [80, 91], "stopwatch": 80, "stove": 80, "strainer": 80, "tram": 80, "stretcher": 80, "couch": 80, "stupa": 80, "submarin": 80, "sundial": 80, "sunglass": 80, "sunscreen": 80, "suspens": 80, "mop": 80, "sweatshirt": 80, "swimsuit": 80, "swing": 80, "switch": 80, "syring": 80, "lamp": 80, "tape": 80, "teapot": 80, "teddi": 80, "televis": [80, 94], "tenni": 80, "thatch": 80, "roof": 80, "front": 80, "thimbl": 80, "thresh": 80, "throne": 80, "tile": 80, "toaster": 80, "tobacco": 80, "toilet": 80, "totem": 80, "tow": 80, "tractor": 80, "semi": 80, "trailer": 80, "trai": 80, "trench": 80, "tricycl": 80, "trimaran": 80, "tripod": 80, "triumphal": 80, "trolleybu": 80, "trombon": 80, "tub": 80, "turnstil": 80, "typewrit": 80, "umbrella": 80, "unicycl": 80, "upright": 80, "vacuum": 80, "cleaner": 80, "vase": 80, "vault": 80, "velvet": 80, "vend": 80, "vestment": 80, "viaduct": 80, "violin": 80, "volleybal": 80, "waffl": 80, "wallet": 80, "wardrob": 80, "sink": 80, "wash": 80, "jug": 80, "tower": 80, "whiskei": 80, "whistl": 80, "wig": 80, "shade": [80, 91], "windsor": 80, "wine": 80, "wok": 80, "wooden": 80, "spoon": 80, "wool": 80, "rail": 80, "shipwreck": 80, "yawl": 80, "yurt": 80, "websit": 80, "comic": 80, "book": 80, "crossword": 80, "traffic": [80, 87, 91], "sign": [80, 91, 94], "dust": 80, "jacket": [80, 87], "menu": 80, "guacamol": 80, "consomm": 80, "trifl": 80, "ic": 80, "cream": 80, "pop": 80, "baguett": 80, "bagel": 80, "pretzel": 80, "cheeseburg": 80, "mash": 80, "potato": 80, "cabbag": 80, "broccoli": 80, "cauliflow": 80, "zucchini": 80, "spaghetti": 80, "squash": 80, "acorn": 80, "butternut": 80, "artichok": 80, "pepper": 80, "cardoon": 80, "mushroom": 80, "granni": 80, "smith": 80, "strawberri": 80, "orang": 80, "lemon": 80, "pineappl": 80, "banana": 80, "jackfruit": 80, "custard": 80, "appl": 80, "pomegran": 80, "hai": 80, "carbonara": 80, "chocol": 80, "syrup": 80, "dough": 80, "meatloaf": 80, "pizza": 80, "pie": 80, "burrito": 80, "eggnog": 80, "alp": 80, "bubbl": 80, "reef": 80, "geyser": 80, "lakeshor": 80, "promontori": 80, "shoal": 80, "seashor": 80, "vallei": 80, "volcano": 80, "bridegroom": 80, "scuba": 80, "diver": 80, "rapese": 80, "daisi": 80, "ladi": 80, "slipper": 80, "corn": 80, "rose": 80, "hip": 80, "chestnut": 80, "fungu": 80, "agar": 80, "gyromitra": 80, "stinkhorn": 80, "earth": 80, "star": 80, "wood": 80, "bolet": 80, "ear": 80, "cifar10_test_set": 80, "airplan": [80, 88], "automobil": [80, 88], "deer": [80, 88], "cifar100_test_set": 80, "aquarium_fish": 80, "babi": 80, "boi": 80, "camel": 80, "caterpillar": 80, "cattl": [80, 94], "cloud": 80, "dinosaur": 80, "dolphin": 80, "flatfish": 80, "forest": 80, "girl": 80, "kangaroo": 80, "lawn_mow": 80, "man": 80, "maple_tre": 80, "motorcycl": [80, 91], "oak_tre": 80, "orchid": 80, "palm_tre": 80, "pear": 80, "pickup_truck": 80, "pine_tre": 80, "plain": 80, "poppi": 80, "possum": 80, "raccoon": 80, "road": [80, 91], "rocket": 80, "seal": 80, "shrew": 80, "skyscrap": 80, "streetcar": 80, "sunflow": 80, "sweet_pepp": 80, "trout": 80, "tulip": 80, "willow_tre": 80, "woman": [80, 87], "caltech256": 80, "ak47": 80, "bat": 80, "glove": 80, "birdbath": 80, "blimp": 80, "bonsai": 80, "boom": 80, "breadmak": 80, "buddha": 80, "bulldoz": 80, "cactu": 80, "cake": 80, "tire": 80, "cartman": 80, "cereal": 80, "chandeli": 80, "chess": 80, "board": 80, "chimp": 80, "chopstick": 80, "coffin": 80, "coin": 80, "comet": 80, "cormor": 80, "globe": 80, "diamond": 80, "dice": 80, "doorknob": 80, "drink": 80, "straw": 80, "dumb": 80, "eiffel": 80, "elk": 80, "ewer": 80, "eyeglass": 80, "fern": 80, "fighter": 80, "jet": [80, 90], "extinguish": 80, "hydrant": 80, "firework": 80, "flashlight": 80, "floppi": 80, "fri": 80, "frisbe": 80, "galaxi": 80, "giraff": 80, "goat": 80, "gate": 80, "grape": 80, "pick": [80, 81], "hamburg": 80, "hammock": 80, "harpsichord": 80, "hawksbil": 80, "helicopt": 80, "hibiscu": 80, "homer": 80, "simpson": 80, "horsesho": 80, "air": 80, "skeleton": 80, "ibi": 80, "cone": 80, "iri": 80, "jesu": 80, "christ": 80, "joi": 80, "kayak": 80, "ketch": 80, "ladder": 80, "lath": 80, "licens": 80, "lightbulb": 80, "lightn": 80, "mandolin": 80, "mar": 80, "mattress": 80, "megaphon": 80, "menorah": 80, "microscop": 80, "minaret": 80, "minotaur": 80, "motorbik": 80, "mussel": 80, "neckti": 80, "octopu": 80, "palm": 80, "pilot": 80, "paperclip": 80, "shredder": 80, "pci": 80, "peopl": [80, 87], "pez": 80, "picnic": 80, "pram": 80, "prai": 80, "pyramid": 80, "rainbow": 80, "roulett": 80, "saddl": 80, "saturn": 80, "segwai": 80, "propel": 80, "sextant": 80, "music": 80, "skateboard": 80, "smokestack": 80, "sneaker": 80, "boat": 80, "stain": 80, "steer": 80, "stirrup": 80, "superman": 80, "sushi": 80, "armi": [80, 94], "sword": 80, "tambourin": 80, "teepe": 80, "court": 80, "theodolit": 80, "tomato": 80, "tombston": 80, "tour": 80, "pisa": 80, "treadmil": 80, "fork": 80, "tweezer": 80, "unicorn": 80, "vcr": 80, "waterfal": 80, "watermelon": 80, "weld": 80, "windmil": 80, "xylophon": 80, "yarmulk": 80, "yo": 80, "toad": 80, "twenty_news_test_set": 80, "alt": 80, "atheism": 80, "comp": 80, "graphic": [80, 91], "misc": [80, 94], "sy": 80, "ibm": 80, "pc": 80, "hardwar": 80, "mac": 80, "forsal": 80, "rec": 80, "sci": 80, "crypt": 80, "electron": 80, "med": 80, "soc": 80, "religion": 80, "christian": [80, 94], "talk": [80, 94], "polit": 80, "gun": 80, "mideast": 80, "amazon": 80, "neutral": 80, "imdb_test_set": 80, "all_class": 80, "20news_test_set": 80, "_load_classes_predprobs_label": 80, "dataset_nam": 80, "labelerror": 80, "url_bas": 80, "5392f6c71473055060be3044becdde1cbc18284d": 80, "url_label": 80, "original_test_label": 80, "_original_label": 80, "url_prob": 80, "cross_validated_predicted_prob": 80, "_pyx": 80, "num_part": 80, "datatset": 80, "bytesio": 80, "allow_pickl": 80, "pred_probs_part": 80, "url": 80, "_of_": 80, "nload": 80, "imdb": 80, "ve": [80, 81, 83, 85, 87], "interpret": [80, 81, 83], "capit": 80, "29780": 80, "256": [80, 81, 87], "780": 80, "medic": [80, 94], "doctor": 80, "254": [80, 87], "359223": 80, "640777": 80, "184": [80, 83], "258427": 80, "341176": 80, "263158": 80, "658824": 80, "337349": 80, "246575": 80, "662651": 80, "248": 80, "330000": 80, "355769": 80, "670000": 80, "251": [80, 87], "167": [80, 83, 87], "252": 80, "112": 80, "253": [80, 87], "022989": 80, "255": [80, 82], "049505": 80, "190": [80, 83, 87], "66": [80, 91], "002216": 80, "000974": 80, "59": [80, 82, 87, 91], "88": [80, 82, 83, 86, 87, 90, 91], "000873": 80, "000739": 80, "79": [80, 87, 91, 92], "32635": 80, "32636": 80, "47": [80, 87, 91], "32637": 80, "32638": 80, "32639": 80, "32640": 80, "051": 80, "002242": 80, "997758": 80, "002088": 80, "001045": 80, "997912": 80, "002053": 80, "997947": 80, "001980": 80, "000991": 80, "998020": 80, "001946": 80, "002915": 80, "998054": 80, "001938": 80, "002904": 80, "998062": 80, "001020": 80, "998980": 80, "001018": 80, "002035": 80, "998982": 80, "999009": 80, "0003": 80, "0002": 80, "36": [80, 82, 91, 94], "44": [80, 86, 87, 91], "71": [80, 83, 87, 91], "071": 80, "067269": 80, "929": 80, "046": 80, "058243": 80, "954": 80, "035": 80, "032096": 80, "965": 80, "031": 80, "012232": 80, "969": 80, "022": 80, "025896": 80, "978": 80, "020": [80, 83], "013092": 80, "018": 80, "013065": 80, "016": 80, "030542": 80, "984": 80, "013": 80, "020833": 80, "987": 80, "012": 80, "010020": 80, "988": 80, "0073": 80, "0020": 80, "0016": 80, "0015": 80, "0013": 80, "0012": 80, "0010": 80, "0008": 80, "0007": 80, "0006": 80, "0005": 80, "0004": 80, "244": [80, 87, 94], "98": [80, 81, 90, 91], "452381": 80, "459770": 80, "72": [80, 83, 86, 90, 91], "523364": 80, "460784": 80, "446602": 80, "57": [80, 82, 83, 91], "68": [80, 82, 83, 87, 91, 92], "103774": 80, "030612": 80, "97": [80, 81, 83, 87, 90, 91, 92, 94], "110092": 80, "049020": 80, "99": [80, 83, 91, 92], "0034": 80, "0032": 80, "0026": 80, "0025": 80, "4945": 80, "4946": 80, "4947": 80, "4948": 80, "4949": 80, "4950": 80, "846": 80, "82": [80, 82, 83, 87, 91], "7532": 80, "532": 80, "034483": 80, "009646": 80, "965517": 80, "030457": 80, "020513": 80, "969543": 80, "028061": 80, "035443": 80, "971939": 80, "025316": 80, "005168": 80, "974684": 80, "049751": 80, "979487": 80, "019920": 80, "042802": 80, "980080": 80, "017677": 80, "005115": 80, "982323": 80, "012987": 80, "005236": 80, "987013": 80, "012723": 80, "025126": 80, "987277": 80, "010989": 80, "008264": 80, "989011": 80, "010283": 80, "027778": 80, "989717": 80, "009677": 80, "990323": 80, "007614": 80, "010127": 80, "992386": 80, "005051": 80, "994949": 80, "005025": 80, "994975": 80, "005013": 80, "994987": 80, "001859": 80, "001328": 80, "000929": 80, "000664": 80, "186": [80, 83], "188": [80, 83, 86], "189": [80, 83], "snippet": 81, "nlp": [81, 94], "mind": [81, 83], "number_of_class": 81, "total_number_of_data_point": 81, "drop": [81, 85, 90, 93], "feed": 81, "alphabet": 81, "labels_proper_format": 81, "your_classifi": 81, "issues_datafram": 81, "class_predicted_for_flagged_exampl": 81, "class_predicted_for_all_exampl": 81, "grant": 81, "datataset": 81, "fair": [81, 83], "game": 81, "speedup": [81, 88], "flexibl": 81, "tempfil": 81, "mkdtemp": 81, "sped": 81, "anywai": 81, "pred_probs_merg": 81, "merge_rare_class": 81, "count_threshold": 81, "class_mapping_orig2new": 81, "heath_summari": 81, "num_examples_per_class": 81, "rare_class": 81, "num_classes_merg": 81, "other_class": 81, "labels_merg": 81, "new_c": 81, "merged_prob": 81, "hstack": [81, 82, 83, 85], "new_class": 81, "original_class": 81, "num_check": 81, "ones_array_ref": 81, "isclos": 81, "though": [81, 83, 94], "successfulli": 81, "meaning": [81, 88], "virtuou": [81, 85], "cycl": [81, 85], "jointli": 81, "junk": 81, "clutter": 81, "unknown": 81, "caltech": 81, "combined_boolean_mask": 81, "mask1": 81, "mask2": 81, "gradientboostingclassifi": [81, 83], "true_error": [81, 83, 86], "101": [81, 87], "102": [81, 86, 87], "104": [81, 83, 87], "model_to_find_error": 81, "model_to_return": 81, "cl0": 81, "randomizedsearchcv": 81, "expens": 81, "param_distribut": 81, "learning_r": [81, 83], "max_depth": [81, 83], "magnitud": 81, "coeffici": [81, 90], "optin": 81, "environ": [81, 83], "rerun": [81, 83], "cell": [81, 83], "On": [81, 83, 87], "unabl": [81, 83], "render": [81, 83], "nbviewer": [81, 83], "cleanlearningcleanlearn": [81, 83], "linearregressionlinearregress": 81, "n_init": 81, "fit_predict": 81, "continuous_column": 81, "categorical_column": 81, "data_df": 81, "feature_a": 81, "feature_b": 81, "unexpectedli": 81, "emphas": 81, "especi": [81, 82, 90, 92, 93], "crucial": 81, "merge_duplicate_set": 81, "merge_kei": 81, "construct_group_kei": 81, "merged_set": 81, "consolidate_set": 81, "tolist": [81, 86], "issubset": 81, "frozenset": 81, "sets_list": 81, "mutabl": 81, "new_set": 81, "current_set": 81, "intersecting_set": 81, "lowest_score_strategi": 81, "sub_df": 81, "idxmin": 81, "filter_near_dupl": 81, "strategy_fn": 81, "strategy_kwarg": 81, "duplicate_row": 81, "group_kei": 81, "to_keep_indic": 81, "groupbi": 81, "explod": 81, "to_remov": 81, "isin": [81, 88], "kept": 81, "near_duplicate_issu": [81, 82], "ids_to_remove_seri": 81, "assist": 81, "streamlin": 81, "ux": 81, "agpl": 81, "compani": 81, "commerci": 81, "alter": 81, "email": 81, "discuss": 81, "anywher": 81, "profession": 81, "expert": 81, "60": [82, 83, 91], "excess": 82, "torchvis": [82, 88], "tensordataset": 82, "stratifiedkfold": [82, 86], "tqdm": 82, "fashion_mnist": 82, "num_row": 82, "60000": 82, "pil": 82, "transformed_dataset": 82, "with_format": 82, "unsqueez": 82, "cpu_count": 82, "torch_dataset": 82, "quick": [82, 86], "relu": 82, "batchnorm2d": 82, "maxpool2d": 82, "lazylinear": 82, "flatten": 82, "get_test_accuraci": 82, "testload": [82, 88], "energi": 82, "trainload": [82, 88], "n_epoch": 82, "patienc": 82, "criterion": 82, "crossentropyloss": 82, "adamw": 82, "best_test_accuraci": 82, "start_epoch": 82, "running_loss": 82, "best_epoch": 82, "end_epoch": 82, "3f": [82, 90], "acc": [82, 83], "time_taken": 82, "compute_embed": 82, "compute_pred_prob": 82, "train_batch_s": 82, "num_work": 82, "worker": [82, 94], "train_id_list": 82, "test_id_list": 82, "train_id": 82, "test_id": 82, "embeddings_model": 82, "ntrain": 82, "trainset": 82, "testset": 82, "pin_memori": 82, "fold_embed": 82, "fold_pred_prob": 82, "finish": 82, "483": 82, "835": 82, "560": [82, 90], "331": 82, "310": 82, "349": 82, "stderr": [82, 91], "sphinxverbatim": [82, 91, 94], "97it": 82, "20it": 82, "18it": [82, 91], "62": [82, 83, 87, 90, 91], "74it": [82, 91], "69": [82, 83, 90, 91], "32it": [82, 91], "65it": [82, 91], "01it": [82, 91], "14it": [82, 91], "51it": [82, 91], "34it": [82, 91], "31it": [82, 91], "63": [82, 83, 87, 91], "76it": [82, 91], "492": 82, "085": 82, "550": 82, "290": [82, 87], "337": [82, 87, 93], "95it": [82, 91], "82it": [82, 91], "69it": [82, 91], "40it": [82, 91], "33it": [82, 91], "06it": [82, 91], "93it": [82, 91], "07it": [82, 91], "78it": [82, 91], "476": 82, "305": [82, 90], "749": 82, "328": [82, 87], "335": 82, "346": 82, "26it": [82, 91], "24it": [82, 91], "49it": [82, 91], "00it": 82, "84it": [82, 91], "59it": [82, 91], "63it": [82, 91], "02it": [82, 91], "71it": [82, 91], "reorder": 82, "vision": 82, "grayscal": 82, "exce": 82, "max_preval": 82, "7620": 82, "3692": 82, "3521": 82, "225": [82, 86], "166": 82, "3691": 82, "40378": 82, "943831e": 82, "54473": 82, "066211e": 82, "06": [82, 83, 87, 91, 94], "29412": 82, "899069e": 82, "25316": 82, "984817e": 82, "52247": 82, "245879e": 82, "9581": 82, "19228": 82, "dress": 82, "54078": 82, "000010": 82, "pullov": 82, "32657": 82, "21282": 82, "000011": 82, "11262": 82, "000014": 82, "6294": 82, "30659": 82, "000798": 82, "30968": 82, "000015": 82, "258": 82, "000907": 82, "9762": 82, "54565": 82, "47139": 82, "000017": 82, "001423": 82, "000026": 82, "39992": 82, "39993": 82, "39994": 82, "39995": 82, "7834": 82, "42819": 82, "629362": 82, "51431": 82, "654330": 82, "55548": 82, "658364": 82, "51191": 82, "668572": 82, "50081": 82, "669703": 82, "7834321613629787": 82, "13732": 82, "13733": 82, "13734": 82, "47635": 82, "110901": 82, "974390": 82, "998733": 82, "937117": 82, "998755": 82, "53564": 82, "5473": 82, "trouser": 82, "plot_label_issue_exampl": 82, "ncol": [82, 88], "nrow": [82, 88], "ceil": 82, "axes_list": 82, "label_issue_indic": 82, "gl": 82, "sl": 82, "fontdict": 82, "imshow": [82, 88], "cmap": [82, 90], "grai": 82, "subplots_adjust": 82, "hspace": 82, "outsiz": 82, "outlier_issues_df": 82, "depict": [82, 86, 87, 88, 89, 91], "plot_outlier_issues_exampl": 82, "n_comparison_imag": 82, "sample_from_class": 82, "number_of_sampl": 82, "non_outlier_indic": 82, "isnul": 82, "non_outlier_indices_excluding_curr": 82, "sampled_indic": 82, "label_scores_of_sampl": 82, "top_score_indic": 82, "top_label_indic": 82, "sampled_imag": 82, "get_image_given_label_and_sampl": 82, "image_from_dataset": 82, "corresponding_label": 82, "comparison_imag": 82, "images_to_plot": 82, "idlist": 82, "iterrow": 82, "closest": 82, "counterpart": 82, "near_duplicate_issues_df": 82, "plot_near_duplicate_issue_exampl": 82, "seen_id_pair": 82, "get_image_and_given_label_and_predicted_label": 82, "duplicate_imag": 82, "nd_set": 82, "challeng": 82, "dark_issu": 82, "reveal": [82, 91], "dark_scor": 82, "dark_issues_df": 82, "is_dark_issu": 82, "34848": 82, "203922": 82, "50270": 82, "204588": 82, "3936": 82, "213098": 82, "733": 82, "217686": 82, "8094": 82, "230118": 82, "plot_image_issue_exampl": 82, "difficult": 82, "disproportion": 82, "lowinfo_issu": 82, "low_information_scor": 82, "lowinfo_issues_df": 82, "is_low_information_issu": 82, "53050": 82, "067975": 82, "40875": 82, "089929": 82, "9594": 82, "092601": 82, "34825": 82, "107744": 82, "37530": 82, "108516": 82, "lot": 82, "depth": 83, "survei": [83, 94], "focus": [83, 85], "scienc": 83, "multivariate_norm": [83, 85, 86], "make_data": [83, 85], "cov": [83, 85, 86], "avg_trac": [83, 86], "test_label": [83, 86, 88, 93], "py_tru": 83, "noise_matrix_tru": 83, "noise_marix": 83, "s_test": 83, "noisy_test_label": 83, "purpl": 83, "val": 83, "namespac": 83, "exec": 83, "markerfacecolor": [83, 86], "markeredgecolor": [83, 86, 90], "markers": [83, 86, 90], "markeredgewidth": [83, 86, 90], "realist": 83, "7560": 83, "638483e": 83, "897052e": 83, "548986e": 83, "924634e": 83, "374580e": 83, "3454": 83, "014051": 83, "020451": 83, "249": [83, 87], "042594": 83, "043859": 83, "045954": 83, "6120": 83, "023714": 83, "007136": 83, "119": [83, 87], "107266": 83, "103": [83, 87], "033738": 83, "238": [83, 87], "119505": 83, "236": [83, 87], "037843": 83, "222": 83, "614915": 83, "122": [83, 87], "624422": 83, "625965": 83, "626079": 83, "118": 83, "627675": 83, "158": 83, "159": [83, 86, 87], "161": 83, "1960": 83, "196": [83, 87], "223": [83, 87], "221": 83, "219": [83, 87], "695174": 83, "323529": 83, "522929": 83, "013722": 83, "675606": 83, "646438": 83, "anyth": 83, "enhanc": [83, 85, 87], "magic": 83, "83": [83, 87, 90, 91, 92, 94], "liter": 83, "identif": 83, "x27": 83, "logisticregressionlogisticregress": 83, "ever": 83, "092": 83, "040": 83, "024": 83, "004": 83, "surpris": 83, "1705": 83, "01936": 83, "ton": 83, "yourfavoritemodel1": 83, "merged_label": 83, "merged_test_label": 83, "newli": [83, 85], "yourfavoritemodel2": 83, "yourfavoritemodel3": 83, "cl3": 83, "takeawai": 83, "That": [83, 86], "randomli": 83, "my_test_pred_prob": 83, "my_test_pr": 83, "issues_test": 83, "corrected_test_label": 83, "pretend": 83, "cl_test_pr": 83, "fairli": 83, "label_acc": 83, "percentag": 83, "offset": 83, "nquestion": 83, "overestim": 83, "answer": 83, "experienc": 83, "76": [83, 86, 87, 90, 91, 92], "knowledg": 83, "quantiti": [83, 90], "prioiri": 83, "known": 83, "versatil": 83, "label_issues_indic": 83, "213": [83, 87], "212": [83, 92], "218": [83, 87], "152": 83, "197": [83, 87], "170": 83, "214": 83, "164": [83, 86], "198": [83, 87], "191": [83, 87], "121": [83, 93], "117": [83, 90], "206": [83, 87], "115": [83, 87], "193": 83, "194": 83, "201": [83, 87], "174": 83, "163": 83, "150": [83, 85, 87], "169": 83, "151": [83, 87], "168": 83, "precision_scor": 83, "recall_scor": 83, "f1_score": 83, "true_label_issu": 83, "filter_by_list": 83, "718750": [83, 85], "807018": 83, "912": 83, "733333": 83, "800000": 83, "721311": 83, "792793": 83, "908": 83, "676923": 83, "765217": 83, "892": 83, "567901": 83, "702290": 83, "844": 83, "gaug": 83, "label_issues_count": 83, "155": [83, 87], "172": [83, 86], "easiest": 83, "modular": 83, "penalti": 83, "l2": 83, "model3": 83, "n_estim": 83, "cv_pred_probs_1": 83, "cv_pred_probs_2": 83, "cv_pred_probs_3": 83, "label_quality_scores_best": 83, "cv_pred_probs_ensembl": 83, "label_quality_scores_bett": 83, "superior": [83, 89], "workflow": [84, 90], "speechbrain": 84, "timm": 84, "glad": 85, "multiannotator_label": 85, "noisier": 85, "111": [85, 90], "local_data": [85, 86], "true_labels_train": [85, 86], "noise_matrix_bett": 85, "noise_matrix_wors": 85, "transpos": [85, 88], "dropna": 85, "zfill": 85, "row_na_check": 85, "notna": 85, "reset_index": 85, "a0001": 85, "a0002": 85, "a0003": 85, "a0004": 85, "a0005": 85, "a0006": 85, "a0007": 85, "a0008": 85, "a0009": 85, "a0010": 85, "a0041": 85, "a0042": 85, "a0043": 85, "a0044": 85, "a0045": 85, "a0046": 85, "a0047": 85, "a0048": 85, "a0049": 85, "a0050": 85, "60856743": 85, "41693214": 85, "40908785": 85, "87147629": 85, "64941785": 85, "10774851": 85, "0524466": 85, "71853246": 85, "37169848": 85, "66031048": 85, "multiannotator_util": 85, "crude": 85, "straight": 85, "majority_vote_label": 85, "736157": 85, "757738": 85, "782255": 85, "715585": 85, "824273": 85, "quality_annotator_a0001": 85, "quality_annotator_a0002": 85, "quality_annotator_a0003": 85, "quality_annotator_a0004": 85, "quality_annotator_a0005": 85, "quality_annotator_a0006": 85, "quality_annotator_a0007": 85, "quality_annotator_a0008": 85, "quality_annotator_a0009": 85, "quality_annotator_a0010": 85, "quality_annotator_a0041": 85, "quality_annotator_a0042": 85, "quality_annotator_a0043": 85, "quality_annotator_a0044": 85, "quality_annotator_a0045": 85, "quality_annotator_a0046": 85, "quality_annotator_a0047": 85, "quality_annotator_a0048": 85, "quality_annotator_a0049": 85, "quality_annotator_a0050": 85, "070551": 85, "216064": 85, "119178": 85, "alongisd": 85, "244982": 85, "208333": 85, "295978": 85, "294118": 85, "324194": 85, "310345": 85, "355315": 85, "346154": 85, "439728": 85, "480000": 85, "a0031": 85, "523205": 85, "580645": 85, "a0034": 85, "535313": 85, "607143": 85, "a0021": 85, "607002": 85, "a0015": 85, "609527": 85, "678571": 85, "a0011": 85, "621101": 85, "692308": 85, "wors": 85, "improved_consensus_label": 85, "majority_vote_accuraci": 85, "cleanlab_label_accuraci": 85, "8581081081081081": 85, "9797297297297297": 85, "besid": 85, "sorted_consensus_quality_scor": 85, "worst_qual": 85, "better_qu": 85, "worst_quality_accuraci": 85, "better_quality_accuraci": 85, "9893238434163701": 85, "improved_pred_prob": 85, "treat": [85, 86, 90, 94], "analzi": 85, "copyright": 86, "advertis": 86, "violenc": 86, "nsfw": 86, "ranked_label_issu": [86, 92, 93], "multioutput": 86, "multioutputclassifi": 86, "celeba": 86, "make_multilabel_data": 86, "boxes_coordin": 86, "box_multilabel": 86, "make_multi": 86, "bx1": 86, "by1": 86, "bx2": 86, "by2": 86, "label_list": 86, "ur": 86, "upper": 86, "inidx": 86, "logical_and": 86, "inv_d": 86, "labels_idx": 86, "true_labels_test": 86, "dict_unique_label": 86, "get_color_arrai": 86, "dcolor": 86, "aa4400": 86, "55227f": 86, "55a100": 86, "00ff00": 86, "007f7f": 86, "386b55": 86, "0000ff": 86, "simplic": 86, "advis": 86, "y_onehot": 86, "single_class_label": 86, "stratifi": [86, 89], "kf": 86, "train_index": 86, "test_index": 86, "clf_cv": 86, "x_train_cv": 86, "x_test_cv": 86, "y_train_cv": 86, "y_test_cv": 86, "y_pred_cv": 86, "saw": 86, "num_to_displai": 86, "09": [86, 87, 91], "275": 86, "267": 86, "171": 86, "234": 86, "165": 86, "227": [86, 87], "262": [86, 87], "263": [86, 87], "266": [86, 87], "139": 86, "143": [86, 87], "216": [86, 87, 94], "265": 86, "despit": [86, 94], "suspect": 86, "888": 86, "8224": 86, "9632": 86, "968": 86, "6512": 86, "0444": 86, "774": 86, "labels_binary_format": 86, "labels_list_format": 86, "surround": 87, "scene": 87, "coco": 87, "everydai": 87, "has_label_issu": 87, "insal": 87, "nc": [87, 91, 94], "s3": [87, 91, 94], "amazonaw": [87, 91, 94], "objectdetectionbenchmark": 87, "tutorial_obj": 87, "pkl": 87, "example_imag": 87, "unzip": [87, 94], "begin": 87, "detectron2": 87, "image_path": 87, "rb": 87, "image_to_visu": 87, "seg_map": 87, "334": 87, "float32": 87, "bboxes_ignor": 87, "286": 87, "285": 87, "224": 87, "231": [87, 94], "293": 87, "235": 87, "289": [87, 90], "282": 87, "74": [87, 90, 91, 92], "281": 87, "271": 87, "280": 87, "277": 87, "279": 87, "287": 87, "299": 87, "276": 87, "307": 87, "321": 87, "326": 87, "333": 87, "261": 87, "319": 87, "257": 87, "295": 87, "283": 87, "243": 87, "303": 87, "316": 87, "247": 87, "323": 87, "327": 87, "226": 87, "228": 87, "232": 87, "239": 87, "240": 87, "209": 87, "242": 87, "202": 87, "230": 87, "215": 87, "220": 87, "229": 87, "85": [87, 90, 91], "217": [87, 94], "237": 87, "207": 87, "204": 87, "205": 87, "153": 87, "149": 87, "140": 87, "124": 87, "268": 87, "273": 87, "108": 87, "284": 87, "110": 87, "136": 87, "145": 87, "173": 87, "297": 87, "317": 87, "192": 87, "329": 87, "332": 87, "324": 87, "203": 87, "320": 87, "314": 87, "199": 87, "291": 87, "000000481413": 87, "jpg": 87, "42398": 87, "44503": 87, "29968": 87, "336": 87, "21005": 87, "9978472": 87, "forgot": 87, "drew": 87, "label_issue_idx": 87, "num_examples_to_show": 87, "113": [87, 90], "candid": 87, "97489622": 87, "70610878": 87, "98764951": 87, "88899237": 87, "99085805": 87, "issue_idx": 87, "95569726e": 87, "03354841e": 87, "57510169e": 87, "58447666e": 87, "39755858e": 87, "suppli": 87, "issue_to_visu": 87, "000000009483": 87, "95569726168054e": 87, "addition": [87, 91], "visibl": 87, "missmatch": 87, "likelei": 87, "agnost": 87, "vaidat": 87, "inconsist": 87, "000000395701": 87, "033548411774308e": 87, "armchair": 87, "tv": 87, "000000154004": 87, "38300759625496356": 87, "foreground": 87, "000000448410": 87, "0008575101690203273": 87, "crowd": 87, "alon": 87, "explor": [87, 88], "resembl": [87, 88], "000000499768": 87, "9748962231208227": 87, "000000521141": 87, "8889923658893665": 87, "000000143931": 87, "9876495074395956": 87, "train_feature_embed": 88, "ood_train_feature_scor": 88, "test_feature_embed": 88, "ood_test_feature_scor": 88, "ood_train_predictions_scor": 88, "train_pred_prob": 88, "ood_test_predictions_scor": 88, "test_pred_prob": 88, "pylab": 88, "rcparam": 88, "baggingclassifi": 88, "therebi": 88, "rescal": 88, "transform_norm": 88, "totensor": 88, "root": 88, "animal_class": 88, "non_animal_class": 88, "animal_idx": 88, "test_idx": 88, "toronto": 88, "edu": 88, "kriz": 88, "5000": 88, "plot_imag": 88, "visualize_outli": 88, "txt_class": 88, "img": [88, 90], "npimg": 88, "show_label": 88, "data_subset": 88, "resnet50": 88, "corpu": 88, "2048": 88, "embed_imag": 88, "create_model": 88, "rwightman": 88, "v0": 88, "rsb": 88, "resnet50_a1_0": 88, "14fe96d1": 88, "pth": 88, "checkpoint": 88, "strang": 88, "odd": 88, "train_ood_features_scor": 88, "top_train_ood_features_idx": 88, "fun": 88, "negat": 88, "homogen": 88, "bottom_train_ood_features_idx": 88, "test_ood_features_scor": 88, "top_ood_features_idx": 88, "inevit": 88, "trade": 88, "5th": 88, "percentil": 88, "fifth_percentil": 88, "plt_rang": 88, "hist": 88, "train_outlier_scor": 88, "ylabel": 88, "axvlin": 88, "test_outlier_scor": 88, "ood_features_indic": 88, "revisit": 88, "unusu": 88, "return_invers": 88, "train_feature_embeddings_sc": 88, "test_feature_embeddings_sc": 88, "train_pred_label": 88, "9702": 88, "train_ood_predictions_scor": 88, "test_ood_predictions_scor": 88, "mainli": [88, 94], "lost": 88, "unsuit": 89, "ok": [89, 94], "convention": 89, "aforement": 89, "hypothet": 89, "contrast": 89, "tradit": 89, "disjoint": 89, "out_of_sample_pred_probs_for_a": 89, "out_of_sample_pred_probs_for_b": 89, "out_of_sample_pred_probs_for_c": 89, "out_of_sample_pred_prob": 89, "price": 90, "incom": 90, "ag": 90, "histgradientboostingregressor": 90, "r2_score": 90, "student_grades_r": 90, "final_scor": 90, "true_final_scor": 90, "homework": 90, "3d": 90, "hue": 90, "mpl_toolkit": 90, "mplot3d": 90, "axes3d": 90, "errors_idx": 90, "add_subplot": 90, "z": 90, "colorbar": 90, "errors_mask": 90, "feature_column": 90, "predicted_column": 90, "x_train_raw": 90, "x_test_raw": 90, "categorical_featur": [90, 92], "randomforestregressor": 90, "629763": 90, "521450": 90, "954607": 90, "547234": 90, "338296": 90, "754531": 90, "619090": 90, "312295": 90, "806626": 90, "784048": 90, "identified_issu": [90, 93], "367": 90, "318": 90, "688": 90, "657": 90, "view_datapoint": 90, "concat": 90, "consum": [90, 93], "baseline_model": [90, 93], "preds_og": 90, "r2_og": 90, "838": 90, "robustli": [90, 92, 93], "acceler": [90, 93], "found_label_issu": 90, "preds_cl": 90, "r2_cl": 90, "925": 90, "effort": [90, 92, 93], "favorit": 90, "13091885": 90, "48412548": 90, "00695165": 90, "44421119": 90, "43029854": 90, "synthia": 91, "imagesegment": 91, "given_mask": 91, "predicted_mask": 91, "set_printopt": [91, 94], "sky": 91, "sidewalk": 91, "veget": 91, "terrain": 91, "rider": 91, "pred_probs_filepath": 91, "1088": 91, "1920": 91, "label_filepath": 91, "synthia_class": 91, "maunal": 91, "100000": 91, "244800": 91, "leftmost": 91, "area": 91, "middl": [91, 94], "infact": 91, "rightmost": 91, "discrep": 91, "4997817": 91, "16923": 91, "169217": 91, "33993": 91, "170085": 91, "88it": 91, "51002": 91, "169503": 91, "62it": 91, "68012": 91, "169735": 91, "92it": 91, "84993": 91, "169759": 91, "89it": 91, "101970": 91, "169628": 91, "61it": 91, "118944": 91, "169663": 91, "19it": 91, "135911": 91, "163001": 91, "52it": 91, "152918": 91, "165147": 91, "169974": 91, "166782": 91, "187064": 91, "168021": 91, "67it": 91, "204038": 91, "168537": 91, "30it": 91, "220938": 91, "168672": 91, "47it": 91, "238130": 91, "169647": 91, "75it": 91, "255262": 91, "170147": 91, "90it": 91, "272284": 91, "170019": 91, "289291": 91, "169797": 91, "306465": 91, "170377": 91, "22it": 91, "323703": 91, "170975": 91, "340851": 91, "171125": 91, "25it": 91, "357965": 91, "170735": 91, "16it": 91, "375079": 91, "170854": 91, "392275": 91, "171182": 91, "409485": 91, "171454": 91, "426631": 91, "171181": 91, "443750": 91, "170479": 91, "460916": 91, "170828": 91, "478000": 91, "167094": 91, "77it": 91, "495446": 91, "169263": 91, "15it": 91, "512890": 91, "170794": 91, "12it": 91, "530383": 91, "172020": 91, "72it": 91, "547934": 91, "173058": 91, "565248": 91, "171739": 91, "41it": 91, "582430": 91, "171635": 91, "599599": 91, "171434": 91, "03it": 91, "616746": 91, "171242": 91, "633873": 91, "171131": 91, "650988": 91, "167888": 91, "38it": 91, "667980": 91, "168487": 91, "13it": 91, "685095": 91, "169274": 91, "702365": 91, "170291": 91, "66it": 91, "719508": 91, "170628": 91, "736576": 91, "169810": 91, "36it": 91, "753562": 91, "169771": 91, "99it": 91, "770715": 91, "170294": 91, "50it": 91, "787747": 91, "169972": 91, "43it": 91, "804872": 91, "170350": 91, "821909": 91, "170342": 91, "838945": 91, "170326": 91, "57it": 91, "856345": 91, "171423": 91, "873794": 91, "172340": 91, "891355": 91, "173318": 91, "17it": 91, "908761": 91, "173536": 91, "926115": 91, "173374": 91, "943534": 91, "173615": 91, "960987": 91, "173888": 91, "978376": 91, "173759": 91, "83it": 91, "995883": 91, "174150": 91, "86it": 91, "1013363": 91, "174342": 91, "29it": 91, "1030818": 91, "174402": 91, "1048303": 91, "174534": 91, "1065757": 91, "174515": 91, "39it": 91, "1083209": 91, "174141": 91, "1100652": 91, "174222": 91, "1118075": 91, "173614": 91, "04it": 91, "1135437": 91, "173273": 91, "09it": 91, "1152765": 91, "173078": 91, "85it": 91, "1170074": 91, "172769": 91, "1187352": 91, "166605": 91, "1204651": 91, "168465": 91, "1221930": 91, "1239241": 91, "170729": 91, "1256336": 91, "170593": 91, "1273736": 91, "171604": 91, "1291095": 91, "172192": 91, "1308323": 91, "171512": 91, "1325481": 91, "170300": 91, "1342592": 91, "170538": 91, "44it": 91, "1359667": 91, "170596": 91, "1376730": 91, "170584": 91, "1393808": 91, "170640": 91, "10it": 91, "1410890": 91, "170689": 91, "1428041": 91, "170932": 91, "1445136": 91, "170912": 91, "1462229": 91, "170913": 91, "08it": 91, "1479321": 91, "170499": 91, "1496372": 91, "170482": 91, "1513421": 91, "170395": 91, "1530461": 91, "167319": 91, "27it": 91, "1547481": 91, "168168": 91, "1564657": 91, "169231": 91, "1581588": 91, "169022": 91, "1598553": 91, "169205": 91, "1615478": 91, "168976": 91, "1632488": 91, "169311": 91, "1649496": 91, "169537": 91, "42it": 91, "1666516": 91, "169734": 91, "1683770": 91, "170570": 91, "1700828": 91, "170107": 91, "1718095": 91, "170870": 91, "1735554": 91, "171982": 91, "68it": 91, "1752844": 91, "172253": 91, "1770192": 91, "172616": 91, "1787471": 91, "172664": 91, "1804738": 91, "172460": 91, "1822303": 91, "173414": 91, "1839775": 91, "173804": 91, "1857168": 91, "173839": 91, "96it": 91, "1874674": 91, "174204": 91, "1892095": 91, "173973": 91, "1909493": 91, "173652": 91, "1926956": 91, "173942": 91, "1944351": 91, "173676": 91, "1961719": 91, "173544": 91, "1979205": 91, "173936": 91, "1996632": 91, "174033": 91, "21it": 91, "2014036": 91, "173984": 91, "2031435": 91, "173931": 91, "2048829": 91, "173817": 91, "2066211": 91, "173089": 91, "2083521": 91, "173048": 91, "80it": 91, "2100866": 91, "173165": 91, "2118183": 91, "173142": 91, "2135542": 91, "173274": 91, "37it": 91, "2152870": 91, "172932": 91, "60it": 91, "2170164": 91, "172786": 91, "2187443": 91, "172758": 91, "2204719": 91, "172489": 91, "2221969": 91, "172209": 91, "2239236": 91, "172342": 91, "87it": 91, "2256471": 91, "171712": 91, "2273643": 91, "171115": 91, "35it": 91, "2290884": 91, "171500": 91, "2308082": 91, "171641": 91, "2325509": 91, "172425": 91, "2342779": 91, "172505": 91, "46it": 91, "2360266": 91, "173212": 91, "2377746": 91, "173684": 91, "2395275": 91, "174160": 91, "2412692": 91, "173545": 91, "2430069": 91, "173608": 91, "2447462": 91, "173701": 91, "94it": 91, "2464833": 91, "173310": 91, "2482165": 91, "172931": 91, "2499563": 91, "173242": 91, "2516888": 91, "173011": 91, "2534190": 91, "172835": 91, "54it": 91, "2551474": 91, "172684": 91, "2568743": 91, "171725": 91, "2586056": 91, "172142": 91, "2603272": 91, "172009": 91, "73it": 91, "2620507": 91, "172106": 91, "2637748": 91, "172193": 91, "2655228": 91, "172969": 91, "91it": 91, "2672594": 91, "173172": 91, "2689912": 91, "172973": 91, "2707210": 91, "172673": 91, "2724649": 91, "173183": 91, "2742045": 91, "173411": 91, "2759387": 91, "172797": 91, "2776675": 91, "172818": 91, "2793958": 91, "172443": 91, "2811203": 91, "172181": 91, "2828422": 91, "171875": 91, "2845610": 91, "171605": 91, "2862771": 91, "171158": 91, "2880043": 91, "171620": 91, "2897206": 91, "171545": 91, "2914479": 91, "171897": 91, "64it": 91, "2931670": 91, "171468": 91, "11it": 91, "2948818": 91, "164907": 91, "2965819": 91, "166389": 91, "56it": 91, "2983140": 91, "168389": 91, "3000413": 91, "169668": 91, "3017703": 91, "170624": 91, "3034960": 91, "171201": 91, "3052094": 91, "171123": 91, "3069455": 91, "171864": 91, "3086755": 91, "172200": 91, "3104106": 91, "172590": 91, "3121414": 91, "172732": 91, "3138690": 91, "172414": 91, "3155966": 91, "172513": 91, "3173316": 91, "172807": 91, "3190786": 91, "173371": 91, "3208124": 91, "173368": 91, "3225462": 91, "173291": 91, "3242799": 91, "173312": 91, "3260131": 91, "173229": 91, "3277459": 91, "173241": 91, "3294784": 91, "168720": 91, "3311836": 91, "169246": 91, "3329185": 91, "3346525": 91, "171358": 91, "3363981": 91, "172310": 91, "3381408": 91, "172891": 91, "3398883": 91, "173444": 91, "3416395": 91, "173944": 91, "3433866": 91, "174170": 91, "3451295": 91, "174202": 91, "3468717": 91, "173871": 91, "3486106": 91, "173730": 91, "48it": 91, "3503480": 91, "173576": 91, "3520839": 91, "172430": 91, "3538084": 91, "172099": 91, "3555296": 91, "171901": 91, "3572487": 91, "171608": 91, "3589757": 91, "171930": 91, "3606959": 91, "171954": 91, "3624245": 91, "172222": 91, "3641468": 91, "165178": 91, "3658590": 91, "166933": 91, "3675846": 91, "168580": 91, "3692949": 91, "169301": 91, "3710095": 91, "169941": 91, "3727212": 91, "170305": 91, "3744383": 91, "170721": 91, "3761585": 91, "171106": 91, "3778703": 91, "171043": 91, "3795890": 91, "171287": 91, "3813023": 91, "170783": 91, "3830175": 91, "171001": 91, "3847278": 91, "170875": 91, "3864410": 91, "171005": 91, "3881633": 91, "171370": 91, "3898771": 91, "23it": 91, "3915831": 91, "169965": 91, "3932869": 91, "170087": 91, "3949879": 91, "169990": 91, "3966879": 91, "169949": 91, "3983875": 91, "169772": 91, "79it": 91, "4000853": 91, "169718": 91, "4017853": 91, "169799": 91, "4034999": 91, "45it": 91, "4052095": 91, "170489": 91, "4069145": 91, "170353": 91, "4086208": 91, "170433": 91, "4103252": 91, "169878": 91, "4120406": 91, "170373": 91, "4137520": 91, "170600": 91, "4154679": 91, "170894": 91, "4171769": 91, "170333": 91, "58it": 91, "4188803": 91, "169923": 91, "4205796": 91, "169523": 91, "4222817": 91, "169724": 91, "4239790": 91, "169579": 91, "4256818": 91, "169786": 91, "4273871": 91, "170006": 91, "4291017": 91, "170438": 91, "4308086": 91, "170511": 91, "81it": 91, "4325138": 91, "169547": 91, "4342095": 91, "163414": 91, "55it": 91, "4359299": 91, "165924": 91, "4376343": 91, "167246": 91, "4393409": 91, "168254": 91, "4410560": 91, "169219": 91, "4427598": 91, "169562": 91, "4444705": 91, "170009": 91, "98it": 91, "4461912": 91, "170621": 91, "4478981": 91, "170378": 91, "4496051": 91, "170472": 91, "4513109": 91, "170500": 91, "4530422": 91, "171284": 91, "4547617": 91, "171480": 91, "4564863": 91, "171770": 91, "4582041": 91, "171252": 91, "4599168": 91, "170751": 91, "4616245": 91, "170236": 91, "4633367": 91, "170526": 91, "4650476": 91, "170690": 91, "4667696": 91, "171138": 91, "4684811": 91, "166416": 91, "4702063": 91, "168205": 91, "4719283": 91, "169383": 91, "4736314": 91, "169655": 91, "4753543": 91, "170435": 91, "4770597": 91, "170424": 91, "4787647": 91, "170176": 91, "4804723": 91, "170346": 91, "4821848": 91, "170613": 91, "4838912": 91, "170004": 91, "4856169": 91, "170767": 91, "4873248": 91, "170274": 91, "4890277": 91, "170191": 91, "4907298": 91, "170155": 91, "4924593": 91, "170988": 91, "4941723": 91, "171077": 91, "4958832": 91, "170246": 91, "4975925": 91, "170448": 91, "4992971": 91, "170315": 91, "171134": 91, "3263230": 91, "783379": 91, "275110": 91, "255792": 91, "78225": 91, "55990": 91, "54427": 91, "33591": 91, "24645": 91, "21308": 91, "15045": 91, "14171": 91, "13832": 91, "13498": 91, "11490": 91, "9164": 91, "8769": 91, "6999": 91, "6031": 91, "5011": 91, "mistakenli": 91, "class_issu": 91, "aim": [91, 94], "domin": 91, "extratreesclassifi": 92, "extratre": 92, "labelencod": [92, 93], "labels_raw": 92, "interg": [92, 93], "tress": 92, "827": 92, "cheat": 92, "0pt": 92, "233": 92, "labels_train": 92, "labels_test": 92, "acc_og": [92, 93], "783068783068783": 92, "acc_cl": [92, 93], "8095238095238095": 92, "earlier": [93, 94], "raw_label": 93, "raw_train_text": 93, "raw_test_text": 93, "raw_train_label": 93, "raw_test_label": 93, "encond": 93, "train_text": 93, "test_text": 93, "858050": 93, "545854": 93, "826194": 93, "965814": 93, "791923": 93, "646": 93, "390": 93, "628": 93, "702": 93, "863": 93, "135": 93, "735": 93, "print_as_df": 93, "inverse_transform": 93, "fight": 93, "bunch": 94, "conll": 94, "2003": 94, "love": 94, "n_i": 94, "optional_list_of_ordered_class_nam": 94, "deepai": 94, "conll2003": 94, "rm": 94, "tokenclassif": 94, "2024": 94, "2400": 94, "52e0": 94, "1a00": 94, "871": 94, "connect": 94, "443": 94, "await": 94, "982975": 94, "960k": 94, "kb": 94, "959": 94, "94k": 94, "mb": 94, "directori": 94, "inflat": 94, "195": 94, "17045998": 94, "16m": 94, "octet": 94, "26m": 94, "134": 94, "bert": 94, "read_npz": 94, "filepath": 94, "corrsespond": 94, "iob2": 94, "given_ent": 94, "entity_map": 94, "readfil": 94, "sep": 94, "startswith": 94, "docstart": 94, "isalpha": 94, "isupp": 94, "indices_to_preview": 94, "nsentenc": 94, "eu": 94, "reject": 94, "boycott": 94, "british": 94, "lamb": 94, "00030412": 94, "00023826": 94, "99936208": 94, "00007009": 94, "00002545": 94, "99998795": 94, "00000401": 94, "00000218": 94, "00000455": 94, "00000131": 94, "00000749": 94, "99996115": 94, "00001371": 94, "0000087": 94, "00000895": 94, "99998936": 94, "00000382": 94, "00000178": 94, "00000366": 94, "00000137": 94, "99999101": 94, "00000266": 94, "00000174": 94, "0000035": 94, "00000109": 94, "99998768": 94, "00000482": 94, "00000202": 94, "00000438": 94, "0000011": 94, "00000465": 94, "99996392": 94, "00001105": 94, "0000116": 94, "00000878": 94, "99998671": 94, "00000364": 94, "00000213": 94, "00000472": 94, "00000281": 94, "99999073": 94, "00000211": 94, "00000159": 94, "00000442": 94, "00000115": 94, "peter": 94, "blackburn": 94, "00000358": 94, "00000529": 94, "99995623": 94, "000022": 94, "0000129": 94, "0000024": 94, "00001812": 94, "99994141": 94, "00001645": 94, "00002162": 94, "brussel": 94, "1996": 94, "00001172": 94, "00000821": 94, "00004661": 94, "0000618": 94, "99987167": 94, "99999061": 94, "00000201": 94, "00000195": 94, "00000408": 94, "00000135": 94, "2254": 94, "2907": 94, "19392": 94, "9962": 94, "8904": 94, "19303": 94, "12918": 94, "9256": 94, "11855": 94, "18392": 94, "20426": 94, "19402": 94, "14744": 94, "19371": 94, "4645": 94, "10331": 94, "9430": 94, "6143": 94, "18367": 94, "12914": 94, "todai": 94, "weather": 94, "march": 94, "scalfaro": 94, "northern": 94, "himself": 94, "said": 94, "germani": 94, "nastja": 94, "rysich": 94, "north": 94, "spla": 94, "fought": 94, "khartoum": 94, "govern": 94, "south": 94, "1983": 94, "autonomi": 94, "animist": 94, "region": 94, "moslem": 94, "arabis": 94, "mayor": 94, "antonio": 94, "gonzalez": 94, "garcia": 94, "revolutionari": 94, "parti": 94, "wednesdai": 94, "troop": 94, "raid": 94, "farm": 94, "stole": 94, "rape": 94, "women": 94, "spring": 94, "chg": 94, "hrw": 94, "12pct": 94, "princ": 94, "photo": 94, "moment": 94, "spokeswoman": 94, "rainier": 94, "told": 94, "reuter": 94, "danila": 94, "carib": 94, "w224": 94, "equip": 94, "radiomet": 94, "earn": 94, "19996": 94, "london": 94, "denom": 94, "sale": 94, "uk": 94, "jp": 94, "fr": 94, "maccabi": 94, "hapoel": 94, "haifa": 94, "tel": 94, "aviv": 94, "hospit": 94, "rever": 94, "roman": 94, "cathol": 94, "nun": 94, "admit": 94, "calcutta": 94, "week": 94, "ago": 94, "fever": 94, "vomit": 94, "allianc": 94, "embattl": 94, "kabul": 94, "salang": 94, "highwai": 94, "mondai": 94, "tuesdai": 94, "suprem": 94, "council": 94, "led": 94, "jumbish": 94, "milli": 94, "movement": 94, "warlord": 94, "abdul": 94, "rashid": 94, "dostum": 94, "dollar": 94, "exchang": 94, "3570": 94, "12049": 94, "born": 94, "1937": 94, "provinc": 94, "anhui": 94, "dai": 94, "came": 94, "shanghai": 94, "citi": 94, "prolif": 94, "author": 94, "teacher": 94, "chines": 94, "16764": 94, "1990": 94, "historian": 94, "alan": 94, "john": 94, "percival": 94, "taylor": 94, "di": 94, "20446": 94, "pace": 94, "bowler": 94, "ian": 94, "harvei": 94, "claim": 94, "victoria": 94, "15514": 94, "cotti": 94, "osc": 94, "foreign": 94, "minist": 94, "7525": 94, "sultan": 94, "specter": 94, "met": 94, "crown": 94, "abdullah": 94, "defenc": 94, "aviat": 94, "jeddah": 94, "saudi": 94, "agenc": 94, "2288": 94, "hi": 94, "customari": 94, "outfit": 94, "champion": 94, "damp": 94, "scalp": 94, "canada": 94, "reign": 94, "olymp": 94, "donovan": 94, "bailei": 94, "1992": 94, "linford": 94, "christi": 94, "britain": 94, "1984": 94, "1988": 94, "carl": 94, "lewi": 94, "ambigi": 94, "punctuat": 94, "chicago": 94, "digest": 94, "philadelphia": 94, "usda": 94, "york": 94, "token_issu": 94, "471": 94, "kean": 94, "year": 94, "contract": 94, "manchest": 94, "19072": 94, "societi": 94, "million": 94, "bite": 94, "deliv": 94, "19910": 94, "father": 94, "clarenc": 94, "woolmer": 94, "renam": 94, "uttar": 94, "pradesh": 94, "india": 94, "ranji": 94, "trophi": 94, "nation": 94, "championship": 94, "captain": 94, "1949": 94, "15658": 94, "19879": 94, "iii": 94, "brian": 94, "shimer": 94, "randi": 94, "jone": 94, "19104": 94}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [9, 0, 0, "-", "datalab"], [30, 0, 0, "-", "dataset"], [33, 0, 0, "-", "experimental"], [36, 0, 0, "-", "filter"], [37, 0, 0, "-", "internal"], [48, 0, 0, "-", "models"], [50, 0, 0, "-", "multiannotator"], [53, 0, 0, "-", "multilabel_classification"], [56, 0, 0, "-", "object_detection"], [59, 0, 0, "-", "outlier"], [60, 0, 0, "-", "rank"], [61, 0, 0, "-", "regression"], [65, 0, 0, "-", "segmentation"], [69, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.datalab": [[4, 0, 0, "-", "datalab"], [13, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[4, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[4, 4, 1, "", "class_names"], [4, 3, 1, "", "find_issues"], [4, 3, 1, "", "get_info"], [4, 3, 1, "", "get_issue_summary"], [4, 3, 1, "", "get_issues"], [4, 4, 1, "", "has_labels"], [4, 4, 1, "", "info"], [4, 4, 1, "", "issue_summary"], [4, 4, 1, "", "issues"], [4, 4, 1, "", "labels"], [4, 3, 1, "", "list_default_issue_types"], [4, 3, 1, "", "list_possible_issue_types"], [4, 3, 1, "", "load"], [4, 3, 1, "", "report"], [4, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[10, 0, 0, "-", "data"], [11, 0, 0, "-", "data_issues"], [14, 0, 0, "-", "issue_finder"], [12, 0, 0, "-", "issue_manager_factory"], [28, 0, 0, "-", "report"]], "cleanlab.datalab.internal.data": [[10, 2, 1, "", "Data"], [10, 5, 1, "", "DataFormatError"], [10, 5, 1, "", "DatasetDictError"], [10, 5, 1, "", "DatasetLoadError"], [10, 2, 1, "", "Label"]], "cleanlab.datalab.internal.data.Data": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[11, 2, 1, "", "DataIssues"], [11, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[11, 3, 1, "", "collect_issues_from_imagelab"], [11, 3, 1, "", "collect_issues_from_issue_manager"], [11, 3, 1, "", "collect_statistics"], [11, 3, 1, "", "get_info"], [11, 3, 1, "", "get_issue_summary"], [11, 3, 1, "", "get_issues"], [11, 6, 1, "", "info"], [11, 6, 1, "", "issue_summary"], [11, 6, 1, "", "issues"], [11, 3, 1, "", "set_health_score"], [11, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[14, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[14, 3, 1, "", "find_issues"], [14, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[16, 0, 0, "-", "data_valuation"], [17, 0, 0, "-", "duplicate"], [18, 0, 0, "-", "imbalance"], [20, 0, 0, "-", "issue_manager"], [21, 0, 0, "-", "label"], [22, 0, 0, "-", "noniid"], [23, 0, 0, "-", "null"], [24, 0, 0, "-", "outlier"], [27, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[16, 6, 1, "", "DEFAULT_THRESHOLD"], [16, 3, 1, "", "collect_info"], [16, 6, 1, "", "description"], [16, 3, 1, "", "find_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_name"], [16, 6, 1, "", "issue_score_key"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "make_summary"], [16, 3, 1, "", "report"], [16, 6, 1, "", "summary"], [16, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[17, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[17, 3, 1, "", "collect_info"], [17, 6, 1, "", "description"], [17, 3, 1, "", "find_issues"], [17, 6, 1, "", "info"], [17, 6, 1, "", "issue_name"], [17, 6, 1, "", "issue_score_key"], [17, 6, 1, "", "issues"], [17, 3, 1, "", "make_summary"], [17, 6, 1, "", "near_duplicate_sets"], [17, 3, 1, "", "report"], [17, 6, 1, "", "summary"], [17, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[18, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[18, 3, 1, "", "collect_info"], [18, 6, 1, "", "description"], [18, 3, 1, "", "find_issues"], [18, 6, 1, "", "info"], [18, 6, 1, "", "issue_name"], [18, 6, 1, "", "issue_score_key"], [18, 6, 1, "", "issues"], [18, 3, 1, "", "make_summary"], [18, 3, 1, "", "report"], [18, 6, 1, "", "summary"], [18, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[20, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[21, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 3, 1, "", "get_health_summary"], [21, 6, 1, "", "health_summary_parameters"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[22, 2, 1, "", "NonIIDIssueManager"], [22, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[23, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[24, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[24, 6, 1, "", "DEFAULT_THRESHOLDS"], [24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 6, 1, "", "ood"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[26, 2, 1, "", "RegressionLabelIssueManager"], [26, 1, 1, "", "find_issues_with_features"], [26, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[27, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[27, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [27, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "filter_cluster_ids"], [27, 3, 1, "", "find_issues"], [27, 3, 1, "", "get_worst_cluster"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "perform_clustering"], [27, 3, 1, "", "report"], [27, 3, 1, "", "set_knn_graph"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, 7, 1, "", "REGISTRY"], [12, 1, 1, "", "list_default_issue_types"], [12, 1, 1, "", "list_possible_issue_types"], [12, 1, 1, "", "register"]], "cleanlab.datalab.internal.report": [[28, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[28, 3, 1, "", "get_report"], [28, 3, 1, "", "report"]], "cleanlab.dataset": [[30, 1, 1, "", "find_overlapping_classes"], [30, 1, 1, "", "health_summary"], [30, 1, 1, "", "overall_label_health_score"], [30, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[31, 0, 0, "-", "cifar_cnn"], [32, 0, 0, "-", "coteaching"], [34, 0, 0, "-", "label_issues_batched"], [35, 0, 0, "-", "mnist_pytorch"]], "cleanlab.experimental.cifar_cnn": [[31, 2, 1, "", "CNN"], [31, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[31, 6, 1, "", "T_destination"], [31, 3, 1, "", "__call__"], [31, 3, 1, "", "add_module"], [31, 3, 1, "", "apply"], [31, 3, 1, "", "bfloat16"], [31, 3, 1, "", "buffers"], [31, 3, 1, "", "children"], [31, 3, 1, "", "cpu"], [31, 3, 1, "", "cuda"], [31, 3, 1, "", "double"], [31, 6, 1, "", "dump_patches"], [31, 3, 1, "", "eval"], [31, 3, 1, "", "extra_repr"], [31, 3, 1, "", "float"], [31, 3, 1, "id0", "forward"], [31, 3, 1, "", "get_buffer"], [31, 3, 1, "", "get_extra_state"], [31, 3, 1, "", "get_parameter"], [31, 3, 1, "", "get_submodule"], [31, 3, 1, "", "half"], [31, 3, 1, "", "ipu"], [31, 3, 1, "", "load_state_dict"], [31, 3, 1, "", "modules"], [31, 3, 1, "", "named_buffers"], [31, 3, 1, "", "named_children"], [31, 3, 1, "", "named_modules"], [31, 3, 1, "", "named_parameters"], [31, 3, 1, "", "parameters"], [31, 3, 1, "", "register_backward_hook"], [31, 3, 1, "", "register_buffer"], [31, 3, 1, "", "register_forward_hook"], [31, 3, 1, "", "register_forward_pre_hook"], [31, 3, 1, "", "register_full_backward_hook"], [31, 3, 1, "", "register_load_state_dict_post_hook"], [31, 3, 1, "", "register_module"], [31, 3, 1, "", "register_parameter"], [31, 3, 1, "", "requires_grad_"], [31, 3, 1, "", "set_extra_state"], [31, 3, 1, "", "share_memory"], [31, 3, 1, "", "state_dict"], [31, 3, 1, "", "to"], [31, 3, 1, "", "to_empty"], [31, 3, 1, "", "train"], [31, 6, 1, "", "training"], [31, 3, 1, "", "type"], [31, 3, 1, "", "xpu"], [31, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[32, 1, 1, "", "adjust_learning_rate"], [32, 1, 1, "", "evaluate"], [32, 1, 1, "", "forget_rate_scheduler"], [32, 1, 1, "", "initialize_lr_scheduler"], [32, 1, 1, "", "loss_coteaching"], [32, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[34, 2, 1, "", "LabelInspector"], [34, 7, 1, "", "adj_confident_thresholds_shared"], [34, 1, 1, "", "find_label_issues_batched"], [34, 7, 1, "", "labels_shared"], [34, 7, 1, "", "pred_probs_shared"], [34, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[34, 3, 1, "", "get_confident_thresholds"], [34, 3, 1, "", "get_label_issues"], [34, 3, 1, "", "get_num_issues"], [34, 3, 1, "", "get_quality_scores"], [34, 3, 1, "", "score_label_quality"], [34, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[35, 2, 1, "", "CNN"], [35, 2, 1, "", "SimpleNet"], [35, 1, 1, "", "get_mnist_dataset"], [35, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[35, 3, 1, "", "__init_subclass__"], [35, 6, 1, "", "batch_size"], [35, 6, 1, "", "dataset"], [35, 6, 1, "", "epochs"], [35, 3, 1, "id0", "fit"], [35, 3, 1, "", "get_metadata_routing"], [35, 3, 1, "", "get_params"], [35, 6, 1, "", "loader"], [35, 6, 1, "", "log_interval"], [35, 6, 1, "", "lr"], [35, 6, 1, "", "momentum"], [35, 6, 1, "", "no_cuda"], [35, 3, 1, "id1", "predict"], [35, 3, 1, "id4", "predict_proba"], [35, 6, 1, "", "seed"], [35, 3, 1, "", "set_fit_request"], [35, 3, 1, "", "set_params"], [35, 3, 1, "", "set_predict_proba_request"], [35, 3, 1, "", "set_predict_request"], [35, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[35, 6, 1, "", "T_destination"], [35, 3, 1, "", "__call__"], [35, 3, 1, "", "add_module"], [35, 3, 1, "", "apply"], [35, 3, 1, "", "bfloat16"], [35, 3, 1, "", "buffers"], [35, 3, 1, "", "children"], [35, 3, 1, "", "cpu"], [35, 3, 1, "", "cuda"], [35, 3, 1, "", "double"], [35, 6, 1, "", "dump_patches"], [35, 3, 1, "", "eval"], [35, 3, 1, "", "extra_repr"], [35, 3, 1, "", "float"], [35, 3, 1, "", "forward"], [35, 3, 1, "", "get_buffer"], [35, 3, 1, "", "get_extra_state"], [35, 3, 1, "", "get_parameter"], [35, 3, 1, "", "get_submodule"], [35, 3, 1, "", "half"], [35, 3, 1, "", "ipu"], [35, 3, 1, "", "load_state_dict"], [35, 3, 1, "", "modules"], [35, 3, 1, "", "named_buffers"], [35, 3, 1, "", "named_children"], [35, 3, 1, "", "named_modules"], [35, 3, 1, "", "named_parameters"], [35, 3, 1, "", "parameters"], [35, 3, 1, "", "register_backward_hook"], [35, 3, 1, "", "register_buffer"], [35, 3, 1, "", "register_forward_hook"], [35, 3, 1, "", "register_forward_pre_hook"], [35, 3, 1, "", "register_full_backward_hook"], [35, 3, 1, "", "register_load_state_dict_post_hook"], [35, 3, 1, "", "register_module"], [35, 3, 1, "", "register_parameter"], [35, 3, 1, "", "requires_grad_"], [35, 3, 1, "", "set_extra_state"], [35, 3, 1, "", "share_memory"], [35, 3, 1, "", "state_dict"], [35, 3, 1, "", "to"], [35, 3, 1, "", "to_empty"], [35, 3, 1, "", "train"], [35, 6, 1, "", "training"], [35, 3, 1, "", "type"], [35, 3, 1, "", "xpu"], [35, 3, 1, "", "zero_grad"]], "cleanlab.filter": [[36, 1, 1, "", "find_label_issues"], [36, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [36, 1, 1, "", "find_predicted_neq_given"], [36, 7, 1, "", "pred_probs_by_class"], [36, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[38, 0, 0, "-", "label_quality_utils"], [39, 0, 0, "-", "latent_algebra"], [40, 0, 0, "-", "multiannotator_utils"], [41, 0, 0, "-", "multilabel_scorer"], [42, 0, 0, "-", "multilabel_utils"], [43, 0, 0, "-", "outlier"], [44, 0, 0, "-", "token_classification_utils"], [45, 0, 0, "-", "util"], [46, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[38, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[39, 1, 1, "", "compute_inv_noise_matrix"], [39, 1, 1, "", "compute_noise_matrix_from_inverse"], [39, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [39, 1, 1, "", "compute_py"], [39, 1, 1, "", "compute_py_inv_noise_matrix"], [39, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[40, 1, 1, "", "assert_valid_inputs_multiannotator"], [40, 1, 1, "", "assert_valid_pred_probs"], [40, 1, 1, "", "check_consensus_label_classes"], [40, 1, 1, "", "compute_soft_cross_entropy"], [40, 1, 1, "", "find_best_temp_scaler"], [40, 1, 1, "", "format_multiannotator_labels"], [40, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[41, 2, 1, "", "Aggregator"], [41, 2, 1, "", "ClassLabelScorer"], [41, 2, 1, "", "MultilabelScorer"], [41, 1, 1, "", "exponential_moving_average"], [41, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [41, 1, 1, "", "get_label_quality_scores"], [41, 1, 1, "", "multilabel_py"], [41, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[41, 3, 1, "", "__call__"], [41, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[41, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [41, 6, 1, "", "NORMALIZED_MARGIN"], [41, 6, 1, "", "SELF_CONFIDENCE"], [41, 3, 1, "", "__call__"], [41, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[41, 3, 1, "", "__call__"], [41, 3, 1, "", "aggregate"], [41, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[42, 1, 1, "", "get_onehot_num_classes"], [42, 1, 1, "", "int2onehot"], [42, 1, 1, "", "onehot2int"], [42, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[43, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[44, 1, 1, "", "color_sentence"], [44, 1, 1, "", "filter_sentence"], [44, 1, 1, "", "get_sentence"], [44, 1, 1, "", "mapping"], [44, 1, 1, "", "merge_probs"], [44, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[45, 1, 1, "", "append_extra_datapoint"], [45, 1, 1, "", "clip_noise_rates"], [45, 1, 1, "", "clip_values"], [45, 1, 1, "", "compress_int_array"], [45, 1, 1, "", "confusion_matrix"], [45, 1, 1, "", "csr_vstack"], [45, 1, 1, "", "estimate_pu_f1"], [45, 1, 1, "", "extract_indices_tf"], [45, 1, 1, "", "force_two_dimensions"], [45, 1, 1, "", "format_labels"], [45, 1, 1, "", "get_missing_classes"], [45, 1, 1, "", "get_num_classes"], [45, 1, 1, "", "get_unique_classes"], [45, 1, 1, "", "is_tensorflow_dataset"], [45, 1, 1, "", "is_torch_dataset"], [45, 1, 1, "", "num_unique_classes"], [45, 1, 1, "", "print_inverse_noise_matrix"], [45, 1, 1, "", "print_joint_matrix"], [45, 1, 1, "", "print_noise_matrix"], [45, 1, 1, "", "print_square_matrix"], [45, 1, 1, "", "remove_noise_from_class"], [45, 1, 1, "", "round_preserving_row_totals"], [45, 1, 1, "", "round_preserving_sum"], [45, 1, 1, "", "smart_display_dataframe"], [45, 1, 1, "", "subset_X_y"], [45, 1, 1, "", "subset_data"], [45, 1, 1, "", "subset_labels"], [45, 1, 1, "", "train_val_split"], [45, 1, 1, "", "unshuffle_tensorflow_dataset"], [45, 1, 1, "", "value_counts"], [45, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[46, 1, 1, "", "assert_indexing_works"], [46, 1, 1, "", "assert_nonempty_input"], [46, 1, 1, "", "assert_valid_class_labels"], [46, 1, 1, "", "assert_valid_inputs"], [46, 1, 1, "", "labels_to_array"]], "cleanlab.models": [[49, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[49, 2, 1, "", "KerasWrapperModel"], [49, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[49, 3, 1, "", "fit"], [49, 3, 1, "", "get_params"], [49, 3, 1, "", "predict"], [49, 3, 1, "", "predict_proba"], [49, 3, 1, "", "set_params"], [49, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[49, 3, 1, "", "fit"], [49, 3, 1, "", "get_params"], [49, 3, 1, "", "predict"], [49, 3, 1, "", "predict_proba"], [49, 3, 1, "", "set_params"], [49, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[50, 1, 1, "", "convert_long_to_wide_dataset"], [50, 1, 1, "", "get_active_learning_scores"], [50, 1, 1, "", "get_active_learning_scores_ensemble"], [50, 1, 1, "", "get_label_quality_multiannotator"], [50, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [50, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[51, 0, 0, "-", "dataset"], [52, 0, 0, "-", "filter"], [54, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[51, 1, 1, "", "common_multilabel_issues"], [51, 1, 1, "", "multilabel_health_summary"], [51, 1, 1, "", "overall_multilabel_health_score"], [51, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[52, 1, 1, "", "find_label_issues"], [52, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[54, 1, 1, "", "get_label_quality_scores"], [54, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[55, 0, 0, "-", "filter"], [57, 0, 0, "-", "rank"], [58, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[55, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[57, 1, 1, "", "compute_badloc_box_scores"], [57, 1, 1, "", "compute_overlooked_box_scores"], [57, 1, 1, "", "compute_swap_box_scores"], [57, 1, 1, "", "get_label_quality_scores"], [57, 1, 1, "", "issues_from_scores"], [57, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[58, 1, 1, "", "bounding_box_size_distribution"], [58, 1, 1, "", "calculate_per_class_metrics"], [58, 1, 1, "", "class_label_distribution"], [58, 1, 1, "", "get_average_per_class_confusion_matrix"], [58, 1, 1, "", "get_sorted_bbox_count_idxs"], [58, 1, 1, "", "object_counts_per_image"], [58, 1, 1, "", "plot_class_distribution"], [58, 1, 1, "", "plot_class_size_distributions"], [58, 1, 1, "", "visualize"]], "cleanlab.outlier": [[59, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[59, 3, 1, "", "fit"], [59, 3, 1, "", "fit_score"], [59, 3, 1, "", "score"]], "cleanlab.rank": [[60, 1, 1, "", "find_top_issues"], [60, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [60, 1, 1, "", "get_label_quality_ensemble_scores"], [60, 1, 1, "", "get_label_quality_scores"], [60, 1, 1, "", "get_normalized_margin_for_each_label"], [60, 1, 1, "", "get_self_confidence_for_each_label"], [60, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[62, 0, 0, "-", "learn"], [63, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[62, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[62, 3, 1, "", "__init_subclass__"], [62, 3, 1, "", "find_label_issues"], [62, 3, 1, "", "fit"], [62, 3, 1, "", "get_aleatoric_uncertainty"], [62, 3, 1, "", "get_epistemic_uncertainty"], [62, 3, 1, "", "get_label_issues"], [62, 3, 1, "", "get_metadata_routing"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "save_space"], [62, 3, 1, "", "score"], [62, 3, 1, "", "set_fit_request"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[63, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"], [67, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[64, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[67, 1, 1, "", "common_label_issues"], [67, 1, 1, "", "display_issues"], [67, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[68, 0, 0, "-", "filter"], [70, 0, 0, "-", "rank"], [71, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[68, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[70, 1, 1, "", "get_label_quality_scores"], [70, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[71, 1, 1, "", "common_label_issues"], [71, 1, 1, "", "display_issues"], [71, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 74, 78, 79, 81, 82, 83, 86, 92, 93, 94], "count": [3, 83], "datalab": [4, 5, 7, 8, 9, 75, 76, 77, 78, 79, 83], "creat": [5, 75, 76, 83, 85], "your": [5, 72, 75, 76, 79, 81, 83], "own": 5, "issu": [5, 7, 8, 19, 26, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 91, 92, 94], "manag": [5, 19], "prerequisit": 5, "implement": 5, "issuemanag": [5, 75], "basic": 5, "check": 5, "intermedi": 5, "advanc": [5, 75], "us": [5, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "gener": 6, "cluster": [6, 81], "id": 6, "guid": [7, 9], "type": [7, 8, 83], "custom": [7, 75], "can": [8, 76, 80, 81, 83, 85], "detect": [8, 76, 78, 79, 81, 83, 87, 88], "estim": [8, 83, 85], "each": 8, "label": [8, 21, 26, 72, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 90, 91, 92, 93, 94], "outlier": [8, 24, 43, 59, 78, 79, 82, 88], "Near": [8, 76, 78, 79, 82], "duplic": [8, 17, 76, 78, 79, 81, 82], "non": [8, 79], "iid": [8, 79], "class": [8, 73, 83, 91], "imbal": [8, 18], "imag": [8, 82, 88], "specif": [8, 19, 91], "underperform": [8, 81], "group": [8, 81], "null": [8, 23], "data": [8, 10, 72, 74, 75, 76, 78, 79, 80, 81, 83, 85, 86, 87, 88, 90, 91, 92, 94], "valuat": 8, "option": 8, "paramet": [8, 83], "get": [9, 75, 76, 85, 86, 87, 91, 94], "start": [9, 80], "api": 9, "refer": 9, "data_issu": 11, "factori": 12, "intern": [13, 37], "issue_find": 14, "data_valu": 16, "issue_manag": [19, 20], "regist": 19, "unregist": 19, "ml": [19, 81, 83], "task": 19, "noniid": 22, "regress": [25, 61, 62, 63, 81, 90], "prioriti": 26, "order": 26, "find": [26, 72, 74, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "underperforming_group": 27, "report": [28, 82], "dataset": [30, 51, 72, 76, 79, 80, 81, 82, 83, 86, 87, 88, 90, 91, 93, 94], "cifar_cnn": 31, "coteach": 32, "experiment": 33, "label_issues_batch": 34, "mnist_pytorch": 35, "filter": [36, 52, 55, 64, 68, 83], "label_quality_util": 38, "latent_algebra": 39, "multiannotator_util": 40, "multilabel_scor": 41, "multilabel_util": 42, "token_classification_util": 44, "util": 45, "valid": [46, 82, 89], "fasttext": 47, "model": [48, 72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "kera": 49, "multiannot": [50, 85], "multilabel_classif": 53, "rank": [54, 57, 60, 63, 66, 70, 83], "object_detect": 56, "summari": [58, 67, 71], "learn": [62, 76, 81, 83, 92], "segment": [65, 91], "token_classif": [69, 94], "cleanlab": [72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "open": [72, 81], "sourc": [72, 81], "document": 72, "quickstart": 72, "1": [72, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "instal": [72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "2": [72, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "common": [72, 73, 94], "3": [72, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "handl": [72, 81], "error": [72, 81, 82, 83, 85, 86, 87, 90, 91, 93, 94], "train": [72, 74, 81, 88, 90, 92, 93], "robust": [72, 83, 90, 92, 93], "noisi": [72, 83, 90, 92, 93], "4": [72, 74, 75, 76, 78, 79, 82, 83, 85, 87, 88, 90, 92, 93], "curat": [72, 80], "fix": [72, 81], "level": [72, 80, 83, 94], "5": [72, 74, 76, 78, 82, 83, 85, 90, 92], "improv": [72, 85], "via": [72, 83, 85], "mani": [72, 83], "other": [72, 85, 87, 90], "techniqu": 72, "contribut": 72, "easi": [72, 78, 79, 82], "mode": [72, 78, 79, 82], "how": [73, 81, 83, 85, 86, 94], "migrat": 73, "version": 73, "0": 73, "from": [73, 75, 76, 83, 90, 92, 93], "pre": [73, 74, 81, 88], "function": [73, 75], "name": 73, "chang": 73, "modul": [73, 83], "new": 73, "remov": 73, "argument": [73, 75], "variabl": 73, "audio": 74, "speechbrain": 74, "depend": [74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "import": [74, 75, 76, 80, 82, 83, 85], "them": [74, 80, 83], "load": [74, 75, 76, 78, 79, 90, 92, 93], "featur": [74, 82, 88], "fit": 74, "linear": 74, "comput": [74, 78, 79, 81, 82, 85, 89, 92], "out": [74, 75, 76, 78, 79, 82, 85, 89, 92], "sampl": [74, 75, 76, 78, 79, 82, 85, 89, 92], "predict": [74, 75, 76, 78, 79, 82, 85, 86, 87, 89, 92], "probabl": [74, 75, 76, 78, 79, 82, 85, 89, 92], "workflow": [75, 83], "audit": [75, 76], "requir": [75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "classifi": [75, 76], "instanti": 75, "object": [75, 87], "increment": 75, "search": 75, "specifi": [75, 81], "nondefault": 75, "save": 75, "ad": 75, "A": 76, "unifi": 76, "all": [76, 83], "kind": [76, 87], "skip": [76, 80, 83, 85], "detail": [76, 80, 83, 85], "more": [76, 83, 90, 92, 93], "about": 76, "addit": 76, "inform": [76, 82], "tutori": [77, 80, 84], "tabular": [78, 92], "numer": 78, "categor": 78, "column": 78, "process": [78, 88, 90, 92], "select": [78, 92], "construct": 78, "k": [78, 82, 89], "nearest": 78, "neighbour": 78, "graph": 78, "text": [79, 93, 94], "format": [79, 81, 86, 87, 93], "defin": [79, 82, 90, 93], "drift": 79, "fetch": [80, 82], "evalu": 80, "health": [80, 83], "8": [80, 83], "popular": 80, "faq": 81, "what": [81, 83, 89], "do": [81, 83], "i": [81, 83, 89], "infer": 81, "correct": 81, "exampl": [81, 82, 83, 88], "ha": 81, "flag": 81, "should": 81, "v": 81, "test": [81, 83, 88], "big": 81, "limit": 81, "memori": 81, "why": 81, "isn": 81, "t": 81, "cleanlearn": [81, 83], "work": [81, 83, 85, 94], "me": 81, "differ": [81, 87], "clean": [81, 83], "final": 81, "hyperparamet": 81, "tune": 81, "onli": 81, "one": [81, 83, 86, 91], "doe": [81, 85, 94], "take": 81, "so": 81, "long": 81, "slice": 81, "when": [81, 83], "identifi": [81, 87], "run": 81, "licens": 81, "under": 81, "an": 81, "answer": 81, "question": 81, "pytorch": [82, 88], "normal": 82, "fashion": 82, "mnist": 82, "prepar": 82, "fold": [82, 89], "cross": [82, 89], "embed": [82, 88], "7": [82, 83], "view": 82, "most": [82, 94], "like": 82, "sever": 82, "set": [82, 83], "dark": 82, "top": [82, 91], "low": 82, "The": 83, "centric": 83, "ai": 83, "machin": 83, "find_label_issu": 83, "line": 83, "code": 83, "visual": [83, 87, 88, 91], "twenti": 83, "lowest": 83, "qualiti": [83, 85, 86, 87, 91, 94], "see": 83, "now": 83, "let": 83, "": 83, "happen": 83, "we": 83, "merg": 83, "seafoam": 83, "green": 83, "yellow": 83, "too": 83, "you": 83, "re": 83, "6": 83, "One": 83, "score": [83, 85, 86, 87, 91, 94], "rule": 83, "overal": [83, 91], "accur": 83, "thi": 83, "directli": 83, "fulli": 83, "character": 83, "nois": 83, "matrix": [83, 86], "joint": 83, "prior": 83, "true": 83, "distribut": 83, "flip": 83, "rate": 83, "ani": 83, "again": 83, "support": 83, "lot": 83, "method": 83, "filter_bi": 83, "automat": 83, "everi": 83, "uniqu": 83, "num_label_issu": 83, "threshold": 83, "found": 83, "Not": 83, "sure": 83, "ensembl": 83, "multipl": [83, 85], "predictor": 83, "consensu": 85, "annot": 85, "initi": 85, "major": 85, "vote": 85, "better": 85, "statist": 85, "compar": 85, "inspect": 85, "potenti": [85, 90, 93], "retrain": 85, "further": 85, "multi": 86, "given": 86, "hot": 86, "binari": 86, "download": [87, 91, 94], "objectlab": 87, "timm": 88, "cifar10": 88, "some": 88, "pred_prob": [88, 91, 94], "wai": 90, "semant": 91, "which": 91, "ar": 91, "commonli": 91, "mislabel": [91, 94], "focus": 91, "scikit": 92, "token": 94, "word": 94, "sentenc": 94, "contain": 94, "particular": 94}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 56}}) \ No newline at end of file diff --git a/master/tutorials/audio.html b/master/tutorials/audio.html index d3d09f5b9..9a5a27e14 100644 --- a/master/tutorials/audio.html +++ b/master/tutorials/audio.html @@ -1504,7 +1504,7 @@

