If provided, this must be a 2D array with shape (num_examples, num_features).
knn_graph :
- Sparse matrix representing distances between examples in the dataset in a k nearest neighbor graph.
+ Sparse matrix of precomputed distances between examples in the dataset in a k nearest neighbor graph.
- If provided, this must be a square CSR matrix with shape (num_examples, num_examples) and (k*num_examples) non-zero entries (k is the number of nearest neighbors considered for each example)
+ If provided, this must be a square CSR matrix with shape ``(num_examples, num_examples)`` and ``(k*num_examples)`` non-zero entries (``k`` is the number of nearest neighbors considered for each example),
evenly distributed across the rows.
- The non-zero entries must be the distances between the corresponding examples. Self-distances must be omitted
- (i.e. the diagonal must be all zeros and the k nearest neighbors of each example must not include itself).
+ Each non-zero entry in this matrix is a distance between a pair of examples in the dataset. Self-distances must be omitted
+ (i.e. diagonal must be all zeros, k nearest neighbors for each example do not include the example itself).
+
+ This CSR format uses three 1D arrays (`data`, `indices`, `indptr`) to store a 2D matrix ``M``:
+
+ - `data`: 1D array containing all the non-zero elements of matrix ``M``, listed in a row-wise fashion (but sorted within each row).
+ - `indices`: 1D array storing the column indices in matrix ``M`` of these non-zero elements. Each entry in `indices` corresponds to an entry in `data`, indicating the column of ``M`` containing this entry.
+ - `indptr`: 1D array indicating the start and end indices in `data` for each row of matrix ``M``. The non-zero elements of the i-th row of ``M`` are stored from ``data[indptr[i]]`` to ``data[indptr[i+1]]``.
+
+ Within each row of matrix ``M`` (defined by the ranges in `indptr`), the corresponding non-zero entries (distances) of `knn_graph` must be sorted in ascending order (specifically in the segments of the `data` array that correspond to each row of ``M``). The `indices` array must also reflect this ordering, maintaining the correct column positions for these sorted distances.
+
+ This type of matrix is returned by the method: `sklearn.neighbors.NearestNeighbors.kneighbors_graph <https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors.kneighbors_graph>`_.
+
+ Below is an example to illustrate:
+
+ .. code-block:: python
+
+ knn_graph.todense()
+ # matrix([[0. , 0.3, 0.2],
+ # [0.3, 0. , 0.4],
+ # [0.2, 0.4, 0. ]])
+
+ knn_graph.data
+ # array([0.2, 0.3, 0.3, 0.4, 0.2, 0.4])
+ # Here, 0.2 and 0.3 are the sorted distances in the first row, 0.3 and 0.4 in the second row, and so on.
+
+ knn_graph.indices
+ # array([2, 1, 0, 2, 0, 1])
+ # Corresponding neighbor indices for the distances from the `data` array.
+
+ knn_graph.indptr
+ # array([0, 2, 4, 6])
+ # The non-zero entries in the first row are stored from `knn_graph.data[0]` to `knn_graph.data[2]`, the second row from `knn_graph.data[2]` to `knn_graph.data[4]`, and so on.
For any duplicated examples i,j whose distance is 0, there should be an *explicit* zero stored in the matrix, i.e. ``knn_graph[i,j] = 0``.
@@ -737,6 +768,11 @@ Source code for cleanlab.datalab.datalab
If `knn_graph` is not provided, it is constructed based on the provided `features`.
If neither `knn_graph` nor `features` are provided, certain issue types like (near) duplicates will not be considered.
+ .. seealso::
+ See the
+ `scipy.sparse.csr_matrix documentation <https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html>`_
+ for more details on the CSR matrix format.
+
issue_types :
Collection specifying which types of issues to consider in audit and any non-default parameter settings to use.
If unspecified, a default set of issue types and recommended parameter settings is considered.
diff --git a/master/_modules/cleanlab/datalab/internal/issue_finder.html b/master/_modules/cleanlab/datalab/internal/issue_finder.html
index 75699ced3..f0cf0f36d 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_finder.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_finder.html
@@ -760,16 +760,7 @@ Source code for cleanlab.datalab.internal.issue_finder
knn_graph :
Sparse matrix representing distances between examples in the dataset in a k nearest neighbor graph.
- If provided, this must be a square CSR matrix with shape (num_examples, num_examples) and (k*num_examples) non-zero entries (k is the number of nearest neighbors considered for each example)
- evenly distributed across the rows.
- The non-zero entries must be the distances between the corresponding examples. Self-distances must be omitted
- (i.e. the diagonal must be all zeros and the k nearest neighbors of each example must not include itself).
-
- For any duplicated examples i,j whose distance is 0, there should be an *explicit* zero stored in the matrix, i.e. ``knn_graph[i,j] = 0``.
-
- If both `knn_graph` and `features` are provided, the `knn_graph` will take precendence.
- If `knn_graph` is not provided, it is constructed based on the provided `features`.
- If neither `knn_graph` nor `features` are provided, certain issue types like (near) duplicates will not be considered.
+ For details, refer to the documentation of the same argument in :py:class:`Datalab.find_issues <cleanlab.datalab.datalab.Datalab.find_issues>`
issue_types :
Collection specifying which types of issues to consider in audit and any non-default parameter settings to use.
diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb
index ddb670d4a..830eccd66 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 5983c81f3..ae9056a1d 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 872f7055a..a04a11fe5 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 d0a9cd867..08fa0d0e6 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 3549a5cc8..68b8a5e1d 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 6cb67da1f..3f4cee4a0 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 7c7e763bf..33cb4e139 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 b21e845af..236619040 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 5316902f3..03616c3f1 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -72,7 +72,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 9dc19c62f..c1ec95508 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 25271206b..9456d9559 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 1c0bf7c95..ff8c12eb6 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 bb1efdb68..02ad77271 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 1ecc90115..2fe704b8a 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 5330d3014..4181c147e 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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 23ad56571..47d2f0746 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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/cleanlab/datalab/datalab.html b/master/cleanlab/datalab/datalab.html
index 40ffad715..abd434988 100644
--- a/master/cleanlab/datalab/datalab.html
+++ b/master/cleanlab/datalab/datalab.html
@@ -720,15 +720,48 @@
features (Optional[np.ndarray]) –
Feature embeddings (vector representations) of every example in the dataset.
If provided, this must be a 2D array with shape (num_examples, num_features).
knn_graph (Optional[csr_matrix]) –
Sparse matrix representing distances between examples in the dataset in a k nearest neighbor graph.
-If provided, this must be a square CSR matrix with shape (num_examples, num_examples) and (k*num_examples) non-zero entries (k is the number of nearest neighbors considered for each example)
+
knn_graph (Optional[csr_matrix]) –
Sparse matrix of precomputed distances between examples in the dataset in a k nearest neighbor graph.
+If provided, this must be a square CSR matrix with shape (num_examples, num_examples) and (k*num_examples) non-zero entries (k is the number of nearest neighbors considered for each example),
evenly distributed across the rows.
-The non-zero entries must be the distances between the corresponding examples. Self-distances must be omitted
-(i.e. the diagonal must be all zeros and the k nearest neighbors of each example must not include itself).
+Each non-zero entry in this matrix is a distance between a pair of examples in the dataset. Self-distances must be omitted
+(i.e. diagonal must be all zeros, k nearest neighbors for each example do not include the example itself).
+This CSR format uses three 1D arrays (data, indices, indptr) to store a 2D matrix M:
+
+data: 1D array containing all the non-zero elements of matrix M, listed in a row-wise fashion (but sorted within each row).
indices: 1D array storing the column indices in matrix M of these non-zero elements. Each entry in indices corresponds to an entry in data, indicating the column of M containing this entry.
indptr: 1D array indicating the start and end indices in data for each row of matrix M. The non-zero elements of the i-th row of M are stored from data[indptr[i]] to data[indptr[i+1]].
+Within each row of matrix M (defined by the ranges in indptr), the corresponding non-zero entries (distances) of knn_graph must be sorted in ascending order (specifically in the segments of the data array that correspond to each row of M). The indices array must also reflect this ordering, maintaining the correct column positions for these sorted distances.
+This type of matrix is returned by the method: sklearn.neighbors.NearestNeighbors.kneighbors_graph.
+Below is an example to illustrate:
+knn_graph.todense()
+# matrix([[0. , 0.3, 0.2],
+# [0.3, 0. , 0.4],
+# [0.2, 0.4, 0. ]])
+
+knn_graph.data
+# array([0.2, 0.3, 0.3, 0.4, 0.2, 0.4])
+# Here, 0.2 and 0.3 are the sorted distances in the first row, 0.3 and 0.4 in the second row, and so on.
+
+knn_graph.indices
+# array([2, 1, 0, 2, 0, 1])
+# Corresponding neighbor indices for the distances from the `data` array.
+
+knn_graph.indptr
+# array([0, 2, 4, 6])
+# The non-zero entries in the first row are stored from `knn_graph.data[0]` to `knn_graph.data[2]`, the second row from `knn_graph.data[2]` to `knn_graph.data[4]`, and so on.
+
+
For any duplicated examples i,j whose distance is 0, there should be an explicit zero stored in the matrix, i.e. knn_graph[i,j] = 0.
If both knn_graph and features are provided, the knn_graph will take precendence.
If knn_graph is not provided, it is constructed based on the provided features.
If neither knn_graph nor features are provided, certain issue types like (near) duplicates will not be considered.
+
+See also
+See the
+scipy.sparse.csr_matrix documentation
+for more details on the CSR matrix format.
+
issue_types (Optional[Dict[str, Any]]) –
Collection specifying which types of issues to consider in audit and any non-default parameter settings to use.
