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diff --git a/master/.doctrees/cleanlab/token_classification/filter.doctree b/master/.doctrees/cleanlab/token_classification/filter.doctree
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diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree
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diff --git a/master/.doctrees/cleanlab/token_classification/summary.doctree b/master/.doctrees/cleanlab/token_classification/summary.doctree
index e5edfe267..fc130ded0 100644
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diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index c3c2aa117..246e72ba9 100644
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diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree
index 16123e68d..19e4fda8c 100644
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diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index b0005a62d..ab83e818a 100644
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diff --git a/master/.doctrees/nbsphinx/tutorials/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/audio.ipynb
index bfd51bcd7..133d834f2 100644
--- a/master/.doctrees/nbsphinx/tutorials/audio.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/audio.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
"metadata": {
"execution": {
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- "iopub.status.busy": "2024-02-08T14:16:16.706135Z",
- "iopub.status.idle": "2024-02-08T14:16:21.469375Z",
- "shell.execute_reply": "2024-02-08T14:16:21.468815Z"
+ "iopub.execute_input": "2024-02-09T00:04:09.983398Z",
+ "iopub.status.busy": "2024-02-09T00:04:09.982990Z",
+ "iopub.status.idle": "2024-02-09T00:04:15.080345Z",
+ "shell.execute_reply": "2024-02-09T00:04:15.079734Z"
},
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},
@@ -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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -131,10 +131,10 @@
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@@ -157,10 +157,10 @@
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},
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@@ -208,10 +208,10 @@
"base_uri": "https://localhost:8080/"
},
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- "shell.execute_reply": "2024-02-08T14:16:23.516586Z"
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"outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6"
@@ -242,10 +242,10 @@
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- "shell.execute_reply": "2024-02-08T14:16:23.529727Z"
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"outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895"
@@ -329,10 +329,10 @@
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@@ -380,10 +380,10 @@
"height": 92
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"outputId": "c6a4917f-4a82-4a89-9193-415072e45550"
@@ -435,10 +435,10 @@
"execution_count": 8,
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@@ -474,10 +474,10 @@
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- "shell.execute_reply": "2024-02-08T14:16:25.974349Z"
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"outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4"
@@ -557,10 +557,10 @@
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- "shell.execute_reply": "2024-02-08T14:16:25.979083Z"
+ "iopub.execute_input": "2024-02-09T00:04:18.483551Z",
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},
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@@ -582,10 +582,10 @@
"execution_count": 11,
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},
"id": "2FSQ2GR9R_YA"
},
@@ -627,10 +627,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
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"outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632"
@@ -689,10 +689,10 @@
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@@ -726,10 +726,10 @@
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@@ -776,10 +776,10 @@
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@@ -816,10 +816,10 @@
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"metadata": {
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@@ -874,10 +874,10 @@
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@@ -981,10 +981,10 @@
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@@ -1257,10 +1257,10 @@
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@@ -1301,10 +1301,10 @@
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@@ -1352,10 +1352,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
index 7dcba895d..65d832198 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,
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- "shell.execute_reply": "2024-02-08T14:16:45.764988Z"
+ "iopub.execute_input": "2024-02-09T00:04:37.584876Z",
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+ "shell.execute_reply": "2024-02-09T00:04:38.652123Z"
},
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@@ -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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
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@@ -118,10 +118,10 @@
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@@ -252,10 +252,10 @@
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@@ -353,10 +353,10 @@
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- "shell.execute_reply": "2024-02-08T14:16:45.786625Z"
+ "iopub.execute_input": "2024-02-09T00:04:38.670230Z",
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@@ -445,10 +445,10 @@
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@@ -517,10 +517,10 @@
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@@ -568,10 +568,10 @@
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@@ -607,10 +607,10 @@
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@@ -641,10 +641,10 @@
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@@ -708,10 +708,10 @@
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@@ -820,10 +820,10 @@
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@@ -935,10 +935,10 @@
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@@ -1068,17 +1068,17 @@
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@@ -1295,10 +1295,10 @@
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@@ -1430,7 +1430,7 @@
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@@ -1554,56 +1570,7 @@
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@@ -1772,7 +1676,56 @@
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+ "max": 132.0,
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+ "value": " 132/132 [00:00<00:00, 11710.98 examples/s]"
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@@ -1789,6 +1742,53 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
index cb07825cf..4da0c16b5 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
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- "shell.execute_reply": "2024-02-08T14:16:52.025235Z"
+ "iopub.execute_input": "2024-02-09T00:04:43.655042Z",
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@@ -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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -116,10 +116,10 @@
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@@ -250,10 +250,10 @@
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@@ -356,10 +356,10 @@
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@@ -448,10 +448,10 @@
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@@ -520,10 +520,10 @@
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@@ -559,10 +559,10 @@
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@@ -601,10 +601,10 @@
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@@ -646,10 +646,10 @@
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@@ -701,10 +701,10 @@
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+ "shell.execute_reply": "2024-02-09T00:04:46.944048Z"
}
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"outputs": [
@@ -834,10 +834,10 @@
"execution_count": 11,
"metadata": {
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- "iopub.status.idle": "2024-02-08T14:16:54.238968Z",
- "shell.execute_reply": "2024-02-08T14:16:54.238432Z"
+ "iopub.execute_input": "2024-02-09T00:04:46.946610Z",
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+ "shell.execute_reply": "2024-02-09T00:04:46.952070Z"
}
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"outputs": [
@@ -941,10 +941,10 @@
"execution_count": 12,
"metadata": {
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- "shell.execute_reply": "2024-02-08T14:16:54.245836Z"
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}
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"outputs": [
@@ -1011,10 +1011,10 @@
"execution_count": 13,
"metadata": {
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- "shell.execute_reply": "2024-02-08T14:16:54.256843Z"
+ "iopub.execute_input": "2024-02-09T00:04:46.961633Z",
+ "iopub.status.busy": "2024-02-09T00:04:46.961340Z",
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+ "shell.execute_reply": "2024-02-09T00:04:46.970195Z"
}
},
"outputs": [
@@ -1187,10 +1187,10 @@
"execution_count": 14,
"metadata": {
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- "iopub.execute_input": "2024-02-08T14:16:54.259335Z",
- "iopub.status.busy": "2024-02-08T14:16:54.259005Z",
- "iopub.status.idle": "2024-02-08T14:16:54.267368Z",
- "shell.execute_reply": "2024-02-08T14:16:54.266873Z"
+ "iopub.execute_input": "2024-02-09T00:04:46.972620Z",
+ "iopub.status.busy": "2024-02-09T00:04:46.972318Z",
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+ "shell.execute_reply": "2024-02-09T00:04:46.980499Z"
}
},
"outputs": [
@@ -1306,10 +1306,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:16:54.269447Z",
- "iopub.status.busy": "2024-02-08T14:16:54.269138Z",
- "iopub.status.idle": "2024-02-08T14:16:54.275808Z",
- "shell.execute_reply": "2024-02-08T14:16:54.275364Z"
+ "iopub.execute_input": "2024-02-09T00:04:46.983088Z",
+ "iopub.status.busy": "2024-02-09T00:04:46.982767Z",
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+ "shell.execute_reply": "2024-02-09T00:04:46.989058Z"
},
"scrolled": true
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@@ -1434,10 +1434,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:16:54.277832Z",
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- "shell.execute_reply": "2024-02-08T14:16:54.285606Z"
+ "iopub.execute_input": "2024-02-09T00:04:46.991356Z",
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+ "iopub.status.idle": "2024-02-09T00:04:46.999994Z",
+ "shell.execute_reply": "2024-02-09T00:04:46.999548Z"
}
},
"outputs": [
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
index b153c5e94..fcfa5142e 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": "2024-02-08T14:16:56.799250Z",
- "iopub.status.busy": "2024-02-08T14:16:56.799084Z",
- "iopub.status.idle": "2024-02-08T14:16:57.842049Z",
- "shell.execute_reply": "2024-02-08T14:16:57.841511Z"
+ "iopub.execute_input": "2024-02-09T00:04:49.569629Z",
+ "iopub.status.busy": "2024-02-09T00:04:49.569470Z",
+ "iopub.status.idle": "2024-02-09T00:04:50.581224Z",
+ "shell.execute_reply": "2024-02-09T00:04:50.580689Z"
},
"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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": "2024-02-08T14:16:57.844683Z",
- "iopub.status.busy": "2024-02-08T14:16:57.844278Z",
- "iopub.status.idle": "2024-02-08T14:16:57.877483Z",
- "shell.execute_reply": "2024-02-08T14:16:57.877041Z"
+ "iopub.execute_input": "2024-02-09T00:04:50.583712Z",
+ "iopub.status.busy": "2024-02-09T00:04:50.583366Z",
+ "iopub.status.idle": "2024-02-09T00:04:50.616984Z",
+ "shell.execute_reply": "2024-02-09T00:04:50.616573Z"
}
},
"outputs": [],
@@ -155,10 +155,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:16:57.879807Z",
- "iopub.status.busy": "2024-02-08T14:16:57.879347Z",
- "iopub.status.idle": "2024-02-08T14:16:58.025248Z",
- "shell.execute_reply": "2024-02-08T14:16:58.024590Z"
+ "iopub.execute_input": "2024-02-09T00:04:50.619168Z",
+ "iopub.status.busy": "2024-02-09T00:04:50.618794Z",
+ "iopub.status.idle": "2024-02-09T00:04:50.992473Z",
+ "shell.execute_reply": "2024-02-09T00:04:50.992035Z"
}
},
"outputs": [
@@ -265,10 +265,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:16:58.027533Z",
- "iopub.status.busy": "2024-02-08T14:16:58.027283Z",
- "iopub.status.idle": "2024-02-08T14:16:58.032275Z",
- "shell.execute_reply": "2024-02-08T14:16:58.031769Z"
+ "iopub.execute_input": "2024-02-09T00:04:50.994441Z",
+ "iopub.status.busy": "2024-02-09T00:04:50.994103Z",
+ "iopub.status.idle": "2024-02-09T00:04:50.997515Z",
+ "shell.execute_reply": "2024-02-09T00:04:50.997089Z"
}
},
"outputs": [],
@@ -289,10 +289,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:16:58.034505Z",
- "iopub.status.busy": "2024-02-08T14:16:58.034117Z",
- "iopub.status.idle": "2024-02-08T14:16:58.042432Z",
- "shell.execute_reply": "2024-02-08T14:16:58.042014Z"
+ "iopub.execute_input": "2024-02-09T00:04:50.999530Z",
+ "iopub.status.busy": "2024-02-09T00:04:50.999205Z",
+ "iopub.status.idle": "2024-02-09T00:04:51.006892Z",
+ "shell.execute_reply": "2024-02-09T00:04:51.006421Z"
}
},
"outputs": [],
@@ -337,10 +337,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:16:58.044590Z",
- "iopub.status.busy": "2024-02-08T14:16:58.044252Z",
- "iopub.status.idle": "2024-02-08T14:16:58.046877Z",
- "shell.execute_reply": "2024-02-08T14:16:58.046428Z"
+ "iopub.execute_input": "2024-02-09T00:04:51.008965Z",
+ "iopub.status.busy": "2024-02-09T00:04:51.008654Z",
+ "iopub.status.idle": "2024-02-09T00:04:51.011050Z",
+ "shell.execute_reply": "2024-02-09T00:04:51.010615Z"
}
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"outputs": [],
@@ -362,10 +362,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:16:58.048928Z",
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- "shell.execute_reply": "2024-02-08T14:17:01.044123Z"
+ "iopub.execute_input": "2024-02-09T00:04:51.012938Z",
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+ "shell.execute_reply": "2024-02-09T00:04:53.944719Z"
}
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"outputs": [],
@@ -401,10 +401,10 @@
"execution_count": 8,
"metadata": {
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- "shell.execute_reply": "2024-02-08T14:17:01.056425Z"
+ "iopub.execute_input": "2024-02-09T00:04:53.947815Z",
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+ "shell.execute_reply": "2024-02-09T00:04:53.957041Z"
}
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"outputs": [],
@@ -436,10 +436,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:01.058938Z",
- "iopub.status.busy": "2024-02-08T14:17:01.058610Z",
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- "shell.execute_reply": "2024-02-08T14:17:02.761715Z"
+ "iopub.execute_input": "2024-02-09T00:04:53.959424Z",
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+ "iopub.status.idle": "2024-02-09T00:04:55.647645Z",
+ "shell.execute_reply": "2024-02-09T00:04:55.647018Z"
}
},
"outputs": [
@@ -475,10 +475,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:02.765376Z",
- "iopub.status.busy": "2024-02-08T14:17:02.764607Z",
- "iopub.status.idle": "2024-02-08T14:17:02.784523Z",
- "shell.execute_reply": "2024-02-08T14:17:02.784051Z"
+ "iopub.execute_input": "2024-02-09T00:04:55.651444Z",
+ "iopub.status.busy": "2024-02-09T00:04:55.650131Z",
+ "iopub.status.idle": "2024-02-09T00:04:55.671769Z",
+ "shell.execute_reply": "2024-02-09T00:04:55.671289Z"
},
"scrolled": true
},
@@ -604,10 +604,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:02.786857Z",
- "iopub.status.busy": "2024-02-08T14:17:02.786492Z",
- "iopub.status.idle": "2024-02-08T14:17:02.795412Z",
- "shell.execute_reply": "2024-02-08T14:17:02.794907Z"
+ "iopub.execute_input": "2024-02-09T00:04:55.675134Z",
+ "iopub.status.busy": "2024-02-09T00:04:55.674223Z",
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+ "shell.execute_reply": "2024-02-09T00:04:55.684624Z"
}
},
"outputs": [
@@ -711,10 +711,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:02.797695Z",
- "iopub.status.busy": "2024-02-08T14:17:02.797331Z",
- "iopub.status.idle": "2024-02-08T14:17:02.807642Z",
- "shell.execute_reply": "2024-02-08T14:17:02.807170Z"
+ "iopub.execute_input": "2024-02-09T00:04:55.688421Z",
+ "iopub.status.busy": "2024-02-09T00:04:55.687538Z",
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+ "shell.execute_reply": "2024-02-09T00:04:55.699374Z"
}
},
"outputs": [
@@ -843,10 +843,10 @@
"execution_count": 13,
"metadata": {
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- "iopub.execute_input": "2024-02-08T14:17:02.810673Z",
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- "iopub.status.idle": "2024-02-08T14:17:02.820597Z",
- "shell.execute_reply": "2024-02-08T14:17:02.820115Z"
+ "iopub.execute_input": "2024-02-09T00:04:55.703253Z",
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+ "shell.execute_reply": "2024-02-09T00:04:55.712614Z"
}
},
"outputs": [
@@ -960,10 +960,10 @@
"execution_count": 14,
"metadata": {
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- "iopub.execute_input": "2024-02-08T14:17:02.823989Z",
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- "shell.execute_reply": "2024-02-08T14:17:02.834892Z"
+ "iopub.execute_input": "2024-02-09T00:04:55.716308Z",
+ "iopub.status.busy": "2024-02-09T00:04:55.715458Z",
+ "iopub.status.idle": "2024-02-09T00:04:55.727537Z",
+ "shell.execute_reply": "2024-02-09T00:04:55.727073Z"
}
},
"outputs": [
@@ -1074,10 +1074,10 @@
"execution_count": 15,
"metadata": {
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- "iopub.execute_input": "2024-02-08T14:17:02.838698Z",
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- "iopub.status.idle": "2024-02-08T14:17:02.846936Z",
- "shell.execute_reply": "2024-02-08T14:17:02.846460Z"
+ "iopub.execute_input": "2024-02-09T00:04:55.730683Z",
+ "iopub.status.busy": "2024-02-09T00:04:55.729835Z",
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+ "shell.execute_reply": "2024-02-09T00:04:55.738451Z"
}
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"outputs": [
@@ -1161,10 +1161,10 @@
"execution_count": 16,
"metadata": {
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- "iopub.execute_input": "2024-02-08T14:17:02.850303Z",
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- "iopub.status.idle": "2024-02-08T14:17:02.857439Z",
- "shell.execute_reply": "2024-02-08T14:17:02.857050Z"
+ "iopub.execute_input": "2024-02-09T00:04:55.740941Z",
+ "iopub.status.busy": "2024-02-09T00:04:55.740570Z",
+ "iopub.status.idle": "2024-02-09T00:04:55.747459Z",
+ "shell.execute_reply": "2024-02-09T00:04:55.747065Z"
}
},
"outputs": [
@@ -1257,10 +1257,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:02.860182Z",
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- "iopub.status.idle": "2024-02-08T14:17:02.866031Z",
- "shell.execute_reply": "2024-02-08T14:17:02.865631Z"
+ "iopub.execute_input": "2024-02-09T00:04:55.749215Z",
+ "iopub.status.busy": "2024-02-09T00:04:55.749058Z",
+ "iopub.status.idle": "2024-02-09T00:04:55.755466Z",
+ "shell.execute_reply": "2024-02-09T00:04:55.755063Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
index 5d9d173ae..16c84ba59 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": "2024-02-08T14:17:05.451285Z",
- "iopub.status.busy": "2024-02-08T14:17:05.451126Z",
- "iopub.status.idle": "2024-02-08T14:17:08.245342Z",
- "shell.execute_reply": "2024-02-08T14:17:08.244841Z"
+ "iopub.execute_input": "2024-02-09T00:04:58.357840Z",
+ "iopub.status.busy": "2024-02-09T00:04:58.357376Z",
+ "iopub.status.idle": "2024-02-09T00:05:01.260489Z",
+ "shell.execute_reply": "2024-02-09T00:05:01.259942Z"
},
"nbsphinx": "hidden"
},
@@ -96,7 +96,7 @@
"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -121,10 +121,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:08.247881Z",
- "iopub.status.busy": "2024-02-08T14:17:08.247474Z",
- "iopub.status.idle": "2024-02-08T14:17:08.250677Z",
- "shell.execute_reply": "2024-02-08T14:17:08.250241Z"
+ "iopub.execute_input": "2024-02-09T00:05:01.263091Z",
+ "iopub.status.busy": "2024-02-09T00:05:01.262622Z",
+ "iopub.status.idle": "2024-02-09T00:05:01.265882Z",
+ "shell.execute_reply": "2024-02-09T00:05:01.265443Z"
}
},
"outputs": [],
@@ -145,10 +145,10 @@
"execution_count": 3,
"metadata": {
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+ "iopub.execute_input": "2024-02-09T00:05:01.267841Z",
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@@ -178,10 +178,10 @@
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@@ -271,10 +271,10 @@
"execution_count": 5,
"metadata": {
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}
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"outputs": [
@@ -283,7 +283,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'cancel_transfer', 'visa_or_mastercard', 'getting_spare_card', 'card_payment_fee_charged', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'card_about_to_expire', 'apple_pay_or_google_pay', 'change_pin'}\n"
+ "Classes: {'visa_or_mastercard', 'cancel_transfer', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'getting_spare_card', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'change_pin', 'card_about_to_expire'}\n"
]
}
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@@ -307,10 +307,10 @@
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@@ -365,17 +365,17 @@
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+ "shell.execute_reply": "2024-02-09T00:05:06.902058Z"
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+ "model_id": "aaf2fc7de10f404d810a4e679a934b70",
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@@ -389,7 +389,7 @@
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@@ -445,7 +445,7 @@
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@@ -459,7 +459,7 @@
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@@ -521,10 +521,10 @@
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@@ -556,10 +556,10 @@
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@@ -579,10 +579,10 @@
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@@ -626,10 +626,10 @@
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@@ -756,10 +756,10 @@
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@@ -869,10 +869,10 @@
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@@ -910,10 +910,10 @@
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@@ -1030,10 +1030,10 @@
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@@ -1227,10 +1227,10 @@
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@@ -1341,10 +1341,10 @@
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@@ -1412,10 +1412,10 @@
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@@ -1494,10 +1494,10 @@
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@@ -1545,10 +1545,10 @@
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@@ -1598,30 +1598,7 @@
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- "value": " 2.21k/2.21k [00:00<00:00, 389kB/s]"
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- "02fee8ddaaab40a3ac600f1f5a7e6fe7": {
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@@ -1674,7 +1651,7 @@
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@@ -1727,7 +1704,7 @@
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@@ -1743,40 +1720,35 @@
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@@ -1794,46 +1766,69 @@
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"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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": "2024-02-08T14:17:21.543062Z",
- "iopub.status.busy": "2024-02-08T14:17:21.542778Z",
- "iopub.status.idle": "2024-02-08T14:17:21.545763Z",
- "shell.execute_reply": "2024-02-08T14:17:21.545204Z"
+ "iopub.execute_input": "2024-02-09T00:05:13.657156Z",
+ "iopub.status.busy": "2024-02-09T00:05:13.656802Z",
+ "iopub.status.idle": "2024-02-09T00:05:13.659440Z",
+ "shell.execute_reply": "2024-02-09T00:05:13.659012Z"
},
"id": "_UvI80l42iyi"
},
@@ -201,10 +201,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:21.547870Z",
- "iopub.status.busy": "2024-02-08T14:17:21.547572Z",
- "iopub.status.idle": "2024-02-08T14:17:21.559100Z",
- "shell.execute_reply": "2024-02-08T14:17:21.558630Z"
+ "iopub.execute_input": "2024-02-09T00:05:13.661557Z",
+ "iopub.status.busy": "2024-02-09T00:05:13.661244Z",
+ "iopub.status.idle": "2024-02-09T00:05:13.672927Z",
+ "shell.execute_reply": "2024-02-09T00:05:13.672512Z"
},
"nbsphinx": "hidden"
},
@@ -283,10 +283,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:21.560898Z",
- "iopub.status.busy": "2024-02-08T14:17:21.560724Z",
- "iopub.status.idle": "2024-02-08T14:17:28.152620Z",
- "shell.execute_reply": "2024-02-08T14:17:28.152155Z"
+ "iopub.execute_input": "2024-02-09T00:05:13.674915Z",
+ "iopub.status.busy": "2024-02-09T00:05:13.674571Z",
+ "iopub.status.idle": "2024-02-09T00:05:21.391567Z",
+ "shell.execute_reply": "2024-02-09T00:05:21.390983Z"
},
"id": "dhTHOg8Pyv5G"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
index 04a5e2f21..0185e23f9 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": "2024-02-08T14:17:30.122511Z",
- "iopub.status.busy": "2024-02-08T14:17:30.122025Z",
- "iopub.status.idle": "2024-02-08T14:17:31.138719Z",
- "shell.execute_reply": "2024-02-08T14:17:31.138168Z"
+ "iopub.execute_input": "2024-02-09T00:05:23.356191Z",
+ "iopub.status.busy": "2024-02-09T00:05:23.355787Z",
+ "iopub.status.idle": "2024-02-09T00:05:24.377950Z",
+ "shell.execute_reply": "2024-02-09T00:05:24.377400Z"
},
"nbsphinx": "hidden"
},
@@ -97,10 +97,10 @@
"id": "239d5ee7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:31.141331Z",
- "iopub.status.busy": "2024-02-08T14:17:31.140895Z",
- "iopub.status.idle": "2024-02-08T14:17:31.144171Z",
- "shell.execute_reply": "2024-02-08T14:17:31.143720Z"
+ "iopub.execute_input": "2024-02-09T00:05:24.380666Z",
+ "iopub.status.busy": "2024-02-09T00:05:24.380249Z",
+ "iopub.status.idle": "2024-02-09T00:05:24.383578Z",
+ "shell.execute_reply": "2024-02-09T00:05:24.383094Z"
}
},
"outputs": [],
@@ -136,10 +136,10 @@
"id": "28b324aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:31.146141Z",
- "iopub.status.busy": "2024-02-08T14:17:31.145806Z",
- "iopub.status.idle": "2024-02-08T14:17:34.074162Z",
- "shell.execute_reply": "2024-02-08T14:17:34.073437Z"
+ "iopub.execute_input": "2024-02-09T00:05:24.385569Z",
+ "iopub.status.busy": "2024-02-09T00:05:24.385185Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.324010Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.323272Z"
}
},
"outputs": [],
@@ -162,10 +162,10 @@
"id": "28b324ab",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.077255Z",
- "iopub.status.busy": "2024-02-08T14:17:34.076648Z",
- "iopub.status.idle": "2024-02-08T14:17:34.113431Z",
- "shell.execute_reply": "2024-02-08T14:17:34.112729Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.327139Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.326356Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.367045Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.366280Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "90c10e18",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.116130Z",
- "iopub.status.busy": "2024-02-08T14:17:34.115834Z",
- "iopub.status.idle": "2024-02-08T14:17:34.146858Z",
- "shell.execute_reply": "2024-02-08T14:17:34.146265Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.370047Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.369634Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.406365Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.405763Z"
}
},
"outputs": [],
@@ -213,10 +213,10 @@
"id": "88839519",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.149671Z",
- "iopub.status.busy": "2024-02-08T14:17:34.149205Z",
- "iopub.status.idle": "2024-02-08T14:17:34.152293Z",
- "shell.execute_reply": "2024-02-08T14:17:34.151742Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.409230Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.408727Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.411813Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.411369Z"
}
},
"outputs": [],
@@ -238,10 +238,10 @@
"id": "558490c2",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.154492Z",
- "iopub.status.busy": "2024-02-08T14:17:34.154104Z",
- "iopub.status.idle": "2024-02-08T14:17:34.156689Z",
- "shell.execute_reply": "2024-02-08T14:17:34.156241Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.413867Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.413470Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.416096Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.415654Z"
}
},
"outputs": [],
@@ -298,10 +298,10 @@
"id": "41714b51",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.158774Z",
- "iopub.status.busy": "2024-02-08T14:17:34.158454Z",
- "iopub.status.idle": "2024-02-08T14:17:34.181726Z",
- "shell.execute_reply": "2024-02-08T14:17:34.181225Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.418190Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.417792Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.441537Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.440961Z"
}
},
"outputs": [
@@ -315,7 +315,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f195e2798ebf44cf8d2027e25f93fdb2",
+ "model_id": "2b1c9d6e104e40a1a0ff8382e6c51ae0",
"version_major": 2,
"version_minor": 0
},
@@ -329,7 +329,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "fbe3605a8fec45139ab8d1029db0780a",
+ "model_id": "672cd986abca4a98b9c66f6076d86507",
"version_major": 2,
"version_minor": 0
},
@@ -387,10 +387,10 @@
"id": "20476c70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.188085Z",
- "iopub.status.busy": "2024-02-08T14:17:34.187540Z",
- "iopub.status.idle": "2024-02-08T14:17:34.193766Z",
- "shell.execute_reply": "2024-02-08T14:17:34.193357Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.448213Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.447809Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.454176Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.453658Z"
},
"nbsphinx": "hidden"
},
@@ -421,10 +421,10 @@
"id": "6983cdad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.195676Z",
- "iopub.status.busy": "2024-02-08T14:17:34.195359Z",
- "iopub.status.idle": "2024-02-08T14:17:34.198563Z",
- "shell.execute_reply": "2024-02-08T14:17:34.198154Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.456279Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.455978Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.459401Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.458925Z"
},
"nbsphinx": "hidden"
},
@@ -447,10 +447,10 @@
"id": "9092b8a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.200561Z",
- "iopub.status.busy": "2024-02-08T14:17:34.200252Z",
- "iopub.status.idle": "2024-02-08T14:17:34.206257Z",
- "shell.execute_reply": "2024-02-08T14:17:34.205801Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.461260Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.461086Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.467236Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.466779Z"
}
},
"outputs": [],
@@ -500,10 +500,10 @@
"id": "b0a01109",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.208311Z",
- "iopub.status.busy": "2024-02-08T14:17:34.208001Z",
- "iopub.status.idle": "2024-02-08T14:17:34.242699Z",
- "shell.execute_reply": "2024-02-08T14:17:34.242110Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.468950Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.468778Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.505922Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.505228Z"
}
},
"outputs": [],
@@ -520,10 +520,10 @@
"id": "8b1da032",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.245164Z",
- "iopub.status.busy": "2024-02-08T14:17:34.244876Z",
- "iopub.status.idle": "2024-02-08T14:17:34.276529Z",
- "shell.execute_reply": "2024-02-08T14:17:34.275939Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.508582Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.508292Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.543528Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.542918Z"
},
"nbsphinx": "hidden"
},
@@ -602,10 +602,10 @@
"id": "4c9e9030",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.278975Z",
- "iopub.status.busy": "2024-02-08T14:17:34.278749Z",
- "iopub.status.idle": "2024-02-08T14:17:34.406023Z",
- "shell.execute_reply": "2024-02-08T14:17:34.405376Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.546414Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.545942Z",
+ "iopub.status.idle": "2024-02-09T00:05:27.682536Z",
+ "shell.execute_reply": "2024-02-09T00:05:27.681838Z"
}
},
"outputs": [
@@ -672,10 +672,10 @@
"id": "8751619e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:34.408818Z",
- "iopub.status.busy": "2024-02-08T14:17:34.408242Z",
- "iopub.status.idle": "2024-02-08T14:17:37.472788Z",
- "shell.execute_reply": "2024-02-08T14:17:37.472135Z"
+ "iopub.execute_input": "2024-02-09T00:05:27.685760Z",
+ "iopub.status.busy": "2024-02-09T00:05:27.684851Z",
+ "iopub.status.idle": "2024-02-09T00:05:30.726892Z",
+ "shell.execute_reply": "2024-02-09T00:05:30.726240Z"
}
},
"outputs": [
@@ -761,10 +761,10 @@
"id": "623df36d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:37.474987Z",
- "iopub.status.busy": "2024-02-08T14:17:37.474771Z",
- "iopub.status.idle": "2024-02-08T14:17:37.532706Z",
- "shell.execute_reply": "2024-02-08T14:17:37.532188Z"
+ "iopub.execute_input": "2024-02-09T00:05:30.729357Z",
+ "iopub.status.busy": "2024-02-09T00:05:30.729006Z",
+ "iopub.status.idle": "2024-02-09T00:05:30.786098Z",
+ "shell.execute_reply": "2024-02-09T00:05:30.785552Z"
}
},
"outputs": [
@@ -1206,7 +1206,7 @@
},
{
"cell_type": "markdown",
- "id": "4b08fe83",
+ "id": "f6cca8f6",
"metadata": {},
"source": [
"### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?"
