diff --git a/master/.buildinfo b/master/.buildinfo
index c240822f3..366b4c330 100644
--- a/master/.buildinfo
+++ b/master/.buildinfo
@@ -1,4 +1,4 @@
# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 2bc3cc0663755d61b0e972bc24b1b4d5
+config: c11225a6041692e02a32f19a8892464a
tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree
index 20ffb77f2..681789d84 100644
Binary files a/master/.doctrees/cleanlab/benchmarking/index.doctree and b/master/.doctrees/cleanlab/benchmarking/index.doctree differ
diff --git a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree
index 4b1f98654..090a6748a 100644
Binary files a/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree and b/master/.doctrees/cleanlab/benchmarking/noise_generation.doctree differ
diff --git a/master/.doctrees/cleanlab/classification.doctree b/master/.doctrees/cleanlab/classification.doctree
index cea2a55b1..6d5f9e20c 100644
Binary files a/master/.doctrees/cleanlab/classification.doctree and b/master/.doctrees/cleanlab/classification.doctree differ
diff --git a/master/.doctrees/cleanlab/count.doctree b/master/.doctrees/cleanlab/count.doctree
index 16ea68a3b..ad6c71424 100644
Binary files a/master/.doctrees/cleanlab/count.doctree and b/master/.doctrees/cleanlab/count.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/datalab.doctree b/master/.doctrees/cleanlab/datalab/datalab.doctree
index b97d03b0f..7e2ad93e7 100644
Binary files a/master/.doctrees/cleanlab/datalab/datalab.doctree and b/master/.doctrees/cleanlab/datalab/datalab.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree
index f235e6c8c..f3b7dbb84 100644
Binary files a/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree and b/master/.doctrees/cleanlab/datalab/guide/custom_issue_manager.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree
index 5e83c24ee..ae550cff6 100644
Binary files a/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree and b/master/.doctrees/cleanlab/datalab/guide/generating_cluster_ids.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/guide/index.doctree b/master/.doctrees/cleanlab/datalab/guide/index.doctree
index 8317e20a7..05f5dcdb3 100644
Binary files a/master/.doctrees/cleanlab/datalab/guide/index.doctree and b/master/.doctrees/cleanlab/datalab/guide/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree
index 2e9838a8e..193eaaff1 100644
Binary files a/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree and b/master/.doctrees/cleanlab/datalab/guide/issue_type_description.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/index.doctree b/master/.doctrees/cleanlab/datalab/index.doctree
index 56a6db7aa..1d28d2325 100644
Binary files a/master/.doctrees/cleanlab/datalab/index.doctree and b/master/.doctrees/cleanlab/datalab/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/data.doctree b/master/.doctrees/cleanlab/datalab/internal/data.doctree
index 09963f4d8..9258b1038 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/data.doctree and b/master/.doctrees/cleanlab/datalab/internal/data.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree
index ce398dfb6..8831a0aa3 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree and b/master/.doctrees/cleanlab/datalab/internal/data_issues.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/factory.doctree b/master/.doctrees/cleanlab/datalab/internal/factory.doctree
index 1e160f54f..ff3c63483 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/factory.doctree and b/master/.doctrees/cleanlab/datalab/internal/factory.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/index.doctree b/master/.doctrees/cleanlab/datalab/internal/index.doctree
index 113c7a0c0..f1b9c3c3a 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree
index 8536cff49..1228c7a6a 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_finder.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree
index f56349e2e..36c13cced 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/_notices/not_registered.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree
index fc130abab..438f7faf3 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/data_valuation.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree
index 28d79e4ea..4b4d66e46 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree
index 598ef5216..e08d25293 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/imbalance.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree
index 7a5fb720b..6e5576815 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree
index 8725a9ec0..13311b358 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/issue_manager.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree
index 37580739b..316fc5765 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/label.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree
index 31f966e62..056d50777 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/noniid.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree
index 62e46ed59..36593048a 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/null.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree
index 2095b6d5c..b3e060c74 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/outlier.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree
index b3c64e8ee..019189d37 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/index.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree
index 7c8e23a7c..a88cdaa29 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/regression/label.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree
index a2fc1d940..65a0f8cea 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree and b/master/.doctrees/cleanlab/datalab/internal/issue_manager/underperforming_group.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/internal/report.doctree b/master/.doctrees/cleanlab/datalab/internal/report.doctree
index d0013a77f..847e4828c 100644
Binary files a/master/.doctrees/cleanlab/datalab/internal/report.doctree and b/master/.doctrees/cleanlab/datalab/internal/report.doctree differ
diff --git a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree
index d70d6fdb0..85a4977d6 100644
Binary files a/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree and b/master/.doctrees/cleanlab/datalab/optional_dependencies.doctree differ