If unspecified, a default set of issue types and recommended parameter settings is considered.
diff --git a/master/cleanlab/datalab/internal/issue_finder.html b/master/cleanlab/datalab/internal/issue_finder.html
index d95eef936..0557b901d 100644
--- a/master/cleanlab/datalab/internal/issue_finder.html
+++ b/master/cleanlab/datalab/internal/issue_finder.html
@@ -625,14 +625,7 @@ issue_finderOptional[csr_matrix]) – Sparse matrix representing distances between examples in the dataset in a k nearest neighbor graph.
-If provided, this must be a square CSR matrix with shape (num_examples, num_examples) and (k*num_examples) non-zero entries (k is the number of nearest neighbors considered for each example)
-evenly distributed across the rows.
-The non-zero entries must be the distances between the corresponding examples. Self-distances must be omitted
-(i.e. the diagonal must be all zeros and the k nearest neighbors of each example must not include itself).
-For any duplicated examples i,j whose distance is 0, there should be an explicit zero stored in the matrix, i.e. knn_graph[i,j] = 0.
-If both knn_graph and features are provided, the knn_graph will take precendence.
-If knn_graph is not provided, it is constructed based on the provided features.
-If neither knn_graph nor features are provided, certain issue types like (near) duplicates will not be considered.
+For details, refer to the documentation of the same argument in Datalab.find_issues
issue_types (Optional[Dict[str, Any]]) –
Collection specifying which types of issues to consider in audit and any non-default parameter settings to use.
If unspecified, a default set of issue types and recommended parameter settings is considered.
diff --git a/master/searchindex.js b/master/searchindex.js
index bb830f342..78260711e 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", 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"tutorials/faq", "tutorials/image", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/tabular", "tutorials/text", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", 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\ No newline at end of file
diff --git a/master/tutorials/audio.html b/master/tutorials/audio.html
index 93dc4ddfc..f9be65c87 100644
--- a/master/tutorials/audio.html
+++ b/master/tutorials/audio.html
@@ -1495,7 +1495,7 @@ 5. Use cleanlab to find label issues
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diff --git a/master/tutorials/audio.ipynb b/master/tutorials/audio.ipynb
index f897d9e1d..a22748c45 100644
--- a/master/tutorials/audio.ipynb
+++ b/master/tutorials/audio.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
"metadata": {
"execution": {
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- "iopub.status.busy": "2024-01-08T11:33:53.940747Z",
- "iopub.status.idle": "2024-01-08T11:33:57.298968Z",
- "shell.execute_reply": "2024-01-08T11:33:57.298180Z"
+ "iopub.execute_input": "2024-01-09T02:26:27.022029Z",
+ "iopub.status.busy": "2024-01-09T02:26:27.021835Z",
+ "iopub.status.idle": "2024-01-09T02:26:30.263735Z",
+ "shell.execute_reply": "2024-01-09T02:26:30.263126Z"
},
"nbsphinx": "hidden"
},
@@ -97,7 +97,7 @@
"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -131,10 +131,10 @@
"execution_count": 2,
"metadata": {
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- "shell.execute_reply": "2024-01-08T11:33:57.305393Z"
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},
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},
@@ -157,10 +157,10 @@
"execution_count": 3,
"metadata": {
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- "shell.execute_reply": "2024-01-08T11:33:57.312981Z"
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+ "shell.execute_reply": "2024-01-09T02:26:30.277134Z"
},
"nbsphinx": "hidden"
},
@@ -208,10 +208,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-01-08T11:33:57.316546Z",
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- "iopub.status.idle": "2024-01-08T11:33:59.250582Z",
- "shell.execute_reply": "2024-01-08T11:33:59.249701Z"
+ "iopub.execute_input": "2024-01-09T02:26:30.280176Z",
+ "iopub.status.busy": "2024-01-09T02:26:30.279775Z",
+ "iopub.status.idle": "2024-01-09T02:26:31.713482Z",
+ "shell.execute_reply": "2024-01-09T02:26:31.712701Z"
},
"id": "GRDPEg7-VOQe",
"outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6"
@@ -242,10 +242,10 @@
"base_uri": "https://localhost:8080/"
},
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- "iopub.status.idle": "2024-01-08T11:33:59.269467Z",
- "shell.execute_reply": "2024-01-08T11:33:59.268735Z"
+ "iopub.execute_input": "2024-01-09T02:26:31.716641Z",
+ "iopub.status.busy": "2024-01-09T02:26:31.716199Z",
+ "iopub.status.idle": "2024-01-09T02:26:31.728685Z",
+ "shell.execute_reply": "2024-01-09T02:26:31.728070Z"
},
"id": "FDA5sGZwUSur",
"outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895"
@@ -329,10 +329,10 @@
"execution_count": 6,
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- "iopub.status.idle": "2024-01-08T11:33:59.309602Z",
- "shell.execute_reply": "2024-01-08T11:33:59.308999Z"
+ "iopub.execute_input": "2024-01-09T02:26:31.761839Z",
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@@ -380,10 +380,10 @@
"height": 92
},
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+ "iopub.execute_input": "2024-01-09T02:26:31.769180Z",
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"outputId": "c6a4917f-4a82-4a89-9193-415072e45550"
@@ -435,10 +435,10 @@
"execution_count": 8,
"metadata": {
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- "iopub.execute_input": "2024-01-08T11:34:00.023055Z",
- "iopub.status.busy": "2024-01-08T11:34:00.022633Z",
- "iopub.status.idle": "2024-01-08T11:34:02.395152Z",
- "shell.execute_reply": "2024-01-08T11:34:02.394516Z"
+ "iopub.execute_input": "2024-01-09T02:26:32.454750Z",
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@@ -472,10 +472,10 @@
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- "shell.execute_reply": "2024-01-08T11:34:02.420376Z"
+ "iopub.execute_input": "2024-01-09T02:26:33.355016Z",
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+ "shell.execute_reply": "2024-01-09T02:26:33.376109Z"
},
"id": "obQYDKdLiUU6",
"outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4"
@@ -555,10 +555,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-08T11:34:02.423575Z",
- "iopub.status.busy": "2024-01-08T11:34:02.423152Z",
- "iopub.status.idle": "2024-01-08T11:34:02.426626Z",
- "shell.execute_reply": "2024-01-08T11:34:02.425997Z"
+ "iopub.execute_input": "2024-01-09T02:26:33.378977Z",
+ "iopub.status.busy": "2024-01-09T02:26:33.378770Z",
+ "iopub.status.idle": "2024-01-09T02:26:33.382088Z",
+ "shell.execute_reply": "2024-01-09T02:26:33.381584Z"
},
"id": "I8JqhOZgi94g"
},
@@ -580,10 +580,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-08T11:34:02.429008Z",
- "iopub.status.busy": "2024-01-08T11:34:02.428639Z",
- "iopub.status.idle": "2024-01-08T11:34:22.063114Z",
- "shell.execute_reply": "2024-01-08T11:34:22.062346Z"
+ "iopub.execute_input": "2024-01-09T02:26:33.384452Z",
+ "iopub.status.busy": "2024-01-09T02:26:33.384077Z",
+ "iopub.status.idle": "2024-01-09T02:26:51.740302Z",
+ "shell.execute_reply": "2024-01-09T02:26:51.739676Z"
},
"id": "2FSQ2GR9R_YA"
},
@@ -615,10 +615,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-01-08T11:34:22.066321Z",
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- "iopub.status.idle": "2024-01-08T11:34:22.070076Z",
- "shell.execute_reply": "2024-01-08T11:34:22.069418Z"
+ "iopub.execute_input": "2024-01-09T02:26:51.743521Z",
+ "iopub.status.busy": "2024-01-09T02:26:51.743049Z",
+ "iopub.status.idle": "2024-01-09T02:26:51.747245Z",
+ "shell.execute_reply": "2024-01-09T02:26:51.746598Z"
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"outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632"
@@ -677,10 +677,10 @@
"execution_count": 13,
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- "shell.execute_reply": "2024-01-08T11:34:27.536085Z"
+ "iopub.execute_input": "2024-01-09T02:26:51.749701Z",
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@@ -714,10 +714,10 @@
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+ "iopub.execute_input": "2024-01-09T02:26:57.310568Z",
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@@ -764,10 +764,10 @@
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- "shell.execute_reply": "2024-01-08T11:34:27.657359Z"
+ "iopub.execute_input": "2024-01-09T02:26:57.320209Z",
+ "iopub.status.busy": "2024-01-09T02:26:57.318922Z",
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}
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@@ -804,10 +804,10 @@
"execution_count": 16,
"metadata": {
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@@ -862,10 +862,10 @@
"execution_count": 17,
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@@ -969,10 +969,10 @@
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@@ -1010,10 +1010,10 @@
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@@ -1133,10 +1133,10 @@
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diff --git a/master/tutorials/datalab/datalab_advanced.ipynb b/master/tutorials/datalab/datalab_advanced.ipynb
index b3f918658..62920430a 100644
--- a/master/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/tutorials/datalab/datalab_advanced.ipynb
@@ -80,10 +80,10 @@
"execution_count": 1,
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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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -118,10 +118,10 @@
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@@ -252,10 +252,10 @@
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@@ -353,10 +353,10 @@
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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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"outputs": [
@@ -909,7 +909,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:300: UserWarning: Overwriting columns ['outlier_score', 'is_outlier_issue'] in self.issues with columns from issue manager OutlierIssueManager.\n",
+ "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:300: UserWarning: Overwriting columns ['is_outlier_issue', 'outlier_score'] in self.issues with columns from issue manager OutlierIssueManager.\n",
" warnings.warn(\n",
"/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:330: UserWarning: Overwriting row in self.issue_summary with row from issue manager OutlierIssueManager.\n",
" warnings.warn(\n",
@@ -935,10 +935,10 @@
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@@ -1068,17 +1068,17 @@
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diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb
index 594682e36..33a753919 100644
--- a/master/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
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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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\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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}
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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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@@ -878,10 +878,10 @@
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@@ -985,10 +985,10 @@
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@@ -1055,10 +1055,10 @@
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@@ -1231,10 +1231,10 @@
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@@ -1350,10 +1350,10 @@
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diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb
index 42b44f2b2..cd1a07a8b 100644
--- a/master/tutorials/datalab/tabular.ipynb
+++ b/master/tutorials/datalab/tabular.ipynb
@@ -74,10 +74,10 @@
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@@ -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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -112,10 +112,10 @@
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@@ -155,10 +155,10 @@
"execution_count": 3,
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@@ -265,10 +265,10 @@
"execution_count": 4,
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@@ -289,10 +289,10 @@
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@@ -436,10 +436,10 @@
"execution_count": 9,
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"outputs": [
@@ -475,10 +475,10 @@
"execution_count": 10,
"metadata": {
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@@ -624,10 +624,10 @@
"execution_count": 11,
"metadata": {
"execution": {
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"outputs": [
@@ -731,10 +731,10 @@
"execution_count": 12,
"metadata": {
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@@ -863,10 +863,10 @@
"execution_count": 13,
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"outputs": [
@@ -980,10 +980,10 @@
"execution_count": 14,
"metadata": {
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}
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"outputs": [
@@ -1094,10 +1094,10 @@
"execution_count": 15,
"metadata": {
"execution": {
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}
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"outputs": [
@@ -1181,10 +1181,10 @@
"execution_count": 16,
"metadata": {
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}
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"outputs": [
@@ -1277,10 +1277,10 @@
"execution_count": 17,
"metadata": {
"execution": {
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- "shell.execute_reply": "2024-01-08T11:34:57.250364Z"
+ "iopub.execute_input": "2024-01-09T02:27:25.663447Z",
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},
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diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html
index 0b59e3c01..852ce733d 100644
--- a/master/tutorials/datalab/text.html
+++ b/master/tutorials/datalab/text.html
@@ -943,7 +943,7 @@ 2. Load and format the text dataset
This dataset has 10 classes.