@@ -1214,7 +1214,7 @@
},
{
"cell_type": "markdown",
- "id": "dc4d9608",
+ "id": "f98e37b9",
"metadata": {},
"source": [
"When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n",
@@ -1227,13 +1227,13 @@
{
"cell_type": "code",
"execution_count": 17,
- "id": "a6bcb0ac",
+ "id": "00f83abc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:37.534729Z",
- "iopub.status.busy": "2024-02-08T14:17:37.534551Z",
- "iopub.status.idle": "2024-02-08T14:17:37.636069Z",
- "shell.execute_reply": "2024-02-08T14:17:37.635525Z"
+ "iopub.execute_input": "2024-02-09T00:05:30.788411Z",
+ "iopub.status.busy": "2024-02-09T00:05:30.787979Z",
+ "iopub.status.idle": "2024-02-09T00:05:30.889807Z",
+ "shell.execute_reply": "2024-02-09T00:05:30.889205Z"
}
},
"outputs": [
@@ -1274,7 +1274,7 @@
},
{
"cell_type": "markdown",
- "id": "e9fe17af",
+ "id": "a746198d",
"metadata": {},
"source": [
"For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:"
@@ -1283,13 +1283,13 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "ce424dc2",
+ "id": "d467e08f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:37.638736Z",
- "iopub.status.busy": "2024-02-08T14:17:37.638521Z",
- "iopub.status.idle": "2024-02-08T14:17:37.714330Z",
- "shell.execute_reply": "2024-02-08T14:17:37.713901Z"
+ "iopub.execute_input": "2024-02-09T00:05:30.892949Z",
+ "iopub.status.busy": "2024-02-09T00:05:30.892036Z",
+ "iopub.status.idle": "2024-02-09T00:05:30.967994Z",
+ "shell.execute_reply": "2024-02-09T00:05:30.967399Z"
}
},
"outputs": [
@@ -1325,7 +1325,7 @@
},
{
"cell_type": "markdown",
- "id": "298d89e1",
+ "id": "cb22d225",
"metadata": {},
"source": [
"### How to handle near-duplicate data identified by cleanlab?\n",
@@ -1336,13 +1336,13 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "a623bd21",
+ "id": "d4dc7076",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:37.716464Z",
- "iopub.status.busy": "2024-02-08T14:17:37.716069Z",
- "iopub.status.idle": "2024-02-08T14:17:37.723314Z",
- "shell.execute_reply": "2024-02-08T14:17:37.722778Z"
+ "iopub.execute_input": "2024-02-09T00:05:30.970084Z",
+ "iopub.status.busy": "2024-02-09T00:05:30.969900Z",
+ "iopub.status.idle": "2024-02-09T00:05:30.977565Z",
+ "shell.execute_reply": "2024-02-09T00:05:30.976999Z"
}
},
"outputs": [],
@@ -1444,7 +1444,7 @@
},
{
"cell_type": "markdown",
- "id": "9330130d",
+ "id": "56fbdb38",
"metadata": {},
"source": [
"The functions above collect sets of near-duplicate examples. Within each\n",
@@ -1459,13 +1459,13 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "b22bd75d",
+ "id": "27bb27ed",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:17:37.725452Z",
- "iopub.status.busy": "2024-02-08T14:17:37.725147Z",
- "iopub.status.idle": "2024-02-08T14:17:37.744615Z",
- "shell.execute_reply": "2024-02-08T14:17:37.744057Z"
+ "iopub.execute_input": "2024-02-09T00:05:30.979455Z",
+ "iopub.status.busy": "2024-02-09T00:05:30.979281Z",
+ "iopub.status.idle": "2024-02-09T00:05:30.998554Z",
+ "shell.execute_reply": "2024-02-09T00:05:30.997990Z"
}
},
"outputs": [
@@ -1482,7 +1482,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/tmp/ipykernel_5881/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+ "/tmp/ipykernel_5947/1995098996.py:88: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
" to_keep_indices = duplicate_rows.groupby(group_key).apply(strategy_fn, **strategy_kwargs).explode().values\n"
]
}
@@ -1516,13 +1516,13 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "9efce442",
+ "id": "3b57f4b1",
"metadata": {
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diff --git a/master/.doctrees/nbsphinx/tutorials/image.ipynb b/master/.doctrees/nbsphinx/tutorials/image.ipynb
index 77ef40263..65c66d269 100644
--- a/master/.doctrees/nbsphinx/tutorials/image.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/image.ipynb
@@ -71,10 +71,10 @@
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- "shell.execute_reply": "2024-02-08T14:17:43.579471Z"
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@@ -112,10 +112,10 @@
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"outputs": [],
@@ -152,17 +152,17 @@
"execution_count": 3,
"metadata": {
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+ "iopub.execute_input": "2024-02-09T00:05:36.785915Z",
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+ "shell.execute_reply": "2024-02-09T00:05:40.896138Z"
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+ "model_id": "98404c51963941e6a9027aec614a324e",
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@@ -176,7 +176,7 @@
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+ "model_id": "d6c58ccae87e44019ca4cfe64749f04a",
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@@ -190,7 +190,7 @@
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@@ -204,7 +204,7 @@
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+ "model_id": "ba75d1285b8445f0b5c5ae6b0b1f36e6",
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@@ -246,10 +246,10 @@
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- "shell.execute_reply": "2024-02-08T14:17:51.293750Z"
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@@ -274,17 +274,17 @@
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@@ -399,10 +399,10 @@
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@@ -539,10 +539,10 @@
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@@ -667,10 +667,10 @@
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@@ -707,10 +707,10 @@
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@@ -726,14 +726,14 @@
"name": "stdout",
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"text": [
- "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.831\n"
+ "epoch: 1 loss: 0.482 test acc: 86.720 time_taken: 4.742\n"
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"name": "stdout",
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- "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.600\n",
+ "epoch: 2 loss: 0.329 test acc: 88.195 time_taken: 4.545\n",
"Computing feature embeddings ...\n"
]
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@@ -750,7 +750,7 @@
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+ " 2%|▎ | 1/40 [00:00<00:04, 8.63it/s]"
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@@ -758,7 +758,7 @@
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"\r",
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+ " 18%|█▊ | 7/40 [00:00<00:00, 35.02it/s]"
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@@ -766,7 +766,7 @@
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+ " 35%|███▌ | 14/40 [00:00<00:00, 46.77it/s]"
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@@ -774,7 +774,7 @@
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+ " 52%|█████▎ | 21/40 [00:00<00:00, 52.86it/s]"
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@@ -782,7 +782,7 @@
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+ " 72%|███████▎ | 29/40 [00:00<00:00, 59.36it/s]"
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@@ -790,7 +790,7 @@
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+ " 92%|█████████▎| 37/40 [00:00<00:00, 64.33it/s]"
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@@ -798,7 +798,7 @@
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+ "100%|██████████| 40/40 [00:00<00:00, 55.03it/s]"
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+ " 2%|▎ | 1/40 [00:00<00:04, 9.32it/s]"
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@@ -836,7 +836,7 @@
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+ " 15%|█▌ | 6/40 [00:00<00:01, 31.75it/s]"
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+ " 30%|███ | 12/40 [00:00<00:00, 42.35it/s]"
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+ " 45%|████▌ | 18/40 [00:00<00:00, 47.63it/s]"
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@@ -860,7 +860,7 @@
"output_type": "stream",
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+ " 62%|██████▎ | 25/40 [00:00<00:00, 53.72it/s]"
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+ " 82%|████████▎ | 33/40 [00:00<00:00, 59.91it/s]"
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+ "100%|██████████| 40/40 [00:00<00:00, 53.32it/s]"
]
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{
@@ -898,14 +898,14 @@
"name": "stdout",
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- "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.765\n"
+ "epoch: 1 loss: 0.493 test acc: 87.060 time_taken: 4.753\n"
]
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{
"name": "stdout",
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"text": [
- "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.515\n",
+ "epoch: 2 loss: 0.330 test acc: 88.505 time_taken: 4.676\n",
"Computing feature embeddings ...\n"
]
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@@ -922,7 +922,7 @@
"output_type": "stream",
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+ " 5%|▌ | 2/40 [00:00<00:02, 18.08it/s]"
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+ " 22%|██▎ | 9/40 [00:00<00:00, 46.68it/s]"
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+ " 40%|████ | 16/40 [00:00<00:00, 53.45it/s]"
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"output_type": "stream",
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+ " 57%|█████▊ | 23/40 [00:00<00:00, 58.85it/s]"
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+ " 72%|███████▎ | 29/40 [00:00<00:00, 58.94it/s]"
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"output_type": "stream",
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+ " 90%|█████████ | 36/40 [00:00<00:00, 61.11it/s]"
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+ "100%|██████████| 40/40 [00:00<00:00, 56.57it/s]"
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@@ -1000,7 +1000,7 @@
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+ " 5%|▌ | 2/40 [00:00<00:01, 19.48it/s]"
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+ " 20%|██ | 8/40 [00:00<00:00, 40.29it/s]"
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"output_type": "stream",
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+ " 38%|███▊ | 15/40 [00:00<00:00, 50.94it/s]"
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"output_type": "stream",
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+ " 55%|█████▌ | 22/40 [00:00<00:00, 56.03it/s]"
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+ " 75%|███████▌ | 30/40 [00:00<00:00, 61.60it/s]"
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+ " 95%|█████████▌| 38/40 [00:00<00:00, 66.02it/s]"
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]
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{
@@ -1070,14 +1070,14 @@
"name": "stdout",
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- "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.786\n"
+ "epoch: 1 loss: 0.476 test acc: 86.340 time_taken: 4.754\n"
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"name": "stdout",
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- "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.487\n",
+ "epoch: 2 loss: 0.328 test acc: 86.310 time_taken: 4.532\n",
"Computing feature embeddings ...\n"
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+ " 20%|██ | 8/40 [00:00<00:00, 42.77it/s]"
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@@ -1180,7 +1180,7 @@
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@@ -1188,7 +1188,7 @@
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+ " 35%|███▌ | 14/40 [00:00<00:00, 47.18it/s]"
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@@ -1196,7 +1196,7 @@
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@@ -1204,7 +1204,7 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
index d49bb267c..f819765e3 100644
--- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
@@ -53,10 +53,10 @@
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@@ -68,7 +68,7 @@
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- " %pip install git+https://github.com/cleanlab/cleanlab.git@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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+ "iopub.status.busy": "2024-02-09T00:10:21.156280Z",
+ "iopub.status.idle": "2024-02-09T00:10:21.198834Z",
+ "shell.execute_reply": "2024-02-09T00:10:21.198265Z"
},
"id": "SLq-3q4xjruX"
},
@@ -1918,10 +1918,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:32.017797Z",
- "iopub.status.busy": "2024-02-08T14:22:32.017480Z",
- "iopub.status.idle": "2024-02-08T14:22:32.112535Z",
- "shell.execute_reply": "2024-02-08T14:22:32.111952Z"
+ "iopub.execute_input": "2024-02-09T00:10:21.201121Z",
+ "iopub.status.busy": "2024-02-09T00:10:21.200813Z",
+ "iopub.status.idle": "2024-02-09T00:10:21.308553Z",
+ "shell.execute_reply": "2024-02-09T00:10:21.307808Z"
},
"id": "g5LHhhuqFbXK"
},
@@ -1953,10 +1953,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:32.115093Z",
- "iopub.status.busy": "2024-02-08T14:22:32.114796Z",
- "iopub.status.idle": "2024-02-08T14:22:32.203978Z",
- "shell.execute_reply": "2024-02-08T14:22:32.203407Z"
+ "iopub.execute_input": "2024-02-09T00:10:21.311360Z",
+ "iopub.status.busy": "2024-02-09T00:10:21.311108Z",
+ "iopub.status.idle": "2024-02-09T00:10:21.428366Z",
+ "shell.execute_reply": "2024-02-09T00:10:21.427777Z"
},
"id": "p7w8F8ezBcet"
},
@@ -2013,10 +2013,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:32.206272Z",
- "iopub.status.busy": "2024-02-08T14:22:32.205915Z",
- "iopub.status.idle": "2024-02-08T14:22:32.412399Z",
- "shell.execute_reply": "2024-02-08T14:22:32.411807Z"
+ "iopub.execute_input": "2024-02-09T00:10:21.430821Z",
+ "iopub.status.busy": "2024-02-09T00:10:21.430511Z",
+ "iopub.status.idle": "2024-02-09T00:10:21.646625Z",
+ "shell.execute_reply": "2024-02-09T00:10:21.645977Z"
},
"id": "WETRL74tE_sU"
},
@@ -2051,10 +2051,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:32.414631Z",
- "iopub.status.busy": "2024-02-08T14:22:32.414201Z",
- "iopub.status.idle": "2024-02-08T14:22:32.614020Z",
- "shell.execute_reply": "2024-02-08T14:22:32.613382Z"
+ "iopub.execute_input": "2024-02-09T00:10:21.649133Z",
+ "iopub.status.busy": "2024-02-09T00:10:21.648763Z",
+ "iopub.status.idle": "2024-02-09T00:10:21.877289Z",
+ "shell.execute_reply": "2024-02-09T00:10:21.876616Z"
},
"id": "kCfdx2gOLmXS"
},
@@ -2216,10 +2216,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:32.616984Z",
- "iopub.status.busy": "2024-02-08T14:22:32.616680Z",
- "iopub.status.idle": "2024-02-08T14:22:32.622872Z",
- "shell.execute_reply": "2024-02-08T14:22:32.622406Z"
+ "iopub.execute_input": "2024-02-09T00:10:21.880008Z",
+ "iopub.status.busy": "2024-02-09T00:10:21.879602Z",
+ "iopub.status.idle": "2024-02-09T00:10:21.886275Z",
+ "shell.execute_reply": "2024-02-09T00:10:21.885682Z"
},
"id": "-uogYRWFYnuu"
},
@@ -2273,10 +2273,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:32.624973Z",
- "iopub.status.busy": "2024-02-08T14:22:32.624581Z",
- "iopub.status.idle": "2024-02-08T14:22:32.842329Z",
- "shell.execute_reply": "2024-02-08T14:22:32.841761Z"
+ "iopub.execute_input": "2024-02-09T00:10:21.888373Z",
+ "iopub.status.busy": "2024-02-09T00:10:21.888185Z",
+ "iopub.status.idle": "2024-02-09T00:10:22.107047Z",
+ "shell.execute_reply": "2024-02-09T00:10:22.106569Z"
},
"id": "pG-ljrmcYp9Q"
},
@@ -2323,10 +2323,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:32.844547Z",
- "iopub.status.busy": "2024-02-08T14:22:32.844209Z",
- "iopub.status.idle": "2024-02-08T14:22:33.903247Z",
- "shell.execute_reply": "2024-02-08T14:22:33.902681Z"
+ "iopub.execute_input": "2024-02-09T00:10:22.109310Z",
+ "iopub.status.busy": "2024-02-09T00:10:22.108965Z",
+ "iopub.status.idle": "2024-02-09T00:10:23.172158Z",
+ "shell.execute_reply": "2024-02-09T00:10:23.171613Z"
},
"id": "wL3ngCnuLEWd"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
index e73ea405d..71257d6f5 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": "2024-02-08T14:22:37.136864Z",
- "iopub.status.busy": "2024-02-08T14:22:37.136686Z",
- "iopub.status.idle": "2024-02-08T14:22:38.171469Z",
- "shell.execute_reply": "2024-02-08T14:22:38.170896Z"
+ "iopub.execute_input": "2024-02-09T00:10:27.224779Z",
+ "iopub.status.busy": "2024-02-09T00:10:27.224228Z",
+ "iopub.status.idle": "2024-02-09T00:10:28.322944Z",
+ "shell.execute_reply": "2024-02-09T00:10:28.322344Z"
},
"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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": "2024-02-08T14:22:38.173847Z",
- "iopub.status.busy": "2024-02-08T14:22:38.173579Z",
- "iopub.status.idle": "2024-02-08T14:22:38.176777Z",
- "shell.execute_reply": "2024-02-08T14:22:38.176221Z"
+ "iopub.execute_input": "2024-02-09T00:10:28.325762Z",
+ "iopub.status.busy": "2024-02-09T00:10:28.325167Z",
+ "iopub.status.idle": "2024-02-09T00:10:28.328831Z",
+ "shell.execute_reply": "2024-02-09T00:10:28.328294Z"
}
},
"outputs": [],
@@ -264,10 +264,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:38.178902Z",
- "iopub.status.busy": "2024-02-08T14:22:38.178584Z",
- "iopub.status.idle": "2024-02-08T14:22:38.186277Z",
- "shell.execute_reply": "2024-02-08T14:22:38.185731Z"
+ "iopub.execute_input": "2024-02-09T00:10:28.331106Z",
+ "iopub.status.busy": "2024-02-09T00:10:28.330806Z",
+ "iopub.status.idle": "2024-02-09T00:10:28.338613Z",
+ "shell.execute_reply": "2024-02-09T00:10:28.338084Z"
},
"nbsphinx": "hidden"
},
@@ -351,10 +351,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:38.188267Z",
- "iopub.status.busy": "2024-02-08T14:22:38.187962Z",
- "iopub.status.idle": "2024-02-08T14:22:38.234659Z",
- "shell.execute_reply": "2024-02-08T14:22:38.234223Z"
+ "iopub.execute_input": "2024-02-09T00:10:28.340559Z",
+ "iopub.status.busy": "2024-02-09T00:10:28.340254Z",
+ "iopub.status.idle": "2024-02-09T00:10:28.393711Z",
+ "shell.execute_reply": "2024-02-09T00:10:28.393105Z"
}
},
"outputs": [],
@@ -380,10 +380,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:38.236882Z",
- "iopub.status.busy": "2024-02-08T14:22:38.236539Z",
- "iopub.status.idle": "2024-02-08T14:22:38.252891Z",
- "shell.execute_reply": "2024-02-08T14:22:38.252461Z"
+ "iopub.execute_input": "2024-02-09T00:10:28.396067Z",
+ "iopub.status.busy": "2024-02-09T00:10:28.395878Z",
+ "iopub.status.idle": "2024-02-09T00:10:28.414263Z",
+ "shell.execute_reply": "2024-02-09T00:10:28.413704Z"
}
},
"outputs": [
@@ -598,10 +598,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:38.254823Z",
- "iopub.status.busy": "2024-02-08T14:22:38.254563Z",
- "iopub.status.idle": "2024-02-08T14:22:38.258383Z",
- "shell.execute_reply": "2024-02-08T14:22:38.257933Z"
+ "iopub.execute_input": "2024-02-09T00:10:28.416550Z",
+ "iopub.status.busy": "2024-02-09T00:10:28.416240Z",
+ "iopub.status.idle": "2024-02-09T00:10:28.420131Z",
+ "shell.execute_reply": "2024-02-09T00:10:28.419599Z"
}
},
"outputs": [
@@ -672,10 +672,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:38.260398Z",
- "iopub.status.busy": "2024-02-08T14:22:38.260125Z",
- "iopub.status.idle": "2024-02-08T14:22:38.288559Z",
- "shell.execute_reply": "2024-02-08T14:22:38.288112Z"
+ "iopub.execute_input": "2024-02-09T00:10:28.422231Z",
+ "iopub.status.busy": "2024-02-09T00:10:28.421872Z",
+ "iopub.status.idle": "2024-02-09T00:10:28.451318Z",
+ "shell.execute_reply": "2024-02-09T00:10:28.450734Z"
}
},
"outputs": [],
@@ -699,10 +699,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:38.290742Z",
- "iopub.status.busy": "2024-02-08T14:22:38.290405Z",
- "iopub.status.idle": "2024-02-08T14:22:38.316321Z",
- "shell.execute_reply": "2024-02-08T14:22:38.315890Z"
+ "iopub.execute_input": "2024-02-09T00:10:28.453712Z",
+ "iopub.status.busy": "2024-02-09T00:10:28.453381Z",
+ "iopub.status.idle": "2024-02-09T00:10:28.480731Z",
+ "shell.execute_reply": "2024-02-09T00:10:28.480138Z"
}
},
"outputs": [],
@@ -739,10 +739,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:38.318387Z",
- "iopub.status.busy": "2024-02-08T14:22:38.318061Z",
- "iopub.status.idle": "2024-02-08T14:22:40.011675Z",
- "shell.execute_reply": "2024-02-08T14:22:40.011111Z"
+ "iopub.execute_input": "2024-02-09T00:10:28.483193Z",
+ "iopub.status.busy": "2024-02-09T00:10:28.482871Z",
+ "iopub.status.idle": "2024-02-09T00:10:30.320484Z",
+ "shell.execute_reply": "2024-02-09T00:10:30.319888Z"
}
},