diff --git a/master/.doctrees/cleanlab/dataset.doctree b/master/.doctrees/cleanlab/dataset.doctree
index dbbfdfb08..9d3d7d370 100644
Binary files a/master/.doctrees/cleanlab/dataset.doctree and b/master/.doctrees/cleanlab/dataset.doctree differ
diff --git a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree
index 7d6dc6cd7..7b94c1509 100644
Binary files a/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree and b/master/.doctrees/cleanlab/experimental/cifar_cnn.doctree differ
diff --git a/master/.doctrees/cleanlab/experimental/coteaching.doctree b/master/.doctrees/cleanlab/experimental/coteaching.doctree
index 5203a4434..a5f148211 100644
Binary files a/master/.doctrees/cleanlab/experimental/coteaching.doctree and b/master/.doctrees/cleanlab/experimental/coteaching.doctree differ
diff --git a/master/.doctrees/cleanlab/experimental/index.doctree b/master/.doctrees/cleanlab/experimental/index.doctree
index c194a1756..f629cde60 100644
Binary files a/master/.doctrees/cleanlab/experimental/index.doctree and b/master/.doctrees/cleanlab/experimental/index.doctree differ
diff --git a/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree b/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree
index a67c7ab6d..3129d0e9d 100644
Binary files a/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree and b/master/.doctrees/cleanlab/experimental/label_issues_batched.doctree differ
diff --git a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree
index 96bf6a232..ed0083653 100644
Binary files a/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree and b/master/.doctrees/cleanlab/experimental/mnist_pytorch.doctree differ
diff --git a/master/.doctrees/cleanlab/filter.doctree b/master/.doctrees/cleanlab/filter.doctree
index d713ddea8..8a3bccd0a 100644
Binary files a/master/.doctrees/cleanlab/filter.doctree and b/master/.doctrees/cleanlab/filter.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/index.doctree b/master/.doctrees/cleanlab/internal/index.doctree
index 2e3e0a1c4..cb2b41138 100644
Binary files a/master/.doctrees/cleanlab/internal/index.doctree and b/master/.doctrees/cleanlab/internal/index.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree
index afcd46a46..bb79605df 100644
Binary files a/master/.doctrees/cleanlab/internal/label_quality_utils.doctree and b/master/.doctrees/cleanlab/internal/label_quality_utils.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/latent_algebra.doctree b/master/.doctrees/cleanlab/internal/latent_algebra.doctree
index e91a5137f..7577e4afd 100644
Binary files a/master/.doctrees/cleanlab/internal/latent_algebra.doctree and b/master/.doctrees/cleanlab/internal/latent_algebra.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree
index 9e9448939..8d9f03cdc 100644
Binary files a/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree and b/master/.doctrees/cleanlab/internal/multiannotator_utils.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree b/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree
index 50aee6b53..79942491d 100644
Binary files a/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree and b/master/.doctrees/cleanlab/internal/multilabel_scorer.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree
index 071660ddb..d06ef86fe 100644
Binary files a/master/.doctrees/cleanlab/internal/multilabel_utils.doctree and b/master/.doctrees/cleanlab/internal/multilabel_utils.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/outlier.doctree b/master/.doctrees/cleanlab/internal/outlier.doctree
index 9dc80ede2..9966ab459 100644
Binary files a/master/.doctrees/cleanlab/internal/outlier.doctree and b/master/.doctrees/cleanlab/internal/outlier.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/token_classification_utils.doctree b/master/.doctrees/cleanlab/internal/token_classification_utils.doctree
index 3ce1d52b7..158cb6317 100644
Binary files a/master/.doctrees/cleanlab/internal/token_classification_utils.doctree and b/master/.doctrees/cleanlab/internal/token_classification_utils.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/util.doctree b/master/.doctrees/cleanlab/internal/util.doctree
index 14f850e07..da84edfb8 100644
Binary files a/master/.doctrees/cleanlab/internal/util.doctree and b/master/.doctrees/cleanlab/internal/util.doctree differ
diff --git a/master/.doctrees/cleanlab/internal/validation.doctree b/master/.doctrees/cleanlab/internal/validation.doctree
index f470aaae2..6b2fb1d20 100644
Binary files a/master/.doctrees/cleanlab/internal/validation.doctree and b/master/.doctrees/cleanlab/internal/validation.doctree differ
diff --git a/master/.doctrees/cleanlab/models/fasttext.doctree b/master/.doctrees/cleanlab/models/fasttext.doctree
index 92979eebc..fa42e2c01 100644
Binary files a/master/.doctrees/cleanlab/models/fasttext.doctree and b/master/.doctrees/cleanlab/models/fasttext.doctree differ
diff --git a/master/.doctrees/cleanlab/models/index.doctree b/master/.doctrees/cleanlab/models/index.doctree
index 0404f7482..b2f634175 100644
Binary files a/master/.doctrees/cleanlab/models/index.doctree and b/master/.doctrees/cleanlab/models/index.doctree differ
diff --git a/master/.doctrees/cleanlab/models/keras.doctree b/master/.doctrees/cleanlab/models/keras.doctree
index 7f21a289b..cc4981aa7 100644
Binary files a/master/.doctrees/cleanlab/models/keras.doctree and b/master/.doctrees/cleanlab/models/keras.doctree differ
diff --git a/master/.doctrees/cleanlab/multiannotator.doctree b/master/.doctrees/cleanlab/multiannotator.doctree
index 5761dac0d..52c53c9f3 100644
Binary files a/master/.doctrees/cleanlab/multiannotator.doctree and b/master/.doctrees/cleanlab/multiannotator.doctree differ
diff --git a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree
index 0d3703860..08b34383f 100644
Binary files a/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree and b/master/.doctrees/cleanlab/multilabel_classification/dataset.doctree differ
diff --git a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree
index e7c2a5541..fa59ceede 100644
Binary files a/master/.doctrees/cleanlab/multilabel_classification/filter.doctree and b/master/.doctrees/cleanlab/multilabel_classification/filter.doctree differ