-Classes: {'change_pin', 'apple_pay_or_google_pay', 'supported_cards_and_currencies', 'getting_spare_card', 'cancel_transfer', 'visa_or_mastercard', 'card_payment_fee_charged', 'beneficiary_not_allowed', 'lost_or_stolen_phone', 'card_about_to_expire'}
+Classes: {'beneficiary_not_allowed', 'supported_cards_and_currencies', 'change_pin', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'card_about_to_expire', 'getting_spare_card', 'lost_or_stolen_phone', 'card_payment_fee_charged', 'cancel_transfer'}
Let’s view the i-th example in the dataset:
@@ -990,43 +990,43 @@ 2. Load and format the text dataset
Professional support and services are also available from our ML experts, learn more by emailing: info@cleanlab.ai
Convert the transformed dataset to a torch dataset. Torch datasets are more efficient with dataloading in practice.
-epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.704
+epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.662
-epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.430
+epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.346
Computing feature embeddings ...
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-epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.677
+epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.688
-epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.306
+epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.257
Computing feature embeddings ...
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-
+Finished Training
-Finished Training
+
Reorder rows of the dataset based on row order in features and pred_probs. Carefully ensure your ordering of the dataset matches these objects!
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+9%|▉ | 471548/4997817 [00:02<00:25, 174226.13it/s]
+- 10%|▉ | 488972/4997817 [00:02<00:25, 174051.97it/s]
- -
</pre>
- 10%|▉ | 475970/4997817 [00:02<00:26, 170172.63it/s]
- 10%|▉ | 488972/4997817 [00:02<00:25, 174051.97it/s]
end{sphinxVerbatim}
10%|▉ | 475970/4997817 [00:02<00:26, 170172.63it/s]
+10%|▉ | 488972/4997817 [00:02<00:25, 174051.97it/s]
+- 10%|█ | 506378/4997817 [00:02<00:25, 173741.09it/s]
- -
</pre>
- 10%|▉ | 493293/4997817 [00:02<00:26, 171080.88it/s]
- 10%|█ | 506378/4997817 [00:02<00:25, 173741.09it/s]
end{sphinxVerbatim}
10%|▉ | 493293/4997817 [00:02<00:26, 171080.88it/s]
+10%|█ | 506378/4997817 [00:02<00:25, 173741.09it/s]
+- 10%|█ | 523916/4997817 [00:03<00:25, 174229.02it/s]
- -
</pre>
- 10%|█ | 510610/4997817 [00:03<00:26, 171701.31it/s]
- 10%|█ | 523916/4997817 [00:03<00:25, 174229.02it/s]
end{sphinxVerbatim}
10%|█ | 510610/4997817 [00:03<00:26, 171701.31it/s]
+10%|█ | 523916/4997817 [00:03<00:25, 174229.02it/s]
+- 11%|█ | 541494/4997817 [00:03<00:25, 174691.21it/s]
- -
</pre>
- 11%|█ | 527886/4997817 [00:03<00:25, 172014.12it/s]
- 11%|█ | 541494/4997817 [00:03<00:25, 174691.21it/s]
end{sphinxVerbatim}
11%|█ | 527886/4997817 [00:03<00:25, 172014.12it/s]
+11%|█ | 541494/4997817 [00:03<00:25, 174691.21it/s]
+- 11%|█ | 558964/4997817 [00:03<00:25, 174558.81it/s]
- -
</pre>
- 11%|█ | 545093/4997817 [00:03<00:25, 171956.90it/s]
- 11%|█ | 558964/4997817 [00:03<00:25, 174558.81it/s]
end{sphinxVerbatim}
11%|█ | 545093/4997817 [00:03<00:25, 171956.90it/s]
+11%|█ | 558964/4997817 [00:03<00:25, 174558.81it/s]
+- 12%|█▏ | 576421/4997817 [00:03<00:25, 174151.31it/s]
- -
</pre>
- 11%|█▏ | 562493/4997817 [00:03<00:25, 172565.25it/s]
- 12%|█▏ | 576421/4997817 [00:03<00:25, 174151.31it/s]
end{sphinxVerbatim}
11%|█▏ | 562493/4997817 [00:03<00:25, 172565.25it/s]
+12%|█▏ | 576421/4997817 [00:03<00:25, 174151.31it/s]
+- 12%|█▏ | 593977/4997817 [00:03<00:25, 174569.48it/s]
- -
</pre>
- 12%|█▏ | 579783/4997817 [00:03<00:25, 172664.11it/s]
- 12%|█▏ | 593977/4997817 [00:03<00:25, 174569.48it/s]
end{sphinxVerbatim}
12%|█▏ | 579783/4997817 [00:03<00:25, 172664.11it/s]
+12%|█▏ | 593977/4997817 [00:03<00:25, 174569.48it/s]
+- 12%|█▏ | 611494/4997817 [00:03<00:25, 174744.69it/s]
- -
</pre>
- 12%|█▏ | 597082/4997817 [00:03<00:25, 172757.76it/s]
- 12%|█▏ | 611494/4997817 [00:03<00:25, 174744.69it/s]
end{sphinxVerbatim}
12%|█▏ | 597082/4997817 [00:03<00:25, 172757.76it/s]
+12%|█▏ | 611494/4997817 [00:03<00:25, 174744.69it/s]
+- 13%|█▎ | 628969/4997817 [00:03<00:25, 172753.15it/s]
- -
</pre>
- 12%|█▏ | 614377/4997817 [00:03<00:25, 172811.61it/s]
- 13%|█▎ | 628969/4997817 [00:03<00:25, 172753.15it/s]
end{sphinxVerbatim}
12%|█▏ | 614377/4997817 [00:03<00:25, 172811.61it/s]
+13%|█▎ | 628969/4997817 [00:03<00:25, 172753.15it/s]
+- 13%|█▎ | 646321/4997817 [00:03<00:25, 172979.58it/s]
- -
</pre>
- 13%|█▎ | 631661/4997817 [00:03<00:25, 172816.22it/s]
- 13%|█▎ | 646321/4997817 [00:03<00:25, 172979.58it/s]
end{sphinxVerbatim}
13%|█▎ | 631661/4997817 [00:03<00:25, 172816.22it/s]
+13%|█▎ | 646321/4997817 [00:03<00:25, 172979.58it/s]
+- 13%|█▎ | 663679/4997817 [00:03<00:25, 173154.91it/s]
- -
</pre>
- 13%|█▎ | 648944/4997817 [00:03<00:25, 172530.14it/s]
- 13%|█▎ | 663679/4997817 [00:03<00:25, 173154.91it/s]
end{sphinxVerbatim}
13%|█▎ | 648944/4997817 [00:03<00:25, 172530.14it/s]
+13%|█▎ | 663679/4997817 [00:03<00:25, 173154.91it/s]
+- 14%|█▎ | 681030/4997817 [00:03<00:24, 173259.04it/s]
- -
</pre>
- 13%|█▎ | 666266/4997817 [00:03<00:25, 172730.07it/s]
- 14%|█▎ | 681030/4997817 [00:03<00:24, 173259.04it/s]
end{sphinxVerbatim}
13%|█▎ | 666266/4997817 [00:03<00:25, 172730.07it/s]
+14%|█▎ | 681030/4997817 [00:03<00:24, 173259.04it/s]
+- 14%|█▍ | 698362/4997817 [00:04<00:24, 173275.22it/s]
- -
</pre>
- 14%|█▎ | 683540/4997817 [00:04<00:24, 172712.98it/s]
- 14%|█▍ | 698362/4997817 [00:04<00:24, 173275.22it/s]
end{sphinxVerbatim}
14%|█▎ | 683540/4997817 [00:04<00:24, 172712.98it/s]
+14%|█▍ | 698362/4997817 [00:04<00:24, 173275.22it/s]
+- 14%|█▍ | 715691/4997817 [00:04<00:24, 173269.06it/s]
- -
</pre>
- 14%|█▍ | 700812/4997817 [00:04<00:24, 172596.96it/s]
- 14%|█▍ | 715691/4997817 [00:04<00:24, 173269.06it/s]
end{sphinxVerbatim}
14%|█▍ | 700812/4997817 [00:04<00:24, 172596.96it/s]
+14%|█▍ | 715691/4997817 [00:04<00:24, 173269.06it/s]
+- 15%|█▍ | 733023/4997817 [00:04<00:24, 173282.63it/s]
- -
</pre>
- 14%|█▍ | 718156/4997817 [00:04<00:24, 172846.08it/s]
- 15%|█▍ | 733023/4997817 [00:04<00:24, 173282.63it/s]
end{sphinxVerbatim}
14%|█▍ | 718156/4997817 [00:04<00:24, 172846.08it/s]
+15%|█▍ | 733023/4997817 [00:04<00:24, 173282.63it/s]
+- 15%|█▌ | 750352/4997817 [00:04<00:24, 172883.34it/s]
- -
</pre>
- 15%|█▍ | 735441/4997817 [00:04<00:24, 172372.99it/s]
- 15%|█▌ | 750352/4997817 [00:04<00:24, 172883.34it/s]
end{sphinxVerbatim}
15%|█▍ | 735441/4997817 [00:04<00:24, 172372.99it/s]
+15%|█▌ | 750352/4997817 [00:04<00:24, 172883.34it/s]
+- 15%|█▌ | 767641/4997817 [00:04<00:24, 172695.49it/s]
- -
</pre>
- 15%|█▌ | 752761/4997817 [00:04<00:24, 172619.67it/s]
- 15%|█▌ | 767641/4997817 [00:04<00:24, 172695.49it/s]
end{sphinxVerbatim}
15%|█▌ | 752761/4997817 [00:04<00:24, 172619.67it/s]
+15%|█▌ | 767641/4997817 [00:04<00:24, 172695.49it/s]
+- 16%|█▌ | 784963/4997817 [00:04<00:24, 172849.42it/s]
- -
</pre>
- 15%|█▌ | 770137/4997817 [00:04<00:24, 172959.01it/s]
- 16%|█▌ | 784963/4997817 [00:04<00:24, 172849.42it/s]
end{sphinxVerbatim}
15%|█▌ | 770137/4997817 [00:04<00:24, 172959.01it/s]
+16%|█▌ | 784963/4997817 [00:04<00:24, 172849.42it/s]
+- 16%|█▌ | 802249/4997817 [00:04<00:24, 172543.53it/s]
- -
</pre>
- 16%|█▌ | 787570/4997817 [00:04<00:24, 173367.13it/s]
- 16%|█▌ | 802249/4997817 [00:04<00:24, 172543.53it/s]
end{sphinxVerbatim}
16%|█▌ | 787570/4997817 [00:04<00:24, 173367.13it/s]
+16%|█▌ | 802249/4997817 [00:04<00:24, 172543.53it/s]
+- 16%|█▋ | 819504/4997817 [00:04<00:24, 172408.48it/s]
- -
</pre>
- 16%|█▌ | 804992/4997817 [00:04<00:24, 173620.09it/s]
- 16%|█▋ | 819504/4997817 [00:04<00:24, 172408.48it/s]
end{sphinxVerbatim}
16%|█▌ | 804992/4997817 [00:04<00:24, 173620.09it/s]
+16%|█▋ | 819504/4997817 [00:04<00:24, 172408.48it/s]