"outputs": [],
@@ -772,10 +772,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:40.014261Z",
- "iopub.status.busy": "2024-02-08T14:22:40.013826Z",
- "iopub.status.idle": "2024-02-08T14:22:40.020595Z",
- "shell.execute_reply": "2024-02-08T14:22:40.020150Z"
+ "iopub.execute_input": "2024-02-09T00:10:30.323196Z",
+ "iopub.status.busy": "2024-02-09T00:10:30.322686Z",
+ "iopub.status.idle": "2024-02-09T00:10:30.329546Z",
+ "shell.execute_reply": "2024-02-09T00:10:30.329003Z"
},
"scrolled": true
},
@@ -886,10 +886,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:40.022619Z",
- "iopub.status.busy": "2024-02-08T14:22:40.022229Z",
- "iopub.status.idle": "2024-02-08T14:22:40.034693Z",
- "shell.execute_reply": "2024-02-08T14:22:40.034159Z"
+ "iopub.execute_input": "2024-02-09T00:10:30.331614Z",
+ "iopub.status.busy": "2024-02-09T00:10:30.331317Z",
+ "iopub.status.idle": "2024-02-09T00:10:30.344193Z",
+ "shell.execute_reply": "2024-02-09T00:10:30.343609Z"
}
},
"outputs": [
@@ -1139,10 +1139,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:40.036651Z",
- "iopub.status.busy": "2024-02-08T14:22:40.036341Z",
- "iopub.status.idle": "2024-02-08T14:22:40.042447Z",
- "shell.execute_reply": "2024-02-08T14:22:40.041936Z"
+ "iopub.execute_input": "2024-02-09T00:10:30.346405Z",
+ "iopub.status.busy": "2024-02-09T00:10:30.346066Z",
+ "iopub.status.idle": "2024-02-09T00:10:30.352708Z",
+ "shell.execute_reply": "2024-02-09T00:10:30.352266Z"
},
"scrolled": true
},
@@ -1316,10 +1316,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:40.044583Z",
- "iopub.status.busy": "2024-02-08T14:22:40.044256Z",
- "iopub.status.idle": "2024-02-08T14:22:40.046874Z",
- "shell.execute_reply": "2024-02-08T14:22:40.046436Z"
+ "iopub.execute_input": "2024-02-09T00:10:30.354960Z",
+ "iopub.status.busy": "2024-02-09T00:10:30.354523Z",
+ "iopub.status.idle": "2024-02-09T00:10:30.357309Z",
+ "shell.execute_reply": "2024-02-09T00:10:30.356859Z"
}
},
"outputs": [],
@@ -1341,10 +1341,10 @@
"id": "cdf061df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:40.048827Z",
- "iopub.status.busy": "2024-02-08T14:22:40.048500Z",
- "iopub.status.idle": "2024-02-08T14:22:40.051992Z",
- "shell.execute_reply": "2024-02-08T14:22:40.051548Z"
+ "iopub.execute_input": "2024-02-09T00:10:30.359377Z",
+ "iopub.status.busy": "2024-02-09T00:10:30.359024Z",
+ "iopub.status.idle": "2024-02-09T00:10:30.362816Z",
+ "shell.execute_reply": "2024-02-09T00:10:30.362347Z"
},
"scrolled": true
},
@@ -1396,10 +1396,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:40.054144Z",
- "iopub.status.busy": "2024-02-08T14:22:40.053644Z",
- "iopub.status.idle": "2024-02-08T14:22:40.056546Z",
- "shell.execute_reply": "2024-02-08T14:22:40.056015Z"
+ "iopub.execute_input": "2024-02-09T00:10:30.364884Z",
+ "iopub.status.busy": "2024-02-09T00:10:30.364563Z",
+ "iopub.status.idle": "2024-02-09T00:10:30.367231Z",
+ "shell.execute_reply": "2024-02-09T00:10:30.366785Z"
}
},
"outputs": [],
@@ -1423,10 +1423,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:40.058485Z",
- "iopub.status.busy": "2024-02-08T14:22:40.058181Z",
- "iopub.status.idle": "2024-02-08T14:22:40.062342Z",
- "shell.execute_reply": "2024-02-08T14:22:40.061826Z"
+ "iopub.execute_input": "2024-02-09T00:10:30.369182Z",
+ "iopub.status.busy": "2024-02-09T00:10:30.368864Z",
+ "iopub.status.idle": "2024-02-09T00:10:30.373012Z",
+ "shell.execute_reply": "2024-02-09T00:10:30.372462Z"
}
},
"outputs": [
@@ -1481,10 +1481,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:40.064274Z",
- "iopub.status.busy": "2024-02-08T14:22:40.064099Z",
- "iopub.status.idle": "2024-02-08T14:22:40.092636Z",
- "shell.execute_reply": "2024-02-08T14:22:40.092205Z"
+ "iopub.execute_input": "2024-02-09T00:10:30.375068Z",
+ "iopub.status.busy": "2024-02-09T00:10:30.374761Z",
+ "iopub.status.idle": "2024-02-09T00:10:30.404880Z",
+ "shell.execute_reply": "2024-02-09T00:10:30.404262Z"
}
},
"outputs": [],
@@ -1527,10 +1527,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:40.094570Z",
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- "shell.execute_reply": "2024-02-08T14:22:40.098593Z"
+ "iopub.execute_input": "2024-02-09T00:10:30.407575Z",
+ "iopub.status.busy": "2024-02-09T00:10:30.407231Z",
+ "iopub.status.idle": "2024-02-09T00:10:30.411944Z",
+ "shell.execute_reply": "2024-02-09T00:10:30.411495Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
index 2aec9ad39..91736dacc 100644
--- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
@@ -64,10 +64,10 @@
"id": "7383d024-8273-4039-bccd-aab3020d331f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:42.879065Z",
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- "shell.execute_reply": "2024-02-08T14:22:43.956173Z"
+ "iopub.execute_input": "2024-02-09T00:10:33.308307Z",
+ "iopub.status.busy": "2024-02-09T00:10:33.307827Z",
+ "iopub.status.idle": "2024-02-09T00:10:34.442106Z",
+ "shell.execute_reply": "2024-02-09T00:10:34.441479Z"
},
"nbsphinx": "hidden"
},
@@ -79,7 +79,7 @@
"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -105,10 +105,10 @@
"id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:43.959425Z",
- "iopub.status.busy": "2024-02-08T14:22:43.959012Z",
- "iopub.status.idle": "2024-02-08T14:22:44.149841Z",
- "shell.execute_reply": "2024-02-08T14:22:44.149246Z"
+ "iopub.execute_input": "2024-02-09T00:10:34.444758Z",
+ "iopub.status.busy": "2024-02-09T00:10:34.444473Z",
+ "iopub.status.idle": "2024-02-09T00:10:34.641736Z",
+ "shell.execute_reply": "2024-02-09T00:10:34.641118Z"
}
},
"outputs": [],
@@ -268,10 +268,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:44.152446Z",
- "iopub.status.busy": "2024-02-08T14:22:44.152166Z",
- "iopub.status.idle": "2024-02-08T14:22:44.165110Z",
- "shell.execute_reply": "2024-02-08T14:22:44.164687Z"
+ "iopub.execute_input": "2024-02-09T00:10:34.644581Z",
+ "iopub.status.busy": "2024-02-09T00:10:34.644170Z",
+ "iopub.status.idle": "2024-02-09T00:10:34.657385Z",
+ "shell.execute_reply": "2024-02-09T00:10:34.656797Z"
},
"nbsphinx": "hidden"
},
@@ -407,10 +407,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:44.166862Z",
- "iopub.status.busy": "2024-02-08T14:22:44.166690Z",
- "iopub.status.idle": "2024-02-08T14:22:46.782126Z",
- "shell.execute_reply": "2024-02-08T14:22:46.781621Z"
+ "iopub.execute_input": "2024-02-09T00:10:34.659552Z",
+ "iopub.status.busy": "2024-02-09T00:10:34.659156Z",
+ "iopub.status.idle": "2024-02-09T00:10:37.284148Z",
+ "shell.execute_reply": "2024-02-09T00:10:37.283542Z"
}
},
"outputs": [
@@ -452,10 +452,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:46.784533Z",
- "iopub.status.busy": "2024-02-08T14:22:46.784058Z",
- "iopub.status.idle": "2024-02-08T14:22:48.123242Z",
- "shell.execute_reply": "2024-02-08T14:22:48.122619Z"
+ "iopub.execute_input": "2024-02-09T00:10:37.286421Z",
+ "iopub.status.busy": "2024-02-09T00:10:37.286074Z",
+ "iopub.status.idle": "2024-02-09T00:10:38.633346Z",
+ "shell.execute_reply": "2024-02-09T00:10:38.632707Z"
}
},
"outputs": [],
@@ -497,10 +497,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:48.126271Z",
- "iopub.status.busy": "2024-02-08T14:22:48.125802Z",
- "iopub.status.idle": "2024-02-08T14:22:48.129852Z",
- "shell.execute_reply": "2024-02-08T14:22:48.129404Z"
+ "iopub.execute_input": "2024-02-09T00:10:38.636078Z",
+ "iopub.status.busy": "2024-02-09T00:10:38.635829Z",
+ "iopub.status.idle": "2024-02-09T00:10:38.639903Z",
+ "shell.execute_reply": "2024-02-09T00:10:38.639367Z"
}
},
"outputs": [
@@ -542,10 +542,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:48.131671Z",
- "iopub.status.busy": "2024-02-08T14:22:48.131502Z",
- "iopub.status.idle": "2024-02-08T14:22:49.821407Z",
- "shell.execute_reply": "2024-02-08T14:22:49.820801Z"
+ "iopub.execute_input": "2024-02-09T00:10:38.641841Z",
+ "iopub.status.busy": "2024-02-09T00:10:38.641661Z",
+ "iopub.status.idle": "2024-02-09T00:10:40.378171Z",
+ "shell.execute_reply": "2024-02-09T00:10:40.377507Z"
}
},
"outputs": [
@@ -592,10 +592,10 @@
"id": "c18dd83b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:49.824131Z",
- "iopub.status.busy": "2024-02-08T14:22:49.823387Z",
- "iopub.status.idle": "2024-02-08T14:22:49.830952Z",
- "shell.execute_reply": "2024-02-08T14:22:49.830491Z"
+ "iopub.execute_input": "2024-02-09T00:10:40.381072Z",
+ "iopub.status.busy": "2024-02-09T00:10:40.380314Z",
+ "iopub.status.idle": "2024-02-09T00:10:40.569151Z",
+ "shell.execute_reply": "2024-02-09T00:10:40.568545Z"
}
},
"outputs": [
@@ -631,10 +631,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:49.833119Z",
- "iopub.status.busy": "2024-02-08T14:22:49.832696Z",
- "iopub.status.idle": "2024-02-08T14:22:52.366638Z",
- "shell.execute_reply": "2024-02-08T14:22:52.366053Z"
+ "iopub.execute_input": "2024-02-09T00:10:40.571499Z",
+ "iopub.status.busy": "2024-02-09T00:10:40.571107Z",
+ "iopub.status.idle": "2024-02-09T00:10:43.163241Z",
+ "shell.execute_reply": "2024-02-09T00:10:43.162614Z"
}
},
"outputs": [
@@ -669,10 +669,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:52.368851Z",
- "iopub.status.busy": "2024-02-08T14:22:52.368522Z",
- "iopub.status.idle": "2024-02-08T14:22:52.371773Z",
- "shell.execute_reply": "2024-02-08T14:22:52.371250Z"
+ "iopub.execute_input": "2024-02-09T00:10:43.165634Z",
+ "iopub.status.busy": "2024-02-09T00:10:43.165250Z",
+ "iopub.status.idle": "2024-02-09T00:10:43.169030Z",
+ "shell.execute_reply": "2024-02-09T00:10:43.168555Z"
}
},
"outputs": [
@@ -717,10 +717,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:52.373800Z",
- "iopub.status.busy": "2024-02-08T14:22:52.373517Z",
- "iopub.status.idle": "2024-02-08T14:22:52.377287Z",
- "shell.execute_reply": "2024-02-08T14:22:52.376879Z"
+ "iopub.execute_input": "2024-02-09T00:10:43.171166Z",
+ "iopub.status.busy": "2024-02-09T00:10:43.170830Z",
+ "iopub.status.idle": "2024-02-09T00:10:43.175024Z",
+ "shell.execute_reply": "2024-02-09T00:10:43.174559Z"
}
},
"outputs": [],
@@ -743,10 +743,10 @@
"id": "d1a2c008",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:52.379296Z",
- "iopub.status.busy": "2024-02-08T14:22:52.378971Z",
- "iopub.status.idle": "2024-02-08T14:22:52.381834Z",
- "shell.execute_reply": "2024-02-08T14:22:52.381413Z"
+ "iopub.execute_input": "2024-02-09T00:10:43.177052Z",
+ "iopub.status.busy": "2024-02-09T00:10:43.176724Z",
+ "iopub.status.idle": "2024-02-09T00:10:43.179767Z",
+ "shell.execute_reply": "2024-02-09T00:10:43.179312Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index 1c1d3591c..13ae402e5 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": "2024-02-08T14:22:54.789790Z",
- "iopub.status.busy": "2024-02-08T14:22:54.789619Z",
- "iopub.status.idle": "2024-02-08T14:22:55.867460Z",
- "shell.execute_reply": "2024-02-08T14:22:55.866922Z"
+ "iopub.execute_input": "2024-02-09T00:10:45.803290Z",
+ "iopub.status.busy": "2024-02-09T00:10:45.803124Z",
+ "iopub.status.idle": "2024-02-09T00:10:46.944656Z",
+ "shell.execute_reply": "2024-02-09T00:10:46.944065Z"
},
"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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": "2024-02-08T14:22:55.870105Z",
- "iopub.status.busy": "2024-02-08T14:22:55.869576Z",
- "iopub.status.idle": "2024-02-08T14:22:58.466883Z",
- "shell.execute_reply": "2024-02-08T14:22:58.466207Z"
+ "iopub.execute_input": "2024-02-09T00:10:46.947693Z",
+ "iopub.status.busy": "2024-02-09T00:10:46.947073Z",
+ "iopub.status.idle": "2024-02-09T00:10:49.613006Z",
+ "shell.execute_reply": "2024-02-09T00:10:49.612291Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:58.469267Z",
- "iopub.status.busy": "2024-02-08T14:22:58.469069Z",
- "iopub.status.idle": "2024-02-08T14:22:58.472238Z",
- "shell.execute_reply": "2024-02-08T14:22:58.471790Z"
+ "iopub.execute_input": "2024-02-09T00:10:49.615627Z",
+ "iopub.status.busy": "2024-02-09T00:10:49.615396Z",
+ "iopub.status.idle": "2024-02-09T00:10:49.618657Z",
+ "shell.execute_reply": "2024-02-09T00:10:49.618200Z"
}
},
"outputs": [],
@@ -169,10 +169,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:58.474254Z",
- "iopub.status.busy": "2024-02-08T14:22:58.473928Z",
- "iopub.status.idle": "2024-02-08T14:22:58.480218Z",
- "shell.execute_reply": "2024-02-08T14:22:58.479806Z"
+ "iopub.execute_input": "2024-02-09T00:10:49.620834Z",
+ "iopub.status.busy": "2024-02-09T00:10:49.620497Z",
+ "iopub.status.idle": "2024-02-09T00:10:49.627030Z",
+ "shell.execute_reply": "2024-02-09T00:10:49.626551Z"
}
},
"outputs": [],
@@ -198,10 +198,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:58.482113Z",
- "iopub.status.busy": "2024-02-08T14:22:58.481933Z",
- "iopub.status.idle": "2024-02-08T14:22:58.980033Z",
- "shell.execute_reply": "2024-02-08T14:22:58.979409Z"
+ "iopub.execute_input": "2024-02-09T00:10:49.629211Z",
+ "iopub.status.busy": "2024-02-09T00:10:49.628864Z",
+ "iopub.status.idle": "2024-02-09T00:10:50.124582Z",
+ "shell.execute_reply": "2024-02-09T00:10:50.123953Z"
},
"scrolled": true
},
@@ -242,10 +242,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:58.982550Z",
- "iopub.status.busy": "2024-02-08T14:22:58.982360Z",
- "iopub.status.idle": "2024-02-08T14:22:58.987621Z",
- "shell.execute_reply": "2024-02-08T14:22:58.987174Z"
+ "iopub.execute_input": "2024-02-09T00:10:50.127122Z",
+ "iopub.status.busy": "2024-02-09T00:10:50.126706Z",
+ "iopub.status.idle": "2024-02-09T00:10:50.132043Z",
+ "shell.execute_reply": "2024-02-09T00:10:50.131509Z"
}
},
"outputs": [
@@ -497,10 +497,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:58.989592Z",
- "iopub.status.busy": "2024-02-08T14:22:58.989301Z",
- "iopub.status.idle": "2024-02-08T14:22:58.993229Z",
- "shell.execute_reply": "2024-02-08T14:22:58.992795Z"
+ "iopub.execute_input": "2024-02-09T00:10:50.134295Z",
+ "iopub.status.busy": "2024-02-09T00:10:50.133966Z",
+ "iopub.status.idle": "2024-02-09T00:10:50.138010Z",
+ "shell.execute_reply": "2024-02-09T00:10:50.137484Z"
}
},
"outputs": [
@@ -557,10 +557,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:58.995032Z",
- "iopub.status.busy": "2024-02-08T14:22:58.994841Z",
- "iopub.status.idle": "2024-02-08T14:22:59.634808Z",
- "shell.execute_reply": "2024-02-08T14:22:59.634169Z"
+ "iopub.execute_input": "2024-02-09T00:10:50.140242Z",
+ "iopub.status.busy": "2024-02-09T00:10:50.139940Z",
+ "iopub.status.idle": "2024-02-09T00:10:50.845235Z",
+ "shell.execute_reply": "2024-02-09T00:10:50.844562Z"
}
},
"outputs": [
@@ -616,10 +616,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:59.637152Z",
- "iopub.status.busy": "2024-02-08T14:22:59.636724Z",
- "iopub.status.idle": "2024-02-08T14:22:59.837142Z",
- "shell.execute_reply": "2024-02-08T14:22:59.836698Z"
+ "iopub.execute_input": "2024-02-09T00:10:50.847495Z",
+ "iopub.status.busy": "2024-02-09T00:10:50.847286Z",
+ "iopub.status.idle": "2024-02-09T00:10:51.019140Z",
+ "shell.execute_reply": "2024-02-09T00:10:51.018538Z"
}
},
"outputs": [
@@ -660,10 +660,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:59.839101Z",
- "iopub.status.busy": "2024-02-08T14:22:59.838788Z",
- "iopub.status.idle": "2024-02-08T14:22:59.842850Z",
- "shell.execute_reply": "2024-02-08T14:22:59.842340Z"
+ "iopub.execute_input": "2024-02-09T00:10:51.021572Z",
+ "iopub.status.busy": "2024-02-09T00:10:51.021101Z",
+ "iopub.status.idle": "2024-02-09T00:10:51.025473Z",
+ "shell.execute_reply": "2024-02-09T00:10:51.025037Z"
}
},
"outputs": [
@@ -700,10 +700,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:22:59.844707Z",
- "iopub.status.busy": "2024-02-08T14:22:59.844529Z",
- "iopub.status.idle": "2024-02-08T14:23:00.296156Z",
- "shell.execute_reply": "2024-02-08T14:23:00.295577Z"
+ "iopub.execute_input": "2024-02-09T00:10:51.027519Z",
+ "iopub.status.busy": "2024-02-09T00:10:51.027343Z",
+ "iopub.status.idle": "2024-02-09T00:10:51.498859Z",
+ "shell.execute_reply": "2024-02-09T00:10:51.498219Z"
}
},
"outputs": [
@@ -762,10 +762,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:00.298830Z",
- "iopub.status.busy": "2024-02-08T14:23:00.298610Z",
- "iopub.status.idle": "2024-02-08T14:23:00.628065Z",
- "shell.execute_reply": "2024-02-08T14:23:00.627518Z"
+ "iopub.execute_input": "2024-02-09T00:10:51.501949Z",
+ "iopub.status.busy": "2024-02-09T00:10:51.501565Z",
+ "iopub.status.idle": "2024-02-09T00:10:51.840251Z",
+ "shell.execute_reply": "2024-02-09T00:10:51.839663Z"
}
},
"outputs": [
@@ -812,10 +812,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:00.630528Z",
- "iopub.status.busy": "2024-02-08T14:23:00.630315Z",
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- "shell.execute_reply": "2024-02-08T14:23:00.987117Z"
+ "iopub.execute_input": "2024-02-09T00:10:51.843343Z",
+ "iopub.status.busy": "2024-02-09T00:10:51.842973Z",
+ "iopub.status.idle": "2024-02-09T00:10:52.183410Z",
+ "shell.execute_reply": "2024-02-09T00:10:52.182752Z"
}
},
"outputs": [
@@ -862,10 +862,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:00.990737Z",
- "iopub.status.busy": "2024-02-08T14:23:00.990515Z",
- "iopub.status.idle": "2024-02-08T14:23:01.425886Z",
- "shell.execute_reply": "2024-02-08T14:23:01.425318Z"
+ "iopub.execute_input": "2024-02-09T00:10:52.187667Z",
+ "iopub.status.busy": "2024-02-09T00:10:52.187252Z",
+ "iopub.status.idle": "2024-02-09T00:10:52.635281Z",
+ "shell.execute_reply": "2024-02-09T00:10:52.634587Z"
}
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"outputs": [
@@ -925,10 +925,10 @@
"id": "7e770d23",
"metadata": {
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- "shell.execute_reply": "2024-02-08T14:23:01.846266Z"
+ "iopub.execute_input": "2024-02-09T00:10:52.639934Z",
+ "iopub.status.busy": "2024-02-09T00:10:52.639416Z",
+ "iopub.status.idle": "2024-02-09T00:10:53.070984Z",
+ "shell.execute_reply": "2024-02-09T00:10:53.070367Z"
}
},
"outputs": [
@@ -971,10 +971,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:01.850069Z",
- "iopub.status.busy": "2024-02-08T14:23:01.849692Z",
- "iopub.status.idle": "2024-02-08T14:23:02.041760Z",
- "shell.execute_reply": "2024-02-08T14:23:02.041181Z"
+ "iopub.execute_input": "2024-02-09T00:10:53.074513Z",
+ "iopub.status.busy": "2024-02-09T00:10:53.074005Z",
+ "iopub.status.idle": "2024-02-09T00:10:53.292324Z",
+ "shell.execute_reply": "2024-02-09T00:10:53.291725Z"
}
},
"outputs": [
@@ -1017,10 +1017,10 @@
"id": "0302818a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:02.044327Z",
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- "iopub.status.idle": "2024-02-08T14:23:02.225719Z",
- "shell.execute_reply": "2024-02-08T14:23:02.225171Z"
+ "iopub.execute_input": "2024-02-09T00:10:53.294783Z",
+ "iopub.status.busy": "2024-02-09T00:10:53.294392Z",
+ "iopub.status.idle": "2024-02-09T00:10:53.496903Z",
+ "shell.execute_reply": "2024-02-09T00:10:53.496329Z"
}
},
"outputs": [
@@ -1067,10 +1067,10 @@
"id": "5cacec81-2adf-46a8-82c5-7ec0185d4356",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:02.228455Z",
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- "iopub.status.idle": "2024-02-08T14:23:02.230923Z",
- "shell.execute_reply": "2024-02-08T14:23:02.230502Z"
+ "iopub.execute_input": "2024-02-09T00:10:53.499564Z",
+ "iopub.status.busy": "2024-02-09T00:10:53.499191Z",
+ "iopub.status.idle": "2024-02-09T00:10:53.502253Z",
+ "shell.execute_reply": "2024-02-09T00:10:53.501783Z"
}
},
"outputs": [],
@@ -1090,10 +1090,10 @@
"id": "3335b8a3-d0b4-415a-a97d-c203088a124e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:02.232668Z",
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- "iopub.status.idle": "2024-02-08T14:23:03.182630Z",
- "shell.execute_reply": "2024-02-08T14:23:03.182052Z"
+ "iopub.execute_input": "2024-02-09T00:10:53.504217Z",
+ "iopub.status.busy": "2024-02-09T00:10:53.503942Z",
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+ "shell.execute_reply": "2024-02-09T00:10:54.485393Z"
}
},
"outputs": [
@@ -1172,10 +1172,10 @@
"id": "9d4b7677-6ebd-447d-b0a1-76e094686628",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:03.185707Z",
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- "iopub.status.idle": "2024-02-08T14:23:03.375924Z",
- "shell.execute_reply": "2024-02-08T14:23:03.375383Z"
+ "iopub.execute_input": "2024-02-09T00:10:54.489059Z",
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+ "shell.execute_reply": "2024-02-09T00:10:54.604552Z"
}
},
"outputs": [
@@ -1214,10 +1214,10 @@