diff --git a/master/.doctrees/cleanlab/multilabel_classification/index.doctree b/master/.doctrees/cleanlab/multilabel_classification/index.doctree
index a2faee9f8..1712c1ffb 100644
Binary files a/master/.doctrees/cleanlab/multilabel_classification/index.doctree and b/master/.doctrees/cleanlab/multilabel_classification/index.doctree differ
diff --git a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree
index 9caebffc6..7e167fd55 100644
Binary files a/master/.doctrees/cleanlab/multilabel_classification/rank.doctree and b/master/.doctrees/cleanlab/multilabel_classification/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/object_detection/filter.doctree b/master/.doctrees/cleanlab/object_detection/filter.doctree
index 9603dd4aa..1526e9d8e 100644
Binary files a/master/.doctrees/cleanlab/object_detection/filter.doctree and b/master/.doctrees/cleanlab/object_detection/filter.doctree differ
diff --git a/master/.doctrees/cleanlab/object_detection/index.doctree b/master/.doctrees/cleanlab/object_detection/index.doctree
index cf1cd1952..5338a1e55 100644
Binary files a/master/.doctrees/cleanlab/object_detection/index.doctree and b/master/.doctrees/cleanlab/object_detection/index.doctree differ
diff --git a/master/.doctrees/cleanlab/object_detection/rank.doctree b/master/.doctrees/cleanlab/object_detection/rank.doctree
index 685f6fe39..28f96c836 100644
Binary files a/master/.doctrees/cleanlab/object_detection/rank.doctree and b/master/.doctrees/cleanlab/object_detection/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/object_detection/summary.doctree b/master/.doctrees/cleanlab/object_detection/summary.doctree
index 46ea65ac0..10ab844dc 100644
Binary files a/master/.doctrees/cleanlab/object_detection/summary.doctree and b/master/.doctrees/cleanlab/object_detection/summary.doctree differ
diff --git a/master/.doctrees/cleanlab/outlier.doctree b/master/.doctrees/cleanlab/outlier.doctree
index 1da345cb6..720ec6c57 100644
Binary files a/master/.doctrees/cleanlab/outlier.doctree and b/master/.doctrees/cleanlab/outlier.doctree differ
diff --git a/master/.doctrees/cleanlab/rank.doctree b/master/.doctrees/cleanlab/rank.doctree
index 013d83807..7a6bf53af 100644
Binary files a/master/.doctrees/cleanlab/rank.doctree and b/master/.doctrees/cleanlab/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/regression/index.doctree b/master/.doctrees/cleanlab/regression/index.doctree
index b7ae781b7..67428e608 100644
Binary files a/master/.doctrees/cleanlab/regression/index.doctree and b/master/.doctrees/cleanlab/regression/index.doctree differ
diff --git a/master/.doctrees/cleanlab/regression/learn.doctree b/master/.doctrees/cleanlab/regression/learn.doctree
index af35ba407..bea82e226 100644
Binary files a/master/.doctrees/cleanlab/regression/learn.doctree and b/master/.doctrees/cleanlab/regression/learn.doctree differ
diff --git a/master/.doctrees/cleanlab/regression/rank.doctree b/master/.doctrees/cleanlab/regression/rank.doctree
index 82e7f90fa..81a77cc9b 100644
Binary files a/master/.doctrees/cleanlab/regression/rank.doctree and b/master/.doctrees/cleanlab/regression/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/segmentation/filter.doctree b/master/.doctrees/cleanlab/segmentation/filter.doctree
index a502a536c..c49dfbd2d 100644
Binary files a/master/.doctrees/cleanlab/segmentation/filter.doctree and b/master/.doctrees/cleanlab/segmentation/filter.doctree differ
diff --git a/master/.doctrees/cleanlab/segmentation/index.doctree b/master/.doctrees/cleanlab/segmentation/index.doctree
index 67a2a1337..4b0ac068a 100644
Binary files a/master/.doctrees/cleanlab/segmentation/index.doctree and b/master/.doctrees/cleanlab/segmentation/index.doctree differ
diff --git a/master/.doctrees/cleanlab/segmentation/rank.doctree b/master/.doctrees/cleanlab/segmentation/rank.doctree
index eef1a3ba9..d9a19a274 100644
Binary files a/master/.doctrees/cleanlab/segmentation/rank.doctree and b/master/.doctrees/cleanlab/segmentation/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/segmentation/summary.doctree b/master/.doctrees/cleanlab/segmentation/summary.doctree
index 600c2df67..bea969301 100644
Binary files a/master/.doctrees/cleanlab/segmentation/summary.doctree and b/master/.doctrees/cleanlab/segmentation/summary.doctree differ
diff --git a/master/.doctrees/cleanlab/token_classification/filter.doctree b/master/.doctrees/cleanlab/token_classification/filter.doctree
index 1543e7db1..affdc4f83 100644
Binary files a/master/.doctrees/cleanlab/token_classification/filter.doctree and b/master/.doctrees/cleanlab/token_classification/filter.doctree differ
diff --git a/master/.doctrees/cleanlab/token_classification/index.doctree b/master/.doctrees/cleanlab/token_classification/index.doctree
index 709d78528..5bb43e128 100644
Binary files a/master/.doctrees/cleanlab/token_classification/index.doctree and b/master/.doctrees/cleanlab/token_classification/index.doctree differ
diff --git a/master/.doctrees/cleanlab/token_classification/rank.doctree b/master/.doctrees/cleanlab/token_classification/rank.doctree
index c89399438..98c38c755 100644
Binary files a/master/.doctrees/cleanlab/token_classification/rank.doctree and b/master/.doctrees/cleanlab/token_classification/rank.doctree differ
diff --git a/master/.doctrees/cleanlab/token_classification/summary.doctree b/master/.doctrees/cleanlab/token_classification/summary.doctree
index b249cfeb2..dd2875dc2 100644
Binary files a/master/.doctrees/cleanlab/token_classification/summary.doctree and b/master/.doctrees/cleanlab/token_classification/summary.doctree differ
diff --git a/master/.doctrees/environment.pickle b/master/.doctrees/environment.pickle
index 8603fad50..ac9d9d7ec 100644
Binary files a/master/.doctrees/environment.pickle and b/master/.doctrees/environment.pickle differ
diff --git a/master/.doctrees/index.doctree b/master/.doctrees/index.doctree
index 16d308b6f..e2f6803ec 100644
Binary files a/master/.doctrees/index.doctree and b/master/.doctrees/index.doctree differ
diff --git a/master/.doctrees/migrating/migrate_v2.doctree b/master/.doctrees/migrating/migrate_v2.doctree
index 49b85afd7..6882fa2b6 100644
Binary files a/master/.doctrees/migrating/migrate_v2.doctree and b/master/.doctrees/migrating/migrate_v2.doctree differ