+- 17%|█▋ | 836746/4997817 [00:04<00:24, 172216.20it/s]
- -
</pre>
- 16%|█▋ | 822398/4997817 [00:04<00:24, 173748.68it/s]
- 17%|█▋ | 836746/4997817 [00:04<00:24, 172216.20it/s]
end{sphinxVerbatim}
16%|█▋ | 822398/4997817 [00:04<00:24, 173748.68it/s]
+17%|█▋ | 836746/4997817 [00:04<00:24, 172216.20it/s]
+- 17%|█▋ | 853968/4997817 [00:04<00:24, 172122.21it/s]
- -
</pre>
- 17%|█▋ | 839858/4997817 [00:04<00:23, 174001.28it/s]
- 17%|█▋ | 853968/4997817 [00:04<00:24, 172122.21it/s]
end{sphinxVerbatim}
17%|█▋ | 839858/4997817 [00:04<00:23, 174001.28it/s]
+17%|█▋ | 853968/4997817 [00:04<00:24, 172122.21it/s]
+- 17%|█▋ | 871181/4997817 [00:05<00:23, 171981.09it/s]
- -
</pre>
- 17%|█▋ | 857366/4997817 [00:05<00:23, 174322.85it/s]
- 17%|█▋ | 871181/4997817 [00:05<00:23, 171981.09it/s]
end{sphinxVerbatim}
17%|█▋ | 857366/4997817 [00:05<00:23, 174322.85it/s]
+17%|█▋ | 871181/4997817 [00:05<00:23, 171981.09it/s]
+- 18%|█▊ | 888380/4997817 [00:05<00:23, 171753.81it/s]
- -
</pre>
- 18%|█▊ | 874799/4997817 [00:05<00:23, 173567.56it/s]
- 18%|█▊ | 888380/4997817 [00:05<00:23, 171753.81it/s]
end{sphinxVerbatim}
18%|█▊ | 874799/4997817 [00:05<00:23, 173567.56it/s]
+18%|█▊ | 888380/4997817 [00:05<00:23, 171753.81it/s]
+- 18%|█▊ | 905557/4997817 [00:05<00:23, 171754.41it/s]
- -
</pre>
- 18%|█▊ | 892157/4997817 [00:05<00:23, 173440.50it/s]
- 18%|█▊ | 905557/4997817 [00:05<00:23, 171754.41it/s]
end{sphinxVerbatim}
18%|█▊ | 892157/4997817 [00:05<00:23, 173440.50it/s]
+18%|█▊ | 905557/4997817 [00:05<00:23, 171754.41it/s]
+- 18%|█▊ | 922933/4997817 [00:05<00:23, 172352.92it/s]
- -
</pre>
- 18%|█▊ | 909502/4997817 [00:05<00:23, 173143.04it/s]
- 18%|█▊ | 922933/4997817 [00:05<00:23, 172352.92it/s]
end{sphinxVerbatim}
18%|█▊ | 909502/4997817 [00:05<00:23, 173143.04it/s]
+18%|█▊ | 922933/4997817 [00:05<00:23, 172352.92it/s]
+- 19%|█▉ | 940465/4997817 [00:05<00:23, 173240.44it/s]
- -
</pre>
- 19%|█▊ | 926835/4997817 [00:05<00:23, 173197.03it/s]
- 19%|█▉ | 940465/4997817 [00:05<00:23, 173240.44it/s]
end{sphinxVerbatim}
19%|█▊ | 926835/4997817 [00:05<00:23, 173197.03it/s]
+19%|█▉ | 940465/4997817 [00:05<00:23, 173240.44it/s]
+- 19%|█▉ | 958213/4997817 [00:05<00:23, 174508.18it/s]
- -
</pre>
- 19%|█▉ | 944156/4997817 [00:05<00:24, 166202.97it/s]
- 19%|█▉ | 958213/4997817 [00:05<00:23, 174508.18it/s]
end{sphinxVerbatim}
19%|█▉ | 944156/4997817 [00:05<00:24, 166202.97it/s]
+19%|█▉ | 958213/4997817 [00:05<00:23, 174508.18it/s]
+- 20%|█▉ | 975677/4997817 [00:05<00:23, 174543.64it/s]
- -
</pre>
- 19%|█▉ | 961364/4997817 [00:05<00:24, 167910.74it/s]
- 20%|█▉ | 975677/4997817 [00:05<00:23, 174543.64it/s]
end{sphinxVerbatim}
19%|█▉ | 961364/4997817 [00:05<00:24, 167910.74it/s]
+20%|█▉ | 975677/4997817 [00:05<00:23, 174543.64it/s]
+- 20%|█▉ | 993172/4997817 [00:05<00:22, 174661.67it/s]
- -
</pre>
- 20%|█▉ | 978704/4997817 [00:05<00:23, 169521.29it/s]
- 20%|█▉ | 993172/4997817 [00:05<00:22, 174661.67it/s]
end{sphinxVerbatim}
20%|█▉ | 978704/4997817 [00:05<00:23, 169521.29it/s]
+20%|█▉ | 993172/4997817 [00:05<00:22, 174661.67it/s]
+- 20%|██ | 1010639/4997817 [00:05<00:22, 174191.42it/s]
- -
</pre>
- 20%|█▉ | 996048/4997817 [00:05<00:23, 170676.17it/s]
- 20%|██ | 1010639/4997817 [00:05<00:22, 174191.42it/s]
end{sphinxVerbatim}
20%|█▉ | 996048/4997817 [00:05<00:23, 170676.17it/s]
+20%|██ | 1010639/4997817 [00:05<00:22, 174191.42it/s]
+- 21%|██ | 1028059/4997817 [00:05<00:22, 173875.72it/s]
- -
</pre>
- 20%|██ | 1013347/4997817 [00:05<00:23, 171362.05it/s]
- 21%|██ | 1028059/4997817 [00:05<00:22, 173875.72it/s]
end{sphinxVerbatim}
20%|██ | 1013347/4997817 [00:05<00:23, 171362.05it/s]
+21%|██ | 1028059/4997817 [00:05<00:22, 173875.72it/s]
+- 21%|██ | 1045447/4997817 [00:06<00:22, 173714.48it/s]
- -
</pre>
- 21%|██ | 1030732/4997817 [00:06<00:23, 172101.84it/s]
- 21%|██ | 1045447/4997817 [00:06<00:22, 173714.48it/s]
end{sphinxVerbatim}
21%|██ | 1030732/4997817 [00:06<00:23, 172101.84it/s]
+21%|██ | 1045447/4997817 [00:06<00:22, 173714.48it/s]
+- 21%|██▏ | 1062819/4997817 [00:06<00:22, 173555.59it/s]
- -
</pre>
- 21%|██ | 1048019/4997817 [00:06<00:22, 172330.03it/s]
- 21%|██▏ | 1062819/4997817 [00:06<00:22, 173555.59it/s]
end{sphinxVerbatim}
21%|██ | 1048019/4997817 [00:06<00:22, 172330.03it/s]
+21%|██▏ | 1062819/4997817 [00:06<00:22, 173555.59it/s]
+- 22%|██▏ | 1080175/4997817 [00:06<00:22, 173322.86it/s]
- -
</pre>
- 21%|██▏ | 1065263/4997817 [00:06<00:22, 171709.76it/s]
- 22%|██▏ | 1080175/4997817 [00:06<00:22, 173322.86it/s]
end{sphinxVerbatim}
21%|██▏ | 1065263/4997817 [00:06<00:22, 171709.76it/s]
+22%|██▏ | 1080175/4997817 [00:06<00:22, 173322.86it/s]
+- 22%|██▏ | 1097508/4997817 [00:06<00:22, 172751.89it/s]
- -
</pre>
- 22%|██▏ | 1082628/4997817 [00:06<00:22, 172285.29it/s]
- 22%|██▏ | 1097508/4997817 [00:06<00:22, 172751.89it/s]
end{sphinxVerbatim}
22%|██▏ | 1082628/4997817 [00:06<00:22, 172285.29it/s]
+22%|██▏ | 1097508/4997817 [00:06<00:22, 172751.89it/s]
+- 22%|██▏ | 1114784/4997817 [00:06<00:22, 172069.99it/s]
- -
</pre>
- 22%|██▏ | 1099931/4997817 [00:06<00:22, 172504.86it/s]
- 22%|██▏ | 1114784/4997817 [00:06<00:22, 172069.99it/s]
end{sphinxVerbatim}
22%|██▏ | 1099931/4997817 [00:06<00:22, 172504.86it/s]
+22%|██▏ | 1114784/4997817 [00:06<00:22, 172069.99it/s]
+- 23%|██▎ | 1132096/4997817 [00:06<00:22, 172355.18it/s]
- -
</pre>
- 22%|██▏ | 1117194/4997817 [00:06<00:22, 172539.89it/s]
- 23%|██▎ | 1132096/4997817 [00:06<00:22, 172355.18it/s]
end{sphinxVerbatim}
22%|██▏ | 1117194/4997817 [00:06<00:22, 172539.89it/s]
+23%|██▎ | 1132096/4997817 [00:06<00:22, 172355.18it/s]
+- 23%|██▎ | 1149354/4997817 [00:06<00:22, 172417.36it/s]
- -
</pre>
- 23%|██▎ | 1134546/4997817 [00:06<00:22, 172830.60it/s]
- 23%|██▎ | 1149354/4997817 [00:06<00:22, 172417.36it/s]
end{sphinxVerbatim}
23%|██▎ | 1134546/4997817 [00:06<00:22, 172830.60it/s]
+23%|██▎ | 1149354/4997817 [00:06<00:22, 172417.36it/s]
+- 23%|██▎ | 1166597/4997817 [00:06<00:22, 172128.78it/s]
- -
</pre>
- 23%|██▎ | 1151895/4997817 [00:06<00:22, 173026.36it/s]
- 23%|██▎ | 1166597/4997817 [00:06<00:22, 172128.78it/s]
end{sphinxVerbatim}
23%|██▎ | 1151895/4997817 [00:06<00:22, 173026.36it/s]
+23%|██▎ | 1166597/4997817 [00:06<00:22, 172128.78it/s]
+- 24%|██▎ | 1183811/4997817 [00:06<00:22, 171851.61it/s]
- -
</pre>
- 23%|██▎ | 1169210/4997817 [00:06<00:22, 173059.88it/s]
- 24%|██▎ | 1183811/4997817 [00:06<00:22, 171851.61it/s]
end{sphinxVerbatim}
23%|██▎ | 1169210/4997817 [00:06<00:22, 173059.88it/s]
+24%|██▎ | 1183811/4997817 [00:06<00:22, 171851.61it/s]
+- 24%|██▍ | 1201022/4997817 [00:06<00:22, 171926.83it/s]
- -
</pre>
- 24%|██▎ | 1186517/4997817 [00:06<00:22, 172852.31it/s]
- 24%|██▍ | 1201022/4997817 [00:06<00:22, 171926.83it/s]
end{sphinxVerbatim}
24%|██▎ | 1186517/4997817 [00:06<00:22, 172852.31it/s]
+24%|██▍ | 1201022/4997817 [00:06<00:22, 171926.83it/s]
+- 24%|██▍ | 1218296/4997817 [00:07<00:21, 172168.16it/s]
- -
</pre>
- 24%|██▍ | 1203803/4997817 [00:07<00:21, 172564.61it/s]
- 24%|██▍ | 1218296/4997817 [00:07<00:21, 172168.16it/s]
end{sphinxVerbatim}
24%|██▍ | 1203803/4997817 [00:07<00:21, 172564.61it/s]
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- -
</pre>
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end{sphinxVerbatim}
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- -
</pre>
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end{sphinxVerbatim}
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- -
</pre>
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end{sphinxVerbatim}
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- -
</pre>
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end{sphinxVerbatim}
25%|██▌ | 1273066/4997817 [00:07<00:21, 172589.94it/s]
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- -
</pre>
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end{sphinxVerbatim}
26%|██▌ | 1290398/4997817 [00:07<00:21, 172806.58it/s]