"id": "59d7ee39-3785-434b-8680-9133014851cd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:03.377942Z",
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- "shell.execute_reply": "2024-02-08T14:23:03.543484Z"
+ "iopub.execute_input": "2024-02-09T00:10:54.607408Z",
+ "iopub.status.busy": "2024-02-09T00:10:54.607051Z",
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+ "shell.execute_reply": "2024-02-09T00:10:54.729966Z"
}
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"outputs": [],
@@ -1266,10 +1266,10 @@
"id": "47b6a8ff-7a58-4a1f-baee-e6cfe7a85a6d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:03.546439Z",
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- "shell.execute_reply": "2024-02-08T14:23:04.283532Z"
+ "iopub.execute_input": "2024-02-09T00:10:54.732934Z",
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+ "shell.execute_reply": "2024-02-09T00:10:55.523378Z"
}
},
"outputs": [
@@ -1351,10 +1351,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:04.286279Z",
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- "iopub.status.idle": "2024-02-08T14:23:04.289390Z",
- "shell.execute_reply": "2024-02-08T14:23:04.288896Z"
+ "iopub.execute_input": "2024-02-09T00:10:55.526524Z",
+ "iopub.status.busy": "2024-02-09T00:10:55.526072Z",
+ "iopub.status.idle": "2024-02-09T00:10:55.529946Z",
+ "shell.execute_reply": "2024-02-09T00:10:55.529481Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index 374b8fcfb..f2b0a4460 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": "2024-02-08T14:23:06.345281Z",
- "iopub.status.busy": "2024-02-08T14:23:06.345109Z",
- "iopub.status.idle": "2024-02-08T14:23:08.942692Z",
- "shell.execute_reply": "2024-02-08T14:23:08.942154Z"
+ "iopub.execute_input": "2024-02-09T00:10:58.031021Z",
+ "iopub.status.busy": "2024-02-09T00:10:58.030844Z",
+ "iopub.status.idle": "2024-02-09T00:11:00.830378Z",
+ "shell.execute_reply": "2024-02-09T00:11:00.829767Z"
},
"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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": "2024-02-08T14:23:08.945321Z",
- "iopub.status.busy": "2024-02-08T14:23:08.944923Z",
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- "shell.execute_reply": "2024-02-08T14:23:09.258358Z"
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+ "shell.execute_reply": "2024-02-09T00:11:01.180405Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:23:09.261422Z",
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- "shell.execute_reply": "2024-02-08T14:23:09.264561Z"
+ "iopub.execute_input": "2024-02-09T00:11:01.183715Z",
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+ "shell.execute_reply": "2024-02-09T00:11:01.187308Z"
},
"nbsphinx": "hidden"
},
@@ -225,10 +225,10 @@
"id": "fd853a54",
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- "iopub.execute_input": "2024-02-08T14:23:09.266785Z",
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- "shell.execute_reply": "2024-02-08T14:23:16.912219Z"
+ "iopub.execute_input": "2024-02-09T00:11:01.189740Z",
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+ "iopub.status.idle": "2024-02-09T00:11:08.466862Z",
+ "shell.execute_reply": "2024-02-09T00:11:08.466311Z"
}
},
"outputs": [
@@ -252,7 +252,7 @@
"output_type": "stream",
"text": [
"\r",
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+ " 0%| | 32768/170498071 [00:00<10:19, 274980.75it/s]"
]
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{
@@ -260,7 +260,7 @@
"output_type": "stream",
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+ " 0%| | 196608/170498071 [00:00<03:07, 910597.90it/s]"
]
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{
@@ -268,7 +268,7 @@
"output_type": "stream",
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+ " 0%| | 819200/170498071 [00:00<00:59, 2841595.62it/s]"
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+ " 2%|▏ | 2981888/170498071 [00:00<00:19, 8760311.78it/s]"
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+ " 5%|▌ | 8683520/170498071 [00:00<00:06, 23520557.70it/s]"
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+ " 8%|▊ | 13959168/170498071 [00:00<00:04, 32488324.14it/s]"
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+ " 11%|█ | 18546688/170498071 [00:00<00:04, 36492412.27it/s]"
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+ " 38%|███▊ | 64356352/170498071 [00:01<00:02, 48327134.04it/s]"
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{
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@@ -396,7 +396,7 @@
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@@ -412,7 +412,7 @@
"output_type": "stream",
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diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index 6c12e2de3..6e74c8790 100644
--- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
@@ -94,10 +94,10 @@
"id": "2e1af7d8",
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},
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},
@@ -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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -135,10 +135,10 @@
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@@ -157,10 +157,10 @@
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@@ -191,10 +191,10 @@
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- "shell.execute_reply": "2024-02-08T14:23:52.181254Z"
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+ "shell.execute_reply": "2024-02-09T00:11:43.650721Z"
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@@ -367,10 +367,10 @@
"id": "55513fed",
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@@ -410,10 +410,10 @@
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@@ -449,10 +449,10 @@
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@@ -470,10 +470,10 @@
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@@ -520,10 +520,10 @@
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@@ -538,10 +538,10 @@
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@@ -565,10 +565,10 @@
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@@ -671,10 +671,10 @@
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@@ -689,10 +689,10 @@
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@@ -727,10 +727,10 @@
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@@ -749,10 +749,10 @@
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@@ -894,10 +894,10 @@
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@@ -936,10 +936,10 @@
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@@ -995,10 +995,10 @@
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@@ -1014,10 +1014,10 @@
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@@ -1055,10 +1055,10 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index 6d37e6190..932d1d202 100644
--- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
@@ -61,10 +61,10 @@
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- "shell.execute_reply": "2024-02-08T14:24:06.263981Z"
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+ "shell.execute_reply": "2024-02-09T00:11:58.408524Z"
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"outputs": [],
@@ -79,10 +79,10 @@
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"outputs": [],
@@ -97,10 +97,10 @@
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- "iopub.status.busy": "2024-02-08T14:25:00.952671Z",
- "iopub.status.idle": "2024-02-08T14:25:01.985876Z",
- "shell.execute_reply": "2024-02-08T14:25:01.985324Z"
+ "iopub.execute_input": "2024-02-09T00:16:12.523122Z",
+ "iopub.status.busy": "2024-02-09T00:16:12.522932Z",
+ "iopub.status.idle": "2024-02-09T00:16:13.561109Z",
+ "shell.execute_reply": "2024-02-09T00:16:13.560569Z"
},
"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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": "2024-02-08T14:25:01.988452Z",
- "iopub.status.busy": "2024-02-08T14:25:01.987961Z",
- "iopub.status.idle": "2024-02-08T14:25:01.991234Z",
- "shell.execute_reply": "2024-02-08T14:25:01.990702Z"
+ "iopub.execute_input": "2024-02-09T00:16:13.563706Z",
+ "iopub.status.busy": "2024-02-09T00:16:13.563270Z",
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+ "shell.execute_reply": "2024-02-09T00:16:13.566044Z"
}
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"outputs": [],
@@ -203,10 +203,10 @@
"id": "07dc5678",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:25:01.993300Z",
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- "shell.execute_reply": "2024-02-08T14:25:01.996404Z"
+ "iopub.execute_input": "2024-02-09T00:16:13.568560Z",
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+ "shell.execute_reply": "2024-02-09T00:16:13.571602Z"
}
},
"outputs": [
@@ -247,10 +247,10 @@
"id": "25ebe22a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:25:01.999046Z",
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- "iopub.status.idle": "2024-02-08T14:25:02.002246Z",
- "shell.execute_reply": "2024-02-08T14:25:02.001745Z"
+ "iopub.execute_input": "2024-02-09T00:16:13.573969Z",
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+ "shell.execute_reply": "2024-02-09T00:16:13.576801Z"
}
},
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@@ -290,10 +290,10 @@
"id": "3faedea9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:25:02.004444Z",
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- "iopub.status.idle": "2024-02-08T14:25:02.006724Z",
- "shell.execute_reply": "2024-02-08T14:25:02.006292Z"
+ "iopub.execute_input": "2024-02-09T00:16:13.579148Z",
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+ "shell.execute_reply": "2024-02-09T00:16:13.581134Z"
}
},
"outputs": [],
@@ -333,17 +333,17 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:25:02.008777Z",
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- "iopub.status.idle": "2024-02-08T14:26:17.950141Z",
- "shell.execute_reply": "2024-02-08T14:26:17.949536Z"
+ "iopub.execute_input": "2024-02-09T00:16:13.583565Z",
+ "iopub.status.busy": "2024-02-09T00:16:13.583250Z",
+ "iopub.status.idle": "2024-02-09T00:17:29.534773Z",
+ "shell.execute_reply": "2024-02-09T00:17:29.534063Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "f94cde7b85b8462293dd9990e3c52464",
+ "model_id": "b5fe880c9c944d13a507143c65c6969e",
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"version_minor": 0
},
@@ -357,7 +357,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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+ "model_id": "436bcafa5cf8491c9ab4a7016809e293",
"version_major": 2,
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@@ -400,10 +400,10 @@
"id": "95dc7268",
"metadata": {
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- "shell.execute_reply": "2024-02-08T14:26:18.607257Z"
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+ "shell.execute_reply": "2024-02-09T00:17:30.196213Z"
}
},
"outputs": [
@@ -446,10 +446,10 @@
"id": "57fed473",
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-02-08T14:26:21.286905Z"
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}
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@@ -519,10 +519,10 @@
"id": "e4a006bd",
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}
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@@ -539,7 +539,7 @@
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diff --git a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb
index 1b33489a6..0659cb517 100644
--- a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb
@@ -112,10 +112,10 @@
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@@ -125,7 +125,7 @@
"dependencies = [\"cleanlab\"]\n",
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"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
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- "shell.execute_reply": "2024-02-08T14:27:25.104546Z"
+ "iopub.execute_input": "2024-02-09T00:18:35.877027Z",
+ "iopub.status.busy": "2024-02-09T00:18:35.876667Z",
+ "iopub.status.idle": "2024-02-09T00:18:35.990307Z",
+ "shell.execute_reply": "2024-02-09T00:18:35.989781Z"
}
},
"outputs": [
@@ -304,10 +304,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:25.107096Z",
- "iopub.status.busy": "2024-02-08T14:27:25.106688Z",
- "iopub.status.idle": "2024-02-08T14:27:25.110892Z",
- "shell.execute_reply": "2024-02-08T14:27:25.110370Z"
+ "iopub.execute_input": "2024-02-09T00:18:35.992500Z",
+ "iopub.status.busy": "2024-02-09T00:18:35.992081Z",
+ "iopub.status.idle": "2024-02-09T00:18:35.995641Z",
+ "shell.execute_reply": "2024-02-09T00:18:35.995109Z"
}
},
"outputs": [],
@@ -328,10 +328,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:25.112912Z",
- "iopub.status.busy": "2024-02-08T14:27:25.112535Z",
- "iopub.status.idle": "2024-02-08T14:27:25.120751Z",
- "shell.execute_reply": "2024-02-08T14:27:25.120347Z"
+ "iopub.execute_input": "2024-02-09T00:18:35.997629Z",
+ "iopub.status.busy": "2024-02-09T00:18:35.997299Z",
+ "iopub.status.idle": "2024-02-09T00:18:36.005226Z",
+ "shell.execute_reply": "2024-02-09T00:18:36.004712Z"
}
},
"outputs": [],
@@ -383,10 +383,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:25.122791Z",
- "iopub.status.busy": "2024-02-08T14:27:25.122459Z",
- "iopub.status.idle": "2024-02-08T14:27:25.124896Z",
- "shell.execute_reply": "2024-02-08T14:27:25.124469Z"
+ "iopub.execute_input": "2024-02-09T00:18:36.007559Z",
+ "iopub.status.busy": "2024-02-09T00:18:36.007251Z",
+ "iopub.status.idle": "2024-02-09T00:18:36.009871Z",
+ "shell.execute_reply": "2024-02-09T00:18:36.009320Z"
}
},
"outputs": [],
@@ -408,10 +408,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:25.126805Z",
- "iopub.status.busy": "2024-02-08T14:27:25.126493Z",
- "iopub.status.idle": "2024-02-08T14:27:25.642785Z",
- "shell.execute_reply": "2024-02-08T14:27:25.642180Z"
+ "iopub.execute_input": "2024-02-09T00:18:36.011745Z",
+ "iopub.status.busy": "2024-02-09T00:18:36.011570Z",
+ "iopub.status.idle": "2024-02-09T00:18:36.533177Z",
+ "shell.execute_reply": "2024-02-09T00:18:36.532558Z"
}
},
"outputs": [],
@@ -445,10 +445,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:25.645257Z",
- "iopub.status.busy": "2024-02-08T14:27:25.645076Z",
- "iopub.status.idle": "2024-02-08T14:27:27.286919Z",
- "shell.execute_reply": "2024-02-08T14:27:27.286290Z"
+ "iopub.execute_input": "2024-02-09T00:18:36.535697Z",
+ "iopub.status.busy": "2024-02-09T00:18:36.535500Z",
+ "iopub.status.idle": "2024-02-09T00:18:38.139182Z",
+ "shell.execute_reply": "2024-02-09T00:18:38.138532Z"
}
},
"outputs": [
@@ -480,10 +480,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:27.289990Z",
- "iopub.status.busy": "2024-02-08T14:27:27.289045Z",
- "iopub.status.idle": "2024-02-08T14:27:27.299053Z",
- "shell.execute_reply": "2024-02-08T14:27:27.298619Z"
+ "iopub.execute_input": "2024-02-09T00:18:38.141773Z",
+ "iopub.status.busy": "2024-02-09T00:18:38.141256Z",
+ "iopub.status.idle": "2024-02-09T00:18:38.151171Z",
+ "shell.execute_reply": "2024-02-09T00:18:38.150656Z"
}
},
"outputs": [
@@ -604,10 +604,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:27.301278Z",
- "iopub.status.busy": "2024-02-08T14:27:27.300855Z",
- "iopub.status.idle": "2024-02-08T14:27:27.304611Z",
- "shell.execute_reply": "2024-02-08T14:27:27.304194Z"
+ "iopub.execute_input": "2024-02-09T00:18:38.153259Z",
+ "iopub.status.busy": "2024-02-09T00:18:38.152943Z",
+ "iopub.status.idle": "2024-02-09T00:18:38.156723Z",
+ "shell.execute_reply": "2024-02-09T00:18:38.156280Z"
}
},
"outputs": [],
@@ -632,10 +632,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:27.306583Z",
- "iopub.status.busy": "2024-02-08T14:27:27.306265Z",
- "iopub.status.idle": "2024-02-08T14:27:27.313141Z",
- "shell.execute_reply": "2024-02-08T14:27:27.312733Z"
+ "iopub.execute_input": "2024-02-09T00:18:38.158787Z",
+ "iopub.status.busy": "2024-02-09T00:18:38.158338Z",
+ "iopub.status.idle": "2024-02-09T00:18:38.165281Z",
+ "shell.execute_reply": "2024-02-09T00:18:38.164867Z"
}
},
"outputs": [],
@@ -657,10 +657,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:27.315134Z",
- "iopub.status.busy": "2024-02-08T14:27:27.314793Z",
- "iopub.status.idle": "2024-02-08T14:27:27.426264Z",
- "shell.execute_reply": "2024-02-08T14:27:27.425672Z"
+ "iopub.execute_input": "2024-02-09T00:18:38.167378Z",
+ "iopub.status.busy": "2024-02-09T00:18:38.167056Z",
+ "iopub.status.idle": "2024-02-09T00:18:38.277715Z",
+ "shell.execute_reply": "2024-02-09T00:18:38.277132Z"
}
},
"outputs": [
@@ -690,10 +690,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:27.428538Z",
- "iopub.status.busy": "2024-02-08T14:27:27.428170Z",
- "iopub.status.idle": "2024-02-08T14:27:27.431075Z",
- "shell.execute_reply": "2024-02-08T14:27:27.430580Z"
+ "iopub.execute_input": "2024-02-09T00:18:38.280101Z",
+ "iopub.status.busy": "2024-02-09T00:18:38.279720Z",
+ "iopub.status.idle": "2024-02-09T00:18:38.282390Z",
+ "shell.execute_reply": "2024-02-09T00:18:38.281969Z"
}
},
"outputs": [],
@@ -714,10 +714,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:27.433121Z",
- "iopub.status.busy": "2024-02-08T14:27:27.432789Z",
- "iopub.status.idle": "2024-02-08T14:27:29.373969Z",
- "shell.execute_reply": "2024-02-08T14:27:29.373229Z"
+ "iopub.execute_input": "2024-02-09T00:18:38.284337Z",
+ "iopub.status.busy": "2024-02-09T00:18:38.284127Z",
+ "iopub.status.idle": "2024-02-09T00:18:40.250999Z",
+ "shell.execute_reply": "2024-02-09T00:18:40.250341Z"
}
},
"outputs": [],
@@ -737,10 +737,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:29.376990Z",
- "iopub.status.busy": "2024-02-08T14:27:29.376399Z",
- "iopub.status.idle": "2024-02-08T14:27:29.387660Z",
- "shell.execute_reply": "2024-02-08T14:27:29.387089Z"
+ "iopub.execute_input": "2024-02-09T00:18:40.253972Z",
+ "iopub.status.busy": "2024-02-09T00:18:40.253228Z",
+ "iopub.status.idle": "2024-02-09T00:18:40.264455Z",
+ "shell.execute_reply": "2024-02-09T00:18:40.263907Z"
}
},
"outputs": [
@@ -770,10 +770,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:29.389725Z",
- "iopub.status.busy": "2024-02-08T14:27:29.389357Z",
- "iopub.status.idle": "2024-02-08T14:27:29.529033Z",
- "shell.execute_reply": "2024-02-08T14:27:29.528506Z"
+ "iopub.execute_input": "2024-02-09T00:18:40.266531Z",
+ "iopub.status.busy": "2024-02-09T00:18:40.266229Z",
+ "iopub.status.idle": "2024-02-09T00:18:40.286648Z",
+ "shell.execute_reply": "2024-02-09T00:18:40.286215Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/text.ipynb b/master/.doctrees/nbsphinx/tutorials/text.ipynb
index a5b2a02d5..097217444 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": "2024-02-08T14:27:32.204126Z",
- "iopub.status.busy": "2024-02-08T14:27:32.203935Z",
- "iopub.status.idle": "2024-02-08T14:27:34.742594Z",
- "shell.execute_reply": "2024-02-08T14:27:34.741969Z"
+ "iopub.execute_input": "2024-02-09T00:18:43.092031Z",
+ "iopub.status.busy": "2024-02-09T00:18:43.091860Z",
+ "iopub.status.idle": "2024-02-09T00:18:45.719204Z",
+ "shell.execute_reply": "2024-02-09T00:18:45.718566Z"
},
"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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": "2024-02-08T14:27:34.745277Z",
- "iopub.status.busy": "2024-02-08T14:27:34.744930Z",
- "iopub.status.idle": "2024-02-08T14:27:34.748478Z",
- "shell.execute_reply": "2024-02-08T14:27:34.747942Z"
+ "iopub.execute_input": "2024-02-09T00:18:45.721879Z",
+ "iopub.status.busy": "2024-02-09T00:18:45.721579Z",
+ "iopub.status.idle": "2024-02-09T00:18:45.725027Z",
+ "shell.execute_reply": "2024-02-09T00:18:45.724581Z"
}
},
"outputs": [],
@@ -184,10 +184,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:34.750440Z",
- "iopub.status.busy": "2024-02-08T14:27:34.750133Z",
- "iopub.status.idle": "2024-02-08T14:27:34.753219Z",
- "shell.execute_reply": "2024-02-08T14:27:34.752696Z"
+ "iopub.execute_input": "2024-02-09T00:18:45.727032Z",
+ "iopub.status.busy": "2024-02-09T00:18:45.726723Z",
+ "iopub.status.idle": "2024-02-09T00:18:45.729765Z",
+ "shell.execute_reply": "2024-02-09T00:18:45.729325Z"
},
"nbsphinx": "hidden"
},
@@ -218,10 +218,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:34.755230Z",
- "iopub.status.busy": "2024-02-08T14:27:34.754907Z",
- "iopub.status.idle": "2024-02-08T14:27:34.906355Z",
- "shell.execute_reply": "2024-02-08T14:27:34.905931Z"
+ "iopub.execute_input": "2024-02-09T00:18:45.731693Z",