5. Use cleanlab to find label issues -{"state": {"ccb07543f38c422eba6e58deccf2a389": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "55ca6bb4d3414d2091e2d6cc744d0227": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "42cde93ea0f44516bebd28767667c2fa": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_ccb07543f38c422eba6e58deccf2a389", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_55ca6bb4d3414d2091e2d6cc744d0227", "value": 2041.0}}, "8c2848fd80e94dfeba2260153670f5b5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d73d40dffae24b919506395235104b28": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "799d78d15e1549e58376f474b256b3f1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_8c2848fd80e94dfeba2260153670f5b5", "placeholder": "\u200b", "style": "IPY_MODEL_d73d40dffae24b919506395235104b28", "value": "hyperparams.yaml: 100%"}}, "5105284d7b9346b09f3d678a6f15a3cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "db5c6c408f9147df9a5d44f8f886ab61": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "53e5f5516c1940eb84c59305f879b33d": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5105284d7b9346b09f3d678a6f15a3cb", "placeholder": "\u200b", "style": "IPY_MODEL_db5c6c408f9147df9a5d44f8f886ab61", "value": " 2.04k/2.04k [00:00<00:00, 352kB/s]"}}, "32761d387fb34f55a8662c19898e2e19": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "a2cd93bedff346d4b94c8c101e88352e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_799d78d15e1549e58376f474b256b3f1", "IPY_MODEL_42cde93ea0f44516bebd28767667c2fa", "IPY_MODEL_53e5f5516c1940eb84c59305f879b33d"], "layout": "IPY_MODEL_32761d387fb34f55a8662c19898e2e19"}}, "bbb0a42cbeb44cd2807289cb84e06c66": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "660387b90c454207a12955625f60a888": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "886f26d855ae49049a473788aaf13fdd": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_bbb0a42cbeb44cd2807289cb84e06c66", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_660387b90c454207a12955625f60a888", "value": 16887676.0}}, "e38a6eaeba49436d880afec59461ee06": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9a471f99ebec460f82f340da4bf084a3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "04545eb82c614214a57ff48711e5ef69": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_e38a6eaeba49436d880afec59461ee06", "placeholder": "\u200b", "style": "IPY_MODEL_9a471f99ebec460f82f340da4bf084a3", "value": "embedding_model.ckpt: 100%"}}, "5836728e785d44ce8ebe512cb1ffe735": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "dae854f92b7548ddb4b4981bb4a8c9d2": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "2fb3c50dfd544cf297447c4dd5e4bdfa": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5836728e785d44ce8ebe512cb1ffe735", "placeholder": "\u200b", "style": "IPY_MODEL_dae854f92b7548ddb4b4981bb4a8c9d2", "value": " 16.9M/16.9M [00:00<00:00, 96.3MB/s]"}}, "77fefa233aa04e3ba284da2418760799": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "69f5f977755e4a4b82b5ca684955dd9e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_04545eb82c614214a57ff48711e5ef69", "IPY_MODEL_886f26d855ae49049a473788aaf13fdd", "IPY_MODEL_2fb3c50dfd544cf297447c4dd5e4bdfa"], "layout": "IPY_MODEL_77fefa233aa04e3ba284da2418760799"}}, "c0de636f211e43f793ee2c8ccaccead2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "95589bfbae6545eea65cdda502cb8591": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "c9aad8223fed4fd79a1fa84e675e9c34": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c0de636f211e43f793ee2c8ccaccead2", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_95589bfbae6545eea65cdda502cb8591", "value": 3201.0}}, "4bdbef4700e34979a7835f1c9cb5f63b": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "41336a38204142909d6d6151660b30ef": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "649eb4698e3443adb0fd32629e36ece6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_4bdbef4700e34979a7835f1c9cb5f63b", "placeholder": "\u200b", "style": "IPY_MODEL_41336a38204142909d6d6151660b30ef", "value": "mean_var_norm_emb.ckpt: 100%"}}, "617fa189f462470a937beee9505799fa": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9fca2e61f7d64cea930c8b07bdcbc59e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "0fd11d936da54fa68096671994e1275f": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_617fa189f462470a937beee9505799fa", "placeholder": "\u200b", "style": "IPY_MODEL_9fca2e61f7d64cea930c8b07bdcbc59e", "value": " 3.20k/3.20k [00:00<00:00, 549kB/s]"}}, "c3b13273fa614276aa8cd31a0d3ba6a0": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4a8b0b00351a48b1bd3c4ffb02422045": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_649eb4698e3443adb0fd32629e36ece6", "IPY_MODEL_c9aad8223fed4fd79a1fa84e675e9c34", "IPY_MODEL_0fd11d936da54fa68096671994e1275f"], "layout": "IPY_MODEL_c3b13273fa614276aa8cd31a0d3ba6a0"}}, "e52c6bc80973455ab0806594b5177ef7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "159b496215244dc799dd25dae2e0470d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "fc52934f923d48f6815ef3ea863cb962": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_e52c6bc80973455ab0806594b5177ef7", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_159b496215244dc799dd25dae2e0470d", "value": 15856877.0}}, "a70c7781a1174beb8b08bdb996fa2bc8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4d632ff2b07b4dffa2ead315843c6e6c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "db86af8139ff41018b497ff1758537de": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_a70c7781a1174beb8b08bdb996fa2bc8", "placeholder": "\u200b", "style": "IPY_MODEL_4d632ff2b07b4dffa2ead315843c6e6c", "value": "classifier.ckpt: 100%"}}, "c30853559a364583b1f650a150b6654a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "24e87b41e33643a0a449e4e5f4a04a21": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "ebe5bf1237e54a5cb31a7953735ed040": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c30853559a364583b1f650a150b6654a", "placeholder": "\u200b", "style": "IPY_MODEL_24e87b41e33643a0a449e4e5f4a04a21", "value": " 15.9M/15.9M [00:00<00:00, 302MB/s]"}}, "7fda371c9fba45549d352696e38b0c16": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1acfc2e65c71473497f43eb9e974bc92": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_db86af8139ff41018b497ff1758537de", "IPY_MODEL_fc52934f923d48f6815ef3ea863cb962", "IPY_MODEL_ebe5bf1237e54a5cb31a7953735ed040"], "layout": "IPY_MODEL_7fda371c9fba45549d352696e38b0c16"}}, "78e330e8413b4c638b5f5ad1892ffc7c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "25691bde0c394a299f774959e32e539d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "7137378c46a441c9b8f6dd4cd7cd9ee2": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_78e330e8413b4c638b5f5ad1892ffc7c", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_25691bde0c394a299f774959e32e539d", "value": 128619.0}}, "da8efe510bcf414b9d2c76f5f791e9ea": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "220c65a6f5bb442e8b509643c78298cb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "1cca100b2cea4a77bbbcc5a23f19e1d7": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_da8efe510bcf414b9d2c76f5f791e9ea", "placeholder": "\u200b", "style": "IPY_MODEL_220c65a6f5bb442e8b509643c78298cb", "value": "label_encoder.txt: 100%"}}, "61d06a92b987421cb46d8a89d1975bb7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f1827a2afaa3497796df337825e00c5d": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "7aaa5224bfe44036a333755a894e18b5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_61d06a92b987421cb46d8a89d1975bb7", "placeholder": "\u200b", "style": "IPY_MODEL_f1827a2afaa3497796df337825e00c5d", "value": " 129k/129k [00:00<00:00, 13.1MB/s]"}}, "25c36bd0682a44a1af90eecaec670b14": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "64de8f2a3f18422084ccd72434dca5cc": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_1cca100b2cea4a77bbbcc5a23f19e1d7", "IPY_MODEL_7137378c46a441c9b8f6dd4cd7cd9ee2", "IPY_MODEL_7aaa5224bfe44036a333755a894e18b5"], "layout": "IPY_MODEL_25c36bd0682a44a1af90eecaec670b14"}}}, "version_major": 2, "version_minor": 0} +{"state": {"26707147e6f24b498f499aec25d3879a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d21e1fb2327a464b8b743384ffa08cc0": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "cc503c61cdb6438ea915c9aa1032c63c": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_26707147e6f24b498f499aec25d3879a", "max": 2041.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_d21e1fb2327a464b8b743384ffa08cc0", "value": 2041.0}}, "1eb624a3dad54e7fa6b146681c26c1fd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "8699a0a7cb124a369027b929c14aca25": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "ed7a2be85ae641c2b6cb2f1e8fb8054b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1eb624a3dad54e7fa6b146681c26c1fd", "placeholder": "\u200b", "style": "IPY_MODEL_8699a0a7cb124a369027b929c14aca25", "value": "hyperparams.yaml: 100%"}}, "83f7b9bff2ec40f19a358ae6e9071f3a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bf1dc44a81b64e408396f375ab6db707": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "b6341a7e935242c2adc1364ec71311bf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_83f7b9bff2ec40f19a358ae6e9071f3a", "placeholder": "\u200b", "style": "IPY_MODEL_bf1dc44a81b64e408396f375ab6db707", "value": " 2.04k/2.04k [00:00<00:00, 337kB/s]"}}, "fa264daf6009497ca63787f2eb2c7503": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c5b91c7d529d4e1a9a36a41238ba0244": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_ed7a2be85ae641c2b6cb2f1e8fb8054b", "IPY_MODEL_cc503c61cdb6438ea915c9aa1032c63c", "IPY_MODEL_b6341a7e935242c2adc1364ec71311bf"], "layout": "IPY_MODEL_fa264daf6009497ca63787f2eb2c7503"}}, "ff2eea9324ab4fdebb914b6830cf4e4e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b391f6736d6440f5977ea7e4075132b1": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "4013766ebdd64bcdaa57852522f87774": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_ff2eea9324ab4fdebb914b6830cf4e4e", "max": 16887676.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_b391f6736d6440f5977ea7e4075132b1", "value": 16887676.0}}, "2415134083d142f18e75d9c1fd90ad9e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "424d86c2319543f0b0d18ccae78cd508": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "8d69a36aa0ef4ac398e5473efe8facaf": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_2415134083d142f18e75d9c1fd90ad9e", "placeholder": "\u200b", "style": "IPY_MODEL_424d86c2319543f0b0d18ccae78cd508", "value": "embedding_model.ckpt: 100%"}}, "3b6ccb1c3d224e82864d450d361cb50f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3bfd515955e24904b4dc7bac5512c42f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "7b3c7b951bc643fea2c2d55d50e02569": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_3b6ccb1c3d224e82864d450d361cb50f", "placeholder": "\u200b", "style": "IPY_MODEL_3bfd515955e24904b4dc7bac5512c42f", "value": " 16.9M/16.9M [00:00<00:00, 74.2MB/s]"}}, "46c6f6135bb9449482ba0294d1515087": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1bdf3168328746ddb97b58d22771e706": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_8d69a36aa0ef4ac398e5473efe8facaf", "IPY_MODEL_4013766ebdd64bcdaa57852522f87774", "IPY_MODEL_7b3c7b951bc643fea2c2d55d50e02569"], "layout": "IPY_MODEL_46c6f6135bb9449482ba0294d1515087"}}, "d3db5175210c48839bd72a54bbd9e709": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "aa7002ac34ad461c971cb567fbaed7c7": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "b3b1a6b802c2438aa6b7345347be1daa": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_d3db5175210c48839bd72a54bbd9e709", "max": 3201.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_aa7002ac34ad461c971cb567fbaed7c7", "value": 3201.0}}, "ee5f56e9b26d44c29b98957642b5cbe6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4aba10cb04694ab4bf360dfefe9871d7": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "851a3e78f4cb49c28fa6d9d3f6ad533c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_ee5f56e9b26d44c29b98957642b5cbe6", "placeholder": "\u200b", "style": "IPY_MODEL_4aba10cb04694ab4bf360dfefe9871d7", "value": "mean_var_norm_emb.ckpt: 100%"}}, "53a59a607d394f2e832ba2a5f8cd33f2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "0ff00007ea674a8496fed6616e438639": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "48805febf856433c84dda4034fb9c0c6": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_53a59a607d394f2e832ba2a5f8cd33f2", "placeholder": "\u200b", "style": "IPY_MODEL_0ff00007ea674a8496fed6616e438639", "value": " 3.20k/3.20k [00:00<00:00, 511kB/s]"}}, "cc06c8dc25a146d1949bbc6f284744e5": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5fd842770f3c46198750ec0bc065bbec": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_851a3e78f4cb49c28fa6d9d3f6ad533c", "IPY_MODEL_b3b1a6b802c2438aa6b7345347be1daa", "IPY_MODEL_48805febf856433c84dda4034fb9c0c6"], "layout": "IPY_MODEL_cc06c8dc25a146d1949bbc6f284744e5"}}, "508a9cef5621429ca2ad08b2e5a5b906": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2105d31075094aec8143dc36c2cc4c8a": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "7694a31ea9a04edcb6864f25b28b15cb": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_508a9cef5621429ca2ad08b2e5a5b906", "max": 15856877.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_2105d31075094aec8143dc36c2cc4c8a", "value": 15856877.0}}, "7eb2a98375b74c91b45dc5b5accd9bca": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "609ef4dd1eb940adb8bd7ff656af9b5e": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "f2c87a26b3e843afa6644bed13f5508c": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_7eb2a98375b74c91b45dc5b5accd9bca", "placeholder": "\u200b", "style": "IPY_MODEL_609ef4dd1eb940adb8bd7ff656af9b5e", "value": "classifier.ckpt: 100%"}}, "21a9a1365c8149ebafde1882681607b7": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "030585901c0a49c3976e336a1ab51ee1": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "959ba9c2758b476f942db7be7ea533c2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_21a9a1365c8149ebafde1882681607b7", "placeholder": "\u200b", "style": "IPY_MODEL_030585901c0a49c3976e336a1ab51ee1", "value": " 15.9M/15.9M [00:00<00:00, 89.8MB/s]"}}, "965eba21261d4a2e9ee7513ec333adc2": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "289a6e5f2aa24222a97d08ebc952a72e": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_f2c87a26b3e843afa6644bed13f5508c", "IPY_MODEL_7694a31ea9a04edcb6864f25b28b15cb", "IPY_MODEL_959ba9c2758b476f942db7be7ea533c2"], "layout": "IPY_MODEL_965eba21261d4a2e9ee7513ec333adc2"}}, "c4ebdef1b4f14614814debc42af581c4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f8c0ae7aca4c47fdb99824d3a5c6c79b": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "e364bdc739a344f9b5a54749543225af": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c4ebdef1b4f14614814debc42af581c4", "max": 128619.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f8c0ae7aca4c47fdb99824d3a5c6c79b", "value": 128619.0}}, "766477efa9484d7b8b8cc8a77a1b6f7d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "d3cc29ff5820423989ce16d9d70bcd31": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "8d8b99dd01c64a7ca9041d32353eac13": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_766477efa9484d7b8b8cc8a77a1b6f7d", "placeholder": "\u200b", "style": "IPY_MODEL_d3cc29ff5820423989ce16d9d70bcd31", "value": "label_encoder.txt: 100%"}}, "da437028c14a404b9d0753eab94e3c55": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "df6099d96cc04692aa1804b28f0d3165": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "320bad48fde04048bd4f6c3c7723b882": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_da437028c14a404b9d0753eab94e3c55", "placeholder": "\u200b", "style": "IPY_MODEL_df6099d96cc04692aa1804b28f0d3165", "value": " 129k/129k [00:00<00:00, 12.9MB/s]"}}, "d9416e2443dc46e89ea2b97a96b892cb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4a7fe1b9aff24fc7a7e179b7f11d5bbd": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_8d8b99dd01c64a7ca9041d32353eac13", "IPY_MODEL_e364bdc739a344f9b5a54749543225af", "IPY_MODEL_320bad48fde04048bd4f6c3c7723b882"], "layout": "IPY_MODEL_d9416e2443dc46e89ea2b97a96b892cb"}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/audio.ipynb b/master/tutorials/audio.ipynb index 5d67baf75..a64acbf25 100644 --- a/master/tutorials/audio.ipynb +++ b/master/tutorials/audio.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:03:56.246621Z", - "iopub.status.busy": "2024-01-17T18:03:56.246426Z", - "iopub.status.idle": "2024-01-17T18:03:59.462158Z", - "shell.execute_reply": "2024-01-17T18:03:59.461536Z" + "iopub.execute_input": "2024-01-17T23:06:03.241225Z", + "iopub.status.busy": "2024-01-17T23:06:03.241029Z", + "iopub.status.idle": "2024-01-17T23:06:06.464107Z", + "shell.execute_reply": "2024-01-17T23:06:06.463420Z" }, "nbsphinx": "hidden" }, @@ -97,7 +97,7 @@ "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -131,10 +131,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:03:59.465517Z", - "iopub.status.busy": "2024-01-17T18:03:59.464868Z", - "iopub.status.idle": "2024-01-17T18:03:59.468353Z", - "shell.execute_reply": "2024-01-17T18:03:59.467776Z" + "iopub.execute_input": "2024-01-17T23:06:06.467168Z", + "iopub.status.busy": "2024-01-17T23:06:06.466788Z", + "iopub.status.idle": "2024-01-17T23:06:06.470304Z", + "shell.execute_reply": "2024-01-17T23:06:06.469673Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:03:59.470782Z", - "iopub.status.busy": "2024-01-17T18:03:59.470350Z", - "iopub.status.idle": "2024-01-17T18:03:59.475553Z", - "shell.execute_reply": "2024-01-17T18:03:59.475067Z" + "iopub.execute_input": "2024-01-17T23:06:06.472648Z", + "iopub.status.busy": "2024-01-17T23:06:06.472216Z", + "iopub.status.idle": "2024-01-17T23:06:06.477236Z", + "shell.execute_reply": "2024-01-17T23:06:06.476626Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-17T18:03:59.477904Z", - "iopub.status.busy": "2024-01-17T18:03:59.477550Z", - "iopub.status.idle": "2024-01-17T18:04:01.172783Z", - "shell.execute_reply": "2024-01-17T18:04:01.171901Z" + "iopub.execute_input": "2024-01-17T23:06:06.479737Z", + "iopub.status.busy": "2024-01-17T23:06:06.479248Z", + "iopub.status.idle": "2024-01-17T23:06:07.960092Z", + "shell.execute_reply": "2024-01-17T23:06:07.959366Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:01.175803Z", - "iopub.status.busy": "2024-01-17T18:04:01.175584Z", - "iopub.status.idle": "2024-01-17T18:04:01.187892Z", - "shell.execute_reply": "2024-01-17T18:04:01.187256Z" + "iopub.execute_input": "2024-01-17T23:06:07.963314Z", + "iopub.status.busy": "2024-01-17T23:06:07.962895Z", + "iopub.status.idle": "2024-01-17T23:06:07.975189Z", + "shell.execute_reply": "2024-01-17T23:06:07.974586Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:01.221136Z", - "iopub.status.busy": "2024-01-17T18:04:01.220534Z", - "iopub.status.idle": "2024-01-17T18:04:01.227460Z", - "shell.execute_reply": "2024-01-17T18:04:01.226813Z" + "iopub.execute_input": "2024-01-17T23:06:08.007234Z", + "iopub.status.busy": "2024-01-17T23:06:08.006813Z", + "iopub.status.idle": "2024-01-17T23:06:08.013562Z", + "shell.execute_reply": "2024-01-17T23:06:08.013029Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:01.230129Z", - "iopub.status.busy": "2024-01-17T18:04:01.229643Z", - "iopub.status.idle": "2024-01-17T18:04:01.936049Z", - "shell.execute_reply": "2024-01-17T18:04:01.935379Z" + "iopub.execute_input": "2024-01-17T23:06:08.015975Z", + "iopub.status.busy": "2024-01-17T23:06:08.015602Z", + "iopub.status.idle": "2024-01-17T23:06:08.737027Z", + "shell.execute_reply": "2024-01-17T23:06:08.736373Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:01.938750Z", - "iopub.status.busy": "2024-01-17T18:04:01.938361Z", - "iopub.status.idle": "2024-01-17T18:04:02.839523Z", - "shell.execute_reply": "2024-01-17T18:04:02.838812Z" + "iopub.execute_input": "2024-01-17T23:06:08.739545Z", + "iopub.status.busy": "2024-01-17T23:06:08.739230Z", + "iopub.status.idle": "2024-01-17T23:06:10.120689Z", + "shell.execute_reply": "2024-01-17T23:06:10.120102Z" }, "id": "vL9lkiKsHvKr" }, @@ -472,10 +472,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:02.842451Z", - "iopub.status.busy": "2024-01-17T18:04:02.842179Z", - "iopub.status.idle": "2024-01-17T18:04:02.864885Z", - "shell.execute_reply": "2024-01-17T18:04:02.864263Z" + "iopub.execute_input": "2024-01-17T23:06:10.123621Z", + "iopub.status.busy": "2024-01-17T23:06:10.123219Z", + "iopub.status.idle": "2024-01-17T23:06:10.145672Z", + "shell.execute_reply": "2024-01-17T23:06:10.145076Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -555,10 +555,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:02.867335Z", - "iopub.status.busy": "2024-01-17T18:04:02.866965Z", - "iopub.status.idle": "2024-01-17T18:04:02.870237Z", - "shell.execute_reply": "2024-01-17T18:04:02.869668Z" + "iopub.execute_input": "2024-01-17T23:06:10.148144Z", + "iopub.status.busy": "2024-01-17T23:06:10.147843Z", + "iopub.status.idle": "2024-01-17T23:06:10.151186Z", + "shell.execute_reply": "2024-01-17T23:06:10.150643Z" }, "id": "I8JqhOZgi94g" }, @@ -580,10 +580,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:02.872601Z", - "iopub.status.busy": "2024-01-17T18:04:02.872244Z", - "iopub.status.idle": "2024-01-17T18:04:21.805805Z", - "shell.execute_reply": "2024-01-17T18:04:21.805139Z" + "iopub.execute_input": "2024-01-17T23:06:10.153485Z", + "iopub.status.busy": "2024-01-17T23:06:10.153191Z", + "iopub.status.idle": "2024-01-17T23:06:28.541137Z", + "shell.execute_reply": "2024-01-17T23:06:28.540500Z" }, "id": "2FSQ2GR9R_YA" }, @@ -615,10 +615,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:21.809284Z", - "iopub.status.busy": "2024-01-17T18:04:21.808690Z", - "iopub.status.idle": "2024-01-17T18:04:21.813229Z", - "shell.execute_reply": "2024-01-17T18:04:21.812575Z" + "iopub.execute_input": "2024-01-17T23:06:28.544247Z", + "iopub.status.busy": "2024-01-17T23:06:28.543816Z", + "iopub.status.idle": "2024-01-17T23:06:28.548440Z", + "shell.execute_reply": "2024-01-17T23:06:28.547908Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -677,10 +677,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:21.815784Z", - "iopub.status.busy": "2024-01-17T18:04:21.815395Z", - "iopub.status.idle": "2024-01-17T18:04:27.285963Z", - "shell.execute_reply": "2024-01-17T18:04:27.285265Z" + "iopub.execute_input": "2024-01-17T23:06:28.550975Z", + "iopub.status.busy": "2024-01-17T23:06:28.550597Z", + "iopub.status.idle": "2024-01-17T23:06:34.059947Z", + "shell.execute_reply": "2024-01-17T23:06:34.059266Z" }, "id": "i_drkY9YOcw4" }, @@ -714,10 +714,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.289545Z", - "iopub.status.busy": "2024-01-17T18:04:27.289071Z", - "iopub.status.idle": "2024-01-17T18:04:27.294502Z", - "shell.execute_reply": "2024-01-17T18:04:27.293903Z" + "iopub.execute_input": "2024-01-17T23:06:34.063475Z", + "iopub.status.busy": "2024-01-17T23:06:34.062997Z", + "iopub.status.idle": "2024-01-17T23:06:34.068792Z", + "shell.execute_reply": "2024-01-17T23:06:34.068163Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -764,10 +764,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.297552Z", - "iopub.status.busy": "2024-01-17T18:04:27.297119Z", - "iopub.status.idle": "2024-01-17T18:04:27.391354Z", - "shell.execute_reply": "2024-01-17T18:04:27.390637Z" + "iopub.execute_input": "2024-01-17T23:06:34.071846Z", + "iopub.status.busy": "2024-01-17T23:06:34.071416Z", + "iopub.status.idle": "2024-01-17T23:06:34.185550Z", + "shell.execute_reply": "2024-01-17T23:06:34.184822Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.394410Z", - "iopub.status.busy": "2024-01-17T18:04:27.394019Z", - "iopub.status.idle": "2024-01-17T18:04:27.404313Z", - "shell.execute_reply": "2024-01-17T18:04:27.403781Z" + "iopub.execute_input": "2024-01-17T23:06:34.188375Z", + "iopub.status.busy": "2024-01-17T23:06:34.188110Z", + "iopub.status.idle": "2024-01-17T23:06:34.198290Z", + "shell.execute_reply": "2024-01-17T23:06:34.197648Z" }, "scrolled": true }, @@ -862,10 +862,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.406721Z", - "iopub.status.busy": "2024-01-17T18:04:27.406410Z", - "iopub.status.idle": "2024-01-17T18:04:27.414725Z", - "shell.execute_reply": "2024-01-17T18:04:27.414071Z" + "iopub.execute_input": "2024-01-17T23:06:34.200848Z", + "iopub.status.busy": "2024-01-17T23:06:34.200521Z", + "iopub.status.idle": "2024-01-17T23:06:34.208862Z", + "shell.execute_reply": "2024-01-17T23:06:34.208245Z" } }, "outputs": [ @@ -969,10 +969,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.417120Z", - "iopub.status.busy": "2024-01-17T18:04:27.416724Z", - "iopub.status.idle": "2024-01-17T18:04:27.421580Z", - "shell.execute_reply": "2024-01-17T18:04:27.420916Z" + "iopub.execute_input": "2024-01-17T23:06:34.211398Z", + "iopub.status.busy": "2024-01-17T23:06:34.211050Z", + "iopub.status.idle": "2024-01-17T23:06:34.215872Z", + "shell.execute_reply": "2024-01-17T23:06:34.215362Z" } }, "outputs": [ @@ -1010,10 +1010,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.423983Z", - "iopub.status.busy": "2024-01-17T18:04:27.423639Z", - "iopub.status.idle": "2024-01-17T18:04:27.429656Z", - "shell.execute_reply": "2024-01-17T18:04:27.429010Z" + "iopub.execute_input": "2024-01-17T23:06:34.218162Z", + "iopub.status.busy": "2024-01-17T23:06:34.217800Z", + "iopub.status.idle": "2024-01-17T23:06:34.224079Z", + "shell.execute_reply": "2024-01-17T23:06:34.223545Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1133,10 +1133,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.432082Z", - "iopub.status.busy": "2024-01-17T18:04:27.431713Z", - "iopub.status.idle": "2024-01-17T18:04:27.545999Z", - "shell.execute_reply": "2024-01-17T18:04:27.545312Z" + "iopub.execute_input": "2024-01-17T23:06:34.226655Z", + "iopub.status.busy": "2024-01-17T23:06:34.226193Z", + "iopub.status.idle": "2024-01-17T23:06:34.342868Z", + "shell.execute_reply": "2024-01-17T23:06:34.342208Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1190,10 +1190,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.548644Z", - "iopub.status.busy": "2024-01-17T18:04:27.548226Z", - "iopub.status.idle": "2024-01-17T18:04:27.658900Z", - "shell.execute_reply": "2024-01-17T18:04:27.658242Z" + "iopub.execute_input": "2024-01-17T23:06:34.345463Z", + "iopub.status.busy": "2024-01-17T23:06:34.345103Z", + "iopub.status.idle": "2024-01-17T23:06:34.452029Z", + "shell.execute_reply": "2024-01-17T23:06:34.451378Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1238,10 +1238,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.661353Z", - "iopub.status.busy": "2024-01-17T18:04:27.661065Z", - "iopub.status.idle": "2024-01-17T18:04:27.770988Z", - "shell.execute_reply": "2024-01-17T18:04:27.770337Z" + "iopub.execute_input": "2024-01-17T23:06:34.454818Z", + "iopub.status.busy": "2024-01-17T23:06:34.454245Z", + "iopub.status.idle": "2024-01-17T23:06:34.561699Z", + "shell.execute_reply": "2024-01-17T23:06:34.561010Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1282,10 +1282,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.773635Z", - "iopub.status.busy": "2024-01-17T18:04:27.773261Z", - "iopub.status.idle": "2024-01-17T18:04:27.880054Z", - "shell.execute_reply": "2024-01-17T18:04:27.879383Z" + "iopub.execute_input": "2024-01-17T23:06:34.564138Z", + "iopub.status.busy": "2024-01-17T23:06:34.563922Z", + "iopub.status.idle": "2024-01-17T23:06:34.673537Z", + "shell.execute_reply": "2024-01-17T23:06:34.672870Z" } }, "outputs": [ @@ -1333,10 +1333,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:27.882841Z", - "iopub.status.busy": "2024-01-17T18:04:27.882447Z", - "iopub.status.idle": "2024-01-17T18:04:27.885966Z", - "shell.execute_reply": "2024-01-17T18:04:27.885407Z" + "iopub.execute_input": "2024-01-17T23:06:34.675986Z", + "iopub.status.busy": "2024-01-17T23:06:34.675761Z", + "iopub.status.idle": "2024-01-17T23:06:34.679368Z", + "shell.execute_reply": "2024-01-17T23:06:34.678729Z" }, "nbsphinx": "hidden" }, @@ -1377,65 +1377,37 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "04545eb82c614214a57ff48711e5ef69": { + "030585901c0a49c3976e336a1ab51ee1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e38a6eaeba49436d880afec59461ee06", - "placeholder": "​", - "style": "IPY_MODEL_9a471f99ebec460f82f340da4bf084a3", - "value": "embedding_model.ckpt: 100%" - } - }, - "0fd11d936da54fa68096671994e1275f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_617fa189f462470a937beee9505799fa", - "placeholder": "​", - "style": "IPY_MODEL_9fca2e61f7d64cea930c8b07bdcbc59e", - "value": " 3.20k/3.20k [00:00<00:00, 549kB/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "159b496215244dc799dd25dae2e0470d": { + "0ff00007ea674a8496fed6616e438639": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "1acfc2e65c71473497f43eb9e974bc92": { + "1bdf3168328746ddb97b58d22771e706": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -1450,65 +1422,66 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_db86af8139ff41018b497ff1758537de", - "IPY_MODEL_fc52934f923d48f6815ef3ea863cb962", - "IPY_MODEL_ebe5bf1237e54a5cb31a7953735ed040" + "IPY_MODEL_8d69a36aa0ef4ac398e5473efe8facaf", + "IPY_MODEL_4013766ebdd64bcdaa57852522f87774", + "IPY_MODEL_7b3c7b951bc643fea2c2d55d50e02569" ], - "layout": "IPY_MODEL_7fda371c9fba45549d352696e38b0c16" - } - }, - "1cca100b2cea4a77bbbcc5a23f19e1d7": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_da8efe510bcf414b9d2c76f5f791e9ea", - "placeholder": "​", - "style": "IPY_MODEL_220c65a6f5bb442e8b509643c78298cb", - "value": "label_encoder.txt: 100%" - } - }, - "220c65a6f5bb442e8b509643c78298cb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "layout": "IPY_MODEL_46c6f6135bb9449482ba0294d1515087" } }, - "24e87b41e33643a0a449e4e5f4a04a21": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "1eb624a3dad54e7fa6b146681c26c1fd": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "25691bde0c394a299f774959e32e539d": { + "2105d31075094aec8143dc36c2cc4c8a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -1524,7 +1497,7 @@ "description_width": "" } }, - "25c36bd0682a44a1af90eecaec670b14": { + "21a9a1365c8149ebafde1882681607b7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1576,28 +1549,7 @@ "width": null } }, - "2fb3c50dfd544cf297447c4dd5e4bdfa": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_5836728e785d44ce8ebe512cb1ffe735", - "placeholder": "​", - "style": "IPY_MODEL_dae854f92b7548ddb4b4981bb4a8c9d2", - "value": " 16.9M/16.9M [00:00<00:00, 96.3MB/s]" - } - }, - "32761d387fb34f55a8662c19898e2e19": { + "2415134083d142f18e75d9c1fd90ad9e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1649,68 +1601,102 @@ "width": null } }, - "41336a38204142909d6d6151660b30ef": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "26707147e6f24b498f499aec25d3879a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "42cde93ea0f44516bebd28767667c2fa": { + "289a6e5f2aa24222a97d08ebc952a72e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_ccb07543f38c422eba6e58deccf2a389", - "max": 2041.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_55ca6bb4d3414d2091e2d6cc744d0227", - "value": 2041.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_f2c87a26b3e843afa6644bed13f5508c", + "IPY_MODEL_7694a31ea9a04edcb6864f25b28b15cb", + "IPY_MODEL_959ba9c2758b476f942db7be7ea533c2" + ], + "layout": "IPY_MODEL_965eba21261d4a2e9ee7513ec333adc2" } }, - "4a8b0b00351a48b1bd3c4ffb02422045": { + "320bad48fde04048bd4f6c3c7723b882": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_649eb4698e3443adb0fd32629e36ece6", - "IPY_MODEL_c9aad8223fed4fd79a1fa84e675e9c34", - "IPY_MODEL_0fd11d936da54fa68096671994e1275f" - ], - "layout": "IPY_MODEL_c3b13273fa614276aa8cd31a0d3ba6a0" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_da437028c14a404b9d0753eab94e3c55", + "placeholder": "​", + "style": "IPY_MODEL_df6099d96cc04692aa1804b28f0d3165", + "value": " 129k/129k [00:00<00:00, 12.9MB/s]" } }, - "4bdbef4700e34979a7835f1c9cb5f63b": { + "3b6ccb1c3d224e82864d450d361cb50f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1762,7 +1748,46 @@ "width": null } }, - "4d632ff2b07b4dffa2ead315843c6e6c": { + "3bfd515955e24904b4dc7bac5512c42f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4013766ebdd64bcdaa57852522f87774": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_ff2eea9324ab4fdebb914b6830cf4e4e", + "max": 16887676.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b391f6736d6440f5977ea7e4075132b1", + "value": 16887676.0 + } + }, + "424d86c2319543f0b0d18ccae78cd508": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1777,7 +1802,7 @@ "description_width": "" } }, - "5105284d7b9346b09f3d678a6f15a3cb": { + "46c6f6135bb9449482ba0294d1515087": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1829,44 +1854,65 @@ "width": null } }, - "53e5f5516c1940eb84c59305f879b33d": { + "48805febf856433c84dda4034fb9c0c6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_53a59a607d394f2e832ba2a5f8cd33f2", + "placeholder": "​", + "style": "IPY_MODEL_0ff00007ea674a8496fed6616e438639", + "value": " 3.20k/3.20k [00:00<00:00, 511kB/s]" + } + }, + "4a7fe1b9aff24fc7a7e179b7f11d5bbd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_5105284d7b9346b09f3d678a6f15a3cb", - "placeholder": "​", - "style": "IPY_MODEL_db5c6c408f9147df9a5d44f8f886ab61", - "value": " 2.04k/2.04k [00:00<00:00, 352kB/s]" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_8d8b99dd01c64a7ca9041d32353eac13", + "IPY_MODEL_e364bdc739a344f9b5a54749543225af", + "IPY_MODEL_320bad48fde04048bd4f6c3c7723b882" + ], + "layout": "IPY_MODEL_d9416e2443dc46e89ea2b97a96b892cb" } }, - "55ca6bb4d3414d2091e2d6cc744d0227": { + "4aba10cb04694ab4bf360dfefe9871d7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "5836728e785d44ce8ebe512cb1ffe735": { + "508a9cef5621429ca2ad08b2e5a5b906": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1918,7 +1964,7 @@ "width": null } }, - "617fa189f462470a937beee9505799fa": { + "53a59a607d394f2e832ba2a5f8cd33f2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1970,7 +2016,44 @@ "width": null } }, - "61d06a92b987421cb46d8a89d1975bb7": { + "5fd842770f3c46198750ec0bc065bbec": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_851a3e78f4cb49c28fa6d9d3f6ad533c", + "IPY_MODEL_b3b1a6b802c2438aa6b7345347be1daa", + "IPY_MODEL_48805febf856433c84dda4034fb9c0c6" + ], + "layout": "IPY_MODEL_cc06c8dc25a146d1949bbc6f284744e5" + } + }, + "609ef4dd1eb940adb8bd7ff656af9b5e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "766477efa9484d7b8b8cc8a77a1b6f7d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2022,112 +2105,52 @@ "width": null } }, - "649eb4698e3443adb0fd32629e36ece6": { + "7694a31ea9a04edcb6864f25b28b15cb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_4bdbef4700e34979a7835f1c9cb5f63b", - "placeholder": "​", - "style": "IPY_MODEL_41336a38204142909d6d6151660b30ef", - "value": "mean_var_norm_emb.ckpt: 100%" - } - }, - "64de8f2a3f18422084ccd72434dca5cc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_1cca100b2cea4a77bbbcc5a23f19e1d7", - "IPY_MODEL_7137378c46a441c9b8f6dd4cd7cd9ee2", - "IPY_MODEL_7aaa5224bfe44036a333755a894e18b5" - ], - "layout": "IPY_MODEL_25c36bd0682a44a1af90eecaec670b14" - } - }, - "660387b90c454207a12955625f60a888": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "69f5f977755e4a4b82b5ca684955dd9e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_04545eb82c614214a57ff48711e5ef69", - "IPY_MODEL_886f26d855ae49049a473788aaf13fdd", - "IPY_MODEL_2fb3c50dfd544cf297447c4dd5e4bdfa" - ], - "layout": "IPY_MODEL_77fefa233aa04e3ba284da2418760799" + "layout": "IPY_MODEL_508a9cef5621429ca2ad08b2e5a5b906", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_2105d31075094aec8143dc36c2cc4c8a", + "value": 15856877.0 } }, - "7137378c46a441c9b8f6dd4cd7cd9ee2": { + "7b3c7b951bc643fea2c2d55d50e02569": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_78e330e8413b4c638b5f5ad1892ffc7c", - "max": 128619.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_25691bde0c394a299f774959e32e539d", - "value": 128619.0 + "layout": "IPY_MODEL_3b6ccb1c3d224e82864d450d361cb50f", + "placeholder": "​", + "style": "IPY_MODEL_3bfd515955e24904b4dc7bac5512c42f", + "value": " 16.9M/16.9M [00:00<00:00, 74.2MB/s]" } }, - "77fefa233aa04e3ba284da2418760799": { + "7eb2a98375b74c91b45dc5b5accd9bca": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2179,7 +2202,7 @@ "width": null } }, - "78e330e8413b4c638b5f5ad1892ffc7c": { + "83f7b9bff2ec40f19a358ae6e9071f3a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2231,7 +2254,7 @@ "width": null } }, - "799d78d15e1549e58376f474b256b3f1": { + "851a3e78f4cb49c28fa6d9d3f6ad533c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2246,13 +2269,28 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_8c2848fd80e94dfeba2260153670f5b5", + "layout": "IPY_MODEL_ee5f56e9b26d44c29b98957642b5cbe6", "placeholder": "​", - "style": "IPY_MODEL_d73d40dffae24b919506395235104b28", - "value": "hyperparams.yaml: 100%" + "style": "IPY_MODEL_4aba10cb04694ab4bf360dfefe9871d7", + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "8699a0a7cb124a369027b929c14aca25": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "7aaa5224bfe44036a333755a894e18b5": { + "8d69a36aa0ef4ac398e5473efe8facaf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2267,89 +2305,55 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_61d06a92b987421cb46d8a89d1975bb7", + "layout": "IPY_MODEL_2415134083d142f18e75d9c1fd90ad9e", "placeholder": "​", - "style": "IPY_MODEL_f1827a2afaa3497796df337825e00c5d", - "value": " 129k/129k [00:00<00:00, 13.1MB/s]" + "style": "IPY_MODEL_424d86c2319543f0b0d18ccae78cd508", + "value": "embedding_model.ckpt: 100%" } }, - "7fda371c9fba45549d352696e38b0c16": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "8d8b99dd01c64a7ca9041d32353eac13": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_766477efa9484d7b8b8cc8a77a1b6f7d", + "placeholder": "​", + "style": "IPY_MODEL_d3cc29ff5820423989ce16d9d70bcd31", + "value": "label_encoder.txt: 100%" } }, - "886f26d855ae49049a473788aaf13fdd": { + "959ba9c2758b476f942db7be7ea533c2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_bbb0a42cbeb44cd2807289cb84e06c66", - "max": 16887676.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_660387b90c454207a12955625f60a888", - "value": 16887676.0 + "layout": "IPY_MODEL_21a9a1365c8149ebafde1882681607b7", + "placeholder": "​", + "style": "IPY_MODEL_030585901c0a49c3976e336a1ab51ee1", + "value": " 15.9M/15.9M [00:00<00:00, 89.8MB/s]" } }, - "8c2848fd80e94dfeba2260153670f5b5": { + "965eba21261d4a2e9ee7513ec333adc2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2401,7 +2405,7 @@ "width": null } }, - "95589bfbae6545eea65cdda502cb8591": { + "aa7002ac34ad461c971cb567fbaed7c7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -2417,59 +2421,83 @@ "description_width": "" } }, - "9a471f99ebec460f82f340da4bf084a3": { + "b391f6736d6440f5977ea7e4075132b1": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "9fca2e61f7d64cea930c8b07bdcbc59e": { + "b3b1a6b802c2438aa6b7345347be1daa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d3db5175210c48839bd72a54bbd9e709", + "max": 3201.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_aa7002ac34ad461c971cb567fbaed7c7", + "value": 3201.0 } }, - "a2cd93bedff346d4b94c8c101e88352e": { + "b6341a7e935242c2adc1364ec71311bf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_83f7b9bff2ec40f19a358ae6e9071f3a", + "placeholder": "​", + "style": "IPY_MODEL_bf1dc44a81b64e408396f375ab6db707", + "value": " 2.04k/2.04k [00:00<00:00, 337kB/s]" + } + }, + "bf1dc44a81b64e408396f375ab6db707": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_799d78d15e1549e58376f474b256b3f1", - "IPY_MODEL_42cde93ea0f44516bebd28767667c2fa", - "IPY_MODEL_53e5f5516c1940eb84c59305f879b33d" - ], - "layout": "IPY_MODEL_32761d387fb34f55a8662c19898e2e19" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "a70c7781a1174beb8b08bdb996fa2bc8": { + "c4ebdef1b4f14614814debc42af581c4": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2521,7 +2549,29 @@ "width": null } }, - "bbb0a42cbeb44cd2807289cb84e06c66": { + "c5b91c7d529d4e1a9a36a41238ba0244": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ed7a2be85ae641c2b6cb2f1e8fb8054b", + "IPY_MODEL_cc503c61cdb6438ea915c9aa1032c63c", + "IPY_MODEL_b6341a7e935242c2adc1364ec71311bf" + ], + "layout": "IPY_MODEL_fa264daf6009497ca63787f2eb2c7503" + } + }, + "cc06c8dc25a146d1949bbc6f284744e5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2573,7 +2623,62 @@ "width": null } }, - "c0de636f211e43f793ee2c8ccaccead2": { + "cc503c61cdb6438ea915c9aa1032c63c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_26707147e6f24b498f499aec25d3879a", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_d21e1fb2327a464b8b743384ffa08cc0", + "value": 2041.0 + } + }, + "d21e1fb2327a464b8b743384ffa08cc0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "d3cc29ff5820423989ce16d9d70bcd31": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "d3db5175210c48839bd72a54bbd9e709": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2625,7 +2730,7 @@ "width": null } }, - "c30853559a364583b1f650a150b6654a": { + "d9416e2443dc46e89ea2b97a96b892cb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2677,7 +2782,7 @@ "width": null } }, - "c3b13273fa614276aa8cd31a0d3ba6a0": { + "da437028c14a404b9d0753eab94e3c55": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2729,7 +2834,22 @@ "width": null } }, - "c9aad8223fed4fd79a1fa84e675e9c34": { + "df6099d96cc04692aa1804b28f0d3165": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "e364bdc739a344f9b5a54749543225af": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -2745,82 +2865,36 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c0de636f211e43f793ee2c8ccaccead2", - "max": 3201.0, + "layout": "IPY_MODEL_c4ebdef1b4f14614814debc42af581c4", + "max": 128619.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_95589bfbae6545eea65cdda502cb8591", - "value": 3201.0 - } - }, - "ccb07543f38c422eba6e58deccf2a389": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "style": "IPY_MODEL_f8c0ae7aca4c47fdb99824d3a5c6c79b", + "value": 128619.0 } }, - "d73d40dffae24b919506395235104b28": { + "ed7a2be85ae641c2b6cb2f1e8fb8054b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_1eb624a3dad54e7fa6b146681c26c1fd", + "placeholder": "​", + "style": "IPY_MODEL_8699a0a7cb124a369027b929c14aca25", + "value": "hyperparams.yaml: 100%" } }, - "da8efe510bcf414b9d2c76f5f791e9ea": { + "ee5f56e9b26d44c29b98957642b5cbe6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2872,58 +2946,44 @@ "width": null } }, - "dae854f92b7548ddb4b4981bb4a8c9d2": { + "f2c87a26b3e843afa6644bed13f5508c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HTMLModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HTMLModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7eb2a98375b74c91b45dc5b5accd9bca", + "placeholder": "​", + "style": "IPY_MODEL_609ef4dd1eb940adb8bd7ff656af9b5e", + "value": "classifier.ckpt: 100%" } }, - "db5c6c408f9147df9a5d44f8f886ab61": { + "f8c0ae7aca4c47fdb99824d3a5c6c79b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "db86af8139ff41018b497ff1758537de": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_a70c7781a1174beb8b08bdb996fa2bc8", - "placeholder": "​", - "style": "IPY_MODEL_4d632ff2b07b4dffa2ead315843c6e6c", - "value": "classifier.ckpt: 100%" - } - }, - "e38a6eaeba49436d880afec59461ee06": { + "fa264daf6009497ca63787f2eb2c7503": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2975,7 +3035,7 @@ "width": null } }, - "e52c6bc80973455ab0806594b5177ef7": { + "ff2eea9324ab4fdebb914b6830cf4e4e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3026,66 +3086,6 @@ "visibility": null, "width": null } - }, - "ebe5bf1237e54a5cb31a7953735ed040": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_c30853559a364583b1f650a150b6654a", - "placeholder": "​", - "style": "IPY_MODEL_24e87b41e33643a0a449e4e5f4a04a21", - "value": " 15.9M/15.9M [00:00<00:00, 302MB/s]" - } - }, - "f1827a2afaa3497796df337825e00c5d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "fc52934f923d48f6815ef3ea863cb962": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e52c6bc80973455ab0806594b5177ef7", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_159b496215244dc799dd25dae2e0470d", - "value": 15856877.0 - } } }, "version_major": 2, diff --git a/master/tutorials/datalab/datalab_advanced.html b/master/tutorials/datalab/datalab_advanced.html index 3437f6583..2c01ab7db 100644 --- a/master/tutorials/datalab/datalab_advanced.html +++ b/master/tutorials/datalab/datalab_advanced.html @@ -1453,7 +1453,7 @@

Functionality 3: Save and load Datalab objects
-
+
@@ -1718,7 +1718,7 @@

Functionality 4: Adding a custom IssueManager -{"state": {"8524043e6e114e199d262dc88bfc22da": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4a135c98d58f4354bffda310f2a613d9": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "051bf949fb4a4c34977914b2fdbd1a46": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_8524043e6e114e199d262dc88bfc22da", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_4a135c98d58f4354bffda310f2a613d9", "value": 132.0}}, "6ea64771986e42b6b69a518953aae518": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "5f0f68db532f4983830e8d3d0e1d9c55": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "f247eef24dd44de39c6212b2720239c1": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_6ea64771986e42b6b69a518953aae518", "placeholder": "\u200b", "style": "IPY_MODEL_5f0f68db532f4983830e8d3d0e1d9c55", "value": "Saving the dataset (1/1 shards): 100%"}}, "1bec11a906f14351943811da375f1b68": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "328dc3e51eef4f61ada827866ade843a": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "016da6d5adb14dfcab43c2faeffdcac2": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1bec11a906f14351943811da375f1b68", "placeholder": "\u200b", "style": "IPY_MODEL_328dc3e51eef4f61ada827866ade843a", "value": " 132/132 [00:00<00:00, 11203.37 examples/s]"}}, "b3956bd3c1b34e468eb324a8aa73cd4e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ce0df2e674ad49f289b9c6629eb9a93b": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_f247eef24dd44de39c6212b2720239c1", "IPY_MODEL_051bf949fb4a4c34977914b2fdbd1a46", "IPY_MODEL_016da6d5adb14dfcab43c2faeffdcac2"], "layout": "IPY_MODEL_b3956bd3c1b34e468eb324a8aa73cd4e"}}}, "version_major": 2, "version_minor": 0} +{"state": {"2c9ed523e681457c97733b484f5d2302": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "dded8d49e1ea43ad9f1ab1944f0fa28e": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "ffd5efcaf7a945d984571d2a9f2057ad": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_2c9ed523e681457c97733b484f5d2302", "max": 132.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_dded8d49e1ea43ad9f1ab1944f0fa28e", "value": 132.0}}, "11eac3b2f4164e5694fdb5152fff54f1": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "9d1d43035b6d471cbf16064de0792a70": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "d48408d0b5804d8c8f562072e9542410": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_11eac3b2f4164e5694fdb5152fff54f1", "placeholder": "\u200b", "style": "IPY_MODEL_9d1d43035b6d471cbf16064de0792a70", "value": "Saving the dataset (1/1 shards): 100%"}}, "67e30ead652c4e178b1a2fa25ba4149c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "de85a0cc75b345a8b0257d3379f3b927": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "7c7a8b3a377f4891984c98cc48b480b5": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_67e30ead652c4e178b1a2fa25ba4149c", "placeholder": "\u200b", "style": "IPY_MODEL_de85a0cc75b345a8b0257d3379f3b927", "value": " 132/132 [00:00<00:00, 11349.20 examples/s]"}}, "bdeb99c1040f435d9ea59551e490a482": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "f9c446a6ae0e481a8f4425281acd2812": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_d48408d0b5804d8c8f562072e9542410", "IPY_MODEL_ffd5efcaf7a945d984571d2a9f2057ad", "IPY_MODEL_7c7a8b3a377f4891984c98cc48b480b5"], "layout": "IPY_MODEL_bdeb99c1040f435d9ea59551e490a482"}}}, "version_major": 2, "version_minor": 0} diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb index 1d05a4ac7..90cafbf27 100644 --- a/master/tutorials/datalab/datalab_advanced.ipynb +++ b/master/tutorials/datalab/datalab_advanced.ipynb @@ -80,10 +80,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:33.483819Z", - "iopub.status.busy": "2024-01-17T18:04:33.483262Z", - "iopub.status.idle": "2024-01-17T18:04:34.563147Z", - "shell.execute_reply": "2024-01-17T18:04:34.562532Z" + "iopub.execute_input": "2024-01-17T23:06:39.342500Z", + "iopub.status.busy": "2024-01-17T23:06:39.342321Z", + "iopub.status.idle": "2024-01-17T23:06:40.409562Z", + "shell.execute_reply": "2024-01-17T23:06:40.408997Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -118,10 +118,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:34.566022Z", - "iopub.status.busy": "2024-01-17T18:04:34.565584Z", - "iopub.status.idle": "2024-01-17T18:04:34.568829Z", - "shell.execute_reply": "2024-01-17T18:04:34.568346Z" + "iopub.execute_input": "2024-01-17T23:06:40.412370Z", + "iopub.status.busy": "2024-01-17T23:06:40.412089Z", + "iopub.status.idle": "2024-01-17T23:06:40.415239Z", + "shell.execute_reply": "2024-01-17T23:06:40.414697Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:34.571312Z", - "iopub.status.busy": "2024-01-17T18:04:34.571026Z", - "iopub.status.idle": "2024-01-17T18:04:34.580263Z", - "shell.execute_reply": "2024-01-17T18:04:34.579715Z" + "iopub.execute_input": "2024-01-17T23:06:40.417686Z", + "iopub.status.busy": "2024-01-17T23:06:40.417328Z", + "iopub.status.idle": "2024-01-17T23:06:40.426661Z", + "shell.execute_reply": "2024-01-17T23:06:40.426085Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:34.582624Z", - "iopub.status.busy": "2024-01-17T18:04:34.582267Z", - "iopub.status.idle": "2024-01-17T18:04:34.587260Z", - "shell.execute_reply": "2024-01-17T18:04:34.586787Z" + "iopub.execute_input": "2024-01-17T23:06:40.429048Z", + "iopub.status.busy": "2024-01-17T23:06:40.428682Z", + "iopub.status.idle": "2024-01-17T23:06:40.433294Z", + "shell.execute_reply": "2024-01-17T23:06:40.432812Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:34.589869Z", - "iopub.status.busy": "2024-01-17T18:04:34.589376Z", - "iopub.status.idle": "2024-01-17T18:04:34.868264Z", - "shell.execute_reply": "2024-01-17T18:04:34.867632Z" + "iopub.execute_input": "2024-01-17T23:06:40.435792Z", + "iopub.status.busy": "2024-01-17T23:06:40.435424Z", + "iopub.status.idle": "2024-01-17T23:06:40.706242Z", + "shell.execute_reply": "2024-01-17T23:06:40.705511Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:34.871315Z", - "iopub.status.busy": "2024-01-17T18:04:34.870886Z", - "iopub.status.idle": "2024-01-17T18:04:35.179091Z", - "shell.execute_reply": "2024-01-17T18:04:35.178424Z" + "iopub.execute_input": "2024-01-17T23:06:40.709010Z", + "iopub.status.busy": "2024-01-17T23:06:40.708793Z", + "iopub.status.idle": "2024-01-17T23:06:41.078542Z", + "shell.execute_reply": "2024-01-17T23:06:41.077853Z" } }, "outputs": [ @@ -568,10 +568,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:35.181786Z", - "iopub.status.busy": "2024-01-17T18:04:35.181405Z", - "iopub.status.idle": "2024-01-17T18:04:35.205575Z", - "shell.execute_reply": "2024-01-17T18:04:35.205040Z" + "iopub.execute_input": "2024-01-17T23:06:41.081271Z", + "iopub.status.busy": "2024-01-17T23:06:41.081043Z", + "iopub.status.idle": "2024-01-17T23:06:41.105700Z", + "shell.execute_reply": "2024-01-17T23:06:41.105204Z" } }, "outputs": [], @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:35.208176Z", - "iopub.status.busy": "2024-01-17T18:04:35.207800Z", - "iopub.status.idle": "2024-01-17T18:04:35.219387Z", - "shell.execute_reply": "2024-01-17T18:04:35.218891Z" + "iopub.execute_input": "2024-01-17T23:06:41.108273Z", + "iopub.status.busy": "2024-01-17T23:06:41.107900Z", + "iopub.status.idle": "2024-01-17T23:06:41.119557Z", + "shell.execute_reply": "2024-01-17T23:06:41.119070Z" } }, "outputs": [], @@ -641,10 +641,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:35.221936Z", - "iopub.status.busy": "2024-01-17T18:04:35.221576Z", - "iopub.status.idle": "2024-01-17T18:04:36.514513Z", - "shell.execute_reply": "2024-01-17T18:04:36.513826Z" + "iopub.execute_input": "2024-01-17T23:06:41.121850Z", + "iopub.status.busy": "2024-01-17T23:06:41.121645Z", + "iopub.status.idle": "2024-01-17T23:06:42.395090Z", + "shell.execute_reply": "2024-01-17T23:06:42.394350Z" } }, "outputs": [ @@ -708,10 +708,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.517319Z", - "iopub.status.busy": "2024-01-17T18:04:36.516739Z", - "iopub.status.idle": "2024-01-17T18:04:36.540062Z", - "shell.execute_reply": "2024-01-17T18:04:36.539415Z" + "iopub.execute_input": "2024-01-17T23:06:42.397825Z", + "iopub.status.busy": "2024-01-17T23:06:42.397474Z", + "iopub.status.idle": "2024-01-17T23:06:42.421815Z", + "shell.execute_reply": "2024-01-17T23:06:42.421176Z" } }, "outputs": [ @@ -820,10 +820,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.542755Z", - "iopub.status.busy": "2024-01-17T18:04:36.542357Z", - "iopub.status.idle": "2024-01-17T18:04:36.565338Z", - "shell.execute_reply": "2024-01-17T18:04:36.564636Z" + "iopub.execute_input": "2024-01-17T23:06:42.424433Z", + "iopub.status.busy": "2024-01-17T23:06:42.424030Z", + "iopub.status.idle": "2024-01-17T23:06:42.444464Z", + "shell.execute_reply": "2024-01-17T23:06:42.443784Z" } }, "outputs": [ @@ -935,10 +935,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.568303Z", - "iopub.status.busy": "2024-01-17T18:04:36.567786Z", - "iopub.status.idle": "2024-01-17T18:04:36.582311Z", - "shell.execute_reply": "2024-01-17T18:04:36.581766Z" + "iopub.execute_input": "2024-01-17T23:06:42.446887Z", + "iopub.status.busy": "2024-01-17T23:06:42.446518Z", + "iopub.status.idle": "2024-01-17T23:06:42.461229Z", + "shell.execute_reply": "2024-01-17T23:06:42.460605Z" } }, "outputs": [ @@ -1068,17 +1068,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.584583Z", - "iopub.status.busy": "2024-01-17T18:04:36.584386Z", - "iopub.status.idle": "2024-01-17T18:04:36.606116Z", - "shell.execute_reply": "2024-01-17T18:04:36.605481Z" + "iopub.execute_input": "2024-01-17T23:06:42.463847Z", + "iopub.status.busy": "2024-01-17T23:06:42.463265Z", + "iopub.status.idle": "2024-01-17T23:06:42.485045Z", + "shell.execute_reply": "2024-01-17T23:06:42.484382Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce0df2e674ad49f289b9c6629eb9a93b", + "model_id": "f9c446a6ae0e481a8f4425281acd2812", "version_major": 2, "version_minor": 0 }, @@ -1114,10 +1114,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.608360Z", - "iopub.status.busy": "2024-01-17T18:04:36.608121Z", - "iopub.status.idle": "2024-01-17T18:04:36.623341Z", - "shell.execute_reply": "2024-01-17T18:04:36.622732Z" + "iopub.execute_input": "2024-01-17T23:06:42.487982Z", + "iopub.status.busy": "2024-01-17T23:06:42.487461Z", + "iopub.status.idle": "2024-01-17T23:06:42.502778Z", + "shell.execute_reply": "2024-01-17T23:06:42.502139Z" } }, "outputs": [ @@ -1235,10 +1235,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.625892Z", - "iopub.status.busy": "2024-01-17T18:04:36.625457Z", - "iopub.status.idle": "2024-01-17T18:04:36.631828Z", - "shell.execute_reply": "2024-01-17T18:04:36.631217Z" + "iopub.execute_input": "2024-01-17T23:06:42.505343Z", + "iopub.status.busy": "2024-01-17T23:06:42.504854Z", + "iopub.status.idle": "2024-01-17T23:06:42.511266Z", + "shell.execute_reply": "2024-01-17T23:06:42.510747Z" } }, "outputs": [], @@ -1295,10 +1295,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:36.634265Z", - "iopub.status.busy": "2024-01-17T18:04:36.633885Z", - "iopub.status.idle": "2024-01-17T18:04:36.652691Z", - "shell.execute_reply": "2024-01-17T18:04:36.652133Z" + "iopub.execute_input": "2024-01-17T23:06:42.513763Z", + "iopub.status.busy": "2024-01-17T23:06:42.513319Z", + "iopub.status.idle": "2024-01-17T23:06:42.532164Z", + "shell.execute_reply": "2024-01-17T23:06:42.531605Z" } }, "outputs": [ @@ -1430,52 +1430,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "016da6d5adb14dfcab43c2faeffdcac2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_1bec11a906f14351943811da375f1b68", - "placeholder": "​", - "style": "IPY_MODEL_328dc3e51eef4f61ada827866ade843a", - "value": " 132/132 [00:00<00:00, 11203.37 examples/s]" - } - }, - "051bf949fb4a4c34977914b2fdbd1a46": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_8524043e6e114e199d262dc88bfc22da", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4a135c98d58f4354bffda310f2a613d9", - "value": 132.0 - } - }, - "1bec11a906f14351943811da375f1b68": { + "11eac3b2f4164e5694fdb5152fff54f1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1527,53 +1482,7 @@ "width": null } }, - "328dc3e51eef4f61ada827866ade843a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "4a135c98d58f4354bffda310f2a613d9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "5f0f68db532f4983830e8d3d0e1d9c55": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "6ea64771986e42b6b69a518953aae518": { + "2c9ed523e681457c97733b484f5d2302": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1625,7 +1534,7 @@ "width": null } }, - "8524043e6e114e199d262dc88bfc22da": { + "67e30ead652c4e178b1a2fa25ba4149c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1677,7 +1586,43 @@ "width": null } }, - "b3956bd3c1b34e468eb324a8aa73cd4e": { + "7c7a8b3a377f4891984c98cc48b480b5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_67e30ead652c4e178b1a2fa25ba4149c", + "placeholder": "​", + "style": "IPY_MODEL_de85a0cc75b345a8b0257d3379f3b927", + "value": " 132/132 [00:00<00:00, 11349.20 examples/s]" + } + }, + "9d1d43035b6d471cbf16064de0792a70": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "bdeb99c1040f435d9ea59551e490a482": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1729,7 +1674,59 @@ "width": null } }, - "ce0df2e674ad49f289b9c6629eb9a93b": { + "d48408d0b5804d8c8f562072e9542410": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_11eac3b2f4164e5694fdb5152fff54f1", + "placeholder": "​", + "style": "IPY_MODEL_9d1d43035b6d471cbf16064de0792a70", + "value": "Saving the dataset (1/1 shards): 100%" + } + }, + "dded8d49e1ea43ad9f1ab1944f0fa28e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "de85a0cc75b345a8b0257d3379f3b927": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f9c446a6ae0e481a8f4425281acd2812": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -1744,32 +1741,35 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_f247eef24dd44de39c6212b2720239c1", - "IPY_MODEL_051bf949fb4a4c34977914b2fdbd1a46", - "IPY_MODEL_016da6d5adb14dfcab43c2faeffdcac2" + "IPY_MODEL_d48408d0b5804d8c8f562072e9542410", + "IPY_MODEL_ffd5efcaf7a945d984571d2a9f2057ad", + "IPY_MODEL_7c7a8b3a377f4891984c98cc48b480b5" ], - "layout": "IPY_MODEL_b3956bd3c1b34e468eb324a8aa73cd4e" + "layout": "IPY_MODEL_bdeb99c1040f435d9ea59551e490a482" } }, - "f247eef24dd44de39c6212b2720239c1": { + "ffd5efcaf7a945d984571d2a9f2057ad": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_6ea64771986e42b6b69a518953aae518", - "placeholder": "​", - "style": "IPY_MODEL_5f0f68db532f4983830e8d3d0e1d9c55", - "value": "Saving the dataset (1/1 shards): 100%" + "layout": "IPY_MODEL_2c9ed523e681457c97733b484f5d2302", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_dded8d49e1ea43ad9f1ab1944f0fa28e", + "value": 132.0 } } }, diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index 1697ceead..d3df4ed89 100644 --- a/master/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:41.467132Z", - "iopub.status.busy": "2024-01-17T18:04:41.466518Z", - "iopub.status.idle": "2024-01-17T18:04:42.555591Z", - "shell.execute_reply": "2024-01-17T18:04:42.554980Z" + "iopub.execute_input": "2024-01-17T23:06:47.569861Z", + "iopub.status.busy": "2024-01-17T23:06:47.569660Z", + "iopub.status.idle": "2024-01-17T23:06:48.641275Z", + "shell.execute_reply": "2024-01-17T23:06:48.640563Z" }, "nbsphinx": "hidden" }, @@ -91,7 +91,7 @@ "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -116,10 +116,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:42.558508Z", - "iopub.status.busy": "2024-01-17T18:04:42.558086Z", - "iopub.status.idle": "2024-01-17T18:04:42.561388Z", - "shell.execute_reply": "2024-01-17T18:04:42.560867Z" + "iopub.execute_input": "2024-01-17T23:06:48.644454Z", + "iopub.status.busy": "2024-01-17T23:06:48.643822Z", + "iopub.status.idle": "2024-01-17T23:06:48.647145Z", + "shell.execute_reply": "2024-01-17T23:06:48.646571Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:42.563994Z", - "iopub.status.busy": "2024-01-17T18:04:42.563549Z", - "iopub.status.idle": "2024-01-17T18:04:42.573492Z", - "shell.execute_reply": "2024-01-17T18:04:42.572963Z" + "iopub.execute_input": "2024-01-17T23:06:48.649505Z", + "iopub.status.busy": "2024-01-17T23:06:48.649176Z", + "iopub.status.idle": "2024-01-17T23:06:48.658971Z", + "shell.execute_reply": "2024-01-17T23:06:48.658347Z" }, "nbsphinx": "hidden" }, @@ -356,10 +356,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:42.575559Z", - "iopub.status.busy": "2024-01-17T18:04:42.575357Z", - "iopub.status.idle": "2024-01-17T18:04:42.579953Z", - "shell.execute_reply": "2024-01-17T18:04:42.579468Z" + "iopub.execute_input": "2024-01-17T23:06:48.661358Z", + "iopub.status.busy": "2024-01-17T23:06:48.661025Z", + "iopub.status.idle": "2024-01-17T23:06:48.666186Z", + "shell.execute_reply": "2024-01-17T23:06:48.665652Z" } }, "outputs": [], @@ -448,10 +448,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:42.582528Z", - "iopub.status.busy": "2024-01-17T18:04:42.582149Z", - "iopub.status.idle": "2024-01-17T18:04:42.863268Z", - "shell.execute_reply": "2024-01-17T18:04:42.862639Z" + "iopub.execute_input": "2024-01-17T23:06:48.668478Z", + "iopub.status.busy": "2024-01-17T23:06:48.668137Z", + "iopub.status.idle": "2024-01-17T23:06:48.950866Z", + "shell.execute_reply": "2024-01-17T23:06:48.950180Z" }, "nbsphinx": "hidden" }, @@ -520,10 +520,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:42.866012Z", - "iopub.status.busy": "2024-01-17T18:04:42.865802Z", - "iopub.status.idle": "2024-01-17T18:04:43.236893Z", - "shell.execute_reply": "2024-01-17T18:04:43.236235Z" + "iopub.execute_input": "2024-01-17T23:06:48.953576Z", + "iopub.status.busy": "2024-01-17T23:06:48.953323Z", + "iopub.status.idle": "2024-01-17T23:06:49.325530Z", + "shell.execute_reply": "2024-01-17T23:06:49.324859Z" } }, "outputs": [ @@ -559,10 +559,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:43.239627Z", - "iopub.status.busy": "2024-01-17T18:04:43.239261Z", - "iopub.status.idle": "2024-01-17T18:04:43.242241Z", - "shell.execute_reply": "2024-01-17T18:04:43.241645Z" + "iopub.execute_input": "2024-01-17T23:06:49.328443Z", + "iopub.status.busy": "2024-01-17T23:06:49.327940Z", + "iopub.status.idle": "2024-01-17T23:06:49.331128Z", + "shell.execute_reply": "2024-01-17T23:06:49.330575Z" } }, "outputs": [], @@ -601,10 +601,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:43.244798Z", - "iopub.status.busy": "2024-01-17T18:04:43.244450Z", - "iopub.status.idle": "2024-01-17T18:04:43.282859Z", - "shell.execute_reply": "2024-01-17T18:04:43.282243Z" + "iopub.execute_input": "2024-01-17T23:06:49.333565Z", + "iopub.status.busy": "2024-01-17T23:06:49.333216Z", + "iopub.status.idle": "2024-01-17T23:06:49.371453Z", + "shell.execute_reply": "2024-01-17T23:06:49.370801Z" } }, "outputs": [ @@ -646,10 +646,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:43.285162Z", - "iopub.status.busy": "2024-01-17T18:04:43.284947Z", - "iopub.status.idle": "2024-01-17T18:04:44.608959Z", - "shell.execute_reply": "2024-01-17T18:04:44.608186Z" + "iopub.execute_input": "2024-01-17T23:06:49.373991Z", + "iopub.status.busy": "2024-01-17T23:06:49.373529Z", + "iopub.status.idle": "2024-01-17T23:06:50.680046Z", + "shell.execute_reply": "2024-01-17T23:06:50.679378Z" } }, "outputs": [ @@ -701,10 +701,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.611888Z", - "iopub.status.busy": "2024-01-17T18:04:44.611313Z", - "iopub.status.idle": "2024-01-17T18:04:44.636218Z", - "shell.execute_reply": "2024-01-17T18:04:44.635671Z" + "iopub.execute_input": "2024-01-17T23:06:50.682695Z", + "iopub.status.busy": "2024-01-17T23:06:50.682347Z", + "iopub.status.idle": "2024-01-17T23:06:50.707371Z", + "shell.execute_reply": "2024-01-17T23:06:50.706854Z" } }, "outputs": [ @@ -878,10 +878,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.638722Z", - "iopub.status.busy": "2024-01-17T18:04:44.638518Z", - "iopub.status.idle": "2024-01-17T18:04:44.646754Z", - "shell.execute_reply": "2024-01-17T18:04:44.646231Z" + "iopub.execute_input": "2024-01-17T23:06:50.709806Z", + "iopub.status.busy": "2024-01-17T23:06:50.709606Z", + "iopub.status.idle": "2024-01-17T23:06:50.716253Z", + "shell.execute_reply": "2024-01-17T23:06:50.715721Z" } }, "outputs": [ @@ -985,10 +985,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.649051Z", - "iopub.status.busy": "2024-01-17T18:04:44.648690Z", - "iopub.status.idle": "2024-01-17T18:04:44.654993Z", - "shell.execute_reply": "2024-01-17T18:04:44.654393Z" + "iopub.execute_input": "2024-01-17T23:06:50.718504Z", + "iopub.status.busy": "2024-01-17T23:06:50.718307Z", + "iopub.status.idle": "2024-01-17T23:06:50.724463Z", + "shell.execute_reply": "2024-01-17T23:06:50.723963Z" } }, "outputs": [ @@ -1055,10 +1055,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.657457Z", - "iopub.status.busy": "2024-01-17T18:04:44.657064Z", - "iopub.status.idle": "2024-01-17T18:04:44.667959Z", - "shell.execute_reply": "2024-01-17T18:04:44.667358Z" + "iopub.execute_input": "2024-01-17T23:06:50.726704Z", + "iopub.status.busy": "2024-01-17T23:06:50.726464Z", + "iopub.status.idle": "2024-01-17T23:06:50.737224Z", + "shell.execute_reply": "2024-01-17T23:06:50.736715Z" } }, "outputs": [ @@ -1231,10 +1231,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.670277Z", - "iopub.status.busy": "2024-01-17T18:04:44.670077Z", - "iopub.status.idle": "2024-01-17T18:04:44.679556Z", - "shell.execute_reply": "2024-01-17T18:04:44.679053Z" + "iopub.execute_input": "2024-01-17T23:06:50.739595Z", + "iopub.status.busy": "2024-01-17T23:06:50.739250Z", + "iopub.status.idle": "2024-01-17T23:06:50.748736Z", + "shell.execute_reply": "2024-01-17T23:06:50.748121Z" } }, "outputs": [ @@ -1350,10 +1350,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.681741Z", - "iopub.status.busy": "2024-01-17T18:04:44.681546Z", - "iopub.status.idle": "2024-01-17T18:04:44.689258Z", - "shell.execute_reply": "2024-01-17T18:04:44.688717Z" + "iopub.execute_input": "2024-01-17T23:06:50.751142Z", + "iopub.status.busy": "2024-01-17T23:06:50.750687Z", + "iopub.status.idle": "2024-01-17T23:06:50.758345Z", + "shell.execute_reply": "2024-01-17T23:06:50.757710Z" }, "scrolled": true }, @@ -1478,10 +1478,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:44.691411Z", - "iopub.status.busy": "2024-01-17T18:04:44.691219Z", - "iopub.status.idle": "2024-01-17T18:04:44.701261Z", - "shell.execute_reply": "2024-01-17T18:04:44.700743Z" + "iopub.execute_input": "2024-01-17T23:06:50.760695Z", + "iopub.status.busy": "2024-01-17T23:06:50.760357Z", + "iopub.status.idle": "2024-01-17T23:06:50.770230Z", + "shell.execute_reply": "2024-01-17T23:06:50.769519Z" } }, "outputs": [ diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index 4d347a0cb..89e9e8e2e 100644 --- a/master/tutorials/datalab/tabular.ipynb +++ b/master/tutorials/datalab/tabular.ipynb @@ -74,10 +74,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:49.517933Z", - "iopub.status.busy": "2024-01-17T18:04:49.517732Z", - "iopub.status.idle": "2024-01-17T18:04:50.545443Z", - "shell.execute_reply": "2024-01-17T18:04:50.544850Z" + "iopub.execute_input": "2024-01-17T23:06:55.582283Z", + "iopub.status.busy": "2024-01-17T23:06:55.581731Z", + "iopub.status.idle": "2024-01-17T23:06:56.607603Z", + "shell.execute_reply": "2024-01-17T23:06:56.606957Z" }, "nbsphinx": "hidden" }, @@ -87,7 +87,7 @@ "dependencies = [\"cleanlab\", \"datasets\"]\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.548446Z", - "iopub.status.busy": "2024-01-17T18:04:50.547847Z", - "iopub.status.idle": "2024-01-17T18:04:50.564313Z", - "shell.execute_reply": "2024-01-17T18:04:50.563695Z" + "iopub.execute_input": "2024-01-17T23:06:56.610616Z", + "iopub.status.busy": "2024-01-17T23:06:56.610107Z", + "iopub.status.idle": "2024-01-17T23:06:56.627639Z", + "shell.execute_reply": "2024-01-17T23:06:56.627094Z" } }, "outputs": [], @@ -155,10 +155,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.566981Z", - "iopub.status.busy": "2024-01-17T18:04:50.566555Z", - "iopub.status.idle": "2024-01-17T18:04:50.730069Z", - "shell.execute_reply": "2024-01-17T18:04:50.729433Z" + "iopub.execute_input": "2024-01-17T23:06:56.630524Z", + "iopub.status.busy": "2024-01-17T23:06:56.630127Z", + "iopub.status.idle": "2024-01-17T23:06:56.764165Z", + "shell.execute_reply": "2024-01-17T23:06:56.763459Z" } }, "outputs": [ @@ -265,10 +265,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.732586Z", - "iopub.status.busy": "2024-01-17T18:04:50.732381Z", - "iopub.status.idle": "2024-01-17T18:04:50.736190Z", - "shell.execute_reply": "2024-01-17T18:04:50.735672Z" + "iopub.execute_input": "2024-01-17T23:06:56.766653Z", + "iopub.status.busy": "2024-01-17T23:06:56.766289Z", + "iopub.status.idle": "2024-01-17T23:06:56.770134Z", + "shell.execute_reply": "2024-01-17T23:06:56.769507Z" } }, "outputs": [], @@ -289,10 +289,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.738648Z", - "iopub.status.busy": "2024-01-17T18:04:50.738199Z", - "iopub.status.idle": "2024-01-17T18:04:50.746501Z", - "shell.execute_reply": "2024-01-17T18:04:50.746013Z" + "iopub.execute_input": "2024-01-17T23:06:56.772612Z", + "iopub.status.busy": "2024-01-17T23:06:56.772309Z", + "iopub.status.idle": "2024-01-17T23:06:56.780263Z", + "shell.execute_reply": "2024-01-17T23:06:56.779767Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.749021Z", - "iopub.status.busy": "2024-01-17T18:04:50.748698Z", - "iopub.status.idle": "2024-01-17T18:04:50.751419Z", - "shell.execute_reply": "2024-01-17T18:04:50.750879Z" + "iopub.execute_input": "2024-01-17T23:06:56.782699Z", + "iopub.status.busy": "2024-01-17T23:06:56.782326Z", + "iopub.status.idle": "2024-01-17T23:06:56.785122Z", + "shell.execute_reply": "2024-01-17T23:06:56.784591Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:50.753826Z", - "iopub.status.busy": "2024-01-17T18:04:50.753466Z", - "iopub.status.idle": "2024-01-17T18:04:54.360252Z", - "shell.execute_reply": "2024-01-17T18:04:54.359622Z" + "iopub.execute_input": "2024-01-17T23:06:56.787751Z", + "iopub.status.busy": "2024-01-17T23:06:56.787454Z", + "iopub.status.idle": "2024-01-17T23:07:00.383276Z", + "shell.execute_reply": "2024-01-17T23:07:00.382535Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:54.363603Z", - "iopub.status.busy": "2024-01-17T18:04:54.363144Z", - "iopub.status.idle": "2024-01-17T18:04:54.373021Z", - "shell.execute_reply": "2024-01-17T18:04:54.372497Z" + "iopub.execute_input": "2024-01-17T23:07:00.386618Z", + "iopub.status.busy": "2024-01-17T23:07:00.386346Z", + "iopub.status.idle": "2024-01-17T23:07:00.396061Z", + "shell.execute_reply": "2024-01-17T23:07:00.395412Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:54.375544Z", - "iopub.status.busy": "2024-01-17T18:04:54.375164Z", - "iopub.status.idle": "2024-01-17T18:04:55.734156Z", - "shell.execute_reply": "2024-01-17T18:04:55.733429Z" + "iopub.execute_input": "2024-01-17T23:07:00.398720Z", + "iopub.status.busy": "2024-01-17T23:07:00.398246Z", + "iopub.status.idle": "2024-01-17T23:07:01.717594Z", + "shell.execute_reply": "2024-01-17T23:07:01.716841Z" } }, "outputs": [ @@ -475,10 +475,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.738536Z", - "iopub.status.busy": "2024-01-17T18:04:55.737191Z", - "iopub.status.idle": "2024-01-17T18:04:55.765750Z", - "shell.execute_reply": "2024-01-17T18:04:55.765138Z" + "iopub.execute_input": "2024-01-17T23:07:01.722190Z", + "iopub.status.busy": "2024-01-17T23:07:01.720796Z", + "iopub.status.idle": "2024-01-17T23:07:01.749134Z", + "shell.execute_reply": "2024-01-17T23:07:01.748523Z" }, "scrolled": true }, @@ -624,10 +624,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.770227Z", - "iopub.status.busy": "2024-01-17T18:04:55.769054Z", - "iopub.status.idle": "2024-01-17T18:04:55.781862Z", - "shell.execute_reply": "2024-01-17T18:04:55.781261Z" + "iopub.execute_input": "2024-01-17T23:07:01.753395Z", + "iopub.status.busy": "2024-01-17T23:07:01.752274Z", + "iopub.status.idle": "2024-01-17T23:07:01.764948Z", + "shell.execute_reply": "2024-01-17T23:07:01.764266Z" } }, "outputs": [ @@ -731,10 +731,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.786261Z", - "iopub.status.busy": "2024-01-17T18:04:55.785118Z", - "iopub.status.idle": "2024-01-17T18:04:55.799914Z", - "shell.execute_reply": "2024-01-17T18:04:55.799303Z" + "iopub.execute_input": "2024-01-17T23:07:01.769149Z", + "iopub.status.busy": "2024-01-17T23:07:01.768032Z", + "iopub.status.idle": "2024-01-17T23:07:01.782499Z", + "shell.execute_reply": "2024-01-17T23:07:01.781898Z" } }, "outputs": [ @@ -863,10 +863,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.804376Z", - "iopub.status.busy": "2024-01-17T18:04:55.803247Z", - "iopub.status.idle": "2024-01-17T18:04:55.816361Z", - "shell.execute_reply": "2024-01-17T18:04:55.815747Z" + "iopub.execute_input": "2024-01-17T23:07:01.786729Z", + "iopub.status.busy": "2024-01-17T23:07:01.785586Z", + "iopub.status.idle": "2024-01-17T23:07:01.798260Z", + "shell.execute_reply": "2024-01-17T23:07:01.797656Z" } }, "outputs": [ @@ -980,10 +980,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.820790Z", - "iopub.status.busy": "2024-01-17T18:04:55.819659Z", - "iopub.status.idle": "2024-01-17T18:04:55.832665Z", - "shell.execute_reply": "2024-01-17T18:04:55.832191Z" + "iopub.execute_input": "2024-01-17T23:07:01.802498Z", + "iopub.status.busy": "2024-01-17T23:07:01.801367Z", + "iopub.status.idle": "2024-01-17T23:07:01.814051Z", + "shell.execute_reply": "2024-01-17T23:07:01.813478Z" } }, "outputs": [ @@ -1094,10 +1094,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.835067Z", - "iopub.status.busy": "2024-01-17T18:04:55.834854Z", - "iopub.status.idle": "2024-01-17T18:04:55.842208Z", - "shell.execute_reply": "2024-01-17T18:04:55.841666Z" + "iopub.execute_input": "2024-01-17T23:07:01.816878Z", + "iopub.status.busy": "2024-01-17T23:07:01.816671Z", + "iopub.status.idle": "2024-01-17T23:07:01.823646Z", + "shell.execute_reply": "2024-01-17T23:07:01.823005Z" } }, "outputs": [ @@ -1181,10 +1181,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.844882Z", - "iopub.status.busy": "2024-01-17T18:04:55.844409Z", - "iopub.status.idle": "2024-01-17T18:04:55.851860Z", - "shell.execute_reply": "2024-01-17T18:04:55.851312Z" + "iopub.execute_input": "2024-01-17T23:07:01.826161Z", + "iopub.status.busy": "2024-01-17T23:07:01.825755Z", + "iopub.status.idle": "2024-01-17T23:07:01.832679Z", + "shell.execute_reply": "2024-01-17T23:07:01.832073Z" } }, "outputs": [ @@ -1277,10 +1277,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:04:55.854459Z", - "iopub.status.busy": "2024-01-17T18:04:55.854085Z", - "iopub.status.idle": "2024-01-17T18:04:55.861606Z", - "shell.execute_reply": "2024-01-17T18:04:55.860960Z" + "iopub.execute_input": "2024-01-17T23:07:01.835198Z", + "iopub.status.busy": "2024-01-17T23:07:01.834825Z", + "iopub.status.idle": "2024-01-17T23:07:01.842060Z", + "shell.execute_reply": "2024-01-17T23:07:01.841527Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 0759512f0..b732725e3 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -952,7 +952,7 @@

2. Load and format the text dataset 
 This dataset has 10 classes.
-Classes: {'apple_pay_or_google_pay', 'getting_spare_card', 'visa_or_mastercard', 'cancel_transfer', 'beneficiary_not_allowed', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'change_pin'}
+Classes: {'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'getting_spare_card', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'cancel_transfer', 'beneficiary_not_allowed', 'change_pin', 'visa_or_mastercard'}

Let’s view the i-th example in the dataset:

@@ -999,43 +999,43 @@

2. Load and format the text dataset
-
+
-
+
-
+
-
+
-
+
-
+
-
+
@@ -1798,7 +1798,7 @@

Easy ModeCleanlab Studio which will automatically produce one for you. Super easy to use, Cleanlab Studio is no-code platform for data-centric AI that automatically: detects data issues (more types of issues than this cleanlab package), helps you quickly correct these data issues, confidently labels large subsets of an unlabeled dataset, and provides other smart metadata about each of your data points – all powered by a system that automatically trains/deploys the best ML model for your data. Try it for free!

diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 4e71fc4dd..9f92665d5 100644 --- a/master/tutorials/datalab/text.ipynb +++ b/master/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:00.739310Z", - "iopub.status.busy": "2024-01-17T18:05:00.739115Z", - "iopub.status.idle": "2024-01-17T18:05:03.063769Z", - "shell.execute_reply": "2024-01-17T18:05:03.063206Z" + "iopub.execute_input": "2024-01-17T23:07:06.736514Z", + "iopub.status.busy": "2024-01-17T23:07:06.736135Z", + "iopub.status.idle": "2024-01-17T23:07:09.007449Z", + "shell.execute_reply": "2024-01-17T23:07:09.006832Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cf63fee22bbf401492c9f2f6f74d206a", + "model_id": "735576d8959e46f3826a38708cf752de", "version_major": 2, "version_minor": 0 }, @@ -118,7 +118,7 @@ "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n", "\n", "if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n", - " %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -143,10 +143,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.066797Z", - "iopub.status.busy": "2024-01-17T18:05:03.066295Z", - "iopub.status.idle": "2024-01-17T18:05:03.069771Z", - "shell.execute_reply": "2024-01-17T18:05:03.069245Z" + "iopub.execute_input": "2024-01-17T23:07:09.010579Z", + "iopub.status.busy": "2024-01-17T23:07:09.009974Z", + "iopub.status.idle": "2024-01-17T23:07:09.013430Z", + "shell.execute_reply": "2024-01-17T23:07:09.012886Z" } }, "outputs": [], @@ -167,10 +167,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.072156Z", - "iopub.status.busy": "2024-01-17T18:05:03.071788Z", - "iopub.status.idle": "2024-01-17T18:05:03.075200Z", - "shell.execute_reply": "2024-01-17T18:05:03.074554Z" + "iopub.execute_input": "2024-01-17T23:07:09.015859Z", + "iopub.status.busy": "2024-01-17T23:07:09.015504Z", + "iopub.status.idle": "2024-01-17T23:07:09.018792Z", + "shell.execute_reply": "2024-01-17T23:07:09.018271Z" }, "nbsphinx": "hidden" }, @@ -200,10 +200,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.077484Z", - "iopub.status.busy": "2024-01-17T18:05:03.077149Z", - "iopub.status.idle": "2024-01-17T18:05:03.143138Z", - "shell.execute_reply": "2024-01-17T18:05:03.142505Z" + "iopub.execute_input": "2024-01-17T23:07:09.021102Z", + "iopub.status.busy": "2024-01-17T23:07:09.020714Z", + "iopub.status.idle": "2024-01-17T23:07:09.058694Z", + "shell.execute_reply": "2024-01-17T23:07:09.058068Z" } }, "outputs": [ @@ -293,10 +293,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.145703Z", - "iopub.status.busy": "2024-01-17T18:05:03.145221Z", - "iopub.status.idle": "2024-01-17T18:05:03.149435Z", - "shell.execute_reply": "2024-01-17T18:05:03.148814Z" + "iopub.execute_input": "2024-01-17T23:07:09.061088Z", + "iopub.status.busy": "2024-01-17T23:07:09.060763Z", + "iopub.status.idle": "2024-01-17T23:07:09.064868Z", + "shell.execute_reply": "2024-01-17T23:07:09.064298Z" } }, "outputs": [ @@ -305,7 +305,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'apple_pay_or_google_pay', 'getting_spare_card', 'visa_or_mastercard', 'cancel_transfer', 'beneficiary_not_allowed', 'card_about_to_expire', 'lost_or_stolen_phone', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'change_pin'}\n" + "Classes: {'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'getting_spare_card', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'cancel_transfer', 'beneficiary_not_allowed', 'change_pin', 'visa_or_mastercard'}\n" ] } ], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.151986Z", - "iopub.status.busy": "2024-01-17T18:05:03.151547Z", - "iopub.status.idle": "2024-01-17T18:05:03.155066Z", - "shell.execute_reply": "2024-01-17T18:05:03.154495Z" + "iopub.execute_input": "2024-01-17T23:07:09.067157Z", + "iopub.status.busy": "2024-01-17T23:07:09.066816Z", + "iopub.status.idle": "2024-01-17T23:07:09.070335Z", + "shell.execute_reply": "2024-01-17T23:07:09.069700Z" } }, "outputs": [ @@ -387,17 +387,17 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:03.157519Z", - "iopub.status.busy": "2024-01-17T18:05:03.157070Z", - "iopub.status.idle": "2024-01-17T18:05:12.348322Z", - "shell.execute_reply": "2024-01-17T18:05:12.347594Z" + "iopub.execute_input": "2024-01-17T23:07:09.072907Z", + "iopub.status.busy": "2024-01-17T23:07:09.072479Z", + "iopub.status.idle": "2024-01-17T23:07:19.691854Z", + "shell.execute_reply": "2024-01-17T23:07:19.691230Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "45ac3af2e1ed4f4c8dfdabe525afe3d2", + "model_id": "eae3f2bc15824aa1945e4a9709a7cb7c", "version_major": 2, "version_minor": 0 }, @@ -411,7 +411,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e185e5cf5c0548fbbc30b1069d606797", + "model_id": "aebe7c43a94b411b875f55c85e5bbf99", "version_major": 2, "version_minor": 0 }, @@ -425,7 +425,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b94b659172c4c808567a150cf38153c", + "model_id": "ba2d5d53d9a94b1896142b5a30bbd514", "version_major": 2, "version_minor": 0 }, @@ -439,7 +439,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cac01943eb874158907dd23885f1ee1a", + "model_id": "0065750b88fd406396533e490ff9a0ca", "version_major": 2, "version_minor": 0 }, @@ -453,7 +453,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3d414f4be0554dc48490d27044d55d7b", + "model_id": "3b25cf0897af4c389e5bb84dce0e453a", "version_major": 2, "version_minor": 0 }, @@ -467,7 +467,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "696fed9227a24944a318a7e13ec558c1", + "model_id": "116e4a0b97db4d0c884059f25c064c6a", "version_major": 2, "version_minor": 0 }, @@ -481,7 +481,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "48cf8f69b83648bc957e73f69c88e66a", + "model_id": "253f1e63067b4f78a688136462184c85", "version_major": 2, "version_minor": 0 }, @@ -535,10 +535,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:12.351622Z", - "iopub.status.busy": "2024-01-17T18:05:12.351228Z", - "iopub.status.idle": "2024-01-17T18:05:13.520684Z", - "shell.execute_reply": "2024-01-17T18:05:13.519985Z" + "iopub.execute_input": "2024-01-17T23:07:19.695009Z", + "iopub.status.busy": "2024-01-17T23:07:19.694579Z", + "iopub.status.idle": "2024-01-17T23:07:20.868638Z", + "shell.execute_reply": "2024-01-17T23:07:20.867960Z" }, "scrolled": true }, @@ -570,10 +570,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:13.524656Z", - "iopub.status.busy": "2024-01-17T18:05:13.524249Z", - "iopub.status.idle": "2024-01-17T18:05:13.527395Z", - "shell.execute_reply": "2024-01-17T18:05:13.526816Z" + "iopub.execute_input": "2024-01-17T23:07:20.873083Z", + "iopub.status.busy": "2024-01-17T23:07:20.871780Z", + "iopub.status.idle": "2024-01-17T23:07:20.876509Z", + "shell.execute_reply": "2024-01-17T23:07:20.875949Z" } }, "outputs": [], @@ -593,10 +593,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:13.531364Z", - "iopub.status.busy": "2024-01-17T18:05:13.530212Z", - "iopub.status.idle": "2024-01-17T18:05:14.861750Z", - "shell.execute_reply": "2024-01-17T18:05:14.860991Z" + "iopub.execute_input": "2024-01-17T23:07:20.880805Z", + "iopub.status.busy": "2024-01-17T23:07:20.879680Z", + "iopub.status.idle": "2024-01-17T23:07:22.198191Z", + "shell.execute_reply": "2024-01-17T23:07:22.197409Z" }, "scrolled": true }, @@ -640,10 +640,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.866391Z", - "iopub.status.busy": "2024-01-17T18:05:14.865013Z", - "iopub.status.idle": "2024-01-17T18:05:14.901758Z", - "shell.execute_reply": "2024-01-17T18:05:14.901142Z" + "iopub.execute_input": "2024-01-17T23:07:22.201762Z", + "iopub.status.busy": "2024-01-17T23:07:22.201081Z", + "iopub.status.idle": "2024-01-17T23:07:22.236485Z", + "shell.execute_reply": "2024-01-17T23:07:22.235879Z" }, "scrolled": true }, @@ -808,10 +808,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.906114Z", - "iopub.status.busy": "2024-01-17T18:05:14.904971Z", - "iopub.status.idle": "2024-01-17T18:05:14.917983Z", - "shell.execute_reply": "2024-01-17T18:05:14.917381Z" + "iopub.execute_input": "2024-01-17T23:07:22.239702Z", + "iopub.status.busy": "2024-01-17T23:07:22.239312Z", + "iopub.status.idle": "2024-01-17T23:07:22.249593Z", + "shell.execute_reply": "2024-01-17T23:07:22.249016Z" }, "scrolled": true }, @@ -921,10 +921,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.920990Z", - "iopub.status.busy": "2024-01-17T18:05:14.920788Z", - "iopub.status.idle": "2024-01-17T18:05:14.925853Z", - "shell.execute_reply": "2024-01-17T18:05:14.925068Z" + "iopub.execute_input": "2024-01-17T23:07:22.252711Z", + "iopub.status.busy": "2024-01-17T23:07:22.252340Z", + "iopub.status.idle": "2024-01-17T23:07:22.256985Z", + "shell.execute_reply": "2024-01-17T23:07:22.256523Z" } }, "outputs": [ @@ -962,10 +962,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.928490Z", - "iopub.status.busy": "2024-01-17T18:05:14.928107Z", - "iopub.status.idle": "2024-01-17T18:05:14.935276Z", - "shell.execute_reply": "2024-01-17T18:05:14.934787Z" + "iopub.execute_input": "2024-01-17T23:07:22.259203Z", + "iopub.status.busy": "2024-01-17T23:07:22.258912Z", + "iopub.status.idle": "2024-01-17T23:07:22.264975Z", + "shell.execute_reply": "2024-01-17T23:07:22.264510Z" } }, "outputs": [ @@ -1082,10 +1082,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.937647Z", - "iopub.status.busy": "2024-01-17T18:05:14.937445Z", - "iopub.status.idle": "2024-01-17T18:05:14.945173Z", - "shell.execute_reply": "2024-01-17T18:05:14.944393Z" + "iopub.execute_input": "2024-01-17T23:07:22.267168Z", + "iopub.status.busy": "2024-01-17T23:07:22.266883Z", + "iopub.status.idle": "2024-01-17T23:07:22.272743Z", + "shell.execute_reply": "2024-01-17T23:07:22.272288Z" } }, "outputs": [ @@ -1168,10 +1168,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.947361Z", - "iopub.status.busy": "2024-01-17T18:05:14.947162Z", - "iopub.status.idle": "2024-01-17T18:05:14.953478Z", - "shell.execute_reply": "2024-01-17T18:05:14.952831Z" + "iopub.execute_input": "2024-01-17T23:07:22.274875Z", + "iopub.status.busy": "2024-01-17T23:07:22.274585Z", + "iopub.status.idle": "2024-01-17T23:07:22.280068Z", + "shell.execute_reply": "2024-01-17T23:07:22.279615Z" } }, "outputs": [ @@ -1279,10 +1279,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.955611Z", - "iopub.status.busy": "2024-01-17T18:05:14.955414Z", - "iopub.status.idle": "2024-01-17T18:05:14.965109Z", - "shell.execute_reply": "2024-01-17T18:05:14.964575Z" + "iopub.execute_input": "2024-01-17T23:07:22.282238Z", + "iopub.status.busy": "2024-01-17T23:07:22.281951Z", + "iopub.status.idle": "2024-01-17T23:07:22.290181Z", + "shell.execute_reply": "2024-01-17T23:07:22.289592Z" } }, "outputs": [ @@ -1393,10 +1393,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.967441Z", - "iopub.status.busy": "2024-01-17T18:05:14.967073Z", - "iopub.status.idle": "2024-01-17T18:05:14.972810Z", - "shell.execute_reply": "2024-01-17T18:05:14.972298Z" + "iopub.execute_input": "2024-01-17T23:07:22.292538Z", + "iopub.status.busy": "2024-01-17T23:07:22.292335Z", + "iopub.status.idle": "2024-01-17T23:07:22.475829Z", + "shell.execute_reply": "2024-01-17T23:07:22.475150Z" } }, "outputs": [ @@ -1464,10 +1464,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:14.975149Z", - "iopub.status.busy": "2024-01-17T18:05:14.974766Z", - "iopub.status.idle": "2024-01-17T18:05:15.144852Z", - "shell.execute_reply": "2024-01-17T18:05:15.144184Z" + "iopub.execute_input": "2024-01-17T23:07:22.478452Z", + "iopub.status.busy": "2024-01-17T23:07:22.478023Z", + "iopub.status.idle": "2024-01-17T23:07:22.484232Z", + "shell.execute_reply": "2024-01-17T23:07:22.483639Z" } }, "outputs": [ @@ -1546,10 +1546,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:15.147389Z", - "iopub.status.busy": "2024-01-17T18:05:15.147036Z", - "iopub.status.idle": "2024-01-17T18:05:15.151030Z", - "shell.execute_reply": "2024-01-17T18:05:15.150478Z" + "iopub.execute_input": "2024-01-17T23:07:22.486793Z", + "iopub.status.busy": "2024-01-17T23:07:22.486423Z", + "iopub.status.idle": "2024-01-17T23:07:22.490377Z", + "shell.execute_reply": "2024-01-17T23:07:22.489770Z" } }, "outputs": [ @@ -1597,10 +1597,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:15.153611Z", - "iopub.status.busy": "2024-01-17T18:05:15.153229Z", - "iopub.status.idle": "2024-01-17T18:05:15.159064Z", - "shell.execute_reply": "2024-01-17T18:05:15.158439Z" + "iopub.execute_input": "2024-01-17T23:07:22.492585Z", + "iopub.status.busy": "2024-01-17T23:07:22.492384Z", + "iopub.status.idle": "2024-01-17T23:07:22.498186Z", + "shell.execute_reply": "2024-01-17T23:07:22.497549Z" }, "nbsphinx": "hidden" }, @@ -1650,73 +1650,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "04bf7ee7a9c34eeeaa4d9b286f8d7f9d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_27abf396b79145cd917bff0ccd201c7e", - "placeholder": "​", - "style": "IPY_MODEL_4af4550bfcf047a5b3c8e1e3d3cd4b87", - "value": " 466k/466k [00:00<00:00, 29.4MB/s]" - } - }, - "0753cc2aba574e4da97f9bf330792b5f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "075782102ee04e45ac26c0953b93dd10": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "093ca90351304fdc8d2445f60be4b830": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "0b94b659172c4c808567a150cf38153c": { + "0065750b88fd406396533e490ff9a0ca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -1731,14 +1665,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_a40a416e6a3b4891b9b8d98a976ad7ed", - "IPY_MODEL_631251cb36704a7e85d67bbb798aaeec", - "IPY_MODEL_1f21207692b5457abe47bd5134b48d96" + "IPY_MODEL_c3a4ce2dd9534c0dae13baffc4bd8242", + "IPY_MODEL_a6798e924bf145afa6fb5a4f09ee6212", + "IPY_MODEL_f01c428443be4e759a8006b89c1f9698" ], - "layout": "IPY_MODEL_681e924901d943949a488b29a505cff0" + "layout": "IPY_MODEL_016aa72c5a294f7d8d81a1b8202fd993" } }, - "0fbbf4fdb51b413294041b296f5b2a74": { + "016aa72c5a294f7d8d81a1b8202fd993": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1790,59 +1724,105 @@ "width": null } }, - "13ff262aa4b44a0e8255af196d81fd7d": { + "06bdb1c3f8ce403faee0d760e00db57e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_ad72a839c6e047a1827fda170388e992", - "placeholder": "​", - "style": "IPY_MODEL_d44a2401009343aeae443f41faabbafa", - "value": "tokenizer_config.json: 100%" + "layout": "IPY_MODEL_98d9d39b1f014399a851cb3246835792", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_b73ee3bd3bc9482f9e9a6daf96958b25", + "value": 665.0 } }, - "156b2e945a6b44ddbdf2da4443a15de5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "0ec758d764544f0c95b7109cd53210d6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "1cd9e82053dd4f6e9275c8fa64b04d24": { + "116e4a0b97db4d0c884059f25c064c6a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6c4d8a52a4a0431d9bf8fa248904a023", + "IPY_MODEL_81190e134e0f4f15b3e4fd3bbe41be0c", + "IPY_MODEL_989066e3c5524adaa8b8a66f2f5ad9e5" + ], + "layout": "IPY_MODEL_3b8bd17f043a430fb0597488d2d6d5f2" } }, - "1e756c095e0949b1a3e12c3fd5657b28": { + "167dfa55e2104cc999179284d62f3179": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1894,70 +1874,46 @@ "width": null } }, - "1ec07e59e71d462894df6ddac86d79db": { + "17086bcb496d418a808e0606b0e2a048": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_56eb788d2ed34127ad6946586c6057e4", - "placeholder": "​", - "style": "IPY_MODEL_0753cc2aba574e4da97f9bf330792b5f", - "value": " 391/391 [00:00<00:00, 49.9kB/s]" - } - }, - "1f21207692b5457abe47bd5134b48d96": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e55f2cd15ee649ac846a97aaf09d6567", - "placeholder": "​", - "style": "IPY_MODEL_075782102ee04e45ac26c0953b93dd10", - "value": " 665/665 [00:00<00:00, 82.4kB/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "24800faa4aa049179ae0e1a942a1bdf6": { + "1be99f53fcc64c409edb1ddbd07de2b0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_f5e134f5de9e4373a4455cf6307bb806", - "placeholder": "​", - "style": "IPY_MODEL_7736ef16b7ec4b909c412b03cfe285dc", - "value": " 29.0/29.0 [00:00<00:00, 3.70kB/s]" + "layout": "IPY_MODEL_b1fbb37072854152a3a3b16caf0ab204", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ba078714339f4da593e623adf2222bd7", + "value": 2211.0 } }, - "27abf396b79145cd917bff0ccd201c7e": { + "1e09d7da26ff491d9d5a848cc7f15be9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2009,7 +1965,7 @@ "width": null } }, - "2a69838d719f4df6a92e28d9ece1f08d": { + "2367326c6c9a474ab889cf6d74faf9aa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2045,195 +2001,23 @@ "left": null, "margin": null, "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "33db2753de344f9b9a86f2af38acd19e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_690fccf3b2e142c4aab722075bd4f649", - "placeholder": "​", - "style": "IPY_MODEL_5d8a1655567a40f081ab0f97f931d0fd", - "value": ".gitattributes: 100%" - } - }, - "37196e3822914cc9a99aadf1179c6517": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "3b4ab4c5daaf45d7b33c2799316d07cf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "3d414f4be0554dc48490d27044d55d7b": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_98ec89e168f64dfbafb57d9076bd2975", - "IPY_MODEL_ecd151fdeecb4f5393b6dc51ca91a59e", - "IPY_MODEL_04bf7ee7a9c34eeeaa4d9b286f8d7f9d" - ], - "layout": "IPY_MODEL_7c80669ce4b44eb091bc16d3833d05ef" - } - }, - "45ac3af2e1ed4f4c8dfdabe525afe3d2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_33db2753de344f9b9a86f2af38acd19e", - "IPY_MODEL_4ca1fe21051a4502b3b9f11c97612ceb", - "IPY_MODEL_1ec07e59e71d462894df6ddac86d79db" - ], - "layout": "IPY_MODEL_648615497a3f4b90ad7c7b1c678d5f5f" - } - }, - "48cf8f69b83648bc957e73f69c88e66a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_abf34ac7904346f0af3ff73344ab92dd", - "IPY_MODEL_ab6d14ed062546a7bc3a37486ca8e7f2", - "IPY_MODEL_99287ca3d43a478fa3a68b0140e5b6cc" - ], - "layout": "IPY_MODEL_e88376b3ed14410a951ad244ba1c84a3" - } - }, - "4af4550bfcf047a5b3c8e1e3d3cd4b87": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "4afd2fbb4d864842b5974978b9c8d333": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4ca1fe21051a4502b3b9f11c97612ceb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_d4076baf5b434bb2adf7f2348373cb42", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_976b837ca52c4769859ed9e693f46300", - "value": 391.0 + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "553f932b2e7648b68c7c9aa3f3257737": { + "247e42b59dc0445aaaa5f7084dd20cf8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2285,23 +2069,44 @@ "width": null } }, - "55be5ce9aa9e4a548b0ad61273477bd1": { + "253f1e63067b4f78a688136462184c85": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_4efdd1e0ae254291ba5a3223a84bc808", + "IPY_MODEL_5fd1f24864bd4064949a3e592679a012", + "IPY_MODEL_ccb22378b232470890186d60d7e8a7ea" + ], + "layout": "IPY_MODEL_9904071098db436e985611826d978697" + } + }, + "2e0c8591af124e1e862ae0bbee13ab5a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "56eb788d2ed34127ad6946586c6057e4": { + "30aa87d3494840c4aa464ef74d9e0db1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2353,22 +2158,74 @@ "width": null } }, - "5d8a1655567a40f081ab0f97f931d0fd": { + "3164b830fbfe43a3b05f9dcada4619b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3dcbe3b63dd44a6ca4dc5a4e53ac44d9", + "max": 1.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e4ce483df8b44f459c167932952d75d6", + "value": 0.0 + } + }, + "34de74288e484d6392837444b0960b3b": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_8ab3108545484488b542fe77f6de60f6", + "placeholder": "​", + "style": "IPY_MODEL_3c9c5368618d46b89d1f97ee8461b90b", + "value": "config.json: 100%" + } + }, + "3b25cf0897af4c389e5bb84dce0e453a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_932c39ca775540ec91faed2d09952439", + "IPY_MODEL_ff6d854ab28e41d9b10ad56ffdd1d632", + "IPY_MODEL_41d630690c6c48ef936615876fa0652c" + ], + "layout": "IPY_MODEL_30aa87d3494840c4aa464ef74d9e0db1" } }, - "601383fd35904b3d948030127529c5b7": { + "3b8bd17f043a430fb0597488d2d6d5f2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2420,52 +2277,37 @@ "width": null } }, - "62c627b523ce4d0680d7ef46df4046b2": { + "3c9c5368618d46b89d1f97ee8461b90b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6eb61a2631024a97b38409576940d076", - "placeholder": "​", - "style": "IPY_MODEL_6f7185636dfd445bbcfa590f6f91decd", - "value": " 2.21k/2.21k [00:00<00:00, 288kB/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "631251cb36704a7e85d67bbb798aaeec": { + "3d7b642ec81c487dab799950929baded": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_1e756c095e0949b1a3e12c3fd5657b28", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4afd2fbb4d864842b5974978b9c8d333", - "value": 665.0 + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "648615497a3f4b90ad7c7b1c678d5f5f": { + "3dcbe3b63dd44a6ca4dc5a4e53ac44d9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2514,10 +2356,25 @@ "right": null, "top": null, "visibility": null, - "width": null + "width": "20px" + } + }, + "3dd0e3791e974382a9f78275796c9be1": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "681e924901d943949a488b29a505cff0": { + "3e17f05dde9d401b9fbb2d95c15d1fba": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2569,7 +2426,49 @@ "width": null } }, - "68aac3d30f1c485fb01431c3acce067f": { + "41a6a383eaa443cba8c9a8e78ec48ecd": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_95b27ef1300348efbc7ad67edd3d9b7a", + "placeholder": "​", + "style": "IPY_MODEL_dae97db7d3c64df7896a80c486b3450f", + "value": " 2.21k/2.21k [00:00<00:00, 299kB/s]" + } + }, + "41d630690c6c48ef936615876fa0652c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_57d3bf10275b4091992aa1a8a28265d8", + "placeholder": "​", + "style": "IPY_MODEL_6504d792bf274abaadea897aedb15878", + "value": " 466k/466k [00:00<00:00, 33.2MB/s]" + } + }, + "431d2da6f0804f3790d3cc8f87036067": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2621,7 +2520,100 @@ "width": null } }, - "690fccf3b2e142c4aab722075bd4f649": { + "43c253f77bd74d59bb9b5eb8c0dad2f8": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "46c6854e27ef4c689e054b875c34f02d": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_3e17f05dde9d401b9fbb2d95c15d1fba", + "placeholder": "​", + "style": "IPY_MODEL_826c4413b0c040c9b8a8c022223e7ab0", + "value": " 0/0 [00:00<?, ?it/s]" + } + }, + "494ad30da7f845f995cfe5a7b7812ca2": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6d6c91a6a97947bca8cef6f298aee849", + "placeholder": "​", + "style": "IPY_MODEL_3d7b642ec81c487dab799950929baded", + "value": ".gitattributes: 100%" + } + }, + "49def5d883e3468bb75ff61ae24e8374": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4efdd1e0ae254291ba5a3223a84bc808": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_0ec758d764544f0c95b7109cd53210d6", + "placeholder": "​", + "style": "IPY_MODEL_2e0c8591af124e1e862ae0bbee13ab5a", + "value": "vocab.txt: 100%" + } + }, + "51b14007f9df43c2ae6ae64965212985": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2673,7 +2665,38 @@ "width": null } }, - "6950f4f6ccd84fec8817d0612d30f52c": { + "5521b2020c564b9892aa36630243ff48": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "55a8e271f7084ea88ce61eb6cea457ad": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "579ddcecd4ad41ff943769c38a61dcc1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2725,29 +2748,7 @@ "width": null } }, - "696fed9227a24944a318a7e13ec558c1": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_13ff262aa4b44a0e8255af196d81fd7d", - "IPY_MODEL_b99461c80ba342a19bb61225b5a1c6ab", - "IPY_MODEL_24800faa4aa049179ae0e1a942a1bdf6" - ], - "layout": "IPY_MODEL_2a69838d719f4df6a92e28d9ece1f08d" - } - }, - "6cdbd6e2878d4ad0a30ac4e4f00206cb": { + "57d3bf10275b4091992aa1a8a28265d8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2796,26 +2797,10 @@ "right": null, "top": null, "visibility": null, - "width": "20px" - } - }, - "6df9e8714cf44f9cb0c6adc1e2801a7d": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "width": null } }, - "6eb61a2631024a97b38409576940d076": { + "5da50ac709c04ceebc042c02d11780bb": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2867,7 +2852,31 @@ "width": null } }, - "6f7185636dfd445bbcfa590f6f91decd": { + "5fd1f24864bd4064949a3e592679a012": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a59b8ca3d88645ca9287af80e876815b", + "max": 231508.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6985d8eda2514934b005c470e8949eb2", + "value": 231508.0 + } + }, + "6504d792bf274abaadea897aedb15878": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -2882,74 +2891,59 @@ "description_width": "" } }, - "705ab5d8864749feba2e94565f234f24": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "653bb2ad40a74c2392bb1d2735dea51e": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" } }, - "7736ef16b7ec4b909c412b03cfe285dc": { + "6985d8eda2514934b005c470e8949eb2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "7a8a403f88844204b88b6b499f25620b": { + "6a18b3dd9d7049169469a3a4798f15b9": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c2f3f723831e4429ba055d875902387a", + "placeholder": "​", + "style": "IPY_MODEL_c8a57340288d4909ade8e4646100002d", + "value": "" + } + }, + "6baeb4add86f4ccea1fa84f9697170d3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3001,7 +2995,28 @@ "width": null } }, - "7c80669ce4b44eb091bc16d3833d05ef": { + "6c4d8a52a4a0431d9bf8fa248904a023": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9174cdb1a46c4b888353513a0a8b4639", + "placeholder": "​", + "style": "IPY_MODEL_7e47396caa2d4e8ba0db916be172b240", + "value": "tokenizer_config.json: 100%" + } + }, + "6cad7a8d33ba491babcc8162595fad2a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3053,7 +3068,7 @@ "width": null } }, - "7da1806fce7e4909bc926a3bb7935ad7": { + "6d6c91a6a97947bca8cef6f298aee849": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3105,28 +3120,29 @@ "width": null } }, - "8717845bbc894b72935b209a75bb2f16": { + "735576d8959e46f3826a38708cf752de": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_e5067484ef674c0380e65aaa243e5a78", - "placeholder": "​", - "style": "IPY_MODEL_d5bd9e0905d947c6b4a03b3be26daf04", - "value": "pytorch_model.bin: 100%" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6a18b3dd9d7049169469a3a4798f15b9", + "IPY_MODEL_3164b830fbfe43a3b05f9dcada4619b7", + "IPY_MODEL_46c6854e27ef4c689e054b875c34f02d" + ], + "layout": "IPY_MODEL_579ddcecd4ad41ff943769c38a61dcc1" } }, - "8e2803fbb174478d972207388a2cb9ff": { + "741743a689ed408fabb5c8b7ae249ad3": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3141,44 +3157,61 @@ "description_width": "" } }, - "90d2e4a16ad541c380a1dff4cfde1f90": { + "7e47396caa2d4e8ba0db916be172b240": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "9407545d118c45a89c4d6605a80d0fff": { + "81190e134e0f4f15b3e4fd3bbe41be0c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HTMLView", + "_view_name": "ProgressView", + "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_c3b4b53286594ff5bb5a5fd3d6d3eef3", - "placeholder": "​", - "style": "IPY_MODEL_093ca90351304fdc8d2445f60be4b830", - "value": " 0/0 [00:00<?, ?it/s]" + "layout": "IPY_MODEL_431d2da6f0804f3790d3cc8f87036067", + "max": 29.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_971a763adca04a4898d2ea87d1e3b531", + "value": 29.0 + } + }, + "826c4413b0c040c9b8a8c022223e7ab0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "976b837ca52c4769859ed9e693f46300": { + "890e919775c548d29334fc80abf8dd41": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -3194,7 +3227,59 @@ "description_width": "" } }, - "97a28fb79228459592c99ca4714195d8": { + "8ab3108545484488b542fe77f6de60f6": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9174cdb1a46c4b888353513a0a8b4639": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3246,73 +3331,7 @@ "width": null } }, - "982f196d95cf48388e934100fe105449": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6cdbd6e2878d4ad0a30ac4e4f00206cb", - "max": 1.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_156b2e945a6b44ddbdf2da4443a15de5", - "value": 0.0 - } - }, - "98ec89e168f64dfbafb57d9076bd2975": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_601383fd35904b3d948030127529c5b7", - "placeholder": "​", - "style": "IPY_MODEL_37196e3822914cc9a99aadf1179c6517", - "value": "tokenizer.json: 100%" - } - }, - "99287ca3d43a478fa3a68b0140e5b6cc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6950f4f6ccd84fec8817d0612d30f52c", - "placeholder": "​", - "style": "IPY_MODEL_a4b986613dd3460fb27e399d856b5baf", - "value": " 232k/232k [00:00<00:00, 26.4MB/s]" - } - }, - "9b6a79f738174dc4a1d6e6c7665fb9c5": { + "92cc65daed6c4dd6a66605efa7d7c4f8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3364,38 +3383,7 @@ "width": null } }, - "9b70999254274e98a23fd7d37ed2000f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "a30da11f6f264d78801d7be28ba245f6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "a40a416e6a3b4891b9b8d98a976ad7ed": { + "932c39ca775540ec91faed2d09952439": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3410,28 +3398,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_eaf16cd449ad4d9ba049f1736192243c", + "layout": "IPY_MODEL_1e09d7da26ff491d9d5a848cc7f15be9", "placeholder": "​", - "style": "IPY_MODEL_3b4ab4c5daaf45d7b33c2799316d07cf", - "value": "config.json: 100%" - } - }, - "a4b986613dd3460fb27e399d856b5baf": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "style": "IPY_MODEL_741743a689ed408fabb5c8b7ae249ad3", + "value": "tokenizer.json: 100%" } }, - "a92177c88acf40c5ababe4a7a348a09e": { + "95b27ef1300348efbc7ad67edd3d9b7a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3483,52 +3456,7 @@ "width": null } }, - "ab6d14ed062546a7bc3a37486ca8e7f2": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_cc2919ce84274a69aae9ff4e90a80906", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_55be5ce9aa9e4a548b0ad61273477bd1", - "value": 231508.0 - } - }, - "abf34ac7904346f0af3ff73344ab92dd": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_0fbbf4fdb51b413294041b296f5b2a74", - "placeholder": "​", - "style": "IPY_MODEL_e797f27a584b45b88287c7dfb530657c", - "value": "vocab.txt: 100%" - } - }, - "ad72a839c6e047a1827fda170388e992": { + "9694ffca6f9a4f48be09b09321d00477": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3580,7 +3508,7 @@ "width": null } }, - "b31ba758e76b4e649b3bc97ebfcfb764": { + "971a763adca04a4898d2ea87d1e3b531": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -3596,7 +3524,28 @@ "description_width": "" } }, - "b773da097d5d41d2bd390767173f453b": { + "989066e3c5524adaa8b8a66f2f5ad9e5": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_a369cc74356749659c4fdffbd4415a89", + "placeholder": "​", + "style": "IPY_MODEL_5521b2020c564b9892aa36630243ff48", + "value": " 29.0/29.0 [00:00<00:00, 3.64kB/s]" + } + }, + "98d9d39b1f014399a851cb3246835792": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3648,31 +3597,7 @@ "width": null } }, - "b99461c80ba342a19bb61225b5a1c6ab": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_cb539705caae4b2888bf430cbdae00f9", - "max": 29.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9b70999254274e98a23fd7d37ed2000f", - "value": 29.0 - } - }, - "c3b4b53286594ff5bb5a5fd3d6d3eef3": { + "9904071098db436e985611826d978697": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3724,50 +3649,7 @@ "width": null } }, - "c44c22e3abfa4e00b6be10158490dcc5": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_553f932b2e7648b68c7c9aa3f3257737", - "placeholder": "​", - "style": "IPY_MODEL_1cd9e82053dd4f6e9275c8fa64b04d24", - "value": "" - } - }, - "cac01943eb874158907dd23885f1ee1a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_8717845bbc894b72935b209a75bb2f16", - "IPY_MODEL_d26bd1de66324c479d2abcd64629946d", - "IPY_MODEL_f5732d71a1fa4ad285feb8ad5d719a36" - ], - "layout": "IPY_MODEL_705ab5d8864749feba2e94565f234f24" - } - }, - "cb539705caae4b2888bf430cbdae00f9": { + "a369cc74356749659c4fdffbd4415a89": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3819,7 +3701,7 @@ "width": null } }, - "cc2919ce84274a69aae9ff4e90a80906": { + "a59b8ca3d88645ca9287af80e876815b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3861,39 +3743,17 @@ "object_fit": null, "object_position": null, "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "cf63fee22bbf401492c9f2f6f74d206a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c44c22e3abfa4e00b6be10158490dcc5", - "IPY_MODEL_982f196d95cf48388e934100fe105449", - "IPY_MODEL_9407545d118c45a89c4d6605a80d0fff" - ], - "layout": "IPY_MODEL_b773da097d5d41d2bd390767173f453b" + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "d26bd1de66324c479d2abcd64629946d": { + "a6798e924bf145afa6fb5a4f09ee6212": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -3909,39 +3769,37 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_a92177c88acf40c5ababe4a7a348a09e", + "layout": "IPY_MODEL_6baeb4add86f4ccea1fa84f9697170d3", "max": 54245363.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_90d2e4a16ad541c380a1dff4cfde1f90", + "style": "IPY_MODEL_890e919775c548d29334fc80abf8dd41", "value": 54245363.0 } }, - "d2af69c7dfa5442bae97f456950de975": { + "aebe7c43a94b411b875f55c85e5bbf99": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_68aac3d30f1c485fb01431c3acce067f", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6df9e8714cf44f9cb0c6adc1e2801a7d", - "value": 2211.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_e0f7b67a7ef14e7f81bcde4581d41777", + "IPY_MODEL_1be99f53fcc64c409edb1ddbd07de2b0", + "IPY_MODEL_41a6a383eaa443cba8c9a8e78ec48ecd" + ], + "layout": "IPY_MODEL_fe5812eb7c1945458f0517ab7e8b8982" } }, - "d4076baf5b434bb2adf7f2348373cb42": { + "b1fbb37072854152a3a3b16caf0ab204": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3993,37 +3851,39 @@ "width": null } }, - "d44a2401009343aeae443f41faabbafa": { + "b73ee3bd3bc9482f9e9a6daf96958b25": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "d5bd9e0905d947c6b4a03b3be26daf04": { + "ba078714339f4da593e623adf2222bd7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "e185e5cf5c0548fbbc30b1069d606797": { + "ba2d5d53d9a94b1896142b5a30bbd514": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -4038,14 +3898,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_eebfeb338a314755a90465c813e21dc1", - "IPY_MODEL_d2af69c7dfa5442bae97f456950de975", - "IPY_MODEL_62c627b523ce4d0680d7ef46df4046b2" + "IPY_MODEL_34de74288e484d6392837444b0960b3b", + "IPY_MODEL_06bdb1c3f8ce403faee0d760e00db57e", + "IPY_MODEL_c8688c529ee943f0a17f150a07147e22" ], - "layout": "IPY_MODEL_7a8a403f88844204b88b6b499f25620b" + "layout": "IPY_MODEL_92cc65daed6c4dd6a66605efa7d7c4f8" } }, - "e5067484ef674c0380e65aaa243e5a78": { + "c2eb7e0981334b8d9993c8d3ef5a5d69": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4097,7 +3957,7 @@ "width": null } }, - "e55f2cd15ee649ac846a97aaf09d6567": { + "c2f3f723831e4429ba055d875902387a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4149,7 +4009,49 @@ "width": null } }, - "e797f27a584b45b88287c7dfb530657c": { + "c3a4ce2dd9534c0dae13baffc4bd8242": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_51b14007f9df43c2ae6ae64965212985", + "placeholder": "​", + "style": "IPY_MODEL_3dd0e3791e974382a9f78275796c9be1", + "value": "pytorch_model.bin: 100%" + } + }, + "c8688c529ee943f0a17f150a07147e22": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_cbef96f2bab2484681f3c8921fa2c6ec", + "placeholder": "​", + "style": "IPY_MODEL_ea74d452fbb34576ab157f19520a727a", + "value": " 665/665 [00:00<00:00, 84.2kB/s]" + } + }, + "c8a57340288d4909ade8e4646100002d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4164,7 +4066,7 @@ "description_width": "" } }, - "e88376b3ed14410a951ad244ba1c84a3": { + "cbef96f2bab2484681f3c8921fa2c6ec": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4216,83 +4118,80 @@ "width": null } }, - "eaf16cd449ad4d9ba049f1736192243c": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "ccb22378b232470890186d60d7e8a7ea": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_247e42b59dc0445aaaa5f7084dd20cf8", + "placeholder": "​", + "style": "IPY_MODEL_653bb2ad40a74c2392bb1d2735dea51e", + "value": " 232k/232k [00:00<00:00, 27.0MB/s]" + } + }, + "dae97db7d3c64df7896a80c486b3450f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" } }, - "ecd151fdeecb4f5393b6dc51ca91a59e": { + "e0f7b67a7ef14e7f81bcde4581d41777": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", + "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_7da1806fce7e4909bc926a3bb7935ad7", - "max": 466062.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_b31ba758e76b4e649b3bc97ebfcfb764", - "value": 466062.0 + "layout": "IPY_MODEL_167dfa55e2104cc999179284d62f3179", + "placeholder": "​", + "style": "IPY_MODEL_49def5d883e3468bb75ff61ae24e8374", + "value": "README.md: 100%" + } + }, + "e4ce483df8b44f459c167932952d75d6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "eebfeb338a314755a90465c813e21dc1": { + "e98bd145421044fba60d1778da8b0bc6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4307,13 +4206,74 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_9b6a79f738174dc4a1d6e6c7665fb9c5", + "layout": "IPY_MODEL_5da50ac709c04ceebc042c02d11780bb", "placeholder": "​", - "style": "IPY_MODEL_a30da11f6f264d78801d7be28ba245f6", - "value": "README.md: 100%" + "style": "IPY_MODEL_43c253f77bd74d59bb9b5eb8c0dad2f8", + "value": " 391/391 [00:00<00:00, 44.5kB/s]" + } + }, + "ea74d452fbb34576ab157f19520a727a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "eae3f2bc15824aa1945e4a9709a7cb7c": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_494ad30da7f845f995cfe5a7b7812ca2", + "IPY_MODEL_efd392f0de6b42a3b5b343acd7c8211a", + "IPY_MODEL_e98bd145421044fba60d1778da8b0bc6" + ], + "layout": "IPY_MODEL_6cad7a8d33ba491babcc8162595fad2a" + } + }, + "efd392f0de6b42a3b5b343acd7c8211a": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9694ffca6f9a4f48be09b09321d00477", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f18d2ca563c44c24a00e3946ef1e6c94", + "value": 391.0 } }, - "f5732d71a1fa4ad285feb8ad5d719a36": { + "f01c428443be4e759a8006b89c1f9698": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4328,13 +4288,29 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_97a28fb79228459592c99ca4714195d8", + "layout": "IPY_MODEL_2367326c6c9a474ab889cf6d74faf9aa", "placeholder": "​", - "style": "IPY_MODEL_8e2803fbb174478d972207388a2cb9ff", - "value": " 54.2M/54.2M [00:00<00:00, 250MB/s]" + "style": "IPY_MODEL_17086bcb496d418a808e0606b0e2a048", + "value": " 54.2M/54.2M [00:01<00:00, 41.9MB/s]" + } + }, + "f18d2ca563c44c24a00e3946ef1e6c94": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "f5e134f5de9e4373a4455cf6307bb806": { + "fe5812eb7c1945458f0517ab7e8b8982": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4385,6 +4361,30 @@ "visibility": null, "width": null } + }, + "ff6d854ab28e41d9b10ad56ffdd1d632": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c2eb7e0981334b8d9993c8d3ef5a5d69", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_55a8e271f7084ea88ce61eb6cea457ad", + "value": 466062.0 + } } }, "version_major": 2, diff --git a/master/tutorials/dataset_health.html b/master/tutorials/dataset_health.html index a93bb2c2b..8fda8582e 100644 --- a/master/tutorials/dataset_health.html +++ b/master/tutorials/dataset_health.html @@ -956,6 +956,9 @@