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- -
</pre>
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end{sphinxVerbatim}
26%|██▌ | 1307679/4997817 [00:07<00:22, 166160.47it/s]
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- -
</pre>
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end{sphinxVerbatim}
27%|██▋ | 1325085/4997817 [00:07<00:21, 168461.57it/s]
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- -
</pre>
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end{sphinxVerbatim}
27%|██▋ | 1342482/4997817 [00:07<00:21, 170080.23it/s]
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- -
</pre>
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end{sphinxVerbatim}
27%|██▋ | 1359770/4997817 [00:07<00:21, 170905.76it/s]
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- -
</pre>
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end{sphinxVerbatim}
28%|██▊ | 1377262/4997817 [00:08<00:21, 172095.37it/s]
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- -
</pre>
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end{sphinxVerbatim}
28%|██▊ | 1394654/4997817 [00:08<00:20, 172637.04it/s]
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- -
</pre>
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end{sphinxVerbatim}
28%|██▊ | 1411932/4997817 [00:08<00:20, 172322.24it/s]
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- -
</pre>
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end{sphinxVerbatim}
29%|██▊ | 1429364/4997817 [00:08<00:20, 172916.11it/s]
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+- 29%|██▉ | 1459635/4997817 [00:08<00:20, 172334.32it/s]
- -
</pre>
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- 29%|██▉ | 1459635/4997817 [00:08<00:20, 172334.32it/s]
end{sphinxVerbatim}
29%|██▉ | 1446805/4997817 [00:08<00:20, 173360.81it/s]
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+- 30%|██▉ | 1476885/4997817 [00:08<00:20, 172381.08it/s]
- -
</pre>
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- 30%|██▉ | 1476885/4997817 [00:08<00:20, 172381.08it/s]
end{sphinxVerbatim}
29%|██▉ | 1464219/4997817 [00:08<00:20, 173591.08it/s]
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- -
</pre>
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- 30%|██▉ | 1494124/4997817 [00:08<00:20, 172109.18it/s]
end{sphinxVerbatim}
30%|██▉ | 1481618/4997817 [00:08<00:20, 173705.89it/s]
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+- 30%|███ | 1511467/4997817 [00:08<00:20, 172501.07it/s]
- -
</pre>
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- 30%|███ | 1511467/4997817 [00:08<00:20, 172501.07it/s]
end{sphinxVerbatim}
30%|██▉ | 1498992/4997817 [00:08<00:20, 173642.85it/s]
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- -
</pre>
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- 31%|███ | 1528718/4997817 [00:08<00:20, 172226.43it/s]
end{sphinxVerbatim}
30%|███ | 1516359/4997817 [00:08<00:20, 173555.56it/s]
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- -
</pre>
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end{sphinxVerbatim}
31%|███ | 1533819/4997817 [00:08<00:19, 173865.84it/s]
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- -
</pre>
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end{sphinxVerbatim}
31%|███ | 1551313/4997817 [00:09<00:19, 174184.25it/s]
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- -
</pre>
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end{sphinxVerbatim}
31%|███▏ | 1568733/4997817 [00:09<00:19, 174021.96it/s]
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- -
</pre>
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end{sphinxVerbatim}
32%|███▏ | 1586136/4997817 [00:09<00:19, 173365.83it/s]
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- -
</pre>
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end{sphinxVerbatim}
32%|███▏ | 1603562/4997817 [00:09<00:19, 173631.98it/s]
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- -
</pre>
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end{sphinxVerbatim}
32%|███▏ | 1620931/4997817 [00:09<00:19, 173647.16it/s]
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- -
</pre>
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end{sphinxVerbatim}
33%|███▎ | 1638297/4997817 [00:09<00:19, 173646.47it/s]
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- -
</pre>
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end{sphinxVerbatim}
33%|███▎ | 1655662/4997817 [00:09<00:19, 173105.92it/s]
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- -
</pre>
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end{sphinxVerbatim}
33%|███▎ | 1672974/4997817 [00:09<00:19, 173039.35it/s]
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- -
</pre>
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end{sphinxVerbatim}
34%|███▍ | 1690279/4997817 [00:09<00:19, 173003.33it/s]
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- -
</pre>
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end{sphinxVerbatim}
34%|███▍ | 1707672/4997817 [00:09<00:18, 173279.01it/s]
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- -
</pre>
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end{sphinxVerbatim}
35%|███▍ | 1725006/4997817 [00:10<00:18, 173295.60it/s]
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- -
</pre>
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end{sphinxVerbatim}
35%|███▍ | 1742344/4997817 [00:10<00:18, 173319.21it/s]
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- -
</pre>
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end{sphinxVerbatim}
35%|███▌ | 1759688/4997817 [00:10<00:18, 173351.88it/s]
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- -
</pre>
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end{sphinxVerbatim}
36%|███▌ | 1777038/4997817 [00:10<00:18, 173394.86it/s]
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- -
</pre>
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end{sphinxVerbatim}
36%|███▌ | 1794436/4997817 [00:10<00:18, 173569.40it/s]
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- -
</pre>
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end{sphinxVerbatim}
36%|███▋ | 1811793/4997817 [00:10<00:18, 173517.02it/s]
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- -
</pre>
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end{sphinxVerbatim}
37%|███▋ | 1829145/4997817 [00:10<00:19, 166757.09it/s]
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- -
</pre>
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end{sphinxVerbatim}
37%|███▋ | 1846395/4997817 [00:10<00:18, 168429.79it/s]
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- -
</pre>
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end{sphinxVerbatim}
37%|███▋ | 1863765/4997817 [00:10<00:18, 169977.65it/s]
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- -
</pre>
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end{sphinxVerbatim}
38%|███▊ | 1881203/4997817 [00:10<00:18, 171277.32it/s]
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- -
</pre>
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end{sphinxVerbatim}
38%|███▊ | 1898452/4997817 [00:11<00:18, 171635.92it/s]
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- -
</pre>
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end{sphinxVerbatim}
38%|███▊ | 1915886/4997817 [00:11<00:17, 172439.30it/s]
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+- 39%|███▉ | 1950428/4997817 [00:11<00:17, 176258.83it/s]
- -
</pre>
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Beyond scoring the overall label quality of each image, the above method produces a (0 to 1) quality score for each pixel. We can apply a thresholding function to these scores in order to extract the same style True or False mask as find_label_issues().