+ "iopub.status.busy": "2024-02-09T00:18:45.731367Z",
+ "iopub.status.idle": "2024-02-09T00:18:45.845990Z",
+ "shell.execute_reply": "2024-02-09T00:18:45.845460Z"
}
},
"outputs": [
@@ -311,10 +311,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:34.908383Z",
- "iopub.status.busy": "2024-02-08T14:27:34.908131Z",
- "iopub.status.idle": "2024-02-08T14:27:34.911634Z",
- "shell.execute_reply": "2024-02-08T14:27:34.911104Z"
+ "iopub.execute_input": "2024-02-09T00:18:45.848136Z",
+ "iopub.status.busy": "2024-02-09T00:18:45.847858Z",
+ "iopub.status.idle": "2024-02-09T00:18:45.851188Z",
+ "shell.execute_reply": "2024-02-09T00:18:45.850729Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:34.913686Z",
- "iopub.status.busy": "2024-02-08T14:27:34.913377Z",
- "iopub.status.idle": "2024-02-08T14:27:34.916837Z",
- "shell.execute_reply": "2024-02-08T14:27:34.916382Z"
+ "iopub.execute_input": "2024-02-09T00:18:45.853196Z",
+ "iopub.status.busy": "2024-02-09T00:18:45.852875Z",
+ "iopub.status.idle": "2024-02-09T00:18:45.856174Z",
+ "shell.execute_reply": "2024-02-09T00:18:45.855631Z"
}
},
"outputs": [
@@ -341,7 +341,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'card_payment_fee_charged', 'getting_spare_card', 'lost_or_stolen_phone', 'beneficiary_not_allowed', 'change_pin', 'visa_or_mastercard', 'card_about_to_expire', 'supported_cards_and_currencies', 'cancel_transfer', 'apple_pay_or_google_pay'}\n"
+ "Classes: {'getting_spare_card', 'cancel_transfer', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'visa_or_mastercard', 'apple_pay_or_google_pay', 'change_pin', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'card_about_to_expire'}\n"
]
}
],
@@ -364,10 +364,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:34.918764Z",
- "iopub.status.busy": "2024-02-08T14:27:34.918447Z",
- "iopub.status.idle": "2024-02-08T14:27:34.921398Z",
- "shell.execute_reply": "2024-02-08T14:27:34.920863Z"
+ "iopub.execute_input": "2024-02-09T00:18:45.858156Z",
+ "iopub.status.busy": "2024-02-09T00:18:45.857843Z",
+ "iopub.status.idle": "2024-02-09T00:18:45.860792Z",
+ "shell.execute_reply": "2024-02-09T00:18:45.860270Z"
}
},
"outputs": [
@@ -408,10 +408,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:34.923447Z",
- "iopub.status.busy": "2024-02-08T14:27:34.923127Z",
- "iopub.status.idle": "2024-02-08T14:27:34.926192Z",
- "shell.execute_reply": "2024-02-08T14:27:34.925770Z"
+ "iopub.execute_input": "2024-02-09T00:18:45.862822Z",
+ "iopub.status.busy": "2024-02-09T00:18:45.862466Z",
+ "iopub.status.idle": "2024-02-09T00:18:45.865679Z",
+ "shell.execute_reply": "2024-02-09T00:18:45.865242Z"
}
},
"outputs": [],
@@ -452,10 +452,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:34.928180Z",
- "iopub.status.busy": "2024-02-08T14:27:34.927859Z",
- "iopub.status.idle": "2024-02-08T14:27:39.131556Z",
- "shell.execute_reply": "2024-02-08T14:27:39.131002Z"
+ "iopub.execute_input": "2024-02-09T00:18:45.867706Z",
+ "iopub.status.busy": "2024-02-09T00:18:45.867392Z",
+ "iopub.status.idle": "2024-02-09T00:18:50.141569Z",
+ "shell.execute_reply": "2024-02-09T00:18:50.141035Z"
}
},
"outputs": [
@@ -510,10 +510,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:39.134249Z",
- "iopub.status.busy": "2024-02-08T14:27:39.133873Z",
- "iopub.status.idle": "2024-02-08T14:27:39.136873Z",
- "shell.execute_reply": "2024-02-08T14:27:39.136390Z"
+ "iopub.execute_input": "2024-02-09T00:18:50.144320Z",
+ "iopub.status.busy": "2024-02-09T00:18:50.143885Z",
+ "iopub.status.idle": "2024-02-09T00:18:50.146801Z",
+ "shell.execute_reply": "2024-02-09T00:18:50.146226Z"
}
},
"outputs": [],
@@ -535,10 +535,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:39.138829Z",
- "iopub.status.busy": "2024-02-08T14:27:39.138502Z",
- "iopub.status.idle": "2024-02-08T14:27:39.141136Z",
- "shell.execute_reply": "2024-02-08T14:27:39.140702Z"
+ "iopub.execute_input": "2024-02-09T00:18:50.148918Z",
+ "iopub.status.busy": "2024-02-09T00:18:50.148575Z",
+ "iopub.status.idle": "2024-02-09T00:18:50.151317Z",
+ "shell.execute_reply": "2024-02-09T00:18:50.150770Z"
}
},
"outputs": [],
@@ -553,10 +553,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:39.142971Z",
- "iopub.status.busy": "2024-02-08T14:27:39.142659Z",
- "iopub.status.idle": "2024-02-08T14:27:41.368853Z",
- "shell.execute_reply": "2024-02-08T14:27:41.368128Z"
+ "iopub.execute_input": "2024-02-09T00:18:50.153241Z",
+ "iopub.status.busy": "2024-02-09T00:18:50.152913Z",
+ "iopub.status.idle": "2024-02-09T00:18:52.453367Z",
+ "shell.execute_reply": "2024-02-09T00:18:52.452720Z"
},
"scrolled": true
},
@@ -579,10 +579,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:41.371805Z",
- "iopub.status.busy": "2024-02-08T14:27:41.371238Z",
- "iopub.status.idle": "2024-02-08T14:27:41.378737Z",
- "shell.execute_reply": "2024-02-08T14:27:41.378284Z"
+ "iopub.execute_input": "2024-02-09T00:18:52.456211Z",
+ "iopub.status.busy": "2024-02-09T00:18:52.455635Z",
+ "iopub.status.idle": "2024-02-09T00:18:52.463412Z",
+ "shell.execute_reply": "2024-02-09T00:18:52.462950Z"
}
},
"outputs": [
@@ -683,10 +683,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:41.380700Z",
- "iopub.status.busy": "2024-02-08T14:27:41.380363Z",
- "iopub.status.idle": "2024-02-08T14:27:41.384008Z",
- "shell.execute_reply": "2024-02-08T14:27:41.383559Z"
+ "iopub.execute_input": "2024-02-09T00:18:52.465564Z",
+ "iopub.status.busy": "2024-02-09T00:18:52.465145Z",
+ "iopub.status.idle": "2024-02-09T00:18:52.469053Z",
+ "shell.execute_reply": "2024-02-09T00:18:52.468609Z"
}
},
"outputs": [],
@@ -700,10 +700,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:41.385893Z",
- "iopub.status.busy": "2024-02-08T14:27:41.385556Z",
- "iopub.status.idle": "2024-02-08T14:27:41.388767Z",
- "shell.execute_reply": "2024-02-08T14:27:41.388324Z"
+ "iopub.execute_input": "2024-02-09T00:18:52.471055Z",
+ "iopub.status.busy": "2024-02-09T00:18:52.470611Z",
+ "iopub.status.idle": "2024-02-09T00:18:52.473960Z",
+ "shell.execute_reply": "2024-02-09T00:18:52.473503Z"
}
},
"outputs": [
@@ -738,10 +738,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:41.390727Z",
- "iopub.status.busy": "2024-02-08T14:27:41.390420Z",
- "iopub.status.idle": "2024-02-08T14:27:41.393251Z",
- "shell.execute_reply": "2024-02-08T14:27:41.392824Z"
+ "iopub.execute_input": "2024-02-09T00:18:52.475995Z",
+ "iopub.status.busy": "2024-02-09T00:18:52.475676Z",
+ "iopub.status.idle": "2024-02-09T00:18:52.478705Z",
+ "shell.execute_reply": "2024-02-09T00:18:52.478221Z"
}
},
"outputs": [],
@@ -761,10 +761,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:41.395292Z",
- "iopub.status.busy": "2024-02-08T14:27:41.394954Z",
- "iopub.status.idle": "2024-02-08T14:27:41.401357Z",
- "shell.execute_reply": "2024-02-08T14:27:41.400936Z"
+ "iopub.execute_input": "2024-02-09T00:18:52.480587Z",
+ "iopub.status.busy": "2024-02-09T00:18:52.480244Z",
+ "iopub.status.idle": "2024-02-09T00:18:52.487024Z",
+ "shell.execute_reply": "2024-02-09T00:18:52.486469Z"
}
},
"outputs": [
@@ -889,10 +889,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:41.403438Z",
- "iopub.status.busy": "2024-02-08T14:27:41.403120Z",
- "iopub.status.idle": "2024-02-08T14:27:41.626428Z",
- "shell.execute_reply": "2024-02-08T14:27:41.625913Z"
+ "iopub.execute_input": "2024-02-09T00:18:52.489149Z",
+ "iopub.status.busy": "2024-02-09T00:18:52.488746Z",
+ "iopub.status.idle": "2024-02-09T00:18:52.751106Z",
+ "shell.execute_reply": "2024-02-09T00:18:52.750578Z"
},
"scrolled": true
},
@@ -931,10 +931,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:41.628842Z",
- "iopub.status.busy": "2024-02-08T14:27:41.628467Z",
- "iopub.status.idle": "2024-02-08T14:27:41.804111Z",
- "shell.execute_reply": "2024-02-08T14:27:41.803643Z"
+ "iopub.execute_input": "2024-02-09T00:18:52.753638Z",
+ "iopub.status.busy": "2024-02-09T00:18:52.753273Z",
+ "iopub.status.idle": "2024-02-09T00:18:52.931483Z",
+ "shell.execute_reply": "2024-02-09T00:18:52.930895Z"
},
"scrolled": true
},
@@ -967,10 +967,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:41.806451Z",
- "iopub.status.busy": "2024-02-08T14:27:41.806094Z",
- "iopub.status.idle": "2024-02-08T14:27:41.809763Z",
- "shell.execute_reply": "2024-02-08T14:27:41.809286Z"
+ "iopub.execute_input": "2024-02-09T00:18:52.935035Z",
+ "iopub.status.busy": "2024-02-09T00:18:52.934051Z",
+ "iopub.status.idle": "2024-02-09T00:18:52.939066Z",
+ "shell.execute_reply": "2024-02-09T00:18:52.938542Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
index 669fea6f7..0c11fcccb 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": "2024-02-08T14:27:44.620201Z",
- "iopub.status.busy": "2024-02-08T14:27:44.619732Z",
- "iopub.status.idle": "2024-02-08T14:27:48.290139Z",
- "shell.execute_reply": "2024-02-08T14:27:48.289531Z"
+ "iopub.execute_input": "2024-02-09T00:18:57.048750Z",
+ "iopub.status.busy": "2024-02-09T00:18:57.048302Z",
+ "iopub.status.idle": "2024-02-09T00:18:58.991824Z",
+ "shell.execute_reply": "2024-02-09T00:18:58.991242Z"
}
},
"outputs": [
@@ -86,7 +86,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-02-08 14:27:44-- https://data.deepai.org/conll2003.zip\r\n",
+ "--2024-02-09 00:18:57-- https://data.deepai.org/conll2003.zip\r\n",
"Resolving data.deepai.org (data.deepai.org)... "
]
},
@@ -94,9 +94,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "169.150.249.169, 2400:52e0:1a01::987:1\r\n",
- "Connecting to data.deepai.org (data.deepai.org)|169.150.249.169|:443... connected.\r\n",
- "HTTP request sent, awaiting response... 200 OK\r\n",
+ "143.244.50.84, 2400:52e0:1a01::1002:1\r\n",
+ "Connecting to data.deepai.org (data.deepai.org)|143.244.50.84|:443... connected.\r\n",
+ "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 +116,9 @@
"output_type": "stream",
"text": [
"\r",
- "conll2003.zip 100%[===================>] 959.94K 5.77MB/s in 0.2s \r\n",
+ "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.05s \r\n",
"\r\n",
- "2024-02-08 14:27:44 (5.77 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+ "2024-02-09 00:18:57 (18.8 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
"\r\n",
"mkdir: cannot create directory ‘data’: File exists\r\n"
]
@@ -131,9 +138,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-02-08 14:27:45-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
- "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.25.234, 16.182.34.9, 52.217.191.57, ...\r\n",
- "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.25.234|:443... "
+ "--2024-02-09 00:18:57-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+ "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.35.137, 54.231.223.49, 3.5.6.160, ...\r\n",
+ "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.35.137|:443... "
]
},
{
@@ -167,71 +174,7 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 1%[ ] 263.08K 1.13MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 4%[ ] 806.73K 1.73MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 8%[> ] 1.42M 2.11MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 13%[=> ] 2.20M 2.44MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 19%[==> ] 3.14M 2.76MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 26%[====> ] 4.30M 3.15MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 35%[======> ] 5.70M 3.57MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 45%[========> ] 7.41M 4.06MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 58%[==========> ] 9.48M 4.61MB/s "
+ "pred_probs.npz 0%[ ] 126.53K 597KB/s "
]
},
{
@@ -239,7 +182,7 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 73%[=============> ] 11.98M 5.28MB/s "
+ "pred_probs.npz 7%[> ] 1.16M 2.74MB/s "
]
},
{
@@ -247,7 +190,7 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 92%[=================> ] 15.04M 6.01MB/s "
+ "pred_probs.npz 47%[========> ] 7.66M 12.0MB/s "
]
},
{
@@ -255,9 +198,10 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 100%[===================>] 16.26M 6.33MB/s in 2.6s \r\n",
+ "pred_probs.npz 83%[===============> ] 13.58M 15.8MB/s \r",
+ "pred_probs.npz 100%[===================>] 16.26M 18.7MB/s in 0.9s \r\n",
"\r\n",
- "2024-02-08 14:27:48 (6.33 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-02-09 00:18:58 (18.7 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
"\r\n"
]
}
@@ -274,10 +218,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:48.292712Z",
- "iopub.status.busy": "2024-02-08T14:27:48.292351Z",
- "iopub.status.idle": "2024-02-08T14:27:49.318807Z",
- "shell.execute_reply": "2024-02-08T14:27:49.318266Z"
+ "iopub.execute_input": "2024-02-09T00:18:58.994407Z",
+ "iopub.status.busy": "2024-02-09T00:18:58.994052Z",
+ "iopub.status.idle": "2024-02-09T00:19:00.032835Z",
+ "shell.execute_reply": "2024-02-09T00:19:00.032278Z"
},
"nbsphinx": "hidden"
},
@@ -288,7 +232,7 @@
"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -314,10 +258,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:49.321441Z",
- "iopub.status.busy": "2024-02-08T14:27:49.321001Z",
- "iopub.status.idle": "2024-02-08T14:27:49.324649Z",
- "shell.execute_reply": "2024-02-08T14:27:49.324219Z"
+ "iopub.execute_input": "2024-02-09T00:19:00.035412Z",
+ "iopub.status.busy": "2024-02-09T00:19:00.034998Z",
+ "iopub.status.idle": "2024-02-09T00:19:00.038580Z",
+ "shell.execute_reply": "2024-02-09T00:19:00.038037Z"
}
},
"outputs": [],
@@ -367,10 +311,10 @@
"id": "ab9d59a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:49.326803Z",
- "iopub.status.busy": "2024-02-08T14:27:49.326369Z",
- "iopub.status.idle": "2024-02-08T14:27:49.329270Z",
- "shell.execute_reply": "2024-02-08T14:27:49.328862Z"
+ "iopub.execute_input": "2024-02-09T00:19:00.040669Z",
+ "iopub.status.busy": "2024-02-09T00:19:00.040361Z",
+ "iopub.status.idle": "2024-02-09T00:19:00.043148Z",
+ "shell.execute_reply": "2024-02-09T00:19:00.042725Z"
},
"nbsphinx": "hidden"
},
@@ -388,10 +332,10 @@
"id": "519cb80c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:49.331345Z",
- "iopub.status.busy": "2024-02-08T14:27:49.330958Z",
- "iopub.status.idle": "2024-02-08T14:27:58.410208Z",
- "shell.execute_reply": "2024-02-08T14:27:58.409616Z"
+ "iopub.execute_input": "2024-02-09T00:19:00.045150Z",
+ "iopub.status.busy": "2024-02-09T00:19:00.044840Z",
+ "iopub.status.idle": "2024-02-09T00:19:09.194303Z",
+ "shell.execute_reply": "2024-02-09T00:19:09.193758Z"
}
},
"outputs": [],
@@ -465,10 +409,10 @@
"id": "202f1526",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:58.412728Z",
- "iopub.status.busy": "2024-02-08T14:27:58.412543Z",
- "iopub.status.idle": "2024-02-08T14:27:58.418173Z",
- "shell.execute_reply": "2024-02-08T14:27:58.417624Z"
+ "iopub.execute_input": "2024-02-09T00:19:09.196802Z",
+ "iopub.status.busy": "2024-02-09T00:19:09.196461Z",
+ "iopub.status.idle": "2024-02-09T00:19:09.201982Z",
+ "shell.execute_reply": "2024-02-09T00:19:09.201462Z"
},
"nbsphinx": "hidden"
},
@@ -508,10 +452,10 @@
"id": "a4381f03",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:58.420195Z",
- "iopub.status.busy": "2024-02-08T14:27:58.419875Z",
- "iopub.status.idle": "2024-02-08T14:27:58.748360Z",
- "shell.execute_reply": "2024-02-08T14:27:58.747727Z"
+ "iopub.execute_input": "2024-02-09T00:19:09.203935Z",
+ "iopub.status.busy": "2024-02-09T00:19:09.203620Z",
+ "iopub.status.idle": "2024-02-09T00:19:09.535937Z",
+ "shell.execute_reply": "2024-02-09T00:19:09.535333Z"
}
},
"outputs": [],
@@ -548,10 +492,10 @@
"id": "7842e4a3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:58.750811Z",
- "iopub.status.busy": "2024-02-08T14:27:58.750619Z",
- "iopub.status.idle": "2024-02-08T14:27:58.755066Z",
- "shell.execute_reply": "2024-02-08T14:27:58.754523Z"
+ "iopub.execute_input": "2024-02-09T00:19:09.538380Z",
+ "iopub.status.busy": "2024-02-09T00:19:09.538182Z",
+ "iopub.status.idle": "2024-02-09T00:19:09.542321Z",
+ "shell.execute_reply": "2024-02-09T00:19:09.541826Z"
}
},
"outputs": [
@@ -623,10 +567,10 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:27:58.757107Z",
- "iopub.status.busy": "2024-02-08T14:27:58.756716Z",
- "iopub.status.idle": "2024-02-08T14:28:01.081800Z",
- "shell.execute_reply": "2024-02-08T14:28:01.081151Z"
+ "iopub.execute_input": "2024-02-09T00:19:09.544346Z",
+ "iopub.status.busy": "2024-02-09T00:19:09.544034Z",
+ "iopub.status.idle": "2024-02-09T00:19:11.827569Z",
+ "shell.execute_reply": "2024-02-09T00:19:11.826938Z"
}
},
"outputs": [],
@@ -648,10 +592,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:28:01.084631Z",
- "iopub.status.busy": "2024-02-08T14:28:01.084106Z",
- "iopub.status.idle": "2024-02-08T14:28:01.087890Z",
- "shell.execute_reply": "2024-02-08T14:28:01.087366Z"
+ "iopub.execute_input": "2024-02-09T00:19:11.830609Z",
+ "iopub.status.busy": "2024-02-09T00:19:11.829877Z",
+ "iopub.status.idle": "2024-02-09T00:19:11.833766Z",
+ "shell.execute_reply": "2024-02-09T00:19:11.833237Z"
}
},
"outputs": [
@@ -687,10 +631,10 @@
"id": "e13de188",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:28:01.089754Z",
- "iopub.status.busy": "2024-02-08T14:28:01.089582Z",
- "iopub.status.idle": "2024-02-08T14:28:01.095038Z",
- "shell.execute_reply": "2024-02-08T14:28:01.094482Z"
+ "iopub.execute_input": "2024-02-09T00:19:11.835782Z",
+ "iopub.status.busy": "2024-02-09T00:19:11.835464Z",
+ "iopub.status.idle": "2024-02-09T00:19:11.840749Z",
+ "shell.execute_reply": "2024-02-09T00:19:11.840197Z"
}
},
"outputs": [
@@ -868,10 +812,10 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:28:01.096888Z",
- "iopub.status.busy": "2024-02-08T14:28:01.096718Z",
- "iopub.status.idle": "2024-02-08T14:28:01.122512Z",
- "shell.execute_reply": "2024-02-08T14:28:01.121960Z"
+ "iopub.execute_input": "2024-02-09T00:19:11.842787Z",
+ "iopub.status.busy": "2024-02-09T00:19:11.842520Z",
+ "iopub.status.idle": "2024-02-09T00:19:11.869333Z",
+ "shell.execute_reply": "2024-02-09T00:19:11.868916Z"
}
},
"outputs": [
@@ -973,10 +917,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:28:01.124389Z",
- "iopub.status.busy": "2024-02-08T14:28:01.124221Z",
- "iopub.status.idle": "2024-02-08T14:28:01.128790Z",
- "shell.execute_reply": "2024-02-08T14:28:01.128336Z"
+ "iopub.execute_input": "2024-02-09T00:19:11.871431Z",
+ "iopub.status.busy": "2024-02-09T00:19:11.871119Z",
+ "iopub.status.idle": "2024-02-09T00:19:11.874995Z",
+ "shell.execute_reply": "2024-02-09T00:19:11.874443Z"
}
},
"outputs": [
@@ -1050,10 +994,10 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:28:01.130630Z",
- "iopub.status.busy": "2024-02-08T14:28:01.130463Z",
- "iopub.status.idle": "2024-02-08T14:28:02.551229Z",
- "shell.execute_reply": "2024-02-08T14:28:02.550712Z"
+ "iopub.execute_input": "2024-02-09T00:19:11.877031Z",
+ "iopub.status.busy": "2024-02-09T00:19:11.876726Z",
+ "iopub.status.idle": "2024-02-09T00:19:13.283142Z",
+ "shell.execute_reply": "2024-02-09T00:19:13.282587Z"
}
},
"outputs": [
@@ -1225,10 +1169,10 @@
"id": "a18795eb",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-08T14:28:02.553241Z",
- "iopub.status.busy": "2024-02-08T14:28:02.553068Z",
- "iopub.status.idle": "2024-02-08T14:28:02.556966Z",
- "shell.execute_reply": "2024-02-08T14:28:02.556551Z"
+ "iopub.execute_input": "2024-02-09T00:19:13.285106Z",
+ "iopub.status.busy": "2024-02-09T00:19:13.284921Z",
+ "iopub.status.idle": "2024-02-09T00:19:13.288805Z",
+ "shell.execute_reply": "2024-02-09T00:19:13.288390Z"
},
"nbsphinx": "hidden"
},
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index 15354bdc9..09c872df3 100644
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diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree
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diff --git a/master/_modules/cleanlab/benchmarking/noise_generation.html b/master/_modules/cleanlab/benchmarking/noise_generation.html
index 3e88d9cc5..8a57810a7 100644
--- a/master/_modules/cleanlab/benchmarking/noise_generation.html
+++ b/master/_modules/cleanlab/benchmarking/noise_generation.html
@@ -238,6 +238,7 @@
list_possible_issue_types as _list_possible_issue_types ,
)
from cleanlab.datalab.internal.serialize import _Serializer
+from cleanlab.datalab.internal.task import Task
if TYPE_CHECKING : # pragma: no cover
import numpy.typing as npt
@@ -609,7 +611,11 @@ Source code for cleanlab.datalab.datalab
task : str
The type of machine learning task that the dataset is used for.