Start of tutorial: Evaluate the health of 8 popular dataset 🎯 Caltech256 🎯 + +Loaded the 'caltech256' dataset with predicted probabilities of shape (29780, 256) +

@@ -963,9 +966,6 @@

Start of tutorial: Evaluate the health of 8 popular dataset 

-
-Loaded the 'caltech256' dataset with predicted probabilities of shape (29780, 256)
-
 -------------------------------------------------------------
 |  Generating a Cleanlab Dataset Health Summary             |
 |   for your dataset with 29,780 examples and 256 classes.  |
@@ -1291,13 +1291,6 @@ 
Start of tutorial: Evaluate the health of 8 popular dataset
 
 🎯 Mnist_test_set 🎯
 
-
- -
-
-
-
-
 
 Loaded the 'mnist_test_set' dataset with predicted probabilities of shape (10000, 10)
 
@@ -2535,13 +2528,6 @@ 
Start of tutorial: Evaluate the health of 8 popular dataset
 
 🎯 Cifar100_test_set 🎯
 
-
-
-
-
-
-
-
 
 Loaded the 'cifar100_test_set' dataset with predicted probabilities of shape (10000, 100)
 
diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb
index dee038810..b276fa39c 100644
--- a/master/tutorials/dataset_health.ipynb
+++ b/master/tutorials/dataset_health.ipynb
@@ -68,10 +68,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:05:19.808883Z",
-     "iopub.status.busy": "2024-01-17T18:05:19.808324Z",
-     "iopub.status.idle": "2024-01-17T18:05:20.839884Z",
-     "shell.execute_reply": "2024-01-17T18:05:20.839261Z"
+     "iopub.execute_input": "2024-01-17T23:07:27.832889Z",
+     "iopub.status.busy": "2024-01-17T23:07:27.832698Z",
+     "iopub.status.idle": "2024-01-17T23:07:28.833865Z",
+     "shell.execute_reply": "2024-01-17T23:07:28.833240Z"
     },
     "nbsphinx": "hidden"
    },
@@ -83,7 +83,7 @@
     "dependencies = [\"cleanlab\", \"requests\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -108,10 +108,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:05:20.842981Z",
-     "iopub.status.busy": "2024-01-17T18:05:20.842479Z",
-     "iopub.status.idle": "2024-01-17T18:05:20.845564Z",
-     "shell.execute_reply": "2024-01-17T18:05:20.844934Z"
+     "iopub.execute_input": "2024-01-17T23:07:28.836757Z",
+     "iopub.status.busy": "2024-01-17T23:07:28.836298Z",
+     "iopub.status.idle": "2024-01-17T23:07:28.839253Z",
+     "shell.execute_reply": "2024-01-17T23:07:28.838765Z"
     },
     "id": "_UvI80l42iyi"
    },
@@ -201,10 +201,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:05:20.848194Z",
-     "iopub.status.busy": "2024-01-17T18:05:20.847848Z",
-     "iopub.status.idle": "2024-01-17T18:05:20.860567Z",
-     "shell.execute_reply": "2024-01-17T18:05:20.860055Z"
+     "iopub.execute_input": "2024-01-17T23:07:28.841805Z",
+     "iopub.status.busy": "2024-01-17T23:07:28.841328Z",
+     "iopub.status.idle": "2024-01-17T23:07:28.854079Z",
+     "shell.execute_reply": "2024-01-17T23:07:28.853457Z"
     },
     "nbsphinx": "hidden"
    },
@@ -283,10 +283,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:05:20.862951Z",
-     "iopub.status.busy": "2024-01-17T18:05:20.862575Z",
-     "iopub.status.idle": "2024-01-17T18:05:25.011623Z",
-     "shell.execute_reply": "2024-01-17T18:05:25.011065Z"
+     "iopub.execute_input": "2024-01-17T23:07:28.856642Z",
+     "iopub.status.busy": "2024-01-17T23:07:28.856314Z",
+     "iopub.status.idle": "2024-01-17T23:07:31.662775Z",
+     "shell.execute_reply": "2024-01-17T23:07:31.662089Z"
     },
     "id": "dhTHOg8Pyv5G"
    },
@@ -297,6 +297,9 @@
      "text": [
       "\n",
       "🎯 Caltech256 🎯\n",
+      "\n",
+      "\n",
+      "Loaded the 'caltech256' dataset with predicted probabilities of shape (29780, 256)\n",
       "\n"
      ]
     },
@@ -304,9 +307,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "Loaded the 'caltech256' dataset with predicted probabilities of shape (29780, 256)\n",
-      "\n",
       "-------------------------------------------------------------\n",
       "|  Generating a Cleanlab Dataset Health Summary             |\n",
       "|   for your dataset with 29,780 examples and 256 classes.  |\n",
@@ -692,13 +692,7 @@
       "\n",
       "\n",
       "🎯 Mnist_test_set 🎯\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "\n",
       "Loaded the 'mnist_test_set' dataset with predicted probabilities of shape (10000, 10)\n",
       "\n",
@@ -2182,13 +2176,7 @@
       "\n",
       "\n",
       "🎯 Cifar100_test_set 🎯\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "\n",
       "Loaded the 'cifar100_test_set' dataset with predicted probabilities of shape (10000, 100)\n",
       "\n",
diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html
index a1fa9648c..046a9b1a0 100644
--- a/master/tutorials/faq.html
+++ b/master/tutorials/faq.html
@@ -946,13 +946,13 @@ 
How can I find label issues in big datasets with limited memory?
-
+
-
+
@@ -1453,7 +1453,7 @@

Can’t find an answer to your question?new Github issue. Our developers may also provide personalized assistance in our Slack Community.

Professional support and services are also available from our ML experts, learn more by emailing: info@cleanlab.ai

diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index 24103a62b..c60bdad38 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:29.575294Z", - "iopub.status.busy": "2024-01-17T18:05:29.575098Z", - "iopub.status.idle": "2024-01-17T18:05:30.601638Z", - "shell.execute_reply": "2024-01-17T18:05:30.600990Z" + "iopub.execute_input": "2024-01-17T23:07:36.495882Z", + "iopub.status.busy": "2024-01-17T23:07:36.495688Z", + "iopub.status.idle": "2024-01-17T23:07:37.512326Z", + "shell.execute_reply": "2024-01-17T23:07:37.511707Z" }, "nbsphinx": "hidden" }, @@ -97,10 +97,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:30.604812Z", - "iopub.status.busy": "2024-01-17T18:05:30.604317Z", - "iopub.status.idle": "2024-01-17T18:05:30.607977Z", - "shell.execute_reply": "2024-01-17T18:05:30.607450Z" + "iopub.execute_input": "2024-01-17T23:07:37.515517Z", + "iopub.status.busy": "2024-01-17T23:07:37.514954Z", + "iopub.status.idle": "2024-01-17T23:07:37.518606Z", + "shell.execute_reply": "2024-01-17T23:07:37.517983Z" } }, "outputs": [], @@ -136,10 +136,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:30.610519Z", - "iopub.status.busy": "2024-01-17T18:05:30.610067Z", - "iopub.status.idle": "2024-01-17T18:05:32.632789Z", - "shell.execute_reply": "2024-01-17T18:05:32.632102Z" + "iopub.execute_input": "2024-01-17T23:07:37.521046Z", + "iopub.status.busy": "2024-01-17T23:07:37.520609Z", + "iopub.status.idle": "2024-01-17T23:07:39.512127Z", + "shell.execute_reply": "2024-01-17T23:07:39.511435Z" } }, "outputs": [], @@ -162,10 +162,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.636274Z", - "iopub.status.busy": "2024-01-17T18:05:32.635511Z", - "iopub.status.idle": "2024-01-17T18:05:32.677507Z", - "shell.execute_reply": "2024-01-17T18:05:32.676720Z" + "iopub.execute_input": "2024-01-17T23:07:39.515319Z", + "iopub.status.busy": "2024-01-17T23:07:39.514761Z", + "iopub.status.idle": "2024-01-17T23:07:39.553653Z", + "shell.execute_reply": "2024-01-17T23:07:39.552872Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.680506Z", - "iopub.status.busy": "2024-01-17T18:05:32.680097Z", - "iopub.status.idle": "2024-01-17T18:05:32.719749Z", - "shell.execute_reply": "2024-01-17T18:05:32.719066Z" + "iopub.execute_input": "2024-01-17T23:07:39.556731Z", + "iopub.status.busy": "2024-01-17T23:07:39.556457Z", + "iopub.status.idle": "2024-01-17T23:07:39.590911Z", + "shell.execute_reply": "2024-01-17T23:07:39.590128Z" } }, "outputs": [], @@ -213,10 +213,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.722770Z", - "iopub.status.busy": "2024-01-17T18:05:32.722366Z", - "iopub.status.idle": "2024-01-17T18:05:32.725463Z", - "shell.execute_reply": "2024-01-17T18:05:32.724891Z" + "iopub.execute_input": "2024-01-17T23:07:39.593846Z", + "iopub.status.busy": "2024-01-17T23:07:39.593576Z", + "iopub.status.idle": "2024-01-17T23:07:39.596849Z", + "shell.execute_reply": "2024-01-17T23:07:39.596247Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.727932Z", - "iopub.status.busy": "2024-01-17T18:05:32.727487Z", - "iopub.status.idle": "2024-01-17T18:05:32.730280Z", - "shell.execute_reply": "2024-01-17T18:05:32.729759Z" + "iopub.execute_input": "2024-01-17T23:07:39.599261Z", + "iopub.status.busy": "2024-01-17T23:07:39.598893Z", + "iopub.status.idle": "2024-01-17T23:07:39.601723Z", + "shell.execute_reply": "2024-01-17T23:07:39.601200Z" } }, "outputs": [], @@ -298,10 +298,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.732729Z", - "iopub.status.busy": "2024-01-17T18:05:32.732328Z", - "iopub.status.idle": "2024-01-17T18:05:32.759985Z", - "shell.execute_reply": "2024-01-17T18:05:32.759356Z" + "iopub.execute_input": "2024-01-17T23:07:39.604155Z", + "iopub.status.busy": "2024-01-17T23:07:39.603812Z", + "iopub.status.idle": "2024-01-17T23:07:39.631912Z", + "shell.execute_reply": "2024-01-17T23:07:39.631291Z" } }, "outputs": [ @@ -315,7 +315,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d2907ab9e816444c921cba3edd090d1c", + "model_id": "c8d58b7026a04e969e05f5cbd2b99e14", "version_major": 2, "version_minor": 0 }, @@ -329,7 +329,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e1401ba3b9414469b83ae25c9cddabf3", + "model_id": "8fc0f21110364ef8b4a28d24e2bd55e7", "version_major": 2, "version_minor": 0 }, @@ -387,10 +387,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.767178Z", - "iopub.status.busy": "2024-01-17T18:05:32.766941Z", - "iopub.status.idle": "2024-01-17T18:05:32.774377Z", - "shell.execute_reply": "2024-01-17T18:05:32.773883Z" + "iopub.execute_input": "2024-01-17T23:07:39.638587Z", + "iopub.status.busy": "2024-01-17T23:07:39.638170Z", + "iopub.status.idle": "2024-01-17T23:07:39.644966Z", + "shell.execute_reply": "2024-01-17T23:07:39.644435Z" }, "nbsphinx": "hidden" }, @@ -421,10 +421,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.776777Z", - "iopub.status.busy": "2024-01-17T18:05:32.776413Z", - "iopub.status.idle": "2024-01-17T18:05:32.780193Z", - "shell.execute_reply": "2024-01-17T18:05:32.779640Z" + "iopub.execute_input": "2024-01-17T23:07:39.647202Z", + "iopub.status.busy": "2024-01-17T23:07:39.646994Z", + "iopub.status.idle": "2024-01-17T23:07:39.650818Z", + "shell.execute_reply": "2024-01-17T23:07:39.650290Z" }, "nbsphinx": "hidden" }, @@ -447,10 +447,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.782656Z", - "iopub.status.busy": "2024-01-17T18:05:32.782291Z", - "iopub.status.idle": "2024-01-17T18:05:32.789381Z", - "shell.execute_reply": "2024-01-17T18:05:32.788810Z" + "iopub.execute_input": "2024-01-17T23:07:39.653116Z", + "iopub.status.busy": "2024-01-17T23:07:39.652913Z", + "iopub.status.idle": "2024-01-17T23:07:39.659830Z", + "shell.execute_reply": "2024-01-17T23:07:39.659314Z" } }, "outputs": [], @@ -500,10 +500,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.791749Z", - "iopub.status.busy": "2024-01-17T18:05:32.791389Z", - "iopub.status.idle": "2024-01-17T18:05:32.834263Z", - "shell.execute_reply": "2024-01-17T18:05:32.833469Z" + "iopub.execute_input": "2024-01-17T23:07:39.662042Z", + "iopub.status.busy": "2024-01-17T23:07:39.661827Z", + "iopub.status.idle": "2024-01-17T23:07:39.700248Z", + "shell.execute_reply": "2024-01-17T23:07:39.699558Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.837464Z", - "iopub.status.busy": "2024-01-17T18:05:32.836999Z", - "iopub.status.idle": "2024-01-17T18:05:32.879044Z", - "shell.execute_reply": "2024-01-17T18:05:32.878377Z" + "iopub.execute_input": "2024-01-17T23:07:39.703097Z", + "iopub.status.busy": "2024-01-17T23:07:39.702829Z", + "iopub.status.idle": "2024-01-17T23:07:39.740445Z", + "shell.execute_reply": "2024-01-17T23:07:39.739778Z" }, "nbsphinx": "hidden" }, @@ -602,10 +602,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:32.882160Z", - "iopub.status.busy": "2024-01-17T18:05:32.881824Z", - "iopub.status.idle": "2024-01-17T18:05:33.000399Z", - "shell.execute_reply": "2024-01-17T18:05:32.999631Z" + "iopub.execute_input": "2024-01-17T23:07:39.743730Z", + "iopub.status.busy": "2024-01-17T23:07:39.743262Z", + "iopub.status.idle": "2024-01-17T23:07:39.857929Z", + "shell.execute_reply": "2024-01-17T23:07:39.857221Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:33.003322Z", - "iopub.status.busy": "2024-01-17T18:05:33.003098Z", - "iopub.status.idle": "2024-01-17T18:05:35.514808Z", - "shell.execute_reply": "2024-01-17T18:05:35.514063Z" + "iopub.execute_input": "2024-01-17T23:07:39.860566Z", + "iopub.status.busy": "2024-01-17T23:07:39.860345Z", + "iopub.status.idle": "2024-01-17T23:07:42.349955Z", + "shell.execute_reply": "2024-01-17T23:07:42.349281Z" } }, "outputs": [ @@ -761,10 +761,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.517610Z", - "iopub.status.busy": "2024-01-17T18:05:35.517166Z", - "iopub.status.idle": "2024-01-17T18:05:35.576901Z", - "shell.execute_reply": "2024-01-17T18:05:35.576188Z" + "iopub.execute_input": "2024-01-17T23:07:42.352843Z", + "iopub.status.busy": "2024-01-17T23:07:42.352472Z", + "iopub.status.idle": "2024-01-17T23:07:42.410242Z", + "shell.execute_reply": "2024-01-17T23:07:42.409702Z" } }, "outputs": [ @@ -802,7 +802,7 @@ }, { "cell_type": "markdown", - "id": "37949d7a", + "id": "78363458", "metadata": {}, "source": [ "### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?" @@ -810,7 +810,7 @@ }, { "cell_type": "markdown", - "id": "dfe41b86", + "id": "d2a5e8b7", "metadata": {}, "source": [ "When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n", @@ -823,13 +823,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "17100cb9", + "id": "c950fb91", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.579654Z", - "iopub.status.busy": "2024-01-17T18:05:35.579165Z", - "iopub.status.idle": "2024-01-17T18:05:35.693837Z", - "shell.execute_reply": "2024-01-17T18:05:35.692940Z" + "iopub.execute_input": "2024-01-17T23:07:42.412848Z", + "iopub.status.busy": "2024-01-17T23:07:42.412446Z", + "iopub.status.idle": "2024-01-17T23:07:42.526424Z", + "shell.execute_reply": "2024-01-17T23:07:42.525736Z" } }, "outputs": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "a03bf6f2", + "id": "8ab3357a", "metadata": {}, "source": [ "For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:" @@ -879,13 +879,13 @@ { "cell_type": "code", "execution_count": 18, - "id": "6ef2cce4", + "id": "1c9ad48b", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.697709Z", - "iopub.status.busy": "2024-01-17T18:05:35.696542Z", - "iopub.status.idle": "2024-01-17T18:05:35.771866Z", - "shell.execute_reply": "2024-01-17T18:05:35.771185Z" + "iopub.execute_input": "2024-01-17T23:07:42.530165Z", + "iopub.status.busy": "2024-01-17T23:07:42.529379Z", + "iopub.status.idle": "2024-01-17T23:07:42.607790Z", + "shell.execute_reply": "2024-01-17T23:07:42.607189Z" } }, "outputs": [ @@ -921,7 +921,7 @@ }, { "cell_type": "markdown", - "id": "8ca4b358", + "id": "4fc657f4", "metadata": {}, "source": [ "### How to handle near-duplicate data identified by cleanlab?\n", @@ -932,13 +932,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "66ce860e", + "id": "8fa90df4", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.774640Z", - "iopub.status.busy": "2024-01-17T18:05:35.774188Z", - "iopub.status.idle": "2024-01-17T18:05:35.782861Z", - "shell.execute_reply": "2024-01-17T18:05:35.782309Z" + "iopub.execute_input": "2024-01-17T23:07:42.610437Z", + "iopub.status.busy": "2024-01-17T23:07:42.610059Z", + "iopub.status.idle": "2024-01-17T23:07:42.618357Z", + "shell.execute_reply": "2024-01-17T23:07:42.617793Z" } }, "outputs": [], @@ -1040,7 +1040,7 @@ }, { "cell_type": "markdown", - "id": "5f1c9f23", + "id": "cf99e781", "metadata": {}, "source": [ "The functions above collect sets of near-duplicate examples. Within each\n", @@ -1055,13 +1055,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "7dce32d6", + "id": "98118892", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.785295Z", - "iopub.status.busy": "2024-01-17T18:05:35.784899Z", - "iopub.status.idle": "2024-01-17T18:05:35.805010Z", - "shell.execute_reply": "2024-01-17T18:05:35.804458Z" + "iopub.execute_input": "2024-01-17T23:07:42.620700Z", + "iopub.status.busy": "2024-01-17T23:07:42.620256Z", + "iopub.status.idle": "2024-01-17T23:07:42.638982Z", + "shell.execute_reply": "2024-01-17T23:07:42.638447Z" } }, "outputs": [ @@ -1104,13 +1104,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "47046a1c", + "id": "e6faf2ef", "metadata": { "execution": { - "iopub.execute_input": "2024-01-17T18:05:35.807439Z", - "iopub.status.busy": "2024-01-17T18:05:35.807048Z", - "iopub.status.idle": "2024-01-17T18:05:35.811530Z", - "shell.execute_reply": "2024-01-17T18:05:35.811000Z" + "iopub.execute_input": "2024-01-17T23:07:42.641306Z", + "iopub.status.busy": "2024-01-17T23:07:42.640932Z", + "iopub.status.idle": "2024-01-17T23:07:42.645229Z", + "shell.execute_reply": "2024-01-17T23:07:42.644699Z" } }, "outputs": [ @@ -1205,38 +1205,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "2effe8a32eaa4e92abaa5cfd66d81dcb": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "36e767e7e95d43ffb851228c4f91edbe": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" - } - }, - "4af3a63525c440a4abc52d769e9017dd": { + "01401acd053d494aa90753fa1b8c29ba": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1288,37 +1257,68 @@ "width": null } }, - "522f0096addf4e0cbafbc1d1348408a1": { + "07a3f5a6aaad401a972197c77e87bf79": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", + "bar_color": null, "description_width": "" } }, - "5e22bd91d1694cfd8c3fe6d8d8c39dff": { + "0a9f4395be2b49e484d8a8639bea82b5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "model_name": "FloatProgressModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", + "_model_name": "FloatProgressModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_9cc15f84dcd54b2ca4ee56b56c2a8d26", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_50bbf71c81b94d23aeb3814fd48c052f", + "value": 50.0 + } + }, + "1f5c5a8eafb94b9a9b11d9a2524373d7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d62ab3c07fc64753b2a9c29aea8050a2", + "placeholder": "​", + "style": "IPY_MODEL_63793e7ce97b43db816b5f644c926e7f", + "value": "number of examples processed for checking labels: " } }, - "5f05395507ac4367ae64c6ba9a41e376": { + "265276564615465aae7792742a910cf0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1370,7 +1370,74 @@ "width": null } }, - "6100ae8ba0d54e21a507da746704062e": { + "381f4ed9e1cc4936a05b827e22b7d226": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "4c99538a52524c55b6909964ce7c9d68": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c5b4c3e45e8848ffbc301b4507e649f1", + "placeholder": "​", + "style": "IPY_MODEL_ad51724cc1d6431d904bb6e18167ea25", + "value": " 10000/? [00:00<00:00, 1165602.49it/s]" + } + }, + "50bbf71c81b94d23aeb3814fd48c052f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "63793e7ce97b43db816b5f644c926e7f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "8187bd04584f405d9507f19902206d11": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1422,7 +1489,29 @@ "width": null } }, - "63d1a448b6a746f18ee2f037a92ae878": { + "8fc0f21110364ef8b4a28d24e2bd55e7": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_1f5c5a8eafb94b9a9b11d9a2524373d7", + "IPY_MODEL_ce759ae3b428466aa400b60523204cbc", + "IPY_MODEL_4c99538a52524c55b6909964ce7c9d68" + ], + "layout": "IPY_MODEL_8187bd04584f405d9507f19902206d11" + } + }, + "91b32b8f1849447099270e102d55f348": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1437,13 +1526,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_a80a6611d34f4705b4f07ebf44d92707", + "layout": "IPY_MODEL_960a5752066f4191bb38b7c279cd71ee", "placeholder": "​", - "style": "IPY_MODEL_2effe8a32eaa4e92abaa5cfd66d81dcb", - "value": " 10000/? [00:00<00:00, 1183093.76it/s]" + "style": "IPY_MODEL_381f4ed9e1cc4936a05b827e22b7d226", + "value": " 10000/? [00:00<00:00, 913831.54it/s]" } }, - "67bf9df6950044db8352f04efbecf277": { + "960a5752066f4191bb38b7c279cd71ee": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1495,7 +1584,7 @@ "width": null } }, - "69ec8fb5a49d4072a4b0ee16e18334df": { + "9bee374c69a64257bb771b86156bbde2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1510,13 +1599,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_5f05395507ac4367ae64c6ba9a41e376", + "layout": "IPY_MODEL_01401acd053d494aa90753fa1b8c29ba", "placeholder": "​", - "style": "IPY_MODEL_522f0096addf4e0cbafbc1d1348408a1", - "value": " 10000/? [00:00<00:00, 948101.00it/s]" + "style": "IPY_MODEL_a813a7209e3749ad8bce32b87d8a1474", + "value": "number of examples processed for estimating thresholds: " } }, - "984a0aef565c492c93122b0c6cd2e272": { + "9cc15f84dcd54b2ca4ee56b56c2a8d26": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1568,23 +1657,37 @@ "width": null } }, - "a60031b8b82f49fbafcdb6359ff9ad02": { + "a813a7209e3749ad8bce32b87d8a1474": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "DescriptionStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "ad51724cc1d6431d904bb6e18167ea25": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", - "bar_color": null, "description_width": "" } }, - "a80a6611d34f4705b4f07ebf44d92707": { + "c5b4c3e45e8848ffbc301b4507e649f1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1636,53 +1739,53 @@ "width": null } }, - "c481e1fa4e204113b169f35e94dcd64d": { + "c8d58b7026a04e969e05f5cbd2b99e14": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", + "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_6100ae8ba0d54e21a507da746704062e", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_a60031b8b82f49fbafcdb6359ff9ad02", - "value": 50.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_9bee374c69a64257bb771b86156bbde2", + "IPY_MODEL_0a9f4395be2b49e484d8a8639bea82b5", + "IPY_MODEL_91b32b8f1849447099270e102d55f348" + ], + "layout": "IPY_MODEL_e591d29a3749442799df27c12ffbaf94" } }, - "d2907ab9e816444c921cba3edd090d1c": { + "ce759ae3b428466aa400b60523204cbc": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_e69126c6e0524cfcafeb8b4ee9fa96bc", - "IPY_MODEL_f89f3d1d85684857aa38609ce181730f", - "IPY_MODEL_69ec8fb5a49d4072a4b0ee16e18334df" - ], - "layout": "IPY_MODEL_ee0899a9ff57472586211760d9d7a753" + "_view_name": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_265276564615465aae7792742a910cf0", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_07a3f5a6aaad401a972197c77e87bf79", + "value": 50.0 } }, - "e1135200e6bc4c8d898c8dc36eb2cd8e": { + "d62ab3c07fc64753b2a9c29aea8050a2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1734,50 +1837,7 @@ "width": null } }, - "e1401ba3b9414469b83ae25c9cddabf3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_f7eb91c94209438b8648fd260292716e", - "IPY_MODEL_c481e1fa4e204113b169f35e94dcd64d", - "IPY_MODEL_63d1a448b6a746f18ee2f037a92ae878" - ], - "layout": "IPY_MODEL_e1135200e6bc4c8d898c8dc36eb2cd8e" - } - }, - "e69126c6e0524cfcafeb8b4ee9fa96bc": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_4af3a63525c440a4abc52d769e9017dd", - "placeholder": "​", - "style": "IPY_MODEL_5e22bd91d1694cfd8c3fe6d8d8c39dff", - "value": "number of examples processed for estimating thresholds: " - } - }, - "ee0899a9ff57472586211760d9d7a753": { + "e591d29a3749442799df27c12ffbaf94": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1828,66 +1888,6 @@ "visibility": null, "width": null } - }, - "ee272707d3f34718a00e655664ab8731": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" - } - }, - "f7eb91c94209438b8648fd260292716e": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_67bf9df6950044db8352f04efbecf277", - "placeholder": "​", - "style": "IPY_MODEL_ee272707d3f34718a00e655664ab8731", - "value": "number of examples processed for checking labels: " - } - }, - "f89f3d1d85684857aa38609ce181730f": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "ProgressView", - "bar_style": "success", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_984a0aef565c492c93122b0c6cd2e272", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_36e767e7e95d43ffb851228c4f91edbe", - "value": 50.0 - } } }, "version_major": 2, diff --git a/master/tutorials/image.html b/master/tutorials/image.html index 533d95455..e0e73013f 100644 --- a/master/tutorials/image.html +++ b/master/tutorials/image.html @@ -888,25 +888,25 @@

2. Fetch and normalize the Fashion-MNIST dataset

-
+
-
+
-
+
-
+
[3]:
@@ -961,7 +961,7 @@ 
2. Fetch and normalize the Fashion-MNIST dataset
-
+

Convert the transformed dataset to a torch dataset. Torch datasets are more efficient with dataloading in practice.

@@ -1217,7 +1217,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings 
-epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.896
+epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.560

@@ -1225,7 +1225,7 @@

5. Compute out-of-sample predicted probabilities and feature embeddings 
-epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.667
+epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.349
 Computing feature embeddings ...

@@ -1269,16 +1269,16 @@

5. Compute out-of-sample predicted probabilities and feature embeddings 

 

-
2%|▎         | 1/40 [00:00&lt;00:04,  9.04it/s]

+
2%|▎         | 1/40 [00:00&lt;00:03,  9.97it/s]

 
</pre>

 

 

-
2%|▎         | 1/40 [00:00<00:04,  9.04it/s]

+
2%|▎         | 1/40 [00:00<00:03,  9.97it/s]

 
end{sphinxVerbatim}

 

 

 

-
2%|▎         | 1/40 [00:00<00:04,  9.04it/s]

+
2%|▎         | 1/40 [00:00<00:03,  9.97it/s]

 
 
 

@@ -1295,16 +1295,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
20%|██        | 8/40 [00:00&lt;00:00, 42.57it/s]

+
22%|██▎       | 9/40 [00:00&lt;00:00, 48.20it/s]

 
</pre>

 

 

-
20%|██        | 8/40 [00:00<00:00, 42.57it/s]

+
22%|██▎       | 9/40 [00:00<00:00, 48.20it/s]

 
end{sphinxVerbatim}

 

 

 

-
20%|██        | 8/40 [00:00<00:00, 42.57it/s]

+
22%|██▎       | 9/40 [00:00<00:00, 48.20it/s]

 
 
 

@@ -1321,16 +1321,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
40%|████      | 16/40 [00:00&lt;00:00, 57.36it/s]

+
42%|████▎     | 17/40 [00:00&lt;00:00, 60.18it/s]

 
</pre>

 

 

-
40%|████      | 16/40 [00:00<00:00, 57.36it/s]

+
42%|████▎     | 17/40 [00:00<00:00, 60.18it/s]

 
end{sphinxVerbatim}

 

 

 

-
40%|████      | 16/40 [00:00<00:00, 57.36it/s]

+
42%|████▎     | 17/40 [00:00<00:00, 60.18it/s]

 
 
 

@@ -1347,16 +1347,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
60%|██████    | 24/40 [00:00&lt;00:00, 64.11it/s]

+
62%|██████▎   | 25/40 [00:00&lt;00:00, 65.74it/s]

 
</pre>

 

 

-
60%|██████    | 24/40 [00:00<00:00, 64.11it/s]

+
62%|██████▎   | 25/40 [00:00<00:00, 65.74it/s]

 
end{sphinxVerbatim}

 

 

 

-
60%|██████    | 24/40 [00:00<00:00, 64.11it/s]

+
62%|██████▎   | 25/40 [00:00<00:00, 65.74it/s]

 
 
 

@@ -1373,16 +1373,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
80%|████████  | 32/40 [00:00&lt;00:00, 68.31it/s]

+
82%|████████▎ | 33/40 [00:00&lt;00:00, 69.32it/s]

 
</pre>

 

 

-
80%|████████  | 32/40 [00:00<00:00, 68.31it/s]

+
82%|████████▎ | 33/40 [00:00<00:00, 69.32it/s]

 
end{sphinxVerbatim}

 

 

 

-
80%|████████  | 32/40 [00:00<00:00, 68.31it/s]

+
82%|████████▎ | 33/40 [00:00<00:00, 69.32it/s]

 
 
 

@@ -1392,16 +1392,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings

 
 

-
100%|██████████| 40/40 [00:00&lt;00:00, 62.78it/s]

+
100%|██████████| 40/40 [00:00&lt;00:00, 62.65it/s]

 
</pre>

 

 

-
100%|██████████| 40/40 [00:00<00:00, 62.78it/s]

+
100%|██████████| 40/40 [00:00<00:00, 62.65it/s]

 
end{sphinxVerbatim}

 

 

 

-
100%|██████████| 40/40 [00:00<00:00, 62.78it/s]

+
100%|██████████| 40/40 [00:00<00:00, 62.65it/s]

 

 

 

@@ -1458,16 +1458,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
5%|▌         | 2/40 [00:00&lt;00:01, 19.18it/s]

+
5%|▌         | 2/40 [00:00&lt;00:02, 18.01it/s]

 
</pre>

 

 

-
5%|▌         | 2/40 [00:00<00:01, 19.18it/s]

+
5%|▌         | 2/40 [00:00<00:02, 18.01it/s]

 
end{sphinxVerbatim}

 

 

 

-
5%|▌         | 2/40 [00:00<00:01, 19.18it/s]

+
5%|▌         | 2/40 [00:00<00:02, 18.01it/s]

 
 
 

@@ -1484,16 +1484,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
25%|██▌       | 10/40 [00:00&lt;00:00, 52.54it/s]

+
25%|██▌       | 10/40 [00:00&lt;00:00, 50.14it/s]

 
</pre>

 

 

-
25%|██▌       | 10/40 [00:00<00:00, 52.54it/s]

+
25%|██▌       | 10/40 [00:00<00:00, 50.14it/s]

 
end{sphinxVerbatim}

 

 

 

-
25%|██▌       | 10/40 [00:00<00:00, 52.54it/s]

+
25%|██▌       | 10/40 [00:00<00:00, 50.14it/s]

 
 
 

@@ -1510,16 +1510,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
45%|████▌     | 18/40 [00:00&lt;00:00, 63.06it/s]

+
45%|████▌     | 18/40 [00:00&lt;00:00, 60.51it/s]

 
</pre>

 

 

-
45%|████▌     | 18/40 [00:00<00:00, 63.06it/s]

+
45%|████▌     | 18/40 [00:00<00:00, 60.51it/s]

 
end{sphinxVerbatim}

 

 

 

-
45%|████▌     | 18/40 [00:00<00:00, 63.06it/s]

+
45%|████▌     | 18/40 [00:00<00:00, 60.51it/s]

 
 
 

@@ -1536,16 +1536,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
65%|██████▌   | 26/40 [00:00&lt;00:00, 68.34it/s]

+
62%|██████▎   | 25/40 [00:00&lt;00:00, 62.34it/s]

 
</pre>

 

 

-
65%|██████▌   | 26/40 [00:00<00:00, 68.34it/s]

+
62%|██████▎   | 25/40 [00:00<00:00, 62.34it/s]

 
end{sphinxVerbatim}

 

 

 

-
65%|██████▌   | 26/40 [00:00<00:00, 68.34it/s]

+
62%|██████▎   | 25/40 [00:00<00:00, 62.34it/s]

 
 
 

@@ -1562,16 +1562,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
85%|████████▌ | 34/40 [00:00&lt;00:00, 71.92it/s]

+
82%|████████▎ | 33/40 [00:00&lt;00:00, 67.31it/s]

 
</pre>

 

 

-
85%|████████▌ | 34/40 [00:00<00:00, 71.92it/s]

+
82%|████████▎ | 33/40 [00:00<00:00, 67.31it/s]

 
end{sphinxVerbatim}

 

 

 

-
85%|████████▌ | 34/40 [00:00<00:00, 71.92it/s]

+
82%|████████▎ | 33/40 [00:00<00:00, 67.31it/s]

 
 
 

@@ -1581,16 +1581,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings

 
 

-
100%|██████████| 40/40 [00:00&lt;00:00, 65.99it/s]

+
100%|██████████| 40/40 [00:00&lt;00:00, 63.76it/s]

 
</pre>

 

 

-
100%|██████████| 40/40 [00:00<00:00, 65.99it/s]

+
100%|██████████| 40/40 [00:00<00:00, 63.76it/s]

 
end{sphinxVerbatim}

 

 

 

-
100%|██████████| 40/40 [00:00<00:00, 65.99it/s]

+
100%|██████████| 40/40 [00:00<00:00, 63.76it/s]

 

 

 

@@ -1613,7 +1613,7 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 
-epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.738
+epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.550
 

 

 

@@ -1621,7 +1621,7 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 
-epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.632
+epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.337
 Computing feature embeddings ...
 

 

@@ -1665,16 +1665,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
5%|▌         | 2/40 [00:00&lt;00:01, 19.17it/s]

+
8%|▊         | 3/40 [00:00&lt;00:01, 25.95it/s]

 
</pre>

 

 

-
5%|▌         | 2/40 [00:00<00:01, 19.17it/s]

+
8%|▊         | 3/40 [00:00<00:01, 25.95it/s]

 
end{sphinxVerbatim}

 

 

 

-
5%|▌         | 2/40 [00:00<00:01, 19.17it/s]

+
8%|▊         | 3/40 [00:00<00:01, 25.95it/s]

 
 
 

@@ -1691,16 +1691,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
22%|██▎       | 9/40 [00:00&lt;00:00, 47.23it/s]

+
28%|██▊       | 11/40 [00:00&lt;00:00, 53.82it/s]

 
</pre>

 

 

-
22%|██▎       | 9/40 [00:00<00:00, 47.23it/s]

+
28%|██▊       | 11/40 [00:00<00:00, 53.82it/s]

 
end{sphinxVerbatim}

 

 

 

-
22%|██▎       | 9/40 [00:00<00:00, 47.23it/s]

+
28%|██▊       | 11/40 [00:00<00:00, 53.82it/s]

 
 
 

@@ -1717,16 +1717,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
42%|████▎     | 17/40 [00:00&lt;00:00, 59.67it/s]

+
48%|████▊     | 19/40 [00:00&lt;00:00, 63.69it/s]

 
</pre>

 

 

-
42%|████▎     | 17/40 [00:00<00:00, 59.67it/s]

+
48%|████▊     | 19/40 [00:00<00:00, 63.69it/s]

 
end{sphinxVerbatim}

 

 

 

-
42%|████▎     | 17/40 [00:00<00:00, 59.67it/s]

+
48%|████▊     | 19/40 [00:00<00:00, 63.69it/s]

 
 
 

@@ -1743,16 +1743,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
62%|██████▎   | 25/40 [00:00&lt;00:00, 65.28it/s]

+
65%|██████▌   | 26/40 [00:00&lt;00:00, 64.34it/s]

 
</pre>

 

 

-
62%|██████▎   | 25/40 [00:00<00:00, 65.28it/s]

+
65%|██████▌   | 26/40 [00:00<00:00, 64.34it/s]

 
end{sphinxVerbatim}

 

 

 

-
62%|██████▎   | 25/40 [00:00<00:00, 65.28it/s]

+
65%|██████▌   | 26/40 [00:00<00:00, 64.34it/s]

 
 
 

@@ -1769,16 +1769,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
82%|████████▎ | 33/40 [00:00&lt;00:00, 68.98it/s]

+
82%|████████▎ | 33/40 [00:00&lt;00:00, 65.40it/s]

 
</pre>

 

 

-
82%|████████▎ | 33/40 [00:00<00:00, 68.98it/s]

+
82%|████████▎ | 33/40 [00:00<00:00, 65.40it/s]

 
end{sphinxVerbatim}

 

 

 

-
82%|████████▎ | 33/40 [00:00<00:00, 68.98it/s]

+
82%|████████▎ | 33/40 [00:00<00:00, 65.40it/s]

 
 
 

@@ -1788,16 +1788,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings

 
 

-
100%|██████████| 40/40 [00:00&lt;00:00, 62.09it/s]

+
100%|██████████| 40/40 [00:00&lt;00:00, 64.01it/s]

 
</pre>

 

 

-
100%|██████████| 40/40 [00:00<00:00, 62.09it/s]

+
100%|██████████| 40/40 [00:00<00:00, 64.01it/s]

 
end{sphinxVerbatim}

 

 

 

-
100%|██████████| 40/40 [00:00<00:00, 62.09it/s]

+
100%|██████████| 40/40 [00:00<00:00, 64.01it/s]

 

 

 

@@ -1854,16 +1854,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
2%|▎         | 1/40 [00:00&lt;00:05,  7.50it/s]

+
5%|▌         | 2/40 [00:00&lt;00:02, 17.33it/s]

 
</pre>

 

 

-
2%|▎         | 1/40 [00:00<00:05,  7.50it/s]

+
5%|▌         | 2/40 [00:00<00:02, 17.33it/s]

 
end{sphinxVerbatim}

 

 

 

-
2%|▎         | 1/40 [00:00<00:05,  7.50it/s]

+
5%|▌         | 2/40 [00:00<00:02, 17.33it/s]

 
 
 

@@ -1880,16 +1880,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
22%|██▎       | 9/40 [00:00&lt;00:00, 43.21it/s]

+
22%|██▎       | 9/40 [00:00&lt;00:00, 43.06it/s]

 
</pre>

 

 

-
22%|██▎       | 9/40 [00:00<00:00, 43.21it/s]

+
22%|██▎       | 9/40 [00:00<00:00, 43.06it/s]

 
end{sphinxVerbatim}

 

 

 

-
22%|██▎       | 9/40 [00:00<00:00, 43.21it/s]

+
22%|██▎       | 9/40 [00:00<00:00, 43.06it/s]

 
 
 

@@ -1906,16 +1906,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
42%|████▎     | 17/40 [00:00&lt;00:00, 56.82it/s]

+
40%|████      | 16/40 [00:00&lt;00:00, 52.93it/s]

 
</pre>

 

 

-
42%|████▎     | 17/40 [00:00<00:00, 56.82it/s]

+
40%|████      | 16/40 [00:00<00:00, 52.93it/s]

 
end{sphinxVerbatim}

 

 

 

-
42%|████▎     | 17/40 [00:00<00:00, 56.82it/s]

+
40%|████      | 16/40 [00:00<00:00, 52.93it/s]

 
 
 

@@ -1932,16 +1932,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
62%|██████▎   | 25/40 [00:00&lt;00:00, 63.54it/s]

+
57%|█████▊    | 23/40 [00:00&lt;00:00, 57.07it/s]

 
</pre>

 

 

-
62%|██████▎   | 25/40 [00:00<00:00, 63.54it/s]

+
57%|█████▊    | 23/40 [00:00<00:00, 57.07it/s]

 
end{sphinxVerbatim}

 

 

 

-
62%|██████▎   | 25/40 [00:00<00:00, 63.54it/s]

+
57%|█████▊    | 23/40 [00:00<00:00, 57.07it/s]

 
 
 

@@ -1958,16 +1958,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
82%|████████▎ | 33/40 [00:00&lt;00:00, 68.22it/s]

+
78%|███████▊  | 31/40 [00:00&lt;00:00, 62.51it/s]

 
</pre>

 

 

-
82%|████████▎ | 33/40 [00:00<00:00, 68.22it/s]

+
78%|███████▊  | 31/40 [00:00<00:00, 62.51it/s]

 
end{sphinxVerbatim}

 

 

 

-
82%|████████▎ | 33/40 [00:00<00:00, 68.22it/s]

+
78%|███████▊  | 31/40 [00:00<00:00, 62.51it/s]

 
 
 

@@ -1977,16 +1977,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings

 
 

-
100%|██████████| 40/40 [00:00&lt;00:00, 61.30it/s]

+
100%|██████████| 40/40 [00:00&lt;00:00, 59.78it/s]

 
</pre>

 

 

-
100%|██████████| 40/40 [00:00<00:00, 61.30it/s]

+
100%|██████████| 40/40 [00:00<00:00, 59.78it/s]

 
end{sphinxVerbatim}

 

 

 

-
100%|██████████| 40/40 [00:00<00:00, 61.30it/s]

+
100%|██████████| 40/40 [00:00<00:00, 59.78it/s]

 

 

 

@@ -2009,7 +2009,7 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 
-epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.767
+epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.749
 

 

 

@@ -2017,7 +2017,7 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 
-epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.551
+epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.346
 Computing feature embeddings ...
 

 

@@ -2061,16 +2061,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
5%|▌         | 2/40 [00:00&lt;00:02, 17.49it/s]

+
8%|▊         | 3/40 [00:00&lt;00:01, 27.26it/s]

 
</pre>

 

 

-
5%|▌         | 2/40 [00:00<00:02, 17.49it/s]

+
8%|▊         | 3/40 [00:00<00:01, 27.26it/s]

 
end{sphinxVerbatim}

 

 

 

-
5%|▌         | 2/40 [00:00<00:02, 17.49it/s]

+
8%|▊         | 3/40 [00:00<00:01, 27.26it/s]

 
 
 

@@ -2087,16 +2087,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
25%|██▌       | 10/40 [00:00&lt;00:00, 50.44it/s]

+
28%|██▊       | 11/40 [00:00&lt;00:00, 55.24it/s]

 
</pre>

 

 

-
25%|██▌       | 10/40 [00:00<00:00, 50.44it/s]

+
28%|██▊       | 11/40 [00:00<00:00, 55.24it/s]

 
end{sphinxVerbatim}

 

 

 

-
25%|██▌       | 10/40 [00:00<00:00, 50.44it/s]

+
28%|██▊       | 11/40 [00:00<00:00, 55.24it/s]

 
 
 

@@ -2113,16 +2113,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
45%|████▌     | 18/40 [00:00&lt;00:00, 60.96it/s]

+
48%|████▊     | 19/40 [00:00&lt;00:00, 64.49it/s]

 
</pre>

 

 

-
45%|████▌     | 18/40 [00:00<00:00, 60.96it/s]

+
48%|████▊     | 19/40 [00:00<00:00, 64.49it/s]

 
end{sphinxVerbatim}

 

 

 

-
45%|████▌     | 18/40 [00:00<00:00, 60.96it/s]

+
48%|████▊     | 19/40 [00:00<00:00, 64.49it/s]

 
 
 

@@ -2139,16 +2139,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
62%|██████▎   | 25/40 [00:00&lt;00:00, 63.96it/s]

+
68%|██████▊   | 27/40 [00:00&lt;00:00, 69.00it/s]

 
</pre>

 

 

-
62%|██████▎   | 25/40 [00:00<00:00, 63.96it/s]

+
68%|██████▊   | 27/40 [00:00<00:00, 69.00it/s]

 
end{sphinxVerbatim}

 

 

 

-
62%|██████▎   | 25/40 [00:00<00:00, 63.96it/s]

+
68%|██████▊   | 27/40 [00:00<00:00, 69.00it/s]

 
 
 

@@ -2165,16 +2165,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
82%|████████▎ | 33/40 [00:00&lt;00:00, 67.69it/s]

+
90%|█████████ | 36/40 [00:00&lt;00:00, 73.84it/s]

 
</pre>

 

 

-
82%|████████▎ | 33/40 [00:00<00:00, 67.69it/s]

+
90%|█████████ | 36/40 [00:00<00:00, 73.84it/s]

 
end{sphinxVerbatim}

 

 

 

-
82%|████████▎ | 33/40 [00:00<00:00, 67.69it/s]

+
90%|█████████ | 36/40 [00:00<00:00, 73.84it/s]

 
 
 

@@ -2184,16 +2184,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings

 
 

-
100%|██████████| 40/40 [00:00&lt;00:00, 63.32it/s]

+
100%|██████████| 40/40 [00:00&lt;00:00, 67.59it/s]

 
</pre>

 

 

-
100%|██████████| 40/40 [00:00<00:00, 63.32it/s]

+
100%|██████████| 40/40 [00:00<00:00, 67.59it/s]

 
end{sphinxVerbatim}

 

 

 

-
100%|██████████| 40/40 [00:00<00:00, 63.32it/s]

+
100%|██████████| 40/40 [00:00<00:00, 67.59it/s]

 

 

 

@@ -2250,16 +2250,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
2%|▎         | 1/40 [00:00&lt;00:04,  9.55it/s]

+
5%|▌         | 2/40 [00:00&lt;00:02, 19.00it/s]

 
</pre>

 

 

-
2%|▎         | 1/40 [00:00<00:04,  9.55it/s]

+
5%|▌         | 2/40 [00:00<00:02, 19.00it/s]

 
end{sphinxVerbatim}

 

 

 

-
2%|▎         | 1/40 [00:00<00:04,  9.55it/s]

+
5%|▌         | 2/40 [00:00<00:02, 19.00it/s]

 
 
 

@@ -2276,16 +2276,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
20%|██        | 8/40 [00:00&lt;00:00, 44.04it/s]

+
25%|██▌       | 10/40 [00:00&lt;00:00, 52.63it/s]

 
</pre>

 

 

-
20%|██        | 8/40 [00:00<00:00, 44.04it/s]

+
25%|██▌       | 10/40 [00:00<00:00, 52.63it/s]

 
end{sphinxVerbatim}

 

 

 

-
20%|██        | 8/40 [00:00<00:00, 44.04it/s]

+
25%|██▌       | 10/40 [00:00<00:00, 52.63it/s]

 
 
 

@@ -2302,16 +2302,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
38%|███▊      | 15/40 [00:00&lt;00:00, 54.25it/s]

+
45%|████▌     | 18/40 [00:00&lt;00:00, 63.02it/s]

 
</pre>

 

 

-
38%|███▊      | 15/40 [00:00<00:00, 54.25it/s]

+
45%|████▌     | 18/40 [00:00<00:00, 63.02it/s]

 
end{sphinxVerbatim}

 

 

 

-
38%|███▊      | 15/40 [00:00<00:00, 54.25it/s]

+
45%|████▌     | 18/40 [00:00<00:00, 63.02it/s]

 
 
 

@@ -2328,16 +2328,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
57%|█████▊    | 23/40 [00:00&lt;00:00, 62.12it/s]

+
65%|██████▌   | 26/40 [00:00&lt;00:00, 68.31it/s]

 
</pre>

 

 

-
57%|█████▊    | 23/40 [00:00<00:00, 62.12it/s]

+
65%|██████▌   | 26/40 [00:00<00:00, 68.31it/s]

 
end{sphinxVerbatim}

 

 

 

-
57%|█████▊    | 23/40 [00:00<00:00, 62.12it/s]

+
65%|██████▌   | 26/40 [00:00<00:00, 68.31it/s]

 
 
 

@@ -2354,42 +2354,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings
 

 

-
78%|███████▊  | 31/40 [00:00&lt;00:00, 67.11it/s]

+
88%|████████▊ | 35/40 [00:00&lt;00:00, 73.71it/s]

 
</pre>

 

 

-
78%|███████▊  | 31/40 [00:00<00:00, 67.11it/s]

+
88%|████████▊ | 35/40 [00:00<00:00, 73.71it/s]

 
end{sphinxVerbatim}

 

 

 

-
78%|███████▊  | 31/40 [00:00<00:00, 67.11it/s]

-
-
-

-

-

-

-

-

-
more-to-come:

-

-
class:

-
stderr

-

-

-

-

-
98%|█████████▊| 39/40 [00:00&lt;00:00, 69.06it/s]

-
</pre>

-

-

-
98%|█████████▊| 39/40 [00:00<00:00, 69.06it/s]

-
end{sphinxVerbatim}

-

-

-

-
98%|█████████▊| 39/40 [00:00<00:00, 69.06it/s]

+
88%|████████▊ | 35/40 [00:00<00:00, 73.71it/s]

 

 

 

@@ -2399,16 +2373,16 @@ 
5. Compute out-of-sample predicted probabilities and feature embeddings

 
 

-
100%|██████████| 40/40 [00:00&lt;00:00, 60.60it/s]

+
100%|██████████| 40/40 [00:00&lt;00:00, 67.51it/s]

 
</pre>

 

 

-
100%|██████████| 40/40 [00:00<00:00, 60.60it/s]

+
100%|██████████| 40/40 [00:00<00:00, 67.51it/s]

 
end{sphinxVerbatim}

 

 

 

-
100%|██████████| 40/40 [00:00<00:00, 60.60it/s]

+
100%|██████████| 40/40 [00:00<00:00, 67.51it/s]

 

 

 

@@ -2498,7 +2472,7 @@ 
7. Use cleanlab to find issues
 

 

-

+
 
 

 

@@ -3279,35 +3253,35 @@ 
Dark images
       
-      dark_score
       is_dark_issue
+      dark_score
     
   
   
     
       34848
-      0.203922
       True
+      0.203922
     
     
       50270
-      0.204588
       True
+      0.204588
     
     
       3936
-      0.213098
       True
+      0.213098
     
     
       733
-      0.217686
       True
+      0.217686
     
     
       8094
-      0.230118
       True
+      0.230118
     
   
 
@@ -3457,7 +3431,7 @@ 
Easy ModeCleanlab Studio which will automatically produce one for you. Super easy to use, Cleanlab Studio is no-code platform for data-centric AI that automatically: detects data
 issues (more types of issues than this cleanlab package), helps you quickly correct these data issues, confidently labels large subsets of an unlabeled dataset, and provides other smart metadata about each of your data points – all powered by a system that automatically trains/deploys the best ML model for your data. Try it for free!

 
 