This dataset has 10 classes.
-Classes: {'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_about_to_expire', 'supported_cards_and_currencies', 'getting_spare_card', 'visa_or_mastercard', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'cancel_transfer', 'change_pin'}
+Classes: {'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'change_pin', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'visa_or_mastercard'}
Let’s print the first example in the train set.
diff --git a/master/tutorials/text.ipynb b/master/tutorials/text.ipynb index 8132e6b9f..a5204c0a8 100644 --- a/master/tutorials/text.ipynb +++ b/master/tutorials/text.ipynb @@ -114,10 +114,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:12.834865Z", - "iopub.status.busy": "2024-01-08T11:46:12.834657Z", - "iopub.status.idle": "2024-01-08T11:46:14.955723Z", - "shell.execute_reply": "2024-01-08T11:46:14.955049Z" + "iopub.execute_input": "2024-01-09T02:38:17.736971Z", + "iopub.status.busy": "2024-01-09T02:38:17.736775Z", + "iopub.status.idle": "2024-01-09T02:38:19.785413Z", + "shell.execute_reply": "2024-01-09T02:38:19.784759Z" }, "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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -159,10 +159,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:14.958732Z", - "iopub.status.busy": "2024-01-08T11:46:14.958324Z", - "iopub.status.idle": "2024-01-08T11:46:14.962048Z", - "shell.execute_reply": "2024-01-08T11:46:14.961433Z" + "iopub.execute_input": "2024-01-09T02:38:19.788389Z", + "iopub.status.busy": "2024-01-09T02:38:19.787863Z", + "iopub.status.idle": "2024-01-09T02:38:19.791600Z", + "shell.execute_reply": "2024-01-09T02:38:19.791079Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:14.964403Z", - "iopub.status.busy": "2024-01-08T11:46:14.964046Z", - "iopub.status.idle": "2024-01-08T11:46:14.967395Z", - "shell.execute_reply": "2024-01-08T11:46:14.966785Z" + "iopub.execute_input": "2024-01-09T02:38:19.793926Z", + "iopub.status.busy": "2024-01-09T02:38:19.793560Z", + "iopub.status.idle": "2024-01-09T02:38:19.796843Z", + "shell.execute_reply": "2024-01-09T02:38:19.796334Z" }, "nbsphinx": "hidden" }, @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:14.969913Z", - "iopub.status.busy": "2024-01-08T11:46:14.969542Z", - "iopub.status.idle": "2024-01-08T11:46:15.113411Z", - "shell.execute_reply": "2024-01-08T11:46:15.112720Z" + "iopub.execute_input": "2024-01-09T02:38:19.799089Z", + "iopub.status.busy": "2024-01-09T02:38:19.798722Z", + "iopub.status.idle": "2024-01-09T02:38:19.846151Z", + "shell.execute_reply": "2024-01-09T02:38:19.845530Z" } }, "outputs": [ @@ -311,10 +311,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:15.115927Z", - "iopub.status.busy": "2024-01-08T11:46:15.115573Z", - "iopub.status.idle": "2024-01-08T11:46:15.119394Z", - "shell.execute_reply": "2024-01-08T11:46:15.118790Z" + "iopub.execute_input": "2024-01-09T02:38:19.849016Z", + "iopub.status.busy": "2024-01-09T02:38:19.848628Z", + "iopub.status.idle": "2024-01-09T02:38:19.852329Z", + "shell.execute_reply": "2024-01-09T02:38:19.851803Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:15.121624Z", - "iopub.status.busy": "2024-01-08T11:46:15.121268Z", - "iopub.status.idle": "2024-01-08T11:46:15.125229Z", - "shell.execute_reply": "2024-01-08T11:46:15.124605Z" + "iopub.execute_input": "2024-01-09T02:38:19.854688Z", + "iopub.status.busy": "2024-01-09T02:38:19.854315Z", + "iopub.status.idle": "2024-01-09T02:38:19.858368Z", + "shell.execute_reply": "2024-01-09T02:38:19.857837Z" } }, "outputs": [ @@ -341,7 +341,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'card_about_to_expire', 'supported_cards_and_currencies', 'getting_spare_card', 'visa_or_mastercard', 'card_payment_fee_charged', 'lost_or_stolen_phone', 'cancel_transfer', 'change_pin'}\n" + "Classes: {'getting_spare_card', 'card_about_to_expire', 'lost_or_stolen_phone', 'change_pin', 'supported_cards_and_currencies', 'cancel_transfer', 'beneficiary_not_allowed', 'card_payment_fee_charged', 'apple_pay_or_google_pay', 'visa_or_mastercard'}\n" ] } ], @@ -364,10 +364,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:15.127588Z", - "iopub.status.busy": "2024-01-08T11:46:15.127098Z", - "iopub.status.idle": "2024-01-08T11:46:15.130732Z", - "shell.execute_reply": "2024-01-08T11:46:15.130136Z" + "iopub.execute_input": "2024-01-09T02:38:19.860686Z", + "iopub.status.busy": "2024-01-09T02:38:19.860322Z", + "iopub.status.idle": "2024-01-09T02:38:19.864124Z", + "shell.execute_reply": "2024-01-09T02:38:19.863584Z" } }, "outputs": [ @@ -408,10 +408,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:15.133181Z", - "iopub.status.busy": "2024-01-08T11:46:15.132695Z", - "iopub.status.idle": "2024-01-08T11:46:15.136260Z", - "shell.execute_reply": "2024-01-08T11:46:15.135695Z" + "iopub.execute_input": "2024-01-09T02:38:19.866426Z", + "iopub.status.busy": "2024-01-09T02:38:19.866228Z", + "iopub.status.idle": "2024-01-09T02:38:19.870061Z", + "shell.execute_reply": "2024-01-09T02:38:19.869523Z" } }, "outputs": [], @@ -452,10 +452,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:15.138494Z", - "iopub.status.busy": "2024-01-08T11:46:15.138294Z", - "iopub.status.idle": "2024-01-08T11:46:24.367103Z", - "shell.execute_reply": "2024-01-08T11:46:24.366447Z" + "iopub.execute_input": "2024-01-09T02:38:19.872274Z", + "iopub.status.busy": "2024-01-09T02:38:19.872081Z", + "iopub.status.idle": "2024-01-09T02:38:28.431076Z", + "shell.execute_reply": "2024-01-09T02:38:28.430439Z" } }, "outputs": [ @@ -502,10 +502,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:24.370167Z", - "iopub.status.busy": "2024-01-08T11:46:24.369737Z", - "iopub.status.idle": "2024-01-08T11:46:24.372928Z", - "shell.execute_reply": "2024-01-08T11:46:24.372406Z" + "iopub.execute_input": "2024-01-09T02:38:28.434230Z", + "iopub.status.busy": "2024-01-09T02:38:28.433777Z", + "iopub.status.idle": "2024-01-09T02:38:28.437031Z", + "shell.execute_reply": "2024-01-09T02:38:28.436510Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:24.375176Z", - "iopub.status.busy": "2024-01-08T11:46:24.374973Z", - "iopub.status.idle": "2024-01-08T11:46:24.377913Z", - "shell.execute_reply": "2024-01-08T11:46:24.377382Z" + "iopub.execute_input": "2024-01-09T02:38:28.439329Z", + "iopub.status.busy": "2024-01-09T02:38:28.439120Z", + "iopub.status.idle": "2024-01-09T02:38:28.441906Z", + "shell.execute_reply": "2024-01-09T02:38:28.441348Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:24.380093Z", - "iopub.status.busy": "2024-01-08T11:46:24.379893Z", - "iopub.status.idle": "2024-01-08T11:46:26.589464Z", - "shell.execute_reply": "2024-01-08T11:46:26.588623Z" + "iopub.execute_input": "2024-01-09T02:38:28.444140Z", + "iopub.status.busy": "2024-01-09T02:38:28.443939Z", + "iopub.status.idle": "2024-01-09T02:38:30.639738Z", + "shell.execute_reply": "2024-01-09T02:38:30.638890Z" }, "scrolled": true }, @@ -571,10 +571,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:26.593237Z", - "iopub.status.busy": "2024-01-08T11:46:26.592349Z", - "iopub.status.idle": "2024-01-08T11:46:26.600575Z", - "shell.execute_reply": "2024-01-08T11:46:26.599963Z" + "iopub.execute_input": "2024-01-09T02:38:30.643324Z", + "iopub.status.busy": "2024-01-09T02:38:30.642519Z", + "iopub.status.idle": "2024-01-09T02:38:30.650603Z", + "shell.execute_reply": "2024-01-09T02:38:30.650087Z" } }, "outputs": [ @@ -675,10 +675,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:26.602973Z", - "iopub.status.busy": "2024-01-08T11:46:26.602595Z", - "iopub.status.idle": "2024-01-08T11:46:26.606506Z", - "shell.execute_reply": "2024-01-08T11:46:26.605958Z" + "iopub.execute_input": "2024-01-09T02:38:30.653107Z", + "iopub.status.busy": "2024-01-09T02:38:30.652606Z", + "iopub.status.idle": "2024-01-09T02:38:30.656810Z", + "shell.execute_reply": "2024-01-09T02:38:30.656299Z" } }, "outputs": [], @@ -692,10 +692,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:26.609182Z", - "iopub.status.busy": "2024-01-08T11:46:26.608668Z", - "iopub.status.idle": "2024-01-08T11:46:26.612360Z", - "shell.execute_reply": "2024-01-08T11:46:26.611783Z" + "iopub.execute_input": "2024-01-09T02:38:30.659198Z", + "iopub.status.busy": "2024-01-09T02:38:30.658840Z", + "iopub.status.idle": "2024-01-09T02:38:30.662238Z", + "shell.execute_reply": "2024-01-09T02:38:30.661599Z" } }, "outputs": [ @@ -730,10 +730,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:26.615004Z", - "iopub.status.busy": "2024-01-08T11:46:26.614525Z", - "iopub.status.idle": "2024-01-08T11:46:26.617879Z", - "shell.execute_reply": "2024-01-08T11:46:26.617349Z" + "iopub.execute_input": "2024-01-09T02:38:30.664638Z", + "iopub.status.busy": "2024-01-09T02:38:30.664280Z", + "iopub.status.idle": "2024-01-09T02:38:30.667517Z", + "shell.execute_reply": "2024-01-09T02:38:30.666979Z" } }, "outputs": [], @@ -753,10 +753,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:26.620357Z", - "iopub.status.busy": "2024-01-08T11:46:26.619902Z", - "iopub.status.idle": "2024-01-08T11:46:26.627192Z", - "shell.execute_reply": "2024-01-08T11:46:26.626543Z" + "iopub.execute_input": "2024-01-09T02:38:30.669830Z", + "iopub.status.busy": "2024-01-09T02:38:30.669457Z", + "iopub.status.idle": "2024-01-09T02:38:30.676655Z", + "shell.execute_reply": "2024-01-09T02:38:30.675973Z" } }, "outputs": [ @@ -881,10 +881,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:26.629855Z", - "iopub.status.busy": "2024-01-08T11:46:26.629406Z", - "iopub.status.idle": "2024-01-08T11:46:26.875361Z", - "shell.execute_reply": "2024-01-08T11:46:26.874719Z" + "iopub.execute_input": "2024-01-09T02:38:30.679300Z", + "iopub.status.busy": "2024-01-09T02:38:30.678928Z", + "iopub.status.idle": "2024-01-09T02:38:30.922557Z", + "shell.execute_reply": "2024-01-09T02:38:30.921826Z" }, "scrolled": true }, @@ -923,10 +923,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:26.878500Z", - "iopub.status.busy": "2024-01-08T11:46:26.878039Z", - "iopub.status.idle": "2024-01-08T11:46:27.176040Z", - "shell.execute_reply": "2024-01-08T11:46:27.175408Z" + "iopub.execute_input": "2024-01-09T02:38:30.925622Z", + "iopub.status.busy": "2024-01-09T02:38:30.925132Z", + "iopub.status.idle": "2024-01-09T02:38:31.203728Z", + "shell.execute_reply": "2024-01-09T02:38:31.202990Z" }, "scrolled": true }, @@ -959,10 +959,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:27.179200Z", - "iopub.status.busy": "2024-01-08T11:46:27.178738Z", - "iopub.status.idle": "2024-01-08T11:46:27.183002Z", - "shell.execute_reply": "2024-01-08T11:46:27.182380Z" + "iopub.execute_input": "2024-01-09T02:38:31.208016Z", + "iopub.status.busy": "2024-01-09T02:38:31.206845Z", + "iopub.status.idle": "2024-01-09T02:38:31.212490Z", + "shell.execute_reply": "2024-01-09T02:38:31.211882Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index 62d530b9b..10d480055 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -862,7 +862,7 @@
---2024-01-08 11:46:31-- https://data.deepai.org/conll2003.zip
+--2024-01-09 02:38:35-- https://data.deepai.org/conll2003.zip
Resolving data.deepai.org (data.deepai.org)...
-143.244.50.88, 2400:52e0:1a01::996:1
-Connecting to data.deepai.org (data.deepai.org)|143.244.50.88|:443... connected.
+185.93.1.247, 2400:52e0:1a00::1068:1
+Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443...
+
+connected.
HTTP request sent, awaiting response...
conll2003.zip 100%[===================>] 959.94K –.-KB/s in 0.05s
+conll2003.zip 100%[===================>] 959.94K 5.93MB/s in 0.2s
--2024-01-08 11:46:32 (17.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-01-09 02:38:36 (5.93 MB/s) - ‘conll2003.zip’ saved [982975/982975]
mkdir: cannot create directory ‘data’: File exists </pre>
conll2003.zip 100%[===================>] 959.94K –.-KB/s in 0.05s
+conll2003.zip 100%[===================>] 959.94K 5.93MB/s in 0.2s
--2024-01-08 11:46:32 (17.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-01-09 02:38:36 (5.93 MB/s) - ‘conll2003.zip’ saved [982975/982975]
mkdir: cannot create directory ‘data’: File exists end{sphinxVerbatim}
conll2003.zip 100%[===================>] 959.94K –.-KB/s in 0.05s
+conll2003.zip 100%[===================>] 959.94K 5.93MB/s in 0.2s
-2024-01-08 11:46:32 (17.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2024-01-09 02:38:36 (5.93 MB/s) - ‘conll2003.zip’ saved [982975/982975]
mkdir: cannot create directory ‘data’: File exists
@@ -940,24 +948,9 @@-1. Install required dependencies and download data
----2024-01-08 11:46:32-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz -Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.226.249, 52.217.9.148, 3.5.7.165, ... -Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.226.249|:443... --------connected. --@@ -981,66 +974,29 @@---+--2024-01-09 02:38:36-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz +Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.11.201, 52.217.232.201, 3.5.27.107, ... +Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.11.201|:443... connected. HTTP request sent, awaiting response...1. Install required dependencies and download data
pred_probs.npz 0%[ ] 0 –.-KB/s
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--</pre>
-- pred_probs.npz 1%[ ] 278.53K 1.31MB/s
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-pred_probs.npz 97%[==================> ] 15.93M 25.9MB/s -pred_probs.npz 100%[===================>] 16.26M 25.9MB/s in 0.6s
+pred_probs.npz 100%[===================>] 16.26M –.-KB/s in 0.1s
--2024-01-08 11:46:33 (25.9 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
+2024-01-09 02:38:36 (150 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
</pre>
pred_probs.npz 97%[==================> ] 15.93M 25.9MB/s -pred_probs.npz 100%[===================>] 16.26M 25.9MB/s in 0.6s
+pred_probs.npz 100%[===================>] 16.26M –.-KB/s in 0.1s
--2024-01-08 11:46:33 (25.9 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
+2024-01-09 02:38:36 (150 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
end{sphinxVerbatim}
pred_probs.npz 97%[==================> ] 15.93M 25.9MB/s -pred_probs.npz 100%[===================>] 16.26M 25.9MB/s in 0.6s
+pred_probs.npz 100%[===================>] 16.26M –.-KB/s in 0.1s
-2024-01-08 11:46:33 (25.9 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
+2024-01-09 02:38:36 (150 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]
[3]: diff --git a/master/tutorials/token_classification.ipynb b/master/tutorials/token_classification.ipynb index a86b6aa2a..0195394fb 100644 --- a/master/tutorials/token_classification.ipynb +++ b/master/tutorials/token_classification.ipynb @@ -75,10 +75,10 @@ "id": "ae8a08e0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:31.949730Z", - "iopub.status.busy": "2024-01-08T11:46:31.949540Z", - "iopub.status.idle": "2024-01-08T11:46:33.725399Z", - "shell.execute_reply": "2024-01-08T11:46:33.724750Z" + "iopub.execute_input": "2024-01-09T02:38:35.691342Z", + "iopub.status.busy": "2024-01-09T02:38:35.690883Z", + "iopub.status.idle": "2024-01-09T02:38:36.831466Z", + "shell.execute_reply": "2024-01-09T02:38:36.830783Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-01-08 11:46:31-- https://data.deepai.org/conll2003.zip\r\n", + "--2024-01-09 02:38:35-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "143.244.50.88, 2400:52e0:1a01::996:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|143.244.50.88|:443... connected.\r\n", + "185.93.1.247, 2400:52e0:1a00::1068:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -116,9 +123,9 @@ "output_type": "stream", "text": [ "\r", - "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.05s \r\n", + "conll2003.zip 100%[===================>] 959.94K 5.93MB/s in 0.2s \r\n", "\r\n", - "2024-01-08 11:46:32 (17.4 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2024-01-09 02:38:36 (5.93 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -138,22 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2024-01-08 11:46:32-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.226.249, 52.217.9.148, 3.5.7.165, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.226.249|:443... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "connected.\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "--2024-01-09 02:38:36-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 3.5.11.201, 52.217.232.201, 3.5.27.107, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|3.5.11.201|:443... connected.