diff --git a/master/.doctrees/nbsphinx/tutorials/audio.ipynb b/master/.doctrees/nbsphinx/tutorials/audio.ipynb
index c0740d96a..d344caa69 100644
--- a/master/.doctrees/nbsphinx/tutorials/audio.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/audio.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:49:57.262898Z",
- "iopub.status.busy": "2024-01-19T12:49:57.262709Z",
- "iopub.status.idle": "2024-01-19T12:50:00.537477Z",
- "shell.execute_reply": "2024-01-19T12:50:00.536799Z"
+ "iopub.execute_input": "2024-01-19T13:07:18.957405Z",
+ "iopub.status.busy": "2024-01-19T13:07:18.957212Z",
+ "iopub.status.idle": "2024-01-19T13:07:22.234748Z",
+ "shell.execute_reply": "2024-01-19T13:07:22.233965Z"
},
"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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -131,10 +131,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:00.540554Z",
- "iopub.status.busy": "2024-01-19T12:50:00.540097Z",
- "iopub.status.idle": "2024-01-19T12:50:00.543607Z",
- "shell.execute_reply": "2024-01-19T12:50:00.543090Z"
+ "iopub.execute_input": "2024-01-19T13:07:22.238142Z",
+ "iopub.status.busy": "2024-01-19T13:07:22.237465Z",
+ "iopub.status.idle": "2024-01-19T13:07:22.241450Z",
+ "shell.execute_reply": "2024-01-19T13:07:22.240855Z"
},
"id": "LaEiwXUiVHCS"
},
@@ -157,10 +157,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:00.545811Z",
- "iopub.status.busy": "2024-01-19T12:50:00.545618Z",
- "iopub.status.idle": "2024-01-19T12:50:00.550466Z",
- "shell.execute_reply": "2024-01-19T12:50:00.549876Z"
+ "iopub.execute_input": "2024-01-19T13:07:22.244182Z",
+ "iopub.status.busy": "2024-01-19T13:07:22.243688Z",
+ "iopub.status.idle": "2024-01-19T13:07:22.249351Z",
+ "shell.execute_reply": "2024-01-19T13:07:22.248860Z"
},
"nbsphinx": "hidden"
},
@@ -208,10 +208,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:00.553073Z",
- "iopub.status.busy": "2024-01-19T12:50:00.552706Z",
- "iopub.status.idle": "2024-01-19T12:50:02.345002Z",
- "shell.execute_reply": "2024-01-19T12:50:02.344156Z"
+ "iopub.execute_input": "2024-01-19T13:07:22.251956Z",
+ "iopub.status.busy": "2024-01-19T13:07:22.251431Z",
+ "iopub.status.idle": "2024-01-19T13:07:23.866596Z",
+ "shell.execute_reply": "2024-01-19T13:07:23.865852Z"
},
"id": "GRDPEg7-VOQe",
"outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6"
@@ -242,10 +242,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:02.348316Z",
- "iopub.status.busy": "2024-01-19T12:50:02.347889Z",
- "iopub.status.idle": "2024-01-19T12:50:02.360581Z",
- "shell.execute_reply": "2024-01-19T12:50:02.359901Z"
+ "iopub.execute_input": "2024-01-19T13:07:23.869813Z",
+ "iopub.status.busy": "2024-01-19T13:07:23.869385Z",
+ "iopub.status.idle": "2024-01-19T13:07:23.881488Z",
+ "shell.execute_reply": "2024-01-19T13:07:23.880847Z"
},
"id": "FDA5sGZwUSur",
"outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895"
@@ -329,10 +329,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:02.394722Z",
- "iopub.status.busy": "2024-01-19T12:50:02.394318Z",
- "iopub.status.idle": "2024-01-19T12:50:02.400974Z",
- "shell.execute_reply": "2024-01-19T12:50:02.400368Z"
+ "iopub.execute_input": "2024-01-19T13:07:23.915158Z",
+ "iopub.status.busy": "2024-01-19T13:07:23.914675Z",
+ "iopub.status.idle": "2024-01-19T13:07:23.921567Z",
+ "shell.execute_reply": "2024-01-19T13:07:23.921026Z"
},
"nbsphinx": "hidden"
},
@@ -380,10 +380,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:02.403322Z",
- "iopub.status.busy": "2024-01-19T12:50:02.402961Z",
- "iopub.status.idle": "2024-01-19T12:50:03.179927Z",
- "shell.execute_reply": "2024-01-19T12:50:03.179264Z"
+ "iopub.execute_input": "2024-01-19T13:07:23.923890Z",
+ "iopub.status.busy": "2024-01-19T13:07:23.923683Z",
+ "iopub.status.idle": "2024-01-19T13:07:24.670346Z",
+ "shell.execute_reply": "2024-01-19T13:07:24.669675Z"
},
"id": "dLBvUZLlII5w",
"outputId": "c6a4917f-4a82-4a89-9193-415072e45550"
@@ -435,10 +435,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:03.182552Z",
- "iopub.status.busy": "2024-01-19T12:50:03.182188Z",
- "iopub.status.idle": "2024-01-19T12:50:04.348638Z",
- "shell.execute_reply": "2024-01-19T12:50:04.347935Z"
+ "iopub.execute_input": "2024-01-19T13:07:24.672804Z",
+ "iopub.status.busy": "2024-01-19T13:07:24.672599Z",
+ "iopub.status.idle": "2024-01-19T13:07:25.412758Z",
+ "shell.execute_reply": "2024-01-19T13:07:25.412163Z"
},
"id": "vL9lkiKsHvKr"
},
@@ -472,10 +472,10 @@
"height": 143
},
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:04.351816Z",
- "iopub.status.busy": "2024-01-19T12:50:04.351230Z",
- "iopub.status.idle": "2024-01-19T12:50:04.373328Z",
- "shell.execute_reply": "2024-01-19T12:50:04.372670Z"
+ "iopub.execute_input": "2024-01-19T13:07:25.415877Z",
+ "iopub.status.busy": "2024-01-19T13:07:25.415455Z",
+ "iopub.status.idle": "2024-01-19T13:07:25.439326Z",
+ "shell.execute_reply": "2024-01-19T13:07:25.438714Z"
},
"id": "obQYDKdLiUU6",
"outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4"
@@ -555,10 +555,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:04.375674Z",
- "iopub.status.busy": "2024-01-19T12:50:04.375297Z",
- "iopub.status.idle": "2024-01-19T12:50:04.378661Z",
- "shell.execute_reply": "2024-01-19T12:50:04.378148Z"
+ "iopub.execute_input": "2024-01-19T13:07:25.441887Z",
+ "iopub.status.busy": "2024-01-19T13:07:25.441558Z",
+ "iopub.status.idle": "2024-01-19T13:07:25.444964Z",
+ "shell.execute_reply": "2024-01-19T13:07:25.444418Z"
},
"id": "I8JqhOZgi94g"
},
@@ -580,10 +580,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:04.380903Z",
- "iopub.status.busy": "2024-01-19T12:50:04.380541Z",
- "iopub.status.idle": "2024-01-19T12:50:22.612244Z",
- "shell.execute_reply": "2024-01-19T12:50:22.611594Z"
+ "iopub.execute_input": "2024-01-19T13:07:25.447343Z",
+ "iopub.status.busy": "2024-01-19T13:07:25.446985Z",
+ "iopub.status.idle": "2024-01-19T13:07:44.317040Z",
+ "shell.execute_reply": "2024-01-19T13:07:44.316337Z"
},
"id": "2FSQ2GR9R_YA"
},
@@ -615,10 +615,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:22.615279Z",
- "iopub.status.busy": "2024-01-19T12:50:22.614853Z",
- "iopub.status.idle": "2024-01-19T12:50:22.619505Z",
- "shell.execute_reply": "2024-01-19T12:50:22.618953Z"