 
diff --git a/master/tutorials/image.ipynb b/master/tutorials/image.ipynb
index e02fa69d1..fbad8e084 100644
--- a/master/tutorials/image.ipynb
+++ b/master/tutorials/image.ipynb
@@ -71,10 +71,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:05:41.067246Z",
-     "iopub.status.busy": "2024-01-17T18:05:41.067031Z",
-     "iopub.status.idle": "2024-01-17T18:05:43.291099Z",
-     "shell.execute_reply": "2024-01-17T18:05:43.290418Z"
+     "iopub.execute_input": "2024-01-17T23:07:47.931457Z",
+     "iopub.status.busy": "2024-01-17T23:07:47.931264Z",
+     "iopub.status.idle": "2024-01-17T23:07:50.035403Z",
+     "shell.execute_reply": "2024-01-17T23:07:50.034793Z"
     },
     "nbsphinx": "hidden"
    },
@@ -112,10 +112,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:05:43.294169Z",
-     "iopub.status.busy": "2024-01-17T18:05:43.293669Z",
-     "iopub.status.idle": "2024-01-17T18:05:43.297424Z",
-     "shell.execute_reply": "2024-01-17T18:05:43.296885Z"
+     "iopub.execute_input": "2024-01-17T23:07:50.038356Z",
+     "iopub.status.busy": "2024-01-17T23:07:50.037866Z",
+     "iopub.status.idle": "2024-01-17T23:07:50.041629Z",
+     "shell.execute_reply": "2024-01-17T23:07:50.041029Z"
     }
    },
    "outputs": [],
@@ -152,17 +152,17 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:05:43.299869Z",
-     "iopub.status.busy": "2024-01-17T18:05:43.299508Z",
-     "iopub.status.idle": "2024-01-17T18:05:45.744718Z",
-     "shell.execute_reply": "2024-01-17T18:05:45.744111Z"
+     "iopub.execute_input": "2024-01-17T23:07:50.043951Z",
+     "iopub.status.busy": "2024-01-17T23:07:50.043515Z",
+     "iopub.status.idle": "2024-01-17T23:07:53.352869Z",
+     "shell.execute_reply": "2024-01-17T23:07:53.352216Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9218ba50145b4c1abb05285d39489f7b",
+       "model_id": "79ccb0a1555b42b5a881bd1c68892db9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -176,7 +176,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "411e7b1250d04fceabc4a56d3aa7c5ba",
+       "model_id": "3b8311a276a84a2b810f57fb87ac7a1c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -190,7 +190,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "34ddbf7efb384ee4960a77089187f278",
+       "model_id": "19d4a001660c44e8a52d8d2f5e1ea989",
        "version_major": 2,
        "version_minor": 0
       },
@@ -204,7 +204,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "13b1818046c2493686e01a759bad0eef",
+       "model_id": "7ce4749d923744c09bd675ee944baaad",
        "version_major": 2,
        "version_minor": 0
       },
@@ -246,10 +246,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:05:45.747577Z",
-     "iopub.status.busy": "2024-01-17T18:05:45.747182Z",
-     "iopub.status.idle": "2024-01-17T18:05:45.751308Z",
-     "shell.execute_reply": "2024-01-17T18:05:45.750698Z"
+     "iopub.execute_input": "2024-01-17T23:07:53.355203Z",
+     "iopub.status.busy": "2024-01-17T23:07:53.354998Z",
+     "iopub.status.idle": "2024-01-17T23:07:53.359311Z",
+     "shell.execute_reply": "2024-01-17T23:07:53.358704Z"
     }
    },
    "outputs": [
@@ -274,17 +274,17 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:05:45.753514Z",
-     "iopub.status.busy": "2024-01-17T18:05:45.753224Z",
-     "iopub.status.idle": "2024-01-17T18:05:58.059408Z",
-     "shell.execute_reply": "2024-01-17T18:05:58.058802Z"
+     "iopub.execute_input": "2024-01-17T23:07:53.361599Z",
+     "iopub.status.busy": "2024-01-17T23:07:53.361259Z",
+     "iopub.status.idle": "2024-01-17T23:08:05.481673Z",
+     "shell.execute_reply": "2024-01-17T23:08:05.480925Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "76f30844c4444b9cb566e2bb926b7b40",
+       "model_id": "cf1bbec90cc743c19c044238bc5cd410",
        "version_major": 2,
        "version_minor": 0
       },
@@ -322,10 +322,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:05:58.062215Z",
-     "iopub.status.busy": "2024-01-17T18:05:58.061961Z",
-     "iopub.status.idle": "2024-01-17T18:06:20.156233Z",
-     "shell.execute_reply": "2024-01-17T18:06:20.155534Z"
+     "iopub.execute_input": "2024-01-17T23:08:05.484696Z",
+     "iopub.status.busy": "2024-01-17T23:08:05.484433Z",
+     "iopub.status.idle": "2024-01-17T23:08:26.795293Z",
+     "shell.execute_reply": "2024-01-17T23:08:26.794657Z"
     }
    },
    "outputs": [],
@@ -358,10 +358,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:06:20.159472Z",
-     "iopub.status.busy": "2024-01-17T18:06:20.159075Z",
-     "iopub.status.idle": "2024-01-17T18:06:20.164287Z",
-     "shell.execute_reply": "2024-01-17T18:06:20.163771Z"
+     "iopub.execute_input": "2024-01-17T23:08:26.798340Z",
+     "iopub.status.busy": "2024-01-17T23:08:26.797902Z",
+     "iopub.status.idle": "2024-01-17T23:08:26.804100Z",
+     "shell.execute_reply": "2024-01-17T23:08:26.803564Z"
     }
    },
    "outputs": [],
@@ -399,10 +399,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:06:20.166658Z",
-     "iopub.status.busy": "2024-01-17T18:06:20.166314Z",
-     "iopub.status.idle": "2024-01-17T18:06:20.170443Z",
-     "shell.execute_reply": "2024-01-17T18:06:20.169973Z"
+     "iopub.execute_input": "2024-01-17T23:08:26.806531Z",
+     "iopub.status.busy": "2024-01-17T23:08:26.806178Z",
+     "iopub.status.idle": "2024-01-17T23:08:26.810192Z",
+     "shell.execute_reply": "2024-01-17T23:08:26.809670Z"
     },
     "nbsphinx": "hidden"
    },
@@ -539,10 +539,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:06:20.172709Z",
-     "iopub.status.busy": "2024-01-17T18:06:20.172347Z",
-     "iopub.status.idle": "2024-01-17T18:06:20.181874Z",
-     "shell.execute_reply": "2024-01-17T18:06:20.181379Z"
+     "iopub.execute_input": "2024-01-17T23:08:26.812487Z",
+     "iopub.status.busy": "2024-01-17T23:08:26.812126Z",
+     "iopub.status.idle": "2024-01-17T23:08:26.821683Z",
+     "shell.execute_reply": "2024-01-17T23:08:26.821162Z"
     },
     "nbsphinx": "hidden"
    },
@@ -667,10 +667,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:06:20.184367Z",
-     "iopub.status.busy": "2024-01-17T18:06:20.183884Z",
-     "iopub.status.idle": "2024-01-17T18:06:20.214975Z",
-     "shell.execute_reply": "2024-01-17T18:06:20.214272Z"
+     "iopub.execute_input": "2024-01-17T23:08:26.823908Z",
+     "iopub.status.busy": "2024-01-17T23:08:26.823540Z",
+     "iopub.status.idle": "2024-01-17T23:08:26.852714Z",
+     "shell.execute_reply": "2024-01-17T23:08:26.852213Z"
     }
    },
    "outputs": [],
@@ -707,10 +707,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:06:20.218114Z",
-     "iopub.status.busy": "2024-01-17T18:06:20.217484Z",
-     "iopub.status.idle": "2024-01-17T18:06:52.341728Z",
-     "shell.execute_reply": "2024-01-17T18:06:52.340867Z"
+     "iopub.execute_input": "2024-01-17T23:08:26.855055Z",
+     "iopub.status.busy": "2024-01-17T23:08:26.854682Z",
+     "iopub.status.idle": "2024-01-17T23:08:57.530989Z",
+     "shell.execute_reply": "2024-01-17T23:08:57.530120Z"
     }
    },
    "outputs": [
@@ -726,14 +726,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.896\n"
+      "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.560\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.667\n",
+      "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.349\n",
       "Computing feature embeddings ...\n"
      ]
     },
@@ -750,7 +750,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  2%|▎         | 1/40 [00:00<00:04,  9.04it/s]"
+      "  2%|▎         | 1/40 [00:00<00:03,  9.97it/s]"
      ]
     },
     {
@@ -758,7 +758,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 20%|██        | 8/40 [00:00<00:00, 42.57it/s]"
+      " 22%|██▎       | 9/40 [00:00<00:00, 48.20it/s]"
      ]
     },
     {
@@ -766,7 +766,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 40%|████      | 16/40 [00:00<00:00, 57.36it/s]"
+      " 42%|████▎     | 17/40 [00:00<00:00, 60.18it/s]"
      ]
     },
     {
@@ -774,7 +774,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 60%|██████    | 24/40 [00:00<00:00, 64.11it/s]"
+      " 62%|██████▎   | 25/40 [00:00<00:00, 65.74it/s]"
      ]
     },
     {
@@ -782,7 +782,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 80%|████████  | 32/40 [00:00<00:00, 68.31it/s]"
+      " 82%|████████▎ | 33/40 [00:00<00:00, 69.32it/s]"
      ]
     },
     {
@@ -790,7 +790,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "100%|██████████| 40/40 [00:00<00:00, 62.78it/s]"
+      "100%|██████████| 40/40 [00:00<00:00, 62.65it/s]"
      ]
     },
     {
@@ -820,7 +820,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  5%|▌         | 2/40 [00:00<00:01, 19.18it/s]"
+      "  5%|▌         | 2/40 [00:00<00:02, 18.01it/s]"
      ]
     },
     {
@@ -828,7 +828,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 25%|██▌       | 10/40 [00:00<00:00, 52.54it/s]"
+      " 25%|██▌       | 10/40 [00:00<00:00, 50.14it/s]"
      ]
     },
     {
@@ -836,7 +836,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 45%|████▌     | 18/40 [00:00<00:00, 63.06it/s]"
+      " 45%|████▌     | 18/40 [00:00<00:00, 60.51it/s]"
      ]
     },
     {
@@ -844,7 +844,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 65%|██████▌   | 26/40 [00:00<00:00, 68.34it/s]"
+      " 62%|██████▎   | 25/40 [00:00<00:00, 62.34it/s]"
      ]
     },
     {
@@ -852,7 +852,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 85%|████████▌ | 34/40 [00:00<00:00, 71.92it/s]"
+      " 82%|████████▎ | 33/40 [00:00<00:00, 67.31it/s]"
      ]
     },
     {
@@ -860,7 +860,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "100%|██████████| 40/40 [00:00<00:00, 65.99it/s]"
+      "100%|██████████| 40/40 [00:00<00:00, 63.76it/s]"
      ]
     },
     {
@@ -882,14 +882,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.738\n"
+      "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.550\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.632\n",
+      "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.337\n",
       "Computing feature embeddings ...\n"
      ]
     },
@@ -906,7 +906,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  5%|▌         | 2/40 [00:00<00:01, 19.17it/s]"
+      "  8%|▊         | 3/40 [00:00<00:01, 25.95it/s]"
      ]
     },
     {
@@ -914,7 +914,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 22%|██▎       | 9/40 [00:00<00:00, 47.23it/s]"
+      " 28%|██▊       | 11/40 [00:00<00:00, 53.82it/s]"
      ]
     },
     {
@@ -922,7 +922,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 42%|████▎     | 17/40 [00:00<00:00, 59.67it/s]"
+      " 48%|████▊     | 19/40 [00:00<00:00, 63.69it/s]"
      ]
     },
     {
@@ -930,7 +930,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 62%|██████▎   | 25/40 [00:00<00:00, 65.28it/s]"
+      " 65%|██████▌   | 26/40 [00:00<00:00, 64.34it/s]"
      ]
     },
     {
@@ -938,7 +938,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 82%|████████▎ | 33/40 [00:00<00:00, 68.98it/s]"
+      " 82%|████████▎ | 33/40 [00:00<00:00, 65.40it/s]"
      ]
     },
     {
@@ -946,7 +946,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "100%|██████████| 40/40 [00:00<00:00, 62.09it/s]"
+      "100%|██████████| 40/40 [00:00<00:00, 64.01it/s]"
      ]
     },
     {
@@ -976,7 +976,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  2%|▎         | 1/40 [00:00<00:05,  7.50it/s]"
+      "  5%|▌         | 2/40 [00:00<00:02, 17.33it/s]"
      ]
     },
     {
@@ -984,7 +984,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 22%|██▎       | 9/40 [00:00<00:00, 43.21it/s]"
+      " 22%|██▎       | 9/40 [00:00<00:00, 43.06it/s]"
      ]
     },
     {
@@ -992,7 +992,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 42%|████▎     | 17/40 [00:00<00:00, 56.82it/s]"
+      " 40%|████      | 16/40 [00:00<00:00, 52.93it/s]"
      ]
     },
     {
@@ -1000,7 +1000,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 62%|██████▎   | 25/40 [00:00<00:00, 63.54it/s]"
+      " 57%|█████▊    | 23/40 [00:00<00:00, 57.07it/s]"
      ]
     },
     {
@@ -1008,7 +1008,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 82%|████████▎ | 33/40 [00:00<00:00, 68.22it/s]"
+      " 78%|███████▊  | 31/40 [00:00<00:00, 62.51it/s]"
      ]
     },
     {
@@ -1016,7 +1016,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "100%|██████████| 40/40 [00:00<00:00, 61.30it/s]"
+      "100%|██████████| 40/40 [00:00<00:00, 59.78it/s]"
      ]
     },
     {
@@ -1038,14 +1038,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.767\n"
+      "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.749\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.551\n",
+      "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.346\n",
       "Computing feature embeddings ...\n"
      ]
     },
@@ -1062,7 +1062,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  5%|▌         | 2/40 [00:00<00:02, 17.49it/s]"
+      "  8%|▊         | 3/40 [00:00<00:01, 27.26it/s]"
      ]
     },
     {
@@ -1070,7 +1070,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 25%|██▌       | 10/40 [00:00<00:00, 50.44it/s]"
+      " 28%|██▊       | 11/40 [00:00<00:00, 55.24it/s]"
      ]
     },
     {
@@ -1078,7 +1078,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 45%|████▌     | 18/40 [00:00<00:00, 60.96it/s]"
+      " 48%|████▊     | 19/40 [00:00<00:00, 64.49it/s]"
      ]
     },
     {
@@ -1086,7 +1086,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 62%|██████▎   | 25/40 [00:00<00:00, 63.96it/s]"
+      " 68%|██████▊   | 27/40 [00:00<00:00, 69.00it/s]"
      ]
     },
     {
@@ -1094,7 +1094,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 82%|████████▎ | 33/40 [00:00<00:00, 67.69it/s]"
+      " 90%|█████████ | 36/40 [00:00<00:00, 73.84it/s]"
      ]
     },
     {
@@ -1102,7 +1102,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "100%|██████████| 40/40 [00:00<00:00, 63.32it/s]"
+      "100%|██████████| 40/40 [00:00<00:00, 67.59it/s]"
      ]
     },
     {
@@ -1132,7 +1132,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  2%|▎         | 1/40 [00:00<00:04,  9.55it/s]"
+      "  5%|▌         | 2/40 [00:00<00:02, 19.00it/s]"
      ]
     },
     {
@@ -1140,7 +1140,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 20%|██        | 8/40 [00:00<00:00, 44.04it/s]"
+      " 25%|██▌       | 10/40 [00:00<00:00, 52.63it/s]"
      ]
     },
     {
@@ -1148,7 +1148,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 38%|███▊      | 15/40 [00:00<00:00, 54.25it/s]"
+      " 45%|████▌     | 18/40 [00:00<00:00, 63.02it/s]"
      ]
     },
     {
@@ -1156,7 +1156,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 57%|█████▊    | 23/40 [00:00<00:00, 62.12it/s]"
+      " 65%|██████▌   | 26/40 [00:00<00:00, 68.31it/s]"
      ]
     },
     {
@@ -1164,7 +1164,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 78%|███████▊  | 31/40 [00:00<00:00, 67.11it/s]"
+      " 88%|████████▊ | 35/40 [00:00<00:00, 73.71it/s]"
      ]
     },
     {
@@ -1172,15 +1172,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 98%|█████████▊| 39/40 [00:00<00:00, 69.06it/s]"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\r",
-      "100%|██████████| 40/40 [00:00<00:00, 60.60it/s]"
+      "100%|██████████| 40/40 [00:00<00:00, 67.51it/s]"
      ]
     },
     {
@@ -1257,10 +1249,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:06:52.344865Z",
-     "iopub.status.busy": "2024-01-17T18:06:52.344261Z",
-     "iopub.status.idle": "2024-01-17T18:06:52.360939Z",
-     "shell.execute_reply": "2024-01-17T18:06:52.360351Z"
+     "iopub.execute_input": "2024-01-17T23:08:57.533946Z",
+     "iopub.status.busy": "2024-01-17T23:08:57.533629Z",
+     "iopub.status.idle": "2024-01-17T23:08:57.550228Z",
+     "shell.execute_reply": "2024-01-17T23:08:57.549699Z"
     }
    },
    "outputs": [],
@@ -1285,10 +1277,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:06:52.364131Z",
-     "iopub.status.busy": "2024-01-17T18:06:52.363638Z",
-     "iopub.status.idle": "2024-01-17T18:06:52.849592Z",
-     "shell.execute_reply": "2024-01-17T18:06:52.848859Z"
+     "iopub.execute_input": "2024-01-17T23:08:57.552782Z",
+     "iopub.status.busy": "2024-01-17T23:08:57.552226Z",
+     "iopub.status.idle": "2024-01-17T23:08:57.986311Z",
+     "shell.execute_reply": "2024-01-17T23:08:57.985707Z"
     }
    },
    "outputs": [],
@@ -1308,10 +1300,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:06:52.852535Z",
-     "iopub.status.busy": "2024-01-17T18:06:52.852301Z",
-     "iopub.status.idle": "2024-01-17T18:10:13.364394Z",
-     "shell.execute_reply": "2024-01-17T18:10:13.363707Z"
+     "iopub.execute_input": "2024-01-17T23:08:57.989260Z",
+     "iopub.status.busy": "2024-01-17T23:08:57.988841Z",
+     "iopub.status.idle": "2024-01-17T23:12:17.592442Z",
+     "shell.execute_reply": "2024-01-17T23:12:17.591804Z"
     }
    },
    "outputs": [
@@ -1350,7 +1342,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "4788f80918f340f199482171f3731dd8",
+       "model_id": "c1f8c848d7954850a757e5e8fa83f920",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1389,10 +1381,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:13.367055Z",
-     "iopub.status.busy": "2024-01-17T18:10:13.366655Z",
-     "iopub.status.idle": "2024-01-17T18:10:13.890209Z",
-     "shell.execute_reply": "2024-01-17T18:10:13.889535Z"
+     "iopub.execute_input": "2024-01-17T23:12:17.595341Z",
+     "iopub.status.busy": "2024-01-17T23:12:17.594687Z",
+     "iopub.status.idle": "2024-01-17T23:12:18.109017Z",
+     "shell.execute_reply": "2024-01-17T23:12:18.108362Z"
     }
    },
    "outputs": [
@@ -1604,10 +1596,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:13.893666Z",
-     "iopub.status.busy": "2024-01-17T18:10:13.893098Z",
-     "iopub.status.idle": "2024-01-17T18:10:13.957139Z",
-     "shell.execute_reply": "2024-01-17T18:10:13.956469Z"
+     "iopub.execute_input": "2024-01-17T23:12:18.112328Z",
+     "iopub.status.busy": "2024-01-17T23:12:18.111889Z",
+     "iopub.status.idle": "2024-01-17T23:12:18.175100Z",
+     "shell.execute_reply": "2024-01-17T23:12:18.174534Z"
     }
    },
    "outputs": [
@@ -1711,10 +1703,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:13.959807Z",
-     "iopub.status.busy": "2024-01-17T18:10:13.959450Z",
-     "iopub.status.idle": "2024-01-17T18:10:13.968741Z",
-     "shell.execute_reply": "2024-01-17T18:10:13.968232Z"
+     "iopub.execute_input": "2024-01-17T23:12:18.177637Z",
+     "iopub.status.busy": "2024-01-17T23:12:18.177428Z",
+     "iopub.status.idle": "2024-01-17T23:12:18.186532Z",
+     "shell.execute_reply": "2024-01-17T23:12:18.185876Z"
     }
    },
    "outputs": [
@@ -1844,10 +1836,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:13.971381Z",
-     "iopub.status.busy": "2024-01-17T18:10:13.970900Z",
-     "iopub.status.idle": "2024-01-17T18:10:13.976151Z",
-     "shell.execute_reply": "2024-01-17T18:10:13.975669Z"
+     "iopub.execute_input": "2024-01-17T23:12:18.188808Z",
+     "iopub.status.busy": "2024-01-17T23:12:18.188604Z",
+     "iopub.status.idle": "2024-01-17T23:12:18.193610Z",
+     "shell.execute_reply": "2024-01-17T23:12:18.193087Z"
     },
     "nbsphinx": "hidden"
    },
@@ -1893,10 +1885,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:13.978642Z",
-     "iopub.status.busy": "2024-01-17T18:10:13.978133Z",
-     "iopub.status.idle": "2024-01-17T18:10:14.471555Z",
-     "shell.execute_reply": "2024-01-17T18:10:14.470916Z"
+     "iopub.execute_input": "2024-01-17T23:12:18.195809Z",
+     "iopub.status.busy": "2024-01-17T23:12:18.195608Z",
+     "iopub.status.idle": "2024-01-17T23:12:18.652330Z",
+     "shell.execute_reply": "2024-01-17T23:12:18.651636Z"
     }
    },
    "outputs": [
@@ -1931,10 +1923,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:14.474235Z",
-     "iopub.status.busy": "2024-01-17T18:10:14.473823Z",
-     "iopub.status.idle": "2024-01-17T18:10:14.482948Z",
-     "shell.execute_reply": "2024-01-17T18:10:14.482455Z"
+     "iopub.execute_input": "2024-01-17T23:12:18.655261Z",
+     "iopub.status.busy": "2024-01-17T23:12:18.654743Z",
+     "iopub.status.idle": "2024-01-17T23:12:18.663764Z",
+     "shell.execute_reply": "2024-01-17T23:12:18.663258Z"
     }
    },
    "outputs": [
@@ -2101,10 +2093,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:14.485528Z",
-     "iopub.status.busy": "2024-01-17T18:10:14.485164Z",
-     "iopub.status.idle": "2024-01-17T18:10:14.492848Z",
-     "shell.execute_reply": "2024-01-17T18:10:14.492364Z"
+     "iopub.execute_input": "2024-01-17T23:12:18.666187Z",
+     "iopub.status.busy": "2024-01-17T23:12:18.665737Z",
+     "iopub.status.idle": "2024-01-17T23:12:18.674326Z",
+     "shell.execute_reply": "2024-01-17T23:12:18.673700Z"
     },
     "nbsphinx": "hidden"
    },
@@ -2180,10 +2172,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:14.496054Z",
-     "iopub.status.busy": "2024-01-17T18:10:14.495663Z",
-     "iopub.status.idle": "2024-01-17T18:10:14.964665Z",
-     "shell.execute_reply": "2024-01-17T18:10:14.963989Z"
+     "iopub.execute_input": "2024-01-17T23:12:18.676786Z",
+     "iopub.status.busy": "2024-01-17T23:12:18.676313Z",
+     "iopub.status.idle": "2024-01-17T23:12:19.151323Z",
+     "shell.execute_reply": "2024-01-17T23:12:19.150735Z"
     }
    },
    "outputs": [
@@ -2220,10 +2212,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:14.967054Z",
-     "iopub.status.busy": "2024-01-17T18:10:14.966847Z",
-     "iopub.status.idle": "2024-01-17T18:10:14.982866Z",
-     "shell.execute_reply": "2024-01-17T18:10:14.982341Z"
+     "iopub.execute_input": "2024-01-17T23:12:19.153906Z",
+     "iopub.status.busy": "2024-01-17T23:12:19.153526Z",
+     "iopub.status.idle": "2024-01-17T23:12:19.169696Z",
+     "shell.execute_reply": "2024-01-17T23:12:19.169068Z"
     }
    },
    "outputs": [
@@ -2380,10 +2372,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:14.985211Z",
-     "iopub.status.busy": "2024-01-17T18:10:14.984998Z",
-     "iopub.status.idle": "2024-01-17T18:10:14.990999Z",
-     "shell.execute_reply": "2024-01-17T18:10:14.990478Z"
+     "iopub.execute_input": "2024-01-17T23:12:19.172357Z",
+     "iopub.status.busy": "2024-01-17T23:12:19.172040Z",
+     "iopub.status.idle": "2024-01-17T23:12:19.178064Z",
+     "shell.execute_reply": "2024-01-17T23:12:19.177456Z"
     },
     "nbsphinx": "hidden"
    },
@@ -2428,10 +2420,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:14.993471Z",
-     "iopub.status.busy": "2024-01-17T18:10:14.993012Z",
-     "iopub.status.idle": "2024-01-17T18:10:15.582338Z",
-     "shell.execute_reply": "2024-01-17T18:10:15.581670Z"
+     "iopub.execute_input": "2024-01-17T23:12:19.180611Z",
+     "iopub.status.busy": "2024-01-17T23:12:19.180056Z",
+     "iopub.status.idle": "2024-01-17T23:12:19.840971Z",
+     "shell.execute_reply": "2024-01-17T23:12:19.840346Z"
     }
    },
    "outputs": [
@@ -2513,10 +2505,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:15.585070Z",
-     "iopub.status.busy": "2024-01-17T18:10:15.584860Z",
-     "iopub.status.idle": "2024-01-17T18:10:15.594353Z",
-     "shell.execute_reply": "2024-01-17T18:10:15.593626Z"
+     "iopub.execute_input": "2024-01-17T23:12:19.844161Z",
+     "iopub.status.busy": "2024-01-17T23:12:19.843784Z",
+     "iopub.status.idle": "2024-01-17T23:12:19.853652Z",
+     "shell.execute_reply": "2024-01-17T23:12:19.853100Z"
     }
    },
    "outputs": [
@@ -2541,47 +2533,47 @@
        "  \n",
        "    \n",
        "      \n",
-       "      dark_score\n",
        "      is_dark_issue\n",
+       "      dark_score\n",
        "    \n",
        "  \n",
        "  \n",
        "    \n",
        "      34848\n",
-       "      0.203922\n",
        "      True\n",
+       "      0.203922\n",
        "    \n",
        "    \n",
        "      50270\n",
-       "      0.204588\n",
        "      True\n",
+       "      0.204588\n",
        "    \n",
        "    \n",
        "      3936\n",
-       "      0.213098\n",
        "      True\n",
+       "      0.213098\n",
        "    \n",
        "    \n",
        "      733\n",
-       "      0.217686\n",
        "      True\n",
+       "      0.217686\n",
        "    \n",
        "    \n",
        "      8094\n",
-       "      0.230118\n",
        "      True\n",
+       "      0.230118\n",
        "    \n",
        "  \n",
        "\n",
        "
"
       ],
       "text/plain": [
-       "       dark_score  is_dark_issue\n",
-       "34848    0.203922           True\n",
-       "50270    0.204588           True\n",
-       "3936     0.213098           True\n",
-       "733      0.217686           True\n",
-       "8094     0.230118           True"
+       "       is_dark_issue  dark_score\n",
+       "34848           True    0.203922\n",
+       "50270           True    0.204588\n",
+       "3936            True    0.213098\n",
+       "733             True    0.217686\n",
+       "8094            True    0.230118"
       ]
      },
      "execution_count": 26,
@@ -2644,10 +2636,10 @@
    "execution_count": 27,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:15.597141Z",
-     "iopub.status.busy": "2024-01-17T18:10:15.596921Z",
-     "iopub.status.idle": "2024-01-17T18:10:15.602211Z",
-     "shell.execute_reply": "2024-01-17T18:10:15.601473Z"
+     "iopub.execute_input": "2024-01-17T23:12:19.856574Z",
+     "iopub.status.busy": "2024-01-17T23:12:19.856209Z",
+     "iopub.status.idle": "2024-01-17T23:12:19.862408Z",
+     "shell.execute_reply": "2024-01-17T23:12:19.861843Z"
     },
     "nbsphinx": "hidden"
    },
@@ -2684,10 +2676,10 @@
    "execution_count": 28,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:15.604750Z",
-     "iopub.status.busy": "2024-01-17T18:10:15.604550Z",
-     "iopub.status.idle": "2024-01-17T18:10:15.778107Z",
-     "shell.execute_reply": "2024-01-17T18:10:15.777326Z"
+     "iopub.execute_input": "2024-01-17T23:12:19.865248Z",
+     "iopub.status.busy": "2024-01-17T23:12:19.864885Z",
+     "iopub.status.idle": "2024-01-17T23:12:20.064339Z",
+     "shell.execute_reply": "2024-01-17T23:12:20.063776Z"
     }
    },
    "outputs": [
@@ -2729,10 +2721,10 @@
    "execution_count": 29,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:15.781004Z",
-     "iopub.status.busy": "2024-01-17T18:10:15.780794Z",
-     "iopub.status.idle": "2024-01-17T18:10:15.789706Z",
-     "shell.execute_reply": "2024-01-17T18:10:15.788980Z"
+     "iopub.execute_input": "2024-01-17T23:12:20.066913Z",
+     "iopub.status.busy": "2024-01-17T23:12:20.066523Z",
+     "iopub.status.idle": "2024-01-17T23:12:20.074947Z",
+     "shell.execute_reply": "2024-01-17T23:12:20.074451Z"
     }
    },
    "outputs": [
@@ -2818,10 +2810,10 @@
    "execution_count": 30,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:15.792222Z",
-     "iopub.status.busy": "2024-01-17T18:10:15.791846Z",
-     "iopub.status.idle": "2024-01-17T18:10:15.991891Z",
-     "shell.execute_reply": "2024-01-17T18:10:15.991225Z"
+     "iopub.execute_input": "2024-01-17T23:12:20.077342Z",
+     "iopub.status.busy": "2024-01-17T23:12:20.076942Z",
+     "iopub.status.idle": "2024-01-17T23:12:20.273417Z",
+     "shell.execute_reply": "2024-01-17T23:12:20.272758Z"
     }
    },
    "outputs": [
@@ -2861,10 +2853,10 @@
    "execution_count": 31,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:15.994477Z",
-     "iopub.status.busy": "2024-01-17T18:10:15.994081Z",
-     "iopub.status.idle": "2024-01-17T18:10:15.998830Z",
-     "shell.execute_reply": "2024-01-17T18:10:15.998287Z"
+     "iopub.execute_input": "2024-01-17T23:12:20.275994Z",
+     "iopub.status.busy": "2024-01-17T23:12:20.275783Z",
+     "iopub.status.idle": "2024-01-17T23:12:20.280537Z",
+     "shell.execute_reply": "2024-01-17T23:12:20.280012Z"
     },
     "nbsphinx": "hidden"
    },
@@ -2901,39 +2893,7 @@
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "0422f06140e1429bb9df4d5e22a01ac2": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "ProgressStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
-      }
-     },
-     "06b2bf8222ef4b6db00a974dc321d1d2": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "ProgressStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
-      }
-     },
-     "09b79e0723394c75a9295ba283a8fd52": {
+     "0397c83a64194b3e8f302bed7a8d9f8c": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "HTMLModel",
@@ -2948,13 +2908,13 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_6c248b337d0c458ead2d440b3472437f",
+       "layout": "IPY_MODEL_e6236cd92f06491a83ea7c38bf31a7e7",
        "placeholder": "​",
-       "style": "IPY_MODEL_0ddd9623827d4a419d5c51467297b6bd",
-       "value": " 60000/0 [00:00<00:00, 817475.64 examples/s]"
+       "style": "IPY_MODEL_2bfaa3482cd24c6f8a7f4c4e881e2ca7",
+       "value": "Generating train split: "
       }
      },
-     "0a024c8dbb214427b3250ba948806841": {
+     "045208935e1f4bd6a391b26b3983d049": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "FloatProgressModel",
@@ -2970,31 +2930,15 @@
        "bar_style": "success",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_19629acb9010471ba50b5b29e00183c6",
-       "max": 1.0,
+       "layout": "IPY_MODEL_7b8800a4904440598f0e45fab5fbd35a",
+       "max": 60000.0,
        "min": 0.0,
        "orientation": "horizontal",
-       "style": "IPY_MODEL_f72cf07726314106aaa73a8aa0dbb3d9",
-       "value": 1.0
-      }
-     },
-     "0d884ce3ab3b4e51bd48a78309442fe2": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "ProgressStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
+       "style": "IPY_MODEL_98fe2af32bbc4615b9fd39e09236fd2f",
+       "value": 60000.0
       }
      },
-     "0ddd9623827d4a419d5c51467297b6bd": {
+     "06760201f05c4000afc116288d98641b": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "DescriptionStyleModel",
@@ -3009,53 +2953,28 @@
        "description_width": ""
       }
      },
-     "1236cf2a98e4455b930f467d1e95e56f": {
+     "0a530e5055f0497090b37f9ec22dadae": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "FloatProgressModel",
+      "model_name": "HTMLModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "FloatProgressModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
+       "_view_name": "HTMLView",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_a649432ed8c04a3b87f55c6e1cbc806d",
-       "max": 60000.0,
-       "min": 0.0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_6616b30d88f3472290070dde94c3d289",
-       "value": 60000.0
-      }
-     },
-     "13b1818046c2493686e01a759bad0eef": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_3dd51d9190ef4190bd322c16bed1fb06",
-        "IPY_MODEL_5c339eb01faf4cf5a621c0edb6c44b68",
-        "IPY_MODEL_cf40c5cc5c3e42069f474bc592b94779"
-       ],
-       "layout": "IPY_MODEL_5d82cde87f5c4f48b90eca1c0c7219d2"
+       "layout": "IPY_MODEL_3bf8c457de024516a37553baea232c37",
+       "placeholder": "​",
+       "style": "IPY_MODEL_1ed5c49b1ddb4b7dbf0853bcd1a3778a",
+       "value": "Downloading data: 100%"
       }
      },
-     "13e232e4e5e148d8a3faa3c2f89ea970": {
+     "0c58d5d607b74c128d2fed5574d2b2f0": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3107,7 +3026,7 @@
        "width": null
       }
      },
-     "14970ee75a2d4d2b84651021085a0bab": {
+     "179264665b3d4da192240f1ac09ac5e5": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "HTMLModel",
@@ -3122,37 +3041,35 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_2dbb0555cbba41679ae0e974d1f96ac2",
+       "layout": "IPY_MODEL_c3549b4dff2849a49f4c582646e80469",
        "placeholder": "​",
-       "style": "IPY_MODEL_94474ce5fa7c49859fb2ba027b64aef7",
-       "value": "Computing checksums: 100%"
+       "style": "IPY_MODEL_99d94d93be9e48819c0c81e7e63b86e8",
+       "value": "Map (num_proc=4): 100%"
       }
      },
-     "18e94be5479c4f7bade2bb296c771679": {
+     "19d4a001660c44e8a52d8d2f5e1ea989": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "FloatProgressModel",
+      "model_name": "HBoxModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "FloatProgressModel",
+       "_model_name": "HBoxModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_cf90eb9f6391477bbf013ddee2032497",
-       "max": 2.0,
-       "min": 0.0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_9af6f90090b44f5a994f4d2ef2c28c38",
-       "value": 2.0
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_0397c83a64194b3e8f302bed7a8d9f8c",
+        "IPY_MODEL_231dea10656441a1a0519b295f0d7bfd",
+        "IPY_MODEL_84270cee9acd46f7b5711cc0cb1d1790"
+       ],
+       "layout": "IPY_MODEL_80393a3a390947c29ad3e5befeee9f1a"
       }
      },
-     "19629acb9010471ba50b5b29e00183c6": {
+     "1a9050a96ba840b1981a446cc79d9a2a": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3201,25 +3118,62 @@
        "right": null,
        "top": null,
        "visibility": null,
-       "width": "20px"
+       "width": null
       }
      },
-     "1e1ecba39033420591ada0b76d527bf1": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
+     "1a99e1fffc83437580ec94981db5304f": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
       "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "1.2.0",
+       "_model_name": "LayoutModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "overflow_x": null,
+       "overflow_y": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      },
-     "20170352b29c453e997c9a95c3a33b80": {
+     "1c435eee98164c94875a24dc9213ab07": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3271,7 +3225,22 @@
        "width": null
       }
      },
-     "25cccd29d96c4634a04b0cb4c0ae7a99": {
+     "1ed5c49b1ddb4b7dbf0853bcd1a3778a": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "231dea10656441a1a0519b295f0d7bfd": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "FloatProgressModel",
@@ -3287,58 +3256,61 @@
        "bar_style": "success",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_adfa225ec8f64e7880afb3cb066db908",
-       "max": 60000.0,
+       "layout": "IPY_MODEL_f2f5ea97fde04ab1992313b89ce1e044",
+       "max": 1.0,
        "min": 0.0,
        "orientation": "horizontal",
-       "style": "IPY_MODEL_f7614c62ca994b079963f2061f90aec2",
-       "value": 60000.0
+       "style": "IPY_MODEL_995e09d244bd49bd8d7ac45836ec79f9",
+       "value": 1.0
       }
      },
-     "293d1b710eea4f9fa017893a7a977f6d": {
+     "2bfaa3482cd24c6f8a7f4c4e881e2ca7": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "HBoxModel",
+      "model_name": "DescriptionStyleModel",
       "state": {
-       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "HBoxModel",
+       "_model_name": "DescriptionStyleModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_14970ee75a2d4d2b84651021085a0bab",
-        "IPY_MODEL_18e94be5479c4f7bade2bb296c771679",
-        "IPY_MODEL_41a78828843c457294fc38e7f334ef9a"
-       ],
-       "layout": "IPY_MODEL_819ad8fec46043248eb022c81bb1b5b6"
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
       }
      },
-     "2b8505492b124436bf13b9605b3b7dd8": {
+     "2f977c6c9def44e5a688a578fdbe8f46": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
+      "model_name": "DescriptionStyleModel",
       "state": {
-       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "DescriptionStyleModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_9e9f3c1977bc4cc18ccf7c0091220fdd",
-       "placeholder": "​",
-       "style": "IPY_MODEL_a8a2c7b2b22842f5be552d65b32d2f6c",
-       "value": " 60000/60000 [00:12<00:00, 6511.95 examples/s]"
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "38f79432d4ea43f3ad8e5a6a05b9cfd7": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
       }
      },
-     "2dbb0555cbba41679ae0e974d1f96ac2": {
+     "39cced6b09354583a41a423a7f3697f2": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3390,50 +3362,59 @@
        "width": null
       }
      },
-     "34ddbf7efb384ee4960a77089187f278": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HBoxModel",
+     "3b5c42a953034dfbb32825a03164a0f7": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
       "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HBoxModel",
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "1.2.0",
+       "_model_name": "LayoutModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_daf93e367bcd41009e494ece90f80fde",
-        "IPY_MODEL_0a024c8dbb214427b3250ba948806841",
-        "IPY_MODEL_09b79e0723394c75a9295ba283a8fd52"
-       ],
-       "layout": "IPY_MODEL_e57d19b9905849f58864c0e2ae9f2c73"
-      }
-     },
-     "3dd51d9190ef4190bd322c16bed1fb06": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_67575008a0c84c898646dedc03acf942",
-       "placeholder": "​",
-       "style": "IPY_MODEL_fe2e11a13fdb46e8b74279e56b89a038",
-       "value": "Generating test split: "
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "overflow_x": null,
+       "overflow_y": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      },
-     "411e7b1250d04fceabc4a56d3aa7c5ba": {
+     "3b8311a276a84a2b810f57fb87ac7a1c": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "HBoxModel",
@@ -3448,35 +3429,14 @@
        "_view_name": "HBoxView",
        "box_style": "",
        "children": [
-        "IPY_MODEL_5fc122f493644e7ca3b284b5bf922da7",
-        "IPY_MODEL_6af063d78a0b4668bb022ad287881b3a",
-        "IPY_MODEL_6a561cf815bc411bbf8042951da94f0f"
+        "IPY_MODEL_0a530e5055f0497090b37f9ec22dadae",
+        "IPY_MODEL_4975b121fca840d69ee14918cd0d217d",
+        "IPY_MODEL_fd2ab6faeeeb4055bbcc5fe64bac125d"
        ],
-       "layout": "IPY_MODEL_8388e5c0bcdd44658f6c1d9fe032adde"
-      }
-     },
-     "41a78828843c457294fc38e7f334ef9a": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_20170352b29c453e997c9a95c3a33b80",
-       "placeholder": "​",
-       "style": "IPY_MODEL_af1dd8d8ecef45a48f86fa83500577b4",
-       "value": " 2/2 [00:00<00:00, 346.19it/s]"
+       "layout": "IPY_MODEL_94881b422b624471974fef993ac1b9e3"
       }
      },
-     "43118ea6bbea4a20b0f36f67ad05ec28": {
+     "3bf8c457de024516a37553baea232c37": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3528,29 +3488,83 @@
        "width": null
       }
      },
-     "4788f80918f340f199482171f3731dd8": {
+     "3dceb99795994c2c9faef0d8437d26df": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "HBoxModel",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "3f168cb3d1c94b1ba4a217cd6467dbc6": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "HBoxModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_d80873115a3d48b4a783d8a26120c697",
-        "IPY_MODEL_1236cf2a98e4455b930f467d1e95e56f",
-        "IPY_MODEL_56ab71b0a1ee4eb8b1ab047505bc017c"
-       ],
-       "layout": "IPY_MODEL_b950eeaac0b8423fbc3d696ee313e0f7"
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_0c58d5d607b74c128d2fed5574d2b2f0",
+       "placeholder": "​",
+       "style": "IPY_MODEL_481f7c373c054bd1a8ed7c673293ca90",
+       "value": " 30.9M/30.9M [00:01<00:00, 16.7MB/s]"
+      }
+     },
+     "406fe6cbb9a34dca80a0ecaff800df90": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_1a9050a96ba840b1981a446cc79d9a2a",
+       "max": 2.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_38f79432d4ea43f3ad8e5a6a05b9cfd7",
+       "value": 2.0
+      }
+     },
+     "41327ec0739e4dcd957c5ee2898e050a": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
       }
      },
-     "56ab71b0a1ee4eb8b1ab047505bc017c": {
+     "44346c7bc1cf426680dd5db30374b0c0": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "HTMLModel",
@@ -3565,13 +3579,13 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_13e232e4e5e148d8a3faa3c2f89ea970",
+       "layout": "IPY_MODEL_a392060d75f04f79b300b2254520405a",
        "placeholder": "​",
-       "style": "IPY_MODEL_59b669434d304300ae56a27d14f49bf5",
-       "value": " 60000/60000 [00:34<00:00, 1771.33it/s]"
+       "style": "IPY_MODEL_2f977c6c9def44e5a688a578fdbe8f46",
+       "value": "Computing checksums: 100%"
       }
      },
-     "58930589e42646c7a4adf4cd6bcd6371": {
+     "44d4dca1913340ce8b9c507cd312f8c9": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3623,7 +3637,46 @@
        "width": null
       }
      },
-     "59b669434d304300ae56a27d14f49bf5": {
+     "481f7c373c054bd1a8ed7c673293ca90": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "4975b121fca840d69ee14918cd0d217d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_3b5c42a953034dfbb32825a03164a0f7",
+       "max": 5175617.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_9fafe542e2ed4c18aab17f4dc9200a15",
+       "value": 5175617.0
+      }
+     },
+     "4ece4d1132ab47b39c47d91a5c14bc90": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "DescriptionStyleModel",
@@ -3638,7 +3691,7 @@
        "description_width": ""
       }
      },
-     "5a1b5f56c91c4c2aa69d687eae32d787": {
+     "4f39a6854c894be9b84b9424dc291601": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3690,31 +3743,44 @@
        "width": null
       }
      },
-     "5c339eb01faf4cf5a621c0edb6c44b68": {
+     "522cf4a497fa4a07ae98cbe1f5c211c0": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "FloatProgressModel",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "57b755a1482c49a2be45568b6bdd0f3e": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "FloatProgressModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
+       "_view_name": "HTMLView",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_ae038169fdbf4ff78f655a94650c3c2b",
-       "max": 1.0,
-       "min": 0.0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_0d884ce3ab3b4e51bd48a78309442fe2",
-       "value": 1.0
+       "layout": "IPY_MODEL_c83a7ea94765464f9172b04328ecf7cb",
+       "placeholder": "​",
+       "style": "IPY_MODEL_9ae996a06a074f598ffdd1896fc06e5f",
+       "value": " 60000/60000 [00:34<00:00, 1762.24it/s]"
       }
      },
-     "5d82cde87f5c4f48b90eca1c0c7219d2": {
+     "58928fb735cd44a8ab6e8c0edf078872": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3766,44 +3832,31 @@
        "width": null
       }
      },
-     "5fc122f493644e7ca3b284b5bf922da7": {
+     "59a3fa3e95574a8b814d146dfed3c4b7": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
+      "model_name": "FloatProgressModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "FloatProgressModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_fd329fbe811246e8a0566112ee0206c9",
-       "placeholder": "​",
-       "style": "IPY_MODEL_fdd50d7c13524c3c882b9c849c0fb910",
-       "value": "Downloading data: 100%"
-      }
-     },
-     "6616b30d88f3472290070dde94c3d289": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "ProgressStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
+       "layout": "IPY_MODEL_5e5f26489aa44b649b2a01102f0c989a",
+       "max": 1.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_522cf4a497fa4a07ae98cbe1f5c211c0",
+       "value": 1.0
       }
      },
-     "67575008a0c84c898646dedc03acf942": {
+     "5e5f26489aa44b649b2a01102f0c989a": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3852,10 +3905,10 @@
        "right": null,
        "top": null,
        "visibility": null,
-       "width": null
+       "width": "20px"
       }
      },
-     "6a561cf815bc411bbf8042951da94f0f": {
+     "6f35b4075f004e12901e63c69bb0356a": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "HTMLModel",
@@ -3870,13 +3923,13 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_85537fd6b7504ad187504946935cc321",
+       "layout": "IPY_MODEL_8e7dbb79e5a543c7885714426be3c75c",
        "placeholder": "​",
-       "style": "IPY_MODEL_8b814b38f0104752b04bd4a5310bb6b1",
-       "value": " 5.18M/5.18M [00:00<00:00, 52.6MB/s]"
+       "style": "IPY_MODEL_e9e90e1e28aa4e3588e479ab25012fbf",
+       "value": " 60000/60000 [00:12<00:00, 5989.87 examples/s]"
       }
      },
-     "6af063d78a0b4668bb022ad287881b3a": {
+     "748f41179a3a40d5bfb7a4edfe8d13ff": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "FloatProgressModel",
@@ -3892,15 +3945,37 @@
        "bar_style": "success",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_58930589e42646c7a4adf4cd6bcd6371",
-       "max": 5175617.0,
+       "layout": "IPY_MODEL_e35a74ec5bd6445e96da7f49abfe6c0c",
+       "max": 60000.0,
        "min": 0.0,
        "orientation": "horizontal",
-       "style": "IPY_MODEL_0422f06140e1429bb9df4d5e22a01ac2",
-       "value": 5175617.0
+       "style": "IPY_MODEL_41327ec0739e4dcd957c5ee2898e050a",
+       "value": 60000.0
+      }
+     },
+     "79ccb0a1555b42b5a881bd1c68892db9": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_8a776487ef584aacb78e7a4c24fc20f0",
+        "IPY_MODEL_f9341d1dc6404c1194db44e60a6c230e",
+        "IPY_MODEL_3f168cb3d1c94b1ba4a217cd6467dbc6"
+       ],
+       "layout": "IPY_MODEL_1c435eee98164c94875a24dc9213ab07"
       }
      },
-     "6c248b337d0c458ead2d440b3472437f": {
+     "7b8800a4904440598f0e45fab5fbd35a": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3952,7 +4027,7 @@
        "width": null
       }
      },
-     "76f30844c4444b9cb566e2bb926b7b40": {
+     "7ce4749d923744c09bd675ee944baaad": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "HBoxModel",
@@ -3967,14 +4042,14 @@
        "_view_name": "HBoxView",
        "box_style": "",
        "children": [
-        "IPY_MODEL_fc146736a7804106a33260d3a605aae1",
-        "IPY_MODEL_25cccd29d96c4634a04b0cb4c0ae7a99",
-        "IPY_MODEL_2b8505492b124436bf13b9605b3b7dd8"
+        "IPY_MODEL_fba3a6dbeac647a58b86ca3c2387dea1",
+        "IPY_MODEL_59a3fa3e95574a8b814d146dfed3c4b7",
+        "IPY_MODEL_9122abc397764bf2888a6ee466622011"
        ],
-       "layout": "IPY_MODEL_da553b2e323e4d9e9a3dfc9c117bcd77"
+       "layout": "IPY_MODEL_857bf7c092c443779ed47daba1a805c1"
       }
      },
-     "78bbe03efe744a16b7517c70dee50f2a": {
+     "80393a3a390947c29ad3e5befeee9f1a": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4026,7 +4101,7 @@
        "width": null
       }
      },
-     "7d62cb9c305943eab8fadf60d62315d0": {
+     "80ebd1685921401db08c4590b9baa83c": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "DescriptionStyleModel",
@@ -4041,59 +4116,28 @@
        "description_width": ""
       }
      },
-     "819ad8fec46043248eb022c81bb1b5b6": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "1.2.0",
-      "model_name": "LayoutModel",
+     "84270cee9acd46f7b5711cc0cb1d1790": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
       "state": {
-       "_model_module": "@jupyter-widgets/base",
-       "_model_module_version": "1.2.0",
-       "_model_name": "LayoutModel",
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "HTMLModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "LayoutView",
-       "align_content": null,
-       "align_items": null,
-       "align_self": null,
-       "border": null,
-       "bottom": null,
-       "display": null,
-       "flex": null,
-       "flex_flow": null,
-       "grid_area": null,
-       "grid_auto_columns": null,
-       "grid_auto_flow": null,
-       "grid_auto_rows": null,
-       "grid_column": null,
-       "grid_gap": null,
-       "grid_row": null,
-       "grid_template_areas": null,
-       "grid_template_columns": null,
-       "grid_template_rows": null,
-       "height": null,
-       "justify_content": null,
-       "justify_items": null,
-       "left": null,
-       "margin": null,
-       "max_height": null,
-       "max_width": null,
-       "min_height": null,
-       "min_width": null,
-       "object_fit": null,
-       "object_position": null,
-       "order": null,
-       "overflow": null,
-       "overflow_x": null,
-       "overflow_y": null,
-       "padding": null,
-       "right": null,
-       "top": null,
-       "visibility": null,
-       "width": null
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_eac75786bd0747edbc8069694b24a47f",
+       "placeholder": "​",
+       "style": "IPY_MODEL_c200da24b8d24af2a8729220de3fc9f5",
+       "value": " 60000/0 [00:00<00:00, 822434.05 examples/s]"
       }
      },
-     "8388e5c0bcdd44658f6c1d9fe032adde": {
+     "857bf7c092c443779ed47daba1a805c1": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4145,7 +4189,28 @@
        "width": null
       }
      },
-     "85537fd6b7504ad187504946935cc321": {
+     "8a776487ef584aacb78e7a4c24fc20f0": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_be2fb1d47c9b4ddca4f55cd862ed528f",
+       "placeholder": "​",
+       "style": "IPY_MODEL_9f3c94882ba84a3188e74bcaadb84860",
+       "value": "Downloading data: 100%"
+      }
+     },
+     "8e7dbb79e5a543c7885714426be3c75c": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4197,58 +4262,29 @@
        "width": null
       }
      },
-     "886d87adc7e14bb181d14fe73356712c": {
+     "8f5c519e8d9d42d7a8b129396063163d": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
+      "model_name": "HBoxModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "HBoxModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_ecec36d46b93457e812be0045f430615",
-       "placeholder": "​",
-       "style": "IPY_MODEL_92480a4d5aa542e4b3c245d5eaef0719",
-       "value": " 30.9M/30.9M [00:00<00:00, 54.1MB/s]"
-      }
-     },
-     "8a59d3978538419bb1a7ae4f2c634c57": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
-      }
-     },
-     "8b814b38f0104752b04bd4a5310bb6b1": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_44346c7bc1cf426680dd5db30374b0c0",
+        "IPY_MODEL_406fe6cbb9a34dca80a0ecaff800df90",
+        "IPY_MODEL_8f6e5f7512ee4ff7ad72e4cc5dca9461"
+       ],
+       "layout": "IPY_MODEL_58928fb735cd44a8ab6e8c0edf078872"
       }
      },
-     "8ec077adf7584cd58c173b40862ef286": {
+     "8f6e5f7512ee4ff7ad72e4cc5dca9461": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "HTMLModel",
@@ -4263,81 +4299,34 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_d080bdb537ff418c9cc35048f6e03b22",
+       "layout": "IPY_MODEL_e5a47d68a2cd473f951c59fcf76d01f1",
        "placeholder": "​",
-       "style": "IPY_MODEL_7d62cb9c305943eab8fadf60d62315d0",
-       "value": "Downloading data: 100%"
+       "style": "IPY_MODEL_4ece4d1132ab47b39c47d91a5c14bc90",
+       "value": " 2/2 [00:00<00:00, 299.27it/s]"
       }
      },
-     "9218ba50145b4c1abb05285d39489f7b": {
+     "9122abc397764bf2888a6ee466622011": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "HBoxModel",
+      "model_name": "HTMLModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "HBoxModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_8ec077adf7584cd58c173b40862ef286",
-        "IPY_MODEL_dc3e760d54c94143afafcc4928690c1e",
-        "IPY_MODEL_886d87adc7e14bb181d14fe73356712c"
-       ],
-       "layout": "IPY_MODEL_43118ea6bbea4a20b0f36f67ad05ec28"
-      }
-     },
-     "92480a4d5aa542e4b3c245d5eaef0719": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
-      }
-     },
-     "94474ce5fa7c49859fb2ba027b64aef7": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
-      }
-     },
-     "9af6f90090b44f5a994f4d2ef2c28c38": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "ProgressStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_44d4dca1913340ce8b9c507cd312f8c9",
+       "placeholder": "​",
+       "style": "IPY_MODEL_3dceb99795994c2c9faef0d8437d26df",
+       "value": " 10000/0 [00:00<00:00, 481500.65 examples/s]"
       }
      },
-     "9e9f3c1977bc4cc18ccf7c0091220fdd": {
+     "94881b422b624471974fef993ac1b9e3": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4389,7 +4378,7 @@
        "width": null
       }
      },
-     "a649432ed8c04a3b87f55c6e1cbc806d": {
+     "9531113f8f814de2b0a6e754c456bf1f": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4441,22 +4430,7 @@
        "width": null
       }
      },
-     "a8a2c7b2b22842f5be552d65b32d2f6c": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
-      }
-     },
-     "adfa225ec8f64e7880afb3cb066db908": {
+     "9700769e28304398b3004f50582ae2ae": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4508,59 +4482,39 @@
        "width": null
       }
      },
-     "ae038169fdbf4ff78f655a94650c3c2b": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "1.2.0",
-      "model_name": "LayoutModel",
+     "98fe2af32bbc4615b9fd39e09236fd2f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
       "state": {
-       "_model_module": "@jupyter-widgets/base",
-       "_model_module_version": "1.2.0",
-       "_model_name": "LayoutModel",
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "ProgressStyleModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "1.2.0",
-       "_view_name": "LayoutView",
-       "align_content": null,
-       "align_items": null,
-       "align_self": null,
-       "border": null,
-       "bottom": null,
-       "display": null,
-       "flex": null,
-       "flex_flow": null,
-       "grid_area": null,
-       "grid_auto_columns": null,
-       "grid_auto_flow": null,
-       "grid_auto_rows": null,
-       "grid_column": null,
-       "grid_gap": null,
-       "grid_row": null,
-       "grid_template_areas": null,
-       "grid_template_columns": null,
-       "grid_template_rows": null,
-       "height": null,
-       "justify_content": null,
-       "justify_items": null,
-       "left": null,
-       "margin": null,
-       "max_height": null,
-       "max_width": null,
-       "min_height": null,
-       "min_width": null,
-       "object_fit": null,
-       "object_position": null,
-       "order": null,
-       "overflow": null,
-       "overflow_x": null,
-       "overflow_y": null,
-       "padding": null,
-       "right": null,
-       "top": null,
-       "visibility": null,
-       "width": "20px"
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "995e09d244bd49bd8d7ac45836ec79f9": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
       }
      },
-     "af1dd8d8ecef45a48f86fa83500577b4": {
+     "99d94d93be9e48819c0c81e7e63b86e8": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "DescriptionStyleModel",
@@ -4575,59 +4529,68 @@
        "description_width": ""
       }
      },
-     "b950eeaac0b8423fbc3d696ee313e0f7": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "1.2.0",
-      "model_name": "LayoutModel",
+     "9ae996a06a074f598ffdd1896fc06e5f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
       "state": {
-       "_model_module": "@jupyter-widgets/base",
-       "_model_module_version": "1.2.0",
-       "_model_name": "LayoutModel",
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "1.2.0",
-       "_view_name": "LayoutView",
-       "align_content": null,
-       "align_items": null,
-       "align_self": null,
-       "border": null,
-       "bottom": null,
-       "display": null,
-       "flex": null,
-       "flex_flow": null,
-       "grid_area": null,
-       "grid_auto_columns": null,
-       "grid_auto_flow": null,
-       "grid_auto_rows": null,
-       "grid_column": null,
-       "grid_gap": null,
-       "grid_row": null,
-       "grid_template_areas": null,
-       "grid_template_columns": null,
-       "grid_template_rows": null,
-       "height": null,
-       "justify_content": null,
-       "justify_items": null,
-       "left": null,
-       "margin": null,
-       "max_height": null,
-       "max_width": null,
-       "min_height": null,
-       "min_width": null,
-       "object_fit": null,
-       "object_position": null,
-       "order": null,
-       "overflow": null,
-       "overflow_x": null,
-       "overflow_y": null,
-       "padding": null,
-       "right": null,
-       "top": null,
-       "visibility": null,
-       "width": null
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "9d080255b4c84e7c91b438dd5562134b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "9f3c94882ba84a3188e74bcaadb84860": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "9fafe542e2ed4c18aab17f4dc9200a15": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
       }
      },
-     "c8c2dd564bcd47159c5d7da2cd990067": {
+     "a392060d75f04f79b300b2254520405a": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4679,22 +4642,23 @@
        "width": null
       }
      },
-     "cee108b3a0fc446daf791fcfa739d5f9": {
+     "b36fa317529346c8837fe628e9b58225": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "ProgressStyleModel",
       "state": {
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
+       "_model_name": "ProgressStyleModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "1.2.0",
        "_view_name": "StyleView",
+       "bar_color": null,
        "description_width": ""
       }
      },
-     "cf40c5cc5c3e42069f474bc592b94779": {
+     "b94cbe2658d747a2b3051c57bb7a1145": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "HTMLModel",
@@ -4709,13 +4673,13 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_c8c2dd564bcd47159c5d7da2cd990067",
+       "layout": "IPY_MODEL_1a99e1fffc83437580ec94981db5304f",
        "placeholder": "​",
-       "style": "IPY_MODEL_cee108b3a0fc446daf791fcfa739d5f9",
-       "value": " 10000/0 [00:00<00:00, 518981.42 examples/s]"
+       "style": "IPY_MODEL_06760201f05c4000afc116288d98641b",
+       "value": "100%"
       }
      },
-     "cf90eb9f6391477bbf013ddee2032497": {
+     "be2fb1d47c9b4ddca4f55cd862ed528f": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4767,7 +4731,44 @@
        "width": null
       }
      },
-     "d0382716c80644e3ba550bc5ed756161": {
+     "c1f8c848d7954850a757e5e8fa83f920": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_b94cbe2658d747a2b3051c57bb7a1145",
+        "IPY_MODEL_748f41179a3a40d5bfb7a4edfe8d13ff",
+        "IPY_MODEL_57b755a1482c49a2be45568b6bdd0f3e"
+       ],
+       "layout": "IPY_MODEL_4f39a6854c894be9b84b9424dc291601"
+      }
+     },
+     "c200da24b8d24af2a8729220de3fc9f5": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "c3549b4dff2849a49f4c582646e80469": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4819,7 +4820,7 @@
        "width": null
       }
      },
-     "d080bdb537ff418c9cc35048f6e03b22": {
+     "c83a7ea94765464f9172b04328ecf7cb": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4871,28 +4872,29 @@
        "width": null
       }
      },
-     "d80873115a3d48b4a783d8a26120c697": {
+     "cf1bbec90cc743c19c044238bc5cd410": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
+      "model_name": "HBoxModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "HBoxModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_5a1b5f56c91c4c2aa69d687eae32d787",
-       "placeholder": "​",
-       "style": "IPY_MODEL_de829f78730844509bffcc9f0cf8f531",
-       "value": "100%"
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_179264665b3d4da192240f1ac09ac5e5",
+        "IPY_MODEL_045208935e1f4bd6a391b26b3983d049",
+        "IPY_MODEL_6f35b4075f004e12901e63c69bb0356a"
+       ],
+       "layout": "IPY_MODEL_39cced6b09354583a41a423a7f3697f2"
       }
      },
-     "da553b2e323e4d9e9a3dfc9c117bcd77": {
+     "e35a74ec5bd6445e96da7f49abfe6c0c": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4944,67 +4946,7 @@
        "width": null
       }
      },
-     "daf93e367bcd41009e494ece90f80fde": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_d0382716c80644e3ba550bc5ed756161",
-       "placeholder": "​",
-       "style": "IPY_MODEL_1e1ecba39033420591ada0b76d527bf1",
-       "value": "Generating train split: "
-      }
-     },
-     "dc3e760d54c94143afafcc4928690c1e": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "FloatProgressModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "FloatProgressModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_78bbe03efe744a16b7517c70dee50f2a",
-       "max": 30931277.0,
-       "min": 0.0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_06b2bf8222ef4b6db00a974dc321d1d2",
-       "value": 30931277.0
-      }
-     },
-     "de829f78730844509bffcc9f0cf8f531": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
-      }
-     },
-     "e57d19b9905849f58864c0e2ae9f2c73": {
+     "e5a47d68a2cd473f951c59fcf76d01f1": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -5056,7 +4998,7 @@
        "width": null
       }
      },
-     "ecec36d46b93457e812be0045f430615": {
+     "e6236cd92f06491a83ea7c38bf31a7e7": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -5108,60 +5050,74 @@
        "width": null
       }
      },
-     "f72cf07726314106aaa73a8aa0dbb3d9": {
+     "e9e90e1e28aa4e3588e479ab25012fbf": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "ProgressStyleModel",
+      "model_name": "DescriptionStyleModel",
       "state": {
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
+       "_model_name": "DescriptionStyleModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "1.2.0",
        "_view_name": "StyleView",
-       "bar_color": null,
        "description_width": ""
       }
      },
-     "f7614c62ca994b079963f2061f90aec2": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "ProgressStyleModel",
+     "eac75786bd0747edbc8069694b24a47f": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
       "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "1.2.0",
+       "_model_name": "LayoutModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
-      }
-     },
-     "fc146736a7804106a33260d3a605aae1": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_fcad613391c24914b64a652e2c514fcb",
-       "placeholder": "​",
-       "style": "IPY_MODEL_8a59d3978538419bb1a7ae4f2c634c57",
-       "value": "Map (num_proc=4): 100%"
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "overflow_x": null,
+       "overflow_y": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      },
-     "fcad613391c24914b64a652e2c514fcb": {
+     "f21c6c99bca84e06b8d1299f3b53db6d": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -5213,7 +5169,7 @@
        "width": null
       }
      },
-     "fd329fbe811246e8a0566112ee0206c9": {
+     "f2f5ea97fde04ab1992313b89ce1e044": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -5262,37 +5218,73 @@
        "right": null,
        "top": null,
        "visibility": null,
-       "width": null
+       "width": "20px"
       }
      },
-     "fdd50d7c13524c3c882b9c849c0fb910": {
+     "f9341d1dc6404c1194db44e60a6c230e": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "FloatProgressModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
+       "_model_name": "FloatProgressModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_f21c6c99bca84e06b8d1299f3b53db6d",
+       "max": 30931277.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_b36fa317529346c8837fe628e9b58225",
+       "value": 30931277.0
       }
      },
-     "fe2e11a13fdb46e8b74279e56b89a038": {
+     "fba3a6dbeac647a58b86ca3c2387dea1": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "HTMLModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_9531113f8f814de2b0a6e754c456bf1f",
+       "placeholder": "​",
+       "style": "IPY_MODEL_9d080255b4c84e7c91b438dd5562134b",
+       "value": "Generating test split: "
+      }
+     },
+     "fd2ab6faeeeb4055bbcc5fe64bac125d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_9700769e28304398b3004f50582ae2ae",
+       "placeholder": "​",
+       "style": "IPY_MODEL_80ebd1685921401db08c4590b9baa83c",
+       "value": " 5.18M/5.18M [00:00<00:00, 38.7MB/s]"
       }
      }
     },
diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb
index f96a2b6cb..36e58d2a0 100644
--- a/master/tutorials/indepth_overview.ipynb
+++ b/master/tutorials/indepth_overview.ipynb
@@ -53,10 +53,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:21.766507Z",
-     "iopub.status.busy": "2024-01-17T18:10:21.766309Z",
-     "iopub.status.idle": "2024-01-17T18:10:22.883924Z",
-     "shell.execute_reply": "2024-01-17T18:10:22.882998Z"
+     "iopub.execute_input": "2024-01-17T23:12:25.645173Z",
+     "iopub.status.busy": "2024-01-17T23:12:25.644719Z",
+     "iopub.status.idle": "2024-01-17T23:12:26.712765Z",
+     "shell.execute_reply": "2024-01-17T23:12:26.712142Z"
     },
     "nbsphinx": "hidden"
    },
@@ -68,7 +68,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -95,10 +95,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:22.886848Z",
-     "iopub.status.busy": "2024-01-17T18:10:22.886478Z",
-     "iopub.status.idle": "2024-01-17T18:10:23.166976Z",
-     "shell.execute_reply": "2024-01-17T18:10:23.166265Z"
+     "iopub.execute_input": "2024-01-17T23:12:26.715639Z",
+     "iopub.status.busy": "2024-01-17T23:12:26.715237Z",
+     "iopub.status.idle": "2024-01-17T23:12:26.981319Z",
+     "shell.execute_reply": "2024-01-17T23:12:26.980689Z"
     },
     "id": "avXlHJcXjruP"
    },
@@ -234,10 +234,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:23.170233Z",
-     "iopub.status.busy": "2024-01-17T18:10:23.169703Z",
-     "iopub.status.idle": "2024-01-17T18:10:23.182165Z",
-     "shell.execute_reply": "2024-01-17T18:10:23.181510Z"
+     "iopub.execute_input": "2024-01-17T23:12:26.984263Z",
+     "iopub.status.busy": "2024-01-17T23:12:26.983847Z",
+     "iopub.status.idle": "2024-01-17T23:12:26.995890Z",
+     "shell.execute_reply": "2024-01-17T23:12:26.995368Z"
     },
     "nbsphinx": "hidden"
    },
@@ -340,10 +340,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:23.184824Z",
-     "iopub.status.busy": "2024-01-17T18:10:23.184445Z",
-     "iopub.status.idle": "2024-01-17T18:10:23.391408Z",
-     "shell.execute_reply": "2024-01-17T18:10:23.390738Z"
+     "iopub.execute_input": "2024-01-17T23:12:26.998151Z",
+     "iopub.status.busy": "2024-01-17T23:12:26.997887Z",
+     "iopub.status.idle": "2024-01-17T23:12:27.219836Z",
+     "shell.execute_reply": "2024-01-17T23:12:27.219174Z"
     }
    },
    "outputs": [
@@ -393,10 +393,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:23.394226Z",
-     "iopub.status.busy": "2024-01-17T18:10:23.393866Z",
-     "iopub.status.idle": "2024-01-17T18:10:23.421258Z",
-     "shell.execute_reply": "2024-01-17T18:10:23.420553Z"
+     "iopub.execute_input": "2024-01-17T23:12:27.222804Z",
+     "iopub.status.busy": "2024-01-17T23:12:27.222337Z",
+     "iopub.status.idle": "2024-01-17T23:12:27.249283Z",
+     "shell.execute_reply": "2024-01-17T23:12:27.248679Z"
     }
    },
    "outputs": [],
@@ -427,10 +427,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:23.423997Z",
-     "iopub.status.busy": "2024-01-17T18:10:23.423593Z",
-     "iopub.status.idle": "2024-01-17T18:10:24.787226Z",
-     "shell.execute_reply": "2024-01-17T18:10:24.786489Z"
+     "iopub.execute_input": "2024-01-17T23:12:27.251883Z",
+     "iopub.status.busy": "2024-01-17T23:12:27.251444Z",
+     "iopub.status.idle": "2024-01-17T23:12:28.555194Z",
+     "shell.execute_reply": "2024-01-17T23:12:28.554473Z"
     }
    },
    "outputs": [
@@ -473,10 +473,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:24.790031Z",
-     "iopub.status.busy": "2024-01-17T18:10:24.789628Z",
-     "iopub.status.idle": "2024-01-17T18:10:24.814871Z",
-     "shell.execute_reply": "2024-01-17T18:10:24.814207Z"
+     "iopub.execute_input": "2024-01-17T23:12:28.558034Z",
+     "iopub.status.busy": "2024-01-17T23:12:28.557654Z",
+     "iopub.status.idle": "2024-01-17T23:12:28.582599Z",
+     "shell.execute_reply": "2024-01-17T23:12:28.581962Z"
     },
     "scrolled": true
    },
@@ -641,10 +641,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:24.817303Z",
-     "iopub.status.busy": "2024-01-17T18:10:24.816930Z",
-     "iopub.status.idle": "2024-01-17T18:10:25.718347Z",
-     "shell.execute_reply": "2024-01-17T18:10:25.717676Z"
+     "iopub.execute_input": "2024-01-17T23:12:28.584982Z",
+     "iopub.status.busy": "2024-01-17T23:12:28.584634Z",
+     "iopub.status.idle": "2024-01-17T23:12:29.463581Z",
+     "shell.execute_reply": "2024-01-17T23:12:29.462958Z"
     },
     "id": "AaHC5MRKjruT"
    },
@@ -763,10 +763,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:25.721086Z",
-     "iopub.status.busy": "2024-01-17T18:10:25.720678Z",
-     "iopub.status.idle": "2024-01-17T18:10:25.735438Z",
-     "shell.execute_reply": "2024-01-17T18:10:25.734893Z"
+     "iopub.execute_input": "2024-01-17T23:12:29.466240Z",
+     "iopub.status.busy": "2024-01-17T23:12:29.465885Z",
+     "iopub.status.idle": "2024-01-17T23:12:29.480184Z",
+     "shell.execute_reply": "2024-01-17T23:12:29.479506Z"
     },
     "id": "Wy27rvyhjruU"
    },
@@ -815,10 +815,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:25.738066Z",
-     "iopub.status.busy": "2024-01-17T18:10:25.737688Z",
-     "iopub.status.idle": "2024-01-17T18:10:25.834468Z",
-     "shell.execute_reply": "2024-01-17T18:10:25.833833Z"
+     "iopub.execute_input": "2024-01-17T23:12:29.482535Z",
+     "iopub.status.busy": "2024-01-17T23:12:29.482170Z",
+     "iopub.status.idle": "2024-01-17T23:12:29.563194Z",
+     "shell.execute_reply": "2024-01-17T23:12:29.562462Z"
     },
     "id": "Db8YHnyVjruU"
    },
@@ -925,10 +925,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:25.837117Z",
-     "iopub.status.busy": "2024-01-17T18:10:25.836764Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.040217Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.039537Z"
+     "iopub.execute_input": "2024-01-17T23:12:29.565726Z",
+     "iopub.status.busy": "2024-01-17T23:12:29.565468Z",
+     "iopub.status.idle": "2024-01-17T23:12:29.770924Z",
+     "shell.execute_reply": "2024-01-17T23:12:29.770408Z"
     },
     "id": "iJqAHuS2jruV"
    },
@@ -965,10 +965,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.042998Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.042584Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.060028Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.059516Z"
+     "iopub.execute_input": "2024-01-17T23:12:29.773611Z",
+     "iopub.status.busy": "2024-01-17T23:12:29.773086Z",
+     "iopub.status.idle": "2024-01-17T23:12:29.790463Z",
+     "shell.execute_reply": "2024-01-17T23:12:29.789935Z"
     },
     "id": "PcPTZ_JJG3Cx"
    },
@@ -1030,10 +1030,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.062644Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.062148Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.072694Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.072095Z"
+     "iopub.execute_input": "2024-01-17T23:12:29.792922Z",
+     "iopub.status.busy": "2024-01-17T23:12:29.792556Z",
+     "iopub.status.idle": "2024-01-17T23:12:29.802644Z",
+     "shell.execute_reply": "2024-01-17T23:12:29.802136Z"
     },
     "id": "0lonvOYvjruV"
    },
@@ -1180,10 +1180,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.075434Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.074917Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.191355Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.190725Z"
+     "iopub.execute_input": "2024-01-17T23:12:29.805003Z",
+     "iopub.status.busy": "2024-01-17T23:12:29.804543Z",
+     "iopub.status.idle": "2024-01-17T23:12:29.898840Z",
+     "shell.execute_reply": "2024-01-17T23:12:29.898192Z"
     },
     "id": "MfqTCa3kjruV"
    },
@@ -1264,10 +1264,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.194148Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.193847Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.344135Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.343417Z"
+     "iopub.execute_input": "2024-01-17T23:12:29.901475Z",
+     "iopub.status.busy": "2024-01-17T23:12:29.901217Z",
+     "iopub.status.idle": "2024-01-17T23:12:30.040827Z",
+     "shell.execute_reply": "2024-01-17T23:12:30.040112Z"
     },
     "id": "9ZtWAYXqMAPL"
    },
@@ -1327,10 +1327,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.347074Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.346637Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.350908Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.350355Z"
+     "iopub.execute_input": "2024-01-17T23:12:30.043470Z",
+     "iopub.status.busy": "2024-01-17T23:12:30.043221Z",
+     "iopub.status.idle": "2024-01-17T23:12:30.047307Z",
+     "shell.execute_reply": "2024-01-17T23:12:30.046685Z"
     },
     "id": "0rXP3ZPWjruW"
    },
@@ -1368,10 +1368,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.353694Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.352956Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.358205Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.357689Z"
+     "iopub.execute_input": "2024-01-17T23:12:30.049676Z",
+     "iopub.status.busy": "2024-01-17T23:12:30.049240Z",
+     "iopub.status.idle": "2024-01-17T23:12:30.053883Z",
+     "shell.execute_reply": "2024-01-17T23:12:30.053284Z"
     },
     "id": "-iRPe8KXjruW"
    },
@@ -1426,10 +1426,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.360522Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.360138Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.400534Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.399805Z"
+     "iopub.execute_input": "2024-01-17T23:12:30.056405Z",
+     "iopub.status.busy": "2024-01-17T23:12:30.055960Z",
+     "iopub.status.idle": "2024-01-17T23:12:30.095504Z",
+     "shell.execute_reply": "2024-01-17T23:12:30.094989Z"
     },
     "id": "ZpipUliyjruW"
    },
@@ -1480,10 +1480,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.403784Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.403295Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.450103Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.449493Z"
+     "iopub.execute_input": "2024-01-17T23:12:30.097960Z",
+     "iopub.status.busy": "2024-01-17T23:12:30.097577Z",
+     "iopub.status.idle": "2024-01-17T23:12:30.144004Z",
+     "shell.execute_reply": "2024-01-17T23:12:30.143423Z"
     },
     "id": "SLq-3q4xjruX"
    },
@@ -1552,10 +1552,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.452754Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.452361Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.564403Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.563611Z"
+     "iopub.execute_input": "2024-01-17T23:12:30.146635Z",
+     "iopub.status.busy": "2024-01-17T23:12:30.146169Z",
+     "iopub.status.idle": "2024-01-17T23:12:30.252527Z",
+     "shell.execute_reply": "2024-01-17T23:12:30.251862Z"
     },
     "id": "g5LHhhuqFbXK"
    },
@@ -1587,10 +1587,10 @@
    "execution_count": 21,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.567555Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.567278Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.680006Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.679288Z"
+     "iopub.execute_input": "2024-01-17T23:12:30.255773Z",
+     "iopub.status.busy": "2024-01-17T23:12:30.255280Z",
+     "iopub.status.idle": "2024-01-17T23:12:30.355493Z",
+     "shell.execute_reply": "2024-01-17T23:12:30.354791Z"
     },
     "id": "p7w8F8ezBcet"
    },
@@ -1647,10 +1647,10 @@
    "execution_count": 22,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.682699Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.682408Z",
-     "iopub.status.idle": "2024-01-17T18:10:26.885900Z",
-     "shell.execute_reply": "2024-01-17T18:10:26.885214Z"
+     "iopub.execute_input": "2024-01-17T23:12:30.358291Z",
+     "iopub.status.busy": "2024-01-17T23:12:30.358031Z",
+     "iopub.status.idle": "2024-01-17T23:12:30.561214Z",
+     "shell.execute_reply": "2024-01-17T23:12:30.560494Z"
     },
     "id": "WETRL74tE_sU"
    },
@@ -1685,10 +1685,10 @@
    "execution_count": 23,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:26.888642Z",
-     "iopub.status.busy": "2024-01-17T18:10:26.888408Z",
-     "iopub.status.idle": "2024-01-17T18:10:27.119260Z",
-     "shell.execute_reply": "2024-01-17T18:10:27.118551Z"
+     "iopub.execute_input": "2024-01-17T23:12:30.563781Z",
+     "iopub.status.busy": "2024-01-17T23:12:30.563569Z",
+     "iopub.status.idle": "2024-01-17T23:12:30.778738Z",
+     "shell.execute_reply": "2024-01-17T23:12:30.778106Z"
     },
     "id": "kCfdx2gOLmXS"
    },
@@ -1850,10 +1850,10 @@
    "execution_count": 24,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:27.121834Z",
-     "iopub.status.busy": "2024-01-17T18:10:27.121615Z",
-     "iopub.status.idle": "2024-01-17T18:10:27.128106Z",
-     "shell.execute_reply": "2024-01-17T18:10:27.127583Z"
+     "iopub.execute_input": "2024-01-17T23:12:30.781569Z",
+     "iopub.status.busy": "2024-01-17T23:12:30.781097Z",
+     "iopub.status.idle": "2024-01-17T23:12:30.787611Z",
+     "shell.execute_reply": "2024-01-17T23:12:30.787090Z"
     },
     "id": "-uogYRWFYnuu"
    },
@@ -1907,10 +1907,10 @@
    "execution_count": 25,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:27.130325Z",
-     "iopub.status.busy": "2024-01-17T18:10:27.130128Z",
-     "iopub.status.idle": "2024-01-17T18:10:27.340313Z",
-     "shell.execute_reply": "2024-01-17T18:10:27.339598Z"
+     "iopub.execute_input": "2024-01-17T23:12:30.789959Z",
+     "iopub.status.busy": "2024-01-17T23:12:30.789573Z",
+     "iopub.status.idle": "2024-01-17T23:12:30.995874Z",
+     "shell.execute_reply": "2024-01-17T23:12:30.995216Z"
     },
     "id": "pG-ljrmcYp9Q"
    },
@@ -1957,10 +1957,10 @@
    "execution_count": 26,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:27.342786Z",
-     "iopub.status.busy": "2024-01-17T18:10:27.342557Z",
-     "iopub.status.idle": "2024-01-17T18:10:28.416510Z",
-     "shell.execute_reply": "2024-01-17T18:10:28.415789Z"
+     "iopub.execute_input": "2024-01-17T23:12:30.998644Z",
+     "iopub.status.busy": "2024-01-17T23:12:30.998256Z",
+     "iopub.status.idle": "2024-01-17T23:12:32.077472Z",
+     "shell.execute_reply": "2024-01-17T23:12:32.076842Z"
     },
     "id": "wL3ngCnuLEWd"
    },
diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb
index cfe891b73..7aab0ca60 100644
--- a/master/tutorials/multiannotator.ipynb
+++ b/master/tutorials/multiannotator.ipynb
@@ -89,10 +89,10 @@
    "id": "a3ddc95f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:34.066939Z",
-     "iopub.status.busy": "2024-01-17T18:10:34.066726Z",
-     "iopub.status.idle": "2024-01-17T18:10:35.107469Z",
-     "shell.execute_reply": "2024-01-17T18:10:35.106848Z"
+     "iopub.execute_input": "2024-01-17T23:12:37.119091Z",
+     "iopub.status.busy": "2024-01-17T23:12:37.118893Z",
+     "iopub.status.idle": "2024-01-17T23:12:38.145112Z",
+     "shell.execute_reply": "2024-01-17T23:12:38.144486Z"
     },
     "nbsphinx": "hidden"
    },
@@ -102,7 +102,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -136,10 +136,10 @@
    "id": "c4efd119",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:35.110492Z",
-     "iopub.status.busy": "2024-01-17T18:10:35.110165Z",
-     "iopub.status.idle": "2024-01-17T18:10:35.113533Z",
-     "shell.execute_reply": "2024-01-17T18:10:35.112907Z"
+     "iopub.execute_input": "2024-01-17T23:12:38.148219Z",
+     "iopub.status.busy": "2024-01-17T23:12:38.147778Z",
+     "iopub.status.idle": "2024-01-17T23:12:38.151072Z",
+     "shell.execute_reply": "2024-01-17T23:12:38.150569Z"
     }
    },
    "outputs": [],
@@ -264,10 +264,10 @@
    "id": "c37c0a69",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:35.116141Z",
-     "iopub.status.busy": "2024-01-17T18:10:35.115710Z",
-     "iopub.status.idle": "2024-01-17T18:10:35.124258Z",
-     "shell.execute_reply": "2024-01-17T18:10:35.123628Z"
+     "iopub.execute_input": "2024-01-17T23:12:38.153453Z",
+     "iopub.status.busy": "2024-01-17T23:12:38.153123Z",
+     "iopub.status.idle": "2024-01-17T23:12:38.161595Z",
+     "shell.execute_reply": "2024-01-17T23:12:38.160994Z"
     },
     "nbsphinx": "hidden"
    },
@@ -351,10 +351,10 @@
    "id": "99f69523",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:35.126617Z",
-     "iopub.status.busy": "2024-01-17T18:10:35.126181Z",
-     "iopub.status.idle": "2024-01-17T18:10:35.174782Z",
-     "shell.execute_reply": "2024-01-17T18:10:35.174277Z"
+     "iopub.execute_input": "2024-01-17T23:12:38.163930Z",
+     "iopub.status.busy": "2024-01-17T23:12:38.163445Z",
+     "iopub.status.idle": "2024-01-17T23:12:38.215430Z",
+     "shell.execute_reply": "2024-01-17T23:12:38.214906Z"
     }
    },
    "outputs": [],
@@ -380,10 +380,10 @@
    "id": "8f241c16",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:35.177183Z",
-     "iopub.status.busy": "2024-01-17T18:10:35.176965Z",
-     "iopub.status.idle": "2024-01-17T18:10:35.196729Z",
-     "shell.execute_reply": "2024-01-17T18:10:35.196205Z"
+     "iopub.execute_input": "2024-01-17T23:12:38.217958Z",
+     "iopub.status.busy": "2024-01-17T23:12:38.217555Z",
+     "iopub.status.idle": "2024-01-17T23:12:38.236753Z",
+     "shell.execute_reply": "2024-01-17T23:12:38.236121Z"
     }
    },
    "outputs": [
@@ -598,10 +598,10 @@
    "id": "4f0819ba",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:35.199192Z",
-     "iopub.status.busy": "2024-01-17T18:10:35.198837Z",
-     "iopub.status.idle": "2024-01-17T18:10:35.202844Z",
-     "shell.execute_reply": "2024-01-17T18:10:35.202255Z"
+     "iopub.execute_input": "2024-01-17T23:12:38.239132Z",
+     "iopub.status.busy": "2024-01-17T23:12:38.238775Z",
+     "iopub.status.idle": "2024-01-17T23:12:38.242790Z",
+     "shell.execute_reply": "2024-01-17T23:12:38.242281Z"
     }
    },
    "outputs": [
@@ -672,10 +672,10 @@
    "id": "d009f347",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:35.205415Z",
-     "iopub.status.busy": "2024-01-17T18:10:35.204931Z",
-     "iopub.status.idle": "2024-01-17T18:10:35.232341Z",
-     "shell.execute_reply": "2024-01-17T18:10:35.231693Z"
+     "iopub.execute_input": "2024-01-17T23:12:38.245093Z",
+     "iopub.status.busy": "2024-01-17T23:12:38.244847Z",
+     "iopub.status.idle": "2024-01-17T23:12:38.275707Z",
+     "shell.execute_reply": "2024-01-17T23:12:38.275228Z"
     }
    },
    "outputs": [],
@@ -699,10 +699,10 @@
    "id": "cbd1e415",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:35.234865Z",
-     "iopub.status.busy": "2024-01-17T18:10:35.234654Z",
-     "iopub.status.idle": "2024-01-17T18:10:35.262331Z",
-     "shell.execute_reply": "2024-01-17T18:10:35.261831Z"
+     "iopub.execute_input": "2024-01-17T23:12:38.277989Z",
+     "iopub.status.busy": "2024-01-17T23:12:38.277604Z",
+     "iopub.status.idle": "2024-01-17T23:12:38.304773Z",
+     "shell.execute_reply": "2024-01-17T23:12:38.304291Z"
     }
    },
    "outputs": [],
@@ -739,10 +739,10 @@
    "id": "6ca92617",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:35.264679Z",
-     "iopub.status.busy": "2024-01-17T18:10:35.264478Z",
-     "iopub.status.idle": "2024-01-17T18:10:36.634552Z",
-     "shell.execute_reply": "2024-01-17T18:10:36.633890Z"
+     "iopub.execute_input": "2024-01-17T23:12:38.307191Z",
+     "iopub.status.busy": "2024-01-17T23:12:38.306725Z",
+     "iopub.status.idle": "2024-01-17T23:12:39.660035Z",
+     "shell.execute_reply": "2024-01-17T23:12:39.659311Z"
     }
    },
    "outputs": [],
@@ -772,10 +772,10 @@
    "id": "bf945113",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:36.637556Z",
-     "iopub.status.busy": "2024-01-17T18:10:36.637138Z",
-     "iopub.status.idle": "2024-01-17T18:10:36.644742Z",
-     "shell.execute_reply": "2024-01-17T18:10:36.644122Z"
+     "iopub.execute_input": "2024-01-17T23:12:39.663258Z",
+     "iopub.status.busy": "2024-01-17T23:12:39.662839Z",
+     "iopub.status.idle": "2024-01-17T23:12:39.670307Z",
+     "shell.execute_reply": "2024-01-17T23:12:39.669734Z"
     },
     "scrolled": true
    },
@@ -886,10 +886,10 @@
    "id": "14251ee0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:36.647074Z",
-     "iopub.status.busy": "2024-01-17T18:10:36.646869Z",
-     "iopub.status.idle": "2024-01-17T18:10:36.661069Z",
-     "shell.execute_reply": "2024-01-17T18:10:36.660517Z"
+     "iopub.execute_input": "2024-01-17T23:12:39.672727Z",
+     "iopub.status.busy": "2024-01-17T23:12:39.672379Z",
+     "iopub.status.idle": "2024-01-17T23:12:39.686172Z",
+     "shell.execute_reply": "2024-01-17T23:12:39.685544Z"
     }
    },
    "outputs": [
@@ -1139,10 +1139,10 @@
    "id": "efe16638",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:36.663330Z",
-     "iopub.status.busy": "2024-01-17T18:10:36.663130Z",
-     "iopub.status.idle": "2024-01-17T18:10:36.670332Z",
-     "shell.execute_reply": "2024-01-17T18:10:36.669799Z"
+     "iopub.execute_input": "2024-01-17T23:12:39.688633Z",
+     "iopub.status.busy": "2024-01-17T23:12:39.688179Z",
+     "iopub.status.idle": "2024-01-17T23:12:39.694998Z",
+     "shell.execute_reply": "2024-01-17T23:12:39.694392Z"
     },
     "scrolled": true
    },
@@ -1316,10 +1316,10 @@
    "id": "abd0fb0b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:36.672921Z",
-     "iopub.status.busy": "2024-01-17T18:10:36.672547Z",
-     "iopub.status.idle": "2024-01-17T18:10:36.675600Z",
-     "shell.execute_reply": "2024-01-17T18:10:36.675078Z"
+     "iopub.execute_input": "2024-01-17T23:12:39.697375Z",
+     "iopub.status.busy": "2024-01-17T23:12:39.696998Z",
+     "iopub.status.idle": "2024-01-17T23:12:39.699962Z",
+     "shell.execute_reply": "2024-01-17T23:12:39.699338Z"
     }
    },
    "outputs": [],
@@ -1341,10 +1341,10 @@
    "id": "cdf061df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:36.677889Z",
-     "iopub.status.busy": "2024-01-17T18:10:36.677688Z",
-     "iopub.status.idle": "2024-01-17T18:10:36.682112Z",
-     "shell.execute_reply": "2024-01-17T18:10:36.681569Z"
+     "iopub.execute_input": "2024-01-17T23:12:39.702455Z",
+     "iopub.status.busy": "2024-01-17T23:12:39.702117Z",
+     "iopub.status.idle": "2024-01-17T23:12:39.706331Z",
+     "shell.execute_reply": "2024-01-17T23:12:39.705698Z"
     },
     "scrolled": true
    },
@@ -1396,10 +1396,10 @@
    "id": "08949890",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:36.684614Z",
-     "iopub.status.busy": "2024-01-17T18:10:36.684230Z",
-     "iopub.status.idle": "2024-01-17T18:10:36.687195Z",
-     "shell.execute_reply": "2024-01-17T18:10:36.686658Z"
+     "iopub.execute_input": "2024-01-17T23:12:39.708839Z",
+     "iopub.status.busy": "2024-01-17T23:12:39.708410Z",
+     "iopub.status.idle": "2024-01-17T23:12:39.711306Z",
+     "shell.execute_reply": "2024-01-17T23:12:39.710774Z"
     }
    },
    "outputs": [],
@@ -1423,10 +1423,10 @@
    "id": "6948b073",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:36.689699Z",
-     "iopub.status.busy": "2024-01-17T18:10:36.689303Z",
-     "iopub.status.idle": "2024-01-17T18:10:36.693988Z",
-     "shell.execute_reply": "2024-01-17T18:10:36.693350Z"
+     "iopub.execute_input": "2024-01-17T23:12:39.713686Z",
+     "iopub.status.busy": "2024-01-17T23:12:39.713257Z",
+     "iopub.status.idle": "2024-01-17T23:12:39.717980Z",
+     "shell.execute_reply": "2024-01-17T23:12:39.717452Z"
     }
    },
    "outputs": [
@@ -1481,10 +1481,10 @@
    "id": "6f8e6914",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:36.696492Z",
-     "iopub.status.busy": "2024-01-17T18:10:36.696127Z",
-     "iopub.status.idle": "2024-01-17T18:10:36.730677Z",
-     "shell.execute_reply": "2024-01-17T18:10:36.730063Z"
+     "iopub.execute_input": "2024-01-17T23:12:39.720498Z",
+     "iopub.status.busy": "2024-01-17T23:12:39.720135Z",
+     "iopub.status.idle": "2024-01-17T23:12:39.753497Z",
+     "shell.execute_reply": "2024-01-17T23:12:39.753003Z"
     }
    },
    "outputs": [],
@@ -1527,10 +1527,10 @@
    "id": "b806d2ea",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:36.733736Z",
-     "iopub.status.busy": "2024-01-17T18:10:36.733290Z",
-     "iopub.status.idle": "2024-01-17T18:10:36.739015Z",
-     "shell.execute_reply": "2024-01-17T18:10:36.738419Z"
+     "iopub.execute_input": "2024-01-17T23:12:39.755769Z",
+     "iopub.status.busy": "2024-01-17T23:12:39.755564Z",
+     "iopub.status.idle": "2024-01-17T23:12:39.760459Z",
+     "shell.execute_reply": "2024-01-17T23:12:39.759917Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb
index 345c02d68..870f9da70 100644
--- a/master/tutorials/multilabel_classification.ipynb
+++ b/master/tutorials/multilabel_classification.ipynb
@@ -63,10 +63,10 @@
    "id": "7383d024-8273-4039-bccd-aab3020d331f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:41.555662Z",
-     "iopub.status.busy": "2024-01-17T18:10:41.555447Z",
-     "iopub.status.idle": "2024-01-17T18:10:42.641450Z",
-     "shell.execute_reply": "2024-01-17T18:10:42.640766Z"
+     "iopub.execute_input": "2024-01-17T23:12:45.381897Z",
+     "iopub.status.busy": "2024-01-17T23:12:45.381706Z",
+     "iopub.status.idle": "2024-01-17T23:12:46.455370Z",
+     "shell.execute_reply": "2024-01-17T23:12:46.454763Z"
     },
     "nbsphinx": "hidden"
    },
@@ -78,7 +78,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -104,10 +104,10 @@
    "id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:42.644299Z",
-     "iopub.status.busy": "2024-01-17T18:10:42.643939Z",
-     "iopub.status.idle": "2024-01-17T18:10:42.934236Z",
-     "shell.execute_reply": "2024-01-17T18:10:42.933614Z"
+     "iopub.execute_input": "2024-01-17T23:12:46.458206Z",
+     "iopub.status.busy": "2024-01-17T23:12:46.457873Z",
+     "iopub.status.idle": "2024-01-17T23:12:46.742282Z",
+     "shell.execute_reply": "2024-01-17T23:12:46.741647Z"
     }
    },
    "outputs": [],
@@ -269,10 +269,10 @@
    "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:42.937365Z",
-     "iopub.status.busy": "2024-01-17T18:10:42.936893Z",
-     "iopub.status.idle": "2024-01-17T18:10:42.950979Z",
-     "shell.execute_reply": "2024-01-17T18:10:42.950488Z"
+     "iopub.execute_input": "2024-01-17T23:12:46.745006Z",
+     "iopub.status.busy": "2024-01-17T23:12:46.744797Z",
+     "iopub.status.idle": "2024-01-17T23:12:46.758584Z",
+     "shell.execute_reply": "2024-01-17T23:12:46.758067Z"
     },
     "nbsphinx": "hidden"
    },
@@ -408,10 +408,10 @@
    "id": "dac65d3b-51e8-4682-b829-beab610b56d6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:42.953251Z",
-     "iopub.status.busy": "2024-01-17T18:10:42.953039Z",
-     "iopub.status.idle": "2024-01-17T18:10:45.626467Z",
-     "shell.execute_reply": "2024-01-17T18:10:45.625783Z"
+     "iopub.execute_input": "2024-01-17T23:12:46.761055Z",
+     "iopub.status.busy": "2024-01-17T23:12:46.760585Z",
+     "iopub.status.idle": "2024-01-17T23:12:49.437361Z",
+     "shell.execute_reply": "2024-01-17T23:12:49.436698Z"
     }
    },
    "outputs": [
@@ -453,10 +453,10 @@
    "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:45.629098Z",
-     "iopub.status.busy": "2024-01-17T18:10:45.628708Z",
-     "iopub.status.idle": "2024-01-17T18:10:47.211836Z",
-     "shell.execute_reply": "2024-01-17T18:10:47.211233Z"
+     "iopub.execute_input": "2024-01-17T23:12:49.440037Z",
+     "iopub.status.busy": "2024-01-17T23:12:49.439727Z",
+     "iopub.status.idle": "2024-01-17T23:12:51.019074Z",
+     "shell.execute_reply": "2024-01-17T23:12:51.018452Z"
     }
    },
    "outputs": [],
@@ -498,10 +498,10 @@
    "id": "ac1a60df",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:47.214903Z",
-     "iopub.status.busy": "2024-01-17T18:10:47.214448Z",
-     "iopub.status.idle": "2024-01-17T18:10:47.220079Z",
-     "shell.execute_reply": "2024-01-17T18:10:47.219546Z"
+     "iopub.execute_input": "2024-01-17T23:12:51.021987Z",
+     "iopub.status.busy": "2024-01-17T23:12:51.021563Z",
+     "iopub.status.idle": "2024-01-17T23:12:51.026386Z",
+     "shell.execute_reply": "2024-01-17T23:12:51.025743Z"
     }
    },
    "outputs": [
@@ -543,10 +543,10 @@
    "id": "d09115b6-ad44-474f-9c8a-85a459586439",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:47.222591Z",
-     "iopub.status.busy": "2024-01-17T18:10:47.222214Z",
-     "iopub.status.idle": "2024-01-17T18:10:48.654551Z",
-     "shell.execute_reply": "2024-01-17T18:10:48.653628Z"
+     "iopub.execute_input": "2024-01-17T23:12:51.028795Z",
+     "iopub.status.busy": "2024-01-17T23:12:51.028423Z",
+     "iopub.status.idle": "2024-01-17T23:12:52.362154Z",
+     "shell.execute_reply": "2024-01-17T23:12:52.361435Z"
     }
    },
    "outputs": [
@@ -584,10 +584,10 @@
    "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:48.658404Z",
-     "iopub.status.busy": "2024-01-17T18:10:48.657649Z",
-     "iopub.status.idle": "2024-01-17T18:10:51.504151Z",
-     "shell.execute_reply": "2024-01-17T18:10:51.503505Z"
+     "iopub.execute_input": "2024-01-17T23:12:52.365370Z",
+     "iopub.status.busy": "2024-01-17T23:12:52.364687Z",
+     "iopub.status.idle": "2024-01-17T23:12:55.189939Z",
+     "shell.execute_reply": "2024-01-17T23:12:55.189338Z"
     }
    },
    "outputs": [
@@ -622,10 +622,10 @@
    "id": "c1198575",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:51.506885Z",
-     "iopub.status.busy": "2024-01-17T18:10:51.506474Z",
-     "iopub.status.idle": "2024-01-17T18:10:51.511559Z",
-     "shell.execute_reply": "2024-01-17T18:10:51.510930Z"
+     "iopub.execute_input": "2024-01-17T23:12:55.192359Z",
+     "iopub.status.busy": "2024-01-17T23:12:55.192152Z",
+     "iopub.status.idle": "2024-01-17T23:12:55.197237Z",
+     "shell.execute_reply": "2024-01-17T23:12:55.196716Z"
     }
    },
    "outputs": [
@@ -662,10 +662,10 @@
    "id": "49161b19-7625-4fb7-add9-607d91a7eca1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:51.514207Z",
-     "iopub.status.busy": "2024-01-17T18:10:51.513861Z",
-     "iopub.status.idle": "2024-01-17T18:10:51.518178Z",
-     "shell.execute_reply": "2024-01-17T18:10:51.517551Z"
+     "iopub.execute_input": "2024-01-17T23:12:55.199466Z",
+     "iopub.status.busy": "2024-01-17T23:12:55.199269Z",
+     "iopub.status.idle": "2024-01-17T23:12:55.203414Z",
+     "shell.execute_reply": "2024-01-17T23:12:55.202885Z"
     }
    },
    "outputs": [],
@@ -688,10 +688,10 @@
    "id": "d1a2c008",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:51.520433Z",
-     "iopub.status.busy": "2024-01-17T18:10:51.520221Z",
-     "iopub.status.idle": "2024-01-17T18:10:51.523664Z",
-     "shell.execute_reply": "2024-01-17T18:10:51.523119Z"
+     "iopub.execute_input": "2024-01-17T23:12:55.205569Z",
+     "iopub.status.busy": "2024-01-17T23:12:55.205370Z",
+     "iopub.status.idle": "2024-01-17T23:12:55.208734Z",
+     "shell.execute_reply": "2024-01-17T23:12:55.208218Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb
index dbc911529..8bd932980 100644
--- a/master/tutorials/object_detection.ipynb
+++ b/master/tutorials/object_detection.ipynb
@@ -70,10 +70,10 @@
    "id": "0ba0dc70",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:56.270011Z",
-     "iopub.status.busy": "2024-01-17T18:10:56.269487Z",
-     "iopub.status.idle": "2024-01-17T18:10:57.381241Z",
-     "shell.execute_reply": "2024-01-17T18:10:57.380625Z"
+     "iopub.execute_input": "2024-01-17T23:13:00.015965Z",
+     "iopub.status.busy": "2024-01-17T23:13:00.015766Z",
+     "iopub.status.idle": "2024-01-17T23:13:01.088856Z",
+     "shell.execute_reply": "2024-01-17T23:13:01.088251Z"
     },
     "nbsphinx": "hidden"
    },
@@ -83,7 +83,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -109,10 +109,10 @@
    "id": "c90449c8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:57.384088Z",
-     "iopub.status.busy": "2024-01-17T18:10:57.383700Z",
-     "iopub.status.idle": "2024-01-17T18:10:58.753470Z",
-     "shell.execute_reply": "2024-01-17T18:10:58.752605Z"
+     "iopub.execute_input": "2024-01-17T23:13:01.091695Z",
+     "iopub.status.busy": "2024-01-17T23:13:01.091313Z",
+     "iopub.status.idle": "2024-01-17T23:13:02.386388Z",
+     "shell.execute_reply": "2024-01-17T23:13:02.385615Z"
     }
    },
    "outputs": [],
@@ -130,10 +130,10 @@
    "id": "df8be4c6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:58.756374Z",
-     "iopub.status.busy": "2024-01-17T18:10:58.756152Z",
-     "iopub.status.idle": "2024-01-17T18:10:58.759433Z",
-     "shell.execute_reply": "2024-01-17T18:10:58.758883Z"
+     "iopub.execute_input": "2024-01-17T23:13:02.389277Z",
+     "iopub.status.busy": "2024-01-17T23:13:02.388865Z",
+     "iopub.status.idle": "2024-01-17T23:13:02.392078Z",
+     "shell.execute_reply": "2024-01-17T23:13:02.391529Z"
     }
    },
    "outputs": [],
@@ -165,10 +165,10 @@
    "id": "2e9ffd6f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:58.761637Z",
-     "iopub.status.busy": "2024-01-17T18:10:58.761440Z",
-     "iopub.status.idle": "2024-01-17T18:10:58.766806Z",
-     "shell.execute_reply": "2024-01-17T18:10:58.766335Z"
+     "iopub.execute_input": "2024-01-17T23:13:02.394243Z",
+     "iopub.status.busy": "2024-01-17T23:13:02.394041Z",
+     "iopub.status.idle": "2024-01-17T23:13:02.399439Z",
+     "shell.execute_reply": "2024-01-17T23:13:02.398974Z"
     }
    },
    "outputs": [],
@@ -194,10 +194,10 @@
    "id": "56705562",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:58.768917Z",
-     "iopub.status.busy": "2024-01-17T18:10:58.768711Z",
-     "iopub.status.idle": "2024-01-17T18:10:59.379953Z",
-     "shell.execute_reply": "2024-01-17T18:10:59.379277Z"
+     "iopub.execute_input": "2024-01-17T23:13:02.401585Z",
+     "iopub.status.busy": "2024-01-17T23:13:02.401389Z",
+     "iopub.status.idle": "2024-01-17T23:13:03.000882Z",
+     "shell.execute_reply": "2024-01-17T23:13:03.000200Z"
     },
     "scrolled": true
    },
@@ -237,10 +237,10 @@
    "id": "b08144d7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:59.383016Z",
-     "iopub.status.busy": "2024-01-17T18:10:59.382511Z",
-     "iopub.status.idle": "2024-01-17T18:10:59.388670Z",
-     "shell.execute_reply": "2024-01-17T18:10:59.388096Z"
+     "iopub.execute_input": "2024-01-17T23:13:03.004104Z",
+     "iopub.status.busy": "2024-01-17T23:13:03.003684Z",
+     "iopub.status.idle": "2024-01-17T23:13:03.009713Z",
+     "shell.execute_reply": "2024-01-17T23:13:03.009212Z"
     }
    },
    "outputs": [
@@ -492,10 +492,10 @@
    "id": "3d70bec6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:59.391089Z",
-     "iopub.status.busy": "2024-01-17T18:10:59.390893Z",
-     "iopub.status.idle": "2024-01-17T18:10:59.394956Z",
-     "shell.execute_reply": "2024-01-17T18:10:59.394468Z"
+     "iopub.execute_input": "2024-01-17T23:13:03.011996Z",
+     "iopub.status.busy": "2024-01-17T23:13:03.011640Z",
+     "iopub.status.idle": "2024-01-17T23:13:03.015800Z",
+     "shell.execute_reply": "2024-01-17T23:13:03.015298Z"
     }
    },
    "outputs": [
@@ -552,10 +552,10 @@
    "id": "4caa635d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:10:59.397137Z",
-     "iopub.status.busy": "2024-01-17T18:10:59.396930Z",
-     "iopub.status.idle": "2024-01-17T18:11:00.095222Z",
-     "shell.execute_reply": "2024-01-17T18:11:00.094559Z"
+     "iopub.execute_input": "2024-01-17T23:13:03.018314Z",
+     "iopub.status.busy": "2024-01-17T23:13:03.017851Z",
+     "iopub.status.idle": "2024-01-17T23:13:03.630150Z",
+     "shell.execute_reply": "2024-01-17T23:13:03.629427Z"
     }
    },
    "outputs": [
@@ -611,10 +611,10 @@
    "id": "a9b4c590",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:00.098096Z",
-     "iopub.status.busy": "2024-01-17T18:11:00.097682Z",
-     "iopub.status.idle": "2024-01-17T18:11:00.189490Z",
-     "shell.execute_reply": "2024-01-17T18:11:00.188840Z"
+     "iopub.execute_input": "2024-01-17T23:13:03.632920Z",
+     "iopub.status.busy": "2024-01-17T23:13:03.632509Z",
+     "iopub.status.idle": "2024-01-17T23:13:03.740338Z",
+     "shell.execute_reply": "2024-01-17T23:13:03.739687Z"
     }
    },
    "outputs": [
@@ -655,10 +655,10 @@
    "id": "ffd9ebcc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:00.192112Z",
-     "iopub.status.busy": "2024-01-17T18:11:00.191749Z",
-     "iopub.status.idle": "2024-01-17T18:11:00.196425Z",
-     "shell.execute_reply": "2024-01-17T18:11:00.195812Z"
+     "iopub.execute_input": "2024-01-17T23:13:03.742826Z",
+     "iopub.status.busy": "2024-01-17T23:13:03.742438Z",
+     "iopub.status.idle": "2024-01-17T23:13:03.746983Z",
+     "shell.execute_reply": "2024-01-17T23:13:03.746385Z"
     }
    },
    "outputs": [
@@ -695,10 +695,10 @@
    "id": "4dd46d67",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:00.198699Z",
-     "iopub.status.busy": "2024-01-17T18:11:00.198356Z",
-     "iopub.status.idle": "2024-01-17T18:11:00.573742Z",
-     "shell.execute_reply": "2024-01-17T18:11:00.573082Z"
+     "iopub.execute_input": "2024-01-17T23:13:03.749393Z",
+     "iopub.status.busy": "2024-01-17T23:13:03.749035Z",
+     "iopub.status.idle": "2024-01-17T23:13:04.126430Z",
+     "shell.execute_reply": "2024-01-17T23:13:04.125638Z"
     }
    },
    "outputs": [
@@ -757,10 +757,10 @@
    "id": "ceec2394",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:00.576878Z",
-     "iopub.status.busy": "2024-01-17T18:11:00.576638Z",
-     "iopub.status.idle": "2024-01-17T18:11:00.916426Z",
-     "shell.execute_reply": "2024-01-17T18:11:00.915786Z"
+     "iopub.execute_input": "2024-01-17T23:13:04.129102Z",
+     "iopub.status.busy": "2024-01-17T23:13:04.128646Z",
+     "iopub.status.idle": "2024-01-17T23:13:04.466798Z",
+     "shell.execute_reply": "2024-01-17T23:13:04.466140Z"
     }
    },
    "outputs": [
@@ -807,10 +807,10 @@
    "id": "94f82b0d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:00.919738Z",
-     "iopub.status.busy": "2024-01-17T18:11:00.919345Z",
-     "iopub.status.idle": "2024-01-17T18:11:01.302132Z",
-     "shell.execute_reply": "2024-01-17T18:11:01.301169Z"
+     "iopub.execute_input": "2024-01-17T23:13:04.469970Z",
+     "iopub.status.busy": "2024-01-17T23:13:04.469557Z",
+     "iopub.status.idle": "2024-01-17T23:13:04.855437Z",
+     "shell.execute_reply": "2024-01-17T23:13:04.854746Z"
     }
    },
    "outputs": [
@@ -857,10 +857,10 @@
    "id": "1ea18c5d",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:01.305447Z",
-     "iopub.status.busy": "2024-01-17T18:11:01.305205Z",
-     "iopub.status.idle": "2024-01-17T18:11:01.743265Z",
-     "shell.execute_reply": "2024-01-17T18:11:01.742611Z"
+     "iopub.execute_input": "2024-01-17T23:13:04.858407Z",
+     "iopub.status.busy": "2024-01-17T23:13:04.858151Z",
+     "iopub.status.idle": "2024-01-17T23:13:05.320000Z",
+     "shell.execute_reply": "2024-01-17T23:13:05.319330Z"
     }
    },
    "outputs": [
@@ -920,10 +920,10 @@
    "id": "7e770d23",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:01.747782Z",
-     "iopub.status.busy": "2024-01-17T18:11:01.747564Z",
-     "iopub.status.idle": "2024-01-17T18:11:02.177235Z",
-     "shell.execute_reply": "2024-01-17T18:11:02.176546Z"
+     "iopub.execute_input": "2024-01-17T23:13:05.324336Z",
+     "iopub.status.busy": "2024-01-17T23:13:05.323918Z",
+     "iopub.status.idle": "2024-01-17T23:13:05.792579Z",
+     "shell.execute_reply": "2024-01-17T23:13:05.791924Z"
     }
    },
    "outputs": [
@@ -966,10 +966,10 @@
    "id": "57e84a27",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:02.180504Z",
-     "iopub.status.busy": "2024-01-17T18:11:02.180026Z",
-     "iopub.status.idle": "2024-01-17T18:11:02.496037Z",
-     "shell.execute_reply": "2024-01-17T18:11:02.495341Z"
+     "iopub.execute_input": "2024-01-17T23:13:05.795897Z",
+     "iopub.status.busy": "2024-01-17T23:13:05.795682Z",
+     "iopub.status.idle": "2024-01-17T23:13:06.121035Z",
+     "shell.execute_reply": "2024-01-17T23:13:06.120426Z"
     }
    },
    "outputs": [
@@ -1012,10 +1012,10 @@
    "id": "0302818a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:02.499471Z",
-     "iopub.status.busy": "2024-01-17T18:11:02.498870Z",
-     "iopub.status.idle": "2024-01-17T18:11:02.702199Z",
-     "shell.execute_reply": "2024-01-17T18:11:02.701491Z"
+     "iopub.execute_input": "2024-01-17T23:13:06.123681Z",
+     "iopub.status.busy": "2024-01-17T23:13:06.123465Z",
+     "iopub.status.idle": "2024-01-17T23:13:06.322246Z",
+     "shell.execute_reply": "2024-01-17T23:13:06.321624Z"
     }
    },
    "outputs": [
@@ -1050,10 +1050,10 @@
    "id": "8ce74938",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:02.704604Z",
-     "iopub.status.busy": "2024-01-17T18:11:02.704393Z",
-     "iopub.status.idle": "2024-01-17T18:11:02.709155Z",
-     "shell.execute_reply": "2024-01-17T18:11:02.708516Z"
+     "iopub.execute_input": "2024-01-17T23:13:06.325012Z",
+     "iopub.status.busy": "2024-01-17T23:13:06.324597Z",
+     "iopub.status.idle": "2024-01-17T23:13:06.328427Z",
+     "shell.execute_reply": "2024-01-17T23:13:06.327899Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html
index 5fc43cf36..cd1b146fd 100644
--- a/master/tutorials/outliers.html
+++ b/master/tutorials/outliers.html
@@ -940,7 +940,7 @@ 
2. Pre-process the Cifar10 dataset
 

 

-

+
 
 

 

@@ -1306,7 +1306,7 @@ 
4. Use cleanlab and here.