- By default, this is set to "classification", but you can also set it to "regression" if you are working with a regression dataset.
+
+ Supported tasks:
+ - "classification" (default): Multiclass classification
+ - "regression" : Regression
+ - "multilabel" : Multilabel classification
label_name : str, optional
The name of the label column in the dataset.
@@ -640,17 +646,11 @@ Source code for cleanlab.datalab.datalab
image_key : Optional [ str ] = None ,
verbosity : int = 1 ,
) -> None :
- self . _validate_task ( task )
# Assume continuous values of labels for regression task
# Map labels to integers for classification task
- map_labels_to_int = task == "classification" # TODO: handle more generally
- is_multilabel = task == "multilabel"
-
- self . _data = Data (
- data , label_name , map_to_int = map_labels_to_int , is_multilabel = is_multilabel
- )
+ self . task = Task . from_str ( task )
+ self . _data = Data ( data , self . task , label_name )
self . data = self . _data . _data
- self . task = task
self . _labels = self . _data . labels
self . _label_map = self . _labels . label_map
self . label_name = self . _labels . label_name
@@ -661,7 +661,7 @@ Source code for cleanlab.datalab.datalab
# Create the builder for DataIssues
builder = _DataIssuesBuilder ( self . _data )
- builder . set_imagelab ( self . _imagelab ) . set_task ( task )
+ builder . set_imagelab ( self . _imagelab ) . set_task ( self . task )
self . data_issues = builder . build ()
# todo: check displayer methods
@@ -671,13 +671,6 @@ Source code for cleanlab.datalab.datalab
def __str__ ( self ) -> str :
return _Displayer ( data_issues = self . data_issues ) . __str__ ()
- def _validate_task ( self , task : str ) -> None :
- """Validates the task parameter passed to the Datalab constructor."""
- _valid_tasks = [ "classification" , "regression" , "multilabel" ]
- if task not in _valid_tasks :
- error_msg = f "Invalid task: { task } . Datalab only supports { _valid_tasks } ."
- raise ValueError ( error_msg )
-
@property
def labels ( self ) -> Union [ np . ndarray , List [ List [ int ]]]:
"""Labels of the dataset, in a [0, 1, ..., K-1] format."""
diff --git a/master/_modules/cleanlab/datalab/internal/data.html b/master/_modules/cleanlab/datalab/internal/data.html
index 405cbacb1..948704e75 100644
--- a/master/_modules/cleanlab/datalab/internal/data.html
+++ b/master/_modules/cleanlab/datalab/internal/data.html
@@ -238,6 +238,7 @@
report
+
task
@@ -547,6 +548,8 @@
Source code for cleanlab.datalab.internal.data import os
from typing import Any , Callable , Dict , List , Mapping , Optional , Union , cast , TYPE_CHECKING , Tuple
+from cleanlab.datalab.internal.task import Task
+
try :
import datasets
except ImportError as error :
@@ -646,9 +649,21 @@
Source code for cleanlab.datalab.internal.data label_name : Union[str, List[str]]
Name of the label column in the dataset.
- map_to_int : bool
- Whether to map the labels to integers, e.g. [0, 1, ..., K-1] where K is the number of classes.
- If False, the labels are not mapped to integers, e.g. for regression tasks.
+ task :
+ The task associated with the dataset. This is used to determine how to
+ to format the labels.
+
+ Note:
+
+ - If the task is a classification task, the labels
+ will be mapped to integers, e.g. [0, 1, ..., K-1] where K is the number
+ of classes. If the task is a regression task, the labels will not be
+ mapped to integers.
+
+ - If the task is a multilabel task, the labels will be formatted as a
+ list of lists, e.g. [[0, 1], [1, 2], [0, 2]] where each sublist contains
+ the labels for a single example. If the task is not a multilabel task,
+ the labels will be formatted as a 1D numpy array.
Warnings
--------
@@ -663,15 +678,15 @@
Source code for cleanlab.datalab.internal.data def __init__ (
self ,
data : "DatasetLike" ,
+ task : Task ,
label_name : Optional [ str ] = None ,
- map_to_int : bool = True ,
- is_multilabel : bool = False ,
) -> None :
self . _validate_data ( data )
self . _data = self . _load_data ( data )
self . _data_hash = hash ( self . _data )
self . labels : Label
- label_class = MultiLabel if is_multilabel else MultiClass
+ label_class = MultiLabel if task . is_multilabel else MultiClass
+ map_to_int = task . is_classification
self . labels = label_class ( data = self . _data , label_name = label_name , map_to_int = map_to_int )
def _load_data ( self , data : "DatasetLike" ) -> Dataset :
diff --git a/master/_modules/cleanlab/datalab/internal/data_issues.html b/master/_modules/cleanlab/datalab/internal/data_issues.html
index 5c5b3ecc4..912eb9d53 100644
--- a/master/_modules/cleanlab/datalab/internal/data_issues.html
+++ b/master/_modules/cleanlab/datalab/internal/data_issues.html
@@ -238,6 +238,7 @@
report
+
task
@@ -559,7 +560,7 @@
Source code for cleanlab.datalab.internal.data_issues import
warnings
from abc import ABC , abstractmethod
-
from typing import TYPE_CHECKING , Any , Dict , List , Optional , Union
+
from typing import TYPE_CHECKING , Any , Dict , List , Optional , Type , Union
import numpy as np
import pandas as pd
@@ -727,7 +728,7 @@
Source code for cleanlab.datalab.internal.data_issues A dictionary that contains information and statistics about the data and each issue type.
"""
-
def __init__ ( self , data : Data , strategy : _InfoStrategy ) -> None :
+
def __init__ ( self , data : Data , strategy : Type [ _InfoStrategy ]) -> None :
self . issues : pd . DataFrame = pd . DataFrame ( index = range ( len ( data )))
self . issue_summary : pd . DataFrame = pd . DataFrame (
columns = [ "issue_type" , "score" , "num_issues" ]
diff --git a/master/_modules/cleanlab/datalab/internal/issue_finder.html b/master/_modules/cleanlab/datalab/internal/issue_finder.html
index 8d440d56a..0ab7a38a3 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_finder.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_finder.html
@@ -238,6 +238,7 @@
report
+
task
@@ -573,6 +574,7 @@
Source code for cleanlab.datalab.internal.issue_finder RegressionPredictions
,
MultiLabelPredProbs ,
)
+
from cleanlab.datalab.internal.task import Task
if TYPE_CHECKING : # pragma: no cover
import numpy.typing as npt
@@ -700,7 +702,7 @@
Source code for cleanlab.datalab.internal.issue_finder return
args_dict
-
def _select_strategy_for_resolving_required_args ( task : str ) -> Callable :
+
def _select_strategy_for_resolving_required_args ( task : Task ) -> Callable :
"""Helper function that selects the strategy for resolving required arguments for each issue type.
Each strategy resolves the required arguments for each issue type.
@@ -721,9 +723,9 @@
Source code for cleanlab.datalab.internal.issue_finder Dictionary of required arguments for each issue type, if available.
"""
strategies = {
-
"classification" : _resolve_required_args_for_classification ,
-
"regression" : _resolve_required_args_for_regression ,
-
"multilabel" : _resolve_required_args_for_multilabel ,
+
Task . CLASSIFICATION : _resolve_required_args_for_classification ,
+
Task . REGRESSION : _resolve_required_args_for_regression ,
+
Task . MULTILABEL : _resolve_required_args_for_multilabel ,
}
selected_strategy = strategies . get ( task , None )
if selected_strategy is None :
@@ -762,7 +764,7 @@
Source code for cleanlab.datalab.internal.issue_finder `Datalab.find_issues` method which internally utilizes an IssueFinder instance.
"""
-
def __init__ ( self , datalab : "Datalab" , task : str , verbosity = 1 ):
+
def __init__ ( self , datalab : "Datalab" , task : Task , verbosity = 1 ):
self . datalab = datalab
self . task = task
self . verbosity = verbosity
@@ -943,9 +945,9 @@
Source code for cleanlab.datalab.internal.issue_finder model_output
= None
if pred_probs is not None :
model_output_dict = {
-
"regression" : RegressionPredictions ,
-
"classification" : MultiClassPredProbs ,
-
"multilabel" : MultiLabelPredProbs ,
+
Task . REGRESSION : RegressionPredictions ,
+
Task . CLASSIFICATION : MultiClassPredProbs ,
+
Task . MULTILABEL : MultiLabelPredProbs ,
}
model_output_class = model_output_dict . get ( self . task )
@@ -976,7 +978,7 @@
Source code for cleanlab.datalab.internal.issue_finder drop_label_check
= (
"label" in issue_types_copy
and not self . datalab . has_labels
-
and self . task != "regression"
+
and self . task != Task . REGRESSION
)
if drop_label_check :
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/data_valuation.html b/master/_modules/cleanlab/datalab/internal/issue_manager/data_valuation.html
index 6f79850ee..d9246cc91 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/data_valuation.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/data_valuation.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/duplicate.html b/master/_modules/cleanlab/datalab/internal/issue_manager/duplicate.html
index b14bea6d1..aa67fab16 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/duplicate.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/duplicate.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/imbalance.html b/master/_modules/cleanlab/datalab/internal/issue_manager/imbalance.html
index 32c84c3ca..d602083ce 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/imbalance.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/imbalance.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/issue_manager.html b/master/_modules/cleanlab/datalab/internal/issue_manager/issue_manager.html
index 95b1bd4f8..45a8ed9d3 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/issue_manager.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/issue_manager.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/label.html b/master/_modules/cleanlab/datalab/internal/issue_manager/label.html
index b4e58eca3..aaa8ff2a1 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/label.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/label.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/noniid.html b/master/_modules/cleanlab/datalab/internal/issue_manager/noniid.html
index f5b422638..a57a0e954 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/noniid.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/noniid.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/null.html b/master/_modules/cleanlab/datalab/internal/issue_manager/null.html
index dac92a906..93062fd4b 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/null.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/null.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/outlier.html b/master/_modules/cleanlab/datalab/internal/issue_manager/outlier.html
index df9be4f26..cc74ba8f5 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/outlier.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/outlier.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/regression/label.html b/master/_modules/cleanlab/datalab/internal/issue_manager/regression/label.html
index dfb385275..26e273d73 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/regression/label.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/regression/label.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager/underperforming_group.html b/master/_modules/cleanlab/datalab/internal/issue_manager/underperforming_group.html
index 670dfe8a7..8028633c2 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager/underperforming_group.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager/underperforming_group.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html b/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html
index debe5ff0e..7c98ea668 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html
@@ -238,6 +238,7 @@
report
+
task
@@ -584,10 +585,11 @@
Source code for cleanlab.datalab.internal.issue_manager_factory )
from cleanlab.datalab.internal.issue_manager.regression import RegressionLabelIssueManager
from cleanlab.datalab.internal.issue_manager.multilabel.label import MultilabelIssueManager
+
from cleanlab.datalab.internal.task import Task
-
REGISTRY : Dict [ str , Dict [ str , Type [ IssueManager ]]] = {
-
"classification" : {
+
REGISTRY : Dict [ Task , Dict [ str , Type [ IssueManager ]]] = {
+
Task . CLASSIFICATION : {
"outlier" : OutlierIssueManager ,
"label" : LabelIssueManager ,
"near_duplicate" : NearDuplicateIssueManager ,
@@ -597,14 +599,14 @@
Source code for cleanlab.datalab.internal.issue_manager_factory "data_valuation"
: DataValuationIssueManager ,
"null" : NullIssueManager ,
},
-
"regression" : {
+
Task . REGRESSION : {
"label" : RegressionLabelIssueManager ,
"outlier" : OutlierIssueManager ,
"near_duplicate" : NearDuplicateIssueManager ,
"non_iid" : NonIIDIssueManager ,
"null" : NullIssueManager ,
},
-
"multilabel" : {
+
Task . MULTILABEL : {
"label" : MultilabelIssueManager ,
"outlier" : OutlierIssueManager ,
"near_duplicate" : NearDuplicateIssueManager ,
@@ -612,16 +614,31 @@
Source code for cleanlab.datalab.internal.issue_manager_factory "null"
: NullIssueManager ,
},
}
-
"""Registry of issue managers that can be constructed from a string
+
"""Registry of issue managers that can be constructed from a task and issue type
and used in the Datalab class.
:meta hide-value:
-
Currently, the following issue managers are registered by default:
+
Currently, the following issue managers are registered by default for a given task:
-
- ``"outlier"``: :py:class:`OutlierIssueManager <cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager>`
-
- ``"near_duplicate"``: :py:class:`NearDuplicateIssueManager <cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager>`
-
- ``"non_iid"``: :py:class:`NonIIDIssueManager <cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager>`
+
- Classification:
+
+
- ``"outlier"``: :py:class:`OutlierIssueManager <cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager>`
+
- ``"label"``: :py:class:`LabelIssueManager <cleanlab.datalab.internal.issue_manager.label.LabelIssueManager>`
+
- ``"near_duplicate"``: :py:class:`NearDuplicateIssueManager <cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager>`
+
- ``"non_iid"``: :py:class:`NonIIDIssueManager <cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager>`
+
- ``"class_imbalance"``: :py:class:`ClassImbalanceIssueManager <cleanlab.datalab.internal.issue_manager.class_imbalance.ClassImbalanceIssueManager>`
+
- ``"underperforming_group"``: :py:class:`UnderperformingGroupIssueManager <cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager>`
+
- ``"data_valuation"``: :py:class:`DataValuationIssueManager <cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager>`
+
- ``"null"``: :py:class:`NullIssueManager <cleanlab.datalab.internal.issue_manager.null.NullIssueManager>`
+
+
- Regression:
+
+
- ``"label"``: :py:class:`RegressionLabelIssueManager <cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager>`
+
+
- Multilabel:
+
+
- ``"label"``: :py:class:`MultilabelIssueManager <cleanlab.datalab.internal.issue_manager.multilabel.label.MultilabelIssueManager>`
Warning
-------
@@ -634,7 +651,7 @@
Source code for cleanlab.datalab.internal.issue_manager_factory """Factory class for constructing concrete issue managers."""
@classmethod
-
def from_str ( cls , issue_type : str , task : str ) -> Type [ IssueManager ]:
+
def from_str ( cls , issue_type : str , task : Task ) -> Type [ IssueManager ]:
"""Constructs a concrete issue manager class from a string."""
if isinstance ( issue_type , list ):
raise ValueError (
@@ -649,12 +666,12 @@
Source code for cleanlab.datalab.internal.issue_manager_factory return
REGISTRY [ task ][ issue_type ]
@classmethod
-
def from_list ( cls , issue_types : List [ str ], task : str ) -> List [ Type [ IssueManager ]]:
+
def from_list ( cls , issue_types : List [ str ], task : Task ) -> List [ Type [ IssueManager ]]:
"""Constructs a list of concrete issue manager classes from a list of strings."""
return [ cls . from_str ( issue_type , task ) for issue_type in issue_types ]
-
[docs] def register ( cls : Type [ IssueManager ], task : str = "classification" ) -> Type [ IssueManager ]:
+
[docs] def register ( cls : Type [ IssueManager ], task : str = str ( Task . CLASSIFICATION )) -> Type [ IssueManager ]:
"""Registers the issue manager factory.
Parameters
@@ -665,6 +682,8 @@
Source code for cleanlab.datalab.internal.issue_manager_factory task :
Specific machine learning task like classification or regression.
+
See :py:meth:`Task.from_str <cleanlab.datalab.internal.task.Task.from_str>`` for more details,
+
to see which task type corresponds to which string.
Returns
-------
@@ -711,20 +730,24 @@
Source code for cleanlab.datalab.internal.issue_manager_factory name: str = str ( cls . issue_name )
- if task not in REGISTRY :
+ try :
+ _task = Task . from_str ( task )
+ if _task not in REGISTRY :
+ raise ValueError ( f "Invalid task type: { _task } , must be in { list ( REGISTRY . keys ()) } " )
+ except KeyError :
raise ValueError ( f "Invalid task type: { task } , must be in { list ( REGISTRY . keys ()) } " )
- if name in REGISTRY [ task ]:
+ if name in REGISTRY [ _task ]:
print (
- f "Warning: Overwriting existing issue manager { name } with { cls } for task { task } ."
+ f "Warning: Overwriting existing issue manager { name } with { cls } for task { _task } ."
"This may cause unexpected behavior."
)
- REGISTRY [ task ][ name ] = cls
+ REGISTRY [ _task ][ name ] = cls
return cls
-
[docs] def list_possible_issue_types ( task : str ) -> List [ str ]:
+
[docs] def list_possible_issue_types ( task : Task ) -> List [ str ]:
"""Returns a list of all registered issue types.
Any issue type that is not in this list cannot be used in the :py:meth:`find_issues` method.
@@ -736,16 +759,19 @@
Source code for cleanlab.datalab.internal.issue_manager_factory return list ( REGISTRY . get ( task , []))
-
[docs] def list_default_issue_types ( task : str ) -> List [ str ]:
+
[docs] def list_default_issue_types ( task : Task ) -> List [ str ]:
"""Returns a list of the issue types that are run by default
when :py:meth:`find_issues` is called without specifying `issue_types`.
+
task :
+
Specific machine learning task supported by Datalab.
+
See Also
--------
:py:class:`REGISTRY <cleanlab.datalab.internal.issue_manager_factory.REGISTRY>` : All available issue types and their corresponding issue managers can be found here.
"""
default_issue_types_dict = {
-
"classification" : [
+
Task . CLASSIFICATION : [
"null" ,
"label" ,
"outlier" ,
@@ -753,14 +779,14 @@
Source code for cleanlab.datalab.internal.issue_manager_factory "non_iid"
,
"class_imbalance" ,
],
-
"regression" : [
+
Task . REGRESSION : [
"null" ,
"label" ,
"outlier" ,
"near_duplicate" ,
"non_iid" ,
],
-
"multilabel" : [
+
Task . MULTILABEL : [
"null" ,
"label" ,
"outlier" ,
@@ -769,7 +795,7 @@
Source code for cleanlab.datalab.internal.issue_manager_factory ],
}
if task not in default_issue_types_dict :
- task = "classification"
+ task = Task . CLASSIFICATION
default_issue_types = default_issue_types_dict [ task ]
return default_issue_types
diff --git a/master/_modules/cleanlab/datalab/internal/report.html b/master/_modules/cleanlab/datalab/internal/report.html
index 76298e5c2..24b19b43e 100644
--- a/master/_modules/cleanlab/datalab/internal/report.html
+++ b/master/_modules/cleanlab/datalab/internal/report.html
@@ -238,6 +238,7 @@
report
+
task
@@ -552,6 +553,7 @@
Source code for cleanlab.datalab.internal.report
from cleanlab.datalab.internal.adapter.constants import DEFAULT_CLEANVISION_ISSUES
from cleanlab.datalab.internal.issue_manager_factory import _IssueManagerFactory
+
from cleanlab.datalab.internal.task import Task
if TYPE_CHECKING : # pragma: no cover
from cleanlab.datalab.internal.data_issues import DataIssues
@@ -568,7 +570,8 @@
Source code for cleanlab.datalab.internal.report
and then passed to the :py:class:`Reporter` class to generate a report.
task :
-
Specific machine learning task like classification or regression.
+
Specific machine learning task that the datset is intended for.
+
See details about supported tasks in :py:class:`Task <cleanlab.datalab.internal.task.Task>`.
verbosity :
The default verbosity of the report to generate. Each :py:class`IssueManager`
@@ -588,7 +591,7 @@
Source code for cleanlab.datalab.internal.report
def __init__ (
self ,
data_issues : "DataIssues" ,
-
task : str ,
+
task : Task ,
verbosity : int = 1 ,
include_description : bool = True ,
show_summary_score : bool = False ,
diff --git a/master/_modules/cleanlab/datalab/internal/task.html b/master/_modules/cleanlab/datalab/internal/task.html
new file mode 100644
index 000000000..1c092e013
--- /dev/null
+++ b/master/_modules/cleanlab/datalab/internal/task.html
@@ -0,0 +1,733 @@
+
+
+
+
+
+
+
+
+
+
+
cleanlab.datalab.internal.task - cleanlab
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Contents
+
+
+
+
+
+
+ Expand
+
+
+
+
+
+ Light mode
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Dark mode
+
+
+
+
+
+
+ Auto light/dark mode
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Hide table of contents sidebar
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Back to top
+
+
+
+
+ Toggle Light / Dark / Auto color theme
+
+
+
+
+
+
+ Toggle table of contents sidebar
+
+
+
+
+
+
+
+
+
Warning
+
Parts of this site uses JavaScript, but your browser does not support it.
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Source code for cleanlab.datalab.internal.task
+# Copyright (C) 2017-2024 Cleanlab Inc.
+# This file is part of cleanlab.
+#
+# cleanlab is free software: you can redistribute it and/or modify
+# it under the terms of the GNU Affero General Public License as published
+# by the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# cleanlab is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+# GNU Affero General Public License for more details.
+#
+# You should have received a copy of the GNU Affero General Public License
+# along with cleanlab. If not, see <https://www.gnu.org/licenses/>.
+from enum import Enum
+
+
+[docs] class Task ( Enum ):
+
"""
+
Represents a task supported by Datalab.
+
+
Datalab supports the following tasks:
+
+
* **Classification**: for predicting discrete class labels.
+
* **Regression**: for predicting continuous numerical values.
+
* **Multilabel**: for predicting multiple binary labels simultaneously.
+
+
Example
+
-------
+
>>> task = Task.CLASSIFICATION
+
>>> task
+
<Task.CLASSIFICATION: 'classification'>
+
"""
+
+
CLASSIFICATION = "classification"
+
"""Classification task."""
+
REGRESSION = "regression"
+
"""Regression task."""
+
MULTILABEL = "multilabel"
+
"""Multilabel task."""
+
+
def __str__ ( self ):
+
"""
+
Returns the string representation of the task.
+
+
Returns:
+
str: The string representation of the task.
+
"""
+
return self . value
+
+
[docs] @classmethod
+
def from_str ( cls , task_str : str ) -> "Task" :
+
"""
+
Converts a string representation of a task to a Task enum value.
+
+
Parameters
+
----------
+
task_str :
+
The string representation of the task.