+ "iopub.execute_input": "2024-01-19T13:07:44.320383Z",
+ "iopub.status.busy": "2024-01-19T13:07:44.319966Z",
+ "iopub.status.idle": "2024-01-19T13:07:44.324597Z",
+ "shell.execute_reply": "2024-01-19T13:07:44.324040Z"
},
"id": "kAkY31IVXyr8",
"outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632"
@@ -677,10 +677,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:22.622151Z",
- "iopub.status.busy": "2024-01-19T12:50:22.621704Z",
- "iopub.status.idle": "2024-01-19T12:50:28.055287Z",
- "shell.execute_reply": "2024-01-19T12:50:28.054633Z"
+ "iopub.execute_input": "2024-01-19T13:07:44.326888Z",
+ "iopub.status.busy": "2024-01-19T13:07:44.326686Z",
+ "iopub.status.idle": "2024-01-19T13:07:49.871297Z",
+ "shell.execute_reply": "2024-01-19T13:07:49.870600Z"
},
"id": "i_drkY9YOcw4"
},
@@ -714,10 +714,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:28.059805Z",
- "iopub.status.busy": "2024-01-19T12:50:28.058649Z",
- "iopub.status.idle": "2024-01-19T12:50:28.066422Z",
- "shell.execute_reply": "2024-01-19T12:50:28.065820Z"
+ "iopub.execute_input": "2024-01-19T13:07:49.874991Z",
+ "iopub.status.busy": "2024-01-19T13:07:49.874323Z",
+ "iopub.status.idle": "2024-01-19T13:07:49.879921Z",
+ "shell.execute_reply": "2024-01-19T13:07:49.879304Z"
},
"id": "_b-AQeoXOc7q",
"outputId": "15ae534a-f517-4906-b177-ca91931a8954"
@@ -764,10 +764,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:28.070834Z",
- "iopub.status.busy": "2024-01-19T12:50:28.069701Z",
- "iopub.status.idle": "2024-01-19T12:50:28.167012Z",
- "shell.execute_reply": "2024-01-19T12:50:28.166241Z"
+ "iopub.execute_input": "2024-01-19T13:07:49.882949Z",
+ "iopub.status.busy": "2024-01-19T13:07:49.882525Z",
+ "iopub.status.idle": "2024-01-19T13:07:49.980739Z",
+ "shell.execute_reply": "2024-01-19T13:07:49.980001Z"
}
},
"outputs": [
@@ -804,10 +804,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:28.169623Z",
- "iopub.status.busy": "2024-01-19T12:50:28.169390Z",
- "iopub.status.idle": "2024-01-19T12:50:28.179521Z",
- "shell.execute_reply": "2024-01-19T12:50:28.178969Z"
+ "iopub.execute_input": "2024-01-19T13:07:49.983463Z",
+ "iopub.status.busy": "2024-01-19T13:07:49.983123Z",
+ "iopub.status.idle": "2024-01-19T13:07:49.993389Z",
+ "shell.execute_reply": "2024-01-19T13:07:49.992841Z"
},
"scrolled": true
},
@@ -862,10 +862,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:28.181979Z",
- "iopub.status.busy": "2024-01-19T12:50:28.181590Z",
- "iopub.status.idle": "2024-01-19T12:50:28.189902Z",
- "shell.execute_reply": "2024-01-19T12:50:28.189365Z"
+ "iopub.execute_input": "2024-01-19T13:07:49.995821Z",
+ "iopub.status.busy": "2024-01-19T13:07:49.995517Z",
+ "iopub.status.idle": "2024-01-19T13:07:50.003896Z",
+ "shell.execute_reply": "2024-01-19T13:07:50.003309Z"
}
},
"outputs": [
@@ -969,10 +969,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:28.192280Z",
- "iopub.status.busy": "2024-01-19T12:50:28.191878Z",
- "iopub.status.idle": "2024-01-19T12:50:28.196703Z",
- "shell.execute_reply": "2024-01-19T12:50:28.196147Z"
+ "iopub.execute_input": "2024-01-19T13:07:50.006538Z",
+ "iopub.status.busy": "2024-01-19T13:07:50.006074Z",
+ "iopub.status.idle": "2024-01-19T13:07:50.010690Z",
+ "shell.execute_reply": "2024-01-19T13:07:50.010037Z"
}
},
"outputs": [
@@ -1010,10 +1010,10 @@
"height": 237
},
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:28.199130Z",
- "iopub.status.busy": "2024-01-19T12:50:28.198753Z",
- "iopub.status.idle": "2024-01-19T12:50:28.204732Z",
- "shell.execute_reply": "2024-01-19T12:50:28.204092Z"
+ "iopub.execute_input": "2024-01-19T13:07:50.013192Z",
+ "iopub.status.busy": "2024-01-19T13:07:50.012830Z",
+ "iopub.status.idle": "2024-01-19T13:07:50.018948Z",
+ "shell.execute_reply": "2024-01-19T13:07:50.018298Z"
},
"id": "FQwRHgbclpsO",
"outputId": "fee5c335-c00e-4fcc-f22b-718705e93182"
@@ -1133,10 +1133,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:28.207304Z",
- "iopub.status.busy": "2024-01-19T12:50:28.206820Z",
- "iopub.status.idle": "2024-01-19T12:50:28.322291Z",
- "shell.execute_reply": "2024-01-19T12:50:28.321641Z"
+ "iopub.execute_input": "2024-01-19T13:07:50.021472Z",
+ "iopub.status.busy": "2024-01-19T13:07:50.021103Z",
+ "iopub.status.idle": "2024-01-19T13:07:50.139310Z",
+ "shell.execute_reply": "2024-01-19T13:07:50.138727Z"
},
"id": "ff1NFVlDoysO",
"outputId": "8141a036-44c1-4349-c338-880432513e37"
@@ -1190,10 +1190,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:28.324982Z",
- "iopub.status.busy": "2024-01-19T12:50:28.324537Z",
- "iopub.status.idle": "2024-01-19T12:50:28.430331Z",
- "shell.execute_reply": "2024-01-19T12:50:28.429688Z"
+ "iopub.execute_input": "2024-01-19T13:07:50.141820Z",
+ "iopub.status.busy": "2024-01-19T13:07:50.141507Z",
+ "iopub.status.idle": "2024-01-19T13:07:50.250092Z",
+ "shell.execute_reply": "2024-01-19T13:07:50.249420Z"
},
"id": "GZgovGkdiaiP",
"outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7"
@@ -1238,10 +1238,10 @@
"height": 92
},
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:28.432687Z",
- "iopub.status.busy": "2024-01-19T12:50:28.432446Z",
- "iopub.status.idle": "2024-01-19T12:50:28.543584Z",
- "shell.execute_reply": "2024-01-19T12:50:28.542953Z"
+ "iopub.execute_input": "2024-01-19T13:07:50.252760Z",
+ "iopub.status.busy": "2024-01-19T13:07:50.252260Z",
+ "iopub.status.idle": "2024-01-19T13:07:50.360137Z",
+ "shell.execute_reply": "2024-01-19T13:07:50.359531Z"
},
"id": "lfa2eHbMwG8R",
"outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c"
@@ -1282,10 +1282,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:28.546199Z",
- "iopub.status.busy": "2024-01-19T12:50:28.545753Z",
- "iopub.status.idle": "2024-01-19T12:50:28.652790Z",
- "shell.execute_reply": "2024-01-19T12:50:28.652143Z"
+ "iopub.execute_input": "2024-01-19T13:07:50.362734Z",
+ "iopub.status.busy": "2024-01-19T13:07:50.362421Z",
+ "iopub.status.idle": "2024-01-19T13:07:50.477254Z",
+ "shell.execute_reply": "2024-01-19T13:07:50.476559Z"
}
},
"outputs": [
@@ -1333,10 +1333,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:28.655070Z",
- "iopub.status.busy": "2024-01-19T12:50:28.654831Z",
- "iopub.status.idle": "2024-01-19T12:50:28.658181Z",