 
 
  
diff --git a/master/tutorials/outliers.ipynb b/master/tutorials/outliers.ipynb
index 4968673b9..bd88b2fad 100644
--- a/master/tutorials/outliers.ipynb
+++ b/master/tutorials/outliers.ipynb
@@ -109,10 +109,10 @@
    "id": "2bbebfc8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:05.142211Z",
-     "iopub.status.busy": "2024-01-17T18:11:05.141650Z",
-     "iopub.status.idle": "2024-01-17T18:11:07.187769Z",
-     "shell.execute_reply": "2024-01-17T18:11:07.187028Z"
+     "iopub.execute_input": "2024-01-17T23:13:08.406000Z",
+     "iopub.status.busy": "2024-01-17T23:13:08.405789Z",
+     "iopub.status.idle": "2024-01-17T23:13:10.340846Z",
+     "shell.execute_reply": "2024-01-17T23:13:10.340212Z"
     },
     "nbsphinx": "hidden"
    },
@@ -125,7 +125,7 @@
     "dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -159,10 +159,10 @@
    "id": "4396f544",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:07.191099Z",
-     "iopub.status.busy": "2024-01-17T18:11:07.190494Z",
-     "iopub.status.idle": "2024-01-17T18:11:07.529231Z",
-     "shell.execute_reply": "2024-01-17T18:11:07.528438Z"
+     "iopub.execute_input": "2024-01-17T23:13:10.343878Z",
+     "iopub.status.busy": "2024-01-17T23:13:10.343422Z",
+     "iopub.status.idle": "2024-01-17T23:13:10.660493Z",
+     "shell.execute_reply": "2024-01-17T23:13:10.659800Z"
     }
    },
    "outputs": [],
@@ -188,10 +188,10 @@
    "id": "3792f82e",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:07.532317Z",
-     "iopub.status.busy": "2024-01-17T18:11:07.531754Z",
-     "iopub.status.idle": "2024-01-17T18:11:07.536174Z",
-     "shell.execute_reply": "2024-01-17T18:11:07.535550Z"
+     "iopub.execute_input": "2024-01-17T23:13:10.663317Z",
+     "iopub.status.busy": "2024-01-17T23:13:10.663104Z",
+     "iopub.status.idle": "2024-01-17T23:13:10.667300Z",
+     "shell.execute_reply": "2024-01-17T23:13:10.666820Z"
     },
     "nbsphinx": "hidden"
    },
@@ -225,10 +225,10 @@
    "id": "fd853a54",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:07.538835Z",
-     "iopub.status.busy": "2024-01-17T18:11:07.538474Z",
-     "iopub.status.idle": "2024-01-17T18:11:11.753022Z",
-     "shell.execute_reply": "2024-01-17T18:11:11.752366Z"
+     "iopub.execute_input": "2024-01-17T23:13:10.669596Z",
+     "iopub.status.busy": "2024-01-17T23:13:10.669232Z",
+     "iopub.status.idle": "2024-01-17T23:13:14.971485Z",
+     "shell.execute_reply": "2024-01-17T23:13:14.970803Z"
     }
    },
    "outputs": [
@@ -242,7 +242,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "94ffd2c8ec814e019f2b36b808770db9",
+       "model_id": "6047426b013f47c49a17843cd40c0b2e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -361,10 +361,10 @@
    "id": "9b64e0aa",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:11.755600Z",
-     "iopub.status.busy": "2024-01-17T18:11:11.755226Z",
-     "iopub.status.idle": "2024-01-17T18:11:11.762083Z",
-     "shell.execute_reply": "2024-01-17T18:11:11.759899Z"
+     "iopub.execute_input": "2024-01-17T23:13:14.974264Z",
+     "iopub.status.busy": "2024-01-17T23:13:14.973841Z",
+     "iopub.status.idle": "2024-01-17T23:13:14.978950Z",
+     "shell.execute_reply": "2024-01-17T23:13:14.978415Z"
     },
     "nbsphinx": "hidden"
    },
@@ -415,10 +415,10 @@
    "id": "a00aa3ed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:11.764664Z",
-     "iopub.status.busy": "2024-01-17T18:11:11.764465Z",
-     "iopub.status.idle": "2024-01-17T18:11:12.328987Z",
-     "shell.execute_reply": "2024-01-17T18:11:12.328323Z"
+     "iopub.execute_input": "2024-01-17T23:13:14.981395Z",
+     "iopub.status.busy": "2024-01-17T23:13:14.980946Z",
+     "iopub.status.idle": "2024-01-17T23:13:15.518999Z",
+     "shell.execute_reply": "2024-01-17T23:13:15.518328Z"
     }
    },
    "outputs": [
@@ -451,10 +451,10 @@
    "id": "41e5cb6b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:12.331547Z",
-     "iopub.status.busy": "2024-01-17T18:11:12.331336Z",
-     "iopub.status.idle": "2024-01-17T18:11:12.972317Z",
-     "shell.execute_reply": "2024-01-17T18:11:12.971642Z"
+     "iopub.execute_input": "2024-01-17T23:13:15.521673Z",
+     "iopub.status.busy": "2024-01-17T23:13:15.521449Z",
+     "iopub.status.idle": "2024-01-17T23:13:16.161297Z",
+     "shell.execute_reply": "2024-01-17T23:13:16.160609Z"
     }
    },
    "outputs": [
@@ -492,10 +492,10 @@
    "id": "1cf25354",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:12.974776Z",
-     "iopub.status.busy": "2024-01-17T18:11:12.974562Z",
-     "iopub.status.idle": "2024-01-17T18:11:12.978442Z",
-     "shell.execute_reply": "2024-01-17T18:11:12.977910Z"
+     "iopub.execute_input": "2024-01-17T23:13:16.164039Z",
+     "iopub.status.busy": "2024-01-17T23:13:16.163636Z",
+     "iopub.status.idle": "2024-01-17T23:13:16.167332Z",
+     "shell.execute_reply": "2024-01-17T23:13:16.166795Z"
     }
    },
    "outputs": [],
@@ -518,10 +518,10 @@
    "id": "85a58d41",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:12.980858Z",
-     "iopub.status.busy": "2024-01-17T18:11:12.980485Z",
-     "iopub.status.idle": "2024-01-17T18:11:25.431905Z",
-     "shell.execute_reply": "2024-01-17T18:11:25.431175Z"
+     "iopub.execute_input": "2024-01-17T23:13:16.169667Z",
+     "iopub.status.busy": "2024-01-17T23:13:16.169312Z",
+     "iopub.status.idle": "2024-01-17T23:13:28.179162Z",
+     "shell.execute_reply": "2024-01-17T23:13:28.178540Z"
     }
    },
    "outputs": [
@@ -580,10 +580,10 @@
    "id": "feb0f519",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:25.434666Z",
-     "iopub.status.busy": "2024-01-17T18:11:25.434434Z",
-     "iopub.status.idle": "2024-01-17T18:11:26.998669Z",
-     "shell.execute_reply": "2024-01-17T18:11:26.997927Z"
+     "iopub.execute_input": "2024-01-17T23:13:28.182004Z",
+     "iopub.status.busy": "2024-01-17T23:13:28.181558Z",
+     "iopub.status.idle": "2024-01-17T23:13:29.717779Z",
+     "shell.execute_reply": "2024-01-17T23:13:29.716989Z"
     }
    },
    "outputs": [
@@ -627,10 +627,10 @@
    "id": "089d5860",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:27.001780Z",
-     "iopub.status.busy": "2024-01-17T18:11:27.001137Z",
-     "iopub.status.idle": "2024-01-17T18:11:27.269493Z",
-     "shell.execute_reply": "2024-01-17T18:11:27.268620Z"
+     "iopub.execute_input": "2024-01-17T23:13:29.720707Z",
+     "iopub.status.busy": "2024-01-17T23:13:29.720296Z",
+     "iopub.status.idle": "2024-01-17T23:13:29.954475Z",
+     "shell.execute_reply": "2024-01-17T23:13:29.953698Z"
     }
    },
    "outputs": [
@@ -666,10 +666,10 @@
    "id": "78b1951c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:27.272731Z",
-     "iopub.status.busy": "2024-01-17T18:11:27.272064Z",
-     "iopub.status.idle": "2024-01-17T18:11:27.957611Z",
-     "shell.execute_reply": "2024-01-17T18:11:27.956708Z"
+     "iopub.execute_input": "2024-01-17T23:13:29.957342Z",
+     "iopub.status.busy": "2024-01-17T23:13:29.957131Z",
+     "iopub.status.idle": "2024-01-17T23:13:30.610677Z",
+     "shell.execute_reply": "2024-01-17T23:13:30.610003Z"
     }
    },
    "outputs": [
@@ -719,10 +719,10 @@
    "id": "e9dff81b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:27.961024Z",
-     "iopub.status.busy": "2024-01-17T18:11:27.960734Z",
-     "iopub.status.idle": "2024-01-17T18:11:28.481913Z",
-     "shell.execute_reply": "2024-01-17T18:11:28.481164Z"
+     "iopub.execute_input": "2024-01-17T23:13:30.613503Z",
+     "iopub.status.busy": "2024-01-17T23:13:30.613295Z",
+     "iopub.status.idle": "2024-01-17T23:13:31.091930Z",
+     "shell.execute_reply": "2024-01-17T23:13:31.091231Z"
     }
    },
    "outputs": [
@@ -770,10 +770,10 @@
    "id": "616769f8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:28.485267Z",
-     "iopub.status.busy": "2024-01-17T18:11:28.484715Z",
-     "iopub.status.idle": "2024-01-17T18:11:28.738717Z",
-     "shell.execute_reply": "2024-01-17T18:11:28.737951Z"
+     "iopub.execute_input": "2024-01-17T23:13:31.094432Z",
+     "iopub.status.busy": "2024-01-17T23:13:31.094207Z",
+     "iopub.status.idle": "2024-01-17T23:13:31.340997Z",
+     "shell.execute_reply": "2024-01-17T23:13:31.340291Z"
     }
    },
    "outputs": [
@@ -829,10 +829,10 @@
    "id": "40fed4ef",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:28.742501Z",
-     "iopub.status.busy": "2024-01-17T18:11:28.741853Z",
-     "iopub.status.idle": "2024-01-17T18:11:28.828263Z",
-     "shell.execute_reply": "2024-01-17T18:11:28.827679Z"
+     "iopub.execute_input": "2024-01-17T23:13:31.344339Z",
+     "iopub.status.busy": "2024-01-17T23:13:31.343982Z",
+     "iopub.status.idle": "2024-01-17T23:13:31.429054Z",
+     "shell.execute_reply": "2024-01-17T23:13:31.428488Z"
     }
    },
    "outputs": [],
@@ -853,10 +853,10 @@
    "id": "89f9db72",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:11:28.831018Z",
-     "iopub.status.busy": "2024-01-17T18:11:28.830693Z",
-     "iopub.status.idle": "2024-01-17T18:12:06.785768Z",
-     "shell.execute_reply": "2024-01-17T18:12:06.784990Z"
+     "iopub.execute_input": "2024-01-17T23:13:31.431988Z",
+     "iopub.status.busy": "2024-01-17T23:13:31.431565Z",
+     "iopub.status.idle": "2024-01-17T23:14:09.484257Z",
+     "shell.execute_reply": "2024-01-17T23:14:09.483536Z"
     }
    },
    "outputs": [
@@ -893,10 +893,10 @@
    "id": "874c885a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:06.788676Z",
-     "iopub.status.busy": "2024-01-17T18:12:06.788248Z",
-     "iopub.status.idle": "2024-01-17T18:12:08.033479Z",
-     "shell.execute_reply": "2024-01-17T18:12:08.032852Z"
+     "iopub.execute_input": "2024-01-17T23:14:09.487213Z",
+     "iopub.status.busy": "2024-01-17T23:14:09.486711Z",
+     "iopub.status.idle": "2024-01-17T23:14:10.689009Z",
+     "shell.execute_reply": "2024-01-17T23:14:10.688273Z"
     }
    },
    "outputs": [
@@ -927,10 +927,10 @@
    "id": "e110fc4b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:08.036962Z",
-     "iopub.status.busy": "2024-01-17T18:12:08.036208Z",
-     "iopub.status.idle": "2024-01-17T18:12:08.232791Z",
-     "shell.execute_reply": "2024-01-17T18:12:08.232181Z"
+     "iopub.execute_input": "2024-01-17T23:14:10.692325Z",
+     "iopub.status.busy": "2024-01-17T23:14:10.691749Z",
+     "iopub.status.idle": "2024-01-17T23:14:10.883450Z",
+     "shell.execute_reply": "2024-01-17T23:14:10.882715Z"
     }
    },
    "outputs": [],
@@ -944,10 +944,10 @@
    "id": "85b60cbf",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:08.235701Z",
-     "iopub.status.busy": "2024-01-17T18:12:08.235355Z",
-     "iopub.status.idle": "2024-01-17T18:12:08.238889Z",
-     "shell.execute_reply": "2024-01-17T18:12:08.238378Z"
+     "iopub.execute_input": "2024-01-17T23:14:10.886396Z",
+     "iopub.status.busy": "2024-01-17T23:14:10.886034Z",
+     "iopub.status.idle": "2024-01-17T23:14:10.889447Z",
+     "shell.execute_reply": "2024-01-17T23:14:10.888823Z"
     }
    },
    "outputs": [],
@@ -969,10 +969,10 @@
    "id": "17f96fa6",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:08.241261Z",
-     "iopub.status.busy": "2024-01-17T18:12:08.241030Z",
-     "iopub.status.idle": "2024-01-17T18:12:08.250335Z",
-     "shell.execute_reply": "2024-01-17T18:12:08.249660Z"
+     "iopub.execute_input": "2024-01-17T23:14:10.891982Z",
+     "iopub.status.busy": "2024-01-17T23:14:10.891602Z",
+     "iopub.status.idle": "2024-01-17T23:14:10.900135Z",
+     "shell.execute_reply": "2024-01-17T23:14:10.899657Z"
     },
     "nbsphinx": "hidden"
    },
@@ -1017,7 +1017,7 @@
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "44fda716909e43628352ea6c49c2ff41": {
+     "2f9ad96375eb43b79434c86af5089657": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -1069,132 +1069,105 @@
        "width": null
       }
      },
-     "4acca3f4f43345808ed1160cde907360": {
-      "model_module": "@jupyter-widgets/base",
-      "model_module_version": "1.2.0",
-      "model_name": "LayoutModel",
+     "3115519043034972b905b0c53494f868": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
       "state": {
-       "_model_module": "@jupyter-widgets/base",
-       "_model_module_version": "1.2.0",
-       "_model_name": "LayoutModel",
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "HTMLModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "LayoutView",
-       "align_content": null,
-       "align_items": null,
-       "align_self": null,
-       "border": null,
-       "bottom": null,
-       "display": null,
-       "flex": null,
-       "flex_flow": null,
-       "grid_area": null,
-       "grid_auto_columns": null,
-       "grid_auto_flow": null,
-       "grid_auto_rows": null,
-       "grid_column": null,
-       "grid_gap": null,
-       "grid_row": null,
-       "grid_template_areas": null,
-       "grid_template_columns": null,
-       "grid_template_rows": null,
-       "height": null,
-       "justify_content": null,
-       "justify_items": null,
-       "left": null,
-       "margin": null,
-       "max_height": null,
-       "max_width": null,
-       "min_height": null,
-       "min_width": null,
-       "object_fit": null,
-       "object_position": null,
-       "order": null,
-       "overflow": null,
-       "overflow_x": null,
-       "overflow_y": null,
-       "padding": null,
-       "right": null,
-       "top": null,
-       "visibility": null,
-       "width": null
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_a13d518f6ab14929a70a4234ad51590d",
+       "placeholder": "​",
+       "style": "IPY_MODEL_f57e4dbd7d6c44deadc2f7b94af1255d",
+       "value": "100%"
       }
      },
-     "4b5c786cd0e44554a0b38a7e4e5e83b0": {
+     "3802f1bde2bf44b184140a42223935a4": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "ProgressStyleModel",
+      "model_name": "DescriptionStyleModel",
       "state": {
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
+       "_model_name": "DescriptionStyleModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "1.2.0",
        "_view_name": "StyleView",
-       "bar_color": null,
        "description_width": ""
       }
      },
-     "6a7a1a6e1cd0483ba26f35bf8df07850": {
+     "3919f9bbffe84644b5c28740e1b586f4": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "ProgressStyleModel",
       "state": {
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
+       "_model_name": "ProgressStyleModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "1.2.0",
        "_view_name": "StyleView",
+       "bar_color": null,
        "description_width": ""
       }
      },
-     "79cb6e9a3ba74463934eec2b0b276fa5": {
+     "549fd896bf6d492d98bc17a3cadcec46": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
+      "model_name": "FloatProgressModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "FloatProgressModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_ecae7d3da8404315949ba1e79252c7e9",
-       "placeholder": "​",
-       "style": "IPY_MODEL_9cfcbc6781604ce8bf02eff15dd91a94",
-       "value": "100%"
+       "layout": "IPY_MODEL_e2da266fb3f04ca79e2519f380d7a53a",
+       "max": 170498071.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_3919f9bbffe84644b5c28740e1b586f4",
+       "value": 170498071.0
       }
      },
-     "888ff16121af4053a4f316377395b6e9": {
+     "6047426b013f47c49a17843cd40c0b2e": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
+      "model_name": "HBoxModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "HBoxModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_44fda716909e43628352ea6c49c2ff41",
-       "placeholder": "​",
-       "style": "IPY_MODEL_6a7a1a6e1cd0483ba26f35bf8df07850",
-       "value": " 170498071/170498071 [00:01<00:00, 113387794.23it/s]"
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_3115519043034972b905b0c53494f868",
+        "IPY_MODEL_549fd896bf6d492d98bc17a3cadcec46",
+        "IPY_MODEL_a16534c9be87478dbdc9bfae4ae7566b"
+       ],
+       "layout": "IPY_MODEL_c2c04344033844fca8dd2955e2dce8d8"
       }
      },
-     "8a1ccbf8e4d1474ca5594d36a574b20a": {
+     "a13d518f6ab14929a70a4234ad51590d": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -1246,68 +1219,80 @@
        "width": null
       }
      },
-     "94ffd2c8ec814e019f2b36b808770db9": {
+     "a16534c9be87478dbdc9bfae4ae7566b": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "HBoxModel",
+      "model_name": "HTMLModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "HBoxModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_79cb6e9a3ba74463934eec2b0b276fa5",
-        "IPY_MODEL_a012aefd9d514d26aacefc9b267c3824",
-        "IPY_MODEL_888ff16121af4053a4f316377395b6e9"
-       ],
-       "layout": "IPY_MODEL_8a1ccbf8e4d1474ca5594d36a574b20a"
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_2f9ad96375eb43b79434c86af5089657",
+       "placeholder": "​",
+       "style": "IPY_MODEL_3802f1bde2bf44b184140a42223935a4",
+       "value": " 170498071/170498071 [00:01<00:00, 97316208.25it/s]"
       }
      },
-     "9cfcbc6781604ce8bf02eff15dd91a94": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
+     "c2c04344033844fca8dd2955e2dce8d8": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.2.0",
+      "model_name": "LayoutModel",
       "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
+       "_model_module": "@jupyter-widgets/base",
+       "_model_module_version": "1.2.0",
+       "_model_name": "LayoutModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/base",
        "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
-      }
-     },
-     "a012aefd9d514d26aacefc9b267c3824": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "FloatProgressModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "FloatProgressModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_4acca3f4f43345808ed1160cde907360",
-       "max": 170498071.0,
-       "min": 0.0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_4b5c786cd0e44554a0b38a7e4e5e83b0",
-       "value": 170498071.0
+       "_view_name": "LayoutView",
+       "align_content": null,
+       "align_items": null,
+       "align_self": null,
+       "border": null,
+       "bottom": null,
+       "display": null,
+       "flex": null,
+       "flex_flow": null,
+       "grid_area": null,
+       "grid_auto_columns": null,
+       "grid_auto_flow": null,
+       "grid_auto_rows": null,
+       "grid_column": null,
+       "grid_gap": null,
+       "grid_row": null,
+       "grid_template_areas": null,
+       "grid_template_columns": null,
+       "grid_template_rows": null,
+       "height": null,
+       "justify_content": null,
+       "justify_items": null,
+       "left": null,
+       "margin": null,
+       "max_height": null,
+       "max_width": null,
+       "min_height": null,
+       "min_width": null,
+       "object_fit": null,
+       "object_position": null,
+       "order": null,
+       "overflow": null,
+       "overflow_x": null,
+       "overflow_y": null,
+       "padding": null,
+       "right": null,
+       "top": null,
+       "visibility": null,
+       "width": null
       }
      },
-     "ecae7d3da8404315949ba1e79252c7e9": {
+     "e2da266fb3f04ca79e2519f380d7a53a": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -1358,6 +1343,21 @@
        "visibility": null,
        "width": null
       }
+     },
+     "f57e4dbd7d6c44deadc2f7b94af1255d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
      }
     },
     "version_major": 2,
diff --git a/master/tutorials/regression.ipynb b/master/tutorials/regression.ipynb
index eafa96774..3de0c3413 100644
--- a/master/tutorials/regression.ipynb
+++ b/master/tutorials/regression.ipynb
@@ -94,10 +94,10 @@
    "id": "2e1af7d8",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:12.975780Z",
-     "iopub.status.busy": "2024-01-17T18:12:12.975329Z",
-     "iopub.status.idle": "2024-01-17T18:12:14.087240Z",
-     "shell.execute_reply": "2024-01-17T18:12:14.086667Z"
+     "iopub.execute_input": "2024-01-17T23:14:15.960182Z",
+     "iopub.status.busy": "2024-01-17T23:14:15.959654Z",
+     "iopub.status.idle": "2024-01-17T23:14:17.062152Z",
+     "shell.execute_reply": "2024-01-17T23:14:17.061449Z"
     },
     "nbsphinx": "hidden"
    },
@@ -109,7 +109,7 @@
     "dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n",
     "    cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -135,10 +135,10 @@
    "id": "4fb10b8f",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:14.090216Z",
-     "iopub.status.busy": "2024-01-17T18:12:14.089748Z",
-     "iopub.status.idle": "2024-01-17T18:12:14.105993Z",
-     "shell.execute_reply": "2024-01-17T18:12:14.105493Z"
+     "iopub.execute_input": "2024-01-17T23:14:17.065102Z",
+     "iopub.status.busy": "2024-01-17T23:14:17.064826Z",
+     "iopub.status.idle": "2024-01-17T23:14:17.081276Z",
+     "shell.execute_reply": "2024-01-17T23:14:17.080798Z"
     }
    },
    "outputs": [],
@@ -157,10 +157,10 @@
    "id": "284dc264",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:14.108511Z",
-     "iopub.status.busy": "2024-01-17T18:12:14.108131Z",
-     "iopub.status.idle": "2024-01-17T18:12:14.111275Z",
-     "shell.execute_reply": "2024-01-17T18:12:14.110730Z"
+     "iopub.execute_input": "2024-01-17T23:14:17.083754Z",
+     "iopub.status.busy": "2024-01-17T23:14:17.083378Z",
+     "iopub.status.idle": "2024-01-17T23:14:17.086439Z",
+     "shell.execute_reply": "2024-01-17T23:14:17.085896Z"
     },
     "nbsphinx": "hidden"
    },
@@ -191,10 +191,10 @@
    "id": "0f7450db",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:14.113627Z",
-     "iopub.status.busy": "2024-01-17T18:12:14.113255Z",
-     "iopub.status.idle": "2024-01-17T18:12:14.211397Z",
-     "shell.execute_reply": "2024-01-17T18:12:14.210759Z"
+     "iopub.execute_input": "2024-01-17T23:14:17.088732Z",
+     "iopub.status.busy": "2024-01-17T23:14:17.088379Z",
+     "iopub.status.idle": "2024-01-17T23:14:17.163683Z",
+     "shell.execute_reply": "2024-01-17T23:14:17.163047Z"
     }
    },
    "outputs": [
@@ -367,10 +367,10 @@
    "id": "55513fed",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:14.214337Z",
-     "iopub.status.busy": "2024-01-17T18:12:14.213937Z",
-     "iopub.status.idle": "2024-01-17T18:12:14.499529Z",
-     "shell.execute_reply": "2024-01-17T18:12:14.498832Z"
+     "iopub.execute_input": "2024-01-17T23:14:17.166444Z",
+     "iopub.status.busy": "2024-01-17T23:14:17.166096Z",
+     "iopub.status.idle": "2024-01-17T23:14:17.450591Z",
+     "shell.execute_reply": "2024-01-17T23:14:17.449863Z"
     },
     "nbsphinx": "hidden"
    },
@@ -410,10 +410,10 @@
    "id": "df5a0f59",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:14.502466Z",
-     "iopub.status.busy": "2024-01-17T18:12:14.502090Z",
-     "iopub.status.idle": "2024-01-17T18:12:14.756785Z",
-     "shell.execute_reply": "2024-01-17T18:12:14.756135Z"
+     "iopub.execute_input": "2024-01-17T23:14:17.453500Z",
+     "iopub.status.busy": "2024-01-17T23:14:17.453276Z",
+     "iopub.status.idle": "2024-01-17T23:14:17.712310Z",
+     "shell.execute_reply": "2024-01-17T23:14:17.711570Z"
     }
    },
    "outputs": [
@@ -449,10 +449,10 @@
    "id": "7af78a8a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:14.759685Z",
-     "iopub.status.busy": "2024-01-17T18:12:14.759113Z",
-     "iopub.status.idle": "2024-01-17T18:12:14.763916Z",
-     "shell.execute_reply": "2024-01-17T18:12:14.763292Z"
+     "iopub.execute_input": "2024-01-17T23:14:17.715207Z",
+     "iopub.status.busy": "2024-01-17T23:14:17.714563Z",
+     "iopub.status.idle": "2024-01-17T23:14:17.719694Z",
+     "shell.execute_reply": "2024-01-17T23:14:17.719140Z"
     }
    },
    "outputs": [],
@@ -470,10 +470,10 @@
    "id": "9556c624",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:14.766230Z",
-     "iopub.status.busy": "2024-01-17T18:12:14.766027Z",
-     "iopub.status.idle": "2024-01-17T18:12:14.772493Z",
-     "shell.execute_reply": "2024-01-17T18:12:14.771995Z"
+     "iopub.execute_input": "2024-01-17T23:14:17.722020Z",
+     "iopub.status.busy": "2024-01-17T23:14:17.721636Z",
+     "iopub.status.idle": "2024-01-17T23:14:17.727795Z",
+     "shell.execute_reply": "2024-01-17T23:14:17.727294Z"
     }
    },
    "outputs": [],
@@ -520,10 +520,10 @@
    "id": "3c2f1ccc",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:14.774939Z",
-     "iopub.status.busy": "2024-01-17T18:12:14.774604Z",
-     "iopub.status.idle": "2024-01-17T18:12:14.777371Z",
-     "shell.execute_reply": "2024-01-17T18:12:14.776796Z"
+     "iopub.execute_input": "2024-01-17T23:14:17.730435Z",
+     "iopub.status.busy": "2024-01-17T23:14:17.729964Z",
+     "iopub.status.idle": "2024-01-17T23:14:17.732895Z",
+     "shell.execute_reply": "2024-01-17T23:14:17.732419Z"
     }
    },
    "outputs": [],
@@ -538,10 +538,10 @@
    "id": "7e1b7860",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:14.779648Z",
-     "iopub.status.busy": "2024-01-17T18:12:14.779287Z",
-     "iopub.status.idle": "2024-01-17T18:12:24.954125Z",
-     "shell.execute_reply": "2024-01-17T18:12:24.953475Z"
+     "iopub.execute_input": "2024-01-17T23:14:17.735253Z",
+     "iopub.status.busy": "2024-01-17T23:14:17.734894Z",
+     "iopub.status.idle": "2024-01-17T23:14:27.746313Z",
+     "shell.execute_reply": "2024-01-17T23:14:27.745534Z"
     }
    },
    "outputs": [],
@@ -565,10 +565,10 @@
    "id": "f407bd69",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:24.957367Z",
-     "iopub.status.busy": "2024-01-17T18:12:24.956688Z",
-     "iopub.status.idle": "2024-01-17T18:12:24.964247Z",
-     "shell.execute_reply": "2024-01-17T18:12:24.963650Z"
+     "iopub.execute_input": "2024-01-17T23:14:27.749932Z",
+     "iopub.status.busy": "2024-01-17T23:14:27.749187Z",
+     "iopub.status.idle": "2024-01-17T23:14:27.757016Z",
+     "shell.execute_reply": "2024-01-17T23:14:27.756392Z"
     }
    },
    "outputs": [
@@ -671,10 +671,10 @@
    "id": "f7385336",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:24.966582Z",
-     "iopub.status.busy": "2024-01-17T18:12:24.966381Z",
-     "iopub.status.idle": "2024-01-17T18:12:24.970309Z",
-     "shell.execute_reply": "2024-01-17T18:12:24.969793Z"
+     "iopub.execute_input": "2024-01-17T23:14:27.759616Z",
+     "iopub.status.busy": "2024-01-17T23:14:27.759241Z",
+     "iopub.status.idle": "2024-01-17T23:14:27.763155Z",
+     "shell.execute_reply": "2024-01-17T23:14:27.762627Z"
     }
    },
    "outputs": [],
@@ -689,10 +689,10 @@
    "id": "59fc3091",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:24.972387Z",
-     "iopub.status.busy": "2024-01-17T18:12:24.972195Z",
-     "iopub.status.idle": "2024-01-17T18:12:24.976119Z",
-     "shell.execute_reply": "2024-01-17T18:12:24.975605Z"
+     "iopub.execute_input": "2024-01-17T23:14:27.765475Z",
+     "iopub.status.busy": "2024-01-17T23:14:27.765102Z",
+     "iopub.status.idle": "2024-01-17T23:14:27.768912Z",
+     "shell.execute_reply": "2024-01-17T23:14:27.768381Z"
     }
    },
    "outputs": [
@@ -727,10 +727,10 @@
    "id": "00949977",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:24.978399Z",
-     "iopub.status.busy": "2024-01-17T18:12:24.978063Z",
-     "iopub.status.idle": "2024-01-17T18:12:24.981351Z",
-     "shell.execute_reply": "2024-01-17T18:12:24.980720Z"
+     "iopub.execute_input": "2024-01-17T23:14:27.771349Z",
+     "iopub.status.busy": "2024-01-17T23:14:27.770935Z",
+     "iopub.status.idle": "2024-01-17T23:14:27.774415Z",
+     "shell.execute_reply": "2024-01-17T23:14:27.773861Z"
     }
    },
    "outputs": [],
@@ -749,10 +749,10 @@
    "id": "b6c1ae3a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:24.983590Z",
-     "iopub.status.busy": "2024-01-17T18:12:24.983226Z",
-     "iopub.status.idle": "2024-01-17T18:12:24.991687Z",
-     "shell.execute_reply": "2024-01-17T18:12:24.991176Z"
+     "iopub.execute_input": "2024-01-17T23:14:27.776712Z",
+     "iopub.status.busy": "2024-01-17T23:14:27.776344Z",
+     "iopub.status.idle": "2024-01-17T23:14:27.785189Z",
+     "shell.execute_reply": "2024-01-17T23:14:27.784641Z"
     }
    },
    "outputs": [
@@ -894,10 +894,10 @@
    "id": "31c704e7",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:24.993898Z",
-     "iopub.status.busy": "2024-01-17T18:12:24.993700Z",
-     "iopub.status.idle": "2024-01-17T18:12:25.140874Z",
-     "shell.execute_reply": "2024-01-17T18:12:25.140179Z"
+     "iopub.execute_input": "2024-01-17T23:14:27.787704Z",
+     "iopub.status.busy": "2024-01-17T23:14:27.787338Z",
+     "iopub.status.idle": "2024-01-17T23:14:27.937319Z",
+     "shell.execute_reply": "2024-01-17T23:14:27.936622Z"
     }
    },
    "outputs": [
@@ -936,10 +936,10 @@
    "id": "0bcc43db",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:25.143690Z",
-     "iopub.status.busy": "2024-01-17T18:12:25.143247Z",
-     "iopub.status.idle": "2024-01-17T18:12:25.277016Z",
-     "shell.execute_reply": "2024-01-17T18:12:25.276372Z"
+     "iopub.execute_input": "2024-01-17T23:14:27.940162Z",
+     "iopub.status.busy": "2024-01-17T23:14:27.939715Z",
+     "iopub.status.idle": "2024-01-17T23:14:28.073020Z",
+     "shell.execute_reply": "2024-01-17T23:14:28.072323Z"
     }
    },
    "outputs": [
@@ -995,10 +995,10 @@
    "id": "7021bd68",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:25.280112Z",
-     "iopub.status.busy": "2024-01-17T18:12:25.279620Z",
-     "iopub.status.idle": "2024-01-17T18:12:25.869524Z",
-     "shell.execute_reply": "2024-01-17T18:12:25.868796Z"
+     "iopub.execute_input": "2024-01-17T23:14:28.075914Z",
+     "iopub.status.busy": "2024-01-17T23:14:28.075472Z",
+     "iopub.status.idle": "2024-01-17T23:14:28.663372Z",
+     "shell.execute_reply": "2024-01-17T23:14:28.662637Z"
     }
    },
    "outputs": [],
@@ -1014,10 +1014,10 @@
    "id": "d49c990b",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:25.872698Z",
-     "iopub.status.busy": "2024-01-17T18:12:25.872237Z",
-     "iopub.status.idle": "2024-01-17T18:12:25.955671Z",
-     "shell.execute_reply": "2024-01-17T18:12:25.954697Z"
+     "iopub.execute_input": "2024-01-17T23:14:28.666533Z",
+     "iopub.status.busy": "2024-01-17T23:14:28.666263Z",
+     "iopub.status.idle": "2024-01-17T23:14:28.748208Z",
+     "shell.execute_reply": "2024-01-17T23:14:28.747515Z"
     }
    },
    "outputs": [
@@ -1055,10 +1055,10 @@
    "id": "95531cda",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:25.958459Z",
-     "iopub.status.busy": "2024-01-17T18:12:25.958243Z",
-     "iopub.status.idle": "2024-01-17T18:12:25.968660Z",
-     "shell.execute_reply": "2024-01-17T18:12:25.968158Z"
+     "iopub.execute_input": "2024-01-17T23:14:28.750983Z",
+     "iopub.status.busy": "2024-01-17T23:14:28.750598Z",
+     "iopub.status.idle": "2024-01-17T23:14:28.760804Z",
+     "shell.execute_reply": "2024-01-17T23:14:28.760302Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/segmentation.html b/master/tutorials/segmentation.html
index 78b66cb67..2dcfe8748 100644
--- a/master/tutorials/segmentation.html
+++ b/master/tutorials/segmentation.html
@@ -969,13 +969,13 @@ 
3. Use cleanlab to find label issues
 

 

-

+

 
 

 

 

 

-

+

 
 

 

@@ -1096,16 +1096,16 @@ 
Classes which are commonly mislabeled overall

 

-
0%|          | 17362/4997817 [00:00&lt;00:28, 173608.58it/s]

+
0%|          | 16923/4997817 [00:00&lt;00:29, 169217.65it/s]

 
</pre>

 

 

-
0%|          | 17362/4997817 [00:00<00:28, 173608.58it/s]

+
0%|          | 16923/4997817 [00:00<00:29, 169217.65it/s]

 
end{sphinxVerbatim}

 

 

 

-
0%|          | 17362/4997817 [00:00<00:28, 173608.58it/s]

+
0%|          | 16923/4997817 [00:00<00:29, 169217.65it/s]

 
 
 

@@ -1122,16 +1122,16 @@ 
Classes which are commonly mislabeled overall

 

-
1%|          | 34895/4997817 [00:00&lt;00:28, 174610.72it/s]

+
1%|          | 33993/4997817 [00:00&lt;00:29, 170085.88it/s]

 
</pre>

 

 

-
1%|          | 34895/4997817 [00:00<00:28, 174610.72it/s]

+
1%|          | 33993/4997817 [00:00<00:29, 170085.88it/s]

 
end{sphinxVerbatim}

 

 

 

-
1%|          | 34895/4997817 [00:00<00:28, 174610.72it/s]

+
1%|          | 33993/4997817 [00:00<00:29, 170085.88it/s]

 
 
 

@@ -1148,16 +1148,16 @@ 
Classes which are commonly mislabeled overall

 

-
1%|          | 52475/4997817 [00:00&lt;00:28, 175150.77it/s]

+
1%|          | 51002/4997817 [00:00&lt;00:29, 169503.62it/s]

 
</pre>

 

 

-
1%|          | 52475/4997817 [00:00<00:28, 175150.77it/s]

+
1%|          | 51002/4997817 [00:00<00:29, 169503.62it/s]

 
end{sphinxVerbatim}

 

 

 

-
1%|          | 52475/4997817 [00:00<00:28, 175150.77it/s]

+
1%|          | 51002/4997817 [00:00<00:29, 169503.62it/s]

 
 
 

@@ -1174,16 +1174,16 @@ 
Classes which are commonly mislabeled overall

 

-
1%|▏         | 69991/4997817 [00:00&lt;00:28, 173938.91it/s]

+
1%|▏         | 68012/4997817 [00:00&lt;00:29, 169735.92it/s]

 
</pre>

 

 

-
1%|▏         | 69991/4997817 [00:00<00:28, 173938.91it/s]

+
1%|▏         | 68012/4997817 [00:00<00:29, 169735.92it/s]

 
end{sphinxVerbatim}

 

 

 

-
1%|▏         | 69991/4997817 [00:00<00:28, 173938.91it/s]

+
1%|▏         | 68012/4997817 [00:00<00:29, 169735.92it/s]

 
 
 

@@ -1200,16 +1200,16 @@ 
Classes which are commonly mislabeled overall

 

-
2%|▏         | 87813/4997817 [00:00&lt;00:27, 175471.95it/s]

+
2%|▏         | 84993/4997817 [00:00&lt;00:28, 169759.89it/s]

 
</pre>

 

 

-
2%|▏         | 87813/4997817 [00:00<00:27, 175471.95it/s]

+
2%|▏         | 84993/4997817 [00:00<00:28, 169759.89it/s]

 
end{sphinxVerbatim}

 

 

 

-
2%|▏         | 87813/4997817 [00:00<00:27, 175471.95it/s]

+
2%|▏         | 84993/4997817 [00:00<00:28, 169759.89it/s]

 
 
 

@@ -1226,16 +1226,16 @@ 
Classes which are commonly mislabeled overall

 

-
2%|▏         | 105688/4997817 [00:00&lt;00:27, 176578.81it/s]

+
2%|▏         | 101970/4997817 [00:00&lt;00:28, 169628.61it/s]

 
</pre>

 

 

-
2%|▏         | 105688/4997817 [00:00<00:27, 176578.81it/s]

+
2%|▏         | 101970/4997817 [00:00<00:28, 169628.61it/s]

 
end{sphinxVerbatim}

 

 

 

-
2%|▏         | 105688/4997817 [00:00<00:27, 176578.81it/s]

+
2%|▏         | 101970/4997817 [00:00<00:28, 169628.61it/s]

 
 
 

@@ -1252,16 +1252,16 @@ 
Classes which are commonly mislabeled overall

 

-
2%|▏         | 123543/4997817 [00:00&lt;00:27, 177216.89it/s]

+
2%|▏         | 118944/4997817 [00:00&lt;00:28, 169663.19it/s]

 
</pre>

 

 

-
2%|▏         | 123543/4997817 [00:00<00:27, 177216.89it/s]

+
2%|▏         | 118944/4997817 [00:00<00:28, 169663.19it/s]

 
end{sphinxVerbatim}

 

 

 

-
2%|▏         | 123543/4997817 [00:00<00:27, 177216.89it/s]

+
2%|▏         | 118944/4997817 [00:00<00:28, 169663.19it/s]

 
 
 

@@ -1278,16 +1278,16 @@ 
Classes which are commonly mislabeled overall

 

-
3%|▎         | 141289/4997817 [00:00&lt;00:27, 177290.20it/s]

+
3%|▎         | 135911/4997817 [00:00&lt;00:29, 163001.52it/s]

 
</pre>

 

 

-
3%|▎         | 141289/4997817 [00:00<00:27, 177290.20it/s]

+
3%|▎         | 135911/4997817 [00:00<00:29, 163001.52it/s]

 
end{sphinxVerbatim}

 

 

 

-
3%|▎         | 141289/4997817 [00:00<00:27, 177290.20it/s]

+
3%|▎         | 135911/4997817 [00:00<00:29, 163001.52it/s]

 
 
 

@@ -1304,16 +1304,16 @@ 
Classes which are commonly mislabeled overall

 

-
3%|▎         | 159076/4997817 [00:00&lt;00:27, 177466.31it/s]

+
3%|▎         | 152918/4997817 [00:00&lt;00:29, 165147.19it/s]

 
</pre>

 

 

-
3%|▎         | 159076/4997817 [00:00<00:27, 177466.31it/s]

+
3%|▎         | 152918/4997817 [00:00<00:29, 165147.19it/s]

 
end{sphinxVerbatim}

 

 

 

-
3%|▎         | 159076/4997817 [00:00<00:27, 177466.31it/s]

+
3%|▎         | 152918/4997817 [00:00<00:29, 165147.19it/s]

 
 
 

@@ -1330,16 +1330,16 @@ 
Classes which are commonly mislabeled overall

 

-
4%|▎         | 176918/4997817 [00:01&lt;00:27, 177754.44it/s]

+
3%|▎         | 169974/4997817 [00:01&lt;00:28, 166782.07it/s]

 
</pre>

 

 

-
4%|▎         | 176918/4997817 [00:01<00:27, 177754.44it/s]

+
3%|▎         | 169974/4997817 [00:01<00:28, 166782.07it/s]

 
end{sphinxVerbatim}

 

 

 

-
4%|▎         | 176918/4997817 [00:01<00:27, 177754.44it/s]

+
3%|▎         | 169974/4997817 [00:01<00:28, 166782.07it/s]

 
 
 

@@ -1356,16 +1356,16 @@ 
Classes which are commonly mislabeled overall

 

-
4%|▍         | 194876/4997817 [00:01&lt;00:26, 178308.24it/s]

+
4%|▎         | 187064/4997817 [00:01&lt;00:28, 168021.67it/s]

 
</pre>

 

 

-
4%|▍         | 194876/4997817 [00:01<00:26, 178308.24it/s]

+
4%|▎         | 187064/4997817 [00:01<00:28, 168021.67it/s]

 
end{sphinxVerbatim}

 

 

 

-
4%|▍         | 194876/4997817 [00:01<00:26, 178308.24it/s]

+
4%|▎         | 187064/4997817 [00:01<00:28, 168021.67it/s]

 
 
 

@@ -1382,16 +1382,16 @@ 
Classes which are commonly mislabeled overall

 

-
4%|▍         | 212708/4997817 [00:01&lt;00:26, 178223.48it/s]

+
4%|▍         | 204038/4997817 [00:01&lt;00:28, 168537.30it/s]

 
</pre>

 

 

-
4%|▍         | 212708/4997817 [00:01<00:26, 178223.48it/s]

+
4%|▍         | 204038/4997817 [00:01<00:28, 168537.30it/s]

 
end{sphinxVerbatim}

 

 

 

-
4%|▍         | 212708/4997817 [00:01<00:26, 178223.48it/s]

+
4%|▍         | 204038/4997817 [00:01<00:28, 168537.30it/s]

 
 
 

@@ -1408,16 +1408,16 @@ 
Classes which are commonly mislabeled overall

 

-
5%|▍         | 230531/4997817 [00:01&lt;00:26, 178136.92it/s]

+
4%|▍         | 220938/4997817 [00:01&lt;00:28, 168672.47it/s]

 
</pre>

 

 

-
5%|▍         | 230531/4997817 [00:01<00:26, 178136.92it/s]

+
4%|▍         | 220938/4997817 [00:01<00:28, 168672.47it/s]

 
end{sphinxVerbatim}

 

 

 

-
5%|▍         | 230531/4997817 [00:01<00:26, 178136.92it/s]

+
4%|▍         | 220938/4997817 [00:01<00:28, 168672.47it/s]

 
 
 

@@ -1434,16 +1434,16 @@ 
Classes which are commonly mislabeled overall

 

-
5%|▍         | 248505/4997817 [00:01&lt;00:26, 178619.00it/s]

+
5%|▍         | 238130/4997817 [00:01&lt;00:28, 169647.75it/s]

 
</pre>

 

 

-
5%|▍         | 248505/4997817 [00:01<00:26, 178619.00it/s]

+
5%|▍         | 238130/4997817 [00:01<00:28, 169647.75it/s]

 
end{sphinxVerbatim}

 

 

 

-
5%|▍         | 248505/4997817 [00:01<00:26, 178619.00it/s]

+
5%|▍         | 238130/4997817 [00:01<00:28, 169647.75it/s]

 
 
 

@@ -1460,16 +1460,16 @@ 
Classes which are commonly mislabeled overall

 

-
5%|▌         | 266377/4997817 [00:01&lt;00:26, 178647.43it/s]

+
5%|▌         | 255262/4997817 [00:01&lt;00:27, 170147.90it/s]

 
</pre>

 

 

-
5%|▌         | 266377/4997817 [00:01<00:26, 178647.43it/s]

+
5%|▌         | 255262/4997817 [00:01<00:27, 170147.90it/s]

 
end{sphinxVerbatim}

 

 

 

-
5%|▌         | 266377/4997817 [00:01<00:26, 178647.43it/s]

+
5%|▌         | 255262/4997817 [00:01<00:27, 170147.90it/s]

 
 
 

@@ -1486,16 +1486,16 @@ 
Classes which are commonly mislabeled overall

 

-
6%|▌         | 284425/4997817 [00:01&lt;00:26, 179193.60it/s]

+
5%|▌         | 272284/4997817 [00:01&lt;00:27, 170019.26it/s]

 
</pre>

 

 

-
6%|▌         | 284425/4997817 [00:01<00:26, 179193.60it/s]

+
5%|▌         | 272284/4997817 [00:01<00:27, 170019.26it/s]

 
end{sphinxVerbatim}

 

 

 

-
6%|▌         | 284425/4997817 [00:01<00:26, 179193.60it/s]

+
5%|▌         | 272284/4997817 [00:01<00:27, 170019.26it/s]

 
 
 

@@ -1512,16 +1512,16 @@ 
Classes which are commonly mislabeled overall

 

-
6%|▌         | 302378/4997817 [00:01&lt;00:26, 179290.92it/s]

+
6%|▌         | 289291/4997817 [00:01&lt;00:27, 169797.69it/s]

 
</pre>

 

 

-
6%|▌         | 302378/4997817 [00:01<00:26, 179290.92it/s]

+
6%|▌         | 289291/4997817 [00:01<00:27, 169797.69it/s]

 
end{sphinxVerbatim}

 

 

 

-
6%|▌         | 302378/4997817 [00:01<00:26, 179290.92it/s]

+
6%|▌         | 289291/4997817 [00:01<00:27, 169797.69it/s]

 
 
 

@@ -1538,16 +1538,16 @@ 
Classes which are commonly mislabeled overall

 

-
6%|▋         | 320347/4997817 [00:01&lt;00:26, 179407.53it/s]

+
6%|▌         | 306465/4997817 [00:01&lt;00:27, 170377.22it/s]

 
</pre>

 

 

-
6%|▋         | 320347/4997817 [00:01<00:26, 179407.53it/s]

+
6%|▌         | 306465/4997817 [00:01<00:27, 170377.22it/s]

 
end{sphinxVerbatim}

 

 

 

-
6%|▋         | 320347/4997817 [00:01<00:26, 179407.53it/s]

+
6%|▌         | 306465/4997817 [00:01<00:27, 170377.22it/s]

 
 
 

@@ -1564,16 +1564,16 @@ 
Classes which are commonly mislabeled overall

 

-
7%|▋         | 338297/4997817 [00:01&lt;00:25, 179431.36it/s]

+
6%|▋         | 323703/4997817 [00:01&lt;00:27, 170975.52it/s]

 
</pre>

 

 

-
7%|▋         | 338297/4997817 [00:01<00:25, 179431.36it/s]

+
6%|▋         | 323703/4997817 [00:01<00:27, 170975.52it/s]

 
end{sphinxVerbatim}

 

 

 

-
7%|▋         | 338297/4997817 [00:01<00:25, 179431.36it/s]

+
6%|▋         | 323703/4997817 [00:01<00:27, 170975.52it/s]

 
 
 

@@ -1590,16 +1590,16 @@ 
Classes which are commonly mislabeled overall

 

-
7%|▋         | 356241/4997817 [00:02&lt;00:25, 179348.77it/s]

+
7%|▋         | 340851/4997817 [00:02&lt;00:27, 171125.25it/s]

 
</pre>

 

 

-
7%|▋         | 356241/4997817 [00:02<00:25, 179348.77it/s]

+
7%|▋         | 340851/4997817 [00:02<00:27, 171125.25it/s]

 
end{sphinxVerbatim}

 

 

 

-
7%|▋         | 356241/4997817 [00:02<00:25, 179348.77it/s]

+
7%|▋         | 340851/4997817 [00:02<00:27, 171125.25it/s]

 
 
 

@@ -1616,16 +1616,16 @@ 
Classes which are commonly mislabeled overall

 

-
7%|▋         | 374208/4997817 [00:02&lt;00:25, 179441.48it/s]

+
7%|▋         | 357965/4997817 [00:02&lt;00:27, 170735.16it/s]

 
</pre>

 

 

-
7%|▋         | 374208/4997817 [00:02<00:25, 179441.48it/s]

+
7%|▋         | 357965/4997817 [00:02<00:27, 170735.16it/s]

 
end{sphinxVerbatim}

 

 

 

-
7%|▋         | 374208/4997817 [00:02<00:25, 179441.48it/s]

+
7%|▋         | 357965/4997817 [00:02<00:27, 170735.16it/s]

 
 
 

@@ -1642,16 +1642,16 @@ 
Classes which are commonly mislabeled overall

 

-
8%|▊         | 392153/4997817 [00:02&lt;00:26, 175413.81it/s]

+
8%|▊         | 375079/4997817 [00:02&lt;00:27, 170854.49it/s]

 
</pre>

 

 

-
8%|▊         | 392153/4997817 [00:02<00:26, 175413.81it/s]

+
8%|▊         | 375079/4997817 [00:02<00:27, 170854.49it/s]

 
end{sphinxVerbatim}

 

 

 

-
8%|▊         | 392153/4997817 [00:02<00:26, 175413.81it/s]

+
8%|▊         | 375079/4997817 [00:02<00:27, 170854.49it/s]

 
 
 

@@ -1668,16 +1668,16 @@ 
Classes which are commonly mislabeled overall

 

-
8%|▊         | 410009/4997817 [00:02&lt;00:26, 176338.11it/s]

+
8%|▊         | 392275/4997817 [00:02&lt;00:26, 171182.95it/s]

 
</pre>

 

 

-
8%|▊         | 410009/4997817 [00:02<00:26, 176338.11it/s]

+
8%|▊         | 392275/4997817 [00:02<00:26, 171182.95it/s]

 
end{sphinxVerbatim}

 

 

 

-
8%|▊         | 410009/4997817 [00:02<00:26, 176338.11it/s]

+
8%|▊         | 392275/4997817 [00:02<00:26, 171182.95it/s]

 
 
 

@@ -1694,16 +1694,16 @@ 
Classes which are commonly mislabeled overall

 

-
9%|▊         | 427853/4997817 [00:02&lt;00:25, 176958.47it/s]

+
8%|▊         | 409485/4997817 [00:02&lt;00:26, 171454.31it/s]

 
</pre>

 

 

-
9%|▊         | 427853/4997817 [00:02<00:25, 176958.47it/s]

+
8%|▊         | 409485/4997817 [00:02<00:26, 171454.31it/s]

 
end{sphinxVerbatim}

 

 

 

-
9%|▊         | 427853/4997817 [00:02<00:25, 176958.47it/s]

+
8%|▊         | 409485/4997817 [00:02<00:26, 171454.31it/s]

 
 
 

@@ -1720,16 +1720,16 @@ 
Classes which are commonly mislabeled overall

 

-
9%|▉         | 445826/4997817 [00:02&lt;00:25, 177779.06it/s]

+
9%|▊         | 426631/4997817 [00:02&lt;00:26, 171181.61it/s]

 
</pre>

 

 

-
9%|▉         | 445826/4997817 [00:02<00:25, 177779.06it/s]

+
9%|▊         | 426631/4997817 [00:02<00:26, 171181.61it/s]

 
end{sphinxVerbatim}

 

 

 

-
9%|▉         | 445826/4997817 [00:02<00:25, 177779.06it/s]

+
9%|▊         | 426631/4997817 [00:02<00:26, 171181.61it/s]

 
 
 

@@ -1746,16 +1746,16 @@ 
Classes which are commonly mislabeled overall

 

-
9%|▉         | 463613/4997817 [00:02&lt;00:25, 177528.30it/s]

+
9%|▉         | 443750/4997817 [00:02&lt;00:26, 170479.76it/s]

 
</pre>

 

 

-
9%|▉         | 463613/4997817 [00:02<00:25, 177528.30it/s]

+
9%|▉         | 443750/4997817 [00:02<00:26, 170479.76it/s]

 
end{sphinxVerbatim}

 

 

 

-
9%|▉         | 463613/4997817 [00:02<00:25, 177528.30it/s]

+
9%|▉         | 443750/4997817 [00:02<00:26, 170479.76it/s]

 
 
 

@@ -1772,16 +1772,16 @@ 
Classes which are commonly mislabeled overall

 

-
10%|▉         | 481507/4997817 [00:02&lt;00:25, 177946.35it/s]

+
9%|▉         | 460916/4997817 [00:02&lt;00:26, 170828.33it/s]

 
</pre>

 

 

-
10%|▉         | 481507/4997817 [00:02<00:25, 177946.35it/s]

+
9%|▉         | 460916/4997817 [00:02<00:26, 170828.33it/s]

 
end{sphinxVerbatim}

 

 

 

-
10%|▉         | 481507/4997817 [00:02<00:25, 177946.35it/s]

+
9%|▉         | 460916/4997817 [00:02<00:26, 170828.33it/s]

 
 
 

@@ -1798,16 +1798,16 @@ 
Classes which are commonly mislabeled overall

 

-
10%|▉         | 499307/4997817 [00:02&lt;00:25, 177661.06it/s]

+
10%|▉         | 478000/4997817 [00:02&lt;00:27, 167094.77it/s]

 
</pre>

 

 

-
10%|▉         | 499307/4997817 [00:02<00:25, 177661.06it/s]

+
10%|▉         | 478000/4997817 [00:02<00:27, 167094.77it/s]

 
end{sphinxVerbatim}

 

 

 

-
10%|▉         | 499307/4997817 [00:02<00:25, 177661.06it/s]

+
10%|▉         | 478000/4997817 [00:02<00:27, 167094.77it/s]

 
 
 

@@ -1824,16 +1824,16 @@ 
Classes which are commonly mislabeled overall

 

-
10%|█         | 517142/4997817 [00:02&lt;00:25, 177861.59it/s]

+
10%|▉         | 495446/4997817 [00:02&lt;00:26, 169263.15it/s]

 
</pre>

 

 

-
10%|█         | 517142/4997817 [00:02<00:25, 177861.59it/s]

+
10%|▉         | 495446/4997817 [00:02<00:26, 169263.15it/s]

 
end{sphinxVerbatim}

 

 

 

-
10%|█         | 517142/4997817 [00:02<00:25, 177861.59it/s]

+
10%|▉         | 495446/4997817 [00:02<00:26, 169263.15it/s]

 
 
 

@@ -1850,16 +1850,16 @@ 
Classes which are commonly mislabeled overall

 

-
11%|█         | 535037/4997817 [00:03&lt;00:25, 178184.28it/s]

+
10%|█         | 512890/4997817 [00:03&lt;00:26, 170794.12it/s]

 
</pre>

 

 

-
11%|█         | 535037/4997817 [00:03<00:25, 178184.28it/s]

+
10%|█         | 512890/4997817 [00:03<00:26, 170794.12it/s]

 
end{sphinxVerbatim}

 

 

 

-
11%|█         | 535037/4997817 [00:03<00:25, 178184.28it/s]

+
10%|█         | 512890/4997817 [00:03<00:26, 170794.12it/s]

 
 
 

@@ -1876,16 +1876,16 @@ 
Classes which are commonly mislabeled overall

 

-
11%|█         | 552923/4997817 [00:03&lt;00:24, 178382.09it/s]

+
11%|█         | 530383/4997817 [00:03&lt;00:25, 172020.72it/s]

 
</pre>

 

 

-
11%|█         | 552923/4997817 [00:03<00:24, 178382.09it/s]

+
11%|█         | 530383/4997817 [00:03<00:25, 172020.72it/s]

 
end{sphinxVerbatim}

 

 

 

-
11%|█         | 552923/4997817 [00:03<00:24, 178382.09it/s]

+
11%|█         | 530383/4997817 [00:03<00:25, 172020.72it/s]

 
 
 

@@ -1902,16 +1902,16 @@ 
Classes which are commonly mislabeled overall

 

-
11%|█▏        | 570763/4997817 [00:03&lt;00:25, 171086.58it/s]

+
11%|█         | 547934/4997817 [00:03&lt;00:25, 173058.88it/s]

 
</pre>

 

 

-
11%|█▏        | 570763/4997817 [00:03<00:25, 171086.58it/s]

+
11%|█         | 547934/4997817 [00:03<00:25, 173058.88it/s]

 
end{sphinxVerbatim}

 

 

 

-
11%|█▏        | 570763/4997817 [00:03<00:25, 171086.58it/s]

+
11%|█         | 547934/4997817 [00:03<00:25, 173058.88it/s]

 
 
 

@@ -1928,16 +1928,16 @@ 
Classes which are commonly mislabeled overall

 

-
12%|█▏        | 588402/4997817 [00:03&lt;00:25, 172627.47it/s]

+
11%|█▏        | 565248/4997817 [00:03&lt;00:25, 171739.41it/s]

 
</pre>

 

 

-
12%|█▏        | 588402/4997817 [00:03<00:25, 172627.47it/s]

+
11%|█▏        | 565248/4997817 [00:03<00:25, 171739.41it/s]

 
end{sphinxVerbatim}

 

 

 

-
12%|█▏        | 588402/4997817 [00:03<00:25, 172627.47it/s]

+
11%|█▏        | 565248/4997817 [00:03<00:25, 171739.41it/s]

 
 
 

@@ -1954,16 +1954,16 @@ 
Classes which are commonly mislabeled overall

 

-
12%|█▏        | 606160/4997817 [00:03&lt;00:25, 174079.34it/s]

+
12%|█▏        | 582430/4997817 [00:03&lt;00:25, 171635.25it/s]

 
</pre>

 

 

-
12%|█▏        | 606160/4997817 [00:03<00:25, 174079.34it/s]

+
12%|█▏        | 582430/4997817 [00:03<00:25, 171635.25it/s]

 
end{sphinxVerbatim}

 

 

 

-
12%|█▏        | 606160/4997817 [00:03<00:25, 174079.34it/s]

+
12%|█▏        | 582430/4997817 [00:03<00:25, 171635.25it/s]

 
 
 

@@ -1980,16 +1980,16 @@ 
Classes which are commonly mislabeled overall

 

-
12%|█▏        | 623955/4997817 [00:03&lt;00:24, 175220.83it/s]

+
12%|█▏        | 599599/4997817 [00:03&lt;00:25, 171434.03it/s]

 
</pre>

 

 

-
12%|█▏        | 623955/4997817 [00:03<00:24, 175220.83it/s]

+
12%|█▏        | 599599/4997817 [00:03<00:25, 171434.03it/s]

 
end{sphinxVerbatim}

 

 

 

-
12%|█▏        | 623955/4997817 [00:03<00:24, 175220.83it/s]

+
12%|█▏        | 599599/4997817 [00:03<00:25, 171434.03it/s]

 
 
 

@@ -2006,16 +2006,16 @@ 
Classes which are commonly mislabeled overall

 

-
13%|█▎        | 641860/4997817 [00:03&lt;00:24, 176352.97it/s]

+
12%|█▏        | 616746/4997817 [00:03&lt;00:25, 171242.34it/s]

 
</pre>

 

 

-
13%|█▎        | 641860/4997817 [00:03<00:24, 176352.97it/s]

+
12%|█▏        | 616746/4997817 [00:03<00:25, 171242.34it/s]

 
end{sphinxVerbatim}

 

 

 

-
13%|█▎        | 641860/4997817 [00:03<00:24, 176352.97it/s]

+
12%|█▏        | 616746/4997817 [00:03<00:25, 171242.34it/s]

 
 
 

@@ -2032,16 +2032,16 @@ 
Classes which are commonly mislabeled overall

 

-
13%|█▎        | 659728/4997817 [00:03&lt;00:24, 177041.83it/s]

+
13%|█▎        | 633873/4997817 [00:03&lt;00:25, 171131.74it/s]

 
</pre>

 

 

-
13%|█▎        | 659728/4997817 [00:03<00:24, 177041.83it/s]

+
13%|█▎        | 633873/4997817 [00:03<00:25, 171131.74it/s]

 
end{sphinxVerbatim}

 

 

 

-
13%|█▎        | 659728/4997817 [00:03<00:24, 177041.83it/s]

+
13%|█▎        | 633873/4997817 [00:03<00:25, 171131.74it/s]

 
 
 

@@ -2058,16 +2058,16 @@ 
Classes which are commonly mislabeled overall

 

-
14%|█▎        | 677498/4997817 [00:03&lt;00:24, 177235.53it/s]

+
13%|█▎        | 650988/4997817 [00:03&lt;00:25, 167888.38it/s]

 
</pre>

 

 

-
14%|█▎        | 677498/4997817 [00:03<00:24, 177235.53it/s]

+
13%|█▎        | 650988/4997817 [00:03<00:25, 167888.38it/s]

 
end{sphinxVerbatim}

 

 

 

-
14%|█▎        | 677498/4997817 [00:03<00:24, 177235.53it/s]

+
13%|█▎        | 650988/4997817 [00:03<00:25, 167888.38it/s]

 
 
 

@@ -2084,16 +2084,16 @@ 
Classes which are commonly mislabeled overall

 

-
14%|█▍        | 695298/4997817 [00:03&lt;00:24, 177460.17it/s]

+
13%|█▎        | 667980/4997817 [00:03&lt;00:25, 168487.13it/s]

 
</pre>

 

 

-
14%|█▍        | 695298/4997817 [00:03<00:24, 177460.17it/s]

+
13%|█▎        | 667980/4997817 [00:03<00:25, 168487.13it/s]

 
end{sphinxVerbatim}

 

 

 

-
14%|█▍        | 695298/4997817 [00:03<00:24, 177460.17it/s]

+
13%|█▎        | 667980/4997817 [00:03<00:25, 168487.13it/s]

 
 
 

@@ -2110,16 +2110,16 @@ 
Classes which are commonly mislabeled overall

 

-
14%|█▍        | 713108/4997817 [00:04&lt;00:24, 177648.89it/s]

+
14%|█▎        | 685095/4997817 [00:04&lt;00:25, 169274.32it/s]

 
</pre>

 

 

-
14%|█▍        | 713108/4997817 [00:04<00:24, 177648.89it/s]

+
14%|█▎        | 685095/4997817 [00:04<00:25, 169274.32it/s]

 
end{sphinxVerbatim}

 

 

 

-
14%|█▍        | 713108/4997817 [00:04<00:24, 177648.89it/s]

+
14%|█▎        | 685095/4997817 [00:04<00:25, 169274.32it/s]

 
 
 

@@ -2136,16 +2136,16 @@ 
Classes which are commonly mislabeled overall

 

-
15%|█▍        | 731013/4997817 [00:04&lt;00:23, 178065.71it/s]

+
14%|█▍        | 702365/4997817 [00:04&lt;00:25, 170291.66it/s]

 
</pre>

 

 

-
15%|█▍        | 731013/4997817 [00:04<00:23, 178065.71it/s]

+
14%|█▍        | 702365/4997817 [00:04<00:25, 170291.66it/s]

 
end{sphinxVerbatim}

 

 

 

-
15%|█▍        | 731013/4997817 [00:04<00:23, 178065.71it/s]

+
14%|█▍        | 702365/4997817 [00:04<00:25, 170291.66it/s]

 
 
 

@@ -2162,16 +2162,16 @@ 
Classes which are commonly mislabeled overall

 

-
15%|█▍        | 748824/4997817 [00:04&lt;00:23, 177652.38it/s]

+
14%|█▍        | 719508/4997817 [00:04&lt;00:25, 170628.16it/s]

 
</pre>

 

 

-
15%|█▍        | 748824/4997817 [00:04<00:23, 177652.38it/s]

+
14%|█▍        | 719508/4997817 [00:04<00:25, 170628.16it/s]

 
end{sphinxVerbatim}

 

 

 

-
15%|█▍        | 748824/4997817 [00:04<00:23, 177652.38it/s]

+
14%|█▍        | 719508/4997817 [00:04<00:25, 170628.16it/s]

 
 
 

@@ -2188,16 +2188,16 @@ 
Classes which are commonly mislabeled overall

 

-
15%|█▌        | 766599/4997817 [00:04&lt;00:23, 177677.08it/s]

+
15%|█▍        | 736576/4997817 [00:04&lt;00:25, 169810.36it/s]

 
</pre>

 

 

-
15%|█▌        | 766599/4997817 [00:04<00:23, 177677.08it/s]

+
15%|█▍        | 736576/4997817 [00:04<00:25, 169810.36it/s]

 
end{sphinxVerbatim}

 

 

 

-
15%|█▌        | 766599/4997817 [00:04<00:23, 177677.08it/s]

+
15%|█▍        | 736576/4997817 [00:04<00:25, 169810.36it/s]

 
 
 

@@ -2214,16 +2214,16 @@ 
Classes which are commonly mislabeled overall

 

-
16%|█▌        | 784420/4997817 [00:04&lt;00:23, 177831.64it/s]

+
15%|█▌        | 753562/4997817 [00:04&lt;00:24, 169771.99it/s]

 
</pre>

 

 

-
16%|█▌        | 784420/4997817 [00:04<00:23, 177831.64it/s]

+
15%|█▌        | 753562/4997817 [00:04<00:24, 169771.99it/s]

 
end{sphinxVerbatim}

 

 

 

-
16%|█▌        | 784420/4997817 [00:04<00:23, 177831.64it/s]

+
15%|█▌        | 753562/4997817 [00:04<00:24, 169771.99it/s]

 
 
 

@@ -2240,16 +2240,16 @@ 
Classes which are commonly mislabeled overall

 

-
16%|█▌        | 802279/4997817 [00:04&lt;00:23, 178056.61it/s]

+
15%|█▌        | 770715/4997817 [00:04&lt;00:24, 170294.50it/s]

 
</pre>

 

 

-
16%|█▌        | 802279/4997817 [00:04<00:23, 178056.61it/s]

+
15%|█▌        | 770715/4997817 [00:04<00:24, 170294.50it/s]

 
end{sphinxVerbatim}

 

 

 

-
16%|█▌        | 802279/4997817 [00:04<00:23, 178056.61it/s]

+
15%|█▌        | 770715/4997817 [00:04<00:24, 170294.50it/s]

 
 
 

@@ -2266,16 +2266,16 @@ 
Classes which are commonly mislabeled overall

 

-
16%|█▋        | 820264/4997817 [00:04&lt;00:23, 178591.12it/s]

+
16%|█▌        | 787747/4997817 [00:04&lt;00:24, 169972.43it/s]

 
</pre>

 

 

-
16%|█▋        | 820264/4997817 [00:04<00:23, 178591.12it/s]

+
16%|█▌        | 787747/4997817 [00:04<00:24, 169972.43it/s]

 
end{sphinxVerbatim}

 

 

 

-
16%|█▋        | 820264/4997817 [00:04<00:23, 178591.12it/s]

+
16%|█▌        | 787747/4997817 [00:04<00:24, 169972.43it/s]

 
 
 

@@ -2292,16 +2292,16 @@ 
Classes which are commonly mislabeled overall

 

-
17%|█▋        | 838197/4997817 [00:04&lt;00:23, 178808.60it/s]

+
16%|█▌        | 804872/4997817 [00:04&lt;00:24, 170350.25it/s]

 
</pre>

 

 

-
17%|█▋        | 838197/4997817 [00:04<00:23, 178808.60it/s]

+
16%|█▌        | 804872/4997817 [00:04<00:24, 170350.25it/s]

 
end{sphinxVerbatim}

 

 

 

-
17%|█▋        | 838197/4997817 [00:04<00:23, 178808.60it/s]

+
16%|█▌        | 804872/4997817 [00:04<00:24, 170350.25it/s]

 
 
 

@@ -2318,16 +2318,16 @@ 
Classes which are commonly mislabeled overall

 

-
17%|█▋        | 856111/4997817 [00:04&lt;00:23, 178905.08it/s]

+
16%|█▋        | 821909/4997817 [00:04&lt;00:24, 170342.59it/s]

 
</pre>

 

 

-
17%|█▋        | 856111/4997817 [00:04<00:23, 178905.08it/s]

+
16%|█▋        | 821909/4997817 [00:04<00:24, 170342.59it/s]

 
end{sphinxVerbatim}

 

 

 

-
17%|█▋        | 856111/4997817 [00:04<00:23, 178905.08it/s]

+
16%|█▋        | 821909/4997817 [00:04<00:24, 170342.59it/s]

 
 
 

@@ -2344,16 +2344,16 @@ 
Classes which are commonly mislabeled overall

 

-
17%|█▋        | 874002/4997817 [00:04&lt;00:23, 178854.94it/s]

+
17%|█▋        | 838945/4997817 [00:04&lt;00:24, 170326.57it/s]

 
</pre>

 

 

-
17%|█▋        | 874002/4997817 [00:04<00:23, 178854.94it/s]

+
17%|█▋        | 838945/4997817 [00:04<00:24, 170326.57it/s]

 
end{sphinxVerbatim}

 

 

 

-
17%|█▋        | 874002/4997817 [00:04<00:23, 178854.94it/s]

+
17%|█▋        | 838945/4997817 [00:04<00:24, 170326.57it/s]

 
 
 

@@ -2370,16 +2370,16 @@ 
Classes which are commonly mislabeled overall

 

-
18%|█▊        | 891888/4997817 [00:05&lt;00:22, 178769.86it/s]

+
17%|█▋        | 856345/4997817 [00:05&lt;00:24, 171423.84it/s]

 
</pre>

 

 

-
18%|█▊        | 891888/4997817 [00:05<00:22, 178769.86it/s]

+
17%|█▋        | 856345/4997817 [00:05<00:24, 171423.84it/s]

 
end{sphinxVerbatim}

 

 

 

-
18%|█▊        | 891888/4997817 [00:05<00:22, 178769.86it/s]

+
17%|█▋        | 856345/4997817 [00:05<00:24, 171423.84it/s]

 
 
 

@@ -2396,16 +2396,16 @@ 
Classes which are commonly mislabeled overall

 

-
18%|█▊        | 909766/4997817 [00:05&lt;00:22, 177999.88it/s]

+
17%|█▋        | 873794/4997817 [00:05&lt;00:23, 172340.75it/s]

 
</pre>

 

 

-
18%|█▊        | 909766/4997817 [00:05<00:22, 177999.88it/s]

+
17%|█▋        | 873794/4997817 [00:05<00:23, 172340.75it/s]

 
end{sphinxVerbatim}

 

 

 

-
18%|█▊        | 909766/4997817 [00:05<00:22, 177999.88it/s]

+
17%|█▋        | 873794/4997817 [00:05<00:23, 172340.75it/s]

 
 
 

@@ -2422,16 +2422,16 @@ 
Classes which are commonly mislabeled overall

 

-
19%|█▊        | 927567/4997817 [00:05&lt;00:23, 175332.09it/s]

+
18%|█▊        | 891355/4997817 [00:05&lt;00:23, 173318.17it/s]

 
</pre>

 

 

-
19%|█▊        | 927567/4997817 [00:05<00:23, 175332.09it/s]

+
18%|█▊        | 891355/4997817 [00:05<00:23, 173318.17it/s]

 
end{sphinxVerbatim}

 

 

 

-
19%|█▊        | 927567/4997817 [00:05<00:23, 175332.09it/s]

+
18%|█▊        | 891355/4997817 [00:05<00:23, 173318.17it/s]

 
 
 

@@ -2448,16 +2448,16 @@ 
Classes which are commonly mislabeled overall

 

-
19%|█▉        | 945109/4997817 [00:05&lt;00:23, 172027.61it/s]

+
18%|█▊        | 908761/4997817 [00:05&lt;00:23, 173536.69it/s]

 
</pre>

 

 

-
19%|█▉        | 945109/4997817 [00:05<00:23, 172027.61it/s]

+
18%|█▊        | 908761/4997817 [00:05<00:23, 173536.69it/s]

 
end{sphinxVerbatim}

 

 

 

-
19%|█▉        | 945109/4997817 [00:05<00:23, 172027.61it/s]

+
18%|█▊        | 908761/4997817 [00:05<00:23, 173536.69it/s]

 
 
 

@@ -2474,16 +2474,16 @@ 
Classes which are commonly mislabeled overall

 

-
19%|█▉        | 962858/4997817 [00:05&lt;00:23, 173626.11it/s]

+
19%|█▊        | 926115/4997817 [00:05&lt;00:23, 173374.72it/s]

 
</pre>

 

 

-
19%|█▉        | 962858/4997817 [00:05<00:23, 173626.11it/s]

+
19%|█▊        | 926115/4997817 [00:05<00:23, 173374.72it/s]

 
end{sphinxVerbatim}

 

 

 

-
19%|█▉        | 962858/4997817 [00:05<00:23, 173626.11it/s]

+
19%|█▊        | 926115/4997817 [00:05<00:23, 173374.72it/s]

 
 
 

@@ -2500,16 +2500,16 @@ 
Classes which are commonly mislabeled overall

 

-
20%|█▉        | 980440/4997817 [00:05&lt;00:23, 174271.59it/s]

+
19%|█▉        | 943534/4997817 [00:05&lt;00:23, 173615.78it/s]

 
</pre>

 

 

-
20%|█▉        | 980440/4997817 [00:05<00:23, 174271.59it/s]

+
19%|█▉        | 943534/4997817 [00:05<00:23, 173615.78it/s]

 
end{sphinxVerbatim}

 

 

 

-
20%|█▉        | 980440/4997817 [00:05<00:23, 174271.59it/s]

+
19%|█▉        | 943534/4997817 [00:05<00:23, 173615.78it/s]

 
 
 

@@ -2526,16 +2526,16 @@ 
Classes which are commonly mislabeled overall

 

-
20%|█▉        | 998071/4997817 [00:05&lt;00:22, 174873.76it/s]

+
19%|█▉        | 960987/4997817 [00:05&lt;00:23, 173888.18it/s]

 
</pre>

 

 

-
20%|█▉        | 998071/4997817 [00:05<00:22, 174873.76it/s]

+
19%|█▉        | 960987/4997817 [00:05<00:23, 173888.18it/s]

 
end{sphinxVerbatim}

 

 

 

-
20%|█▉        | 998071/4997817 [00:05<00:22, 174873.76it/s]

+
19%|█▉        | 960987/4997817 [00:05<00:23, 173888.18it/s]

 
 
 

@@ -2552,16 +2552,16 @@ 
Classes which are commonly mislabeled overall

 

-
20%|██        | 1015609/4997817 [00:05&lt;00:22, 175019.90it/s]

+
20%|█▉        | 978376/4997817 [00:05&lt;00:23, 173759.83it/s]

 
</pre>

 

 

-
20%|██        | 1015609/4997817 [00:05<00:22, 175019.90it/s]

+
20%|█▉        | 978376/4997817 [00:05<00:23, 173759.83it/s]

 
end{sphinxVerbatim}

 

 

 

-
20%|██        | 1015609/4997817 [00:05<00:22, 175019.90it/s]

+
20%|█▉        | 978376/4997817 [00:05<00:23, 173759.83it/s]

 
 
 

@@ -2578,16 +2578,16 @@ 
Classes which are commonly mislabeled overall

 

-
21%|██        | 1033146/4997817 [00:05&lt;00:22, 175119.66it/s]

+
20%|█▉        | 995883/4997817 [00:05&lt;00:22, 174150.86it/s]

 
</pre>

 

 

-
21%|██        | 1033146/4997817 [00:05<00:22, 175119.66it/s]

+
20%|█▉        | 995883/4997817 [00:05<00:22, 174150.86it/s]

 
end{sphinxVerbatim}

 

 

 

-
21%|██        | 1033146/4997817 [00:05<00:22, 175119.66it/s]

+
20%|█▉        | 995883/4997817 [00:05<00:22, 174150.86it/s]

 
 
 

@@ -2604,16 +2604,16 @@ 
Classes which are commonly mislabeled overall

 

-
21%|██        | 1050707/4997817 [00:05&lt;00:22, 175263.37it/s]

+
20%|██        | 1013363/4997817 [00:05&lt;00:22, 174342.29it/s]

 
</pre>

 

 

-
21%|██        | 1050707/4997817 [00:05<00:22, 175263.37it/s]

+
20%|██        | 1013363/4997817 [00:05<00:22, 174342.29it/s]

 
end{sphinxVerbatim}

 

 

 

-
21%|██        | 1050707/4997817 [00:05<00:22, 175263.37it/s]

+
20%|██        | 1013363/4997817 [00:05<00:22, 174342.29it/s]

 
 
 

@@ -2630,16 +2630,16 @@ 
Classes which are commonly mislabeled overall

 

-
21%|██▏       | 1068310/4997817 [00:06&lt;00:22, 175489.78it/s]

+
21%|██        | 1030818/4997817 [00:06&lt;00:22, 174402.88it/s]

 
</pre>

 

 

-
21%|██▏       | 1068310/4997817 [00:06<00:22, 175489.78it/s]

+
21%|██        | 1030818/4997817 [00:06<00:22, 174402.88it/s]

 
end{sphinxVerbatim}

 

 

 

-
21%|██▏       | 1068310/4997817 [00:06<00:22, 175489.78it/s]

+
21%|██        | 1030818/4997817 [00:06<00:22, 174402.88it/s]

 
 
 

@@ -2656,16 +2656,16 @@ 
Classes which are commonly mislabeled overall

 

-
22%|██▏       | 1086006/4997817 [00:06&lt;00:22, 175925.40it/s]

+
21%|██        | 1048303/4997817 [00:06&lt;00:22, 174534.90it/s]

 
</pre>

 

 

-
22%|██▏       | 1086006/4997817 [00:06<00:22, 175925.40it/s]

+
21%|██        | 1048303/4997817 [00:06<00:22, 174534.90it/s]

 
end{sphinxVerbatim}

 

 

 

-
22%|██▏       | 1086006/4997817 [00:06<00:22, 175925.40it/s]

+
21%|██        | 1048303/4997817 [00:06<00:22, 174534.90it/s]

 
 
 

@@ -2682,16 +2682,16 @@ 
Classes which are commonly mislabeled overall

 

-
22%|██▏       | 1103856/4997817 [00:06&lt;00:22, 176693.74it/s]

+
21%|██▏       | 1065757/4997817 [00:06&lt;00:22, 174515.39it/s]

 
</pre>

 

 

-
22%|██▏       | 1103856/4997817 [00:06<00:22, 176693.74it/s]

+
21%|██▏       | 1065757/4997817 [00:06<00:22, 174515.39it/s]

 
end{sphinxVerbatim}

 

 

 

-
22%|██▏       | 1103856/4997817 [00:06<00:22, 176693.74it/s]

+
21%|██▏       | 1065757/4997817 [00:06<00:22, 174515.39it/s]

 
 
 

@@ -2708,16 +2708,16 @@ 
Classes which are commonly mislabeled overall

 

-
22%|██▏       | 1121527/4997817 [00:06&lt;00:21, 176653.73it/s]

+
22%|██▏       | 1083209/4997817 [00:06&lt;00:22, 174141.01it/s]

 
</pre>

 

 

-
22%|██▏       | 1121527/4997817 [00:06<00:21, 176653.73it/s]

+
22%|██▏       | 1083209/4997817 [00:06<00:22, 174141.01it/s]

 
end{sphinxVerbatim}

 

 

 

-
22%|██▏       | 1121527/4997817 [00:06<00:21, 176653.73it/s]

+
22%|██▏       | 1083209/4997817 [00:06<00:22, 174141.01it/s]

 
 
 

@@ -2734,16 +2734,16 @@ 
Classes which are commonly mislabeled overall

 

-
23%|██▎       | 1139194/4997817 [00:06&lt;00:21, 175610.49it/s]

+
22%|██▏       | 1100652/4997817 [00:06&lt;00:22, 174222.63it/s]

 
</pre>

 

 

-
23%|██▎       | 1139194/4997817 [00:06<00:21, 175610.49it/s]

+
22%|██▏       | 1100652/4997817 [00:06<00:22, 174222.63it/s]

 
end{sphinxVerbatim}

 

 

 

-
23%|██▎       | 1139194/4997817 [00:06<00:21, 175610.49it/s]

+
22%|██▏       | 1100652/4997817 [00:06<00:22, 174222.63it/s]

 
 
 

@@ -2760,16 +2760,16 @@ 
Classes which are commonly mislabeled overall

 

-
23%|██▎       | 1156758/4997817 [00:06&lt;00:21, 175340.14it/s]

+
22%|██▏       | 1118075/4997817 [00:06&lt;00:22, 173614.04it/s]

 
</pre>

 

 

-
23%|██▎       | 1156758/4997817 [00:06<00:21, 175340.14it/s]

+
22%|██▏       | 1118075/4997817 [00:06<00:22, 173614.04it/s]

 
end{sphinxVerbatim}

 

 

 

-
23%|██▎       | 1156758/4997817 [00:06<00:21, 175340.14it/s]

+
22%|██▏       | 1118075/4997817 [00:06<00:22, 173614.04it/s]

 
 
 

@@ -2786,16 +2786,16 @@ 
Classes which are commonly mislabeled overall

 

-
23%|██▎       | 1174423/4997817 [00:06&lt;00:21, 175726.52it/s]

+
23%|██▎       | 1135437/4997817 [00:06&lt;00:22, 173273.09it/s]

 
</pre>

 

 

-
23%|██▎       | 1174423/4997817 [00:06<00:21, 175726.52it/s]

+
23%|██▎       | 1135437/4997817 [00:06<00:22, 173273.09it/s]

 
end{sphinxVerbatim}

 

 

 

-
23%|██▎       | 1174423/4997817 [00:06<00:21, 175726.52it/s]

+
23%|██▎       | 1135437/4997817 [00:06<00:22, 173273.09it/s]

 
 
 

@@ -2812,16 +2812,16 @@ 
Classes which are commonly mislabeled overall

 

-
24%|██▍       | 1192038/4997817 [00:06&lt;00:21, 175849.70it/s]

+
23%|██▎       | 1152765/4997817 [00:06&lt;00:22, 173078.85it/s]

 
</pre>

 

 

-
24%|██▍       | 1192038/4997817 [00:06<00:21, 175849.70it/s]

+
23%|██▎       | 1152765/4997817 [00:06<00:22, 173078.85it/s]

 
end{sphinxVerbatim}

 

 

 

-
24%|██▍       | 1192038/4997817 [00:06<00:21, 175849.70it/s]

+
23%|██▎       | 1152765/4997817 [00:06<00:22, 173078.85it/s]

 
 
 

@@ -2838,16 +2838,16 @@ 
Classes which are commonly mislabeled overall

 

-
24%|██▍       | 1209681/4997817 [00:06&lt;00:21, 176020.95it/s]

+
23%|██▎       | 1170074/4997817 [00:06&lt;00:22, 172769.04it/s]

 
</pre>

 

 

-
24%|██▍       | 1209681/4997817 [00:06<00:21, 176020.95it/s]

+
23%|██▎       | 1170074/4997817 [00:06<00:22, 172769.04it/s]

 
end{sphinxVerbatim}

 

 

 

-
24%|██▍       | 1209681/4997817 [00:06<00:21, 176020.95it/s]

+
23%|██▎       | 1170074/4997817 [00:06<00:22, 172769.04it/s]

 
 
 

@@ -2864,16 +2864,16 @@ 
Classes which are commonly mislabeled overall

 

-
25%|██▍       | 1227284/4997817 [00:06&lt;00:21, 175879.77it/s]

+
24%|██▍       | 1187352/4997817 [00:06&lt;00:22, 166605.50it/s]

 
</pre>

 

 

-
25%|██▍       | 1227284/4997817 [00:06<00:21, 175879.77it/s]

+
24%|██▍       | 1187352/4997817 [00:06<00:22, 166605.50it/s]

 
end{sphinxVerbatim}

 

 

 

-
25%|██▍       | 1227284/4997817 [00:06<00:21, 175879.77it/s]

+
24%|██▍       | 1187352/4997817 [00:06<00:22, 166605.50it/s]

 
 
 

@@ -2890,16 +2890,16 @@ 
Classes which are commonly mislabeled overall

 

-
25%|██▍       | 1244924/4997817 [00:07&lt;00:21, 176033.24it/s]

+
24%|██▍       | 1204651/4997817 [00:07&lt;00:22, 168465.26it/s]

 
</pre>

 

 

-
25%|██▍       | 1244924/4997817 [00:07<00:21, 176033.24it/s]

+
24%|██▍       | 1204651/4997817 [00:07<00:22, 168465.26it/s]

 
end{sphinxVerbatim}

 

 

 

-
25%|██▍       | 1244924/4997817 [00:07<00:21, 176033.24it/s]

+
24%|██▍       | 1204651/4997817 [00:07<00:22, 168465.26it/s]

 
 
 

@@ -2916,16 +2916,16 @@ 
Classes which are commonly mislabeled overall

 

-
25%|██▌       | 1262528/4997817 [00:07&lt;00:21, 175800.45it/s]

+
24%|██▍       | 1221930/4997817 [00:07&lt;00:22, 169735.51it/s]

 
</pre>

 

 

-
25%|██▌       | 1262528/4997817 [00:07<00:21, 175800.45it/s]

+
24%|██▍       | 1221930/4997817 [00:07<00:22, 169735.51it/s]

 
end{sphinxVerbatim}

 

 

 

-
25%|██▌       | 1262528/4997817 [00:07<00:21, 175800.45it/s]

+
24%|██▍       | 1221930/4997817 [00:07<00:22, 169735.51it/s]

 
 
 

@@ -2942,16 +2942,16 @@ 
Classes which are commonly mislabeled overall

 

-
26%|██▌       | 1280128/4997817 [00:07&lt;00:21, 175857.50it/s]

+
25%|██▍       | 1239241/4997817 [00:07&lt;00:22, 170729.82it/s]

 
</pre>

 

 

-
26%|██▌       | 1280128/4997817 [00:07<00:21, 175857.50it/s]

+
25%|██▍       | 1239241/4997817 [00:07<00:22, 170729.82it/s]

 
end{sphinxVerbatim}

 

 

 

-
26%|██▌       | 1280128/4997817 [00:07<00:21, 175857.50it/s]

+
25%|██▍       | 1239241/4997817 [00:07<00:22, 170729.82it/s]

 
 
 

@@ -2968,16 +2968,16 @@ 
Classes which are commonly mislabeled overall

 

-
26%|██▌       | 1297844/4997817 [00:07&lt;00:20, 176243.66it/s]

+
25%|██▌       | 1256336/4997817 [00:07&lt;00:21, 170593.49it/s]

 
</pre>

 

 

-
26%|██▌       | 1297844/4997817 [00:07<00:20, 176243.66it/s]

+
25%|██▌       | 1256336/4997817 [00:07<00:21, 170593.49it/s]

 
end{sphinxVerbatim}

 

 

 

-
26%|██▌       | 1297844/4997817 [00:07<00:20, 176243.66it/s]

+
25%|██▌       | 1256336/4997817 [00:07<00:21, 170593.49it/s]

 
 
 

@@ -2994,16 +2994,16 @@ 
Classes which are commonly mislabeled overall

 

-
26%|██▋       | 1315598/4997817 [00:07&lt;00:20, 176628.10it/s]

+
25%|██▌       | 1273736/4997817 [00:07&lt;00:21, 171604.75it/s]

 
</pre>

 

 

-
26%|██▋       | 1315598/4997817 [00:07<00:20, 176628.10it/s]

+
25%|██▌       | 1273736/4997817 [00:07<00:21, 171604.75it/s]

 
end{sphinxVerbatim}

 

 

 

-
26%|██▋       | 1315598/4997817 [00:07<00:20, 176628.10it/s]

+
25%|██▌       | 1273736/4997817 [00:07<00:21, 171604.75it/s]

 
 
 

@@ -3020,16 +3020,16 @@ 
Classes which are commonly mislabeled overall

 

-
27%|██▋       | 1333326/4997817 [00:07&lt;00:20, 176821.58it/s]

+
26%|██▌       | 1291095/4997817 [00:07&lt;00:21, 172192.76it/s]

 
</pre>

 

 

-
27%|██▋       | 1333326/4997817 [00:07<00:20, 176821.58it/s]

+
26%|██▌       | 1291095/4997817 [00:07<00:21, 172192.76it/s]

 
end{sphinxVerbatim}

 

 

 

-
27%|██▋       | 1333326/4997817 [00:07<00:20, 176821.58it/s]

+
26%|██▌       | 1291095/4997817 [00:07<00:21, 172192.76it/s]

 
 
 

@@ -3046,16 +3046,16 @@ 
Classes which are commonly mislabeled overall

 

-
27%|██▋       | 1351011/4997817 [00:07&lt;00:20, 176827.24it/s]

+
26%|██▌       | 1308323/4997817 [00:07&lt;00:21, 171512.26it/s]

 
</pre>

 

 

-
27%|██▋       | 1351011/4997817 [00:07<00:20, 176827.24it/s]

+
26%|██▌       | 1308323/4997817 [00:07<00:21, 171512.26it/s]

 
end{sphinxVerbatim}

 

 

 

-
27%|██▋       | 1351011/4997817 [00:07<00:20, 176827.24it/s]

+
26%|██▌       | 1308323/4997817 [00:07<00:21, 171512.26it/s]

 
 
 

@@ -3072,16 +3072,16 @@ 
Classes which are commonly mislabeled overall

 

-
27%|██▋       | 1368694/4997817 [00:07&lt;00:20, 176802.46it/s]

+
27%|██▋       | 1325481/4997817 [00:07&lt;00:21, 170300.82it/s]

 
</pre>

 

 

-
27%|██▋       | 1368694/4997817 [00:07<00:20, 176802.46it/s]

+
27%|██▋       | 1325481/4997817 [00:07<00:21, 170300.82it/s]

 
end{sphinxVerbatim}

 

 

 

-
27%|██▋       | 1368694/4997817 [00:07<00:20, 176802.46it/s]

+
27%|██▋       | 1325481/4997817 [00:07<00:21, 170300.82it/s]

 
 
 

@@ -3098,16 +3098,16 @@ 
Classes which are commonly mislabeled overall

 

-
28%|██▊       | 1386390/4997817 [00:07&lt;00:20, 176847.35it/s]

+
27%|██▋       | 1342592/4997817 [00:07&lt;00:21, 170538.44it/s]

 
</pre>

 

 

-
28%|██▊       | 1386390/4997817 [00:07<00:20, 176847.35it/s]

+
27%|██▋       | 1342592/4997817 [00:07<00:21, 170538.44it/s]

 
end{sphinxVerbatim}

 

 

 

-
28%|██▊       | 1386390/4997817 [00:07<00:20, 176847.35it/s]

+
27%|██▋       | 1342592/4997817 [00:07<00:21, 170538.44it/s]

 
 
 

@@ -3124,16 +3124,16 @@ 
Classes which are commonly mislabeled overall

 

-
28%|██▊       | 1404075/4997817 [00:07&lt;00:20, 176737.99it/s]

+
27%|██▋       | 1359667/4997817 [00:07&lt;00:21, 170596.95it/s]

 
</pre>

 

 

-
28%|██▊       | 1404075/4997817 [00:07<00:20, 176737.99it/s]

+
27%|██▋       | 1359667/4997817 [00:07<00:21, 170596.95it/s]

 
end{sphinxVerbatim}

 

 

 

-
28%|██▊       | 1404075/4997817 [00:07<00:20, 176737.99it/s]

+
27%|██▋       | 1359667/4997817 [00:07<00:21, 170596.95it/s]

 
 
 

@@ -3150,16 +3150,16 @@ 
Classes which are commonly mislabeled overall

 

-
28%|██▊       | 1421749/4997817 [00:08&lt;00:20, 176144.34it/s]

+
28%|██▊       | 1376730/4997817 [00:08&lt;00:21, 170584.52it/s]

 
</pre>

 

 

-
28%|██▊       | 1421749/4997817 [00:08<00:20, 176144.34it/s]

+
28%|██▊       | 1376730/4997817 [00:08<00:21, 170584.52it/s]

 
end{sphinxVerbatim}

 

 

 

-
28%|██▊       | 1421749/4997817 [00:08<00:20, 176144.34it/s]

+
28%|██▊       | 1376730/4997817 [00:08<00:21, 170584.52it/s]

 
 
 

@@ -3176,16 +3176,16 @@ 
Classes which are commonly mislabeled overall

 

-
29%|██▉       | 1439364/4997817 [00:08&lt;00:20, 175538.08it/s]

+
28%|██▊       | 1393808/4997817 [00:08&lt;00:21, 170640.10it/s]

 
</pre>

 

 

-
29%|██▉       | 1439364/4997817 [00:08<00:20, 175538.08it/s]

+
28%|██▊       | 1393808/4997817 [00:08<00:21, 170640.10it/s]

 
end{sphinxVerbatim}

 

 

 

-
29%|██▉       | 1439364/4997817 [00:08<00:20, 175538.08it/s]

+
28%|██▊       | 1393808/4997817 [00:08<00:21, 170640.10it/s]

 
 
 

@@ -3202,16 +3202,16 @@ 
Classes which are commonly mislabeled overall

 

-
29%|██▉       | 1456919/4997817 [00:08&lt;00:20, 175153.28it/s]

+
28%|██▊       | 1410890/4997817 [00:08&lt;00:21, 170689.62it/s]

 
</pre>

 

 

-
29%|██▉       | 1456919/4997817 [00:08<00:20, 175153.28it/s]

+
28%|██▊       | 1410890/4997817 [00:08<00:21, 170689.62it/s]

 
end{sphinxVerbatim}

 

 

 

-
29%|██▉       | 1456919/4997817 [00:08<00:20, 175153.28it/s]

+
28%|██▊       | 1410890/4997817 [00:08<00:21, 170689.62it/s]

 
 
 

@@ -3228,16 +3228,16 @@ 
Classes which are commonly mislabeled overall

 

-
30%|██▉       | 1474435/4997817 [00:08&lt;00:20, 170969.49it/s]

+
29%|██▊       | 1428041/4997817 [00:08&lt;00:20, 170932.83it/s]

 
</pre>

 

 

-
30%|██▉       | 1474435/4997817 [00:08<00:20, 170969.49it/s]

+
29%|██▊       | 1428041/4997817 [00:08<00:20, 170932.83it/s]

 
end{sphinxVerbatim}

 

 

 

-
30%|██▉       | 1474435/4997817 [00:08<00:20, 170969.49it/s]

+
29%|██▊       | 1428041/4997817 [00:08<00:20, 170932.83it/s]

 
 
 

@@ -3254,16 +3254,16 @@ 
Classes which are commonly mislabeled overall

 

-
30%|██▉       | 1491823/4997817 [00:08&lt;00:20, 171820.74it/s]

+
29%|██▉       | 1445136/4997817 [00:08&lt;00:20, 170912.74it/s]

 
</pre>

 

 

-
30%|██▉       | 1491823/4997817 [00:08<00:20, 171820.74it/s]

+
29%|██▉       | 1445136/4997817 [00:08<00:20, 170912.74it/s]

 
end{sphinxVerbatim}

 

 

 

-
30%|██▉       | 1491823/4997817 [00:08<00:20, 171820.74it/s]

+
29%|██▉       | 1445136/4997817 [00:08<00:20, 170912.74it/s]

 
 
 

@@ -3280,16 +3280,16 @@ 
Classes which are commonly mislabeled overall

 

-
30%|███       | 1509255/4997817 [00:08&lt;00:20, 172556.44it/s]

+
29%|██▉       | 1462229/4997817 [00:08&lt;00:20, 170913.08it/s]

 
</pre>

 

 

-
30%|███       | 1509255/4997817 [00:08<00:20, 172556.44it/s]

+
29%|██▉       | 1462229/4997817 [00:08<00:20, 170913.08it/s]

 
end{sphinxVerbatim}

 

 

 

-
30%|███       | 1509255/4997817 [00:08<00:20, 172556.44it/s]

+
29%|██▉       | 1462229/4997817 [00:08<00:20, 170913.08it/s]

 
 
 

@@ -3306,16 +3306,16 @@ 
Classes which are commonly mislabeled overall

 

-
31%|███       | 1526644/4997817 [00:08&lt;00:20, 172949.51it/s]

+
30%|██▉       | 1479321/4997817 [00:08&lt;00:20, 170499.01it/s]

 
</pre>

 

 

-
31%|███       | 1526644/4997817 [00:08<00:20, 172949.51it/s]

+
30%|██▉       | 1479321/4997817 [00:08<00:20, 170499.01it/s]

 
end{sphinxVerbatim}

 

 

 

-
31%|███       | 1526644/4997817 [00:08<00:20, 172949.51it/s]

+
30%|██▉       | 1479321/4997817 [00:08<00:20, 170499.01it/s]

 
 
 

@@ -3332,16 +3332,16 @@ 
Classes which are commonly mislabeled overall

 

-
31%|███       | 1544068/4997817 [00:08&lt;00:19, 173330.43it/s]

+
30%|██▉       | 1496372/4997817 [00:08&lt;00:20, 170482.52it/s]

 
</pre>

 

 

-
31%|███       | 1544068/4997817 [00:08<00:19, 173330.43it/s]

+
30%|██▉       | 1496372/4997817 [00:08<00:20, 170482.52it/s]

 
end{sphinxVerbatim}

 

 

 

-
31%|███       | 1544068/4997817 [00:08<00:19, 173330.43it/s]

+
30%|██▉       | 1496372/4997817 [00:08<00:20, 170482.52it/s]

 
 
 

@@ -3358,16 +3358,16 @@ 
Classes which are commonly mislabeled overall

 

-
31%|███       | 1561511/4997817 [00:08&lt;00:19, 173656.01it/s]

+
30%|███       | 1513421/4997817 [00:08&lt;00:20, 170395.25it/s]

 
</pre>

 

 

-
31%|███       | 1561511/4997817 [00:08<00:19, 173656.01it/s]

+
30%|███       | 1513421/4997817 [00:08<00:20, 170395.25it/s]

 
end{sphinxVerbatim}

 

 

 

-
31%|███       | 1561511/4997817 [00:08<00:19, 173656.01it/s]

+
30%|███       | 1513421/4997817 [00:08<00:20, 170395.25it/s]

 
 
 

@@ -3384,16 +3384,16 @@ 
Classes which are commonly mislabeled overall

 

-
32%|███▏      | 1578882/4997817 [00:08&lt;00:19, 173600.68it/s]

+
31%|███       | 1530461/4997817 [00:08&lt;00:20, 167319.27it/s]

 
</pre>

 

 

-
32%|███▏      | 1578882/4997817 [00:08<00:19, 173600.68it/s]

+
31%|███       | 1530461/4997817 [00:08<00:20, 167319.27it/s]

 
end{sphinxVerbatim}

 

 

 

-
32%|███▏      | 1578882/4997817 [00:08<00:19, 173600.68it/s]

+
31%|███       | 1530461/4997817 [00:08<00:20, 167319.27it/s]

 
 
 

@@ -3410,16 +3410,16 @@ 
Classes which are commonly mislabeled overall

 

-
32%|███▏      | 1596246/4997817 [00:09&lt;00:19, 173421.86it/s]

+
31%|███       | 1547481/4997817 [00:09&lt;00:20, 168168.22it/s]

 
</pre>

 

 

-
32%|███▏      | 1596246/4997817 [00:09<00:19, 173421.86it/s]

+
31%|███       | 1547481/4997817 [00:09<00:20, 168168.22it/s]

 
end{sphinxVerbatim}

 

 

 

-
32%|███▏      | 1596246/4997817 [00:09<00:19, 173421.86it/s]

+
31%|███       | 1547481/4997817 [00:09<00:20, 168168.22it/s]

 
 
 

@@ -3436,16 +3436,16 @@ 
Classes which are commonly mislabeled overall

 

-
32%|███▏      | 1613759/4997817 [00:09&lt;00:19, 173930.01it/s]

+
31%|███▏      | 1564657/4997817 [00:09&lt;00:20, 169231.06it/s]

 
</pre>

 

 

-
32%|███▏      | 1613759/4997817 [00:09<00:19, 173930.01it/s]

+
31%|███▏      | 1564657/4997817 [00:09<00:20, 169231.06it/s]

 
end{sphinxVerbatim}

 

 

 

-
32%|███▏      | 1613759/4997817 [00:09<00:19, 173930.01it/s]

+
31%|███▏      | 1564657/4997817 [00:09<00:20, 169231.06it/s]

 
 
 

@@ -3462,16 +3462,16 @@ 
Classes which are commonly mislabeled overall

 

-
33%|███▎      | 1631217/4997817 [00:09&lt;00:19, 174122.33it/s]

+
32%|███▏      | 1581588/4997817 [00:09&lt;00:20, 169022.41it/s]

 
</pre>

 

 

-
33%|███▎      | 1631217/4997817 [00:09<00:19, 174122.33it/s]

+
32%|███▏      | 1581588/4997817 [00:09<00:20, 169022.41it/s]

 
end{sphinxVerbatim}

 

 

 

-
33%|███▎      | 1631217/4997817 [00:09<00:19, 174122.33it/s]

+
32%|███▏      | 1581588/4997817 [00:09<00:20, 169022.41it/s]

 
 
 

@@ -3488,16 +3488,16 @@ 
Classes which are commonly mislabeled overall

 

-
33%|███▎      | 1648631/4997817 [00:09&lt;00:19, 170877.37it/s]

+
32%|███▏      | 1598553/4997817 [00:09&lt;00:20, 169205.69it/s]

 
</pre>

 

 

-
33%|███▎      | 1648631/4997817 [00:09<00:19, 170877.37it/s]

+
32%|███▏      | 1598553/4997817 [00:09<00:20, 169205.69it/s]

 
end{sphinxVerbatim}

 

 

 

-
33%|███▎      | 1648631/4997817 [00:09<00:19, 170877.37it/s]

+
32%|███▏      | 1598553/4997817 [00:09<00:20, 169205.69it/s]

 
 
 

@@ -3514,16 +3514,16 @@ 
Classes which are commonly mislabeled overall

 

-
33%|███▎      | 1666045/4997817 [00:09&lt;00:19, 171838.32it/s]

+
32%|███▏      | 1615478/4997817 [00:09&lt;00:20, 168976.78it/s]

 
</pre>

 

 

-
33%|███▎      | 1666045/4997817 [00:09<00:19, 171838.32it/s]

+
32%|███▏      | 1615478/4997817 [00:09<00:20, 168976.78it/s]

 
end{sphinxVerbatim}

 

 

 

-
33%|███▎      | 1666045/4997817 [00:09<00:19, 171838.32it/s]

+
32%|███▏      | 1615478/4997817 [00:09<00:20, 168976.78it/s]

 
 
 

@@ -3540,16 +3540,16 @@ 
Classes which are commonly mislabeled overall

 

-
34%|███▎      | 1683650/4997817 [00:09&lt;00:19, 173084.46it/s]

+
33%|███▎      | 1632488/4997817 [00:09&lt;00:19, 169311.06it/s]

 
</pre>

 

 

-
34%|███▎      | 1683650/4997817 [00:09<00:19, 173084.46it/s]

+
33%|███▎      | 1632488/4997817 [00:09<00:19, 169311.06it/s]

 
end{sphinxVerbatim}

 

 

 

-
34%|███▎      | 1683650/4997817 [00:09<00:19, 173084.46it/s]

+
33%|███▎      | 1632488/4997817 [00:09<00:19, 169311.06it/s]

 
 
 

@@ -3566,16 +3566,16 @@ 
Classes which are commonly mislabeled overall

 

-
34%|███▍      | 1701219/4997817 [00:09&lt;00:18, 173858.01it/s]

+
33%|███▎      | 1649496/4997817 [00:09&lt;00:19, 169537.42it/s]

 
</pre>

 

 

-
34%|███▍      | 1701219/4997817 [00:09<00:18, 173858.01it/s]

+
33%|███▎      | 1649496/4997817 [00:09<00:19, 169537.42it/s]

 
end{sphinxVerbatim}

 

 

 

-
34%|███▍      | 1701219/4997817 [00:09<00:18, 173858.01it/s]

+
33%|███▎      | 1649496/4997817 [00:09<00:19, 169537.42it/s]

 
 
 

@@ -3592,16 +3592,16 @@ 
Classes which are commonly mislabeled overall

 

-
34%|███▍      | 1718873/4997817 [00:09&lt;00:18, 174654.48it/s]

+
33%|███▎      | 1666516/4997817 [00:09&lt;00:19, 169734.29it/s]

 
</pre>

 

 

-
34%|███▍      | 1718873/4997817 [00:09<00:18, 174654.48it/s]

+
33%|███▎      | 1666516/4997817 [00:09<00:19, 169734.29it/s]

 
end{sphinxVerbatim}

 

 

 

-
34%|███▍      | 1718873/4997817 [00:09<00:18, 174654.48it/s]

+
33%|███▎      | 1666516/4997817 [00:09<00:19, 169734.29it/s]

 
 
 

@@ -3618,16 +3618,16 @@ 
Classes which are commonly mislabeled overall

 

-
35%|███▍      | 1736344/4997817 [00:09&lt;00:18, 174622.09it/s]

+
34%|███▎      | 1683770/4997817 [00:09&lt;00:19, 170570.66it/s]

 
</pre>

 

 

-
35%|███▍      | 1736344/4997817 [00:09<00:18, 174622.09it/s]

+
34%|███▎      | 1683770/4997817 [00:09<00:19, 170570.66it/s]

 
end{sphinxVerbatim}

 

 

 

-
35%|███▍      | 1736344/4997817 [00:09<00:18, 174622.09it/s]

+
34%|███▎      | 1683770/4997817 [00:09<00:19, 170570.66it/s]

 
 
 

@@ -3644,16 +3644,16 @@ 
Classes which are commonly mislabeled overall

 

-
35%|███▌      | 1753942/4997817 [00:09&lt;00:18, 175023.95it/s]

+
34%|███▍      | 1700828/4997817 [00:09&lt;00:19, 170107.59it/s]

 
</pre>

 

 

-
35%|███▌      | 1753942/4997817 [00:09<00:18, 175023.95it/s]

+
34%|███▍      | 1700828/4997817 [00:09<00:19, 170107.59it/s]

 
end{sphinxVerbatim}

 

 

 

-
35%|███▌      | 1753942/4997817 [00:09<00:18, 175023.95it/s]

+
34%|███▍      | 1700828/4997817 [00:09<00:19, 170107.59it/s]

 
 
 

@@ -3670,16 +3670,16 @@ 
Classes which are commonly mislabeled overall

 

-
35%|███▌      | 1771448/4997817 [00:10&lt;00:18, 174993.57it/s]

+
34%|███▍      | 1718095/4997817 [00:10&lt;00:19, 170870.72it/s]

 
</pre>

 

 

-
35%|███▌      | 1771448/4997817 [00:10<00:18, 174993.57it/s]

+
34%|███▍      | 1718095/4997817 [00:10<00:19, 170870.72it/s]

 
end{sphinxVerbatim}

 

 

 

-
35%|███▌      | 1771448/4997817 [00:10<00:18, 174993.57it/s]

+
34%|███▍      | 1718095/4997817 [00:10<00:19, 170870.72it/s]

 
 
 

@@ -3696,16 +3696,16 @@ 
Classes which are commonly mislabeled overall

 

-
36%|███▌      | 1788950/4997817 [00:10&lt;00:18, 174751.67it/s]

+
35%|███▍      | 1735554/4997817 [00:10&lt;00:18, 171982.68it/s]

 
</pre>

 

 

-
36%|███▌      | 1788950/4997817 [00:10<00:18, 174751.67it/s]

+
35%|███▍      | 1735554/4997817 [00:10<00:18, 171982.68it/s]

 
end{sphinxVerbatim}

 

 

 

-
36%|███▌      | 1788950/4997817 [00:10<00:18, 174751.67it/s]

+
35%|███▍      | 1735554/4997817 [00:10<00:18, 171982.68it/s]

 
 
 

@@ -3722,16 +3722,16 @@ 
Classes which are commonly mislabeled overall

 

-
36%|███▌      | 1806535/4997817 [00:10&lt;00:18, 175076.18it/s]

+
35%|███▌      | 1752844/4997817 [00:10&lt;00:18, 172253.27it/s]

 
</pre>

 

 

-
36%|███▌      | 1806535/4997817 [00:10<00:18, 175076.18it/s]

+
35%|███▌      | 1752844/4997817 [00:10<00:18, 172253.27it/s]

 
end{sphinxVerbatim}

 

 

 

-
36%|███▌      | 1806535/4997817 [00:10<00:18, 175076.18it/s]

+
35%|███▌      | 1752844/4997817 [00:10<00:18, 172253.27it/s]

 
 
 

@@ -3748,16 +3748,16 @@ 
Classes which are commonly mislabeled overall

 

-
36%|███▋      | 1824044/4997817 [00:10&lt;00:18, 174947.92it/s]

+
35%|███▌      | 1770192/4997817 [00:10&lt;00:18, 172616.19it/s]

 
</pre>

 

 

-
36%|███▋      | 1824044/4997817 [00:10<00:18, 174947.92it/s]

+
35%|███▌      | 1770192/4997817 [00:10<00:18, 172616.19it/s]

 
end{sphinxVerbatim}

 

 

 

-
36%|███▋      | 1824044/4997817 [00:10<00:18, 174947.92it/s]

+
35%|███▌      | 1770192/4997817 [00:10<00:18, 172616.19it/s]

 
 
 

@@ -3774,16 +3774,16 @@ 
Classes which are commonly mislabeled overall

 

-
37%|███▋      | 1841882/4997817 [00:10&lt;00:17, 175973.50it/s]

+
36%|███▌      | 1787471/4997817 [00:10&lt;00:18, 172664.15it/s]

 
</pre>

 

 

-
37%|███▋      | 1841882/4997817 [00:10<00:17, 175973.50it/s]

+
36%|███▌      | 1787471/4997817 [00:10<00:18, 172664.15it/s]

 
end{sphinxVerbatim}

 

 

 

-
37%|███▋      | 1841882/4997817 [00:10<00:17, 175973.50it/s]

+
36%|███▌      | 1787471/4997817 [00:10<00:18, 172664.15it/s]

 
 
 

@@ -3800,16 +3800,16 @@ 
Classes which are commonly mislabeled overall

 

-
37%|███▋      | 1859592/4997817 [00:10&lt;00:17, 176306.73it/s]