+
+
Returns
+
-------
+
Task :
+
The corresponding Task enum value.
+
+
Raises
+
------
+
ValueError :
+
If the provided task_str is not a valid task supported by Datalab.
+
+
Examples
+
--------
+
>>> Task.from_str("classification")
+
<Task.CLASSIFICATION: 'classification'>
+
>>> print(Task.from_str("regression"))
+
regression
+
"""
+
_value_to_enum = { task . value : task for task in Task }
+
try :
+
return _value_to_enum [ task_str ]
+
except KeyError :
+
valid_tasks = list ( _value_to_enum . keys ())
+
raise ValueError ( f "Invalid task: { task_str } . Datalab only supports { valid_tasks } ." )
+
+
@property
+
def is_classification ( self ):
+
"""
+
Checks if the task is classification.
+
+
Returns
+
-------
+
bool :
+
True if the task is classification, False otherwise.
+
+
Examples
+
--------
+
>>> task = Task.CLASSIFICATION
+
>>> print(task.is_classification)
+
True
+
"""
+
return self == Task . CLASSIFICATION
+
+
@property
+
def is_regression ( self ):
+
"""
+
Checks if the task is regression.
+
+
Returns
+
-------
+
bool :
+
True if the task is regression, False otherwise.
+
+
Examples
+
--------
+
>>> task = Task.CLASSIFICATION
+
>>> print(task.is_regression)
+
False
+
"""
+
return self == Task . REGRESSION
+
+
@property
+
def is_multilabel ( self ):
+
"""
+
Checks if the task is multilabel.
+
+
Returns
+
-------
+
bool :
+
True if the task is multilabel, False otherwise.
+
+
Examples
+
--------
+
>>> task = Task.CLASSIFICATION
+
>>> print(task.is_multilabel)
+
False
+
"""
+
return self == Task . MULTILABEL
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/master/_modules/cleanlab/dataset.html b/master/_modules/cleanlab/dataset.html
index 738f8f55c..f613a9fea 100644
--- a/master/_modules/cleanlab/dataset.html
+++ b/master/_modules/cleanlab/dataset.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/experimental/cifar_cnn.html b/master/_modules/cleanlab/experimental/cifar_cnn.html
index 84edc9eba..53f343d19 100644
--- a/master/_modules/cleanlab/experimental/cifar_cnn.html
+++ b/master/_modules/cleanlab/experimental/cifar_cnn.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/experimental/coteaching.html b/master/_modules/cleanlab/experimental/coteaching.html
index 278204bd7..cfe201d20 100644
--- a/master/_modules/cleanlab/experimental/coteaching.html
+++ b/master/_modules/cleanlab/experimental/coteaching.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/experimental/label_issues_batched.html b/master/_modules/cleanlab/experimental/label_issues_batched.html
index 96673cc6b..376288183 100644
--- a/master/_modules/cleanlab/experimental/label_issues_batched.html
+++ b/master/_modules/cleanlab/experimental/label_issues_batched.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/experimental/mnist_pytorch.html b/master/_modules/cleanlab/experimental/mnist_pytorch.html
index 6ef027e5b..b685fee38 100644
--- a/master/_modules/cleanlab/experimental/mnist_pytorch.html
+++ b/master/_modules/cleanlab/experimental/mnist_pytorch.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/filter.html b/master/_modules/cleanlab/filter.html
index bd6edb9b6..f33a309c6 100644
--- a/master/_modules/cleanlab/filter.html
+++ b/master/_modules/cleanlab/filter.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/internal/label_quality_utils.html b/master/_modules/cleanlab/internal/label_quality_utils.html
index aa1821739..4e6618dea 100644
--- a/master/_modules/cleanlab/internal/label_quality_utils.html
+++ b/master/_modules/cleanlab/internal/label_quality_utils.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/internal/latent_algebra.html b/master/_modules/cleanlab/internal/latent_algebra.html
index baef74ae9..cb636bd16 100644
--- a/master/_modules/cleanlab/internal/latent_algebra.html
+++ b/master/_modules/cleanlab/internal/latent_algebra.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/internal/multiannotator_utils.html b/master/_modules/cleanlab/internal/multiannotator_utils.html
index fd93b9ee5..3e66fdf51 100644
--- a/master/_modules/cleanlab/internal/multiannotator_utils.html
+++ b/master/_modules/cleanlab/internal/multiannotator_utils.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/internal/multilabel_scorer.html b/master/_modules/cleanlab/internal/multilabel_scorer.html
index e4df0e9d6..f292889cd 100644
--- a/master/_modules/cleanlab/internal/multilabel_scorer.html
+++ b/master/_modules/cleanlab/internal/multilabel_scorer.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/internal/multilabel_utils.html b/master/_modules/cleanlab/internal/multilabel_utils.html
index 6ffe1c292..9b73a0ca4 100644
--- a/master/_modules/cleanlab/internal/multilabel_utils.html
+++ b/master/_modules/cleanlab/internal/multilabel_utils.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/internal/outlier.html b/master/_modules/cleanlab/internal/outlier.html
index a8183a41f..a03a6aec5 100644
--- a/master/_modules/cleanlab/internal/outlier.html
+++ b/master/_modules/cleanlab/internal/outlier.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/internal/token_classification_utils.html b/master/_modules/cleanlab/internal/token_classification_utils.html
index 7a27fa947..98f44066d 100644
--- a/master/_modules/cleanlab/internal/token_classification_utils.html
+++ b/master/_modules/cleanlab/internal/token_classification_utils.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/internal/util.html b/master/_modules/cleanlab/internal/util.html
index 8e71a20f3..b9ead498a 100644
--- a/master/_modules/cleanlab/internal/util.html
+++ b/master/_modules/cleanlab/internal/util.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/internal/validation.html b/master/_modules/cleanlab/internal/validation.html
index 01c2f68fb..03dbd9e82 100644
--- a/master/_modules/cleanlab/internal/validation.html
+++ b/master/_modules/cleanlab/internal/validation.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/models/keras.html b/master/_modules/cleanlab/models/keras.html
index 4ed0e32a8..89ca1d80c 100644
--- a/master/_modules/cleanlab/models/keras.html
+++ b/master/_modules/cleanlab/models/keras.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/multiannotator.html b/master/_modules/cleanlab/multiannotator.html
index 0864cc174..a5a831d36 100644
--- a/master/_modules/cleanlab/multiannotator.html
+++ b/master/_modules/cleanlab/multiannotator.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/multilabel_classification/dataset.html b/master/_modules/cleanlab/multilabel_classification/dataset.html
index 7a782ff33..d51a0968c 100644
--- a/master/_modules/cleanlab/multilabel_classification/dataset.html
+++ b/master/_modules/cleanlab/multilabel_classification/dataset.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/multilabel_classification/filter.html b/master/_modules/cleanlab/multilabel_classification/filter.html
index c0f7b1234..7798f7251 100644
--- a/master/_modules/cleanlab/multilabel_classification/filter.html
+++ b/master/_modules/cleanlab/multilabel_classification/filter.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/multilabel_classification/rank.html b/master/_modules/cleanlab/multilabel_classification/rank.html
index 8e18e1c6d..46ccae06d 100644
--- a/master/_modules/cleanlab/multilabel_classification/rank.html
+++ b/master/_modules/cleanlab/multilabel_classification/rank.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/object_detection/filter.html b/master/_modules/cleanlab/object_detection/filter.html
index 97ef5c75c..fcc59a390 100644
--- a/master/_modules/cleanlab/object_detection/filter.html
+++ b/master/_modules/cleanlab/object_detection/filter.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/object_detection/rank.html b/master/_modules/cleanlab/object_detection/rank.html
index 4d6c79990..79c1c7fc4 100644
--- a/master/_modules/cleanlab/object_detection/rank.html
+++ b/master/_modules/cleanlab/object_detection/rank.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/object_detection/summary.html b/master/_modules/cleanlab/object_detection/summary.html
index d8c3ca714..022fcc370 100644
--- a/master/_modules/cleanlab/object_detection/summary.html
+++ b/master/_modules/cleanlab/object_detection/summary.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/outlier.html b/master/_modules/cleanlab/outlier.html
index 8a607766d..ced745e1b 100644
--- a/master/_modules/cleanlab/outlier.html
+++ b/master/_modules/cleanlab/outlier.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/rank.html b/master/_modules/cleanlab/rank.html
index e54247f19..4deb09878 100644
--- a/master/_modules/cleanlab/rank.html
+++ b/master/_modules/cleanlab/rank.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/regression/learn.html b/master/_modules/cleanlab/regression/learn.html
index 9db04ed12..d0d5e3489 100644
--- a/master/_modules/cleanlab/regression/learn.html
+++ b/master/_modules/cleanlab/regression/learn.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/regression/rank.html b/master/_modules/cleanlab/regression/rank.html
index de1377cf2..20beb65f0 100644
--- a/master/_modules/cleanlab/regression/rank.html
+++ b/master/_modules/cleanlab/regression/rank.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/segmentation/filter.html b/master/_modules/cleanlab/segmentation/filter.html
index 5017285f7..f05cc2d8f 100644
--- a/master/_modules/cleanlab/segmentation/filter.html
+++ b/master/_modules/cleanlab/segmentation/filter.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/segmentation/rank.html b/master/_modules/cleanlab/segmentation/rank.html
index 9ada56b19..52c4dc808 100644
--- a/master/_modules/cleanlab/segmentation/rank.html
+++ b/master/_modules/cleanlab/segmentation/rank.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/segmentation/summary.html b/master/_modules/cleanlab/segmentation/summary.html
index b597ac2a6..d3498d354 100644
--- a/master/_modules/cleanlab/segmentation/summary.html
+++ b/master/_modules/cleanlab/segmentation/summary.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/token_classification/filter.html b/master/_modules/cleanlab/token_classification/filter.html
index 1f9a8e945..43ae890e4 100644
--- a/master/_modules/cleanlab/token_classification/filter.html
+++ b/master/_modules/cleanlab/token_classification/filter.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/token_classification/rank.html b/master/_modules/cleanlab/token_classification/rank.html
index c56f0b1aa..cb89d85bf 100644
--- a/master/_modules/cleanlab/token_classification/rank.html
+++ b/master/_modules/cleanlab/token_classification/rank.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/cleanlab/token_classification/summary.html b/master/_modules/cleanlab/token_classification/summary.html
index 953d124e2..dde86da10 100644
--- a/master/_modules/cleanlab/token_classification/summary.html
+++ b/master/_modules/cleanlab/token_classification/summary.html
@@ -238,6 +238,7 @@
report
+
task
diff --git a/master/_modules/index.html b/master/_modules/index.html
index db00cc094..be778fc59 100644
--- a/master/_modules/index.html
+++ b/master/_modules/index.html
@@ -238,6 +238,7 @@
report
+
task
@@ -546,6 +547,7 @@
All modules for which code is available
cleanlab.datalab.internal.issue_manager.underperforming_group
cleanlab.datalab.internal.issue_manager_factory
cleanlab.datalab.internal.report
+
cleanlab.datalab.internal.task
cleanlab.dataset
cleanlab.experimental.cifar_cnn
cleanlab.experimental.coteaching
diff --git a/master/_sources/cleanlab/datalab/internal/index.rst b/master/_sources/cleanlab/datalab/internal/index.rst
index 2a5f6050c..6e8d335f7 100644
--- a/master/_sources/cleanlab/datalab/internal/index.rst
+++ b/master/_sources/cleanlab/datalab/internal/index.rst
@@ -21,3 +21,4 @@ internal
factory
issue_manager/index
report
+ task
diff --git a/master/_sources/cleanlab/datalab/internal/task.rst b/master/_sources/cleanlab/datalab/internal/task.rst
new file mode 100644
index 000000000..42ded02f5
--- /dev/null
+++ b/master/_sources/cleanlab/datalab/internal/task.rst
@@ -0,0 +1,11 @@
+task
+====
+
+.. note:: This module is not intended to be used directly by users. It is used by the :mod:`cleanlab.datalab.datalab` module.
+
+.. automodule:: cleanlab.datalab.internal.task
+ :autosummary:
+ :members:
+ :undoc-members:
+ :show-inheritance:
+ :ignore-module-all:
\ No newline at end of file
diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb
index 14895a485..a2c987fb9 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 57628de2b..ef09fc76f 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 7d51cb908..2f9a3eb5c 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 9a1588077..b2db9adb1 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 13339a083..5a14ef4c3 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 75b5b8936..a5797f6ee 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 1979e19cb..1b95dff44 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 06fb97345..3faabb648 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 8dfb97daa..3a2b29659 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -73,7 +73,7 @@
"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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 f491f2948..7163db06a 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 492030165..12eccde38 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 d3e9e9f07..cc4da388c 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 1ec4a4b86..59432150c 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 8b8491d07..b17411a2f 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 3e23a3669..e8f4d7239 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\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 44816420d..7f1825644 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@f03cdd8f3c75a5f550d00a20cb320ddc53d49705\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@16c5866a8b1b16ae3bc83a4f730d0c2a568a41cd\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/cleanlab/benchmarking/index.html b/master/cleanlab/benchmarking/index.html
index 162210136..8a9bb9d26 100644
--- a/master/cleanlab/benchmarking/index.html
+++ b/master/cleanlab/benchmarking/index.html
@@ -247,6 +247,7 @@
report
+
task
diff --git a/master/cleanlab/benchmarking/noise_generation.html b/master/cleanlab/benchmarking/noise_generation.html
index bbd1d658c..8836c2f5c 100644
--- a/master/cleanlab/benchmarking/noise_generation.html
+++ b/master/cleanlab/benchmarking/noise_generation.html
@@ -247,6 +247,7 @@
report
+
task
diff --git a/master/cleanlab/classification.html b/master/cleanlab/classification.html
index f120dc7b1..60c7cdacd 100644
--- a/master/cleanlab/classification.html
+++ b/master/cleanlab/classification.html
@@ -21,7 +21,7 @@
-
+
classification - cleanlab
@@ -247,6 +247,7 @@
report
+
task
@@ -1188,14 +1189,14 @@
-
+
diff --git a/master/cleanlab/count.html b/master/cleanlab/count.html
index 5735b2407..8f941152c 100644
--- a/master/cleanlab/count.html
+++ b/master/cleanlab/count.html
@@ -247,6 +247,7 @@
report
+
task
diff --git a/master/cleanlab/datalab/datalab.html b/master/cleanlab/datalab/datalab.html
index 11891904a..1c4cda8dd 100644
--- a/master/cleanlab/datalab/datalab.html
+++ b/master/cleanlab/datalab/datalab.html
@@ -247,6 +247,7 @@
report
+
task
@@ -590,8 +591,16 @@
-
task (str
) – The type of machine learning task that the dataset is used for.
-By default, this is set to “classification”, but you can also set it to “regression” if you are working with a regression dataset.
+
task (str
) –
The type of machine learning task that the dataset is used for.
+
+Supported tasks:
+”classification” (default): Multiclass classification
+”regression” : Regression
+”multilabel” : Multilabel classification
+
+
+
+
label_name (str
, optional ) – The name of the label column in the dataset.
image_key (str
, optional ) – Optional key that can be specified for image datasets to point to the field containing the actual images themselves.
If specified, additional image-specific issue types can be detected in the dataset.
diff --git a/master/cleanlab/datalab/guide/custom_issue_manager.html b/master/cleanlab/datalab/guide/custom_issue_manager.html
index 615989478..bb3974dcf 100644
--- a/master/cleanlab/datalab/guide/custom_issue_manager.html
+++ b/master/cleanlab/datalab/guide/custom_issue_manager.html
@@ -247,6 +247,7 @@
report
+
task
diff --git a/master/cleanlab/datalab/guide/generating_cluster_ids.html b/master/cleanlab/datalab/guide/generating_cluster_ids.html
index 350d429e2..f2f9a165a 100644
--- a/master/cleanlab/datalab/guide/generating_cluster_ids.html
+++ b/master/cleanlab/datalab/guide/generating_cluster_ids.html
@@ -247,6 +247,7 @@
report
+
task
diff --git a/master/cleanlab/datalab/guide/index.html b/master/cleanlab/datalab/guide/index.html
index ee426e8da..e445bf691 100644
--- a/master/cleanlab/datalab/guide/index.html
+++ b/master/cleanlab/datalab/guide/index.html
@@ -247,6 +247,7 @@
report
+
task
diff --git a/master/cleanlab/datalab/guide/issue_type_description.html b/master/cleanlab/datalab/guide/issue_type_description.html
index d56f01635..840646935 100644
--- a/master/cleanlab/datalab/guide/issue_type_description.html
+++ b/master/cleanlab/datalab/guide/issue_type_description.html
@@ -247,6 +247,7 @@
report
+
task
diff --git a/master/cleanlab/datalab/index.html b/master/cleanlab/datalab/index.html
index f655d21e5..7827d1462 100644
--- a/master/cleanlab/datalab/index.html
+++ b/master/cleanlab/datalab/index.html
@@ -247,6 +247,7 @@
report
+
task
@@ -576,6 +577,7 @@
issue_manager
report
+
task
diff --git a/master/cleanlab/datalab/internal/data.html b/master/cleanlab/datalab/internal/data.html
index e28272cf0..ee68c98ce 100644
--- a/master/cleanlab/datalab/internal/data.html
+++ b/master/cleanlab/datalab/internal/data.html
@@ -247,6 +247,7 @@
report
+
task
@@ -559,7 +560,7 @@
-Data
(data[, label_name, map_to_int, ...])
+Data
(data, task[, label_name])
Class that holds and validates datasets for Datalab.
Label
(*, data[, label_name, map_to_int])
@@ -661,7 +662,7 @@
-class cleanlab.datalab.internal.data. Data ( data , label_name = None , map_to_int = True , is_multilabel = False ) [source]
+class cleanlab.datalab.internal.data. Data ( data , task , label_name = None ) [source]
Bases: object
Class that holds and validates datasets for Datalab.
Internally, the data is stored as a datasets.Dataset object and the labels
@@ -714,8 +715,24 @@
label_name (Union[str
, List[str]]
) – Name of the label column in the dataset.
-map_to_int (bool
) – Whether to map the labels to integers, e.g. [0, 1, …, K-1] where K is the number of classes.
-If False, the labels are not mapped to integers, e.g. for regression tasks.
+task (Task
) –
The task associated with the dataset. This is used to determine how to
+to format the labels.
+Note:
+
+
+
will be mapped to integers, e.g. [0, 1, …, K-1] where K is the number
+of classes. If the task is a regression task, the labels will not be
+mapped to integers.
+
+If the task is a multilabel task, the labels will be formatted as a
+list of lists, e.g. [[0, 1], [1, 2], [0, 2]] where each sublist contains
+the labels for a single example. If the task is not a multilabel task,
+the labels will be formatted as a 1D numpy array.
+
+
+
diff --git a/master/cleanlab/datalab/internal/data_issues.html b/master/cleanlab/datalab/internal/data_issues.html
index 5ce254609..fb2d67592 100644
--- a/master/cleanlab/datalab/internal/data_issues.html
+++ b/master/cleanlab/datalab/internal/data_issues.html
@@ -247,6 +247,7 @@
report
+task
@@ -576,7 +577,7 @@
Parameters:
data (Data
) – The data object for which the issues are being collected.
-strategy (_InfoStrategy
) – Strategy used for processing info dictionaries.
+strategy (Type
[_InfoStrategy
] ) – Strategy used for processing info dictionaries.
diff --git a/master/cleanlab/datalab/internal/factory.html b/master/cleanlab/datalab/internal/factory.html
index bc686db09..d8b6d1571 100644
--- a/master/cleanlab/datalab/internal/factory.html
+++ b/master/cleanlab/datalab/internal/factory.html
@@ -247,6 +247,7 @@
report
+task
@@ -562,7 +563,7 @@
REGISTRY
-Registry of issue managers that can be constructed from a string and used in the Datalab class.
+Registry of issue managers that can be constructed from a task and issue type and used in the Datalab class.
@@ -585,16 +586,41 @@
-cleanlab.datalab.internal.issue_manager_factory. REGISTRY : Dict [ str , Dict [ str , Type [ IssueManager ] ] ]
-Registry of issue managers that can be constructed from a string
+cleanlab.datalab.internal.issue_manager_factory. REGISTRY : Dict [ Task , Dict [ str , Type [ IssueManager ] ] ]
+
Registry of issue managers that can be constructed from a task and issue type
and used in the Datalab class.
-Currently, the following issue managers are registered by default:
-
+Currently, the following issue managers are registered by default for a given task:
+
+Classification:
+
+
+
+Regression:
+
+
+
+Multilabel:
+
+
+
Warning
@@ -611,7 +637,9 @@
cls (Type
[IssueManager
] ) – A subclass of
IssueManager
.
-task (str
) – Specific machine learning task like classification or regression.
+task (str
) – Specific machine learning task like classification or regression.
+See Task.from_str <cleanlab.datalab.internal.task.Task.from_str>`()
for more details,
+to see which task type corresponds to which string.
Return type:
@@ -667,8 +695,16 @@
cleanlab.datalab.internal.issue_manager_factory. list_default_issue_types ( task ) [source]
Returns a list of the issue types that are run by default
-when find_issues()
is called without specifying issue_types .
-:rtype: List
[str
]
+when find_issues()
is called without specifying issue_types .
+
+Return type:
+List
[str
]
+
+
+
+task : Specific machine learning task supported by Datalab.