- "shell.execute_reply": "2024-01-19T12:50:28.657674Z"
+ "iopub.execute_input": "2024-01-19T13:07:50.479769Z",
+ "iopub.status.busy": "2024-01-19T13:07:50.479564Z",
+ "iopub.status.idle": "2024-01-19T13:07:50.483161Z",
+ "shell.execute_reply": "2024-01-19T13:07:50.482627Z"
},
"nbsphinx": "hidden"
},
@@ -1377,59 +1377,29 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0ce3a0c990d047368ccb3d8e543e6a22": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
+ "00b1ad1d0ad64627a8ba1a9d06054bba": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "1.2.0",
- "_model_name": "LayoutModel",
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "overflow_x": null,
- "overflow_y": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_534f415f2a8d48dcbee027b36973ab71",
+ "IPY_MODEL_d46f31bef1dd42c09fcf3c112929a103",
+ "IPY_MODEL_b2b27ad18a0e49e7bf3678b3cd178265"
+ ],
+ "layout": "IPY_MODEL_160eb6e2d63a429199bb9e03ee955d64"
}
},
- "1111c4f255f2436ca6e2fcbc7f9ff997": {
+ "093d2bc34b98452da5a220f007165f58": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1481,7 +1451,7 @@
"width": null
}
},
- "137ae62d19464ed1b8a4e3b4312169d7": {
+ "09c16b5d10c6496986d6f93260f61313": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -1496,7 +1466,7 @@
"description_width": ""
}
},
- "1a232dee230a4624a24d9d002a90faf1": {
+ "0b90500ccdbe4972b9585bc528819b07": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1548,7 +1518,7 @@
"width": null
}
},
- "1cd3312303524f22a46f81a3dc625bf1": {
+ "160eb6e2d63a429199bb9e03ee955d64": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1600,7 +1570,7 @@
"width": null
}
},
- "1da1f64828654973be5d1bc0188eabb9": {
+ "1666b377200d4807a9e0d485ae21c67d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -1615,31 +1585,22 @@
"description_width": ""
}
},
- "1f6936830ee04242861ff388ebc9c5f1": {
+ "1d235530bbba41dfb3163b4c88b05e9d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
+ "model_name": "DescriptionStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "DescriptionStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_1111c4f255f2436ca6e2fcbc7f9ff997",
- "max": 128619.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_7d7e0f7b883a4e689e431030ac9f227c",
- "value": 128619.0
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
}
},
- "2273e8660d2a4608a259b28a781529f6": {
+ "1dc523a2305043daaf8cf8792e8bfd98": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1691,23 +1652,22 @@
"width": null
}
},
- "2a99e31b2be24fc38fa0a6a8a7af32fd": {
+ "212d1d9773554d118a584ed746ac427a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
- "bar_color": null,
"description_width": ""
}
},
- "2a9e8428987c48208eb2bcb506e0e64c": {
+ "2899e0e78f0749f1b677b831b5fd3258": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1759,7 +1719,7 @@
"width": null
}
},
- "2d21af9117f7403299f4ac853bc27c1b": {
+ "31ed60be496d4b9982773866256f4b27": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
@@ -1775,59 +1735,83 @@
"description_width": ""
}
},
- "2f2a515f18fa4fd28e52f5e4d8b05c88": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
+ "35c780d9fff948d9a13e61648118d7f7": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "1.2.0",
- "_model_name": "LayoutModel",
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_60ee438a916e48d382f47c74db3ec3cc",
+ "placeholder": "",
+ "style": "IPY_MODEL_1666b377200d4807a9e0d485ae21c67d",
+ "value": "label_encoder.txt: 100%"
+ }
+ },
+ "438410373dd54605b51b5cfb68e62508": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "overflow_x": null,
- "overflow_y": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "439bd3fd0c304ac78d5530d6c8e137c1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "459cbaab523a4b19b8eb2ca3aea58fcc": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_1dc523a2305043daaf8cf8792e8bfd98",
+ "max": 16887676.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_31ed60be496d4b9982773866256f4b27",
+ "value": 16887676.0
}
},
- "30a2111692aa4fe9b7991c29f2a886a6": {
+ "483a45225b7d4a02aa07a05d6e44909e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1879,7 +1863,7 @@
"width": null
}
},
- "3baf4c0ea31e42319b9c023bc744ed83": {
+ "48d5903919ab47efa333db481f4cb817": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -1894,28 +1878,37 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_2273e8660d2a4608a259b28a781529f6",
+ "layout": "IPY_MODEL_fef60fcf2cb64ccbab845c49811e30c4",
"placeholder": "",
- "style": "IPY_MODEL_4896370d1afd4a91b569801cf1cf47fc",
- "value": "classifier.ckpt: 100%"
+ "style": "IPY_MODEL_cd6bae6e074a4bfd98a28fc1486fc8e1",
+ "value": " 16.9M/16.9M [00:00<00:00, 185MB/s]"
}
},
- "42e59a8af2c046aa839561b1046458c7": {
+ "4a820d8915df418ebb62014e8787c9ae": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_4bfec978ca614e56b835d3291ebd90ac",
+ "max": 3201.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_5b7d1929e1074f33873f4e643cddc0fd",
+ "value": 3201.0
}
},
- "45c60a0d36c549faa01e733154bd3824": {
+ "4bfec978ca614e56b835d3291ebd90ac": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1967,61 +1960,71 @@
"width": null
}
},
- "4896370d1afd4a91b569801cf1cf47fc": {
+ "4e9d28c969db43718ca5c5d0b72655a5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "model_name": "HBoxModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_35c780d9fff948d9a13e61648118d7f7",
+ "IPY_MODEL_c2a53a30f2b445cf82a64a57f571ef9a",
+ "IPY_MODEL_ed3d034ccc1f40659c92df7515c3b47c"
+ ],
+ "layout": "IPY_MODEL_b3302b8ae620478f84ea1bbe60474080"
}
},
- "50f96c279d1d41238c7154680539d3db": {
+ "523527c1f8794a3ca99c9ce51595c818": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "model_name": "HTMLModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_9ce4599c7e3042bdb0fe79f38216ced4",
+ "placeholder": "",
+ "style": "IPY_MODEL_be855abaa09342de95a46d55295a6950",
+ "value": "hyperparams.yaml: 100%"
}
},
- "571ee7f45d2d4c2ba4cbe9e9e89234fc": {
+ "534f415f2a8d48dcbee027b36973ab71": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
+ "_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_9c14978917584d3783b7d6afc0f96cb0",
- "max": 16887676.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_2d21af9117f7403299f4ac853bc27c1b",
- "value": 16887676.0
+ "layout": "IPY_MODEL_7a89ec91763b41bfa51852ebebce4057",