+
36%|███▌      | 1804738/4997817 [00:10&lt;00:18, 172460.42it/s]

 
</pre>

 

 

-
37%|███▋      | 1859592/4997817 [00:10<00:17, 176306.73it/s]

+
36%|███▌      | 1804738/4997817 [00:10<00:18, 172460.42it/s]

 
end{sphinxVerbatim}

 

 

 

-
37%|███▋      | 1859592/4997817 [00:10<00:17, 176306.73it/s]

+
36%|███▌      | 1804738/4997817 [00:10<00:18, 172460.42it/s]

 
 
 

@@ -3826,16 +3826,16 @@ 
Classes which are commonly mislabeled overall

 

-
38%|███▊      | 1877279/4997817 [00:10&lt;00:17, 176473.74it/s]

+
36%|███▋      | 1822303/4997817 [00:10&lt;00:18, 173414.99it/s]

 
</pre>

 

 

-
38%|███▊      | 1877279/4997817 [00:10<00:17, 176473.74it/s]

+
36%|███▋      | 1822303/4997817 [00:10<00:18, 173414.99it/s]

 
end{sphinxVerbatim}

 

 

 

-
38%|███▊      | 1877279/4997817 [00:10<00:17, 176473.74it/s]

+
36%|███▋      | 1822303/4997817 [00:10<00:18, 173414.99it/s]

 
 
 

@@ -3852,16 +3852,16 @@ 
Classes which are commonly mislabeled overall

 

-
38%|███▊      | 1894943/4997817 [00:10&lt;00:17, 176520.23it/s]

+
37%|███▋      | 1839775/4997817 [00:10&lt;00:18, 173804.36it/s]

 
</pre>

 

 

-
38%|███▊      | 1894943/4997817 [00:10<00:17, 176520.23it/s]

+
37%|███▋      | 1839775/4997817 [00:10<00:18, 173804.36it/s]

 
end{sphinxVerbatim}

 

 

 

-
38%|███▊      | 1894943/4997817 [00:10<00:17, 176520.23it/s]

+
37%|███▋      | 1839775/4997817 [00:10<00:18, 173804.36it/s]

 
 
 

@@ -3878,16 +3878,16 @@ 
Classes which are commonly mislabeled overall

 

-
38%|███▊      | 1912596/4997817 [00:10&lt;00:17, 176219.44it/s]

+
37%|███▋      | 1857168/4997817 [00:10&lt;00:18, 173839.96it/s]

 
</pre>

 

 

-
38%|███▊      | 1912596/4997817 [00:10<00:17, 176219.44it/s]

+
37%|███▋      | 1857168/4997817 [00:10<00:18, 173839.96it/s]

 
end{sphinxVerbatim}

 

 

 

-
38%|███▊      | 1912596/4997817 [00:10<00:17, 176219.44it/s]

+
37%|███▋      | 1857168/4997817 [00:10<00:18, 173839.96it/s]

 
 
 

@@ -3904,16 +3904,16 @@ 
Classes which are commonly mislabeled overall

 

-
39%|███▊      | 1930263/4997817 [00:10&lt;00:17, 176352.95it/s]

+
38%|███▊      | 1874674/4997817 [00:10&lt;00:17, 174204.13it/s]

 
</pre>

 

 

-
39%|███▊      | 1930263/4997817 [00:10<00:17, 176352.95it/s]

+
38%|███▊      | 1874674/4997817 [00:10<00:17, 174204.13it/s]

 
end{sphinxVerbatim}

 

 

 

-
39%|███▊      | 1930263/4997817 [00:10<00:17, 176352.95it/s]

+
38%|███▊      | 1874674/4997817 [00:10<00:17, 174204.13it/s]

 
 
 

@@ -3930,16 +3930,16 @@ 
Classes which are commonly mislabeled overall

 

-
39%|███▉      | 1947942/4997817 [00:11&lt;00:17, 176481.33it/s]

+
38%|███▊      | 1892095/4997817 [00:11&lt;00:17, 173973.19it/s]

 
</pre>

 

 

-
39%|███▉      | 1947942/4997817 [00:11<00:17, 176481.33it/s]

+
38%|███▊      | 1892095/4997817 [00:11<00:17, 173973.19it/s]

 
end{sphinxVerbatim}

 

 

 

-
39%|███▉      | 1947942/4997817 [00:11<00:17, 176481.33it/s]

+
38%|███▊      | 1892095/4997817 [00:11<00:17, 173973.19it/s]

 
 
 

@@ -3956,16 +3956,16 @@ 
Classes which are commonly mislabeled overall

 

-
39%|███▉      | 1965591/4997817 [00:11&lt;00:17, 175961.68it/s]

+
38%|███▊      | 1909493/4997817 [00:11&lt;00:17, 173652.09it/s]

 
</pre>

 

 

-
39%|███▉      | 1965591/4997817 [00:11<00:17, 175961.68it/s]

+
38%|███▊      | 1909493/4997817 [00:11<00:17, 173652.09it/s]

 
end{sphinxVerbatim}

 

 

 

-
39%|███▉      | 1965591/4997817 [00:11<00:17, 175961.68it/s]

+
38%|███▊      | 1909493/4997817 [00:11<00:17, 173652.09it/s]

 
 
 

@@ -3982,16 +3982,16 @@ 
Classes which are commonly mislabeled overall

 

-
40%|███▉      | 1983188/4997817 [00:11&lt;00:17, 175748.07it/s]

+
39%|███▊      | 1926956/4997817 [00:11&lt;00:17, 173942.04it/s]

 
</pre>

 

 

-
40%|███▉      | 1983188/4997817 [00:11<00:17, 175748.07it/s]

+
39%|███▊      | 1926956/4997817 [00:11<00:17, 173942.04it/s]

 
end{sphinxVerbatim}

 

 

 

-
40%|███▉      | 1983188/4997817 [00:11<00:17, 175748.07it/s]

+
39%|███▊      | 1926956/4997817 [00:11<00:17, 173942.04it/s]

 
 
 

@@ -4008,16 +4008,16 @@ 
Classes which are commonly mislabeled overall

 

-
40%|████      | 2000787/4997817 [00:11&lt;00:17, 175815.61it/s]

+
39%|███▉      | 1944351/4997817 [00:11&lt;00:17, 173676.68it/s]

 
</pre>

 

 

-
40%|████      | 2000787/4997817 [00:11<00:17, 175815.61it/s]

+
39%|███▉      | 1944351/4997817 [00:11<00:17, 173676.68it/s]

 
end{sphinxVerbatim}

 

 

 

-
40%|████      | 2000787/4997817 [00:11<00:17, 175815.61it/s]

+
39%|███▉      | 1944351/4997817 [00:11<00:17, 173676.68it/s]

 
 
 

@@ -4034,16 +4034,16 @@ 
Classes which are commonly mislabeled overall

 

-
40%|████      | 2018369/4997817 [00:11&lt;00:17, 168730.26it/s]

+
39%|███▉      | 1961719/4997817 [00:11&lt;00:17, 173544.32it/s]

 
</pre>

 

 

-
40%|████      | 2018369/4997817 [00:11<00:17, 168730.26it/s]

+
39%|███▉      | 1961719/4997817 [00:11<00:17, 173544.32it/s]

 
end{sphinxVerbatim}

 

 

 

-
40%|████      | 2018369/4997817 [00:11<00:17, 168730.26it/s]

+
39%|███▉      | 1961719/4997817 [00:11<00:17, 173544.32it/s]

 
 
 

@@ -4060,16 +4060,16 @@ 
Classes which are commonly mislabeled overall

 

-
41%|████      | 2035840/4997817 [00:11&lt;00:17, 170467.93it/s]

+
40%|███▉      | 1979205/4997817 [00:11&lt;00:17, 173936.66it/s]

 
</pre>

 

 

-
41%|████      | 2035840/4997817 [00:11<00:17, 170467.93it/s]

+
40%|███▉      | 1979205/4997817 [00:11<00:17, 173936.66it/s]

 
end{sphinxVerbatim}

 

 

 

-
41%|████      | 2035840/4997817 [00:11<00:17, 170467.93it/s]

+
40%|███▉      | 1979205/4997817 [00:11<00:17, 173936.66it/s]

 
 
 

@@ -4086,16 +4086,16 @@ 
Classes which are commonly mislabeled overall

 

-
41%|████      | 2053380/4997817 [00:11&lt;00:17, 171912.16it/s]

+
40%|███▉      | 1996632/4997817 [00:11&lt;00:17, 174033.21it/s]

 
</pre>

 

 

-
41%|████      | 2053380/4997817 [00:11<00:17, 171912.16it/s]

+
40%|███▉      | 1996632/4997817 [00:11<00:17, 174033.21it/s]

 
end{sphinxVerbatim}

 

 

 

-
41%|████      | 2053380/4997817 [00:11<00:17, 171912.16it/s]

+
40%|███▉      | 1996632/4997817 [00:11<00:17, 174033.21it/s]

 
 
 

@@ -4112,16 +4112,16 @@ 
Classes which are commonly mislabeled overall

 

-
41%|████▏     | 2070874/4997817 [00:11&lt;00:16, 172804.36it/s]

+
40%|████      | 2014036/4997817 [00:11&lt;00:17, 173984.33it/s]

 
</pre>

 

 

-
41%|████▏     | 2070874/4997817 [00:11<00:16, 172804.36it/s]

+
40%|████      | 2014036/4997817 [00:11<00:17, 173984.33it/s]

 
end{sphinxVerbatim}

 

 

 

-
41%|████▏     | 2070874/4997817 [00:11<00:16, 172804.36it/s]

+
40%|████      | 2014036/4997817 [00:11<00:17, 173984.33it/s]

 
 
 

@@ -4138,16 +4138,16 @@ 
Classes which are commonly mislabeled overall

 

-
42%|████▏     | 2088397/4997817 [00:11&lt;00:16, 173519.94it/s]

+
41%|████      | 2031435/4997817 [00:11&lt;00:17, 173931.39it/s]

 
</pre>

 

 

-
42%|████▏     | 2088397/4997817 [00:11<00:16, 173519.94it/s]

+
41%|████      | 2031435/4997817 [00:11<00:17, 173931.39it/s]

 
end{sphinxVerbatim}

 

 

 

-
42%|████▏     | 2088397/4997817 [00:11<00:16, 173519.94it/s]

+
41%|████      | 2031435/4997817 [00:11<00:17, 173931.39it/s]

 
 
 

@@ -4164,16 +4164,16 @@ 
Classes which are commonly mislabeled overall

 

-
42%|████▏     | 2105931/4997817 [00:11&lt;00:16, 174057.90it/s]

+
41%|████      | 2048829/4997817 [00:11&lt;00:16, 173817.50it/s]

 
</pre>

 

 

-
42%|████▏     | 2105931/4997817 [00:11<00:16, 174057.90it/s]

+
41%|████      | 2048829/4997817 [00:11<00:16, 173817.50it/s]

 
end{sphinxVerbatim}

 

 

 

-
42%|████▏     | 2105931/4997817 [00:11<00:16, 174057.90it/s]

+
41%|████      | 2048829/4997817 [00:11<00:16, 173817.50it/s]

 
 
 

@@ -4190,16 +4190,16 @@ 
Classes which are commonly mislabeled overall

 

-
42%|████▏     | 2123526/4997817 [00:12&lt;00:16, 174619.28it/s]

+
41%|████▏     | 2066211/4997817 [00:12&lt;00:16, 173089.88it/s]

 
</pre>

 

 

-
42%|████▏     | 2123526/4997817 [00:12<00:16, 174619.28it/s]

+
41%|████▏     | 2066211/4997817 [00:12<00:16, 173089.88it/s]

 
end{sphinxVerbatim}

 

 

 

-
42%|████▏     | 2123526/4997817 [00:12<00:16, 174619.28it/s]

+
41%|████▏     | 2066211/4997817 [00:12<00:16, 173089.88it/s]

 
 
 

@@ -4216,16 +4216,16 @@ 
Classes which are commonly mislabeled overall

 

-
43%|████▎     | 2140999/4997817 [00:12&lt;00:16, 174636.87it/s]

+
42%|████▏     | 2083521/4997817 [00:12&lt;00:16, 173048.80it/s]

 
</pre>

 

 

-
43%|████▎     | 2140999/4997817 [00:12<00:16, 174636.87it/s]

+
42%|████▏     | 2083521/4997817 [00:12<00:16, 173048.80it/s]

 
end{sphinxVerbatim}

 

 

 

-
43%|████▎     | 2140999/4997817 [00:12<00:16, 174636.87it/s]

+
42%|████▏     | 2083521/4997817 [00:12<00:16, 173048.80it/s]

 
 
 

@@ -4242,16 +4242,16 @@ 
Classes which are commonly mislabeled overall

 

-
43%|████▎     | 2158470/4997817 [00:12&lt;00:16, 174638.99it/s]

+
42%|████▏     | 2100866/4997817 [00:12&lt;00:16, 173165.42it/s]

 
</pre>

 

 

-
43%|████▎     | 2158470/4997817 [00:12<00:16, 174638.99it/s]

+
42%|████▏     | 2100866/4997817 [00:12<00:16, 173165.42it/s]

 
end{sphinxVerbatim}

 

 

 

-
43%|████▎     | 2158470/4997817 [00:12<00:16, 174638.99it/s]

+
42%|████▏     | 2100866/4997817 [00:12<00:16, 173165.42it/s]

 
 
 

@@ -4268,16 +4268,16 @@ 
Classes which are commonly mislabeled overall

 

-
44%|████▎     | 2175968/4997817 [00:12&lt;00:16, 174737.78it/s]

+
42%|████▏     | 2118183/4997817 [00:12&lt;00:16, 173142.49it/s]

 
</pre>

 

 

-
44%|████▎     | 2175968/4997817 [00:12<00:16, 174737.78it/s]

+
42%|████▏     | 2118183/4997817 [00:12<00:16, 173142.49it/s]

 
end{sphinxVerbatim}

 

 

 

-
44%|████▎     | 2175968/4997817 [00:12<00:16, 174737.78it/s]

+
42%|████▏     | 2118183/4997817 [00:12<00:16, 173142.49it/s]

 
 
 

@@ -4294,16 +4294,16 @@ 
Classes which are commonly mislabeled overall

 

-
44%|████▍     | 2193446/4997817 [00:12&lt;00:16, 174633.60it/s]

+
43%|████▎     | 2135542/4997817 [00:12&lt;00:16, 173274.37it/s]

 
</pre>

 

 

-
44%|████▍     | 2193446/4997817 [00:12<00:16, 174633.60it/s]

+
43%|████▎     | 2135542/4997817 [00:12<00:16, 173274.37it/s]

 
end{sphinxVerbatim}

 

 

 

-
44%|████▍     | 2193446/4997817 [00:12<00:16, 174633.60it/s]

+
43%|████▎     | 2135542/4997817 [00:12<00:16, 173274.37it/s]

 
 
 

@@ -4320,16 +4320,16 @@ 
Classes which are commonly mislabeled overall

 

-
44%|████▍     | 2211104/4997817 [00:12&lt;00:15, 175212.99it/s]

+
43%|████▎     | 2152870/4997817 [00:12&lt;00:16, 172932.60it/s]

 
</pre>

 

 

-
44%|████▍     | 2211104/4997817 [00:12<00:15, 175212.99it/s]

+
43%|████▎     | 2152870/4997817 [00:12<00:16, 172932.60it/s]

 
end{sphinxVerbatim}

 

 

 

-
44%|████▍     | 2211104/4997817 [00:12<00:15, 175212.99it/s]

+
43%|████▎     | 2152870/4997817 [00:12<00:16, 172932.60it/s]

 
 
 

@@ -4346,16 +4346,16 @@ 
Classes which are commonly mislabeled overall

 

-
45%|████▍     | 2228891/4997817 [00:12&lt;00:15, 176005.69it/s]

+
43%|████▎     | 2170164/4997817 [00:12&lt;00:16, 172786.75it/s]

 
</pre>

 

 

-
45%|████▍     | 2228891/4997817 [00:12<00:15, 176005.69it/s]

+
43%|████▎     | 2170164/4997817 [00:12<00:16, 172786.75it/s]

 
end{sphinxVerbatim}

 

 

 

-
45%|████▍     | 2228891/4997817 [00:12<00:15, 176005.69it/s]

+
43%|████▎     | 2170164/4997817 [00:12<00:16, 172786.75it/s]

 
 
 

@@ -4372,16 +4372,16 @@ 
Classes which are commonly mislabeled overall

 

-
45%|████▍     | 2246705/4997817 [00:12&lt;00:15, 176642.54it/s]

+
44%|████▍     | 2187443/4997817 [00:12&lt;00:16, 172758.80it/s]

 
</pre>

 

 

-
45%|████▍     | 2246705/4997817 [00:12<00:15, 176642.54it/s]

+
44%|████▍     | 2187443/4997817 [00:12<00:16, 172758.80it/s]

 
end{sphinxVerbatim}

 

 

 

-
45%|████▍     | 2246705/4997817 [00:12<00:15, 176642.54it/s]

+
44%|████▍     | 2187443/4997817 [00:12<00:16, 172758.80it/s]

 
 
 

@@ -4398,16 +4398,16 @@ 
Classes which are commonly mislabeled overall

 

-
45%|████▌     | 2264371/4997817 [00:12&lt;00:15, 176477.26it/s]

+
44%|████▍     | 2204719/4997817 [00:12&lt;00:16, 172489.32it/s]

 
</pre>

 

 

-
45%|████▌     | 2264371/4997817 [00:12<00:15, 176477.26it/s]

+
44%|████▍     | 2204719/4997817 [00:12<00:16, 172489.32it/s]

 
end{sphinxVerbatim}

 

 

 

-
45%|████▌     | 2264371/4997817 [00:12<00:15, 176477.26it/s]

+
44%|████▍     | 2204719/4997817 [00:12<00:16, 172489.32it/s]

 
 
 

@@ -4424,16 +4424,16 @@ 
Classes which are commonly mislabeled overall

 

-
46%|████▌     | 2282020/4997817 [00:12&lt;00:15, 176270.92it/s]

+
44%|████▍     | 2221969/4997817 [00:12&lt;00:16, 172209.27it/s]

 
</pre>

 

 

-
46%|████▌     | 2282020/4997817 [00:12<00:15, 176270.92it/s]

+
44%|████▍     | 2221969/4997817 [00:12<00:16, 172209.27it/s]

 
end{sphinxVerbatim}

 

 

 

-
46%|████▌     | 2282020/4997817 [00:12<00:15, 176270.92it/s]

+
44%|████▍     | 2221969/4997817 [00:12<00:16, 172209.27it/s]

 
 
 

@@ -4450,16 +4450,16 @@ 
Classes which are commonly mislabeled overall

 

-
46%|████▌     | 2299648/4997817 [00:13&lt;00:15, 176268.65it/s]

+
45%|████▍     | 2239236/4997817 [00:13&lt;00:16, 172342.87it/s]

 
</pre>

 

 

-
46%|████▌     | 2299648/4997817 [00:13<00:15, 176268.65it/s]

+
45%|████▍     | 2239236/4997817 [00:13<00:16, 172342.87it/s]

 
end{sphinxVerbatim}

 

 

 

-
46%|████▌     | 2299648/4997817 [00:13<00:15, 176268.65it/s]

+
45%|████▍     | 2239236/4997817 [00:13<00:16, 172342.87it/s]

 
 
 

@@ -4476,16 +4476,16 @@ 
Classes which are commonly mislabeled overall

 

-
46%|████▋     | 2317443/4997817 [00:13&lt;00:15, 176769.77it/s]

+
45%|████▌     | 2256471/4997817 [00:13&lt;00:15, 171712.51it/s]

 
</pre>

 

 

-
46%|████▋     | 2317443/4997817 [00:13<00:15, 176769.77it/s]

+
45%|████▌     | 2256471/4997817 [00:13<00:15, 171712.51it/s]

 
end{sphinxVerbatim}

 

 

 

-
46%|████▋     | 2317443/4997817 [00:13<00:15, 176769.77it/s]

+
45%|████▌     | 2256471/4997817 [00:13<00:15, 171712.51it/s]

 
 
 

@@ -4502,16 +4502,16 @@ 
Classes which are commonly mislabeled overall

 

-
47%|████▋     | 2335287/4997817 [00:13&lt;00:15, 177266.82it/s]

+
45%|████▌     | 2273643/4997817 [00:13&lt;00:15, 171115.35it/s]

 
</pre>

 

 

-
47%|████▋     | 2335287/4997817 [00:13<00:15, 177266.82it/s]

+
45%|████▌     | 2273643/4997817 [00:13<00:15, 171115.35it/s]

 
end{sphinxVerbatim}

 

 

 

-
47%|████▋     | 2335287/4997817 [00:13<00:15, 177266.82it/s]

+
45%|████▌     | 2273643/4997817 [00:13<00:15, 171115.35it/s]

 
 
 

@@ -4528,16 +4528,16 @@ 
Classes which are commonly mislabeled overall

 

-
47%|████▋     | 2353014/4997817 [00:13&lt;00:14, 177237.67it/s]

+
46%|████▌     | 2290884/4997817 [00:13&lt;00:15, 171500.07it/s]

 
</pre>

 

 

-
47%|████▋     | 2353014/4997817 [00:13<00:14, 177237.67it/s]

+
46%|████▌     | 2290884/4997817 [00:13<00:15, 171500.07it/s]

 
end{sphinxVerbatim}

 

 

 

-
47%|████▋     | 2353014/4997817 [00:13<00:14, 177237.67it/s]

+
46%|████▌     | 2290884/4997817 [00:13<00:15, 171500.07it/s]

 
 
 

@@ -4554,16 +4554,16 @@ 
Classes which are commonly mislabeled overall

 

-
47%|████▋     | 2370811/4997817 [00:13&lt;00:14, 177454.13it/s]

+
46%|████▌     | 2308082/4997817 [00:13&lt;00:15, 171641.01it/s]

 
</pre>

 

 

-
47%|████▋     | 2370811/4997817 [00:13<00:14, 177454.13it/s]

+
46%|████▌     | 2308082/4997817 [00:13<00:15, 171641.01it/s]

 
end{sphinxVerbatim}

 

 

 

-
47%|████▋     | 2370811/4997817 [00:13<00:14, 177454.13it/s]

+
46%|████▌     | 2308082/4997817 [00:13<00:15, 171641.01it/s]

 
 
 

@@ -4580,16 +4580,16 @@ 
Classes which are commonly mislabeled overall

 

-
48%|████▊     | 2388644/4997817 [00:13&lt;00:14, 177713.67it/s]

+
47%|████▋     | 2325509/4997817 [00:13&lt;00:15, 172425.40it/s]

 
</pre>

 

 

-
48%|████▊     | 2388644/4997817 [00:13<00:14, 177713.67it/s]

+
47%|████▋     | 2325509/4997817 [00:13<00:15, 172425.40it/s]

 
end{sphinxVerbatim}

 

 

 

-
48%|████▊     | 2388644/4997817 [00:13<00:14, 177713.67it/s]

+
47%|████▋     | 2325509/4997817 [00:13<00:15, 172425.40it/s]

 
 
 

@@ -4606,16 +4606,16 @@ 
Classes which are commonly mislabeled overall

 

-
48%|████▊     | 2406515/4997817 [00:13&lt;00:14, 178008.59it/s]

+
47%|████▋     | 2342779/4997817 [00:13&lt;00:15, 172505.46it/s]

 
</pre>

 

 

-
48%|████▊     | 2406515/4997817 [00:13<00:14, 178008.59it/s]

+
47%|████▋     | 2342779/4997817 [00:13<00:15, 172505.46it/s]

 
end{sphinxVerbatim}

 

 

 

-
48%|████▊     | 2406515/4997817 [00:13<00:14, 178008.59it/s]

+
47%|████▋     | 2342779/4997817 [00:13<00:15, 172505.46it/s]

 
 
 

@@ -4632,16 +4632,16 @@ 
Classes which are commonly mislabeled overall

 

-
49%|████▊     | 2424382/4997817 [00:13&lt;00:14, 178202.69it/s]

+
47%|████▋     | 2360266/4997817 [00:13&lt;00:15, 173212.74it/s]

 
</pre>

 

 

-
49%|████▊     | 2424382/4997817 [00:13<00:14, 178202.69it/s]

+
47%|████▋     | 2360266/4997817 [00:13<00:15, 173212.74it/s]

 
end{sphinxVerbatim}

 

 

 

-
49%|████▊     | 2424382/4997817 [00:13<00:14, 178202.69it/s]

+
47%|████▋     | 2360266/4997817 [00:13<00:15, 173212.74it/s]

 
 
 

@@ -4658,16 +4658,16 @@ 
Classes which are commonly mislabeled overall

 

-
49%|████▉     | 2442203/4997817 [00:13&lt;00:14, 176953.39it/s]

+
48%|████▊     | 2377746/4997817 [00:13&lt;00:15, 173684.69it/s]

 
</pre>

 

 

-
49%|████▉     | 2442203/4997817 [00:13<00:14, 176953.39it/s]

+
48%|████▊     | 2377746/4997817 [00:13<00:15, 173684.69it/s]

 
end{sphinxVerbatim}

 

 

 

-
49%|████▉     | 2442203/4997817 [00:13<00:14, 176953.39it/s]

+
48%|████▊     | 2377746/4997817 [00:13<00:15, 173684.69it/s]

 
 
 

@@ -4684,16 +4684,16 @@ 
Classes which are commonly mislabeled overall

 

-
49%|████▉     | 2460064/4997817 [00:13&lt;00:14, 177445.01it/s]

+
48%|████▊     | 2395275/4997817 [00:13&lt;00:14, 174160.30it/s]

 
</pre>

 

 

-
49%|████▉     | 2460064/4997817 [00:13<00:14, 177445.01it/s]

+
48%|████▊     | 2395275/4997817 [00:13<00:14, 174160.30it/s]

 
end{sphinxVerbatim}

 

 

 

-
49%|████▉     | 2460064/4997817 [00:13<00:14, 177445.01it/s]

+
48%|████▊     | 2395275/4997817 [00:13<00:14, 174160.30it/s]

 
 
 

@@ -4710,16 +4710,16 @@ 
Classes which are commonly mislabeled overall

 

-
50%|████▉     | 2478011/4997817 [00:14&lt;00:14, 178046.47it/s]

+
48%|████▊     | 2412692/4997817 [00:14&lt;00:14, 173545.01it/s]

 
</pre>

 

 

-
50%|████▉     | 2478011/4997817 [00:14<00:14, 178046.47it/s]

+
48%|████▊     | 2412692/4997817 [00:14<00:14, 173545.01it/s]

 
end{sphinxVerbatim}

 

 

 

-
50%|████▉     | 2478011/4997817 [00:14<00:14, 178046.47it/s]

+
48%|████▊     | 2412692/4997817 [00:14<00:14, 173545.01it/s]

 
 
 

@@ -4736,16 +4736,16 @@ 
Classes which are commonly mislabeled overall

 

-
50%|████▉     | 2495859/4997817 [00:14&lt;00:14, 178170.66it/s]

+
49%|████▊     | 2430069/4997817 [00:14&lt;00:14, 173608.65it/s]

 
</pre>

 

 

-
50%|████▉     | 2495859/4997817 [00:14<00:14, 178170.66it/s]

+
49%|████▊     | 2430069/4997817 [00:14<00:14, 173608.65it/s]

 
end{sphinxVerbatim}

 

 

 

-
50%|████▉     | 2495859/4997817 [00:14<00:14, 178170.66it/s]

+
49%|████▊     | 2430069/4997817 [00:14<00:14, 173608.65it/s]

 
 
 

@@ -4762,16 +4762,16 @@ 
Classes which are commonly mislabeled overall

 

-
50%|█████     | 2513719/4997817 [00:14&lt;00:13, 178294.71it/s]

+
49%|████▉     | 2447462/4997817 [00:14&lt;00:14, 173701.94it/s]

 
</pre>

 

 

-
50%|█████     | 2513719/4997817 [00:14<00:13, 178294.71it/s]

+
49%|████▉     | 2447462/4997817 [00:14<00:14, 173701.94it/s]

 
end{sphinxVerbatim}

 

 

 

-
50%|█████     | 2513719/4997817 [00:14<00:13, 178294.71it/s]

+
49%|████▉     | 2447462/4997817 [00:14<00:14, 173701.94it/s]

 
 
 

@@ -4788,16 +4788,16 @@ 
Classes which are commonly mislabeled overall

 

-
51%|█████     | 2531550/4997817 [00:14&lt;00:13, 177984.78it/s]

+
49%|████▉     | 2464833/4997817 [00:14&lt;00:14, 173310.39it/s]

 
</pre>

 

 

-
51%|█████     | 2531550/4997817 [00:14<00:13, 177984.78it/s]

+
49%|████▉     | 2464833/4997817 [00:14<00:14, 173310.39it/s]

 
end{sphinxVerbatim}

 

 

 

-
51%|█████     | 2531550/4997817 [00:14<00:13, 177984.78it/s]

+
49%|████▉     | 2464833/4997817 [00:14<00:14, 173310.39it/s]

 
 
 

@@ -4814,16 +4814,16 @@ 
Classes which are commonly mislabeled overall

 

-
51%|█████     | 2549350/4997817 [00:14&lt;00:13, 177765.45it/s]

+
50%|████▉     | 2482165/4997817 [00:14&lt;00:14, 172931.85it/s]

 
</pre>

 

 

-
51%|█████     | 2549350/4997817 [00:14<00:13, 177765.45it/s]

+
50%|████▉     | 2482165/4997817 [00:14<00:14, 172931.85it/s]

 
end{sphinxVerbatim}

 

 

 

-
51%|█████     | 2549350/4997817 [00:14<00:13, 177765.45it/s]

+
50%|████▉     | 2482165/4997817 [00:14<00:14, 172931.85it/s]

 
 
 

@@ -4840,16 +4840,16 @@ 
Classes which are commonly mislabeled overall

 

-
51%|█████▏    | 2567127/4997817 [00:14&lt;00:13, 177467.75it/s]

+
50%|█████     | 2499563/4997817 [00:14&lt;00:14, 173242.08it/s]

 
</pre>

 

 

-
51%|█████▏    | 2567127/4997817 [00:14<00:13, 177467.75it/s]

+
50%|█████     | 2499563/4997817 [00:14<00:14, 173242.08it/s]

 
end{sphinxVerbatim}

 

 

 

-
51%|█████▏    | 2567127/4997817 [00:14<00:13, 177467.75it/s]

+
50%|█████     | 2499563/4997817 [00:14<00:14, 173242.08it/s]

 
 
 

@@ -4866,16 +4866,16 @@ 
Classes which are commonly mislabeled overall

 

-
52%|█████▏    | 2584875/4997817 [00:14&lt;00:13, 177135.41it/s]

+
50%|█████     | 2516888/4997817 [00:14&lt;00:14, 173011.66it/s]

 
</pre>

 

 

-
52%|█████▏    | 2584875/4997817 [00:14<00:13, 177135.41it/s]

+
50%|█████     | 2516888/4997817 [00:14<00:14, 173011.66it/s]

 
end{sphinxVerbatim}

 

 

 

-
52%|█████▏    | 2584875/4997817 [00:14<00:13, 177135.41it/s]

+
50%|█████     | 2516888/4997817 [00:14<00:14, 173011.66it/s]

 
 
 

@@ -4892,16 +4892,16 @@ 
Classes which are commonly mislabeled overall

 

-
52%|█████▏    | 2602589/4997817 [00:14&lt;00:13, 176761.69it/s]

+
51%|█████     | 2534190/4997817 [00:14&lt;00:14, 172835.54it/s]

 
</pre>

 

 

-
52%|█████▏    | 2602589/4997817 [00:14<00:13, 176761.69it/s]

+
51%|█████     | 2534190/4997817 [00:14<00:14, 172835.54it/s]

 
end{sphinxVerbatim}

 

 

 

-
52%|█████▏    | 2602589/4997817 [00:14<00:13, 176761.69it/s]

+
51%|█████     | 2534190/4997817 [00:14<00:14, 172835.54it/s]

 
 
 

@@ -4918,16 +4918,16 @@ 
Classes which are commonly mislabeled overall

 

-
52%|█████▏    | 2620266/4997817 [00:14&lt;00:13, 176400.10it/s]

+
51%|█████     | 2551474/4997817 [00:14&lt;00:14, 172684.40it/s]

 
</pre>

 

 

-
52%|█████▏    | 2620266/4997817 [00:14<00:13, 176400.10it/s]

+
51%|█████     | 2551474/4997817 [00:14<00:14, 172684.40it/s]

 
end{sphinxVerbatim}

 

 

 

-
52%|█████▏    | 2620266/4997817 [00:14<00:13, 176400.10it/s]

+
51%|█████     | 2551474/4997817 [00:14<00:14, 172684.40it/s]

 
 
 

@@ -4944,16 +4944,16 @@ 
Classes which are commonly mislabeled overall

 

-
53%|█████▎    | 2637907/4997817 [00:14&lt;00:13, 176353.99it/s]

+
51%|█████▏    | 2568743/4997817 [00:14&lt;00:14, 171725.95it/s]

 
</pre>

 

 

-
53%|█████▎    | 2637907/4997817 [00:14<00:13, 176353.99it/s]

+
51%|█████▏    | 2568743/4997817 [00:14<00:14, 171725.95it/s]

 
end{sphinxVerbatim}

 

 

 

-
53%|█████▎    | 2637907/4997817 [00:14<00:13, 176353.99it/s]

+
51%|█████▏    | 2568743/4997817 [00:14<00:14, 171725.95it/s]

 
 
 

@@ -4970,16 +4970,16 @@ 
Classes which are commonly mislabeled overall

 

-
53%|█████▎    | 2655543/4997817 [00:15&lt;00:13, 175946.08it/s]

+
52%|█████▏    | 2586056/4997817 [00:15&lt;00:14, 172142.42it/s]

 
</pre>

 

 

-
53%|█████▎    | 2655543/4997817 [00:15<00:13, 175946.08it/s]

+
52%|█████▏    | 2586056/4997817 [00:15<00:14, 172142.42it/s]

 
end{sphinxVerbatim}

 

 

 

-
53%|█████▎    | 2655543/4997817 [00:15<00:13, 175946.08it/s]

+
52%|█████▏    | 2586056/4997817 [00:15<00:14, 172142.42it/s]

 
 
 

@@ -4996,16 +4996,16 @@ 
Classes which are commonly mislabeled overall

 

-
53%|█████▎    | 2673138/4997817 [00:15&lt;00:13, 175874.49it/s]

+
52%|█████▏    | 2603272/4997817 [00:15&lt;00:13, 172009.73it/s]

 
</pre>

 

 

-
53%|█████▎    | 2673138/4997817 [00:15<00:13, 175874.49it/s]

+
52%|█████▏    | 2603272/4997817 [00:15<00:13, 172009.73it/s]

 
end{sphinxVerbatim}

 

 

 

-
53%|█████▎    | 2673138/4997817 [00:15<00:13, 175874.49it/s]

+
52%|█████▏    | 2603272/4997817 [00:15<00:13, 172009.73it/s]

 
 
 

@@ -5022,16 +5022,16 @@ 
Classes which are commonly mislabeled overall

 

-
54%|█████▍    | 2690726/4997817 [00:15&lt;00:13, 175848.48it/s]

+
52%|█████▏    | 2620507/4997817 [00:15&lt;00:13, 172106.86it/s]

 
</pre>

 

 

-
54%|█████▍    | 2690726/4997817 [00:15<00:13, 175848.48it/s]

+
52%|█████▏    | 2620507/4997817 [00:15<00:13, 172106.86it/s]

 
end{sphinxVerbatim}

 

 

 

-
54%|█████▍    | 2690726/4997817 [00:15<00:13, 175848.48it/s]

+
52%|█████▏    | 2620507/4997817 [00:15<00:13, 172106.86it/s]

 
 
 

@@ -5048,16 +5048,16 @@ 
Classes which are commonly mislabeled overall

 

-
54%|█████▍    | 2708319/4997817 [00:15&lt;00:13, 175869.29it/s]

+
53%|█████▎    | 2637748/4997817 [00:15&lt;00:13, 172193.71it/s]

 
</pre>

 

 

-
54%|█████▍    | 2708319/4997817 [00:15<00:13, 175869.29it/s]

+
53%|█████▎    | 2637748/4997817 [00:15<00:13, 172193.71it/s]

 
end{sphinxVerbatim}

 

 

 

-
54%|█████▍    | 2708319/4997817 [00:15<00:13, 175869.29it/s]

+
53%|█████▎    | 2637748/4997817 [00:15<00:13, 172193.71it/s]

 
 
 

@@ -5074,16 +5074,16 @@ 
Classes which are commonly mislabeled overall

 

-
55%|█████▍    | 2725906/4997817 [00:15&lt;00:13, 170165.16it/s]

+
53%|█████▎    | 2655228/4997817 [00:15&lt;00:13, 172969.91it/s]

 
</pre>

 

 

-
55%|█████▍    | 2725906/4997817 [00:15<00:13, 170165.16it/s]

+
53%|█████▎    | 2655228/4997817 [00:15<00:13, 172969.91it/s]

 
end{sphinxVerbatim}

 

 

 

-
55%|█████▍    | 2725906/4997817 [00:15<00:13, 170165.16it/s]

+
53%|█████▎    | 2655228/4997817 [00:15<00:13, 172969.91it/s]

 
 
 

@@ -5100,16 +5100,16 @@ 
Classes which are commonly mislabeled overall

 

-
55%|█████▍    | 2743530/4997817 [00:15&lt;00:13, 171941.91it/s]

+
53%|█████▎    | 2672594/4997817 [00:15&lt;00:13, 173172.78it/s]

 
</pre>

 

 

-
55%|█████▍    | 2743530/4997817 [00:15<00:13, 171941.91it/s]

+
53%|█████▎    | 2672594/4997817 [00:15<00:13, 173172.78it/s]

 
end{sphinxVerbatim}

 

 

 

-
55%|█████▍    | 2743530/4997817 [00:15<00:13, 171941.91it/s]

+
53%|█████▎    | 2672594/4997817 [00:15<00:13, 173172.78it/s]

 
 
 

@@ -5126,16 +5126,16 @@ 
Classes which are commonly mislabeled overall

 

-
55%|█████▌    | 2761055/4997817 [00:15&lt;00:12, 172913.09it/s]

+
54%|█████▍    | 2689912/4997817 [00:15&lt;00:13, 172973.25it/s]

 
</pre>

 

 

-
55%|█████▌    | 2761055/4997817 [00:15<00:12, 172913.09it/s]

+
54%|█████▍    | 2689912/4997817 [00:15<00:13, 172973.25it/s]

 
end{sphinxVerbatim}

 

 

 

-
55%|█████▌    | 2761055/4997817 [00:15<00:12, 172913.09it/s]

+
54%|█████▍    | 2689912/4997817 [00:15<00:13, 172973.25it/s]

 
 
 

@@ -5152,16 +5152,16 @@ 
Classes which are commonly mislabeled overall

 

-
56%|█████▌    | 2778599/4997817 [00:15&lt;00:12, 173657.51it/s]

+
54%|█████▍    | 2707210/4997817 [00:15&lt;00:13, 172673.04it/s]

 
</pre>

 

 

-
56%|█████▌    | 2778599/4997817 [00:15<00:12, 173657.51it/s]

+
54%|█████▍    | 2707210/4997817 [00:15<00:13, 172673.04it/s]

 
end{sphinxVerbatim}

 

 

 

-
56%|█████▌    | 2778599/4997817 [00:15<00:12, 173657.51it/s]

+
54%|█████▍    | 2707210/4997817 [00:15<00:13, 172673.04it/s]

 
 
 

@@ -5178,16 +5178,16 @@ 
Classes which are commonly mislabeled overall

 

-
56%|█████▌    | 2796132/4997817 [00:15&lt;00:12, 174152.13it/s]

+
55%|█████▍    | 2724649/4997817 [00:15&lt;00:13, 173183.95it/s]

 
</pre>

 

 

-
56%|█████▌    | 2796132/4997817 [00:15<00:12, 174152.13it/s]

+
55%|█████▍    | 2724649/4997817 [00:15<00:13, 173183.95it/s]

 
end{sphinxVerbatim}

 

 

 

-
56%|█████▌    | 2796132/4997817 [00:15<00:12, 174152.13it/s]

+
55%|█████▍    | 2724649/4997817 [00:15<00:13, 173183.95it/s]

 
 
 

@@ -5204,16 +5204,16 @@ 
Classes which are commonly mislabeled overall

 

-
56%|█████▋    | 2813713/4997817 [00:15&lt;00:12, 174642.51it/s]

+
55%|█████▍    | 2742045/4997817 [00:15&lt;00:13, 173411.99it/s]

 
</pre>

 

 

-
56%|█████▋    | 2813713/4997817 [00:15<00:12, 174642.51it/s]

+
55%|█████▍    | 2742045/4997817 [00:15<00:13, 173411.99it/s]

 
end{sphinxVerbatim}

 

 

 

-
56%|█████▋    | 2813713/4997817 [00:15<00:12, 174642.51it/s]

+
55%|█████▍    | 2742045/4997817 [00:15<00:13, 173411.99it/s]

 
 
 

@@ -5230,16 +5230,16 @@ 
Classes which are commonly mislabeled overall

 

-
57%|█████▋    | 2831215/4997817 [00:16&lt;00:12, 174751.85it/s]

+
55%|█████▌    | 2759387/4997817 [00:16&lt;00:12, 172797.03it/s]

 
</pre>

 

 

-
57%|█████▋    | 2831215/4997817 [00:16<00:12, 174751.85it/s]

+
55%|█████▌    | 2759387/4997817 [00:16<00:12, 172797.03it/s]

 
end{sphinxVerbatim}

 

 

 

-
57%|█████▋    | 2831215/4997817 [00:16<00:12, 174751.85it/s]

+
55%|█████▌    | 2759387/4997817 [00:16<00:12, 172797.03it/s]

 
 
 

@@ -5256,16 +5256,16 @@ 
Classes which are commonly mislabeled overall

 

-
57%|█████▋    | 2848781/4997817 [00:16&lt;00:12, 175020.18it/s]

+
56%|█████▌    | 2776675/4997817 [00:16&lt;00:12, 172818.44it/s]

 
</pre>

 

 

-
57%|█████▋    | 2848781/4997817 [00:16<00:12, 175020.18it/s]

+
56%|█████▌    | 2776675/4997817 [00:16<00:12, 172818.44it/s]

 
end{sphinxVerbatim}

 

 

 

-
57%|█████▋    | 2848781/4997817 [00:16<00:12, 175020.18it/s]

+
56%|█████▌    | 2776675/4997817 [00:16<00:12, 172818.44it/s]

 
 
 

@@ -5282,16 +5282,16 @@ 
Classes which are commonly mislabeled overall

 

-
57%|█████▋    | 2866288/4997817 [00:16&lt;00:12, 174922.90it/s]

+
56%|█████▌    | 2793958/4997817 [00:16&lt;00:12, 172443.43it/s]

 
</pre>

 

 

-
57%|█████▋    | 2866288/4997817 [00:16<00:12, 174922.90it/s]

+
56%|█████▌    | 2793958/4997817 [00:16<00:12, 172443.43it/s]

 
end{sphinxVerbatim}

 

 

 

-
57%|█████▋    | 2866288/4997817 [00:16<00:12, 174922.90it/s]

+
56%|█████▌    | 2793958/4997817 [00:16<00:12, 172443.43it/s]

 
 
 

@@ -5308,16 +5308,16 @@ 
Classes which are commonly mislabeled overall

 

-
58%|█████▊    | 2883784/4997817 [00:16&lt;00:12, 174867.39it/s]

+
56%|█████▌    | 2811203/4997817 [00:16&lt;00:12, 172181.49it/s]

 
</pre>

 

 

-
58%|█████▊    | 2883784/4997817 [00:16<00:12, 174867.39it/s]

+
56%|█████▌    | 2811203/4997817 [00:16<00:12, 172181.49it/s]

 
end{sphinxVerbatim}

 

 

 

-
58%|█████▊    | 2883784/4997817 [00:16<00:12, 174867.39it/s]

+
56%|█████▌    | 2811203/4997817 [00:16<00:12, 172181.49it/s]

 
 
 

@@ -5334,16 +5334,16 @@ 
Classes which are commonly mislabeled overall

 

-
58%|█████▊    | 2901274/4997817 [00:16&lt;00:12, 174518.53it/s]

+
57%|█████▋    | 2828422/4997817 [00:16&lt;00:12, 171875.31it/s]

 
</pre>

 

 

-
58%|█████▊    | 2901274/4997817 [00:16<00:12, 174518.53it/s]

+
57%|█████▋    | 2828422/4997817 [00:16<00:12, 171875.31it/s]

 
end{sphinxVerbatim}

 

 

 

-
58%|█████▊    | 2901274/4997817 [00:16<00:12, 174518.53it/s]

+
57%|█████▋    | 2828422/4997817 [00:16<00:12, 171875.31it/s]

 
 
 

@@ -5360,16 +5360,16 @@ 
Classes which are commonly mislabeled overall

 

-
58%|█████▊    | 2918784/4997817 [00:16&lt;00:11, 174687.97it/s]

+
57%|█████▋    | 2845610/4997817 [00:16&lt;00:12, 171605.12it/s]

 
</pre>

 

 

-
58%|█████▊    | 2918784/4997817 [00:16<00:11, 174687.97it/s]

+
57%|█████▋    | 2845610/4997817 [00:16<00:12, 171605.12it/s]

 
end{sphinxVerbatim}

 

 

 

-
58%|█████▊    | 2918784/4997817 [00:16<00:11, 174687.97it/s]

+
57%|█████▋    | 2845610/4997817 [00:16<00:12, 171605.12it/s]

 
 
 

@@ -5386,16 +5386,16 @@ 
Classes which are commonly mislabeled overall

 

-
59%|█████▉    | 2936508/4997817 [00:16&lt;00:11, 175446.36it/s]

+
57%|█████▋    | 2862771/4997817 [00:16&lt;00:12, 171158.71it/s]

 
</pre>

 

 

-
59%|█████▉    | 2936508/4997817 [00:16<00:11, 175446.36it/s]

+
57%|█████▋    | 2862771/4997817 [00:16<00:12, 171158.71it/s]

 
end{sphinxVerbatim}

 

 

 

-
59%|█████▉    | 2936508/4997817 [00:16<00:11, 175446.36it/s]

+
57%|█████▋    | 2862771/4997817 [00:16<00:12, 171158.71it/s]

 
 
 

@@ -5412,16 +5412,16 @@ 
Classes which are commonly mislabeled overall

 

-
59%|█████▉    | 2954190/4997817 [00:16&lt;00:11, 175855.21it/s]

+
58%|█████▊    | 2880043/4997817 [00:16&lt;00:12, 171620.59it/s]

 
</pre>

 

 

-
59%|█████▉    | 2954190/4997817 [00:16<00:11, 175855.21it/s]

+
58%|█████▊    | 2880043/4997817 [00:16<00:12, 171620.59it/s]

 
end{sphinxVerbatim}

 

 

 

-
59%|█████▉    | 2954190/4997817 [00:16<00:11, 175855.21it/s]

+
58%|█████▊    | 2880043/4997817 [00:16<00:12, 171620.59it/s]

 
 
 

@@ -5438,16 +5438,16 @@ 
Classes which are commonly mislabeled overall

 

-
59%|█████▉    | 2971867/4997817 [00:16&lt;00:11, 176126.05it/s]

+
58%|█████▊    | 2897206/4997817 [00:16&lt;00:12, 171545.78it/s]

 
</pre>

 

 

-
59%|█████▉    | 2971867/4997817 [00:16<00:11, 176126.05it/s]

+
58%|█████▊    | 2897206/4997817 [00:16<00:12, 171545.78it/s]

 
end{sphinxVerbatim}

 

 

 

-
59%|█████▉    | 2971867/4997817 [00:16<00:11, 176126.05it/s]

+
58%|█████▊    | 2897206/4997817 [00:16<00:12, 171545.78it/s]

 
 
 

@@ -5464,16 +5464,16 @@ 
Classes which are commonly mislabeled overall

 

-
60%|█████▉    | 2989607/4997817 [00:16&lt;00:11, 176504.50it/s]

+
58%|█████▊    | 2914479/4997817 [00:16&lt;00:12, 171897.64it/s]

 
</pre>

 

 

-
60%|█████▉    | 2989607/4997817 [00:16<00:11, 176504.50it/s]

+
58%|█████▊    | 2914479/4997817 [00:16<00:12, 171897.64it/s]

 
end{sphinxVerbatim}

 

 

 

-
60%|█████▉    | 2989607/4997817 [00:16<00:11, 176504.50it/s]

+
58%|█████▊    | 2914479/4997817 [00:16<00:12, 171897.64it/s]

 
 
 

@@ -5490,16 +5490,16 @@ 
Classes which are commonly mislabeled overall

 

-
60%|██████    | 3007465/4997817 [00:17&lt;00:11, 177122.98it/s]

+
59%|█████▊    | 2931670/4997817 [00:17&lt;00:12, 171468.11it/s]

 
</pre>

 

 

-
60%|██████    | 3007465/4997817 [00:17<00:11, 177122.98it/s]

+
59%|█████▊    | 2931670/4997817 [00:17<00:12, 171468.11it/s]

 
end{sphinxVerbatim}

 

 

 

-
60%|██████    | 3007465/4997817 [00:17<00:11, 177122.98it/s]

+
59%|█████▊    | 2931670/4997817 [00:17<00:12, 171468.11it/s]

 
 
 

@@ -5516,16 +5516,16 @@ 
Classes which are commonly mislabeled overall

 

-
61%|██████    | 3025371/4997817 [00:17&lt;00:11, 177699.75it/s]

+
59%|█████▉    | 2948818/4997817 [00:17&lt;00:12, 164907.42it/s]

 
</pre>

 

 

-
61%|██████    | 3025371/4997817 [00:17<00:11, 177699.75it/s]

+
59%|█████▉    | 2948818/4997817 [00:17<00:12, 164907.42it/s]

 
end{sphinxVerbatim}

 

 

 

-
61%|██████    | 3025371/4997817 [00:17<00:11, 177699.75it/s]

+
59%|█████▉    | 2948818/4997817 [00:17<00:12, 164907.42it/s]

 
 
 

@@ -5542,16 +5542,16 @@ 
Classes which are commonly mislabeled overall

 

-
61%|██████    | 3043142/4997817 [00:17&lt;00:11, 177526.55it/s]

+
59%|█████▉    | 2965819/4997817 [00:17&lt;00:12, 166389.56it/s]

 
</pre>

 

 

-
61%|██████    | 3043142/4997817 [00:17<00:11, 177526.55it/s]

+
59%|█████▉    | 2965819/4997817 [00:17<00:12, 166389.56it/s]

 
end{sphinxVerbatim}

 

 

 

-
61%|██████    | 3043142/4997817 [00:17<00:11, 177526.55it/s]

+
59%|█████▉    | 2965819/4997817 [00:17<00:12, 166389.56it/s]

 
 
 

@@ -5568,16 +5568,16 @@ 
Classes which are commonly mislabeled overall

 

-
61%|██████    | 3060895/4997817 [00:17&lt;00:10, 177261.36it/s]

+
60%|█████▉    | 2983140/4997817 [00:17&lt;00:11, 168389.32it/s]

 
</pre>

 

 

-
61%|██████    | 3060895/4997817 [00:17<00:10, 177261.36it/s]

+
60%|█████▉    | 2983140/4997817 [00:17<00:11, 168389.32it/s]

 
end{sphinxVerbatim}

 

 

 

-
61%|██████    | 3060895/4997817 [00:17<00:10, 177261.36it/s]

+
60%|█████▉    | 2983140/4997817 [00:17<00:11, 168389.32it/s]

 
 
 

@@ -5594,16 +5594,16 @@ 
Classes which are commonly mislabeled overall

 

-
62%|██████▏   | 3078730/4997817 [00:17&lt;00:10, 177583.27it/s]

+
60%|██████    | 3000413/4997817 [00:17&lt;00:11, 169668.46it/s]

 
</pre>

 

 

-
62%|██████▏   | 3078730/4997817 [00:17<00:10, 177583.27it/s]

+
60%|██████    | 3000413/4997817 [00:17<00:11, 169668.46it/s]

 
end{sphinxVerbatim}

 

 

 

-
62%|██████▏   | 3078730/4997817 [00:17<00:10, 177583.27it/s]

+
60%|██████    | 3000413/4997817 [00:17<00:11, 169668.46it/s]

 
 
 

@@ -5620,16 +5620,16 @@ 
Classes which are commonly mislabeled overall

 

-
62%|██████▏   | 3096489/4997817 [00:17&lt;00:10, 173439.53it/s]

+
60%|██████    | 3017703/4997817 [00:17&lt;00:11, 170624.06it/s]

 
</pre>

 

 

-
62%|██████▏   | 3096489/4997817 [00:17<00:10, 173439.53it/s]

+
60%|██████    | 3017703/4997817 [00:17<00:11, 170624.06it/s]

 
end{sphinxVerbatim}

 

 

 

-
62%|██████▏   | 3096489/4997817 [00:17<00:10, 173439.53it/s]

+
60%|██████    | 3017703/4997817 [00:17<00:11, 170624.06it/s]

 
 
 

@@ -5646,16 +5646,16 @@ 
Classes which are commonly mislabeled overall

 

-
62%|██████▏   | 3114319/4997817 [00:17&lt;00:10, 174869.70it/s]

+
61%|██████    | 3034960/4997817 [00:17&lt;00:11, 171201.38it/s]

 
</pre>

 

 

-
62%|██████▏   | 3114319/4997817 [00:17<00:10, 174869.70it/s]

+
61%|██████    | 3034960/4997817 [00:17<00:11, 171201.38it/s]

 
end{sphinxVerbatim}

 

 

 

-
62%|██████▏   | 3114319/4997817 [00:17<00:10, 174869.70it/s]

+
61%|██████    | 3034960/4997817 [00:17<00:11, 171201.38it/s]

 
 
 

@@ -5672,16 +5672,16 @@ 
Classes which are commonly mislabeled overall

 

-
63%|██████▎   | 3132167/4997817 [00:17&lt;00:10, 175934.19it/s]

+
61%|██████    | 3052094/4997817 [00:17&lt;00:11, 171123.09it/s]

 
</pre>

 

 

-
63%|██████▎   | 3132167/4997817 [00:17<00:10, 175934.19it/s]

+
61%|██████    | 3052094/4997817 [00:17<00:11, 171123.09it/s]

 
end{sphinxVerbatim}

 

 

 

-
63%|██████▎   | 3132167/4997817 [00:17<00:10, 175934.19it/s]

+
61%|██████    | 3052094/4997817 [00:17<00:11, 171123.09it/s]

 
 
 

@@ -5698,16 +5698,16 @@ 
Classes which are commonly mislabeled overall

 

-
63%|██████▎   | 3150036/4997817 [00:17&lt;00:10, 176751.52it/s]

+
61%|██████▏   | 3069455/4997817 [00:17&lt;00:11, 171864.37it/s]

 
</pre>

 

 

-
63%|██████▎   | 3150036/4997817 [00:17<00:10, 176751.52it/s]

+
61%|██████▏   | 3069455/4997817 [00:17<00:11, 171864.37it/s]

 
end{sphinxVerbatim}

 

 

 

-
63%|██████▎   | 3150036/4997817 [00:17<00:10, 176751.52it/s]

+
61%|██████▏   | 3069455/4997817 [00:17<00:11, 171864.37it/s]

 
 
 

@@ -5724,16 +5724,16 @@ 
Classes which are commonly mislabeled overall

 

-
63%|██████▎   | 3167884/4997817 [00:18&lt;00:10, 177263.84it/s]

+
62%|██████▏   | 3086755/4997817 [00:18&lt;00:11, 172200.09it/s]

 
</pre>

 

 

-
63%|██████▎   | 3167884/4997817 [00:18<00:10, 177263.84it/s]

+
62%|██████▏   | 3086755/4997817 [00:18<00:11, 172200.09it/s]

 
end{sphinxVerbatim}

 

 

 

-
63%|██████▎   | 3167884/4997817 [00:18<00:10, 177263.84it/s]

+
62%|██████▏   | 3086755/4997817 [00:18<00:11, 172200.09it/s]

 
 
 

@@ -5750,16 +5750,16 @@ 
Classes which are commonly mislabeled overall

 

-
64%|██████▎   | 3185698/4997817 [00:18&lt;00:10, 177523.48it/s]

+
62%|██████▏   | 3104106/4997817 [00:18&lt;00:10, 172590.10it/s]

 
</pre>

 

 

-
64%|██████▎   | 3185698/4997817 [00:18<00:10, 177523.48it/s]

+
62%|██████▏   | 3104106/4997817 [00:18<00:10, 172590.10it/s]

 
end{sphinxVerbatim}

 

 

 

-
64%|██████▎   | 3185698/4997817 [00:18<00:10, 177523.48it/s]

+
62%|██████▏   | 3104106/4997817 [00:18<00:10, 172590.10it/s]

 
 
 

@@ -5776,16 +5776,16 @@ 
Classes which are commonly mislabeled overall

 

-
64%|██████▍   | 3203568/4997817 [00:18&lt;00:10, 177872.39it/s]

+
62%|██████▏   | 3121414/4997817 [00:18&lt;00:10, 172732.84it/s]

 
</pre>

 

 

-
64%|██████▍   | 3203568/4997817 [00:18<00:10, 177872.39it/s]

+
62%|██████▏   | 3121414/4997817 [00:18<00:10, 172732.84it/s]

 
end{sphinxVerbatim}

 

 

 

-
64%|██████▍   | 3203568/4997817 [00:18<00:10, 177872.39it/s]

+
62%|██████▏   | 3121414/4997817 [00:18<00:10, 172732.84it/s]

 
 
 

@@ -5802,16 +5802,16 @@ 
Classes which are commonly mislabeled overall

 

-
64%|██████▍   | 3221360/4997817 [00:18&lt;00:09, 177711.90it/s]

+
63%|██████▎   | 3138690/4997817 [00:18&lt;00:10, 172414.93it/s]

 
</pre>

 

 

-
64%|██████▍   | 3221360/4997817 [00:18<00:09, 177711.90it/s]

+
63%|██████▎   | 3138690/4997817 [00:18<00:10, 172414.93it/s]

 
end{sphinxVerbatim}

 

 

 

-
64%|██████▍   | 3221360/4997817 [00:18<00:09, 177711.90it/s]

+
63%|██████▎   | 3138690/4997817 [00:18<00:10, 172414.93it/s]

 
 
 

@@ -5828,16 +5828,16 @@ 
Classes which are commonly mislabeled overall

 

-
65%|██████▍   | 3239134/4997817 [00:18&lt;00:09, 177577.15it/s]

+
63%|██████▎   | 3155966/4997817 [00:18&lt;00:10, 172513.49it/s]

 
</pre>

 

 

-
65%|██████▍   | 3239134/4997817 [00:18<00:09, 177577.15it/s]

+
63%|██████▎   | 3155966/4997817 [00:18<00:10, 172513.49it/s]

 
end{sphinxVerbatim}

 

 

 

-
65%|██████▍   | 3239134/4997817 [00:18<00:09, 177577.15it/s]

+
63%|██████▎   | 3155966/4997817 [00:18<00:10, 172513.49it/s]

 
 
 

@@ -5854,16 +5854,16 @@ 
Classes which are commonly mislabeled overall

 

-
65%|██████▌   | 3256894/4997817 [00:18&lt;00:09, 176368.10it/s]

+
63%|██████▎   | 3173316/4997817 [00:18&lt;00:10, 172807.21it/s]

 
</pre>

 

 

-
65%|██████▌   | 3256894/4997817 [00:18<00:09, 176368.10it/s]

+
63%|██████▎   | 3173316/4997817 [00:18<00:10, 172807.21it/s]

 
end{sphinxVerbatim}

 

 

 

-
65%|██████▌   | 3256894/4997817 [00:18<00:09, 176368.10it/s]

+
63%|██████▎   | 3173316/4997817 [00:18<00:10, 172807.21it/s]

 
 
 

@@ -5880,16 +5880,16 @@ 
Classes which are commonly mislabeled overall

 

-
66%|██████▌   | 3274697/4997817 [00:18&lt;00:09, 176860.96it/s]

+
64%|██████▍   | 3190786/4997817 [00:18&lt;00:10, 173371.43it/s]

 
</pre>

 

 

-
66%|██████▌   | 3274697/4997817 [00:18<00:09, 176860.96it/s]

+
64%|██████▍   | 3190786/4997817 [00:18<00:10, 173371.43it/s]

 
end{sphinxVerbatim}

 

 

 

-
66%|██████▌   | 3274697/4997817 [00:18<00:09, 176860.96it/s]

+
64%|██████▍   | 3190786/4997817 [00:18<00:10, 173371.43it/s]

 
 
 

@@ -5906,16 +5906,16 @@ 
Classes which are commonly mislabeled overall

 

-
66%|██████▌   | 3292438/4997817 [00:18&lt;00:09, 177021.85it/s]

+
64%|██████▍   | 3208124/4997817 [00:18&lt;00:10, 173368.22it/s]

 
</pre>

 

 

-
66%|██████▌   | 3292438/4997817 [00:18<00:09, 177021.85it/s]

+
64%|██████▍   | 3208124/4997817 [00:18<00:10, 173368.22it/s]

 
end{sphinxVerbatim}

 

 

 

-
66%|██████▌   | 3292438/4997817 [00:18<00:09, 177021.85it/s]

+
64%|██████▍   | 3208124/4997817 [00:18<00:10, 173368.22it/s]

 
 
 

@@ -5932,16 +5932,16 @@ 
Classes which are commonly mislabeled overall

 

-
66%|██████▌   | 3310162/4997817 [00:18&lt;00:09, 177081.84it/s]

+
65%|██████▍   | 3225462/4997817 [00:18&lt;00:10, 173291.90it/s]

 
</pre>

 

 

-
66%|██████▌   | 3310162/4997817 [00:18<00:09, 177081.84it/s]

+
65%|██████▍   | 3225462/4997817 [00:18<00:10, 173291.90it/s]

 
end{sphinxVerbatim}

 

 

 

-
66%|██████▌   | 3310162/4997817 [00:18<00:09, 177081.84it/s]

+
65%|██████▍   | 3225462/4997817 [00:18<00:10, 173291.90it/s]

 
 
 

@@ -5958,16 +5958,16 @@ 
Classes which are commonly mislabeled overall

 

-
67%|██████▋   | 3327911/4997817 [00:18&lt;00:09, 177201.04it/s]

+
65%|██████▍   | 3242799/4997817 [00:18&lt;00:10, 173312.39it/s]

 
</pre>

 

 

-
67%|██████▋   | 3327911/4997817 [00:18<00:09, 177201.04it/s]

+
65%|██████▍   | 3242799/4997817 [00:18<00:10, 173312.39it/s]

 
end{sphinxVerbatim}

 

 

 

-
67%|██████▋   | 3327911/4997817 [00:18<00:09, 177201.04it/s]

+
65%|██████▍   | 3242799/4997817 [00:18<00:10, 173312.39it/s]

 
 
 

@@ -5984,16 +5984,16 @@ 
Classes which are commonly mislabeled overall

 

-
67%|██████▋   | 3345632/4997817 [00:19&lt;00:09, 177070.11it/s]

+
65%|██████▌   | 3260131/4997817 [00:19&lt;00:10, 173229.77it/s]

 
</pre>

 

 

-
67%|██████▋   | 3345632/4997817 [00:19<00:09, 177070.11it/s]

+
65%|██████▌   | 3260131/4997817 [00:19<00:10, 173229.77it/s]

 
end{sphinxVerbatim}

 

 

 

-
67%|██████▋   | 3345632/4997817 [00:19<00:09, 177070.11it/s]

+
65%|██████▌   | 3260131/4997817 [00:19<00:10, 173229.77it/s]

 
 
 

@@ -6010,16 +6010,16 @@ 
Classes which are commonly mislabeled overall

 

-
67%|██████▋   | 3363340/4997817 [00:19&lt;00:09, 176560.09it/s]

+
66%|██████▌   | 3277459/4997817 [00:19&lt;00:09, 173241.78it/s]

 
</pre>

 

 

-
67%|██████▋   | 3363340/4997817 [00:19<00:09, 176560.09it/s]

+
66%|██████▌   | 3277459/4997817 [00:19<00:09, 173241.78it/s]

 
end{sphinxVerbatim}

 

 

 

-
67%|██████▋   | 3363340/4997817 [00:19<00:09, 176560.09it/s]

+
66%|██████▌   | 3277459/4997817 [00:19<00:09, 173241.78it/s]

 
 
 

@@ -6036,16 +6036,16 @@ 
Classes which are commonly mislabeled overall

 

-
68%|██████▊   | 3380997/4997817 [00:19&lt;00:09, 176297.50it/s]

+
66%|██████▌   | 3294784/4997817 [00:19&lt;00:10, 168720.26it/s]

 
</pre>

 

 

-
68%|██████▊   | 3380997/4997817 [00:19<00:09, 176297.50it/s]

+
66%|██████▌   | 3294784/4997817 [00:19<00:10, 168720.26it/s]

 
end{sphinxVerbatim}

 

 

 

-
68%|██████▊   | 3380997/4997817 [00:19<00:09, 176297.50it/s]

+
66%|██████▌   | 3294784/4997817 [00:19<00:10, 168720.26it/s]

 
 
 

@@ -6062,16 +6062,16 @@ 
Classes which are commonly mislabeled overall

 

-
68%|██████▊   | 3398628/4997817 [00:19&lt;00:09, 175923.15it/s]

+
66%|██████▋   | 3311836/4997817 [00:19&lt;00:09, 169246.65it/s]

 
</pre>

 

 

-
68%|██████▊   | 3398628/4997817 [00:19<00:09, 175923.15it/s]

+
66%|██████▋   | 3311836/4997817 [00:19<00:09, 169246.65it/s]

 
end{sphinxVerbatim}

 

 

 

-
68%|██████▊   | 3398628/4997817 [00:19<00:09, 175923.15it/s]

+
66%|██████▋   | 3311836/4997817 [00:19<00:09, 169246.65it/s]

 
 
 

@@ -6088,16 +6088,16 @@ 
Classes which are commonly mislabeled overall

 

-
68%|██████▊   | 3416444/4997817 [00:19&lt;00:08, 176583.41it/s]

+
67%|██████▋   | 3329185/4997817 [00:19&lt;00:09, 170499.74it/s]

 
</pre>

 

 

-
68%|██████▊   | 3416444/4997817 [00:19<00:08, 176583.41it/s]

+
67%|██████▋   | 3329185/4997817 [00:19<00:09, 170499.74it/s]

 
end{sphinxVerbatim}

 

 

 

-
68%|██████▊   | 3416444/4997817 [00:19<00:08, 176583.41it/s]

+
67%|██████▋   | 3329185/4997817 [00:19<00:09, 170499.74it/s]

 
 
 

@@ -6114,16 +6114,16 @@ 
Classes which are commonly mislabeled overall

 

-
69%|██████▊   | 3434103/4997817 [00:19&lt;00:08, 176134.50it/s]

+
67%|██████▋   | 3346525/4997817 [00:19&lt;00:09, 171358.37it/s]

 
</pre>

 

 

-
69%|██████▊   | 3434103/4997817 [00:19<00:08, 176134.50it/s]

+
67%|██████▋   | 3346525/4997817 [00:19<00:09, 171358.37it/s]

 
end{sphinxVerbatim}

 

 

 

-
69%|██████▊   | 3434103/4997817 [00:19<00:08, 176134.50it/s]

+
67%|██████▋   | 3346525/4997817 [00:19<00:09, 171358.37it/s]

 
 
 

@@ -6140,16 +6140,16 @@ 
Classes which are commonly mislabeled overall

 

-
69%|██████▉   | 3451738/4997817 [00:19&lt;00:08, 176195.20it/s]

+
67%|██████▋   | 3363981/4997817 [00:19&lt;00:09, 172310.29it/s]

 
</pre>

 

 

-
69%|██████▉   | 3451738/4997817 [00:19<00:08, 176195.20it/s]

+
67%|██████▋   | 3363981/4997817 [00:19<00:09, 172310.29it/s]

 
end{sphinxVerbatim}

 

 

 

-
69%|██████▉   | 3451738/4997817 [00:19<00:08, 176195.20it/s]

+
67%|██████▋   | 3363981/4997817 [00:19<00:09, 172310.29it/s]

 
 
 

@@ -6166,16 +6166,16 @@ 
Classes which are commonly mislabeled overall

 

-
69%|██████▉   | 3469358/4997817 [00:19&lt;00:08, 176027.78it/s]

+
68%|██████▊   | 3381408/4997817 [00:19&lt;00:09, 172891.73it/s]

 
</pre>

 

 

-
69%|██████▉   | 3469358/4997817 [00:19<00:08, 176027.78it/s]

+
68%|██████▊   | 3381408/4997817 [00:19<00:09, 172891.73it/s]

 
end{sphinxVerbatim}

 

 

 

-
69%|██████▉   | 3469358/4997817 [00:19<00:08, 176027.78it/s]

+
68%|██████▊   | 3381408/4997817 [00:19<00:09, 172891.73it/s]

 
 
 

@@ -6192,16 +6192,16 @@ 
Classes which are commonly mislabeled overall

 

-
70%|██████▉   | 3486962/4997817 [00:19&lt;00:08, 175916.89it/s]

+
68%|██████▊   | 3398883/4997817 [00:19&lt;00:09, 173444.12it/s]

 
</pre>

 

 

-
70%|██████▉   | 3486962/4997817 [00:19<00:08, 175916.89it/s]

+
68%|██████▊   | 3398883/4997817 [00:19<00:09, 173444.12it/s]

 
end{sphinxVerbatim}

 

 

 

-
70%|██████▉   | 3486962/4997817 [00:19<00:08, 175916.89it/s]

+
68%|██████▊   | 3398883/4997817 [00:19<00:09, 173444.12it/s]

 
 
 

@@ -6218,16 +6218,16 @@ 
Classes which are commonly mislabeled overall

 

-
70%|███████   | 3504554/4997817 [00:19&lt;00:08, 175282.26it/s]

+
68%|██████▊   | 3416395/4997817 [00:19&lt;00:09, 173944.67it/s]

 
</pre>

 

 

-
70%|███████   | 3504554/4997817 [00:19<00:08, 175282.26it/s]

+
68%|██████▊   | 3416395/4997817 [00:19<00:09, 173944.67it/s]

 
end{sphinxVerbatim}

 

 

 

-
70%|███████   | 3504554/4997817 [00:19<00:08, 175282.26it/s]

+
68%|██████▊   | 3416395/4997817 [00:19<00:09, 173944.67it/s]

 
 
 

@@ -6244,16 +6244,16 @@ 
Classes which are commonly mislabeled overall

 

-
70%|███████   | 3522083/4997817 [00:20&lt;00:08, 175175.68it/s]

+
69%|██████▊   | 3433866/4997817 [00:20&lt;00:08, 174170.71it/s]

 
</pre>

 

 

-
70%|███████   | 3522083/4997817 [00:20<00:08, 175175.68it/s]

+
69%|██████▊   | 3433866/4997817 [00:20<00:08, 174170.71it/s]

 
end{sphinxVerbatim}

 

 

 

-
70%|███████   | 3522083/4997817 [00:20<00:08, 175175.68it/s]

+
69%|██████▊   | 3433866/4997817 [00:20<00:08, 174170.71it/s]

 
 
 

@@ -6270,16 +6270,16 @@ 
Classes which are commonly mislabeled overall

 

-
71%|███████   | 3539798/4997817 [00:20&lt;00:08, 175762.45it/s]

+
69%|██████▉   | 3451295/4997817 [00:20&lt;00:08, 174202.78it/s]

 
</pre>

 

 

-
71%|███████   | 3539798/4997817 [00:20<00:08, 175762.45it/s]

+
69%|██████▉   | 3451295/4997817 [00:20<00:08, 174202.78it/s]

 
end{sphinxVerbatim}

 

 

 

-
71%|███████   | 3539798/4997817 [00:20<00:08, 175762.45it/s]

+
69%|██████▉   | 3451295/4997817 [00:20<00:08, 174202.78it/s]

 
 
 

@@ -6296,16 +6296,16 @@ 
Classes which are commonly mislabeled overall

 

-
71%|███████   | 3557423/4997817 [00:20&lt;00:08, 175905.54it/s]

+
69%|██████▉   | 3468717/4997817 [00:20&lt;00:08, 173871.49it/s]

 
</pre>

 

 

-
71%|███████   | 3557423/4997817 [00:20<00:08, 175905.54it/s]

+
69%|██████▉   | 3468717/4997817 [00:20<00:08, 173871.49it/s]

 
end{sphinxVerbatim}

 

 

 

-
71%|███████   | 3557423/4997817 [00:20<00:08, 175905.54it/s]

+
69%|██████▉   | 3468717/4997817 [00:20<00:08, 173871.49it/s]

 
 
 

@@ -6322,16 +6322,16 @@ 
Classes which are commonly mislabeled overall

 

-
72%|███████▏  | 3575120/4997817 [00:20&lt;00:08, 176219.37it/s]

+
70%|██████▉   | 3486106/4997817 [00:20&lt;00:08, 173730.48it/s]

 
</pre>

 

 

-
72%|███████▏  | 3575120/4997817 [00:20<00:08, 176219.37it/s]

+
70%|██████▉   | 3486106/4997817 [00:20<00:08, 173730.48it/s]

 
end{sphinxVerbatim}

 

 

 

-
72%|███████▏  | 3575120/4997817 [00:20<00:08, 176219.37it/s]

+
70%|██████▉   | 3486106/4997817 [00:20<00:08, 173730.48it/s]

 
 
 

@@ -6348,16 +6348,16 @@ 
Classes which are commonly mislabeled overall

 

-
72%|███████▏  | 3592743/4997817 [00:20&lt;00:07, 176124.71it/s]

+
70%|███████   | 3503480/4997817 [00:20&lt;00:08, 173576.14it/s]

 
</pre>

 

 

-
72%|███████▏  | 3592743/4997817 [00:20<00:07, 176124.71it/s]

+
70%|███████   | 3503480/4997817 [00:20<00:08, 173576.14it/s]

 
end{sphinxVerbatim}

 

 

 

-
72%|███████▏  | 3592743/4997817 [00:20<00:07, 176124.71it/s]

+
70%|███████   | 3503480/4997817 [00:20<00:08, 173576.14it/s]

 
 
 

@@ -6374,16 +6374,16 @@ 
Classes which are commonly mislabeled overall

 

-
72%|███████▏  | 3610402/4997817 [00:20&lt;00:07, 176260.53it/s]

+
70%|███████   | 3520839/4997817 [00:20&lt;00:08, 172430.10it/s]

 
</pre>

 

 

-
72%|███████▏  | 3610402/4997817 [00:20<00:07, 176260.53it/s]

+
70%|███████   | 3520839/4997817 [00:20<00:08, 172430.10it/s]

 
end{sphinxVerbatim}

 

 

 

-
72%|███████▏  | 3610402/4997817 [00:20<00:07, 176260.53it/s]

+
70%|███████   | 3520839/4997817 [00:20<00:08, 172430.10it/s]

 
 
 

@@ -6400,16 +6400,16 @@ 
Classes which are commonly mislabeled overall

 

-
73%|███████▎  | 3628029/4997817 [00:20&lt;00:07, 176148.24it/s]

+
71%|███████   | 3538084/4997817 [00:20&lt;00:08, 172099.73it/s]

 
</pre>

 

 

-
73%|███████▎  | 3628029/4997817 [00:20<00:07, 176148.24it/s]

+
71%|███████   | 3538084/4997817 [00:20<00:08, 172099.73it/s]

 
end{sphinxVerbatim}

 

 

 

-
73%|███████▎  | 3628029/4997817 [00:20<00:07, 176148.24it/s]

+
71%|███████   | 3538084/4997817 [00:20<00:08, 172099.73it/s]

 
 
 

@@ -6426,16 +6426,16 @@ 
Classes which are commonly mislabeled overall

 

-
73%|███████▎  | 3645692/4997817 [00:20&lt;00:07, 176289.26it/s]

+
71%|███████   | 3555296/4997817 [00:20&lt;00:08, 171901.03it/s]

 
</pre>

 

 

-
73%|███████▎  | 3645692/4997817 [00:20<00:07, 176289.26it/s]

+
71%|███████   | 3555296/4997817 [00:20<00:08, 171901.03it/s]

 
end{sphinxVerbatim}

 

 

 

-
73%|███████▎  | 3645692/4997817 [00:20<00:07, 176289.26it/s]

+
71%|███████   | 3555296/4997817 [00:20<00:08, 171901.03it/s]

 
 
 

@@ -6452,16 +6452,16 @@ 
Classes which are commonly mislabeled overall

 

-
73%|███████▎  | 3663322/4997817 [00:20&lt;00:07, 176222.00it/s]

+
71%|███████▏  | 3572487/4997817 [00:20&lt;00:08, 171608.38it/s]

 
</pre>

 

 

-
73%|███████▎  | 3663322/4997817 [00:20<00:07, 176222.00it/s]

+
71%|███████▏  | 3572487/4997817 [00:20<00:08, 171608.38it/s]

 
end{sphinxVerbatim}

 

 

 

-
73%|███████▎  | 3663322/4997817 [00:20<00:07, 176222.00it/s]

+
71%|███████▏  | 3572487/4997817 [00:20<00:08, 171608.38it/s]

 
 
 

@@ -6478,16 +6478,16 @@ 
Classes which are commonly mislabeled overall

 

-
74%|███████▎  | 3680980/4997817 [00:20&lt;00:07, 176324.07it/s]

+
72%|███████▏  | 3589757/4997817 [00:20&lt;00:08, 171930.02it/s]

 
</pre>

 

 

-
74%|███████▎  | 3680980/4997817 [00:20<00:07, 176324.07it/s]

+
72%|███████▏  | 3589757/4997817 [00:20<00:08, 171930.02it/s]

 
end{sphinxVerbatim}

 

 

 

-
74%|███████▎  | 3680980/4997817 [00:20<00:07, 176324.07it/s]

+
72%|███████▏  | 3589757/4997817 [00:20<00:08, 171930.02it/s]

 
 
 

@@ -6504,16 +6504,16 @@ 
Classes which are commonly mislabeled overall

 

-
74%|███████▍  | 3698735/4997817 [00:21&lt;00:07, 176687.64it/s]

+
72%|███████▏  | 3606959/4997817 [00:21&lt;00:08, 171954.77it/s]

 
</pre>

 

 

-
74%|███████▍  | 3698735/4997817 [00:21<00:07, 176687.64it/s]

+
72%|███████▏  | 3606959/4997817 [00:21<00:08, 171954.77it/s]

 
end{sphinxVerbatim}

 

 

 

-
74%|███████▍  | 3698735/4997817 [00:21<00:07, 176687.64it/s]

+
72%|███████▏  | 3606959/4997817 [00:21<00:08, 171954.77it/s]

 
 
 

@@ -6530,16 +6530,16 @@ 
Classes which are commonly mislabeled overall

 

-
74%|███████▍  | 3716404/4997817 [00:21&lt;00:07, 176628.29it/s]

+
73%|███████▎  | 3624245/4997817 [00:21&lt;00:07, 172222.71it/s]

 
</pre>

 

 

-
74%|███████▍  | 3716404/4997817 [00:21<00:07, 176628.29it/s]

+
73%|███████▎  | 3624245/4997817 [00:21<00:07, 172222.71it/s]

 
end{sphinxVerbatim}

 

 

 

-
74%|███████▍  | 3716404/4997817 [00:21<00:07, 176628.29it/s]

+
73%|███████▎  | 3624245/4997817 [00:21<00:07, 172222.71it/s]

 
 
 

@@ -6556,16 +6556,16 @@ 
Classes which are commonly mislabeled overall

 

-
75%|███████▍  | 3734091/4997817 [00:21&lt;00:07, 176697.15it/s]

+
73%|███████▎  | 3641468/4997817 [00:21&lt;00:08, 165178.06it/s]

 
</pre>

 

 

-
75%|███████▍  | 3734091/4997817 [00:21<00:07, 176697.15it/s]

+
73%|███████▎  | 3641468/4997817 [00:21<00:08, 165178.06it/s]

 
end{sphinxVerbatim}

 

 

 

-
75%|███████▍  | 3734091/4997817 [00:21<00:07, 176697.15it/s]

+
73%|███████▎  | 3641468/4997817 [00:21<00:08, 165178.06it/s]

 
 
 

@@ -6582,16 +6582,16 @@ 
Classes which are commonly mislabeled overall

 

-
75%|███████▌  | 3751761/4997817 [00:21&lt;00:07, 176640.21it/s]

+
73%|███████▎  | 3658590/4997817 [00:21&lt;00:08, 166933.37it/s]

 
</pre>

 

 

-
75%|███████▌  | 3751761/4997817 [00:21<00:07, 176640.21it/s]

+
73%|███████▎  | 3658590/4997817 [00:21<00:08, 166933.37it/s]

 
end{sphinxVerbatim}

 

 

 

-
75%|███████▌  | 3751761/4997817 [00:21<00:07, 176640.21it/s]

+
73%|███████▎  | 3658590/4997817 [00:21<00:08, 166933.37it/s]

 
 
 

@@ -6608,16 +6608,16 @@ 
Classes which are commonly mislabeled overall

 

-
75%|███████▌  | 3769457/4997817 [00:21&lt;00:06, 176732.49it/s]

+
74%|███████▎  | 3675846/4997817 [00:21&lt;00:07, 168580.91it/s]

 
</pre>

 

 

-
75%|███████▌  | 3769457/4997817 [00:21<00:06, 176732.49it/s]

+
74%|███████▎  | 3675846/4997817 [00:21<00:07, 168580.91it/s]

 
end{sphinxVerbatim}

 

 

 

-
75%|███████▌  | 3769457/4997817 [00:21<00:06, 176732.49it/s]

+
74%|███████▎  | 3675846/4997817 [00:21<00:07, 168580.91it/s]

 
 
 

@@ -6634,16 +6634,16 @@ 
Classes which are commonly mislabeled overall

 

-
76%|███████▌  | 3787209/4997817 [00:21&lt;00:06, 176965.92it/s]

+
74%|███████▍  | 3692949/4997817 [00:21&lt;00:07, 169301.80it/s]

 
</pre>

 

 

-
76%|███████▌  | 3787209/4997817 [00:21<00:06, 176965.92it/s]

+
74%|███████▍  | 3692949/4997817 [00:21<00:07, 169301.80it/s]

 
end{sphinxVerbatim}

 

 

 

-
76%|███████▌  | 3787209/4997817 [00:21<00:06, 176965.92it/s]

+
74%|███████▍  | 3692949/4997817 [00:21<00:07, 169301.80it/s]

 
 
 

@@ -6660,16 +6660,16 @@ 
Classes which are commonly mislabeled overall

 

-
76%|███████▌  | 3804906/4997817 [00:21&lt;00:06, 176115.13it/s]

+
74%|███████▍  | 3710095/4997817 [00:21&lt;00:07, 169941.03it/s]

 
</pre>

 

 

-
76%|███████▌  | 3804906/4997817 [00:21<00:06, 176115.13it/s]

+
74%|███████▍  | 3710095/4997817 [00:21<00:07, 169941.03it/s]

 
end{sphinxVerbatim}

 

 

 

-
76%|███████▌  | 3804906/4997817 [00:21<00:06, 176115.13it/s]

+
74%|███████▍  | 3710095/4997817 [00:21<00:07, 169941.03it/s]

 
 
 

@@ -6686,16 +6686,16 @@ 
Classes which are commonly mislabeled overall

 

-
76%|███████▋  | 3822589/4997817 [00:21&lt;00:06, 176324.35it/s]

+
75%|███████▍  | 3727212/4997817 [00:21&lt;00:07, 170305.46it/s]

 
</pre>

 

 

-
76%|███████▋  | 3822589/4997817 [00:21<00:06, 176324.35it/s]

+
75%|███████▍  | 3727212/4997817 [00:21<00:07, 170305.46it/s]

 
end{sphinxVerbatim}

 

 

 

-
76%|███████▋  | 3822589/4997817 [00:21<00:06, 176324.35it/s]

+
75%|███████▍  | 3727212/4997817 [00:21<00:07, 170305.46it/s]

 
 
 

@@ -6712,16 +6712,16 @@ 
Classes which are commonly mislabeled overall

 

-
77%|███████▋  | 3840267/4997817 [00:21&lt;00:06, 176456.98it/s]

+
75%|███████▍  | 3744383/4997817 [00:21&lt;00:07, 170721.03it/s]

 
</pre>

 

 

-
77%|███████▋  | 3840267/4997817 [00:21<00:06, 176456.98it/s]

+
75%|███████▍  | 3744383/4997817 [00:21<00:07, 170721.03it/s]

 
end{sphinxVerbatim}

 

 

 

-
77%|███████▋  | 3840267/4997817 [00:21<00:06, 176456.98it/s]

+
75%|███████▍  | 3744383/4997817 [00:21<00:07, 170721.03it/s]

 
 
 

@@ -6738,16 +6738,16 @@ 
Classes which are commonly mislabeled overall

 

-
77%|███████▋  | 3857949/4997817 [00:21&lt;00:06, 176561.33it/s]

+
75%|███████▌  | 3761585/4997817 [00:21&lt;00:07, 171106.29it/s]

 
</pre>

 

 

-
77%|███████▋  | 3857949/4997817 [00:21<00:06, 176561.33it/s]

+
75%|███████▌  | 3761585/4997817 [00:21<00:07, 171106.29it/s]

 
end{sphinxVerbatim}

 

 

 

-
77%|███████▋  | 3857949/4997817 [00:21<00:06, 176561.33it/s]

+
75%|███████▌  | 3761585/4997817 [00:21<00:07, 171106.29it/s]

 
 
 

@@ -6764,16 +6764,16 @@ 
Classes which are commonly mislabeled overall

 

-
78%|███████▊  | 3875608/4997817 [00:22&lt;00:06, 176566.31it/s]

+
76%|███████▌  | 3778703/4997817 [00:22&lt;00:07, 171043.71it/s]

 
</pre>

 

 

-
78%|███████▊  | 3875608/4997817 [00:22<00:06, 176566.31it/s]

+
76%|███████▌  | 3778703/4997817 [00:22<00:07, 171043.71it/s]

 
end{sphinxVerbatim}

 

 

 

-
78%|███████▊  | 3875608/4997817 [00:22<00:06, 176566.31it/s]

+
76%|███████▌  | 3778703/4997817 [00:22<00:07, 171043.71it/s]

 
 
 

@@ -6790,16 +6790,16 @@ 
Classes which are commonly mislabeled overall

 

-
78%|███████▊  | 3893265/4997817 [00:22&lt;00:06, 176410.01it/s]

+
76%|███████▌  | 3795890/4997817 [00:22&lt;00:07, 171287.44it/s]

 
</pre>

 

 

-
78%|███████▊  | 3893265/4997817 [00:22<00:06, 176410.01it/s]

+
76%|███████▌  | 3795890/4997817 [00:22<00:07, 171287.44it/s]

 
end{sphinxVerbatim}

 

 

 

-
78%|███████▊  | 3893265/4997817 [00:22<00:06, 176410.01it/s]

+
76%|███████▌  | 3795890/4997817 [00:22<00:07, 171287.44it/s]

 
 
 

@@ -6816,16 +6816,16 @@ 
Classes which are commonly mislabeled overall

 

-
78%|███████▊  | 3910907/4997817 [00:22&lt;00:06, 176281.59it/s]

+
76%|███████▋  | 3813023/4997817 [00:22&lt;00:06, 170783.59it/s]

 
</pre>

 

 

-
78%|███████▊  | 3910907/4997817 [00:22<00:06, 176281.59it/s]

+
76%|███████▋  | 3813023/4997817 [00:22<00:06, 170783.59it/s]

 
end{sphinxVerbatim}

 

 

 

-
78%|███████▊  | 3910907/4997817 [00:22<00:06, 176281.59it/s]

+
76%|███████▋  | 3813023/4997817 [00:22<00:06, 170783.59it/s]

 
 
 

@@ -6842,16 +6842,16 @@ 
Classes which are commonly mislabeled overall

 

-
79%|███████▊  | 3928536/4997817 [00:22&lt;00:06, 176164.37it/s]

+
77%|███████▋  | 3830175/4997817 [00:22&lt;00:06, 171001.66it/s]

 
</pre>

 

 

-
79%|███████▊  | 3928536/4997817 [00:22<00:06, 176164.37it/s]

+
77%|███████▋  | 3830175/4997817 [00:22<00:06, 171001.66it/s]

 
end{sphinxVerbatim}

 

 

 

-
79%|███████▊  | 3928536/4997817 [00:22<00:06, 176164.37it/s]

+
77%|███████▋  | 3830175/4997817 [00:22<00:06, 171001.66it/s]

 
 
 

@@ -6868,16 +6868,16 @@ 
Classes which are commonly mislabeled overall

 

-
79%|███████▉  | 3946173/4997817 [00:22&lt;00:05, 176221.61it/s]

+
77%|███████▋  | 3847278/4997817 [00:22&lt;00:06, 170875.44it/s]

 
</pre>

 

 

-
79%|███████▉  | 3946173/4997817 [00:22<00:05, 176221.61it/s]

+
77%|███████▋  | 3847278/4997817 [00:22<00:06, 170875.44it/s]

 
end{sphinxVerbatim}

 

 

 

-
79%|███████▉  | 3946173/4997817 [00:22<00:05, 176221.61it/s]

+
77%|███████▋  | 3847278/4997817 [00:22<00:06, 170875.44it/s]

 
 
 

@@ -6894,16 +6894,16 @@ 
Classes which are commonly mislabeled overall

 

-
79%|███████▉  | 3963796/4997817 [00:22&lt;00:05, 175821.79it/s]

+
77%|███████▋  | 3864410/4997817 [00:22&lt;00:06, 171005.16it/s]

 
</pre>

 

 

-
79%|███████▉  | 3963796/4997817 [00:22<00:05, 175821.79it/s]

+
77%|███████▋  | 3864410/4997817 [00:22<00:06, 171005.16it/s]

 
end{sphinxVerbatim}

 

 

 

-
79%|███████▉  | 3963796/4997817 [00:22<00:05, 175821.79it/s]

+
77%|███████▋  | 3864410/4997817 [00:22<00:06, 171005.16it/s]

 
 
 

@@ -6920,16 +6920,16 @@ 
Classes which are commonly mislabeled overall

 

-
80%|███████▉  | 3981379/4997817 [00:22&lt;00:05, 175679.25it/s]

+
78%|███████▊  | 3881633/4997817 [00:22&lt;00:06, 171370.89it/s]

 
</pre>

 

 

-
80%|███████▉  | 3981379/4997817 [00:22<00:05, 175679.25it/s]

+
78%|███████▊  | 3881633/4997817 [00:22<00:06, 171370.89it/s]

 
end{sphinxVerbatim}

 

 

 

-
80%|███████▉  | 3981379/4997817 [00:22<00:05, 175679.25it/s]

+
78%|███████▊  | 3881633/4997817 [00:22<00:06, 171370.89it/s]

 
 
 

@@ -6946,16 +6946,16 @@ 
Classes which are commonly mislabeled overall

 

-
80%|████████  | 3999029/4997817 [00:22&lt;00:05, 175919.84it/s]

+
78%|███████▊  | 3898771/4997817 [00:22&lt;00:06, 170584.23it/s]

 
</pre>

 

 

-
80%|████████  | 3999029/4997817 [00:22<00:05, 175919.84it/s]

+
78%|███████▊  | 3898771/4997817 [00:22<00:06, 170584.23it/s]

 
end{sphinxVerbatim}

 

 

 

-
80%|████████  | 3999029/4997817 [00:22<00:05, 175919.84it/s]

+
78%|███████▊  | 3898771/4997817 [00:22<00:06, 170584.23it/s]

 
 
 

@@ -6972,16 +6972,16 @@ 
Classes which are commonly mislabeled overall

 

-
80%|████████  | 4016746/4997817 [00:22&lt;00:05, 176291.45it/s]

+
78%|███████▊  | 3915831/4997817 [00:22&lt;00:06, 169965.52it/s]

 
</pre>

 

 

-
80%|████████  | 4016746/4997817 [00:22<00:05, 176291.45it/s]

+
78%|███████▊  | 3915831/4997817 [00:22<00:06, 169965.52it/s]

 
end{sphinxVerbatim}

 

 

 

-
80%|████████  | 4016746/4997817 [00:22<00:05, 176291.45it/s]

+
78%|███████▊  | 3915831/4997817 [00:22<00:06, 169965.52it/s]

 
 
 

@@ -6998,16 +6998,16 @@ 
Classes which are commonly mislabeled overall

 

-
81%|████████  | 4034376/4997817 [00:22&lt;00:05, 176165.06it/s]

+
79%|███████▊  | 3932869/4997817 [00:22&lt;00:06, 170087.91it/s]

 
</pre>

 

 

-
81%|████████  | 4034376/4997817 [00:22<00:05, 176165.06it/s]

+
79%|███████▊  | 3932869/4997817 [00:22<00:06, 170087.91it/s]

 
end{sphinxVerbatim}

 

 

 

-
81%|████████  | 4034376/4997817 [00:22<00:05, 176165.06it/s]

+
79%|███████▊  | 3932869/4997817 [00:22<00:06, 170087.91it/s]

 
 
 

@@ -7024,16 +7024,16 @@ 
Classes which are commonly mislabeled overall

 

-
81%|████████  | 4051993/4997817 [00:23&lt;00:05, 176084.45it/s]

+
79%|███████▉  | 3949879/4997817 [00:23&lt;00:06, 169990.31it/s]

 
</pre>

 

 

-
81%|████████  | 4051993/4997817 [00:23<00:05, 176084.45it/s]

+
79%|███████▉  | 3949879/4997817 [00:23<00:06, 169990.31it/s]

 
end{sphinxVerbatim}

 

 

 

-
81%|████████  | 4051993/4997817 [00:23<00:05, 176084.45it/s]

+
79%|███████▉  | 3949879/4997817 [00:23<00:06, 169990.31it/s]

 
 
 

@@ -7050,16 +7050,16 @@ 
Classes which are commonly mislabeled overall

 

-
81%|████████▏ | 4069602/4997817 [00:23&lt;00:05, 175885.63it/s]

+
79%|███████▉  | 3966879/4997817 [00:23&lt;00:06, 169949.51it/s]

 
</pre>

 

 

-
81%|████████▏ | 4069602/4997817 [00:23<00:05, 175885.63it/s]

+
79%|███████▉  | 3966879/4997817 [00:23<00:06, 169949.51it/s]

 
end{sphinxVerbatim}

 

 

 

-
81%|████████▏ | 4069602/4997817 [00:23<00:05, 175885.63it/s]

+
79%|███████▉  | 3966879/4997817 [00:23<00:06, 169949.51it/s]

 
 
 

@@ -7076,16 +7076,16 @@ 
Classes which are commonly mislabeled overall

 

-
82%|████████▏ | 4087191/4997817 [00:23&lt;00:05, 175394.53it/s]

+
80%|███████▉  | 3983875/4997817 [00:23&lt;00:05, 169772.79it/s]

 
</pre>

 

 

-
82%|████████▏ | 4087191/4997817 [00:23<00:05, 175394.53it/s]

+
80%|███████▉  | 3983875/4997817 [00:23<00:05, 169772.79it/s]

 
end{sphinxVerbatim}

 

 

 

-
82%|████████▏ | 4087191/4997817 [00:23<00:05, 175394.53it/s]

+
80%|███████▉  | 3983875/4997817 [00:23<00:05, 169772.79it/s]

 
 
 

@@ -7102,16 +7102,16 @@ 
Classes which are commonly mislabeled overall

 

-
82%|████████▏ | 4104731/4997817 [00:23&lt;00:05, 175012.49it/s]

+
80%|████████  | 4000853/4997817 [00:23&lt;00:05, 169718.68it/s]

 
</pre>

 

 

-
82%|████████▏ | 4104731/4997817 [00:23<00:05, 175012.49it/s]

+
80%|████████  | 4000853/4997817 [00:23<00:05, 169718.68it/s]

 
end{sphinxVerbatim}

 

 

 

-
82%|████████▏ | 4104731/4997817 [00:23<00:05, 175012.49it/s]

+
80%|████████  | 4000853/4997817 [00:23<00:05, 169718.68it/s]

 
 
 

@@ -7128,16 +7128,16 @@ 
Classes which are commonly mislabeled overall

 

-
82%|████████▏ | 4122236/4997817 [00:23&lt;00:05, 175020.26it/s]

+
80%|████████  | 4017853/4997817 [00:23&lt;00:05, 169799.69it/s]

 
</pre>

 

 

-
82%|████████▏ | 4122236/4997817 [00:23<00:05, 175020.26it/s]