+
+
See also
REGISTRY
: All available issue types and their corresponding issue managers can be found here.
diff --git a/master/cleanlab/datalab/internal/index.html b/master/cleanlab/datalab/internal/index.html
index cabbd4c07..631e9246e 100644
--- a/master/cleanlab/datalab/internal/index.html
+++ b/master/cleanlab/datalab/internal/index.html
@@ -247,6 +247,7 @@
report
+
task
@@ -580,6 +581,10 @@
task
+
diff --git a/master/cleanlab/datalab/internal/issue_finder.html b/master/cleanlab/datalab/internal/issue_finder.html
index 1fd909454..bee50ae2d 100644
--- a/master/cleanlab/datalab/internal/issue_finder.html
+++ b/master/cleanlab/datalab/internal/issue_finder.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html b/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html
index 8ee23a120..b1e56428b 100644
--- a/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html
+++ b/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html
@@ -245,6 +245,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/data_valuation.html b/master/cleanlab/datalab/internal/issue_manager/data_valuation.html
index f891af754..f4f62278e 100644
--- a/master/cleanlab/datalab/internal/issue_manager/data_valuation.html
+++ b/master/cleanlab/datalab/internal/issue_manager/data_valuation.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/duplicate.html b/master/cleanlab/datalab/internal/issue_manager/duplicate.html
index ed90d1972..cedf87269 100644
--- a/master/cleanlab/datalab/internal/issue_manager/duplicate.html
+++ b/master/cleanlab/datalab/internal/issue_manager/duplicate.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/imbalance.html b/master/cleanlab/datalab/internal/issue_manager/imbalance.html
index d37348f25..934ba4d41 100644
--- a/master/cleanlab/datalab/internal/issue_manager/imbalance.html
+++ b/master/cleanlab/datalab/internal/issue_manager/imbalance.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/index.html b/master/cleanlab/datalab/internal/issue_manager/index.html
index 3b05d49d4..819f2fd4a 100644
--- a/master/cleanlab/datalab/internal/issue_manager/index.html
+++ b/master/cleanlab/datalab/internal/issue_manager/index.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/issue_manager.html b/master/cleanlab/datalab/internal/issue_manager/issue_manager.html
index 6fbb7dd33..8afc6ac1a 100644
--- a/master/cleanlab/datalab/internal/issue_manager/issue_manager.html
+++ b/master/cleanlab/datalab/internal/issue_manager/issue_manager.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/label.html b/master/cleanlab/datalab/internal/issue_manager/label.html
index 5132926ac..2e6f06ca3 100644
--- a/master/cleanlab/datalab/internal/issue_manager/label.html
+++ b/master/cleanlab/datalab/internal/issue_manager/label.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/noniid.html b/master/cleanlab/datalab/internal/issue_manager/noniid.html
index 9e7f2509d..c2c0961da 100644
--- a/master/cleanlab/datalab/internal/issue_manager/noniid.html
+++ b/master/cleanlab/datalab/internal/issue_manager/noniid.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/null.html b/master/cleanlab/datalab/internal/issue_manager/null.html
index 2f07f81b2..cb56357f0 100644
--- a/master/cleanlab/datalab/internal/issue_manager/null.html
+++ b/master/cleanlab/datalab/internal/issue_manager/null.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/outlier.html b/master/cleanlab/datalab/internal/issue_manager/outlier.html
index 14e79ef2f..737e4c382 100644
--- a/master/cleanlab/datalab/internal/issue_manager/outlier.html
+++ b/master/cleanlab/datalab/internal/issue_manager/outlier.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/regression/index.html b/master/cleanlab/datalab/internal/issue_manager/regression/index.html
index 85a9c569f..8462621af 100644
--- a/master/cleanlab/datalab/internal/issue_manager/regression/index.html
+++ b/master/cleanlab/datalab/internal/issue_manager/regression/index.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/regression/label.html b/master/cleanlab/datalab/internal/issue_manager/regression/label.html
index c7ee7d744..d632871ce 100644
--- a/master/cleanlab/datalab/internal/issue_manager/regression/label.html
+++ b/master/cleanlab/datalab/internal/issue_manager/regression/label.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html b/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html
index db285bdfa..68132f3c5 100644
--- a/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html
+++ b/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html
@@ -247,6 +247,7 @@
report
+task
diff --git a/master/cleanlab/datalab/internal/report.html b/master/cleanlab/datalab/internal/report.html
index 1b1138e9e..009722b39 100644
--- a/master/cleanlab/datalab/internal/report.html
+++ b/master/cleanlab/datalab/internal/report.html
@@ -21,7 +21,7 @@
-
+
report - cleanlab
@@ -247,6 +247,7 @@
report
+task
@@ -565,7 +566,8 @@ report
@@ -648,6 +665,8 @@
C
(cleanlab.datalab.internal.data.MultiLabel property)
+
CLASSIFICATION (cleanlab.datalab.internal.task.Task attribute)
+
ClassImbalanceIssueManager (class in cleanlab.datalab.internal.issue_manager.imbalance)
ClassLabelScorer (class in cleanlab.internal.multilabel_scorer)
@@ -804,6 +823,13 @@ C
+
+ cleanlab.datalab.internal.task
+
+
@@ -918,6 +944,8 @@ C
module
+
+
cleanlab.internal.validation
@@ -925,8 +953,6 @@ C
module
-
-
- from_str() (cleanlab.internal.multilabel_scorer.ClassLabelScorer class method)
+ from_str() (cleanlab.datalab.internal.task.Task class method)
+
+
@@ -1679,6 +1709,12 @@
I
(cleanlab.datalab.internal.data.MultiLabel property)
+
is_classification (cleanlab.datalab.internal.task.Task property)
+
+
is_multilabel (cleanlab.datalab.internal.task.Task property)
+
+
is_regression (cleanlab.datalab.internal.task.Task property)
+
is_tensorflow_dataset() (in module cleanlab.internal.util)
is_torch_dataset() (in module cleanlab.internal.util)
@@ -1920,6 +1956,8 @@ M
cleanlab.datalab.internal.issue_manager_factory
cleanlab.datalab.internal.report
+
+
cleanlab.datalab.internal.task
cleanlab.dataset
@@ -2017,6 +2055,8 @@
M
MultiClass (class in cleanlab.datalab.internal.data)
MultiLabel (class in cleanlab.datalab.internal.data)
+
+
MULTILABEL (cleanlab.datalab.internal.task.Task attribute)
multilabel_health_summary() (in module cleanlab.multilabel_classification.dataset)
@@ -2249,6 +2289,8 @@
R
REGISTRY (in module cleanlab.datalab.internal.issue_manager_factory)
+
+
REGRESSION (cleanlab.datalab.internal.task.Task attribute)
RegressionLabelIssueManager (class in cleanlab.datalab.internal.issue_manager.regression.label)
@@ -2438,6 +2480,8 @@
T
(cleanlab.experimental.mnist_pytorch.SimpleNet attribute)
+
Task (class in cleanlab.datalab.internal.task)
+
temp_scale_pred_probs() (in module cleanlab.internal.multiannotator_utils)
test_batch_size (cleanlab.experimental.mnist_pytorch.CNN attribute)
diff --git a/master/index.html b/master/index.html
index ed1606852..c0fb0d405 100644
--- a/master/index.html
+++ b/master/index.html
@@ -247,6 +247,7 @@
report
+
task
diff --git a/master/migrating/migrate_v2.html b/master/migrating/migrate_v2.html
index 6a86064be..b3bbe736b 100644
--- a/master/migrating/migrate_v2.html
+++ b/master/migrating/migrate_v2.html
@@ -247,6 +247,7 @@
report
+
task
diff --git a/master/objects.inv b/master/objects.inv
index 077b158ee..1df5aa507 100644
Binary files a/master/objects.inv and b/master/objects.inv differ
diff --git a/master/py-modindex.html b/master/py-modindex.html
index 2c48873ca..78dc5dc48 100644
--- a/master/py-modindex.html
+++ b/master/py-modindex.html
@@ -237,6 +237,7 @@
report
+
task
@@ -632,6 +633,12 @@
Python Module Index
cleanlab.datalab.internal.report
+
+
+
+ cleanlab.datalab.internal.task
+
+
diff --git a/master/search.html b/master/search.html
index 93d328ace..f621cbc68 100644
--- a/master/search.html
+++ b/master/search.html
@@ -236,6 +236,7 @@
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+task
diff --git a/master/searchindex.js b/master/searchindex.js
index b00934886..3be8b3e75 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
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Install and import required dependencies": [[76, "1.-Install-and-import-required-dependencies"], [82, "1.-Install-and-import-required-dependencies"], [85, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[76, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[76, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[76, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[76, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[76, "Get-additional-information"]], "Near duplicate issues": [[76, "Near-duplicate-issues"], [82, "Near-duplicate-issues"]], "Datalab Tutorials": [[77, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[78, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "1. Install required dependencies": [[78, "1.-Install-required-dependencies"], [79, "1.-Install-required-dependencies"], [90, "1.-Install-required-dependencies"], [92, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"]], "2. Load and process the data": [[78, "2.-Load-and-process-the-data"], [90, "2.-Load-and-process-the-data"], [92, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[78, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [92, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Construct K nearest neighbours graph": [[78, "4.-Construct-K-nearest-neighbours-graph"]], "Label issues": [[78, "Label-issues"], [79, "Label-issues"], [82, "Label-issues"]], "Outlier issues": [[78, "Outlier-issues"], [79, "Outlier-issues"], [82, "Outlier-issues"]], "Near-duplicate issues": [[78, "Near-duplicate-issues"], [79, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[79, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "2. Load and format the text dataset": [[79, "2.-Load-and-format-the-text-dataset"], [93, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[79, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[79, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[79, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[80, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[80, "Install-dependencies-and-import-them"], [83, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[80, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[80, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[81, "FAQ"]], "What data can cleanlab detect issues in?": [[81, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[81, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[81, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[81, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[81, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[81, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[81, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[81, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[81, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[81, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[81, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[81, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[81, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[81, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[82, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[82, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[82, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[82, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[82, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[82, "7.-Use-cleanlab-to-find-issues"]], "View report": [[82, "View-report"]], "View most likely examples with label errors": [[82, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[82, "View-most-severe-outliers"]], "View sets of near duplicate images": [[82, "View-sets-of-near-duplicate-images"]], "Dark images": [[82, "Dark-images"]], "View top examples of dark images": [[82, "View-top-examples-of-dark-images"]], "Low information images": [[82, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[83, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[83, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[83, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[83, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[83, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[83, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[83, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[83, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[83, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[83, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[83, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[83, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[83, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[83, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[83, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[83, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[83, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[83, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[83, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[83, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[83, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[83, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[84, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[85, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[85, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[85, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[85, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[85, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[85, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[85, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[85, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[85, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[86, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[86, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[86, "2.-Format-data,-labels,-and-model-predictions"], [87, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[86, "3.-Use-cleanlab-to-find-label-issues"], [87, "3.-Use-cleanlab-to-find-label-issues"], [91, "3.-Use-cleanlab-to-find-label-issues"], [94, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[86, "Label-quality-scores"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[86, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Finding Label Errors in Object Detection Datasets": [[87, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[87, "1.-Install-required-dependencies-and-download-data"], [91, "1.-Install-required-dependencies-and-download-data"], [94, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[87, "Get-label-quality-scores"], [91, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[87, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[87, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[87, "Other-uses-of-visualize"]], "Exploratory data analysis": [[87, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[88, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[88, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[88, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[88, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[88, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[88, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[89, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[89, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[89, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[90, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[90, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[90, "4.-Train-a-more-robust-model-from-noisy-labels"], [93, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[90, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[91, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[91, "2.-Get-data,-labels,-and-pred_probs"], [94, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[91, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[91, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[91, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[92, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[92, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[92, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[93, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[93, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[94, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[94, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[94, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[94, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[94, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.datalab.datalab"], [9, 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cleanlab.datalab.internal.issue_manager.data_valuation)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info 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attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[20, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], 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"experimental"]], "label_issues_batched": [[35, "module-cleanlab.experimental.label_issues_batched"]], "mnist_pytorch": [[36, "module-cleanlab.experimental.mnist_pytorch"]], "filter": [[37, "module-cleanlab.filter"], [53, "module-cleanlab.multilabel_classification.filter"], [56, "filter"], [65, "filter"], [69, "module-cleanlab.token_classification.filter"]], "label_quality_utils": [[39, "module-cleanlab.internal.label_quality_utils"]], "latent_algebra": [[40, "module-cleanlab.internal.latent_algebra"]], "multiannotator_utils": [[41, "module-cleanlab.internal.multiannotator_utils"]], "multilabel_scorer": [[42, "module-cleanlab.internal.multilabel_scorer"]], "multilabel_utils": [[43, "module-cleanlab.internal.multilabel_utils"]], "token_classification_utils": [[45, "module-cleanlab.internal.token_classification_utils"]], "util": [[46, "module-cleanlab.internal.util"]], "validation": [[47, "module-cleanlab.internal.validation"]], "fasttext": [[48, "fasttext"]], "models": [[49, "models"]], "keras": [[50, "module-cleanlab.models.keras"]], "multiannotator": [[51, "module-cleanlab.multiannotator"]], "multilabel_classification": [[54, "multilabel-classification"]], "rank": [[55, "module-cleanlab.multilabel_classification.rank"], [58, "module-cleanlab.object_detection.rank"], [61, "module-cleanlab.rank"], [67, "module-cleanlab.segmentation.rank"], [71, "module-cleanlab.token_classification.rank"]], "object_detection": [[57, "object-detection"]], "summary": [[59, "summary"], [68, "module-cleanlab.segmentation.summary"], [72, "module-cleanlab.token_classification.summary"]], "regression.learn": [[63, "module-cleanlab.regression.learn"]], "regression.rank": [[64, "module-cleanlab.regression.rank"]], "segmentation": [[66, "segmentation"]], "token_classification": [[70, "token-classification"]], "cleanlab open-source documentation": [[73, "cleanlab-open-source-documentation"]], "Quickstart": [[73, "quickstart"]], "1. Install cleanlab": [[73, "install-cleanlab"]], "2. Find common issues in your data": [[73, "find-common-issues-in-your-data"]], "3. Handle label errors and train robust models with noisy labels": [[73, "handle-label-errors-and-train-robust-models-with-noisy-labels"]], "4. Dataset curation: fix dataset-level issues": [[73, "dataset-curation-fix-dataset-level-issues"]], "5. Improve your data via many other techniques": [[73, "improve-your-data-via-many-other-techniques"]], "Contributing": [[73, "contributing"]], "Easy Mode": [[73, "easy-mode"], [79, "Easy-Mode"], [80, "Easy-Mode"], [83, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[74, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[74, "function-and-class-name-changes"]], "Module name changes": [[74, "module-name-changes"]], "New modules": [[74, "new-modules"]], "Removed modules": [[74, "removed-modules"]], "Common argument and variable name changes": [[74, "common-argument-and-variable-name-changes"]], "Audio Classification with SpeechBrain and Cleanlab": [[75, "Audio-Classification-with-SpeechBrain-and-Cleanlab"]], "1. Install dependencies and import them": [[75, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[75, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[75, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[75, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[75, "5.-Use-cleanlab-to-find-label-issues"], [79, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[76, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[76, "Install-and-import-required-dependencies"]], "Create and load the data": [[76, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[76, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[76, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[76, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[76, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[76, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[76, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[77, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[77, "1.-Install-and-import-required-dependencies"], [83, "1.-Install-and-import-required-dependencies"], [86, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[77, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[77, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[77, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[77, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[77, "Get-additional-information"]], "Near duplicate issues": [[77, "Near-duplicate-issues"], [83, "Near-duplicate-issues"]], "Datalab Tutorials": [[78, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[79, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "1. Install required dependencies": [[79, "1.-Install-required-dependencies"], [80, "1.-Install-required-dependencies"], [91, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"], [94, "1.-Install-required-dependencies"]], "2. Load and process the data": [[79, "2.-Load-and-process-the-data"], [91, "2.-Load-and-process-the-data"], [93, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[79, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [93, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Construct K nearest neighbours graph": [[79, "4.-Construct-K-nearest-neighbours-graph"]], "Label issues": [[79, "Label-issues"], [80, "Label-issues"], [83, "Label-issues"]], "Outlier issues": [[79, "Outlier-issues"], [80, "Outlier-issues"], [83, "Outlier-issues"]], "Near-duplicate issues": [[79, "Near-duplicate-issues"], [80, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[80, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "2. Load and format the text dataset": [[80, "2.-Load-and-format-the-text-dataset"], [94, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[80, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[80, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[80, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[81, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[81, "Install-dependencies-and-import-them"], [84, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[81, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[81, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[82, "FAQ"]], "What data can cleanlab detect issues in?": [[82, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[82, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[82, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[82, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[82, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[82, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[82, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[82, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[82, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[82, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[82, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[82, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[82, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[82, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[83, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[83, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[83, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[83, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[83, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[83, "7.-Use-cleanlab-to-find-issues"]], "View report": [[83, "View-report"]], "View most likely examples with label errors": [[83, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[83, "View-most-severe-outliers"]], "View sets of near duplicate images": [[83, "View-sets-of-near-duplicate-images"]], "Dark images": [[83, "Dark-images"]], "View top examples of dark images": [[83, "View-top-examples-of-dark-images"]], "Low information images": [[83, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[84, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[84, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[84, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[84, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[84, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[84, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[84, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[84, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[84, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[84, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[84, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[84, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[84, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[84, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[84, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[84, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[84, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[84, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[84, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[84, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[84, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[84, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[85, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[86, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[86, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[86, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[86, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[86, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[86, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[86, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[86, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[86, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[87, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[87, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[87, "2.-Format-data,-labels,-and-model-predictions"], [88, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[87, "3.-Use-cleanlab-to-find-label-issues"], [88, "3.-Use-cleanlab-to-find-label-issues"], [92, "3.-Use-cleanlab-to-find-label-issues"], [95, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[87, "Label-quality-scores"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[87, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Finding Label Errors in Object Detection Datasets": [[88, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[88, "1.-Install-required-dependencies-and-download-data"], [92, "1.-Install-required-dependencies-and-download-data"], [95, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[88, "Get-label-quality-scores"], [92, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[88, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[88, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[88, "Other-uses-of-visualize"]], "Exploratory data analysis": [[88, "Exploratory-data-analysis"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[89, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[89, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[89, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[89, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[89, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[89, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[90, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[90, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[90, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[91, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[91, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[91, "4.-Train-a-more-robust-model-from-noisy-labels"], [94, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[91, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[92, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[92, "2.-Get-data,-labels,-and-pred_probs"], [95, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[92, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[92, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[92, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[93, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[93, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[93, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[94, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[94, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[95, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[95, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[95, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[95, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[95, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.datalab.datalab"], [9, 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(cleanlab.datalab.internal.data.multilabel property)": [[10, "cleanlab.datalab.internal.data.MultiLabel.class_names"]], "cleanlab.datalab.internal.data": [[10, "module-cleanlab.datalab.internal.data"]], "has_labels (cleanlab.datalab.internal.data.data property)": [[10, "cleanlab.datalab.internal.data.Data.has_labels"]], "is_available (cleanlab.datalab.internal.data.label property)": [[10, "cleanlab.datalab.internal.data.Label.is_available"]], "is_available (cleanlab.datalab.internal.data.multiclass property)": [[10, "cleanlab.datalab.internal.data.MultiClass.is_available"]], "is_available (cleanlab.datalab.internal.data.multilabel property)": [[10, "cleanlab.datalab.internal.data.MultiLabel.is_available"]], "with_traceback() (cleanlab.datalab.internal.data.dataformaterror method)": [[10, "cleanlab.datalab.internal.data.DataFormatError.with_traceback"]], "with_traceback() (cleanlab.datalab.internal.data.datasetdicterror method)": [[10, 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"set_health_score() (cleanlab.datalab.internal.data_issues.dataissues method)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.set_health_score"]], "statistics (cleanlab.datalab.internal.data_issues.dataissues property)": [[11, "cleanlab.datalab.internal.data_issues.DataIssues.statistics"]], "registry (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.REGISTRY"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, "module-cleanlab.datalab.internal.issue_manager_factory"]], "list_default_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_default_issue_types"]], "list_possible_issue_types() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.list_possible_issue_types"]], "register() (in module cleanlab.datalab.internal.issue_manager_factory)": [[12, "cleanlab.datalab.internal.issue_manager_factory.register"]], "cleanlab.datalab.internal": [[13, "module-cleanlab.datalab.internal"]], "issuefinder (class in cleanlab.datalab.internal.issue_finder)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder"]], "cleanlab.datalab.internal.issue_finder": [[14, "module-cleanlab.datalab.internal.issue_finder"]], "find_issues() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.find_issues"]], "get_available_issue_types() (cleanlab.datalab.internal.issue_finder.issuefinder method)": [[14, "cleanlab.datalab.internal.issue_finder.IssueFinder.get_available_issue_types"]], "default_threshold (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.DEFAULT_THRESHOLD"]], "datavaluationissuemanager (class in cleanlab.datalab.internal.issue_manager.data_valuation)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager class method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.verbosity_levels"]], "nearduplicateissuemanager (class in cleanlab.datalab.internal.issue_manager.duplicate)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[17, "module-cleanlab.datalab.internal.issue_manager.duplicate"]], "collect_info() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager class method)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.make_summary"]], "near_duplicate_sets (cleanlab.datalab.internal.issue_manager.duplicate.nearduplicateissuemanager attribute)": [[17, "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager.near_duplicate_sets"]], "report() 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[[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager 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"cleanlab.internal.multilabel_scorer.multilabel_py"]], "possible_methods (cleanlab.internal.multilabel_scorer.aggregator attribute)": [[42, "cleanlab.internal.multilabel_scorer.Aggregator.possible_methods"]], "softmin() (in module cleanlab.internal.multilabel_scorer)": [[42, "cleanlab.internal.multilabel_scorer.softmin"]], "cleanlab.internal.multilabel_utils": [[43, "module-cleanlab.internal.multilabel_utils"]], "get_onehot_num_classes() (in module cleanlab.internal.multilabel_utils)": [[43, "cleanlab.internal.multilabel_utils.get_onehot_num_classes"]], "int2onehot() (in module cleanlab.internal.multilabel_utils)": [[43, "cleanlab.internal.multilabel_utils.int2onehot"]], "onehot2int() (in module cleanlab.internal.multilabel_utils)": [[43, "cleanlab.internal.multilabel_utils.onehot2int"]], "stack_complement() (in module cleanlab.internal.multilabel_utils)": [[43, "cleanlab.internal.multilabel_utils.stack_complement"]], "cleanlab.internal.outlier": [[44, 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"get_normalized_margin_for_each_label() (in module cleanlab.rank)": [[61, "cleanlab.rank.get_normalized_margin_for_each_label"]], "get_self_confidence_for_each_label() (in module cleanlab.rank)": [[61, "cleanlab.rank.get_self_confidence_for_each_label"]], "order_label_issues() (in module cleanlab.rank)": [[61, "cleanlab.rank.order_label_issues"]], "cleanlab.regression": [[62, "module-cleanlab.regression"]], "cleanlearning (class in cleanlab.regression.learn)": [[63, "cleanlab.regression.learn.CleanLearning"]], "__init_subclass__() (cleanlab.regression.learn.cleanlearning class method)": [[63, "cleanlab.regression.learn.CleanLearning.__init_subclass__"]], "cleanlab.regression.learn": [[63, "module-cleanlab.regression.learn"]], "find_label_issues() (cleanlab.regression.learn.cleanlearning method)": [[63, "cleanlab.regression.learn.CleanLearning.find_label_issues"]], "fit() (cleanlab.regression.learn.cleanlearning method)": [[63, "cleanlab.regression.learn.CleanLearning.fit"]], 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"cleanlab.segmentation.rank": [[67, "module-cleanlab.segmentation.rank"]], "get_label_quality_scores() (in module cleanlab.segmentation.rank)": [[67, "cleanlab.segmentation.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.segmentation.rank)": [[67, "cleanlab.segmentation.rank.issues_from_scores"]], "cleanlab.segmentation.summary": [[68, "module-cleanlab.segmentation.summary"]], "common_label_issues() (in module cleanlab.segmentation.summary)": [[68, "cleanlab.segmentation.summary.common_label_issues"]], "display_issues() (in module cleanlab.segmentation.summary)": [[68, "cleanlab.segmentation.summary.display_issues"]], "filter_by_class() (in module cleanlab.segmentation.summary)": [[68, "cleanlab.segmentation.summary.filter_by_class"]], "cleanlab.token_classification.filter": [[69, "module-cleanlab.token_classification.filter"]], "find_label_issues() (in module cleanlab.token_classification.filter)": [[69, "cleanlab.token_classification.filter.find_label_issues"]], "cleanlab.token_classification": [[70, "module-cleanlab.token_classification"]], "cleanlab.token_classification.rank": [[71, "module-cleanlab.token_classification.rank"]], "get_label_quality_scores() (in module cleanlab.token_classification.rank)": [[71, "cleanlab.token_classification.rank.get_label_quality_scores"]], "issues_from_scores() (in module cleanlab.token_classification.rank)": [[71, "cleanlab.token_classification.rank.issues_from_scores"]], "cleanlab.token_classification.summary": [[72, "module-cleanlab.token_classification.summary"]], "common_label_issues() (in module cleanlab.token_classification.summary)": [[72, "cleanlab.token_classification.summary.common_label_issues"]], "display_issues() (in module cleanlab.token_classification.summary)": [[72, "cleanlab.token_classification.summary.display_issues"]], "filter_by_token() (in module cleanlab.token_classification.summary)": [[72, "cleanlab.token_classification.summary.filter_by_token"]]}})
\ No newline at end of file
diff --git a/master/tutorials/audio.html b/master/tutorials/audio.html
index 68762b88b..c547ebe88 100644
--- a/master/tutorials/audio.html
+++ b/master/tutorials/audio.html
@@ -248,6 +248,7 @@
report
+task
@@ -1274,7 +1275,7 @@ 5. Use cleanlab to find label issues