+ "placeholder": "",
+ "style": "IPY_MODEL_439bd3fd0c304ac78d5530d6c8e137c1",
+ "value": "classifier.ckpt: 100%"
}
},
- "58a504493f0941e0a8cf6d3baf185813": {
+ "551f66b5aca5432d87a8a04359b7781c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2073,154 +2076,7 @@
"width": null
}
},
- "5b89a3400138425ca2c530b7c6bd42e8": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_7409c0e69dbf4fe685f415fb5f6a2181",
- "placeholder": "",
- "style": "IPY_MODEL_c86bc3a16bd94e8a934561e17d8668fb",
- "value": " 129k/129k [00:00<00:00, 1.08MB/s]"
- }
- },
- "5c18fdd989c24943a59715dfc999bb8c": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_3baf4c0ea31e42319b9c023bc744ed83",
- "IPY_MODEL_ffac427e768244cb88277658c2f43f83",
- "IPY_MODEL_ccc36be5668f43128252d1fde78a7a0f"
- ],
- "layout": "IPY_MODEL_1cd3312303524f22a46f81a3dc625bf1"
- }
- },
- "5c29756371ae4ef987046e199624a8af": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "648749fa28b440d9a46175a8d29280a3": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_d9bd293308b44ec2bf6516edf71a24e8",
- "IPY_MODEL_6bcad508ef1f417197178fa3100cef50",
- "IPY_MODEL_fde1c71b67c5403088567e76ae4b994d"
- ],
- "layout": "IPY_MODEL_dd228ce142384e2d9edd55a85b76286d"
- }
- },
- "6bcad508ef1f417197178fa3100cef50": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_2a9e8428987c48208eb2bcb506e0e64c",
- "max": 2041.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_2a99e31b2be24fc38fa0a6a8a7af32fd",
- "value": 2041.0
- }
- },
- "6f4a23cf95c14348a3b19314fdd986a6": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_1a232dee230a4624a24d9d002a90faf1",
- "placeholder": "",
- "style": "IPY_MODEL_42e59a8af2c046aa839561b1046458c7",
- "value": "mean_var_norm_emb.ckpt: 100%"
- }
- },
- "7078bd39e9444d5eba94236a8d784a90": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_7aa9df1d1d8e442c8a85708a4ad593f9",
- "IPY_MODEL_571ee7f45d2d4c2ba4cbe9e9e89234fc",
- "IPY_MODEL_f3f6cc5ceb174a59b39229c03624a602"
- ],
- "layout": "IPY_MODEL_c87c7746a1ee49be84c7ff55a06512d5"
- }
- },
- "7409c0e69dbf4fe685f415fb5f6a2181": {
+ "56295cacf7c44bfdbba4b24a052d6476": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2272,28 +2128,7 @@
"width": null
}
},
- "7aa9df1d1d8e442c8a85708a4ad593f9": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_45c60a0d36c549faa01e733154bd3824",
- "placeholder": "",
- "style": "IPY_MODEL_1da1f64828654973be5d1bc0188eabb9",
- "value": "embedding_model.ckpt: 100%"
- }
- },
- "7d7e0f7b883a4e689e431030ac9f227c": {
+ "5b7d1929e1074f33873f4e643cddc0fd": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
@@ -2309,22 +2144,7 @@
"description_width": ""
}
},
- "830db690526543bfbe69899fcf632f8f": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "8dc9a4876b68498bbe96226b0953b140": {
+ "60ee438a916e48d382f47c74db3ec3cc": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2376,44 +2196,28 @@
"width": null
}
},
- "91f3d24ed9164874a46f3c3d6457e43a": {
+ "66cd66ece4c2460da791087c6868c2f7": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HBoxModel",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_f563023d6365418ba5357a23b26e8d90",
- "IPY_MODEL_1f6936830ee04242861ff388ebc9c5f1",
- "IPY_MODEL_5b89a3400138425ca2c530b7c6bd42e8"
- ],
- "layout": "IPY_MODEL_c6eb825c960e48a9a05ec0717cf267e1"
- }
- },
- "930e43a9200348af900b45e086d1a5d5": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_093d2bc34b98452da5a220f007165f58",
+ "placeholder": "",
+ "style": "IPY_MODEL_212d1d9773554d118a584ed746ac427a",
+ "value": "mean_var_norm_emb.ckpt: 100%"
}
},
- "9c14978917584d3783b7d6afc0f96cb0": {
+ "7a89ec91763b41bfa51852ebebce4057": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2465,7 +2269,44 @@
"width": null
}
},
- "9f91095fd21f452cb00e09a0f542ea91": {
+ "7c5020aad22447ec858bc827b1d5faa4": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "7dc53d70873e4403a124da1a86f723cb": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_66cd66ece4c2460da791087c6868c2f7",
+ "IPY_MODEL_4a820d8915df418ebb62014e8787c9ae",
+ "IPY_MODEL_e09a6d388c1a48bfa14c86ce89a8838f"
+ ],
+ "layout": "IPY_MODEL_8329bc304e144d2c8d4b40d98631348b"
+ }
+ },
+ "8329bc304e144d2c8d4b40d98631348b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2517,52 +2358,7 @@
"width": null
}
},
- "a3176b9cf0ec4f1eab163819f91e7fa7": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_0ce3a0c990d047368ccb3d8e543e6a22",
- "max": 3201.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_d55ceb279fda4cf6b1656d7ebff721e3",
- "value": 3201.0
- }
- },
- "a41057563ee24b7dab2778568c1655b7": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_9f91095fd21f452cb00e09a0f542ea91",
- "placeholder": "",
- "style": "IPY_MODEL_fc659d3a8f6c4cab82058ccbac8ba469",
- "value": " 3.20k/3.20k [00:00<00:00, 589kB/s]"
- }
- },
- "b144e5130f8a4bf29ed7aa36b77921aa": {
+ "86584ba92bce4bac90441e91c8eb0e42": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2614,7 +2410,7 @@
"width": null
}
},
- "b288f240d2de4946aa7d3369af98b80b": {
+ "9ce4599c7e3042bdb0fe79f38216ced4": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2666,13 +2462,34 @@
"width": null
}
},
- "b385acfca33743f39792e47766a26dcd": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
+ "b2b27ad18a0e49e7bf3678b3cd178265": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "1.2.0",
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_483a45225b7d4a02aa07a05d6e44909e",
+ "placeholder": "",
+ "style": "IPY_MODEL_7c5020aad22447ec858bc827b1d5faa4",
+ "value": " 15.9M/15.9M [00:00<00:00, 279MB/s]"
+ }
+ },
+ "b3302b8ae620478f84ea1bbe60474080": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "1.2.0",
+ "model_name": "LayoutModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
"_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
@@ -2718,29 +2535,7 @@
"width": null
}
},
- "c5b14b37c04f4b589452d1b7d7eb813f": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_6f4a23cf95c14348a3b19314fdd986a6",
- "IPY_MODEL_a3176b9cf0ec4f1eab163819f91e7fa7",
- "IPY_MODEL_a41057563ee24b7dab2778568c1655b7"
- ],
- "layout": "IPY_MODEL_58a504493f0941e0a8cf6d3baf185813"
- }
- },
- "c6eb825c960e48a9a05ec0717cf267e1": {
+ "b86f2239fbfe41a8aed03ba4a9618e70": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2792,7 +2587,51 @@