+
80%|████████  | 4017853/4997817 [00:23<00:05, 169799.69it/s]

 
end{sphinxVerbatim}

 

 

 

-
82%|████████▏ | 4122236/4997817 [00:23<00:05, 175020.26it/s]

+
80%|████████  | 4017853/4997817 [00:23<00:05, 169799.69it/s]

 
 
 

@@ -7154,16 +7154,16 @@ 
Classes which are commonly mislabeled overall

 

-
83%|████████▎ | 4139739/4997817 [00:23&lt;00:04, 174861.56it/s]

+
81%|████████  | 4034999/4997817 [00:23&lt;00:05, 170291.45it/s]

 
</pre>

 

 

-
83%|████████▎ | 4139739/4997817 [00:23<00:04, 174861.56it/s]

+
81%|████████  | 4034999/4997817 [00:23<00:05, 170291.45it/s]

 
end{sphinxVerbatim}

 

 

 

-
83%|████████▎ | 4139739/4997817 [00:23<00:04, 174861.56it/s]

+
81%|████████  | 4034999/4997817 [00:23<00:05, 170291.45it/s]

 
 
 

@@ -7180,16 +7180,16 @@ 
Classes which are commonly mislabeled overall

 

-
83%|████████▎ | 4157233/4997817 [00:23&lt;00:04, 174879.84it/s]

+
81%|████████  | 4052095/4997817 [00:23&lt;00:05, 170489.07it/s]

 
</pre>

 

 

-
83%|████████▎ | 4157233/4997817 [00:23<00:04, 174879.84it/s]

+
81%|████████  | 4052095/4997817 [00:23<00:05, 170489.07it/s]

 
end{sphinxVerbatim}

 

 

 

-
83%|████████▎ | 4157233/4997817 [00:23<00:04, 174879.84it/s]

+
81%|████████  | 4052095/4997817 [00:23<00:05, 170489.07it/s]

 
 
 

@@ -7206,16 +7206,16 @@ 
Classes which are commonly mislabeled overall

 

-
84%|████████▎ | 4174722/4997817 [00:23&lt;00:04, 167593.86it/s]

+
81%|████████▏ | 4069145/4997817 [00:23&lt;00:05, 170353.71it/s]

 
</pre>

 

 

-
84%|████████▎ | 4174722/4997817 [00:23<00:04, 167593.86it/s]

+
81%|████████▏ | 4069145/4997817 [00:23<00:05, 170353.71it/s]

 
end{sphinxVerbatim}

 

 

 

-
84%|████████▎ | 4174722/4997817 [00:23<00:04, 167593.86it/s]

+
81%|████████▏ | 4069145/4997817 [00:23<00:05, 170353.71it/s]

 
 
 

@@ -7232,16 +7232,16 @@ 
Classes which are commonly mislabeled overall

 

-
84%|████████▍ | 4192083/4997817 [00:23&lt;00:04, 169341.24it/s]

+
82%|████████▏ | 4086208/4997817 [00:23&lt;00:05, 170433.51it/s]

 
</pre>

 

 

-
84%|████████▍ | 4192083/4997817 [00:23<00:04, 169341.24it/s]

+
82%|████████▏ | 4086208/4997817 [00:23<00:05, 170433.51it/s]

 
end{sphinxVerbatim}

 

 

 

-
84%|████████▍ | 4192083/4997817 [00:23<00:04, 169341.24it/s]

+
82%|████████▏ | 4086208/4997817 [00:23<00:05, 170433.51it/s]

 
 
 

@@ -7258,16 +7258,16 @@ 
Classes which are commonly mislabeled overall

 

-
84%|████████▍ | 4209474/4997817 [00:23&lt;00:04, 170677.23it/s]

+
82%|████████▏ | 4103252/4997817 [00:23&lt;00:05, 169878.09it/s]

 
</pre>

 

 

-
84%|████████▍ | 4209474/4997817 [00:23<00:04, 170677.23it/s]

+
82%|████████▏ | 4103252/4997817 [00:23<00:05, 169878.09it/s]

 
end{sphinxVerbatim}

 

 

 

-
84%|████████▍ | 4209474/4997817 [00:23<00:04, 170677.23it/s]

+
82%|████████▏ | 4103252/4997817 [00:23<00:05, 169878.09it/s]

 
 
 

@@ -7284,16 +7284,16 @@ 
Classes which are commonly mislabeled overall

 

-
85%|████████▍ | 4226976/4997817 [00:24&lt;00:04, 171956.01it/s]

+
82%|████████▏ | 4120406/4997817 [00:24&lt;00:05, 170373.48it/s]

 
</pre>

 

 

-
85%|████████▍ | 4226976/4997817 [00:24<00:04, 171956.01it/s]

+
82%|████████▏ | 4120406/4997817 [00:24<00:05, 170373.48it/s]

 
end{sphinxVerbatim}

 

 

 

-
85%|████████▍ | 4226976/4997817 [00:24<00:04, 171956.01it/s]

+
82%|████████▏ | 4120406/4997817 [00:24<00:05, 170373.48it/s]

 
 
 

@@ -7310,16 +7310,16 @@ 
Classes which are commonly mislabeled overall

 

-
85%|████████▍ | 4244411/4997817 [00:24&lt;00:04, 172661.73it/s]

+
83%|████████▎ | 4137520/4997817 [00:24&lt;00:05, 170600.48it/s]

 
</pre>

 

 

-
85%|████████▍ | 4244411/4997817 [00:24<00:04, 172661.73it/s]

+
83%|████████▎ | 4137520/4997817 [00:24<00:05, 170600.48it/s]

 
end{sphinxVerbatim}

 

 

 

-
85%|████████▍ | 4244411/4997817 [00:24<00:04, 172661.73it/s]

+
83%|████████▎ | 4137520/4997817 [00:24<00:05, 170600.48it/s]

 
 
 

@@ -7336,16 +7336,16 @@ 
Classes which are commonly mislabeled overall

 

-
85%|████████▌ | 4261828/4997817 [00:24&lt;00:04, 173107.14it/s]

+
83%|████████▎ | 4154679/4997817 [00:24&lt;00:04, 170894.40it/s]

 
</pre>

 

 

-
85%|████████▌ | 4261828/4997817 [00:24<00:04, 173107.14it/s]

+
83%|████████▎ | 4154679/4997817 [00:24<00:04, 170894.40it/s]

 
end{sphinxVerbatim}

 

 

 

-
85%|████████▌ | 4261828/4997817 [00:24<00:04, 173107.14it/s]

+
83%|████████▎ | 4154679/4997817 [00:24<00:04, 170894.40it/s]

 
 
 

@@ -7362,16 +7362,16 @@ 
Classes which are commonly mislabeled overall

 

-
86%|████████▌ | 4279356/4997817 [00:24&lt;00:04, 173752.22it/s]

+
83%|████████▎ | 4171769/4997817 [00:24&lt;00:04, 170333.58it/s]

 
</pre>

 

 

-
86%|████████▌ | 4279356/4997817 [00:24<00:04, 173752.22it/s]

+
83%|████████▎ | 4171769/4997817 [00:24<00:04, 170333.58it/s]

 
end{sphinxVerbatim}

 

 

 

-
86%|████████▌ | 4279356/4997817 [00:24<00:04, 173752.22it/s]

+
83%|████████▎ | 4171769/4997817 [00:24<00:04, 170333.58it/s]

 
 
 

@@ -7388,16 +7388,16 @@ 
Classes which are commonly mislabeled overall

 

-
86%|████████▌ | 4296896/4997817 [00:24&lt;00:04, 174242.38it/s]

+
84%|████████▍ | 4188803/4997817 [00:24&lt;00:04, 169923.43it/s]

 
</pre>

 

 

-
86%|████████▌ | 4296896/4997817 [00:24<00:04, 174242.38it/s]

+
84%|████████▍ | 4188803/4997817 [00:24<00:04, 169923.43it/s]

 
end{sphinxVerbatim}

 

 

 

-
86%|████████▌ | 4296896/4997817 [00:24<00:04, 174242.38it/s]

+
84%|████████▍ | 4188803/4997817 [00:24<00:04, 169923.43it/s]

 
 
 

@@ -7414,16 +7414,16 @@ 
Classes which are commonly mislabeled overall

 

-
86%|████████▋ | 4314328/4997817 [00:24&lt;00:03, 173327.93it/s]

+
84%|████████▍ | 4205796/4997817 [00:24&lt;00:04, 169523.93it/s]

 
</pre>

 

 

-
86%|████████▋ | 4314328/4997817 [00:24<00:03, 173327.93it/s]

+
84%|████████▍ | 4205796/4997817 [00:24<00:04, 169523.93it/s]

 
end{sphinxVerbatim}

 

 

 

-
86%|████████▋ | 4314328/4997817 [00:24<00:03, 173327.93it/s]

+
84%|████████▍ | 4205796/4997817 [00:24<00:04, 169523.93it/s]

 
 
 

@@ -7440,16 +7440,16 @@ 
Classes which are commonly mislabeled overall

 

-
87%|████████▋ | 4331919/4997817 [00:24&lt;00:03, 174094.65it/s]

+
84%|████████▍ | 4222817/4997817 [00:24&lt;00:04, 169724.32it/s]

 
</pre>

 

 

-
87%|████████▋ | 4331919/4997817 [00:24<00:03, 174094.65it/s]

+
84%|████████▍ | 4222817/4997817 [00:24<00:04, 169724.32it/s]

 
end{sphinxVerbatim}

 

 

 

-
87%|████████▋ | 4331919/4997817 [00:24<00:03, 174094.65it/s]

+
84%|████████▍ | 4222817/4997817 [00:24<00:04, 169724.32it/s]

 
 
 

@@ -7466,16 +7466,16 @@ 
Classes which are commonly mislabeled overall

 

-
87%|████████▋ | 4349427/4997817 [00:24&lt;00:03, 174384.04it/s]

+
85%|████████▍ | 4239790/4997817 [00:24&lt;00:04, 169579.63it/s]

 
</pre>

 

 

-
87%|████████▋ | 4349427/4997817 [00:24<00:03, 174384.04it/s]

+
85%|████████▍ | 4239790/4997817 [00:24<00:04, 169579.63it/s]

 
end{sphinxVerbatim}

 

 

 

-
87%|████████▋ | 4349427/4997817 [00:24<00:03, 174384.04it/s]

+
85%|████████▍ | 4239790/4997817 [00:24<00:04, 169579.63it/s]

 
 
 

@@ -7492,16 +7492,16 @@ 
Classes which are commonly mislabeled overall

 

-
87%|████████▋ | 4367051/4997817 [00:24&lt;00:03, 174935.33it/s]

+
85%|████████▌ | 4256818/4997817 [00:24&lt;00:04, 169786.18it/s]

 
</pre>

 

 

-
87%|████████▋ | 4367051/4997817 [00:24<00:03, 174935.33it/s]

+
85%|████████▌ | 4256818/4997817 [00:24<00:04, 169786.18it/s]

 
end{sphinxVerbatim}

 

 

 

-
87%|████████▋ | 4367051/4997817 [00:24<00:03, 174935.33it/s]

+
85%|████████▌ | 4256818/4997817 [00:24<00:04, 169786.18it/s]

 
 
 

@@ -7518,16 +7518,16 @@ 
Classes which are commonly mislabeled overall

 

-
88%|████████▊ | 4384595/4997817 [00:24&lt;00:03, 175083.73it/s]

+
86%|████████▌ | 4273871/4997817 [00:24&lt;00:04, 170006.16it/s]

 
</pre>

 

 

-
88%|████████▊ | 4384595/4997817 [00:24<00:03, 175083.73it/s]

+
86%|████████▌ | 4273871/4997817 [00:24<00:04, 170006.16it/s]

 
end{sphinxVerbatim}

 

 

 

-
88%|████████▊ | 4384595/4997817 [00:24<00:03, 175083.73it/s]

+
86%|████████▌ | 4273871/4997817 [00:24<00:04, 170006.16it/s]

 
 
 

@@ -7544,16 +7544,16 @@ 
Classes which are commonly mislabeled overall

 

-
88%|████████▊ | 4402158/4997817 [00:25&lt;00:03, 175244.18it/s]

+
86%|████████▌ | 4291017/4997817 [00:25&lt;00:04, 170438.96it/s]

 
</pre>

 

 

-
88%|████████▊ | 4402158/4997817 [00:25<00:03, 175244.18it/s]

+
86%|████████▌ | 4291017/4997817 [00:25<00:04, 170438.96it/s]

 
end{sphinxVerbatim}

 

 

 

-
88%|████████▊ | 4402158/4997817 [00:25<00:03, 175244.18it/s]

+
86%|████████▌ | 4291017/4997817 [00:25<00:04, 170438.96it/s]

 
 
 

@@ -7570,16 +7570,16 @@ 
Classes which are commonly mislabeled overall

 

-
88%|████████▊ | 4419786/4997817 [00:25&lt;00:03, 175552.04it/s]

+
86%|████████▌ | 4308086/4997817 [00:25&lt;00:04, 170511.81it/s]

 
</pre>

 

 

-
88%|████████▊ | 4419786/4997817 [00:25<00:03, 175552.04it/s]

+
86%|████████▌ | 4308086/4997817 [00:25<00:04, 170511.81it/s]

 
end{sphinxVerbatim}

 

 

 

-
88%|████████▊ | 4419786/4997817 [00:25<00:03, 175552.04it/s]

+
86%|████████▌ | 4308086/4997817 [00:25<00:04, 170511.81it/s]

 
 
 

@@ -7596,16 +7596,16 @@ 
Classes which are commonly mislabeled overall

 

-
89%|████████▉ | 4437371/4997817 [00:25&lt;00:03, 175636.52it/s]

+
87%|████████▋ | 4325138/4997817 [00:25&lt;00:03, 169547.73it/s]

 
</pre>

 

 

-
89%|████████▉ | 4437371/4997817 [00:25<00:03, 175636.52it/s]

+
87%|████████▋ | 4325138/4997817 [00:25<00:03, 169547.73it/s]

 
end{sphinxVerbatim}

 

 

 

-
89%|████████▉ | 4437371/4997817 [00:25<00:03, 175636.52it/s]

+
87%|████████▋ | 4325138/4997817 [00:25<00:03, 169547.73it/s]

 
 
 

@@ -7622,16 +7622,16 @@ 
Classes which are commonly mislabeled overall

 

-
89%|████████▉ | 4455003/4997817 [00:25&lt;00:03, 175837.48it/s]

+
87%|████████▋ | 4342095/4997817 [00:25&lt;00:04, 163414.55it/s]

 
</pre>

 

 

-
89%|████████▉ | 4455003/4997817 [00:25<00:03, 175837.48it/s]

+
87%|████████▋ | 4342095/4997817 [00:25<00:04, 163414.55it/s]

 
end{sphinxVerbatim}

 

 

 

-
89%|████████▉ | 4455003/4997817 [00:25<00:03, 175837.48it/s]

+
87%|████████▋ | 4342095/4997817 [00:25<00:04, 163414.55it/s]

 
 
 

@@ -7648,16 +7648,16 @@ 
Classes which are commonly mislabeled overall

 

-
89%|████████▉ | 4472798/4997817 [00:25&lt;00:02, 176468.40it/s]

+
87%|████████▋ | 4359299/4997817 [00:25&lt;00:03, 165924.16it/s]

 
</pre>

 

 

-
89%|████████▉ | 4472798/4997817 [00:25<00:02, 176468.40it/s]

+
87%|████████▋ | 4359299/4997817 [00:25<00:03, 165924.16it/s]

 
end{sphinxVerbatim}

 

 

 

-
89%|████████▉ | 4472798/4997817 [00:25<00:02, 176468.40it/s]

+
87%|████████▋ | 4359299/4997817 [00:25<00:03, 165924.16it/s]

 
 
 

@@ -7674,16 +7674,16 @@ 
Classes which are commonly mislabeled overall

 

-
90%|████████▉ | 4490560/4997817 [00:25&lt;00:02, 176808.91it/s]

+
88%|████████▊ | 4376343/4997817 [00:25&lt;00:03, 167246.68it/s]

 
</pre>

 

 

-
90%|████████▉ | 4490560/4997817 [00:25<00:02, 176808.91it/s]

+
88%|████████▊ | 4376343/4997817 [00:25<00:03, 167246.68it/s]

 
end{sphinxVerbatim}

 

 

 

-
90%|████████▉ | 4490560/4997817 [00:25<00:02, 176808.91it/s]

+
88%|████████▊ | 4376343/4997817 [00:25<00:03, 167246.68it/s]

 
 
 

@@ -7700,16 +7700,16 @@ 
Classes which are commonly mislabeled overall

 

-
90%|█████████ | 4508294/4997817 [00:25&lt;00:02, 176965.60it/s]

+
88%|████████▊ | 4393409/4997817 [00:25&lt;00:03, 168254.06it/s]

 
</pre>

 

 

-
90%|█████████ | 4508294/4997817 [00:25<00:02, 176965.60it/s]

+
88%|████████▊ | 4393409/4997817 [00:25<00:03, 168254.06it/s]

 
end{sphinxVerbatim}

 

 

 

-
90%|█████████ | 4508294/4997817 [00:25<00:02, 176965.60it/s]

+
88%|████████▊ | 4393409/4997817 [00:25<00:03, 168254.06it/s]

 
 
 

@@ -7726,16 +7726,16 @@ 
Classes which are commonly mislabeled overall

 

-
91%|█████████ | 4525991/4997817 [00:25&lt;00:02, 176542.13it/s]

+
88%|████████▊ | 4410560/4997817 [00:25&lt;00:03, 169219.14it/s]

 
</pre>

 

 

-
91%|█████████ | 4525991/4997817 [00:25<00:02, 176542.13it/s]

+
88%|████████▊ | 4410560/4997817 [00:25<00:03, 169219.14it/s]

 
end{sphinxVerbatim}

 

 

 

-
91%|█████████ | 4525991/4997817 [00:25<00:02, 176542.13it/s]

+
88%|████████▊ | 4410560/4997817 [00:25<00:03, 169219.14it/s]

 
 
 

@@ -7752,16 +7752,16 @@ 
Classes which are commonly mislabeled overall

 

-
91%|█████████ | 4543710/4997817 [00:25&lt;00:02, 176732.28it/s]

+
89%|████████▊ | 4427598/4997817 [00:25&lt;00:03, 169562.46it/s]

 
</pre>

 

 

-
91%|█████████ | 4543710/4997817 [00:25<00:02, 176732.28it/s]

+
89%|████████▊ | 4427598/4997817 [00:25<00:03, 169562.46it/s]

 
end{sphinxVerbatim}

 

 

 

-
91%|█████████ | 4543710/4997817 [00:25<00:02, 176732.28it/s]

+
89%|████████▊ | 4427598/4997817 [00:25<00:03, 169562.46it/s]

 
 
 

@@ -7778,16 +7778,16 @@ 
Classes which are commonly mislabeled overall

 

-
91%|█████████▏| 4561463/4997817 [00:25&lt;00:02, 176966.16it/s]

+
89%|████████▉ | 4444705/4997817 [00:25&lt;00:03, 170009.98it/s]

 
</pre>

 

 

-
91%|█████████▏| 4561463/4997817 [00:25<00:02, 176966.16it/s]

+
89%|████████▉ | 4444705/4997817 [00:25<00:03, 170009.98it/s]

 
end{sphinxVerbatim}

 

 

 

-
91%|█████████▏| 4561463/4997817 [00:25<00:02, 176966.16it/s]

+
89%|████████▉ | 4444705/4997817 [00:25<00:03, 170009.98it/s]

 
 
 

@@ -7804,16 +7804,16 @@ 
Classes which are commonly mislabeled overall

 

-
92%|█████████▏| 4579245/4997817 [00:26&lt;00:02, 177220.61it/s]

+
89%|████████▉ | 4461912/4997817 [00:26&lt;00:03, 170621.62it/s]

 
</pre>

 

 

-
92%|█████████▏| 4579245/4997817 [00:26<00:02, 177220.61it/s]

+
89%|████████▉ | 4461912/4997817 [00:26<00:03, 170621.62it/s]

 
end{sphinxVerbatim}

 

 

 

-
92%|█████████▏| 4579245/4997817 [00:26<00:02, 177220.61it/s]

+
89%|████████▉ | 4461912/4997817 [00:26<00:03, 170621.62it/s]

 
 
 

@@ -7830,16 +7830,16 @@ 
Classes which are commonly mislabeled overall

 

-
92%|█████████▏| 4597064/4997817 [00:26&lt;00:02, 177506.60it/s]

+
90%|████████▉ | 4478981/4997817 [00:26&lt;00:03, 170378.09it/s]

 
</pre>

 

 

-
92%|█████████▏| 4597064/4997817 [00:26<00:02, 177506.60it/s]

+
90%|████████▉ | 4478981/4997817 [00:26<00:03, 170378.09it/s]

 
end{sphinxVerbatim}

 

 

 

-
92%|█████████▏| 4597064/4997817 [00:26<00:02, 177506.60it/s]

+
90%|████████▉ | 4478981/4997817 [00:26<00:03, 170378.09it/s]

 
 
 

@@ -7856,16 +7856,16 @@ 
Classes which are commonly mislabeled overall

 

-
92%|█████████▏| 4614841/4997817 [00:26&lt;00:02, 177581.31it/s]

+
90%|████████▉ | 4496051/4997817 [00:26&lt;00:02, 170472.86it/s]

 
</pre>

 

 

-
92%|█████████▏| 4614841/4997817 [00:26<00:02, 177581.31it/s]

+
90%|████████▉ | 4496051/4997817 [00:26<00:02, 170472.86it/s]

 
end{sphinxVerbatim}

 

 

 

-
92%|█████████▏| 4614841/4997817 [00:26<00:02, 177581.31it/s]

+
90%|████████▉ | 4496051/4997817 [00:26<00:02, 170472.86it/s]

 
 
 

@@ -7882,16 +7882,16 @@ 
Classes which are commonly mislabeled overall

 

-
93%|█████████▎| 4632600/4997817 [00:26&lt;00:02, 177414.75it/s]

+
90%|█████████ | 4513109/4997817 [00:26&lt;00:02, 170500.24it/s]

 
</pre>

 

 

-
93%|█████████▎| 4632600/4997817 [00:26<00:02, 177414.75it/s]

+
90%|█████████ | 4513109/4997817 [00:26<00:02, 170500.24it/s]

 
end{sphinxVerbatim}

 

 

 

-
93%|█████████▎| 4632600/4997817 [00:26<00:02, 177414.75it/s]

+
90%|█████████ | 4513109/4997817 [00:26<00:02, 170500.24it/s]

 
 
 

@@ -7908,16 +7908,16 @@ 
Classes which are commonly mislabeled overall

 

-
93%|█████████▎| 4650342/4997817 [00:26&lt;00:01, 176810.52it/s]

+
91%|█████████ | 4530422/4997817 [00:26&lt;00:02, 171284.56it/s]

 
</pre>

 

 

-
93%|█████████▎| 4650342/4997817 [00:26<00:01, 176810.52it/s]

+
91%|█████████ | 4530422/4997817 [00:26<00:02, 171284.56it/s]

 
end{sphinxVerbatim}

 

 

 

-
93%|█████████▎| 4650342/4997817 [00:26<00:01, 176810.52it/s]

+
91%|█████████ | 4530422/4997817 [00:26<00:02, 171284.56it/s]

 
 
 

@@ -7934,16 +7934,16 @@ 
Classes which are commonly mislabeled overall

 

-
93%|█████████▎| 4668024/4997817 [00:26&lt;00:02, 163789.41it/s]

+
91%|█████████ | 4547617/4997817 [00:26&lt;00:02, 171480.12it/s]

 
</pre>

 

 

-
93%|█████████▎| 4668024/4997817 [00:26<00:02, 163789.41it/s]

+
91%|█████████ | 4547617/4997817 [00:26<00:02, 171480.12it/s]

 
end{sphinxVerbatim}

 

 

 

-
93%|█████████▎| 4668024/4997817 [00:26<00:02, 163789.41it/s]

+
91%|█████████ | 4547617/4997817 [00:26<00:02, 171480.12it/s]

 
 
 

@@ -7960,16 +7960,16 @@ 
Classes which are commonly mislabeled overall

 

-
94%|█████████▍| 4685720/4997817 [00:26&lt;00:01, 167519.88it/s]

+
91%|█████████▏| 4564863/4997817 [00:26&lt;00:02, 171770.24it/s]

 
</pre>

 

 

-
94%|█████████▍| 4685720/4997817 [00:26<00:01, 167519.88it/s]

+
91%|█████████▏| 4564863/4997817 [00:26<00:02, 171770.24it/s]

 
end{sphinxVerbatim}

 

 

 

-
94%|█████████▍| 4685720/4997817 [00:26<00:01, 167519.88it/s]

+
91%|█████████▏| 4564863/4997817 [00:26<00:02, 171770.24it/s]

 
 
 

@@ -7986,16 +7986,16 @@ 
Classes which are commonly mislabeled overall

 

-
94%|█████████▍| 4703228/4997817 [00:26&lt;00:01, 169696.70it/s]

+
92%|█████████▏| 4582041/4997817 [00:26&lt;00:02, 171252.29it/s]

 
</pre>

 

 

-
94%|█████████▍| 4703228/4997817 [00:26<00:01, 169696.70it/s]

+
92%|█████████▏| 4582041/4997817 [00:26<00:02, 171252.29it/s]

 
end{sphinxVerbatim}

 

 

 

-
94%|█████████▍| 4703228/4997817 [00:26<00:01, 169696.70it/s]

+
92%|█████████▏| 4582041/4997817 [00:26<00:02, 171252.29it/s]

 
 
 

@@ -8012,16 +8012,16 @@ 
Classes which are commonly mislabeled overall

 

-
94%|█████████▍| 4720997/4997817 [00:26&lt;00:01, 172024.75it/s]

+
92%|█████████▏| 4599168/4997817 [00:26&lt;00:02, 170751.33it/s]

 
</pre>

 

 

-
94%|█████████▍| 4720997/4997817 [00:26<00:01, 172024.75it/s]

+
92%|█████████▏| 4599168/4997817 [00:26<00:02, 170751.33it/s]

 
end{sphinxVerbatim}

 

 

 

-
94%|█████████▍| 4720997/4997817 [00:26<00:01, 172024.75it/s]

+
92%|█████████▏| 4599168/4997817 [00:26<00:02, 170751.33it/s]

 
 
 

@@ -8038,16 +8038,16 @@ 
Classes which are commonly mislabeled overall

 

-
95%|█████████▍| 4738747/4997817 [00:26&lt;00:01, 173631.53it/s]

+
92%|█████████▏| 4616245/4997817 [00:26&lt;00:02, 170236.58it/s]

 
</pre>

 

 

-
95%|█████████▍| 4738747/4997817 [00:26<00:01, 173631.53it/s]

+
92%|█████████▏| 4616245/4997817 [00:26<00:02, 170236.58it/s]

 
end{sphinxVerbatim}

 

 

 

-
95%|█████████▍| 4738747/4997817 [00:26<00:01, 173631.53it/s]

+
92%|█████████▏| 4616245/4997817 [00:26<00:02, 170236.58it/s]

 
 
 

@@ -8064,16 +8064,16 @@ 
Classes which are commonly mislabeled overall

 

-
95%|█████████▌| 4756401/4997817 [00:27&lt;00:01, 174489.53it/s]

+
93%|█████████▎| 4633367/4997817 [00:27&lt;00:02, 170526.11it/s]

 
</pre>

 

 

-
95%|█████████▌| 4756401/4997817 [00:27<00:01, 174489.53it/s]

+
93%|█████████▎| 4633367/4997817 [00:27<00:02, 170526.11it/s]

 
end{sphinxVerbatim}

 

 

 

-
95%|█████████▌| 4756401/4997817 [00:27<00:01, 174489.53it/s]

+
93%|█████████▎| 4633367/4997817 [00:27<00:02, 170526.11it/s]

 
 
 

@@ -8090,16 +8090,16 @@ 
Classes which are commonly mislabeled overall

 

-
96%|█████████▌| 4774026/4997817 [00:27&lt;00:01, 175007.15it/s]

+
93%|█████████▎| 4650476/4997817 [00:27&lt;00:02, 170690.98it/s]

 
</pre>

 

 

-
96%|█████████▌| 4774026/4997817 [00:27<00:01, 175007.15it/s]

+
93%|█████████▎| 4650476/4997817 [00:27<00:02, 170690.98it/s]

 
end{sphinxVerbatim}

 

 

 

-
96%|█████████▌| 4774026/4997817 [00:27<00:01, 175007.15it/s]

+
93%|█████████▎| 4650476/4997817 [00:27<00:02, 170690.98it/s]

 
 
 

@@ -8116,16 +8116,16 @@ 
Classes which are commonly mislabeled overall

 

-
96%|█████████▌| 4791625/4997817 [00:27&lt;00:01, 175295.19it/s]

+
93%|█████████▎| 4667696/4997817 [00:27&lt;00:01, 171138.38it/s]

 
</pre>

 

 

-
96%|█████████▌| 4791625/4997817 [00:27<00:01, 175295.19it/s]

+
93%|█████████▎| 4667696/4997817 [00:27<00:01, 171138.38it/s]

 
end{sphinxVerbatim}

 

 

 

-
96%|█████████▌| 4791625/4997817 [00:27<00:01, 175295.19it/s]

+
93%|█████████▎| 4667696/4997817 [00:27<00:01, 171138.38it/s]

 
 
 

@@ -8142,16 +8142,16 @@ 
Classes which are commonly mislabeled overall

 

-
96%|█████████▌| 4809276/4997817 [00:27&lt;00:01, 175653.18it/s]

+
94%|█████████▎| 4684811/4997817 [00:27&lt;00:01, 166416.31it/s]

 
</pre>

 

 

-
96%|█████████▌| 4809276/4997817 [00:27<00:01, 175653.18it/s]

+
94%|█████████▎| 4684811/4997817 [00:27<00:01, 166416.31it/s]

 
end{sphinxVerbatim}

 

 

 

-
96%|█████████▌| 4809276/4997817 [00:27<00:01, 175653.18it/s]

+
94%|█████████▎| 4684811/4997817 [00:27<00:01, 166416.31it/s]

 
 
 

@@ -8168,16 +8168,16 @@ 
Classes which are commonly mislabeled overall

 

-
97%|█████████▋| 4827007/4997817 [00:27&lt;00:00, 176143.70it/s]

+
94%|█████████▍| 4702063/4997817 [00:27&lt;00:01, 168205.75it/s]

 
</pre>

 

 

-
97%|█████████▋| 4827007/4997817 [00:27<00:00, 176143.70it/s]

+
94%|█████████▍| 4702063/4997817 [00:27<00:01, 168205.75it/s]

 
end{sphinxVerbatim}

 

 

 

-
97%|█████████▋| 4827007/4997817 [00:27<00:00, 176143.70it/s]

+
94%|█████████▍| 4702063/4997817 [00:27<00:01, 168205.75it/s]

 
 
 

@@ -8194,16 +8194,16 @@ 
Classes which are commonly mislabeled overall

 

-
97%|█████████▋| 4844714/4997817 [00:27&lt;00:00, 176418.08it/s]

+
94%|█████████▍| 4719283/4997817 [00:27&lt;00:01, 169383.99it/s]

 
</pre>

 

 

-
97%|█████████▋| 4844714/4997817 [00:27<00:00, 176418.08it/s]

+
94%|█████████▍| 4719283/4997817 [00:27<00:01, 169383.99it/s]

 
end{sphinxVerbatim}

 

 

 

-
97%|█████████▋| 4844714/4997817 [00:27<00:00, 176418.08it/s]

+
94%|█████████▍| 4719283/4997817 [00:27<00:01, 169383.99it/s]

 
 
 

@@ -8220,16 +8220,16 @@ 
Classes which are commonly mislabeled overall

 

-
97%|█████████▋| 4862493/4997817 [00:27&lt;00:00, 176823.46it/s]

+
95%|█████████▍| 4736314/4997817 [00:27&lt;00:01, 169655.39it/s]

 
</pre>

 

 

-
97%|█████████▋| 4862493/4997817 [00:27<00:00, 176823.46it/s]

+
95%|█████████▍| 4736314/4997817 [00:27<00:01, 169655.39it/s]

 
end{sphinxVerbatim}

 

 

 

-
97%|█████████▋| 4862493/4997817 [00:27<00:00, 176823.46it/s]

+
95%|█████████▍| 4736314/4997817 [00:27<00:01, 169655.39it/s]

 
 
 

@@ -8246,16 +8246,16 @@ 
Classes which are commonly mislabeled overall

 

-
98%|█████████▊| 4880182/4997817 [00:27&lt;00:00, 176751.65it/s]

+
95%|█████████▌| 4753543/4997817 [00:27&lt;00:01, 170435.52it/s]

 
</pre>

 

 

-
98%|█████████▊| 4880182/4997817 [00:27<00:00, 176751.65it/s]

+
95%|█████████▌| 4753543/4997817 [00:27<00:01, 170435.52it/s]

 
end{sphinxVerbatim}

 

 

 

-
98%|█████████▊| 4880182/4997817 [00:27<00:00, 176751.65it/s]

+
95%|█████████▌| 4753543/4997817 [00:27<00:01, 170435.52it/s]

 
 
 

@@ -8272,16 +8272,16 @@ 
Classes which are commonly mislabeled overall

 

-
98%|█████████▊| 4897903/4997817 [00:27&lt;00:00, 176884.36it/s]

+
95%|█████████▌| 4770597/4997817 [00:27&lt;00:01, 170424.55it/s]

 
</pre>

 

 

-
98%|█████████▊| 4897903/4997817 [00:27<00:00, 176884.36it/s]

+
95%|█████████▌| 4770597/4997817 [00:27<00:01, 170424.55it/s]

 
end{sphinxVerbatim}

 

 

 

-
98%|█████████▊| 4897903/4997817 [00:27<00:00, 176884.36it/s]

+
95%|█████████▌| 4770597/4997817 [00:27<00:01, 170424.55it/s]

 
 
 

@@ -8298,16 +8298,16 @@ 
Classes which are commonly mislabeled overall

 

-
98%|█████████▊| 4915661/4997817 [00:27&lt;00:00, 177087.36it/s]

+
96%|█████████▌| 4787647/4997817 [00:27&lt;00:01, 170176.12it/s]

 
</pre>

 

 

-
98%|█████████▊| 4915661/4997817 [00:27<00:00, 177087.36it/s]

+
96%|█████████▌| 4787647/4997817 [00:27<00:01, 170176.12it/s]

 
end{sphinxVerbatim}

 

 

 

-
98%|█████████▊| 4915661/4997817 [00:27<00:00, 177087.36it/s]

+
96%|█████████▌| 4787647/4997817 [00:27<00:01, 170176.12it/s]

 
 
 

@@ -8324,16 +8324,16 @@ 
Classes which are commonly mislabeled overall

 

-
99%|█████████▊| 4933428/4997817 [00:28&lt;00:00, 177258.92it/s]

+
96%|█████████▌| 4804723/4997817 [00:28&lt;00:01, 170346.92it/s]

 
</pre>

 

 

-
99%|█████████▊| 4933428/4997817 [00:28<00:00, 177258.92it/s]

+
96%|█████████▌| 4804723/4997817 [00:28<00:01, 170346.92it/s]

 
end{sphinxVerbatim}

 

 

 

-
99%|█████████▊| 4933428/4997817 [00:28<00:00, 177258.92it/s]

+
96%|█████████▌| 4804723/4997817 [00:28<00:01, 170346.92it/s]

 
 
 

@@ -8350,16 +8350,16 @@ 
Classes which are commonly mislabeled overall

 

-
99%|█████████▉| 4951156/4997817 [00:28&lt;00:00, 177082.23it/s]

+
96%|█████████▋| 4821848/4997817 [00:28&lt;00:01, 170613.16it/s]

 
</pre>

 

 

-
99%|█████████▉| 4951156/4997817 [00:28<00:00, 177082.23it/s]

+
96%|█████████▋| 4821848/4997817 [00:28<00:01, 170613.16it/s]

 
end{sphinxVerbatim}

 

 

 

-
99%|█████████▉| 4951156/4997817 [00:28<00:00, 177082.23it/s]

+
96%|█████████▋| 4821848/4997817 [00:28<00:01, 170613.16it/s]

 
 
 

@@ -8376,18 +8376,217 @@ 
Classes which are commonly mislabeled overall

 

-
99%|█████████▉| 4968913/4997817 [00:28&lt;00:00, 177225.04it/s]

+
97%|█████████▋| 4838912/4997817 [00:28&lt;00:00, 170004.32it/s]

 
</pre>

 

 

-
99%|█████████▉| 4968913/4997817 [00:28<00:00, 177225.04it/s]

+
97%|█████████▋| 4838912/4997817 [00:28<00:00, 170004.32it/s]

 
end{sphinxVerbatim}

 

 

 

-
99%|█████████▉| 4968913/4997817 [00:28<00:00, 177225.04it/s]

+
97%|█████████▋| 4838912/4997817 [00:28<00:00, 170004.32it/s]

 
 
+

+

+

+

+

+

+
more-to-come:

+

+
class:

+
stderr

+

+

+

+

+
97%|█████████▋| 4856169/4997817 [00:28&lt;00:00, 170767.84it/s]

+
</pre>

+

+

+
97%|█████████▋| 4856169/4997817 [00:28<00:00, 170767.84it/s]

+
end{sphinxVerbatim}

+

+

+

+
97%|█████████▋| 4856169/4997817 [00:28<00:00, 170767.84it/s]

+

+

+

+

+

+

+

+

+
more-to-come:

+

+
class:

+
stderr

+

+

+

+

+
98%|█████████▊| 4873248/4997817 [00:28&lt;00:00, 170274.10it/s]

+
</pre>

+

+

+
98%|█████████▊| 4873248/4997817 [00:28<00:00, 170274.10it/s]

+
end{sphinxVerbatim}

+

+

+

+
98%|█████████▊| 4873248/4997817 [00:28<00:00, 170274.10it/s]

+

+

+

+

+

+

+

+

+
more-to-come:

+

+
class:

+
stderr

+

+

+

+

+
98%|█████████▊| 4890277/4997817 [00:28&lt;00:00, 170191.72it/s]

+
</pre>

+

+

+
98%|█████████▊| 4890277/4997817 [00:28<00:00, 170191.72it/s]

+
end{sphinxVerbatim}

+

+

+

+
98%|█████████▊| 4890277/4997817 [00:28<00:00, 170191.72it/s]

+

+

+

+

+

+

+

+

+
more-to-come:

+

+
class:

+
stderr

+

+

+

+

+
98%|█████████▊| 4907298/4997817 [00:28&lt;00:00, 170155.86it/s]

+
</pre>

+

+

+
98%|█████████▊| 4907298/4997817 [00:28<00:00, 170155.86it/s]

+
end{sphinxVerbatim}

+

+

+

+
98%|█████████▊| 4907298/4997817 [00:28<00:00, 170155.86it/s]

+

+

+

+

+

+

+

+

+
more-to-come:

+

+
class:

+
stderr

+

+

+

+

+
99%|█████████▊| 4924593/4997817 [00:28&lt;00:00, 170988.03it/s]

+
</pre>

+

+

+
99%|█████████▊| 4924593/4997817 [00:28<00:00, 170988.03it/s]

+
end{sphinxVerbatim}

+

+

+

+
99%|█████████▊| 4924593/4997817 [00:28<00:00, 170988.03it/s]

+

+

+

+

+

+

+

+

+
more-to-come:

+

+
class:

+
stderr

+

+

+

+

+
99%|█████████▉| 4941723/4997817 [00:28&lt;00:00, 171077.12it/s]

+
</pre>

+

+

+
99%|█████████▉| 4941723/4997817 [00:28<00:00, 171077.12it/s]

+
end{sphinxVerbatim}

+

+

+

+
99%|█████████▉| 4941723/4997817 [00:28<00:00, 171077.12it/s]

+

+

+

+

+

+

+

+

+
more-to-come:

+

+
class:

+
stderr

+

+

+

+

+
99%|█████████▉| 4958832/4997817 [00:28&lt;00:00, 170246.67it/s]

+
</pre>

+

+

+
99%|█████████▉| 4958832/4997817 [00:28<00:00, 170246.67it/s]

+
end{sphinxVerbatim}

+

+

+

+
99%|█████████▉| 4958832/4997817 [00:28<00:00, 170246.67it/s]

+

+

+

+

+

+

+

+

+

+
100%|█████████▉| 4975925/4997817 [00:29&lt;00:00, 170448.66it/s]

+
</pre>

+

+

+
100%|█████████▉| 4975925/4997817 [00:29<00:00, 170448.66it/s]

+
end{sphinxVerbatim}

+

+

+

+
100%|█████████▉| 4975925/4997817 [00:29<00:00, 170448.66it/s]

 

 

 

@@ -8395,16 +8594,16 @@ 
Classes which are commonly mislabeled overall

 
 

-
100%|█████████▉| 4986697/4997817 [00:28&lt;00:00, 177405.38it/s]

+
100%|█████████▉| 4992971/4997817 [00:29&lt;00:00, 170315.59it/s]

 
</pre>

 

 

-
100%|█████████▉| 4986697/4997817 [00:28<00:00, 177405.38it/s]

+
100%|█████████▉| 4992971/4997817 [00:29<00:00, 170315.59it/s]

 
end{sphinxVerbatim}

 

 

 

-
100%|█████████▉| 4986697/4997817 [00:28<00:00, 177405.38it/s]

+
100%|█████████▉| 4992971/4997817 [00:29<00:00, 170315.59it/s]

 

 

 

@@ -8412,16 +8611,16 @@ 
Classes which are commonly mislabeled overall

 
 

-
100%|██████████| 4997817/4997817 [00:28&lt;00:00, 175774.30it/s]

+
100%|██████████| 4997817/4997817 [00:29&lt;00:00, 171134.52it/s]

 
</pre>

 

 

-
100%|██████████| 4997817/4997817 [00:28<00:00, 175774.30it/s]

+
100%|██████████| 4997817/4997817 [00:29<00:00, 171134.52it/s]

 
end{sphinxVerbatim}

 

 

 

-
100%|██████████| 4997817/4997817 [00:28<00:00, 175774.30it/s]

+
100%|██████████| 4997817/4997817 [00:29<00:00, 171134.52it/s]

 

 

 

@@ -8677,7 +8876,7 @@ 
Get label quality scores
 

 

-

+
 
 
Beyond scoring the overall label quality of each image, the above method produces a (0 to 1) quality score for each pixel. We can apply a thresholding function to these scores in order to extract the same style True or False mask as find_label_issues().

 

@@ -8733,7 +8932,7 @@ 
Get label quality scores
-{"state": {"cdcc7203a1d34765831634720bdbf052": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "254221792aff42ce82df1c22b6a17fe7": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "92db28b2f6594a47bbecaa39c185f8fd": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_cdcc7203a1d34765831634720bdbf052", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_254221792aff42ce82df1c22b6a17fe7", "value": 30.0}}, "813659abd0ae4858b70121d8e5e42c7a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1cf620e1d88a4e37b2d34ea1fb5e1535": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "7daa21d4a2364d38ad2a974e382c6322": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_813659abd0ae4858b70121d8e5e42c7a", "placeholder": "\u200b", "style": "IPY_MODEL_1cf620e1d88a4e37b2d34ea1fb5e1535", "value": "number of examples processed for estimating thresholds: 100%"}}, "2cef6291580545a795374bc03a6013f9": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "1ed919af8e7844faabecb7a8dd47f285": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "fbe3025786e94c99ab6c633251923c57": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_2cef6291580545a795374bc03a6013f9", "placeholder": "\u200b", "style": "IPY_MODEL_1ed919af8e7844faabecb7a8dd47f285", "value": " 30/30 [00:00<00:00, 414.89it/s]"}}, "f26f2446131342a1b208ddec0b71c771": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ec615bcedf144713a74c0755f4d4a017": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_7daa21d4a2364d38ad2a974e382c6322", "IPY_MODEL_92db28b2f6594a47bbecaa39c185f8fd", "IPY_MODEL_fbe3025786e94c99ab6c633251923c57"], "layout": "IPY_MODEL_f26f2446131342a1b208ddec0b71c771"}}, "587d5b941fbc465faf4eb95923831929": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "70503413664647f98537eb6ceb24c397": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "ef63b67382614e458109cee4652d44da": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_587d5b941fbc465faf4eb95923831929", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_70503413664647f98537eb6ceb24c397", "value": 30.0}}, "9c7f519c6e4e43a983bbd8764c156460": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6422f763cda64280af2c3e65257aeadf": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "660cb6ce052f47b4b6cbe492bceef787": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_9c7f519c6e4e43a983bbd8764c156460", "placeholder": "\u200b", "style": "IPY_MODEL_6422f763cda64280af2c3e65257aeadf", "value": "number of examples processed for checking labels: 100%"}}, "e77f234650f04aceb92afed644f9afbf": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "ab4466bce4fc4e29a5451793442f63f0": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "d603c968a93c4cc180f3f615cff5aa17": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_e77f234650f04aceb92afed644f9afbf", "placeholder": "\u200b", "style": "IPY_MODEL_ab4466bce4fc4e29a5451793442f63f0", "value": " 30/30 [00:39<00:00,  1.18s/it]"}}, "2c30e1b3212d44fcb51934e93bf256f4": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "b6cb19a81c2a486b82141204a442d67b": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_660cb6ce052f47b4b6cbe492bceef787", "IPY_MODEL_ef63b67382614e458109cee4652d44da", "IPY_MODEL_d603c968a93c4cc180f3f615cff5aa17"], "layout": "IPY_MODEL_2c30e1b3212d44fcb51934e93bf256f4"}}, "1772cf6eace14f4f8a042982d8d016a8": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "6493300865974b87a00b6d58ce9d45d3": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "e065034c12da4de986d452fa8e88a13a": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1772cf6eace14f4f8a042982d8d016a8", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_6493300865974b87a00b6d58ce9d45d3", "value": 30.0}}, "7b875172cd764fa9ab4b1d7fdcd93b46": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3d5158c48b6d4e3587ff508936ad7386": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "464b5bb258bd409ab318e6ecab337861": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_7b875172cd764fa9ab4b1d7fdcd93b46", "placeholder": "\u200b", "style": "IPY_MODEL_3d5158c48b6d4e3587ff508936ad7386", "value": "images processed using softmin: 100%"}}, "1a4a06ef208a4aea96192270ec7fb84a": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "98e1388d51f34102aacc21ba7bc431eb": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "4109ef68aa164157869d8efce1002420": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1a4a06ef208a4aea96192270ec7fb84a", "placeholder": "\u200b", "style": "IPY_MODEL_98e1388d51f34102aacc21ba7bc431eb", "value": " 30/30 [00:01<00:00, 23.76it/s]"}}, "a4c1ccb6ff5b40f5b2a9448cc2d9a89d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "7dceb22a611b4605b26d5be95c8f7516": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_464b5bb258bd409ab318e6ecab337861", "IPY_MODEL_e065034c12da4de986d452fa8e88a13a", "IPY_MODEL_4109ef68aa164157869d8efce1002420"], "layout": "IPY_MODEL_a4c1ccb6ff5b40f5b2a9448cc2d9a89d"}}}, "version_major": 2, "version_minor": 0}
+{"state": {"fcc677509e4047feacf1ee2612eb3bf6": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "2d9bd9df2ade4a83ab6231049c45a53b": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "1c9e243742194e9f93f1f1d0f4ade627": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_fcc677509e4047feacf1ee2612eb3bf6", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_2d9bd9df2ade4a83ab6231049c45a53b", "value": 30.0}}, "68e0315ddeeb47aaa3eed64bc0a781bb": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "c0d493b322dc419dbe87e7a6b2af79ce": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "579262aaf92e43c8b403d67fcdbb69de": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_68e0315ddeeb47aaa3eed64bc0a781bb", "placeholder": "\u200b", "style": "IPY_MODEL_c0d493b322dc419dbe87e7a6b2af79ce", "value": "number of examples processed for estimating thresholds: 100%"}}, "11f01422601b4fbfb274288e4cabc8dd": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "337a4acbdc614129ab2ee0180f730c19": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "f8958214798d4aaea3392a34c1132c86": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_11f01422601b4fbfb274288e4cabc8dd", "placeholder": "\u200b", "style": "IPY_MODEL_337a4acbdc614129ab2ee0180f730c19", "value": " 30/30 [00:00<00:00, 415.09it/s]"}}, "e66280fc5b9544d3b2997f0895b86556": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "4fea53dd3c354db89dbd413a514598b1": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_579262aaf92e43c8b403d67fcdbb69de", "IPY_MODEL_1c9e243742194e9f93f1f1d0f4ade627", "IPY_MODEL_f8958214798d4aaea3392a34c1132c86"], "layout": "IPY_MODEL_e66280fc5b9544d3b2997f0895b86556"}}, "3eedef9a02374d1e96abed5e91673616": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "bbeae6affcaf40c7b85b3a80bc472488": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "6c0f1a6b8b0941eb890e179ab021fa32": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_3eedef9a02374d1e96abed5e91673616", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_bbeae6affcaf40c7b85b3a80bc472488", "value": 30.0}}, "1a11c07d013c43bb8d5c2a9a9f51b77c": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "25a7aae549ff4d1fa03ce9aa556d8fc3": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "540cfd5273004c949a94947f00084b1e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1a11c07d013c43bb8d5c2a9a9f51b77c", "placeholder": "\u200b", "style": "IPY_MODEL_25a7aae549ff4d1fa03ce9aa556d8fc3", "value": "number of examples processed for checking labels: 100%"}}, "c2042fce4b7149fbb89d176082a3c94f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "851b95992b3f4ad4a8670c0230fcd73f": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "e15b5be74c0c4d29bdfda502583c695b": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c2042fce4b7149fbb89d176082a3c94f", "placeholder": "\u200b", "style": "IPY_MODEL_851b95992b3f4ad4a8670c0230fcd73f", "value": " 30/30 [00:34<00:00,  1.15s/it]"}}, "606e484d407447bcb09578773cd22d5d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3addf8ea78984dc3bf5ed29c07556bb9": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_540cfd5273004c949a94947f00084b1e", "IPY_MODEL_6c0f1a6b8b0941eb890e179ab021fa32", "IPY_MODEL_e15b5be74c0c4d29bdfda502583c695b"], "layout": "IPY_MODEL_606e484d407447bcb09578773cd22d5d"}}, "2641df8a6b10468c84fd7d0cf8fe4b7f": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "017b88b766dd4b1eb6d2b1e38318808d": {"model_name": "ProgressStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": ""}}, "a29c9371170a4cf195f0623a90a22046": {"model_name": "FloatProgressModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_2641df8a6b10468c84fd7d0cf8fe4b7f", "max": 30.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_017b88b766dd4b1eb6d2b1e38318808d", "value": 30.0}}, "75f17e33a67d4d759b4cd9037c2e49dc": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "dc529557e2e64fcba50b373e9f823e42": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "ba97605c2b714aaeae151edc68655c4e": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_75f17e33a67d4d759b4cd9037c2e49dc", "placeholder": "\u200b", "style": "IPY_MODEL_dc529557e2e64fcba50b373e9f823e42", "value": "images processed using softmin: 100%"}}, "bc89283dc7174f93b8b234407daf2a1d": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "dacdbf7e5c0a4c5a8b089b152056890c": {"model_name": "DescriptionStyleModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": ""}}, "b75dd4684dc541648d9d07c71d611dde": {"model_name": "HTMLModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_bc89283dc7174f93b8b234407daf2a1d", "placeholder": "\u200b", "style": "IPY_MODEL_dacdbf7e5c0a4c5a8b089b152056890c", "value": " 30/30 [00:01<00:00, 23.70it/s]"}}, "3bf0e062325848e38433cfd0a1c1e64e": {"model_name": "LayoutModel", "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "state": {"_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null}}, "3f014168fa4346d1a5243faf468a81d2": {"model_name": "HBoxModel", "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "state": {"_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": ["IPY_MODEL_ba97605c2b714aaeae151edc68655c4e", "IPY_MODEL_a29c9371170a4cf195f0623a90a22046", "IPY_MODEL_b75dd4684dc541648d9d07c71d611dde"], "layout": "IPY_MODEL_3bf0e062325848e38433cfd0a1c1e64e"}}}, "version_major": 2, "version_minor": 0}
 
 
 