\r\n", "HTTP request sent, awaiting response... " ] }, @@ -174,26 +168,9 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 1%[ ] 278.53K 1.31MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 28%[====> ] 4.65M 11.2MB/s " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r", - "pred_probs.npz 97%[==================> ] 15.93M 25.9MB/s \r", - "pred_probs.npz 100%[===================>] 16.26M 25.9MB/s in 0.6s \r\n", + "pred_probs.npz 100%[===================>] 16.26M --.-KB/s in 0.1s \r\n", "\r\n", - "2024-01-08 11:46:33 (25.9 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2024-01-09 02:38:36 (150 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -210,10 +187,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:33.728078Z", - "iopub.status.busy": "2024-01-08T11:46:33.727684Z", - "iopub.status.idle": "2024-01-08T11:46:34.740109Z", - "shell.execute_reply": "2024-01-08T11:46:34.739496Z" + "iopub.execute_input": "2024-01-09T02:38:36.834357Z", + "iopub.status.busy": "2024-01-09T02:38:36.833955Z", + "iopub.status.idle": "2024-01-09T02:38:37.874955Z", + "shell.execute_reply": "2024-01-09T02:38:37.874330Z" }, "nbsphinx": "hidden" }, @@ -224,7 +201,7 @@ "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@0a03742f52fc2b4c54e6274c64867976397f0b0d\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3526e4e8dbd8a5103c3050f41f03eaff284b3ab8\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -250,10 +227,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:34.742946Z", - "iopub.status.busy": "2024-01-08T11:46:34.742446Z", - "iopub.status.idle": "2024-01-08T11:46:34.746122Z", - "shell.execute_reply": "2024-01-08T11:46:34.745496Z" + "iopub.execute_input": "2024-01-09T02:38:37.878027Z", + "iopub.status.busy": "2024-01-09T02:38:37.877578Z", + "iopub.status.idle": "2024-01-09T02:38:37.881195Z", + "shell.execute_reply": "2024-01-09T02:38:37.880644Z" } }, "outputs": [], @@ -303,10 +280,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:34.748638Z", - "iopub.status.busy": "2024-01-08T11:46:34.748268Z", - "iopub.status.idle": "2024-01-08T11:46:34.751511Z", - "shell.execute_reply": "2024-01-08T11:46:34.750857Z" + "iopub.execute_input": "2024-01-09T02:38:37.883800Z", + "iopub.status.busy": "2024-01-09T02:38:37.883428Z", + "iopub.status.idle": "2024-01-09T02:38:37.886626Z", + "shell.execute_reply": "2024-01-09T02:38:37.886095Z" }, "nbsphinx": "hidden" }, @@ -324,10 +301,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:34.753914Z", - "iopub.status.busy": "2024-01-08T11:46:34.753565Z", - "iopub.status.idle": "2024-01-08T11:46:42.670159Z", - "shell.execute_reply": "2024-01-08T11:46:42.669510Z" + "iopub.execute_input": "2024-01-09T02:38:37.889033Z", + "iopub.status.busy": "2024-01-09T02:38:37.888669Z", + "iopub.status.idle": "2024-01-09T02:38:45.878971Z", + "shell.execute_reply": "2024-01-09T02:38:45.878283Z" } }, "outputs": [], @@ -401,10 +378,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:42.673217Z", - "iopub.status.busy": "2024-01-08T11:46:42.672808Z", - "iopub.status.idle": "2024-01-08T11:46:42.678894Z", - "shell.execute_reply": "2024-01-08T11:46:42.678330Z" + "iopub.execute_input": "2024-01-09T02:38:45.881874Z", + "iopub.status.busy": "2024-01-09T02:38:45.881620Z", + "iopub.status.idle": "2024-01-09T02:38:45.887671Z", + "shell.execute_reply": "2024-01-09T02:38:45.887078Z" }, "nbsphinx": "hidden" }, @@ -444,10 +421,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:42.681143Z", - "iopub.status.busy": "2024-01-08T11:46:42.680835Z", - "iopub.status.idle": "2024-01-08T11:46:43.104429Z", - "shell.execute_reply": "2024-01-08T11:46:43.103715Z" + "iopub.execute_input": "2024-01-09T02:38:45.890004Z", + "iopub.status.busy": "2024-01-09T02:38:45.889632Z", + "iopub.status.idle": "2024-01-09T02:38:46.317888Z", + "shell.execute_reply": "2024-01-09T02:38:46.317259Z" } }, "outputs": [], @@ -484,10 +461,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:43.107340Z", - "iopub.status.busy": "2024-01-08T11:46:43.107087Z", - "iopub.status.idle": "2024-01-08T11:46:43.113362Z", - "shell.execute_reply": "2024-01-08T11:46:43.112733Z" + "iopub.execute_input": "2024-01-09T02:38:46.320703Z", + "iopub.status.busy": "2024-01-09T02:38:46.320290Z", + "iopub.status.idle": "2024-01-09T02:38:46.325604Z", + "shell.execute_reply": "2024-01-09T02:38:46.325035Z" } }, "outputs": [ @@ -559,10 +536,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:43.115913Z", - "iopub.status.busy": "2024-01-08T11:46:43.115476Z", - "iopub.status.idle": "2024-01-08T11:46:45.037388Z", - "shell.execute_reply": "2024-01-08T11:46:45.036624Z" + "iopub.execute_input": "2024-01-09T02:38:46.328121Z", + "iopub.status.busy": "2024-01-09T02:38:46.327752Z", + "iopub.status.idle": "2024-01-09T02:38:48.279409Z", + "shell.execute_reply": "2024-01-09T02:38:48.278654Z" } }, "outputs": [], @@ -584,10 +561,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:45.040997Z", - "iopub.status.busy": "2024-01-08T11:46:45.040075Z", - "iopub.status.idle": "2024-01-08T11:46:45.047022Z", - "shell.execute_reply": "2024-01-08T11:46:45.046360Z" + "iopub.execute_input": "2024-01-09T02:38:48.282869Z", + "iopub.status.busy": "2024-01-09T02:38:48.282115Z", + "iopub.status.idle": "2024-01-09T02:38:48.289091Z", + "shell.execute_reply": "2024-01-09T02:38:48.288442Z" } }, "outputs": [ @@ -623,10 +600,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:45.049521Z", - "iopub.status.busy": "2024-01-08T11:46:45.049029Z", - "iopub.status.idle": "2024-01-08T11:46:45.075003Z", - "shell.execute_reply": "2024-01-08T11:46:45.074374Z" + "iopub.execute_input": "2024-01-09T02:38:48.291646Z", + "iopub.status.busy": "2024-01-09T02:38:48.291203Z", + "iopub.status.idle": "2024-01-09T02:38:48.308281Z", + "shell.execute_reply": "2024-01-09T02:38:48.307787Z" } }, "outputs": [ @@ -804,10 +781,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:45.077409Z", - "iopub.status.busy": "2024-01-08T11:46:45.076978Z", - "iopub.status.idle": "2024-01-08T11:46:45.109110Z", - "shell.execute_reply": "2024-01-08T11:46:45.108606Z" + "iopub.execute_input": "2024-01-09T02:38:48.310500Z", + "iopub.status.busy": "2024-01-09T02:38:48.310301Z", + "iopub.status.idle": "2024-01-09T02:38:48.342796Z", + "shell.execute_reply": "2024-01-09T02:38:48.342287Z" } }, "outputs": [ @@ -909,10 +886,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:45.111487Z", - "iopub.status.busy": "2024-01-08T11:46:45.111114Z", - "iopub.status.idle": "2024-01-08T11:46:45.118819Z", - "shell.execute_reply": "2024-01-08T11:46:45.118293Z" + "iopub.execute_input": "2024-01-09T02:38:48.345127Z", + "iopub.status.busy": "2024-01-09T02:38:48.344926Z", + "iopub.status.idle": "2024-01-09T02:38:48.354346Z", + "shell.execute_reply": "2024-01-09T02:38:48.353738Z" } }, "outputs": [ @@ -986,10 +963,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:45.121192Z", - "iopub.status.busy": "2024-01-08T11:46:45.120823Z", - "iopub.status.idle": "2024-01-08T11:46:46.940522Z", - "shell.execute_reply": "2024-01-08T11:46:46.939948Z" + "iopub.execute_input": "2024-01-09T02:38:48.356834Z", + "iopub.status.busy": "2024-01-09T02:38:48.356628Z", + "iopub.status.idle": "2024-01-09T02:38:50.187680Z", + "shell.execute_reply": "2024-01-09T02:38:50.187124Z" } }, "outputs": [ @@ -1161,10 +1138,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2024-01-08T11:46:46.943173Z", - "iopub.status.busy": "2024-01-08T11:46:46.942789Z", - "iopub.status.idle": "2024-01-08T11:46:46.947081Z", - "shell.execute_reply": "2024-01-08T11:46:46.946560Z" + "iopub.execute_input": "2024-01-09T02:38:50.190444Z", + "iopub.status.busy": "2024-01-09T02:38:50.189942Z", + "iopub.status.idle": "2024-01-09T02:38:50.194378Z", + "shell.execute_reply": "2024-01-09T02:38:50.193753Z" }, "nbsphinx": "hidden" }, diff --git a/versioning.js b/versioning.js index f30622fcd..c5d4ae716 100644 --- a/versioning.js +++ b/versioning.js @@ -1,4 +1,4 @@ var Version = { version_number: "v2.5.0", - commit_hash: "0a03742f52fc2b4c54e6274c64867976397f0b0d", + commit_hash: "3526e4e8dbd8a5103c3050f41f03eaff284b3ab8", }; \ No newline at end of file