diff --git a/master/tutorials/segmentation.ipynb b/master/tutorials/segmentation.ipynb
index 277db8411..6376839e6 100644
--- a/master/tutorials/segmentation.ipynb
+++ b/master/tutorials/segmentation.ipynb
@@ -61,10 +61,10 @@
    "id": "ae8a08e0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:31.125213Z",
-     "iopub.status.busy": "2024-01-17T18:12:31.124971Z",
-     "iopub.status.idle": "2024-01-17T18:12:32.801405Z",
-     "shell.execute_reply": "2024-01-17T18:12:32.800636Z"
+     "iopub.execute_input": "2024-01-17T23:14:34.017881Z",
+     "iopub.status.busy": "2024-01-17T23:14:34.017503Z",
+     "iopub.status.idle": "2024-01-17T23:14:36.066016Z",
+     "shell.execute_reply": "2024-01-17T23:14:36.065243Z"
     }
    },
    "outputs": [],
@@ -79,10 +79,10 @@
    "id": "58fd4c55",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:12:32.804526Z",
-     "iopub.status.busy": "2024-01-17T18:12:32.804011Z",
-     "iopub.status.idle": "2024-01-17T18:13:26.043168Z",
-     "shell.execute_reply": "2024-01-17T18:13:26.042378Z"
+     "iopub.execute_input": "2024-01-17T23:14:36.068785Z",
+     "iopub.status.busy": "2024-01-17T23:14:36.068574Z",
+     "iopub.status.idle": "2024-01-17T23:15:29.432226Z",
+     "shell.execute_reply": "2024-01-17T23:15:29.431512Z"
     }
    },
    "outputs": [],
@@ -97,10 +97,10 @@
    "id": "439b0305",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:13:26.046070Z",
-     "iopub.status.busy": "2024-01-17T18:13:26.045857Z",
-     "iopub.status.idle": "2024-01-17T18:13:27.779303Z",
-     "shell.execute_reply": "2024-01-17T18:13:27.778617Z"
+     "iopub.execute_input": "2024-01-17T23:15:29.435338Z",
+     "iopub.status.busy": "2024-01-17T23:15:29.434915Z",
+     "iopub.status.idle": "2024-01-17T23:15:30.460838Z",
+     "shell.execute_reply": "2024-01-17T23:15:30.460233Z"
     },
     "nbsphinx": "hidden"
    },
@@ -111,7 +111,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -137,10 +137,10 @@
    "id": "a1349304",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:13:27.782397Z",
-     "iopub.status.busy": "2024-01-17T18:13:27.781842Z",
-     "iopub.status.idle": "2024-01-17T18:13:27.785561Z",
-     "shell.execute_reply": "2024-01-17T18:13:27.784975Z"
+     "iopub.execute_input": "2024-01-17T23:15:30.463806Z",
+     "iopub.status.busy": "2024-01-17T23:15:30.463391Z",
+     "iopub.status.idle": "2024-01-17T23:15:30.467069Z",
+     "shell.execute_reply": "2024-01-17T23:15:30.466497Z"
     }
    },
    "outputs": [],
@@ -203,10 +203,10 @@
    "id": "07dc5678",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:13:27.787782Z",
-     "iopub.status.busy": "2024-01-17T18:13:27.787576Z",
-     "iopub.status.idle": "2024-01-17T18:13:27.791812Z",
-     "shell.execute_reply": "2024-01-17T18:13:27.791287Z"
+     "iopub.execute_input": "2024-01-17T23:15:30.469583Z",
+     "iopub.status.busy": "2024-01-17T23:15:30.469112Z",
+     "iopub.status.idle": "2024-01-17T23:15:30.473211Z",
+     "shell.execute_reply": "2024-01-17T23:15:30.472586Z"
     }
    },
    "outputs": [
@@ -247,10 +247,10 @@
    "id": "25ebe22a",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:13:27.794277Z",
-     "iopub.status.busy": "2024-01-17T18:13:27.793928Z",
-     "iopub.status.idle": "2024-01-17T18:13:27.797585Z",
-     "shell.execute_reply": "2024-01-17T18:13:27.797052Z"
+     "iopub.execute_input": "2024-01-17T23:15:30.475590Z",
+     "iopub.status.busy": "2024-01-17T23:15:30.475289Z",
+     "iopub.status.idle": "2024-01-17T23:15:30.479220Z",
+     "shell.execute_reply": "2024-01-17T23:15:30.478614Z"
     }
    },
    "outputs": [
@@ -290,10 +290,10 @@
    "id": "3faedea9",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:13:27.799879Z",
-     "iopub.status.busy": "2024-01-17T18:13:27.799457Z",
-     "iopub.status.idle": "2024-01-17T18:13:27.802639Z",
-     "shell.execute_reply": "2024-01-17T18:13:27.802012Z"
+     "iopub.execute_input": "2024-01-17T23:15:30.481531Z",
+     "iopub.status.busy": "2024-01-17T23:15:30.481169Z",
+     "iopub.status.idle": "2024-01-17T23:15:30.484282Z",
+     "shell.execute_reply": "2024-01-17T23:15:30.483769Z"
     }
    },
    "outputs": [],
@@ -333,10 +333,10 @@
    "id": "2c2ad9ad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:13:27.804808Z",
-     "iopub.status.busy": "2024-01-17T18:13:27.804477Z",
-     "iopub.status.idle": "2024-01-17T18:14:55.334560Z",
-     "shell.execute_reply": "2024-01-17T18:14:55.333875Z"
+     "iopub.execute_input": "2024-01-17T23:15:30.486493Z",
+     "iopub.status.busy": "2024-01-17T23:15:30.486198Z",
+     "iopub.status.idle": "2024-01-17T23:16:54.759811Z",
+     "shell.execute_reply": "2024-01-17T23:16:54.759104Z"
     }
    },
    "outputs": [
@@ -350,7 +350,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ec615bcedf144713a74c0755f4d4a017",
+       "model_id": "4fea53dd3c354db89dbd413a514598b1",
        "version_major": 2,
        "version_minor": 0
       },
@@ -364,7 +364,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b6cb19a81c2a486b82141204a442d67b",
+       "model_id": "3addf8ea78984dc3bf5ed29c07556bb9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -407,10 +407,10 @@
    "id": "95dc7268",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:14:55.337672Z",
-     "iopub.status.busy": "2024-01-17T18:14:55.337209Z",
-     "iopub.status.idle": "2024-01-17T18:14:56.101511Z",
-     "shell.execute_reply": "2024-01-17T18:14:56.100932Z"
+     "iopub.execute_input": "2024-01-17T23:16:54.762950Z",
+     "iopub.status.busy": "2024-01-17T23:16:54.762598Z",
+     "iopub.status.idle": "2024-01-17T23:16:55.528775Z",
+     "shell.execute_reply": "2024-01-17T23:16:55.528081Z"
     }
    },
    "outputs": [
@@ -453,10 +453,10 @@
    "id": "57fed473",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:14:56.104021Z",
-     "iopub.status.busy": "2024-01-17T18:14:56.103660Z",
-     "iopub.status.idle": "2024-01-17T18:14:58.199350Z",
-     "shell.execute_reply": "2024-01-17T18:14:58.198693Z"
+     "iopub.execute_input": "2024-01-17T23:16:55.531668Z",
+     "iopub.status.busy": "2024-01-17T23:16:55.531169Z",
+     "iopub.status.idle": "2024-01-17T23:16:57.626271Z",
+     "shell.execute_reply": "2024-01-17T23:16:57.625582Z"
     }
    },
    "outputs": [
@@ -526,10 +526,10 @@
    "id": "e4a006bd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:14:58.202064Z",
-     "iopub.status.busy": "2024-01-17T18:14:58.201668Z",
-     "iopub.status.idle": "2024-01-17T18:15:26.871400Z",
-     "shell.execute_reply": "2024-01-17T18:15:26.870736Z"
+     "iopub.execute_input": "2024-01-17T23:16:57.628737Z",
+     "iopub.status.busy": "2024-01-17T23:16:57.628517Z",
+     "iopub.status.idle": "2024-01-17T23:17:27.065044Z",
+     "shell.execute_reply": "2024-01-17T23:17:27.064412Z"
     }
    },
    "outputs": [
@@ -546,7 +546,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  0%|          | 17362/4997817 [00:00<00:28, 173608.58it/s]"
+      "  0%|          | 16923/4997817 [00:00<00:29, 169217.65it/s]"
      ]
     },
     {
@@ -554,7 +554,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  1%|          | 34895/4997817 [00:00<00:28, 174610.72it/s]"
+      "  1%|          | 33993/4997817 [00:00<00:29, 170085.88it/s]"
      ]
     },
     {
@@ -562,7 +562,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  1%|          | 52475/4997817 [00:00<00:28, 175150.77it/s]"
+      "  1%|          | 51002/4997817 [00:00<00:29, 169503.62it/s]"
      ]
     },
     {
@@ -570,7 +570,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  1%|▏         | 69991/4997817 [00:00<00:28, 173938.91it/s]"
+      "  1%|▏         | 68012/4997817 [00:00<00:29, 169735.92it/s]"
      ]
     },
     {
@@ -578,7 +578,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  2%|▏         | 87813/4997817 [00:00<00:27, 175471.95it/s]"
+      "  2%|▏         | 84993/4997817 [00:00<00:28, 169759.89it/s]"
      ]
     },
     {
@@ -586,7 +586,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  2%|▏         | 105688/4997817 [00:00<00:27, 176578.81it/s]"
+      "  2%|▏         | 101970/4997817 [00:00<00:28, 169628.61it/s]"
      ]
     },
     {
@@ -594,7 +594,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  2%|▏         | 123543/4997817 [00:00<00:27, 177216.89it/s]"
+      "  2%|▏         | 118944/4997817 [00:00<00:28, 169663.19it/s]"
      ]
     },
     {
@@ -602,7 +602,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  3%|▎         | 141289/4997817 [00:00<00:27, 177290.20it/s]"
+      "  3%|▎         | 135911/4997817 [00:00<00:29, 163001.52it/s]"
      ]
     },
     {
@@ -610,7 +610,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  3%|▎         | 159076/4997817 [00:00<00:27, 177466.31it/s]"
+      "  3%|▎         | 152918/4997817 [00:00<00:29, 165147.19it/s]"
      ]
     },
     {
@@ -618,7 +618,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  4%|▎         | 176918/4997817 [00:01<00:27, 177754.44it/s]"
+      "  3%|▎         | 169974/4997817 [00:01<00:28, 166782.07it/s]"
      ]
     },
     {
@@ -626,7 +626,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  4%|▍         | 194876/4997817 [00:01<00:26, 178308.24it/s]"
+      "  4%|▎         | 187064/4997817 [00:01<00:28, 168021.67it/s]"
      ]
     },
     {
@@ -634,7 +634,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  4%|▍         | 212708/4997817 [00:01<00:26, 178223.48it/s]"
+      "  4%|▍         | 204038/4997817 [00:01<00:28, 168537.30it/s]"
      ]
     },
     {
@@ -642,7 +642,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  5%|▍         | 230531/4997817 [00:01<00:26, 178136.92it/s]"
+      "  4%|▍         | 220938/4997817 [00:01<00:28, 168672.47it/s]"
      ]
     },
     {
@@ -650,7 +650,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  5%|▍         | 248505/4997817 [00:01<00:26, 178619.00it/s]"
+      "  5%|▍         | 238130/4997817 [00:01<00:28, 169647.75it/s]"
      ]
     },
     {
@@ -658,7 +658,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  5%|▌         | 266377/4997817 [00:01<00:26, 178647.43it/s]"
+      "  5%|▌         | 255262/4997817 [00:01<00:27, 170147.90it/s]"
      ]
     },
     {
@@ -666,7 +666,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  6%|▌         | 284425/4997817 [00:01<00:26, 179193.60it/s]"
+      "  5%|▌         | 272284/4997817 [00:01<00:27, 170019.26it/s]"
      ]
     },
     {
@@ -674,7 +674,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  6%|▌         | 302378/4997817 [00:01<00:26, 179290.92it/s]"
+      "  6%|▌         | 289291/4997817 [00:01<00:27, 169797.69it/s]"
      ]
     },
     {
@@ -682,7 +682,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  6%|▋         | 320347/4997817 [00:01<00:26, 179407.53it/s]"
+      "  6%|▌         | 306465/4997817 [00:01<00:27, 170377.22it/s]"
      ]
     },
     {
@@ -690,7 +690,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  7%|▋         | 338297/4997817 [00:01<00:25, 179431.36it/s]"
+      "  6%|▋         | 323703/4997817 [00:01<00:27, 170975.52it/s]"
      ]
     },
     {
@@ -698,7 +698,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  7%|▋         | 356241/4997817 [00:02<00:25, 179348.77it/s]"
+      "  7%|▋         | 340851/4997817 [00:02<00:27, 171125.25it/s]"
      ]
     },
     {
@@ -706,7 +706,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  7%|▋         | 374208/4997817 [00:02<00:25, 179441.48it/s]"
+      "  7%|▋         | 357965/4997817 [00:02<00:27, 170735.16it/s]"
      ]
     },
     {
@@ -714,7 +714,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  8%|▊         | 392153/4997817 [00:02<00:26, 175413.81it/s]"
+      "  8%|▊         | 375079/4997817 [00:02<00:27, 170854.49it/s]"
      ]
     },
     {
@@ -722,7 +722,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  8%|▊         | 410009/4997817 [00:02<00:26, 176338.11it/s]"
+      "  8%|▊         | 392275/4997817 [00:02<00:26, 171182.95it/s]"
      ]
     },
     {
@@ -730,7 +730,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  9%|▊         | 427853/4997817 [00:02<00:25, 176958.47it/s]"
+      "  8%|▊         | 409485/4997817 [00:02<00:26, 171454.31it/s]"
      ]
     },
     {
@@ -738,7 +738,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  9%|▉         | 445826/4997817 [00:02<00:25, 177779.06it/s]"
+      "  9%|▊         | 426631/4997817 [00:02<00:26, 171181.61it/s]"
      ]
     },
     {
@@ -746,7 +746,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "  9%|▉         | 463613/4997817 [00:02<00:25, 177528.30it/s]"
+      "  9%|▉         | 443750/4997817 [00:02<00:26, 170479.76it/s]"
      ]
     },
     {
@@ -754,7 +754,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 10%|▉         | 481507/4997817 [00:02<00:25, 177946.35it/s]"
+      "  9%|▉         | 460916/4997817 [00:02<00:26, 170828.33it/s]"
      ]
     },
     {
@@ -762,7 +762,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 10%|▉         | 499307/4997817 [00:02<00:25, 177661.06it/s]"
+      " 10%|▉         | 478000/4997817 [00:02<00:27, 167094.77it/s]"
      ]
     },
     {
@@ -770,7 +770,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 10%|█         | 517142/4997817 [00:02<00:25, 177861.59it/s]"
+      " 10%|▉         | 495446/4997817 [00:02<00:26, 169263.15it/s]"
      ]
     },
     {
@@ -778,7 +778,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 11%|█         | 535037/4997817 [00:03<00:25, 178184.28it/s]"
+      " 10%|█         | 512890/4997817 [00:03<00:26, 170794.12it/s]"
      ]
     },
     {
@@ -786,7 +786,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 11%|█         | 552923/4997817 [00:03<00:24, 178382.09it/s]"
+      " 11%|█         | 530383/4997817 [00:03<00:25, 172020.72it/s]"
      ]
     },
     {
@@ -794,7 +794,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 11%|█▏        | 570763/4997817 [00:03<00:25, 171086.58it/s]"
+      " 11%|█         | 547934/4997817 [00:03<00:25, 173058.88it/s]"
      ]
     },
     {
@@ -802,7 +802,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 12%|█▏        | 588402/4997817 [00:03<00:25, 172627.47it/s]"
+      " 11%|█▏        | 565248/4997817 [00:03<00:25, 171739.41it/s]"
      ]
     },
     {
@@ -810,7 +810,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 12%|█▏        | 606160/4997817 [00:03<00:25, 174079.34it/s]"
+      " 12%|█▏        | 582430/4997817 [00:03<00:25, 171635.25it/s]"
      ]
     },
     {
@@ -818,7 +818,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 12%|█▏        | 623955/4997817 [00:03<00:24, 175220.83it/s]"
+      " 12%|█▏        | 599599/4997817 [00:03<00:25, 171434.03it/s]"
      ]
     },
     {
@@ -826,7 +826,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 13%|█▎        | 641860/4997817 [00:03<00:24, 176352.97it/s]"
+      " 12%|█▏        | 616746/4997817 [00:03<00:25, 171242.34it/s]"
      ]
     },
     {
@@ -834,7 +834,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 13%|█▎        | 659728/4997817 [00:03<00:24, 177041.83it/s]"
+      " 13%|█▎        | 633873/4997817 [00:03<00:25, 171131.74it/s]"
      ]
     },
     {
@@ -842,7 +842,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 14%|█▎        | 677498/4997817 [00:03<00:24, 177235.53it/s]"
+      " 13%|█▎        | 650988/4997817 [00:03<00:25, 167888.38it/s]"
      ]
     },
     {
@@ -850,7 +850,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 14%|█▍        | 695298/4997817 [00:03<00:24, 177460.17it/s]"
+      " 13%|█▎        | 667980/4997817 [00:03<00:25, 168487.13it/s]"
      ]
     },
     {
@@ -858,7 +858,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 14%|█▍        | 713108/4997817 [00:04<00:24, 177648.89it/s]"
+      " 14%|█▎        | 685095/4997817 [00:04<00:25, 169274.32it/s]"
      ]
     },
     {
@@ -866,7 +866,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 15%|█▍        | 731013/4997817 [00:04<00:23, 178065.71it/s]"
+      " 14%|█▍        | 702365/4997817 [00:04<00:25, 170291.66it/s]"
      ]
     },
     {
@@ -874,7 +874,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 15%|█▍        | 748824/4997817 [00:04<00:23, 177652.38it/s]"
+      " 14%|█▍        | 719508/4997817 [00:04<00:25, 170628.16it/s]"
      ]
     },
     {
@@ -882,7 +882,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 15%|█▌        | 766599/4997817 [00:04<00:23, 177677.08it/s]"
+      " 15%|█▍        | 736576/4997817 [00:04<00:25, 169810.36it/s]"
      ]
     },
     {
@@ -890,7 +890,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 16%|█▌        | 784420/4997817 [00:04<00:23, 177831.64it/s]"
+      " 15%|█▌        | 753562/4997817 [00:04<00:24, 169771.99it/s]"
      ]
     },
     {
@@ -898,7 +898,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 16%|█▌        | 802279/4997817 [00:04<00:23, 178056.61it/s]"
+      " 15%|█▌        | 770715/4997817 [00:04<00:24, 170294.50it/s]"
      ]
     },
     {
@@ -906,7 +906,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 16%|█▋        | 820264/4997817 [00:04<00:23, 178591.12it/s]"
+      " 16%|█▌        | 787747/4997817 [00:04<00:24, 169972.43it/s]"
      ]
     },
     {
@@ -914,7 +914,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 17%|█▋        | 838197/4997817 [00:04<00:23, 178808.60it/s]"
+      " 16%|█▌        | 804872/4997817 [00:04<00:24, 170350.25it/s]"
      ]
     },
     {
@@ -922,7 +922,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 17%|█▋        | 856111/4997817 [00:04<00:23, 178905.08it/s]"
+      " 16%|█▋        | 821909/4997817 [00:04<00:24, 170342.59it/s]"
      ]
     },
     {
@@ -930,7 +930,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 17%|█▋        | 874002/4997817 [00:04<00:23, 178854.94it/s]"
+      " 17%|█▋        | 838945/4997817 [00:04<00:24, 170326.57it/s]"
      ]
     },
     {
@@ -938,7 +938,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 18%|█▊        | 891888/4997817 [00:05<00:22, 178769.86it/s]"
+      " 17%|█▋        | 856345/4997817 [00:05<00:24, 171423.84it/s]"
      ]
     },
     {
@@ -946,7 +946,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 18%|█▊        | 909766/4997817 [00:05<00:22, 177999.88it/s]"
+      " 17%|█▋        | 873794/4997817 [00:05<00:23, 172340.75it/s]"
      ]
     },
     {
@@ -954,7 +954,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 19%|█▊        | 927567/4997817 [00:05<00:23, 175332.09it/s]"
+      " 18%|█▊        | 891355/4997817 [00:05<00:23, 173318.17it/s]"
      ]
     },
     {
@@ -962,7 +962,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 19%|█▉        | 945109/4997817 [00:05<00:23, 172027.61it/s]"
+      " 18%|█▊        | 908761/4997817 [00:05<00:23, 173536.69it/s]"
      ]
     },
     {
@@ -970,7 +970,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 19%|█▉        | 962858/4997817 [00:05<00:23, 173626.11it/s]"
+      " 19%|█▊        | 926115/4997817 [00:05<00:23, 173374.72it/s]"
      ]
     },
     {
@@ -978,7 +978,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 20%|█▉        | 980440/4997817 [00:05<00:23, 174271.59it/s]"
+      " 19%|█▉        | 943534/4997817 [00:05<00:23, 173615.78it/s]"
      ]
     },
     {
@@ -986,7 +986,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 20%|█▉        | 998071/4997817 [00:05<00:22, 174873.76it/s]"
+      " 19%|█▉        | 960987/4997817 [00:05<00:23, 173888.18it/s]"
      ]
     },
     {
@@ -994,7 +994,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 20%|██        | 1015609/4997817 [00:05<00:22, 175019.90it/s]"
+      " 20%|█▉        | 978376/4997817 [00:05<00:23, 173759.83it/s]"
      ]
     },
     {
@@ -1002,7 +1002,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 21%|██        | 1033146/4997817 [00:05<00:22, 175119.66it/s]"
+      " 20%|█▉        | 995883/4997817 [00:05<00:22, 174150.86it/s]"
      ]
     },
     {
@@ -1010,7 +1010,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 21%|██        | 1050707/4997817 [00:05<00:22, 175263.37it/s]"
+      " 20%|██        | 1013363/4997817 [00:05<00:22, 174342.29it/s]"
      ]
     },
     {
@@ -1018,7 +1018,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 21%|██▏       | 1068310/4997817 [00:06<00:22, 175489.78it/s]"
+      " 21%|██        | 1030818/4997817 [00:06<00:22, 174402.88it/s]"
      ]
     },
     {
@@ -1026,7 +1026,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 22%|██▏       | 1086006/4997817 [00:06<00:22, 175925.40it/s]"
+      " 21%|██        | 1048303/4997817 [00:06<00:22, 174534.90it/s]"
      ]
     },
     {
@@ -1034,7 +1034,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 22%|██▏       | 1103856/4997817 [00:06<00:22, 176693.74it/s]"
+      " 21%|██▏       | 1065757/4997817 [00:06<00:22, 174515.39it/s]"
      ]
     },
     {
@@ -1042,7 +1042,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 22%|██▏       | 1121527/4997817 [00:06<00:21, 176653.73it/s]"
+      " 22%|██▏       | 1083209/4997817 [00:06<00:22, 174141.01it/s]"
      ]
     },
     {
@@ -1050,7 +1050,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 23%|██▎       | 1139194/4997817 [00:06<00:21, 175610.49it/s]"
+      " 22%|██▏       | 1100652/4997817 [00:06<00:22, 174222.63it/s]"
      ]
     },
     {
@@ -1058,7 +1058,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 23%|██▎       | 1156758/4997817 [00:06<00:21, 175340.14it/s]"
+      " 22%|██▏       | 1118075/4997817 [00:06<00:22, 173614.04it/s]"
      ]
     },
     {
@@ -1066,7 +1066,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 23%|██▎       | 1174423/4997817 [00:06<00:21, 175726.52it/s]"
+      " 23%|██▎       | 1135437/4997817 [00:06<00:22, 173273.09it/s]"
      ]
     },
     {
@@ -1074,7 +1074,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 24%|██▍       | 1192038/4997817 [00:06<00:21, 175849.70it/s]"
+      " 23%|██▎       | 1152765/4997817 [00:06<00:22, 173078.85it/s]"
      ]
     },
     {
@@ -1082,7 +1082,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 24%|██▍       | 1209681/4997817 [00:06<00:21, 176020.95it/s]"
+      " 23%|██▎       | 1170074/4997817 [00:06<00:22, 172769.04it/s]"
      ]
     },
     {
@@ -1090,7 +1090,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 25%|██▍       | 1227284/4997817 [00:06<00:21, 175879.77it/s]"
+      " 24%|██▍       | 1187352/4997817 [00:06<00:22, 166605.50it/s]"
      ]
     },
     {
@@ -1098,7 +1098,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 25%|██▍       | 1244924/4997817 [00:07<00:21, 176033.24it/s]"
+      " 24%|██▍       | 1204651/4997817 [00:07<00:22, 168465.26it/s]"
      ]
     },
     {
@@ -1106,7 +1106,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 25%|██▌       | 1262528/4997817 [00:07<00:21, 175800.45it/s]"
+      " 24%|██▍       | 1221930/4997817 [00:07<00:22, 169735.51it/s]"
      ]
     },
     {
@@ -1114,7 +1114,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 26%|██▌       | 1280128/4997817 [00:07<00:21, 175857.50it/s]"
+      " 25%|██▍       | 1239241/4997817 [00:07<00:22, 170729.82it/s]"
      ]
     },
     {
@@ -1122,7 +1122,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 26%|██▌       | 1297844/4997817 [00:07<00:20, 176243.66it/s]"
+      " 25%|██▌       | 1256336/4997817 [00:07<00:21, 170593.49it/s]"
      ]
     },
     {
@@ -1130,7 +1130,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 26%|██▋       | 1315598/4997817 [00:07<00:20, 176628.10it/s]"
+      " 25%|██▌       | 1273736/4997817 [00:07<00:21, 171604.75it/s]"
      ]
     },
     {
@@ -1138,7 +1138,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 27%|██▋       | 1333326/4997817 [00:07<00:20, 176821.58it/s]"
+      " 26%|██▌       | 1291095/4997817 [00:07<00:21, 172192.76it/s]"
      ]
     },
     {
@@ -1146,7 +1146,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 27%|██▋       | 1351011/4997817 [00:07<00:20, 176827.24it/s]"
+      " 26%|██▌       | 1308323/4997817 [00:07<00:21, 171512.26it/s]"
      ]
     },
     {
@@ -1154,7 +1154,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 27%|██▋       | 1368694/4997817 [00:07<00:20, 176802.46it/s]"
+      " 27%|██▋       | 1325481/4997817 [00:07<00:21, 170300.82it/s]"
      ]
     },
     {
@@ -1162,7 +1162,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 28%|██▊       | 1386390/4997817 [00:07<00:20, 176847.35it/s]"
+      " 27%|██▋       | 1342592/4997817 [00:07<00:21, 170538.44it/s]"
      ]
     },
     {
@@ -1170,7 +1170,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 28%|██▊       | 1404075/4997817 [00:07<00:20, 176737.99it/s]"
+      " 27%|██▋       | 1359667/4997817 [00:07<00:21, 170596.95it/s]"
      ]
     },
     {
@@ -1178,7 +1178,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 28%|██▊       | 1421749/4997817 [00:08<00:20, 176144.34it/s]"
+      " 28%|██▊       | 1376730/4997817 [00:08<00:21, 170584.52it/s]"
      ]
     },
     {
@@ -1186,7 +1186,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 29%|██▉       | 1439364/4997817 [00:08<00:20, 175538.08it/s]"
+      " 28%|██▊       | 1393808/4997817 [00:08<00:21, 170640.10it/s]"
      ]
     },
     {
@@ -1194,7 +1194,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 29%|██▉       | 1456919/4997817 [00:08<00:20, 175153.28it/s]"
+      " 28%|██▊       | 1410890/4997817 [00:08<00:21, 170689.62it/s]"
      ]
     },
     {
@@ -1202,7 +1202,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 30%|██▉       | 1474435/4997817 [00:08<00:20, 170969.49it/s]"
+      " 29%|██▊       | 1428041/4997817 [00:08<00:20, 170932.83it/s]"
      ]
     },
     {
@@ -1210,7 +1210,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 30%|██▉       | 1491823/4997817 [00:08<00:20, 171820.74it/s]"
+      " 29%|██▉       | 1445136/4997817 [00:08<00:20, 170912.74it/s]"
      ]
     },
     {
@@ -1218,7 +1218,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 30%|███       | 1509255/4997817 [00:08<00:20, 172556.44it/s]"
+      " 29%|██▉       | 1462229/4997817 [00:08<00:20, 170913.08it/s]"
      ]
     },
     {
@@ -1226,7 +1226,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 31%|███       | 1526644/4997817 [00:08<00:20, 172949.51it/s]"
+      " 30%|██▉       | 1479321/4997817 [00:08<00:20, 170499.01it/s]"
      ]
     },
     {
@@ -1234,7 +1234,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 31%|███       | 1544068/4997817 [00:08<00:19, 173330.43it/s]"
+      " 30%|██▉       | 1496372/4997817 [00:08<00:20, 170482.52it/s]"
      ]
     },
     {
@@ -1242,7 +1242,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 31%|███       | 1561511/4997817 [00:08<00:19, 173656.01it/s]"
+      " 30%|███       | 1513421/4997817 [00:08<00:20, 170395.25it/s]"
      ]
     },
     {
@@ -1250,7 +1250,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 32%|███▏      | 1578882/4997817 [00:08<00:19, 173600.68it/s]"
+      " 31%|███       | 1530461/4997817 [00:08<00:20, 167319.27it/s]"
      ]
     },
     {
@@ -1258,7 +1258,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 32%|███▏      | 1596246/4997817 [00:09<00:19, 173421.86it/s]"
+      " 31%|███       | 1547481/4997817 [00:09<00:20, 168168.22it/s]"
      ]
     },
     {
@@ -1266,7 +1266,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 32%|███▏      | 1613759/4997817 [00:09<00:19, 173930.01it/s]"
+      " 31%|███▏      | 1564657/4997817 [00:09<00:20, 169231.06it/s]"
      ]
     },
     {
@@ -1274,7 +1274,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 33%|███▎      | 1631217/4997817 [00:09<00:19, 174122.33it/s]"
+      " 32%|███▏      | 1581588/4997817 [00:09<00:20, 169022.41it/s]"
      ]
     },
     {
@@ -1282,7 +1282,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 33%|███▎      | 1648631/4997817 [00:09<00:19, 170877.37it/s]"
+      " 32%|███▏      | 1598553/4997817 [00:09<00:20, 169205.69it/s]"
      ]
     },
     {
@@ -1290,7 +1290,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 33%|███▎      | 1666045/4997817 [00:09<00:19, 171838.32it/s]"
+      " 32%|███▏      | 1615478/4997817 [00:09<00:20, 168976.78it/s]"
      ]
     },
     {
@@ -1298,7 +1298,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 34%|███▎      | 1683650/4997817 [00:09<00:19, 173084.46it/s]"
+      " 33%|███▎      | 1632488/4997817 [00:09<00:19, 169311.06it/s]"
      ]
     },
     {
@@ -1306,7 +1306,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 34%|███▍      | 1701219/4997817 [00:09<00:18, 173858.01it/s]"
+      " 33%|███▎      | 1649496/4997817 [00:09<00:19, 169537.42it/s]"
      ]
     },
     {
@@ -1314,7 +1314,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 34%|███▍      | 1718873/4997817 [00:09<00:18, 174654.48it/s]"
+      " 33%|███▎      | 1666516/4997817 [00:09<00:19, 169734.29it/s]"
      ]
     },
     {
@@ -1322,7 +1322,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 35%|███▍      | 1736344/4997817 [00:09<00:18, 174622.09it/s]"
+      " 34%|███▎      | 1683770/4997817 [00:09<00:19, 170570.66it/s]"
      ]
     },
     {
@@ -1330,7 +1330,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 35%|███▌      | 1753942/4997817 [00:09<00:18, 175023.95it/s]"
+      " 34%|███▍      | 1700828/4997817 [00:09<00:19, 170107.59it/s]"
      ]
     },
     {
@@ -1338,7 +1338,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 35%|███▌      | 1771448/4997817 [00:10<00:18, 174993.57it/s]"
+      " 34%|███▍      | 1718095/4997817 [00:10<00:19, 170870.72it/s]"
      ]
     },
     {
@@ -1346,7 +1346,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 36%|███▌      | 1788950/4997817 [00:10<00:18, 174751.67it/s]"
+      " 35%|███▍      | 1735554/4997817 [00:10<00:18, 171982.68it/s]"
      ]
     },
     {
@@ -1354,7 +1354,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 36%|███▌      | 1806535/4997817 [00:10<00:18, 175076.18it/s]"
+      " 35%|███▌      | 1752844/4997817 [00:10<00:18, 172253.27it/s]"
      ]
     },
     {
@@ -1362,7 +1362,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 36%|███▋      | 1824044/4997817 [00:10<00:18, 174947.92it/s]"
+      " 35%|███▌      | 1770192/4997817 [00:10<00:18, 172616.19it/s]"
      ]
     },
     {
@@ -1370,7 +1370,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 37%|███▋      | 1841882/4997817 [00:10<00:17, 175973.50it/s]"
+      " 36%|███▌      | 1787471/4997817 [00:10<00:18, 172664.15it/s]"
      ]
     },
     {
@@ -1378,7 +1378,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 37%|███▋      | 1859592/4997817 [00:10<00:17, 176306.73it/s]"
+      " 36%|███▌      | 1804738/4997817 [00:10<00:18, 172460.42it/s]"
      ]
     },
     {
@@ -1386,7 +1386,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 38%|███▊      | 1877279/4997817 [00:10<00:17, 176473.74it/s]"
+      " 36%|███▋      | 1822303/4997817 [00:10<00:18, 173414.99it/s]"
      ]
     },
     {
@@ -1394,7 +1394,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 38%|███▊      | 1894943/4997817 [00:10<00:17, 176520.23it/s]"
+      " 37%|███▋      | 1839775/4997817 [00:10<00:18, 173804.36it/s]"
      ]
     },
     {
@@ -1402,7 +1402,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 38%|███▊      | 1912596/4997817 [00:10<00:17, 176219.44it/s]"
+      " 37%|███▋      | 1857168/4997817 [00:10<00:18, 173839.96it/s]"
      ]
     },
     {
@@ -1410,7 +1410,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 39%|███▊      | 1930263/4997817 [00:10<00:17, 176352.95it/s]"
+      " 38%|███▊      | 1874674/4997817 [00:10<00:17, 174204.13it/s]"
      ]
     },
     {
@@ -1418,7 +1418,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 39%|███▉      | 1947942/4997817 [00:11<00:17, 176481.33it/s]"
+      " 38%|███▊      | 1892095/4997817 [00:11<00:17, 173973.19it/s]"
      ]
     },
     {
@@ -1426,7 +1426,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 39%|███▉      | 1965591/4997817 [00:11<00:17, 175961.68it/s]"
+      " 38%|███▊      | 1909493/4997817 [00:11<00:17, 173652.09it/s]"
      ]
     },
     {
@@ -1434,7 +1434,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 40%|███▉      | 1983188/4997817 [00:11<00:17, 175748.07it/s]"
+      " 39%|███▊      | 1926956/4997817 [00:11<00:17, 173942.04it/s]"
      ]
     },
     {
@@ -1442,7 +1442,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 40%|████      | 2000787/4997817 [00:11<00:17, 175815.61it/s]"
+      " 39%|███▉      | 1944351/4997817 [00:11<00:17, 173676.68it/s]"
      ]
     },
     {
@@ -1450,7 +1450,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 40%|████      | 2018369/4997817 [00:11<00:17, 168730.26it/s]"
+      " 39%|███▉      | 1961719/4997817 [00:11<00:17, 173544.32it/s]"
      ]
     },
     {
@@ -1458,7 +1458,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 41%|████      | 2035840/4997817 [00:11<00:17, 170467.93it/s]"
+      " 40%|███▉      | 1979205/4997817 [00:11<00:17, 173936.66it/s]"
      ]
     },
     {
@@ -1466,7 +1466,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 41%|████      | 2053380/4997817 [00:11<00:17, 171912.16it/s]"
+      " 40%|███▉      | 1996632/4997817 [00:11<00:17, 174033.21it/s]"
      ]
     },
     {
@@ -1474,7 +1474,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 41%|████▏     | 2070874/4997817 [00:11<00:16, 172804.36it/s]"
+      " 40%|████      | 2014036/4997817 [00:11<00:17, 173984.33it/s]"
      ]
     },
     {
@@ -1482,7 +1482,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 42%|████▏     | 2088397/4997817 [00:11<00:16, 173519.94it/s]"
+      " 41%|████      | 2031435/4997817 [00:11<00:17, 173931.39it/s]"
      ]
     },
     {
@@ -1490,7 +1490,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 42%|████▏     | 2105931/4997817 [00:11<00:16, 174057.90it/s]"
+      " 41%|████      | 2048829/4997817 [00:11<00:16, 173817.50it/s]"
      ]
     },
     {
@@ -1498,7 +1498,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 42%|████▏     | 2123526/4997817 [00:12<00:16, 174619.28it/s]"
+      " 41%|████▏     | 2066211/4997817 [00:12<00:16, 173089.88it/s]"
      ]
     },
     {
@@ -1506,7 +1506,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 43%|████▎     | 2140999/4997817 [00:12<00:16, 174636.87it/s]"
+      " 42%|████▏     | 2083521/4997817 [00:12<00:16, 173048.80it/s]"
      ]
     },
     {
@@ -1514,7 +1514,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 43%|████▎     | 2158470/4997817 [00:12<00:16, 174638.99it/s]"
+      " 42%|████▏     | 2100866/4997817 [00:12<00:16, 173165.42it/s]"
      ]
     },
     {
@@ -1522,7 +1522,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 44%|████▎     | 2175968/4997817 [00:12<00:16, 174737.78it/s]"
+      " 42%|████▏     | 2118183/4997817 [00:12<00:16, 173142.49it/s]"
      ]
     },
     {
@@ -1530,7 +1530,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 44%|████▍     | 2193446/4997817 [00:12<00:16, 174633.60it/s]"
+      " 43%|████▎     | 2135542/4997817 [00:12<00:16, 173274.37it/s]"
      ]
     },
     {
@@ -1538,7 +1538,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 44%|████▍     | 2211104/4997817 [00:12<00:15, 175212.99it/s]"
+      " 43%|████▎     | 2152870/4997817 [00:12<00:16, 172932.60it/s]"
      ]
     },
     {
@@ -1546,7 +1546,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 45%|████▍     | 2228891/4997817 [00:12<00:15, 176005.69it/s]"
+      " 43%|████▎     | 2170164/4997817 [00:12<00:16, 172786.75it/s]"
      ]
     },
     {
@@ -1554,7 +1554,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 45%|████▍     | 2246705/4997817 [00:12<00:15, 176642.54it/s]"
+      " 44%|████▍     | 2187443/4997817 [00:12<00:16, 172758.80it/s]"
      ]
     },
     {
@@ -1562,7 +1562,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 45%|████▌     | 2264371/4997817 [00:12<00:15, 176477.26it/s]"
+      " 44%|████▍     | 2204719/4997817 [00:12<00:16, 172489.32it/s]"
      ]
     },
     {
@@ -1570,7 +1570,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 46%|████▌     | 2282020/4997817 [00:12<00:15, 176270.92it/s]"
+      " 44%|████▍     | 2221969/4997817 [00:12<00:16, 172209.27it/s]"
      ]
     },
     {
@@ -1578,7 +1578,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 46%|████▌     | 2299648/4997817 [00:13<00:15, 176268.65it/s]"
+      " 45%|████▍     | 2239236/4997817 [00:13<00:16, 172342.87it/s]"
      ]
     },
     {
@@ -1586,7 +1586,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 46%|████▋     | 2317443/4997817 [00:13<00:15, 176769.77it/s]"
+      " 45%|████▌     | 2256471/4997817 [00:13<00:15, 171712.51it/s]"
      ]
     },
     {
@@ -1594,7 +1594,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 47%|████▋     | 2335287/4997817 [00:13<00:15, 177266.82it/s]"
+      " 45%|████▌     | 2273643/4997817 [00:13<00:15, 171115.35it/s]"
      ]
     },
     {
@@ -1602,7 +1602,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 47%|████▋     | 2353014/4997817 [00:13<00:14, 177237.67it/s]"
+      " 46%|████▌     | 2290884/4997817 [00:13<00:15, 171500.07it/s]"
      ]
     },
     {
@@ -1610,7 +1610,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 47%|████▋     | 2370811/4997817 [00:13<00:14, 177454.13it/s]"
+      " 46%|████▌     | 2308082/4997817 [00:13<00:15, 171641.01it/s]"
      ]
     },
     {
@@ -1618,7 +1618,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 48%|████▊     | 2388644/4997817 [00:13<00:14, 177713.67it/s]"
+      " 47%|████▋     | 2325509/4997817 [00:13<00:15, 172425.40it/s]"
      ]
     },
     {
@@ -1626,7 +1626,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 48%|████▊     | 2406515/4997817 [00:13<00:14, 178008.59it/s]"
+      " 47%|████▋     | 2342779/4997817 [00:13<00:15, 172505.46it/s]"
      ]
     },
     {
@@ -1634,7 +1634,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 49%|████▊     | 2424382/4997817 [00:13<00:14, 178202.69it/s]"
+      " 47%|████▋     | 2360266/4997817 [00:13<00:15, 173212.74it/s]"
      ]
     },
     {
@@ -1642,7 +1642,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 49%|████▉     | 2442203/4997817 [00:13<00:14, 176953.39it/s]"
+      " 48%|████▊     | 2377746/4997817 [00:13<00:15, 173684.69it/s]"
      ]
     },
     {
@@ -1650,7 +1650,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 49%|████▉     | 2460064/4997817 [00:13<00:14, 177445.01it/s]"
+      " 48%|████▊     | 2395275/4997817 [00:13<00:14, 174160.30it/s]"
      ]
     },
     {
@@ -1658,7 +1658,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 50%|████▉     | 2478011/4997817 [00:14<00:14, 178046.47it/s]"
+      " 48%|████▊     | 2412692/4997817 [00:14<00:14, 173545.01it/s]"
      ]
     },
     {
@@ -1666,7 +1666,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 50%|████▉     | 2495859/4997817 [00:14<00:14, 178170.66it/s]"
+      " 49%|████▊     | 2430069/4997817 [00:14<00:14, 173608.65it/s]"
      ]
     },
     {
@@ -1674,7 +1674,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 50%|█████     | 2513719/4997817 [00:14<00:13, 178294.71it/s]"
+      " 49%|████▉     | 2447462/4997817 [00:14<00:14, 173701.94it/s]"
      ]
     },
     {
@@ -1682,7 +1682,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 51%|█████     | 2531550/4997817 [00:14<00:13, 177984.78it/s]"
+      " 49%|████▉     | 2464833/4997817 [00:14<00:14, 173310.39it/s]"
      ]
     },
     {
@@ -1690,7 +1690,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 51%|█████     | 2549350/4997817 [00:14<00:13, 177765.45it/s]"
+      " 50%|████▉     | 2482165/4997817 [00:14<00:14, 172931.85it/s]"
      ]
     },
     {
@@ -1698,7 +1698,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 51%|█████▏    | 2567127/4997817 [00:14<00:13, 177467.75it/s]"
+      " 50%|█████     | 2499563/4997817 [00:14<00:14, 173242.08it/s]"
      ]
     },
     {
@@ -1706,7 +1706,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 52%|█████▏    | 2584875/4997817 [00:14<00:13, 177135.41it/s]"
+      " 50%|█████     | 2516888/4997817 [00:14<00:14, 173011.66it/s]"
      ]
     },
     {
@@ -1714,7 +1714,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 52%|█████▏    | 2602589/4997817 [00:14<00:13, 176761.69it/s]"
+      " 51%|█████     | 2534190/4997817 [00:14<00:14, 172835.54it/s]"
      ]
     },
     {
@@ -1722,7 +1722,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 52%|█████▏    | 2620266/4997817 [00:14<00:13, 176400.10it/s]"
+      " 51%|█████     | 2551474/4997817 [00:14<00:14, 172684.40it/s]"
      ]
     },
     {
@@ -1730,7 +1730,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 53%|█████▎    | 2637907/4997817 [00:14<00:13, 176353.99it/s]"
+      " 51%|█████▏    | 2568743/4997817 [00:14<00:14, 171725.95it/s]"
      ]
     },
     {
@@ -1738,7 +1738,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 53%|█████▎    | 2655543/4997817 [00:15<00:13, 175946.08it/s]"
+      " 52%|█████▏    | 2586056/4997817 [00:15<00:14, 172142.42it/s]"
      ]
     },
     {
@@ -1746,7 +1746,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 53%|█████▎    | 2673138/4997817 [00:15<00:13, 175874.49it/s]"
+      " 52%|█████▏    | 2603272/4997817 [00:15<00:13, 172009.73it/s]"
      ]
     },
     {
@@ -1754,7 +1754,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 54%|█████▍    | 2690726/4997817 [00:15<00:13, 175848.48it/s]"
+      " 52%|█████▏    | 2620507/4997817 [00:15<00:13, 172106.86it/s]"
      ]
     },
     {
@@ -1762,7 +1762,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 54%|█████▍    | 2708319/4997817 [00:15<00:13, 175869.29it/s]"
+      " 53%|█████▎    | 2637748/4997817 [00:15<00:13, 172193.71it/s]"
      ]
     },
     {
@@ -1770,7 +1770,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 55%|█████▍    | 2725906/4997817 [00:15<00:13, 170165.16it/s]"
+      " 53%|█████▎    | 2655228/4997817 [00:15<00:13, 172969.91it/s]"
      ]
     },
     {
@@ -1778,7 +1778,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 55%|█████▍    | 2743530/4997817 [00:15<00:13, 171941.91it/s]"
+      " 53%|█████▎    | 2672594/4997817 [00:15<00:13, 173172.78it/s]"
      ]
     },
     {
@@ -1786,7 +1786,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 55%|█████▌    | 2761055/4997817 [00:15<00:12, 172913.09it/s]"
+      " 54%|█████▍    | 2689912/4997817 [00:15<00:13, 172973.25it/s]"
      ]
     },
     {
@@ -1794,7 +1794,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 56%|█████▌    | 2778599/4997817 [00:15<00:12, 173657.51it/s]"
+      " 54%|█████▍    | 2707210/4997817 [00:15<00:13, 172673.04it/s]"
      ]
     },
     {
@@ -1802,7 +1802,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 56%|█████▌    | 2796132/4997817 [00:15<00:12, 174152.13it/s]"
+      " 55%|█████▍    | 2724649/4997817 [00:15<00:13, 173183.95it/s]"
      ]
     },
     {
@@ -1810,7 +1810,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 56%|█████▋    | 2813713/4997817 [00:15<00:12, 174642.51it/s]"
+      " 55%|█████▍    | 2742045/4997817 [00:15<00:13, 173411.99it/s]"
      ]
     },
     {
@@ -1818,7 +1818,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 57%|█████▋    | 2831215/4997817 [00:16<00:12, 174751.85it/s]"
+      " 55%|█████▌    | 2759387/4997817 [00:16<00:12, 172797.03it/s]"
      ]
     },
     {
@@ -1826,7 +1826,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 57%|█████▋    | 2848781/4997817 [00:16<00:12, 175020.18it/s]"
+      " 56%|█████▌    | 2776675/4997817 [00:16<00:12, 172818.44it/s]"
      ]
     },
     {
@@ -1834,7 +1834,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 57%|█████▋    | 2866288/4997817 [00:16<00:12, 174922.90it/s]"
+      " 56%|█████▌    | 2793958/4997817 [00:16<00:12, 172443.43it/s]"
      ]
     },
     {
@@ -1842,7 +1842,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 58%|█████▊    | 2883784/4997817 [00:16<00:12, 174867.39it/s]"
+      " 56%|█████▌    | 2811203/4997817 [00:16<00:12, 172181.49it/s]"
      ]
     },
     {
@@ -1850,7 +1850,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 58%|█████▊    | 2901274/4997817 [00:16<00:12, 174518.53it/s]"
+      " 57%|█████▋    | 2828422/4997817 [00:16<00:12, 171875.31it/s]"
      ]
     },
     {
@@ -1858,7 +1858,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 58%|█████▊    | 2918784/4997817 [00:16<00:11, 174687.97it/s]"
+      " 57%|█████▋    | 2845610/4997817 [00:16<00:12, 171605.12it/s]"
      ]
     },
     {
@@ -1866,7 +1866,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 59%|█████▉    | 2936508/4997817 [00:16<00:11, 175446.36it/s]"
+      " 57%|█████▋    | 2862771/4997817 [00:16<00:12, 171158.71it/s]"
      ]
     },
     {
@@ -1874,7 +1874,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 59%|█████▉    | 2954190/4997817 [00:16<00:11, 175855.21it/s]"
+      " 58%|█████▊    | 2880043/4997817 [00:16<00:12, 171620.59it/s]"
      ]
     },
     {
@@ -1882,7 +1882,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 59%|█████▉    | 2971867/4997817 [00:16<00:11, 176126.05it/s]"
+      " 58%|█████▊    | 2897206/4997817 [00:16<00:12, 171545.78it/s]"
      ]
     },
     {
@@ -1890,7 +1890,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 60%|█████▉    | 2989607/4997817 [00:16<00:11, 176504.50it/s]"
+      " 58%|█████▊    | 2914479/4997817 [00:16<00:12, 171897.64it/s]"
      ]
     },
     {
@@ -1898,7 +1898,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 60%|██████    | 3007465/4997817 [00:17<00:11, 177122.98it/s]"
+      " 59%|█████▊    | 2931670/4997817 [00:17<00:12, 171468.11it/s]"
      ]
     },
     {
@@ -1906,7 +1906,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 61%|██████    | 3025371/4997817 [00:17<00:11, 177699.75it/s]"
+      " 59%|█████▉    | 2948818/4997817 [00:17<00:12, 164907.42it/s]"
      ]
     },
     {
@@ -1914,7 +1914,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 61%|██████    | 3043142/4997817 [00:17<00:11, 177526.55it/s]"
+      " 59%|█████▉    | 2965819/4997817 [00:17<00:12, 166389.56it/s]"
      ]
     },
     {
@@ -1922,7 +1922,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 61%|██████    | 3060895/4997817 [00:17<00:10, 177261.36it/s]"
+      " 60%|█████▉    | 2983140/4997817 [00:17<00:11, 168389.32it/s]"
      ]
     },
     {
@@ -1930,7 +1930,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 62%|██████▏   | 3078730/4997817 [00:17<00:10, 177583.27it/s]"
+      " 60%|██████    | 3000413/4997817 [00:17<00:11, 169668.46it/s]"
      ]
     },
     {
@@ -1938,7 +1938,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 62%|██████▏   | 3096489/4997817 [00:17<00:10, 173439.53it/s]"
+      " 60%|██████    | 3017703/4997817 [00:17<00:11, 170624.06it/s]"
      ]
     },
     {
@@ -1946,7 +1946,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 62%|██████▏   | 3114319/4997817 [00:17<00:10, 174869.70it/s]"
+      " 61%|██████    | 3034960/4997817 [00:17<00:11, 171201.38it/s]"
      ]
     },
     {
@@ -1954,7 +1954,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 63%|██████▎   | 3132167/4997817 [00:17<00:10, 175934.19it/s]"
+      " 61%|██████    | 3052094/4997817 [00:17<00:11, 171123.09it/s]"
      ]
     },
     {
@@ -1962,7 +1962,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 63%|██████▎   | 3150036/4997817 [00:17<00:10, 176751.52it/s]"
+      " 61%|██████▏   | 3069455/4997817 [00:17<00:11, 171864.37it/s]"
      ]
     },
     {
@@ -1970,7 +1970,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 63%|██████▎   | 3167884/4997817 [00:18<00:10, 177263.84it/s]"
+      " 62%|██████▏   | 3086755/4997817 [00:18<00:11, 172200.09it/s]"
      ]
     },
     {
@@ -1978,7 +1978,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 64%|██████▎   | 3185698/4997817 [00:18<00:10, 177523.48it/s]"
+      " 62%|██████▏   | 3104106/4997817 [00:18<00:10, 172590.10it/s]"
      ]
     },
     {
@@ -1986,7 +1986,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 64%|██████▍   | 3203568/4997817 [00:18<00:10, 177872.39it/s]"
+      " 62%|██████▏   | 3121414/4997817 [00:18<00:10, 172732.84it/s]"
      ]
     },
     {
@@ -1994,7 +1994,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 64%|██████▍   | 3221360/4997817 [00:18<00:09, 177711.90it/s]"
+      " 63%|██████▎   | 3138690/4997817 [00:18<00:10, 172414.93it/s]"
      ]
     },
     {
@@ -2002,7 +2002,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 65%|██████▍   | 3239134/4997817 [00:18<00:09, 177577.15it/s]"
+      " 63%|██████▎   | 3155966/4997817 [00:18<00:10, 172513.49it/s]"
      ]
     },
     {
@@ -2010,7 +2010,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 65%|██████▌   | 3256894/4997817 [00:18<00:09, 176368.10it/s]"
+      " 63%|██████▎   | 3173316/4997817 [00:18<00:10, 172807.21it/s]"
      ]
     },
     {
@@ -2018,7 +2018,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 66%|██████▌   | 3274697/4997817 [00:18<00:09, 176860.96it/s]"
+      " 64%|██████▍   | 3190786/4997817 [00:18<00:10, 173371.43it/s]"
      ]
     },
     {
@@ -2026,7 +2026,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 66%|██████▌   | 3292438/4997817 [00:18<00:09, 177021.85it/s]"
+      " 64%|██████▍   | 3208124/4997817 [00:18<00:10, 173368.22it/s]"
      ]
     },
     {
@@ -2034,7 +2034,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 66%|██████▌   | 3310162/4997817 [00:18<00:09, 177081.84it/s]"
+      " 65%|██████▍   | 3225462/4997817 [00:18<00:10, 173291.90it/s]"
      ]
     },
     {
@@ -2042,7 +2042,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 67%|██████▋   | 3327911/4997817 [00:18<00:09, 177201.04it/s]"
+      " 65%|██████▍   | 3242799/4997817 [00:18<00:10, 173312.39it/s]"
      ]
     },
     {
@@ -2050,7 +2050,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 67%|██████▋   | 3345632/4997817 [00:19<00:09, 177070.11it/s]"
+      " 65%|██████▌   | 3260131/4997817 [00:19<00:10, 173229.77it/s]"
      ]
     },
     {
@@ -2058,7 +2058,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 67%|██████▋   | 3363340/4997817 [00:19<00:09, 176560.09it/s]"
+      " 66%|██████▌   | 3277459/4997817 [00:19<00:09, 173241.78it/s]"
      ]
     },
     {
@@ -2066,7 +2066,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 68%|██████▊   | 3380997/4997817 [00:19<00:09, 176297.50it/s]"
+      " 66%|██████▌   | 3294784/4997817 [00:19<00:10, 168720.26it/s]"
      ]
     },
     {
@@ -2074,7 +2074,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 68%|██████▊   | 3398628/4997817 [00:19<00:09, 175923.15it/s]"
+      " 66%|██████▋   | 3311836/4997817 [00:19<00:09, 169246.65it/s]"
      ]
     },
     {
@@ -2082,7 +2082,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 68%|██████▊   | 3416444/4997817 [00:19<00:08, 176583.41it/s]"
+      " 67%|██████▋   | 3329185/4997817 [00:19<00:09, 170499.74it/s]"
      ]
     },
     {
@@ -2090,7 +2090,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 69%|██████▊   | 3434103/4997817 [00:19<00:08, 176134.50it/s]"
+      " 67%|██████▋   | 3346525/4997817 [00:19<00:09, 171358.37it/s]"
      ]
     },
     {
@@ -2098,7 +2098,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 69%|██████▉   | 3451738/4997817 [00:19<00:08, 176195.20it/s]"
+      " 67%|██████▋   | 3363981/4997817 [00:19<00:09, 172310.29it/s]"
      ]
     },
     {
@@ -2106,7 +2106,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 69%|██████▉   | 3469358/4997817 [00:19<00:08, 176027.78it/s]"
+      " 68%|██████▊   | 3381408/4997817 [00:19<00:09, 172891.73it/s]"
      ]
     },
     {
@@ -2114,7 +2114,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 70%|██████▉   | 3486962/4997817 [00:19<00:08, 175916.89it/s]"
+      " 68%|██████▊   | 3398883/4997817 [00:19<00:09, 173444.12it/s]"
      ]
     },
     {
@@ -2122,7 +2122,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 70%|███████   | 3504554/4997817 [00:19<00:08, 175282.26it/s]"
+      " 68%|██████▊   | 3416395/4997817 [00:19<00:09, 173944.67it/s]"
      ]
     },
     {
@@ -2130,7 +2130,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 70%|███████   | 3522083/4997817 [00:20<00:08, 175175.68it/s]"
+      " 69%|██████▊   | 3433866/4997817 [00:20<00:08, 174170.71it/s]"
      ]
     },
     {
@@ -2138,7 +2138,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 71%|███████   | 3539798/4997817 [00:20<00:08, 175762.45it/s]"
+      " 69%|██████▉   | 3451295/4997817 [00:20<00:08, 174202.78it/s]"
      ]
     },
     {
@@ -2146,7 +2146,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 71%|███████   | 3557423/4997817 [00:20<00:08, 175905.54it/s]"
+      " 69%|██████▉   | 3468717/4997817 [00:20<00:08, 173871.49it/s]"
      ]
     },
     {
@@ -2154,7 +2154,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 72%|███████▏  | 3575120/4997817 [00:20<00:08, 176219.37it/s]"
+      " 70%|██████▉   | 3486106/4997817 [00:20<00:08, 173730.48it/s]"
      ]
     },
     {
@@ -2162,7 +2162,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 72%|███████▏  | 3592743/4997817 [00:20<00:07, 176124.71it/s]"
+      " 70%|███████   | 3503480/4997817 [00:20<00:08, 173576.14it/s]"
      ]
     },
     {
@@ -2170,7 +2170,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 72%|███████▏  | 3610402/4997817 [00:20<00:07, 176260.53it/s]"
+      " 70%|███████   | 3520839/4997817 [00:20<00:08, 172430.10it/s]"
      ]
     },
     {
@@ -2178,7 +2178,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 73%|███████▎  | 3628029/4997817 [00:20<00:07, 176148.24it/s]"
+      " 71%|███████   | 3538084/4997817 [00:20<00:08, 172099.73it/s]"
      ]
     },
     {
@@ -2186,7 +2186,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 73%|███████▎  | 3645692/4997817 [00:20<00:07, 176289.26it/s]"
+      " 71%|███████   | 3555296/4997817 [00:20<00:08, 171901.03it/s]"
      ]
     },
     {
@@ -2194,7 +2194,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 73%|███████▎  | 3663322/4997817 [00:20<00:07, 176222.00it/s]"
+      " 71%|███████▏  | 3572487/4997817 [00:20<00:08, 171608.38it/s]"
      ]
     },
     {
@@ -2202,7 +2202,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 74%|███████▎  | 3680980/4997817 [00:20<00:07, 176324.07it/s]"
+      " 72%|███████▏  | 3589757/4997817 [00:20<00:08, 171930.02it/s]"
      ]
     },
     {
@@ -2210,7 +2210,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 74%|███████▍  | 3698735/4997817 [00:21<00:07, 176687.64it/s]"
+      " 72%|███████▏  | 3606959/4997817 [00:21<00:08, 171954.77it/s]"
      ]
     },
     {
@@ -2218,7 +2218,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 74%|███████▍  | 3716404/4997817 [00:21<00:07, 176628.29it/s]"
+      " 73%|███████▎  | 3624245/4997817 [00:21<00:07, 172222.71it/s]"
      ]
     },
     {
@@ -2226,7 +2226,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 75%|███████▍  | 3734091/4997817 [00:21<00:07, 176697.15it/s]"
+      " 73%|███████▎  | 3641468/4997817 [00:21<00:08, 165178.06it/s]"
      ]
     },
     {
@@ -2234,7 +2234,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 75%|███████▌  | 3751761/4997817 [00:21<00:07, 176640.21it/s]"
+      " 73%|███████▎  | 3658590/4997817 [00:21<00:08, 166933.37it/s]"
      ]
     },
     {
@@ -2242,7 +2242,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 75%|███████▌  | 3769457/4997817 [00:21<00:06, 176732.49it/s]"
+      " 74%|███████▎  | 3675846/4997817 [00:21<00:07, 168580.91it/s]"
      ]
     },
     {
@@ -2250,7 +2250,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 76%|███████▌  | 3787209/4997817 [00:21<00:06, 176965.92it/s]"
+      " 74%|███████▍  | 3692949/4997817 [00:21<00:07, 169301.80it/s]"
      ]
     },
     {
@@ -2258,7 +2258,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 76%|███████▌  | 3804906/4997817 [00:21<00:06, 176115.13it/s]"
+      " 74%|███████▍  | 3710095/4997817 [00:21<00:07, 169941.03it/s]"
      ]
     },
     {
@@ -2266,7 +2266,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 76%|███████▋  | 3822589/4997817 [00:21<00:06, 176324.35it/s]"
+      " 75%|███████▍  | 3727212/4997817 [00:21<00:07, 170305.46it/s]"
      ]
     },
     {
@@ -2274,7 +2274,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 77%|███████▋  | 3840267/4997817 [00:21<00:06, 176456.98it/s]"
+      " 75%|███████▍  | 3744383/4997817 [00:21<00:07, 170721.03it/s]"
      ]
     },
     {
@@ -2282,7 +2282,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 77%|███████▋  | 3857949/4997817 [00:21<00:06, 176561.33it/s]"
+      " 75%|███████▌  | 3761585/4997817 [00:21<00:07, 171106.29it/s]"
      ]
     },
     {
@@ -2290,7 +2290,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 78%|███████▊  | 3875608/4997817 [00:22<00:06, 176566.31it/s]"
+      " 76%|███████▌  | 3778703/4997817 [00:22<00:07, 171043.71it/s]"
      ]
     },
     {
@@ -2298,7 +2298,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 78%|███████▊  | 3893265/4997817 [00:22<00:06, 176410.01it/s]"
+      " 76%|███████▌  | 3795890/4997817 [00:22<00:07, 171287.44it/s]"
      ]
     },
     {
@@ -2306,7 +2306,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 78%|███████▊  | 3910907/4997817 [00:22<00:06, 176281.59it/s]"
+      " 76%|███████▋  | 3813023/4997817 [00:22<00:06, 170783.59it/s]"
      ]
     },
     {
@@ -2314,7 +2314,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 79%|███████▊  | 3928536/4997817 [00:22<00:06, 176164.37it/s]"
+      " 77%|███████▋  | 3830175/4997817 [00:22<00:06, 171001.66it/s]"
      ]
     },
     {
@@ -2322,7 +2322,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 79%|███████▉  | 3946173/4997817 [00:22<00:05, 176221.61it/s]"
+      " 77%|███████▋  | 3847278/4997817 [00:22<00:06, 170875.44it/s]"
      ]
     },
     {
@@ -2330,7 +2330,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 79%|███████▉  | 3963796/4997817 [00:22<00:05, 175821.79it/s]"
+      " 77%|███████▋  | 3864410/4997817 [00:22<00:06, 171005.16it/s]"
      ]
     },
     {
@@ -2338,7 +2338,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 80%|███████▉  | 3981379/4997817 [00:22<00:05, 175679.25it/s]"
+      " 78%|███████▊  | 3881633/4997817 [00:22<00:06, 171370.89it/s]"
      ]
     },
     {
@@ -2346,7 +2346,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 80%|████████  | 3999029/4997817 [00:22<00:05, 175919.84it/s]"
+      " 78%|███████▊  | 3898771/4997817 [00:22<00:06, 170584.23it/s]"
      ]
     },
     {
@@ -2354,7 +2354,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 80%|████████  | 4016746/4997817 [00:22<00:05, 176291.45it/s]"
+      " 78%|███████▊  | 3915831/4997817 [00:22<00:06, 169965.52it/s]"
      ]
     },
     {
@@ -2362,7 +2362,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 81%|████████  | 4034376/4997817 [00:22<00:05, 176165.06it/s]"
+      " 79%|███████▊  | 3932869/4997817 [00:22<00:06, 170087.91it/s]"
      ]
     },
     {
@@ -2370,7 +2370,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 81%|████████  | 4051993/4997817 [00:23<00:05, 176084.45it/s]"
+      " 79%|███████▉  | 3949879/4997817 [00:23<00:06, 169990.31it/s]"
      ]
     },
     {
@@ -2378,7 +2378,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 81%|████████▏ | 4069602/4997817 [00:23<00:05, 175885.63it/s]"
+      " 79%|███████▉  | 3966879/4997817 [00:23<00:06, 169949.51it/s]"
      ]
     },
     {
@@ -2386,7 +2386,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 82%|████████▏ | 4087191/4997817 [00:23<00:05, 175394.53it/s]"
+      " 80%|███████▉  | 3983875/4997817 [00:23<00:05, 169772.79it/s]"
      ]
     },
     {
@@ -2394,7 +2394,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 82%|████████▏ | 4104731/4997817 [00:23<00:05, 175012.49it/s]"
+      " 80%|████████  | 4000853/4997817 [00:23<00:05, 169718.68it/s]"
      ]
     },
     {
@@ -2402,7 +2402,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 82%|████████▏ | 4122236/4997817 [00:23<00:05, 175020.26it/s]"
+      " 80%|████████  | 4017853/4997817 [00:23<00:05, 169799.69it/s]"
      ]
     },
     {
@@ -2410,7 +2410,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 83%|████████▎ | 4139739/4997817 [00:23<00:04, 174861.56it/s]"
+      " 81%|████████  | 4034999/4997817 [00:23<00:05, 170291.45it/s]"
      ]
     },
     {
@@ -2418,7 +2418,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 83%|████████▎ | 4157233/4997817 [00:23<00:04, 174879.84it/s]"
+      " 81%|████████  | 4052095/4997817 [00:23<00:05, 170489.07it/s]"
      ]
     },
     {
@@ -2426,7 +2426,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 84%|████████▎ | 4174722/4997817 [00:23<00:04, 167593.86it/s]"
+      " 81%|████████▏ | 4069145/4997817 [00:23<00:05, 170353.71it/s]"
      ]
     },
     {
@@ -2434,7 +2434,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 84%|████████▍ | 4192083/4997817 [00:23<00:04, 169341.24it/s]"
+      " 82%|████████▏ | 4086208/4997817 [00:23<00:05, 170433.51it/s]"
      ]
     },
     {
@@ -2442,7 +2442,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 84%|████████▍ | 4209474/4997817 [00:23<00:04, 170677.23it/s]"
+      " 82%|████████▏ | 4103252/4997817 [00:23<00:05, 169878.09it/s]"
      ]
     },
     {
@@ -2450,7 +2450,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 85%|████████▍ | 4226976/4997817 [00:24<00:04, 171956.01it/s]"
+      " 82%|████████▏ | 4120406/4997817 [00:24<00:05, 170373.48it/s]"
      ]
     },
     {
@@ -2458,7 +2458,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 85%|████████▍ | 4244411/4997817 [00:24<00:04, 172661.73it/s]"
+      " 83%|████████▎ | 4137520/4997817 [00:24<00:05, 170600.48it/s]"
      ]
     },
     {
@@ -2466,7 +2466,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 85%|████████▌ | 4261828/4997817 [00:24<00:04, 173107.14it/s]"
+      " 83%|████████▎ | 4154679/4997817 [00:24<00:04, 170894.40it/s]"
      ]
     },
     {
@@ -2474,7 +2474,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 86%|████████▌ | 4279356/4997817 [00:24<00:04, 173752.22it/s]"
+      " 83%|████████▎ | 4171769/4997817 [00:24<00:04, 170333.58it/s]"
      ]
     },
     {
@@ -2482,7 +2482,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 86%|████████▌ | 4296896/4997817 [00:24<00:04, 174242.38it/s]"
+      " 84%|████████▍ | 4188803/4997817 [00:24<00:04, 169923.43it/s]"
      ]
     },
     {
@@ -2490,7 +2490,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 86%|████████▋ | 4314328/4997817 [00:24<00:03, 173327.93it/s]"
+      " 84%|████████▍ | 4205796/4997817 [00:24<00:04, 169523.93it/s]"
      ]
     },
     {
@@ -2498,7 +2498,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 87%|████████▋ | 4331919/4997817 [00:24<00:03, 174094.65it/s]"
+      " 84%|████████▍ | 4222817/4997817 [00:24<00:04, 169724.32it/s]"
      ]
     },
     {
@@ -2506,7 +2506,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 87%|████████▋ | 4349427/4997817 [00:24<00:03, 174384.04it/s]"
+      " 85%|████████▍ | 4239790/4997817 [00:24<00:04, 169579.63it/s]"
      ]
     },
     {
@@ -2514,7 +2514,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 87%|████████▋ | 4367051/4997817 [00:24<00:03, 174935.33it/s]"
+      " 85%|████████▌ | 4256818/4997817 [00:24<00:04, 169786.18it/s]"
      ]
     },
     {
@@ -2522,7 +2522,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 88%|████████▊ | 4384595/4997817 [00:24<00:03, 175083.73it/s]"
+      " 86%|████████▌ | 4273871/4997817 [00:24<00:04, 170006.16it/s]"
      ]
     },
     {
@@ -2530,7 +2530,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 88%|████████▊ | 4402158/4997817 [00:25<00:03, 175244.18it/s]"
+      " 86%|████████▌ | 4291017/4997817 [00:25<00:04, 170438.96it/s]"
      ]
     },
     {
@@ -2538,7 +2538,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 88%|████████▊ | 4419786/4997817 [00:25<00:03, 175552.04it/s]"
+      " 86%|████████▌ | 4308086/4997817 [00:25<00:04, 170511.81it/s]"
      ]
     },
     {
@@ -2546,7 +2546,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 89%|████████▉ | 4437371/4997817 [00:25<00:03, 175636.52it/s]"
+      " 87%|████████▋ | 4325138/4997817 [00:25<00:03, 169547.73it/s]"
      ]
     },
     {
@@ -2554,7 +2554,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 89%|████████▉ | 4455003/4997817 [00:25<00:03, 175837.48it/s]"
+      " 87%|████████▋ | 4342095/4997817 [00:25<00:04, 163414.55it/s]"
      ]
     },
     {
@@ -2562,7 +2562,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 89%|████████▉ | 4472798/4997817 [00:25<00:02, 176468.40it/s]"
+      " 87%|████████▋ | 4359299/4997817 [00:25<00:03, 165924.16it/s]"
      ]
     },
     {
@@ -2570,7 +2570,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 90%|████████▉ | 4490560/4997817 [00:25<00:02, 176808.91it/s]"
+      " 88%|████████▊ | 4376343/4997817 [00:25<00:03, 167246.68it/s]"
      ]
     },
     {
@@ -2578,7 +2578,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 90%|█████████ | 4508294/4997817 [00:25<00:02, 176965.60it/s]"
+      " 88%|████████▊ | 4393409/4997817 [00:25<00:03, 168254.06it/s]"
      ]
     },
     {
@@ -2586,7 +2586,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 91%|█████████ | 4525991/4997817 [00:25<00:02, 176542.13it/s]"
+      " 88%|████████▊ | 4410560/4997817 [00:25<00:03, 169219.14it/s]"
      ]
     },
     {
@@ -2594,7 +2594,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 91%|█████████ | 4543710/4997817 [00:25<00:02, 176732.28it/s]"
+      " 89%|████████▊ | 4427598/4997817 [00:25<00:03, 169562.46it/s]"
      ]
     },
     {
@@ -2602,7 +2602,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 91%|█████████▏| 4561463/4997817 [00:25<00:02, 176966.16it/s]"
+      " 89%|████████▉ | 4444705/4997817 [00:25<00:03, 170009.98it/s]"
      ]
     },
     {
@@ -2610,7 +2610,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 92%|█████████▏| 4579245/4997817 [00:26<00:02, 177220.61it/s]"
+      " 89%|████████▉ | 4461912/4997817 [00:26<00:03, 170621.62it/s]"
      ]
     },
     {
@@ -2618,7 +2618,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 92%|█████████▏| 4597064/4997817 [00:26<00:02, 177506.60it/s]"
+      " 90%|████████▉ | 4478981/4997817 [00:26<00:03, 170378.09it/s]"
      ]
     },
     {
@@ -2626,7 +2626,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 92%|█████████▏| 4614841/4997817 [00:26<00:02, 177581.31it/s]"
+      " 90%|████████▉ | 4496051/4997817 [00:26<00:02, 170472.86it/s]"
      ]
     },
     {
@@ -2634,7 +2634,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 93%|█████████▎| 4632600/4997817 [00:26<00:02, 177414.75it/s]"
+      " 90%|█████████ | 4513109/4997817 [00:26<00:02, 170500.24it/s]"
      ]
     },
     {
@@ -2642,7 +2642,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 93%|█████████▎| 4650342/4997817 [00:26<00:01, 176810.52it/s]"
+      " 91%|█████████ | 4530422/4997817 [00:26<00:02, 171284.56it/s]"
      ]
     },
     {
@@ -2650,7 +2650,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 93%|█████████▎| 4668024/4997817 [00:26<00:02, 163789.41it/s]"
+      " 91%|█████████ | 4547617/4997817 [00:26<00:02, 171480.12it/s]"
      ]
     },
     {
@@ -2658,7 +2658,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 94%|█████████▍| 4685720/4997817 [00:26<00:01, 167519.88it/s]"
+      " 91%|█████████▏| 4564863/4997817 [00:26<00:02, 171770.24it/s]"
      ]
     },
     {
@@ -2666,7 +2666,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 94%|█████████▍| 4703228/4997817 [00:26<00:01, 169696.70it/s]"
+      " 92%|█████████▏| 4582041/4997817 [00:26<00:02, 171252.29it/s]"
      ]
     },
     {
@@ -2674,7 +2674,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 94%|█████████▍| 4720997/4997817 [00:26<00:01, 172024.75it/s]"
+      " 92%|█████████▏| 4599168/4997817 [00:26<00:02, 170751.33it/s]"
      ]
     },
     {
@@ -2682,7 +2682,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 95%|█████████▍| 4738747/4997817 [00:26<00:01, 173631.53it/s]"
+      " 92%|█████████▏| 4616245/4997817 [00:26<00:02, 170236.58it/s]"
      ]
     },
     {
@@ -2690,7 +2690,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 95%|█████████▌| 4756401/4997817 [00:27<00:01, 174489.53it/s]"
+      " 93%|█████████▎| 4633367/4997817 [00:27<00:02, 170526.11it/s]"
      ]
     },
     {
@@ -2698,7 +2698,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 96%|█████████▌| 4774026/4997817 [00:27<00:01, 175007.15it/s]"
+      " 93%|█████████▎| 4650476/4997817 [00:27<00:02, 170690.98it/s]"
      ]
     },
     {
@@ -2706,7 +2706,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 96%|█████████▌| 4791625/4997817 [00:27<00:01, 175295.19it/s]"
+      " 93%|█████████▎| 4667696/4997817 [00:27<00:01, 171138.38it/s]"
      ]
     },
     {
@@ -2714,7 +2714,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 96%|█████████▌| 4809276/4997817 [00:27<00:01, 175653.18it/s]"
+      " 94%|█████████▎| 4684811/4997817 [00:27<00:01, 166416.31it/s]"
      ]
     },
     {
@@ -2722,7 +2722,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 97%|█████████▋| 4827007/4997817 [00:27<00:00, 176143.70it/s]"
+      " 94%|█████████▍| 4702063/4997817 [00:27<00:01, 168205.75it/s]"
      ]
     },
     {
@@ -2730,7 +2730,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 97%|█████████▋| 4844714/4997817 [00:27<00:00, 176418.08it/s]"
+      " 94%|█████████▍| 4719283/4997817 [00:27<00:01, 169383.99it/s]"
      ]
     },
     {
@@ -2738,7 +2738,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 97%|█████████▋| 4862493/4997817 [00:27<00:00, 176823.46it/s]"
+      " 95%|█████████▍| 4736314/4997817 [00:27<00:01, 169655.39it/s]"
      ]
     },
     {
@@ -2746,7 +2746,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 98%|█████████▊| 4880182/4997817 [00:27<00:00, 176751.65it/s]"
+      " 95%|█████████▌| 4753543/4997817 [00:27<00:01, 170435.52it/s]"
      ]
     },
     {
@@ -2754,7 +2754,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 98%|█████████▊| 4897903/4997817 [00:27<00:00, 176884.36it/s]"
+      " 95%|█████████▌| 4770597/4997817 [00:27<00:01, 170424.55it/s]"
      ]
     },
     {
@@ -2762,7 +2762,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 98%|█████████▊| 4915661/4997817 [00:27<00:00, 177087.36it/s]"
+      " 96%|█████████▌| 4787647/4997817 [00:27<00:01, 170176.12it/s]"
      ]
     },
     {
@@ -2770,7 +2770,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 99%|█████████▊| 4933428/4997817 [00:28<00:00, 177258.92it/s]"
+      " 96%|█████████▌| 4804723/4997817 [00:28<00:01, 170346.92it/s]"
      ]
     },
     {
@@ -2778,7 +2778,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 99%|█████████▉| 4951156/4997817 [00:28<00:00, 177082.23it/s]"
+      " 96%|█████████▋| 4821848/4997817 [00:28<00:01, 170613.16it/s]"
      ]
     },
     {
@@ -2786,7 +2786,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      " 99%|█████████▉| 4968913/4997817 [00:28<00:00, 177225.04it/s]"
+      " 97%|█████████▋| 4838912/4997817 [00:28<00:00, 170004.32it/s]"
      ]
     },
     {
@@ -2794,7 +2794,7 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "100%|█████████▉| 4986697/4997817 [00:28<00:00, 177405.38it/s]"
+      " 97%|█████████▋| 4856169/4997817 [00:28<00:00, 170767.84it/s]"
      ]
     },
     {
@@ -2802,7 +2802,71 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "100%|██████████| 4997817/4997817 [00:28<00:00, 175774.30it/s]"
+      " 98%|█████████▊| 4873248/4997817 [00:28<00:00, 170274.10it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 98%|█████████▊| 4890277/4997817 [00:28<00:00, 170191.72it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 98%|█████████▊| 4907298/4997817 [00:28<00:00, 170155.86it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 99%|█████████▊| 4924593/4997817 [00:28<00:00, 170988.03it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 99%|█████████▉| 4941723/4997817 [00:28<00:00, 171077.12it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 99%|█████████▉| 4958832/4997817 [00:28<00:00, 170246.67it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "100%|█████████▉| 4975925/4997817 [00:29<00:00, 170448.66it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "100%|█████████▉| 4992971/4997817 [00:29<00:00, 170315.59it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "100%|██████████| 4997817/4997817 [00:29<00:00, 171134.52it/s]"
      ]
     },
     {
@@ -3041,10 +3105,10 @@
    "id": "c8f4e163",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:26.873997Z",
-     "iopub.status.busy": "2024-01-17T18:15:26.873608Z",
-     "iopub.status.idle": "2024-01-17T18:15:34.500725Z",
-     "shell.execute_reply": "2024-01-17T18:15:34.500119Z"
+     "iopub.execute_input": "2024-01-17T23:17:27.067514Z",
+     "iopub.status.busy": "2024-01-17T23:17:27.067292Z",
+     "iopub.status.idle": "2024-01-17T23:17:33.987753Z",
+     "shell.execute_reply": "2024-01-17T23:17:33.987100Z"
     }
    },
    "outputs": [],
@@ -3058,10 +3122,10 @@
    "id": "716c74f3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:34.503481Z",
-     "iopub.status.busy": "2024-01-17T18:15:34.503250Z",
-     "iopub.status.idle": "2024-01-17T18:15:37.645465Z",
-     "shell.execute_reply": "2024-01-17T18:15:37.644802Z"
+     "iopub.execute_input": "2024-01-17T23:17:33.990761Z",
+     "iopub.status.busy": "2024-01-17T23:17:33.990234Z",
+     "iopub.status.idle": "2024-01-17T23:17:37.063049Z",
+     "shell.execute_reply": "2024-01-17T23:17:37.062361Z"
     }
    },
    "outputs": [
@@ -3130,17 +3194,17 @@
    "id": "db0b5179",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:37.648166Z",
-     "iopub.status.busy": "2024-01-17T18:15:37.647687Z",
-     "iopub.status.idle": "2024-01-17T18:15:38.942832Z",
-     "shell.execute_reply": "2024-01-17T18:15:38.942205Z"
+     "iopub.execute_input": "2024-01-17T23:17:37.065626Z",
+     "iopub.status.busy": "2024-01-17T23:17:37.065237Z",
+     "iopub.status.idle": "2024-01-17T23:17:38.361596Z",
+     "shell.execute_reply": "2024-01-17T23:17:38.360967Z"
     }
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7dceb22a611b4605b26d5be95c8f7516",
+       "model_id": "3f014168fa4346d1a5243faf468a81d2",
        "version_major": 2,
        "version_minor": 0
       },
@@ -3170,10 +3234,10 @@
    "id": "390780a1",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:38.945502Z",
-     "iopub.status.busy": "2024-01-17T18:15:38.945301Z",
-     "iopub.status.idle": "2024-01-17T18:15:39.161394Z",
-     "shell.execute_reply": "2024-01-17T18:15:39.160716Z"
+     "iopub.execute_input": "2024-01-17T23:17:38.364721Z",
+     "iopub.status.busy": "2024-01-17T23:17:38.364114Z",
+     "iopub.status.idle": "2024-01-17T23:17:38.581707Z",
+     "shell.execute_reply": "2024-01-17T23:17:38.581139Z"
     }
    },
    "outputs": [],
@@ -3187,10 +3251,10 @@
    "id": "933d6ef0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:39.164077Z",
-     "iopub.status.busy": "2024-01-17T18:15:39.163834Z",
-     "iopub.status.idle": "2024-01-17T18:15:43.933619Z",
-     "shell.execute_reply": "2024-01-17T18:15:43.932997Z"
+     "iopub.execute_input": "2024-01-17T23:17:38.584628Z",
+     "iopub.status.busy": "2024-01-17T23:17:38.584131Z",
+     "iopub.status.idle": "2024-01-17T23:17:43.223493Z",
+     "shell.execute_reply": "2024-01-17T23:17:43.222781Z"
     }
    },
    "outputs": [
@@ -3263,10 +3327,10 @@
    "id": "86bac686",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:43.935930Z",
-     "iopub.status.busy": "2024-01-17T18:15:43.935733Z",
-     "iopub.status.idle": "2024-01-17T18:15:43.992260Z",
-     "shell.execute_reply": "2024-01-17T18:15:43.991692Z"
+     "iopub.execute_input": "2024-01-17T23:17:43.226244Z",
+     "iopub.status.busy": "2024-01-17T23:17:43.225777Z",
+     "iopub.status.idle": "2024-01-17T23:17:43.282179Z",
+     "shell.execute_reply": "2024-01-17T23:17:43.281446Z"
     },
     "nbsphinx": "hidden"
    },
@@ -3310,7 +3374,23 @@
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {
     "state": {
-     "1772cf6eace14f4f8a042982d8d016a8": {
+     "017b88b766dd4b1eb6d2b1e38318808d": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "11f01422601b4fbfb274288e4cabc8dd": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3362,7 +3442,7 @@
        "width": null
       }
      },
-     "1a4a06ef208a4aea96192270ec7fb84a": {
+     "1a11c07d013c43bb8d5c2a9a9f51b77c": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3414,22 +3494,31 @@
        "width": null
       }
      },
-     "1cf620e1d88a4e37b2d34ea1fb5e1535": {
+     "1c9e243742194e9f93f1f1d0f4ade627": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "FloatProgressModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
+       "_model_name": "FloatProgressModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_fcc677509e4047feacf1ee2612eb3bf6",
+       "max": 30.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_2d9bd9df2ade4a83ab6231049c45a53b",
+       "value": 30.0
       }
      },
-     "1ed919af8e7844faabecb7a8dd47f285": {
+     "25a7aae549ff4d1fa03ce9aa556d8fc3": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "DescriptionStyleModel",
@@ -3444,23 +3533,7 @@
        "description_width": ""
       }
      },
-     "254221792aff42ce82df1c22b6a17fe7": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "ProgressStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
-      }
-     },
-     "2c30e1b3212d44fcb51934e93bf256f4": {
+     "2641df8a6b10468c84fd7d0cf8fe4b7f": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3512,7 +3585,60 @@
        "width": null
       }
      },
-     "2cef6291580545a795374bc03a6013f9": {
+     "2d9bd9df2ade4a83ab6231049c45a53b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "337a4acbdc614129ab2ee0180f730c19": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "3addf8ea78984dc3bf5ed29c07556bb9": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HBoxModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "HBoxModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_540cfd5273004c949a94947f00084b1e",
+        "IPY_MODEL_6c0f1a6b8b0941eb890e179ab021fa32",
+        "IPY_MODEL_e15b5be74c0c4d29bdfda502583c695b"
+       ],
+       "layout": "IPY_MODEL_606e484d407447bcb09578773cd22d5d"
+      }
+     },
+     "3bf0e062325848e38433cfd0a1c1e64e": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3564,64 +3690,7 @@
        "width": null
       }
      },
-     "3d5158c48b6d4e3587ff508936ad7386": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
-      }
-     },
-     "4109ef68aa164157869d8efce1002420": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_1a4a06ef208a4aea96192270ec7fb84a",
-       "placeholder": "​",
-       "style": "IPY_MODEL_98e1388d51f34102aacc21ba7bc431eb",
-       "value": " 30/30 [00:01<00:00, 23.76it/s]"
-      }
-     },
-     "464b5bb258bd409ab318e6ecab337861": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_7b875172cd764fa9ab4b1d7fdcd93b46",
-       "placeholder": "​",
-       "style": "IPY_MODEL_3d5158c48b6d4e3587ff508936ad7386",
-       "value": "images processed using softmin: 100%"
-      }
-     },
-     "587d5b941fbc465faf4eb95923831929": {
+     "3eedef9a02374d1e96abed5e91673616": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3673,38 +3742,51 @@
        "width": null
       }
      },
-     "6422f763cda64280af2c3e65257aeadf": {
+     "3f014168fa4346d1a5243faf468a81d2": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
+      "model_name": "HBoxModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
+       "_model_name": "HBoxModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_ba97605c2b714aaeae151edc68655c4e",
+        "IPY_MODEL_a29c9371170a4cf195f0623a90a22046",
+        "IPY_MODEL_b75dd4684dc541648d9d07c71d611dde"
+       ],
+       "layout": "IPY_MODEL_3bf0e062325848e38433cfd0a1c1e64e"
       }
      },
-     "6493300865974b87a00b6d58ce9d45d3": {
+     "4fea53dd3c354db89dbd413a514598b1": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "ProgressStyleModel",
+      "model_name": "HBoxModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
+       "_model_name": "HBoxModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HBoxView",
+       "box_style": "",
+       "children": [
+        "IPY_MODEL_579262aaf92e43c8b403d67fcdbb69de",
+        "IPY_MODEL_1c9e243742194e9f93f1f1d0f4ade627",
+        "IPY_MODEL_f8958214798d4aaea3392a34c1132c86"
+       ],
+       "layout": "IPY_MODEL_e66280fc5b9544d3b2997f0895b86556"
       }
      },
-     "660cb6ce052f47b4b6cbe492bceef787": {
+     "540cfd5273004c949a94947f00084b1e": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "HTMLModel",
@@ -3719,29 +3801,34 @@
        "_view_name": "HTMLView",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_9c7f519c6e4e43a983bbd8764c156460",
+       "layout": "IPY_MODEL_1a11c07d013c43bb8d5c2a9a9f51b77c",
        "placeholder": "​",
-       "style": "IPY_MODEL_6422f763cda64280af2c3e65257aeadf",
+       "style": "IPY_MODEL_25a7aae549ff4d1fa03ce9aa556d8fc3",
        "value": "number of examples processed for checking labels: 100%"
       }
      },
-     "70503413664647f98537eb6ceb24c397": {
+     "579262aaf92e43c8b403d67fcdbb69de": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "ProgressStyleModel",
+      "model_name": "HTMLModel",
       "state": {
+       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "ProgressStyleModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "bar_color": null,
-       "description_width": ""
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_68e0315ddeeb47aaa3eed64bc0a781bb",
+       "placeholder": "​",
+       "style": "IPY_MODEL_c0d493b322dc419dbe87e7a6b2af79ce",
+       "value": "number of examples processed for estimating thresholds: 100%"
       }
      },
-     "7b875172cd764fa9ab4b1d7fdcd93b46": {
+     "606e484d407447bcb09578773cd22d5d": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3793,50 +3880,7 @@
        "width": null
       }
      },
-     "7daa21d4a2364d38ad2a974e382c6322": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_813659abd0ae4858b70121d8e5e42c7a",
-       "placeholder": "​",
-       "style": "IPY_MODEL_1cf620e1d88a4e37b2d34ea1fb5e1535",
-       "value": "number of examples processed for estimating thresholds: 100%"
-      }
-     },
-     "7dceb22a611b4605b26d5be95c8f7516": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_464b5bb258bd409ab318e6ecab337861",
-        "IPY_MODEL_e065034c12da4de986d452fa8e88a13a",
-        "IPY_MODEL_4109ef68aa164157869d8efce1002420"
-       ],
-       "layout": "IPY_MODEL_a4c1ccb6ff5b40f5b2a9448cc2d9a89d"
-      }
-     },
-     "813659abd0ae4858b70121d8e5e42c7a": {
+     "68e0315ddeeb47aaa3eed64bc0a781bb": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3888,7 +3932,7 @@
        "width": null
       }
      },
-     "92db28b2f6594a47bbecaa39c185f8fd": {
+     "6c0f1a6b8b0941eb890e179ab021fa32": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "FloatProgressModel",
@@ -3904,30 +3948,15 @@
        "bar_style": "success",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_cdcc7203a1d34765831634720bdbf052",
+       "layout": "IPY_MODEL_3eedef9a02374d1e96abed5e91673616",
        "max": 30.0,
        "min": 0.0,
        "orientation": "horizontal",
-       "style": "IPY_MODEL_254221792aff42ce82df1c22b6a17fe7",
+       "style": "IPY_MODEL_bbeae6affcaf40c7b85b3a80bc472488",
        "value": 30.0
       }
      },
-     "98e1388d51f34102aacc21ba7bc431eb": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "DescriptionStyleModel",
-      "state": {
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "DescriptionStyleModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/base",
-       "_view_module_version": "1.2.0",
-       "_view_name": "StyleView",
-       "description_width": ""
-      }
-     },
-     "9c7f519c6e4e43a983bbd8764c156460": {
+     "75f17e33a67d4d759b4cd9037c2e49dc": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -3979,7 +4008,104 @@
        "width": null
       }
      },
-     "a4c1ccb6ff5b40f5b2a9448cc2d9a89d": {
+     "851b95992b3f4ad4a8670c0230fcd73f": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "a29c9371170a4cf195f0623a90a22046": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "FloatProgressModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "FloatProgressModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "ProgressView",
+       "bar_style": "success",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_2641df8a6b10468c84fd7d0cf8fe4b7f",
+       "max": 30.0,
+       "min": 0.0,
+       "orientation": "horizontal",
+       "style": "IPY_MODEL_017b88b766dd4b1eb6d2b1e38318808d",
+       "value": 30.0
+      }
+     },
+     "b75dd4684dc541648d9d07c71d611dde": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_bc89283dc7174f93b8b234407daf2a1d",
+       "placeholder": "​",
+       "style": "IPY_MODEL_dacdbf7e5c0a4c5a8b089b152056890c",
+       "value": " 30/30 [00:01<00:00, 23.70it/s]"
+      }
+     },
+     "ba97605c2b714aaeae151edc68655c4e": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
+      "state": {
+       "_dom_classes": [],
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "HTMLModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/controls",
+       "_view_module_version": "1.5.0",
+       "_view_name": "HTMLView",
+       "description": "",
+       "description_tooltip": null,
+       "layout": "IPY_MODEL_75f17e33a67d4d759b4cd9037c2e49dc",
+       "placeholder": "​",
+       "style": "IPY_MODEL_dc529557e2e64fcba50b373e9f823e42",
+       "value": "images processed using softmin: 100%"
+      }
+     },
+     "bbeae6affcaf40c7b85b3a80bc472488": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "ProgressStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "ProgressStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "bar_color": null,
+       "description_width": ""
+      }
+     },
+     "bc89283dc7174f93b8b234407daf2a1d": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4031,7 +4157,7 @@
        "width": null
       }
      },
-     "ab4466bce4fc4e29a5451793442f63f0": {
+     "c0d493b322dc419dbe87e7a6b2af79ce": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
       "model_name": "DescriptionStyleModel",
@@ -4046,29 +4172,7 @@
        "description_width": ""
       }
      },
-     "b6cb19a81c2a486b82141204a442d67b": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_660cb6ce052f47b4b6cbe492bceef787",
-        "IPY_MODEL_ef63b67382614e458109cee4652d44da",
-        "IPY_MODEL_d603c968a93c4cc180f3f615cff5aa17"
-       ],
-       "layout": "IPY_MODEL_2c30e1b3212d44fcb51934e93bf256f4"
-      }
-     },
-     "cdcc7203a1d34765831634720bdbf052": {
+     "c2042fce4b7149fbb89d176082a3c94f": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4120,52 +4224,58 @@
        "width": null
       }
      },
-     "d603c968a93c4cc180f3f615cff5aa17": {
+     "dacdbf7e5c0a4c5a8b089b152056890c": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
+      "model_name": "DescriptionStyleModel",
       "state": {
-       "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
+       "_model_name": "DescriptionStyleModel",
        "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_e77f234650f04aceb92afed644f9afbf",
-       "placeholder": "​",
-       "style": "IPY_MODEL_ab4466bce4fc4e29a5451793442f63f0",
-       "value": " 30/30 [00:39<00:00,  1.18s/it]"
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
       }
      },
-     "e065034c12da4de986d452fa8e88a13a": {
+     "dc529557e2e64fcba50b373e9f823e42": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "FloatProgressModel",
+      "model_name": "DescriptionStyleModel",
+      "state": {
+       "_model_module": "@jupyter-widgets/controls",
+       "_model_module_version": "1.5.0",
+       "_model_name": "DescriptionStyleModel",
+       "_view_count": null,
+       "_view_module": "@jupyter-widgets/base",
+       "_view_module_version": "1.2.0",
+       "_view_name": "StyleView",
+       "description_width": ""
+      }
+     },
+     "e15b5be74c0c4d29bdfda502583c695b": {
+      "model_module": "@jupyter-widgets/controls",
+      "model_module_version": "1.5.0",
+      "model_name": "HTMLModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "FloatProgressModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
+       "_view_name": "HTMLView",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_1772cf6eace14f4f8a042982d8d016a8",
-       "max": 30.0,
-       "min": 0.0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_6493300865974b87a00b6d58ce9d45d3",
-       "value": 30.0
+       "layout": "IPY_MODEL_c2042fce4b7149fbb89d176082a3c94f",
+       "placeholder": "​",
+       "style": "IPY_MODEL_851b95992b3f4ad4a8670c0230fcd73f",
+       "value": " 30/30 [00:34<00:00,  1.15s/it]"
       }
      },
-     "e77f234650f04aceb92afed644f9afbf": {
+     "e66280fc5b9544d3b2997f0895b86556": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4217,53 +4327,28 @@
        "width": null
       }
      },
-     "ec615bcedf144713a74c0755f4d4a017": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HBoxModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HBoxModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HBoxView",
-       "box_style": "",
-       "children": [
-        "IPY_MODEL_7daa21d4a2364d38ad2a974e382c6322",
-        "IPY_MODEL_92db28b2f6594a47bbecaa39c185f8fd",
-        "IPY_MODEL_fbe3025786e94c99ab6c633251923c57"
-       ],
-       "layout": "IPY_MODEL_f26f2446131342a1b208ddec0b71c771"
-      }
-     },
-     "ef63b67382614e458109cee4652d44da": {
+     "f8958214798d4aaea3392a34c1132c86": {
       "model_module": "@jupyter-widgets/controls",
       "model_module_version": "1.5.0",
-      "model_name": "FloatProgressModel",
+      "model_name": "HTMLModel",
       "state": {
        "_dom_classes": [],
        "_model_module": "@jupyter-widgets/controls",
        "_model_module_version": "1.5.0",
-       "_model_name": "FloatProgressModel",
+       "_model_name": "HTMLModel",
        "_view_count": null,
        "_view_module": "@jupyter-widgets/controls",
        "_view_module_version": "1.5.0",
-       "_view_name": "ProgressView",
-       "bar_style": "success",
+       "_view_name": "HTMLView",
        "description": "",
        "description_tooltip": null,
-       "layout": "IPY_MODEL_587d5b941fbc465faf4eb95923831929",
-       "max": 30.0,
-       "min": 0.0,
-       "orientation": "horizontal",
-       "style": "IPY_MODEL_70503413664647f98537eb6ceb24c397",
-       "value": 30.0
+       "layout": "IPY_MODEL_11f01422601b4fbfb274288e4cabc8dd",
+       "placeholder": "​",
+       "style": "IPY_MODEL_337a4acbdc614129ab2ee0180f730c19",
+       "value": " 30/30 [00:00<00:00, 415.09it/s]"
       }
      },
-     "f26f2446131342a1b208ddec0b71c771": {
+     "fcc677509e4047feacf1ee2612eb3bf6": {
       "model_module": "@jupyter-widgets/base",
       "model_module_version": "1.2.0",
       "model_name": "LayoutModel",
@@ -4314,27 +4399,6 @@
        "visibility": null,
        "width": null
       }
-     },
-     "fbe3025786e94c99ab6c633251923c57": {
-      "model_module": "@jupyter-widgets/controls",
-      "model_module_version": "1.5.0",
-      "model_name": "HTMLModel",
-      "state": {
-       "_dom_classes": [],
-       "_model_module": "@jupyter-widgets/controls",
-       "_model_module_version": "1.5.0",
-       "_model_name": "HTMLModel",
-       "_view_count": null,
-       "_view_module": "@jupyter-widgets/controls",
-       "_view_module_version": "1.5.0",
-       "_view_name": "HTMLView",
-       "description": "",
-       "description_tooltip": null,
-       "layout": "IPY_MODEL_2cef6291580545a795374bc03a6013f9",
-       "placeholder": "​",
-       "style": "IPY_MODEL_1ed919af8e7844faabecb7a8dd47f285",
-       "value": " 30/30 [00:00<00:00, 414.89it/s]"
-      }
      }
     },
     "version_major": 2,
diff --git a/master/tutorials/tabular.ipynb b/master/tutorials/tabular.ipynb
index 5466270db..17bc8ee87 100644
--- a/master/tutorials/tabular.ipynb
+++ b/master/tutorials/tabular.ipynb
@@ -112,10 +112,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:48.780485Z",
-     "iopub.status.busy": "2024-01-17T18:15:48.780288Z",
-     "iopub.status.idle": "2024-01-17T18:15:49.889722Z",
-     "shell.execute_reply": "2024-01-17T18:15:49.889148Z"
+     "iopub.execute_input": "2024-01-17T23:17:47.734952Z",
+     "iopub.status.busy": "2024-01-17T23:17:47.734771Z",
+     "iopub.status.idle": "2024-01-17T23:17:48.739120Z",
+     "shell.execute_reply": "2024-01-17T23:17:48.738432Z"
     },
     "nbsphinx": "hidden"
    },
@@ -125,7 +125,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -150,10 +150,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:49.892898Z",
-     "iopub.status.busy": "2024-01-17T18:15:49.892481Z",
-     "iopub.status.idle": "2024-01-17T18:15:49.912635Z",
-     "shell.execute_reply": "2024-01-17T18:15:49.912039Z"
+     "iopub.execute_input": "2024-01-17T23:17:48.742367Z",
+     "iopub.status.busy": "2024-01-17T23:17:48.741768Z",
+     "iopub.status.idle": "2024-01-17T23:17:48.758449Z",
+     "shell.execute_reply": "2024-01-17T23:17:48.757950Z"
     }
    },
    "outputs": [],
@@ -194,10 +194,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:49.915208Z",
-     "iopub.status.busy": "2024-01-17T18:15:49.914856Z",
-     "iopub.status.idle": "2024-01-17T18:15:49.961568Z",
-     "shell.execute_reply": "2024-01-17T18:15:49.960846Z"
+     "iopub.execute_input": "2024-01-17T23:17:48.760735Z",
+     "iopub.status.busy": "2024-01-17T23:17:48.760537Z",
+     "iopub.status.idle": "2024-01-17T23:17:48.800098Z",
+     "shell.execute_reply": "2024-01-17T23:17:48.799507Z"
     }
    },
    "outputs": [
@@ -304,10 +304,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:49.964144Z",
-     "iopub.status.busy": "2024-01-17T18:15:49.963669Z",
-     "iopub.status.idle": "2024-01-17T18:15:49.967418Z",
-     "shell.execute_reply": "2024-01-17T18:15:49.966851Z"
+     "iopub.execute_input": "2024-01-17T23:17:48.802608Z",
+     "iopub.status.busy": "2024-01-17T23:17:48.802234Z",
+     "iopub.status.idle": "2024-01-17T23:17:48.805857Z",
+     "shell.execute_reply": "2024-01-17T23:17:48.805334Z"
     }
    },
    "outputs": [],
@@ -328,10 +328,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:49.969921Z",
-     "iopub.status.busy": "2024-01-17T18:15:49.969441Z",
-     "iopub.status.idle": "2024-01-17T18:15:49.978117Z",
-     "shell.execute_reply": "2024-01-17T18:15:49.977519Z"
+     "iopub.execute_input": "2024-01-17T23:17:48.808362Z",
+     "iopub.status.busy": "2024-01-17T23:17:48.807900Z",
+     "iopub.status.idle": "2024-01-17T23:17:48.817003Z",
+     "shell.execute_reply": "2024-01-17T23:17:48.816545Z"
     }
    },
    "outputs": [],
@@ -383,10 +383,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:49.980561Z",
-     "iopub.status.busy": "2024-01-17T18:15:49.980192Z",
-     "iopub.status.idle": "2024-01-17T18:15:49.983567Z",
-     "shell.execute_reply": "2024-01-17T18:15:49.983071Z"
+     "iopub.execute_input": "2024-01-17T23:17:48.819529Z",
+     "iopub.status.busy": "2024-01-17T23:17:48.819050Z",
+     "iopub.status.idle": "2024-01-17T23:17:48.821755Z",
+     "shell.execute_reply": "2024-01-17T23:17:48.821267Z"
     }
    },
    "outputs": [],
@@ -408,10 +408,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:49.985926Z",
-     "iopub.status.busy": "2024-01-17T18:15:49.985561Z",
-     "iopub.status.idle": "2024-01-17T18:15:50.575769Z",
-     "shell.execute_reply": "2024-01-17T18:15:50.575063Z"
+     "iopub.execute_input": "2024-01-17T23:17:48.824069Z",
+     "iopub.status.busy": "2024-01-17T23:17:48.823696Z",
+     "iopub.status.idle": "2024-01-17T23:17:49.401999Z",
+     "shell.execute_reply": "2024-01-17T23:17:49.401363Z"
     }
    },
    "outputs": [],
@@ -445,10 +445,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:50.578728Z",
-     "iopub.status.busy": "2024-01-17T18:15:50.578362Z",
-     "iopub.status.idle": "2024-01-17T18:15:51.817403Z",
-     "shell.execute_reply": "2024-01-17T18:15:51.816705Z"
+     "iopub.execute_input": "2024-01-17T23:17:49.404884Z",
+     "iopub.status.busy": "2024-01-17T23:17:49.404494Z",
+     "iopub.status.idle": "2024-01-17T23:17:50.628963Z",
+     "shell.execute_reply": "2024-01-17T23:17:50.628183Z"
     }
    },
    "outputs": [
@@ -480,10 +480,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:51.820386Z",
-     "iopub.status.busy": "2024-01-17T18:15:51.819691Z",
-     "iopub.status.idle": "2024-01-17T18:15:51.829994Z",
-     "shell.execute_reply": "2024-01-17T18:15:51.829415Z"
+     "iopub.execute_input": "2024-01-17T23:17:50.632065Z",
+     "iopub.status.busy": "2024-01-17T23:17:50.631351Z",
+     "iopub.status.idle": "2024-01-17T23:17:50.641752Z",
+     "shell.execute_reply": "2024-01-17T23:17:50.641138Z"
     }
    },
    "outputs": [
@@ -604,10 +604,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:51.832550Z",
-     "iopub.status.busy": "2024-01-17T18:15:51.832105Z",
-     "iopub.status.idle": "2024-01-17T18:15:51.836560Z",
-     "shell.execute_reply": "2024-01-17T18:15:51.835934Z"
+     "iopub.execute_input": "2024-01-17T23:17:50.644372Z",
+     "iopub.status.busy": "2024-01-17T23:17:50.643967Z",
+     "iopub.status.idle": "2024-01-17T23:17:50.648354Z",
+     "shell.execute_reply": "2024-01-17T23:17:50.647831Z"
     }
    },
    "outputs": [],
@@ -632,10 +632,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:51.839202Z",
-     "iopub.status.busy": "2024-01-17T18:15:51.838732Z",
-     "iopub.status.idle": "2024-01-17T18:15:51.847634Z",
-     "shell.execute_reply": "2024-01-17T18:15:51.847116Z"
+     "iopub.execute_input": "2024-01-17T23:17:50.650701Z",
+     "iopub.status.busy": "2024-01-17T23:17:50.650342Z",
+     "iopub.status.idle": "2024-01-17T23:17:50.659466Z",
+     "shell.execute_reply": "2024-01-17T23:17:50.658916Z"
     }
    },
    "outputs": [],
@@ -657,10 +657,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:51.849889Z",
-     "iopub.status.busy": "2024-01-17T18:15:51.849543Z",
-     "iopub.status.idle": "2024-01-17T18:15:51.974011Z",
-     "shell.execute_reply": "2024-01-17T18:15:51.973351Z"
+     "iopub.execute_input": "2024-01-17T23:17:50.662055Z",
+     "iopub.status.busy": "2024-01-17T23:17:50.661674Z",
+     "iopub.status.idle": "2024-01-17T23:17:50.784615Z",
+     "shell.execute_reply": "2024-01-17T23:17:50.783929Z"
     }
    },
    "outputs": [
@@ -690,10 +690,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:51.976584Z",
-     "iopub.status.busy": "2024-01-17T18:15:51.976090Z",
-     "iopub.status.idle": "2024-01-17T18:15:51.979227Z",
-     "shell.execute_reply": "2024-01-17T18:15:51.978616Z"
+     "iopub.execute_input": "2024-01-17T23:17:50.787531Z",
+     "iopub.status.busy": "2024-01-17T23:17:50.787142Z",
+     "iopub.status.idle": "2024-01-17T23:17:50.790141Z",
+     "shell.execute_reply": "2024-01-17T23:17:50.789587Z"
     }
    },
    "outputs": [],
@@ -714,10 +714,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:51.981454Z",
-     "iopub.status.busy": "2024-01-17T18:15:51.981098Z",
-     "iopub.status.idle": "2024-01-17T18:15:53.416387Z",
-     "shell.execute_reply": "2024-01-17T18:15:53.415644Z"
+     "iopub.execute_input": "2024-01-17T23:17:50.792562Z",
+     "iopub.status.busy": "2024-01-17T23:17:50.792169Z",
+     "iopub.status.idle": "2024-01-17T23:17:52.221345Z",
+     "shell.execute_reply": "2024-01-17T23:17:52.220630Z"
     }
    },
    "outputs": [],
@@ -737,10 +737,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:53.419589Z",
-     "iopub.status.busy": "2024-01-17T18:15:53.419145Z",
-     "iopub.status.idle": "2024-01-17T18:15:53.433267Z",
-     "shell.execute_reply": "2024-01-17T18:15:53.432699Z"
+     "iopub.execute_input": "2024-01-17T23:17:52.224659Z",
+     "iopub.status.busy": "2024-01-17T23:17:52.224238Z",
+     "iopub.status.idle": "2024-01-17T23:17:52.238083Z",
+     "shell.execute_reply": "2024-01-17T23:17:52.237541Z"
     }
    },
    "outputs": [
@@ -770,10 +770,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:53.435679Z",
-     "iopub.status.busy": "2024-01-17T18:15:53.435296Z",
-     "iopub.status.idle": "2024-01-17T18:15:53.480212Z",
-     "shell.execute_reply": "2024-01-17T18:15:53.479691Z"
+     "iopub.execute_input": "2024-01-17T23:17:52.240512Z",
+     "iopub.status.busy": "2024-01-17T23:17:52.240154Z",
+     "iopub.status.idle": "2024-01-17T23:17:52.269107Z",
+     "shell.execute_reply": "2024-01-17T23:17:52.268431Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/text.html b/master/tutorials/text.html
index 3a0f039fa..742b1f6ae 100644
--- a/master/tutorials/text.html
+++ b/master/tutorials/text.html
@@ -978,7 +978,7 @@ 
2. Load and format the text dataset
 
 This dataset has 10 classes.
-Classes: {'beneficiary_not_allowed', 'supported_cards_and_currencies', 'change_pin', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'getting_spare_card', 'card_payment_fee_charged', 'cancel_transfer', 'lost_or_stolen_phone', 'card_about_to_expire'}
+Classes: {'visa_or_mastercard', 'lost_or_stolen_phone', 'card_about_to_expire', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'cancel_transfer', 'getting_spare_card', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'change_pin'}
 

 
 
Let’s print the first example in the train set.

diff --git a/master/tutorials/text.ipynb b/master/tutorials/text.ipynb
index af64e12b3..c78dd02a5 100644
--- a/master/tutorials/text.ipynb
+++ b/master/tutorials/text.ipynb
@@ -114,10 +114,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:15:58.681081Z",
-     "iopub.status.busy": "2024-01-17T18:15:58.680544Z",
-     "iopub.status.idle": "2024-01-17T18:16:00.775060Z",
-     "shell.execute_reply": "2024-01-17T18:16:00.774440Z"
+     "iopub.execute_input": "2024-01-17T23:17:57.742875Z",
+     "iopub.status.busy": "2024-01-17T23:17:57.742428Z",
+     "iopub.status.idle": "2024-01-17T23:17:59.830312Z",
+     "shell.execute_reply": "2024-01-17T23:17:59.829658Z"
     },
     "nbsphinx": "hidden"
    },
@@ -134,7 +134,7 @@
     "os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"  # disable parallelism to avoid deadlocks with huggingface\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -159,10 +159,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:00.778036Z",
-     "iopub.status.busy": "2024-01-17T18:16:00.777514Z",
-     "iopub.status.idle": "2024-01-17T18:16:00.781173Z",
-     "shell.execute_reply": "2024-01-17T18:16:00.780639Z"
+     "iopub.execute_input": "2024-01-17T23:17:59.833303Z",
+     "iopub.status.busy": "2024-01-17T23:17:59.832848Z",
+     "iopub.status.idle": "2024-01-17T23:17:59.836479Z",
+     "shell.execute_reply": "2024-01-17T23:17:59.835915Z"
     }
    },
    "outputs": [],
@@ -184,10 +184,10 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:00.783257Z",
-     "iopub.status.busy": "2024-01-17T18:16:00.782964Z",
-     "iopub.status.idle": "2024-01-17T18:16:00.786185Z",
-     "shell.execute_reply": "2024-01-17T18:16:00.785673Z"
+     "iopub.execute_input": "2024-01-17T23:17:59.838723Z",
+     "iopub.status.busy": "2024-01-17T23:17:59.838522Z",
+     "iopub.status.idle": "2024-01-17T23:17:59.842419Z",
+     "shell.execute_reply": "2024-01-17T23:17:59.841936Z"
     },
     "nbsphinx": "hidden"
    },
@@ -218,10 +218,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:00.788633Z",
-     "iopub.status.busy": "2024-01-17T18:16:00.788231Z",
-     "iopub.status.idle": "2024-01-17T18:16:00.838714Z",
-     "shell.execute_reply": "2024-01-17T18:16:00.838154Z"
+     "iopub.execute_input": "2024-01-17T23:17:59.844709Z",
+     "iopub.status.busy": "2024-01-17T23:17:59.844354Z",
+     "iopub.status.idle": "2024-01-17T23:17:59.883168Z",
+     "shell.execute_reply": "2024-01-17T23:17:59.882480Z"
     }
    },
    "outputs": [
@@ -311,10 +311,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:00.840975Z",
-     "iopub.status.busy": "2024-01-17T18:16:00.840666Z",
-     "iopub.status.idle": "2024-01-17T18:16:00.844276Z",
-     "shell.execute_reply": "2024-01-17T18:16:00.843733Z"
+     "iopub.execute_input": "2024-01-17T23:17:59.885656Z",
+     "iopub.status.busy": "2024-01-17T23:17:59.885435Z",
+     "iopub.status.idle": "2024-01-17T23:17:59.889383Z",
+     "shell.execute_reply": "2024-01-17T23:17:59.888880Z"
     }
    },
    "outputs": [],
@@ -329,10 +329,10 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:00.846573Z",
-     "iopub.status.busy": "2024-01-17T18:16:00.846282Z",
-     "iopub.status.idle": "2024-01-17T18:16:00.849977Z",
-     "shell.execute_reply": "2024-01-17T18:16:00.849375Z"
+     "iopub.execute_input": "2024-01-17T23:17:59.891833Z",
+     "iopub.status.busy": "2024-01-17T23:17:59.891353Z",
+     "iopub.status.idle": "2024-01-17T23:17:59.895138Z",
+     "shell.execute_reply": "2024-01-17T23:17:59.894518Z"
     }
    },
    "outputs": [
@@ -341,7 +341,7 @@
      "output_type": "stream",
      "text": [
       "This dataset has 10 classes.\n",
-      "Classes: {'beneficiary_not_allowed', 'supported_cards_and_currencies', 'change_pin', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'getting_spare_card', 'card_payment_fee_charged', 'cancel_transfer', 'lost_or_stolen_phone', 'card_about_to_expire'}\n"
+      "Classes: {'visa_or_mastercard', 'lost_or_stolen_phone', 'card_about_to_expire', 'supported_cards_and_currencies', 'card_payment_fee_charged', 'cancel_transfer', 'getting_spare_card', 'apple_pay_or_google_pay', 'beneficiary_not_allowed', 'change_pin'}\n"
      ]
     }
    ],
@@ -364,10 +364,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:00.852267Z",
-     "iopub.status.busy": "2024-01-17T18:16:00.851974Z",
-     "iopub.status.idle": "2024-01-17T18:16:00.855468Z",
-     "shell.execute_reply": "2024-01-17T18:16:00.854975Z"
+     "iopub.execute_input": "2024-01-17T23:17:59.897309Z",
+     "iopub.status.busy": "2024-01-17T23:17:59.897107Z",
+     "iopub.status.idle": "2024-01-17T23:17:59.901064Z",
+     "shell.execute_reply": "2024-01-17T23:17:59.900538Z"
     }
    },
    "outputs": [
@@ -408,10 +408,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:00.857729Z",
-     "iopub.status.busy": "2024-01-17T18:16:00.857390Z",
-     "iopub.status.idle": "2024-01-17T18:16:00.860942Z",
-     "shell.execute_reply": "2024-01-17T18:16:00.860317Z"
+     "iopub.execute_input": "2024-01-17T23:17:59.903420Z",
+     "iopub.status.busy": "2024-01-17T23:17:59.903221Z",
+     "iopub.status.idle": "2024-01-17T23:17:59.907092Z",
+     "shell.execute_reply": "2024-01-17T23:17:59.906454Z"
     }
    },
    "outputs": [],
@@ -452,10 +452,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:00.863348Z",
-     "iopub.status.busy": "2024-01-17T18:16:00.862895Z",
-     "iopub.status.idle": "2024-01-17T18:16:09.532434Z",
-     "shell.execute_reply": "2024-01-17T18:16:09.531800Z"
+     "iopub.execute_input": "2024-01-17T23:17:59.909625Z",
+     "iopub.status.busy": "2024-01-17T23:17:59.909179Z",
+     "iopub.status.idle": "2024-01-17T23:18:08.513069Z",
+     "shell.execute_reply": "2024-01-17T23:18:08.512429Z"
     }
    },
    "outputs": [
@@ -502,10 +502,10 @@
    "execution_count": 10,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:09.535736Z",
-     "iopub.status.busy": "2024-01-17T18:16:09.535212Z",
-     "iopub.status.idle": "2024-01-17T18:16:09.538358Z",
-     "shell.execute_reply": "2024-01-17T18:16:09.537737Z"
+     "iopub.execute_input": "2024-01-17T23:18:08.516221Z",
+     "iopub.status.busy": "2024-01-17T23:18:08.516011Z",
+     "iopub.status.idle": "2024-01-17T23:18:08.519002Z",
+     "shell.execute_reply": "2024-01-17T23:18:08.518428Z"
     }
    },
    "outputs": [],
@@ -527,10 +527,10 @@
    "execution_count": 11,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:09.540756Z",
-     "iopub.status.busy": "2024-01-17T18:16:09.540303Z",
-     "iopub.status.idle": "2024-01-17T18:16:09.543296Z",
-     "shell.execute_reply": "2024-01-17T18:16:09.542675Z"
+     "iopub.execute_input": "2024-01-17T23:18:08.521479Z",
+     "iopub.status.busy": "2024-01-17T23:18:08.521036Z",
+     "iopub.status.idle": "2024-01-17T23:18:08.524037Z",
+     "shell.execute_reply": "2024-01-17T23:18:08.523424Z"
     }
    },
    "outputs": [],
@@ -545,10 +545,10 @@
    "execution_count": 12,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:09.545603Z",
-     "iopub.status.busy": "2024-01-17T18:16:09.545232Z",
-     "iopub.status.idle": "2024-01-17T18:16:11.774377Z",
-     "shell.execute_reply": "2024-01-17T18:16:11.773545Z"
+     "iopub.execute_input": "2024-01-17T23:18:08.526322Z",
+     "iopub.status.busy": "2024-01-17T23:18:08.525946Z",
+     "iopub.status.idle": "2024-01-17T23:18:10.782125Z",
+     "shell.execute_reply": "2024-01-17T23:18:10.781246Z"
     },
     "scrolled": true
    },
@@ -571,10 +571,10 @@
    "execution_count": 13,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:11.777880Z",
-     "iopub.status.busy": "2024-01-17T18:16:11.777134Z",
-     "iopub.status.idle": "2024-01-17T18:16:11.785059Z",
-     "shell.execute_reply": "2024-01-17T18:16:11.784473Z"
+     "iopub.execute_input": "2024-01-17T23:18:10.785663Z",
+     "iopub.status.busy": "2024-01-17T23:18:10.784971Z",
+     "iopub.status.idle": "2024-01-17T23:18:10.793126Z",
+     "shell.execute_reply": "2024-01-17T23:18:10.792564Z"
     }
    },
    "outputs": [
@@ -675,10 +675,10 @@
    "execution_count": 14,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:11.787623Z",
-     "iopub.status.busy": "2024-01-17T18:16:11.787145Z",
-     "iopub.status.idle": "2024-01-17T18:16:11.791785Z",
-     "shell.execute_reply": "2024-01-17T18:16:11.791195Z"
+     "iopub.execute_input": "2024-01-17T23:18:10.795673Z",
+     "iopub.status.busy": "2024-01-17T23:18:10.795173Z",
+     "iopub.status.idle": "2024-01-17T23:18:10.799534Z",
+     "shell.execute_reply": "2024-01-17T23:18:10.799023Z"
     }
    },
    "outputs": [],
@@ -692,10 +692,10 @@
    "execution_count": 15,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:11.794363Z",
-     "iopub.status.busy": "2024-01-17T18:16:11.793879Z",
-     "iopub.status.idle": "2024-01-17T18:16:11.797824Z",
-     "shell.execute_reply": "2024-01-17T18:16:11.797308Z"
+     "iopub.execute_input": "2024-01-17T23:18:10.801761Z",
+     "iopub.status.busy": "2024-01-17T23:18:10.801399Z",
+     "iopub.status.idle": "2024-01-17T23:18:10.804803Z",
+     "shell.execute_reply": "2024-01-17T23:18:10.804175Z"
     }
    },
    "outputs": [
@@ -730,10 +730,10 @@
    "execution_count": 16,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:11.800118Z",
-     "iopub.status.busy": "2024-01-17T18:16:11.799769Z",
-     "iopub.status.idle": "2024-01-17T18:16:11.802932Z",
-     "shell.execute_reply": "2024-01-17T18:16:11.802404Z"
+     "iopub.execute_input": "2024-01-17T23:18:10.807088Z",
+     "iopub.status.busy": "2024-01-17T23:18:10.806776Z",
+     "iopub.status.idle": "2024-01-17T23:18:10.809992Z",
+     "shell.execute_reply": "2024-01-17T23:18:10.809457Z"
     }
    },
    "outputs": [],
@@ -753,10 +753,10 @@
    "execution_count": 17,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:11.805206Z",
-     "iopub.status.busy": "2024-01-17T18:16:11.804854Z",
-     "iopub.status.idle": "2024-01-17T18:16:11.812281Z",
-     "shell.execute_reply": "2024-01-17T18:16:11.811672Z"
+     "iopub.execute_input": "2024-01-17T23:18:10.812343Z",
+     "iopub.status.busy": "2024-01-17T23:18:10.811976Z",
+     "iopub.status.idle": "2024-01-17T23:18:10.818970Z",
+     "shell.execute_reply": "2024-01-17T23:18:10.818388Z"
     }
    },
    "outputs": [
@@ -881,10 +881,10 @@
    "execution_count": 18,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:11.814814Z",
-     "iopub.status.busy": "2024-01-17T18:16:11.814358Z",
-     "iopub.status.idle": "2024-01-17T18:16:12.075466Z",
-     "shell.execute_reply": "2024-01-17T18:16:12.074804Z"
+     "iopub.execute_input": "2024-01-17T23:18:10.821471Z",
+     "iopub.status.busy": "2024-01-17T23:18:10.821021Z",
+     "iopub.status.idle": "2024-01-17T23:18:11.086279Z",
+     "shell.execute_reply": "2024-01-17T23:18:11.085605Z"
     },
     "scrolled": true
    },
@@ -923,10 +923,10 @@
    "execution_count": 19,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:12.079661Z",
-     "iopub.status.busy": "2024-01-17T18:16:12.078502Z",
-     "iopub.status.idle": "2024-01-17T18:16:12.366162Z",
-     "shell.execute_reply": "2024-01-17T18:16:12.365485Z"
+     "iopub.execute_input": "2024-01-17T23:18:11.089474Z",
+     "iopub.status.busy": "2024-01-17T23:18:11.089026Z",
+     "iopub.status.idle": "2024-01-17T23:18:11.370620Z",
+     "shell.execute_reply": "2024-01-17T23:18:11.369959Z"
     },
     "scrolled": true
    },
@@ -959,10 +959,10 @@
    "execution_count": 20,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:12.370837Z",
-     "iopub.status.busy": "2024-01-17T18:16:12.369694Z",
-     "iopub.status.idle": "2024-01-17T18:16:12.375305Z",
-     "shell.execute_reply": "2024-01-17T18:16:12.374713Z"
+     "iopub.execute_input": "2024-01-17T23:18:11.373903Z",
+     "iopub.status.busy": "2024-01-17T23:18:11.373450Z",
+     "iopub.status.idle": "2024-01-17T23:18:11.377667Z",
+     "shell.execute_reply": "2024-01-17T23:18:11.377072Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html
index 3eea17820..0f3ff954f 100644
--- a/master/tutorials/token_classification.html
+++ b/master/tutorials/token_classification.html
@@ -871,7 +871,7 @@ 
1. Install required dependencies and download data
 

 
---2024-01-17 18:16:17--  https://data.deepai.org/conll2003.zip
+--2024-01-17 23:18:16--  https://data.deepai.org/conll2003.zip
 Resolving data.deepai.org (data.deepai.org)...
 

 
@@ -880,9 +880,24 @@ 
1. Install required dependencies and download data
 

 
-169.150.236.99, 2400:52e0:1a00::718:1
-Connecting to data.deepai.org (data.deepai.org)|169.150.236.99|:443... connected.
-HTTP request sent, awaiting response... 200 OK
+185.93.1.244, 2400:52e0:1a00::871:1
+Connecting to data.deepai.org (data.deepai.org)|185.93.1.244|:443... connected.
+

+
+

+

+

+

+
+HTTP request sent, awaiting response...
+

+

+

+

+

+

+
+200 OK
 Length: 982975 (960K) [application/zip]
 Saving to: ‘conll2003.zip’

 

@@ -903,25 +918,25 @@ 
1. Install required dependencies and download data
 

 
-
conll2003.zip       100%[===================&gt;] 959.94K  –.-KB/s    in 0.07s

+
conll2003.zip       100%[===================&gt;] 959.94K  –.-KB/s    in 0.1s

 

 

-
2024-01-17 18:16:17 (14.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]

+
2024-01-17 23:18:17 (6.54 MB/s) - ‘conll2003.zip’ saved [982975/982975]

 
mkdir: cannot create directory ‘data’: File exists
 </pre>

 

 

-
conll2003.zip       100%[===================>] 959.94K  –.-KB/s    in 0.07s

+
conll2003.zip       100%[===================>] 959.94K  –.-KB/s    in 0.1s

 

 

-
2024-01-17 18:16:17 (14.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]

+
2024-01-17 23:18:17 (6.54 MB/s) - ‘conll2003.zip’ saved [982975/982975]

 
mkdir: cannot create directory ‘data’: File exists
 end{sphinxVerbatim}

 

 

-
conll2003.zip       100%[===================>] 959.94K  –.-KB/s    in 0.07s

+
conll2003.zip       100%[===================>] 959.94K  –.-KB/s    in 0.1s

 

-
2024-01-17 18:16:17 (14.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]

+
2024-01-17 23:18:17 (6.54 MB/s) - ‘conll2003.zip’ saved [982975/982975]

 
mkdir: cannot create directory ‘data’: File exists

 

 

@@ -941,16 +956,9 @@ 
1. Install required dependencies and download data
 

 
---2024-01-17 18:16:18--  https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz
-Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.69.41, 52.216.35.73, 3.5.25.134, ...
-Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.69.41|:443... connected.
-

-

-

-

-

-

-
+--2024-01-17 23:18:17--  https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz
+Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.195.49, 52.216.244.132, 52.217.89.156, ...
+Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.195.49|:443... connected.
 HTTP request sent, awaiting response...
 

 

@@ -980,26 +988,23 @@ 
1. Install required dependencies and download data
 

 
-
pred_probs.npz       96%[==================&gt; ]  15.71M  56.8MB/s
-pred_probs.npz      100%[===================&gt;]  16.26M  58.4MB/s    in 0.3s

+
pred_probs.npz      100%[===================&gt;]  16.26M  –.-KB/s    in 0.1s

 

 

-
2024-01-17 18:16:18 (58.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

+
2024-01-17 23:18:17 (134 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

 
</pre>

 

 

-
pred_probs.npz       96%[==================> ]  15.71M  56.8MB/s
-pred_probs.npz      100%[===================>]  16.26M  58.4MB/s    in 0.3s

+
pred_probs.npz      100%[===================>]  16.26M  –.-KB/s    in 0.1s

 

 

-
2024-01-17 18:16:18 (58.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

+
2024-01-17 23:18:17 (134 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

 
end{sphinxVerbatim}

 

 

-
pred_probs.npz       96%[==================> ]  15.71M  56.8MB/s
-pred_probs.npz      100%[===================>]  16.26M  58.4MB/s    in 0.3s

+
pred_probs.npz      100%[===================>]  16.26M  –.-KB/s    in 0.1s

 

-
2024-01-17 18:16:18 (58.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

+
2024-01-17 23:18:17 (134 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]

 

 

 
[3]:
diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb
index 8f1cbce7d..8126f8625 100644
--- a/master/tutorials/token_classification.ipynb
+++ b/master/tutorials/token_classification.ipynb
@@ -75,10 +75,10 @@
    "id": "ae8a08e0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:17.683980Z",
-     "iopub.status.busy": "2024-01-17T18:16:17.683539Z",
-     "iopub.status.idle": "2024-01-17T18:16:18.840240Z",
-     "shell.execute_reply": "2024-01-17T18:16:18.839548Z"
+     "iopub.execute_input": "2024-01-17T23:18:16.631328Z",
+     "iopub.status.busy": "2024-01-17T23:18:16.631138Z",
+     "iopub.status.idle": "2024-01-17T23:18:17.707067Z",
+     "shell.execute_reply": "2024-01-17T23:18:17.706406Z"
     }
    },
    "outputs": [
@@ -86,7 +86,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--2024-01-17 18:16:17--  https://data.deepai.org/conll2003.zip\r\n",
+      "--2024-01-17 23:18:16--  https://data.deepai.org/conll2003.zip\r\n",
       "Resolving data.deepai.org (data.deepai.org)... "
      ]
     },
@@ -94,9 +94,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "169.150.236.99, 2400:52e0:1a00::718:1\r\n",
-      "Connecting to data.deepai.org (data.deepai.org)|169.150.236.99|:443... connected.\r\n",
-      "HTTP request sent, awaiting response... 200 OK\r\n",
+      "185.93.1.244, 2400:52e0:1a00::871:1\r\n",
+      "Connecting to data.deepai.org (data.deepai.org)|185.93.1.244|:443... connected.\r\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HTTP request sent, awaiting response... "
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "200 OK\r\n",
       "Length: 982975 (960K) [application/zip]\r\n",
       "Saving to: ‘conll2003.zip’\r\n",
       "\r\n",
@@ -109,9 +122,9 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.07s   \r\n",
+      "conll2003.zip       100%[===================>] 959.94K  --.-KB/s    in 0.1s    \r\n",
       "\r\n",
-      "2024-01-17 18:16:17 (14.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+      "2024-01-17 23:18:17 (6.54 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
       "\r\n",
       "mkdir: cannot create directory ‘data’: File exists\r\n"
      ]
@@ -131,15 +144,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "--2024-01-17 18:16:18--  https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
-      "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.69.41, 52.216.35.73, 3.5.25.134, ...\r\n",
-      "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.69.41|:443... connected.\r\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "--2024-01-17 23:18:17--  https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+      "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 54.231.195.49, 52.216.244.132, 52.217.89.156, ...\r\n",
+      "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|54.231.195.49|:443... connected.\r\n",
       "HTTP request sent, awaiting response... "
      ]
     },
@@ -160,10 +167,9 @@
      "output_type": "stream",
      "text": [
       "\r",
-      "pred_probs.npz       96%[==================> ]  15.71M  56.8MB/s               \r",
-      "pred_probs.npz      100%[===================>]  16.26M  58.4MB/s    in 0.3s    \r\n",
+      "pred_probs.npz      100%[===================>]  16.26M  --.-KB/s    in 0.1s    \r\n",
       "\r\n",
-      "2024-01-17 18:16:18 (58.4 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+      "2024-01-17 23:18:17 (134 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
       "\r\n"
      ]
     }
@@ -180,10 +186,10 @@
    "id": "439b0305",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:18.843321Z",
-     "iopub.status.busy": "2024-01-17T18:16:18.842924Z",
-     "iopub.status.idle": "2024-01-17T18:16:19.868315Z",
-     "shell.execute_reply": "2024-01-17T18:16:19.867697Z"
+     "iopub.execute_input": "2024-01-17T23:18:17.709578Z",
+     "iopub.status.busy": "2024-01-17T23:18:17.709371Z",
+     "iopub.status.idle": "2024-01-17T23:18:18.727351Z",
+     "shell.execute_reply": "2024-01-17T23:18:18.726727Z"
     },
     "nbsphinx": "hidden"
    },
@@ -194,7 +200,7 @@
     "dependencies = [\"cleanlab\"]\n",
     "\n",
     "if \"google.colab\" in str(get_ipython()):  # Check if it's running in Google Colab\n",
-    "    %pip install git+https://github.com/cleanlab/cleanlab.git@89866d53b4074a0103c737ad28c80123f03973de\n",
+    "    %pip install git+https://github.com/cleanlab/cleanlab.git@93154314109f77e58265574da2ab08503d0fd5a2\n",
     "    cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
     "    %pip install $cmd\n",
     "else:\n",
@@ -220,10 +226,10 @@
    "id": "a1349304",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:19.871321Z",
-     "iopub.status.busy": "2024-01-17T18:16:19.870738Z",
-     "iopub.status.idle": "2024-01-17T18:16:19.874378Z",
-     "shell.execute_reply": "2024-01-17T18:16:19.873888Z"
+     "iopub.execute_input": "2024-01-17T23:18:18.730451Z",
+     "iopub.status.busy": "2024-01-17T23:18:18.729940Z",
+     "iopub.status.idle": "2024-01-17T23:18:18.733621Z",
+     "shell.execute_reply": "2024-01-17T23:18:18.733015Z"
     }
    },
    "outputs": [],
@@ -273,10 +279,10 @@
    "id": "ab9d59a0",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:19.876654Z",
-     "iopub.status.busy": "2024-01-17T18:16:19.876350Z",
-     "iopub.status.idle": "2024-01-17T18:16:19.879488Z",
-     "shell.execute_reply": "2024-01-17T18:16:19.878934Z"
+     "iopub.execute_input": "2024-01-17T23:18:18.735946Z",
+     "iopub.status.busy": "2024-01-17T23:18:18.735620Z",
+     "iopub.status.idle": "2024-01-17T23:18:18.739293Z",
+     "shell.execute_reply": "2024-01-17T23:18:18.738779Z"
     },
     "nbsphinx": "hidden"
    },
@@ -294,10 +300,10 @@
    "id": "519cb80c",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:19.881736Z",
-     "iopub.status.busy": "2024-01-17T18:16:19.881370Z",
-     "iopub.status.idle": "2024-01-17T18:16:27.772463Z",
-     "shell.execute_reply": "2024-01-17T18:16:27.771773Z"
+     "iopub.execute_input": "2024-01-17T23:18:18.741545Z",
+     "iopub.status.busy": "2024-01-17T23:18:18.741197Z",
+     "iopub.status.idle": "2024-01-17T23:18:26.628775Z",
+     "shell.execute_reply": "2024-01-17T23:18:26.628162Z"
     }
    },
    "outputs": [],
@@ -371,10 +377,10 @@
    "id": "202f1526",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:27.775590Z",
-     "iopub.status.busy": "2024-01-17T18:16:27.775061Z",
-     "iopub.status.idle": "2024-01-17T18:16:27.781127Z",
-     "shell.execute_reply": "2024-01-17T18:16:27.780571Z"
+     "iopub.execute_input": "2024-01-17T23:18:26.631735Z",
+     "iopub.status.busy": "2024-01-17T23:18:26.631355Z",
+     "iopub.status.idle": "2024-01-17T23:18:26.637292Z",
+     "shell.execute_reply": "2024-01-17T23:18:26.636788Z"
     },
     "nbsphinx": "hidden"
    },
@@ -414,10 +420,10 @@
    "id": "a4381f03",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:27.783496Z",
-     "iopub.status.busy": "2024-01-17T18:16:27.783123Z",
-     "iopub.status.idle": "2024-01-17T18:16:28.214355Z",
-     "shell.execute_reply": "2024-01-17T18:16:28.213734Z"
+     "iopub.execute_input": "2024-01-17T23:18:26.639706Z",
+     "iopub.status.busy": "2024-01-17T23:18:26.639338Z",
+     "iopub.status.idle": "2024-01-17T23:18:27.069318Z",
+     "shell.execute_reply": "2024-01-17T23:18:27.068667Z"
     }
    },
    "outputs": [],
@@ -454,10 +460,10 @@
    "id": "7842e4a3",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:28.217259Z",
-     "iopub.status.busy": "2024-01-17T18:16:28.216794Z",
-     "iopub.status.idle": "2024-01-17T18:16:28.223233Z",
-     "shell.execute_reply": "2024-01-17T18:16:28.222714Z"
+     "iopub.execute_input": "2024-01-17T23:18:27.072250Z",
+     "iopub.status.busy": "2024-01-17T23:18:27.071829Z",
+     "iopub.status.idle": "2024-01-17T23:18:27.078274Z",
+     "shell.execute_reply": "2024-01-17T23:18:27.077724Z"
     }
    },
    "outputs": [
@@ -529,10 +535,10 @@
    "id": "2c2ad9ad",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:28.225511Z",
-     "iopub.status.busy": "2024-01-17T18:16:28.225305Z",
-     "iopub.status.idle": "2024-01-17T18:16:30.205162Z",
-     "shell.execute_reply": "2024-01-17T18:16:30.204242Z"
+     "iopub.execute_input": "2024-01-17T23:18:27.080782Z",
+     "iopub.status.busy": "2024-01-17T23:18:27.080396Z",
+     "iopub.status.idle": "2024-01-17T23:18:29.029614Z",
+     "shell.execute_reply": "2024-01-17T23:18:29.028849Z"
     }
    },
    "outputs": [],
@@ -554,10 +560,10 @@
    "id": "95dc7268",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:30.208772Z",
-     "iopub.status.busy": "2024-01-17T18:16:30.207942Z",
-     "iopub.status.idle": "2024-01-17T18:16:30.215461Z",
-     "shell.execute_reply": "2024-01-17T18:16:30.214886Z"
+     "iopub.execute_input": "2024-01-17T23:18:29.033316Z",
+     "iopub.status.busy": "2024-01-17T23:18:29.032451Z",
+     "iopub.status.idle": "2024-01-17T23:18:29.039639Z",
+     "shell.execute_reply": "2024-01-17T23:18:29.038988Z"
     }
    },
    "outputs": [
@@ -593,10 +599,10 @@
    "id": "e13de188",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:30.218139Z",
-     "iopub.status.busy": "2024-01-17T18:16:30.217665Z",
-     "iopub.status.idle": "2024-01-17T18:16:30.243112Z",
-     "shell.execute_reply": "2024-01-17T18:16:30.242491Z"
+     "iopub.execute_input": "2024-01-17T23:18:29.042085Z",
+     "iopub.status.busy": "2024-01-17T23:18:29.041709Z",
+     "iopub.status.idle": "2024-01-17T23:18:29.066441Z",
+     "shell.execute_reply": "2024-01-17T23:18:29.065766Z"
     }
    },
    "outputs": [
@@ -774,10 +780,10 @@
    "id": "e4a006bd",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:30.245621Z",
-     "iopub.status.busy": "2024-01-17T18:16:30.245250Z",
-     "iopub.status.idle": "2024-01-17T18:16:30.277918Z",
-     "shell.execute_reply": "2024-01-17T18:16:30.277281Z"
+     "iopub.execute_input": "2024-01-17T23:18:29.068812Z",
+     "iopub.status.busy": "2024-01-17T23:18:29.068604Z",
+     "iopub.status.idle": "2024-01-17T23:18:29.100697Z",
+     "shell.execute_reply": "2024-01-17T23:18:29.099970Z"
     }
    },
    "outputs": [
@@ -879,10 +885,10 @@
    "id": "c8f4e163",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:30.280522Z",
-     "iopub.status.busy": "2024-01-17T18:16:30.280142Z",
-     "iopub.status.idle": "2024-01-17T18:16:30.291143Z",
-     "shell.execute_reply": "2024-01-17T18:16:30.290517Z"
+     "iopub.execute_input": "2024-01-17T23:18:29.103479Z",
+     "iopub.status.busy": "2024-01-17T23:18:29.103200Z",
+     "iopub.status.idle": "2024-01-17T23:18:29.112931Z",
+     "shell.execute_reply": "2024-01-17T23:18:29.112353Z"
     }
    },
    "outputs": [
@@ -956,10 +962,10 @@
    "id": "db0b5179",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:30.293427Z",
-     "iopub.status.busy": "2024-01-17T18:16:30.293061Z",
-     "iopub.status.idle": "2024-01-17T18:16:32.143940Z",
-     "shell.execute_reply": "2024-01-17T18:16:32.143364Z"
+     "iopub.execute_input": "2024-01-17T23:18:29.115284Z",
+     "iopub.status.busy": "2024-01-17T23:18:29.115081Z",
+     "iopub.status.idle": "2024-01-17T23:18:30.973062Z",
+     "shell.execute_reply": "2024-01-17T23:18:30.972404Z"
     }
    },
    "outputs": [
@@ -1131,10 +1137,10 @@
    "id": "a18795eb",
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2024-01-17T18:16:32.146497Z",
-     "iopub.status.busy": "2024-01-17T18:16:32.146092Z",
-     "iopub.status.idle": "2024-01-17T18:16:32.150481Z",
-     "shell.execute_reply": "2024-01-17T18:16:32.149957Z"
+     "iopub.execute_input": "2024-01-17T23:18:30.975447Z",
+     "iopub.status.busy": "2024-01-17T23:18:30.975239Z",
+     "iopub.status.idle": "2024-01-17T23:18:30.979718Z",
+     "shell.execute_reply": "2024-01-17T23:18:30.979194Z"
     },
     "nbsphinx": "hidden"
    },
diff --git a/versioning.js b/versioning.js
index 2fbc445e2..3a1c28d3b 100644
--- a/versioning.js
+++ b/versioning.js
@@ -1,4 +1,4 @@
 var Version = {
   version_number: "v2.5.0",
-  commit_hash: "89866d53b4074a0103c737ad28c80123f03973de",
+  commit_hash: "93154314109f77e58265574da2ab08503d0fd5a2",
 };
\ No newline at end of file