"width": null
}
},
- "c86bc3a16bd94e8a934561e17d8668fb": {
+ "bb134285969947e8b5213257338e754b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_523527c1f8794a3ca99c9ce51595c818",
+ "IPY_MODEL_ff213d38fc694ab7b718b44a15a813a9",
+ "IPY_MODEL_defd34453d47481c872a23c616a43610"
+ ],
+ "layout": "IPY_MODEL_2899e0e78f0749f1b677b831b5fd3258"
+ }
+ },
+ "bce9a85715934f1aaaf3f7c7a6633477": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_efc27bfb809641c9a6560326be3a0d67",
+ "IPY_MODEL_459cbaab523a4b19b8eb2ca3aea58fcc",
+ "IPY_MODEL_48d5903919ab47efa333db481f4cb817"
+ ],
+ "layout": "IPY_MODEL_0b90500ccdbe4972b9585bc528819b07"
+ }
+ },
+ "be855abaa09342de95a46d55295a6950": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -2807,7 +2646,7 @@
"description_width": ""
}
},
- "c87c7746a1ee49be84c7ff55a06512d5": {
+ "beff320a729b4fe48cfd460c7d64158d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2859,44 +2698,70 @@
"width": null
}
},
- "c938a24c52c4467c9d9dad0904936e97": {
+ "c2a53a30f2b445cf82a64a57f571ef9a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_b86f2239fbfe41a8aed03ba4a9618e70",
+ "max": 128619.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_438410373dd54605b51b5cfb68e62508",
+ "value": 128619.0
+ }
+ },
+ "cd6bae6e074a4bfd98a28fc1486fc8e1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
- "bar_color": null,
"description_width": ""
}
},
- "ccc36be5668f43128252d1fde78a7a0f": {
+ "d46f31bef1dd42c09fcf3c112929a103": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HTMLModel",
+ "model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_30a2111692aa4fe9b7991c29f2a886a6",
- "placeholder": "",
- "style": "IPY_MODEL_50f96c279d1d41238c7154680539d3db",
- "value": " 15.9M/15.9M [00:00<00:00, 318MB/s]"
+ "layout": "IPY_MODEL_f5435b5ab5e44bb7bad3561a058e1a70",
+ "max": 15856877.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_d6cc96101b24464493fb1b1923f586e7",
+ "value": 15856877.0
}
},
- "d55ceb279fda4cf6b1656d7ebff721e3": {
+ "d6cc96101b24464493fb1b1923f586e7": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
@@ -2912,7 +2777,22 @@
"description_width": ""
}
},
- "d9bd293308b44ec2bf6516edf71a24e8": {
+ "dabda8d267d543ae83abd84b564f6ca3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "defd34453d47481c872a23c616a43610": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -2927,13 +2807,107 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_b288f240d2de4946aa7d3369af98b80b",
+ "layout": "IPY_MODEL_551f66b5aca5432d87a8a04359b7781c",
"placeholder": "",
- "style": "IPY_MODEL_830db690526543bfbe69899fcf632f8f",
- "value": "hyperparams.yaml: 100%"
+ "style": "IPY_MODEL_f097f2a99e2f49818b6687621a501462",
+ "value": " 2.04k/2.04k [00:00<00:00, 348kB/s]"
+ }
+ },
+ "e09a6d388c1a48bfa14c86ce89a8838f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_beff320a729b4fe48cfd460c7d64158d",
+ "placeholder": "",
+ "style": "IPY_MODEL_1d235530bbba41dfb3163b4c88b05e9d",
+ "value": " 3.20k/3.20k [00:00<00:00, 561kB/s]"
+ }
+ },
+ "ed3d034ccc1f40659c92df7515c3b47c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_56295cacf7c44bfdbba4b24a052d6476",
+ "placeholder": "",
+ "style": "IPY_MODEL_09c16b5d10c6496986d6f93260f61313",
+ "value": " 129k/129k [00:00<00:00, 9.05MB/s]"
+ }
+ },
+ "efc27bfb809641c9a6560326be3a0d67": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_86584ba92bce4bac90441e91c8eb0e42",
+ "placeholder": "",
+ "style": "IPY_MODEL_dabda8d267d543ae83abd84b564f6ca3",
+ "value": "embedding_model.ckpt: 100%"
+ }
+ },
+ "efe358bea53145d4a3cf559e36a6170f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "dd228ce142384e2d9edd55a85b76286d": {
+ "f097f2a99e2f49818b6687621a501462": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "f5435b5ab5e44bb7bad3561a058e1a70": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2985,85 +2959,111 @@
"width": null
}
},
- "f3f6cc5ceb174a59b39229c03624a602": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_8dc9a4876b68498bbe96226b0953b140",
- "placeholder": "",
- "style": "IPY_MODEL_5c29756371ae4ef987046e199624a8af",
- "value": " 16.9M/16.9M [00:00<00:00, 259MB/s]"
- }
- },
- "f563023d6365418ba5357a23b26e8d90": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_2f2a515f18fa4fd28e52f5e4d8b05c88",
- "placeholder": "",
- "style": "IPY_MODEL_930e43a9200348af900b45e086d1a5d5",
- "value": "label_encoder.txt: 100%"
- }
- },
- "fc659d3a8f6c4cab82058ccbac8ba469": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "fe7768236d884e30b2a87e144929e4ef": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "1.2.0",
+ "model_name": "LayoutModel",
"state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
}
},
- "fde1c71b67c5403088567e76ae4b994d": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
+ "fef60fcf2cb64ccbab845c49811e30c4": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "1.2.0",
+ "model_name": "LayoutModel",
"state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_b144e5130f8a4bf29ed7aa36b77921aa",
- "placeholder": "",
- "style": "IPY_MODEL_137ae62d19464ed1b8a4e3b4312169d7",
- "value": " 2.04k/2.04k [00:00<00:00, 332kB/s]"
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
}
},
- "ffac427e768244cb88277658c2f43f83": {
+ "ff213d38fc694ab7b718b44a15a813a9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
@@ -3079,12 +3079,12 @@
"bar_style": "success",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_b385acfca33743f39792e47766a26dcd",
- "max": 15856877.0,
+ "layout": "IPY_MODEL_fe7768236d884e30b2a87e144929e4ef",
+ "max": 2041.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_c938a24c52c4467c9d9dad0904936e97",
- "value": 15856877.0
+ "style": "IPY_MODEL_efe358bea53145d4a3cf559e36a6170f",
+ "value": 2041.0
}
}
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
index 6e9a4faf9..ee9d68bac 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb
@@ -80,10 +80,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:33.391934Z",
- "iopub.status.busy": "2024-01-19T12:50:33.391326Z",
- "iopub.status.idle": "2024-01-19T12:50:34.465093Z",
- "shell.execute_reply": "2024-01-19T12:50:34.464427Z"
+ "iopub.execute_input": "2024-01-19T13:07:55.048921Z",
+ "iopub.status.busy": "2024-01-19T13:07:55.048382Z",
+ "iopub.status.idle": "2024-01-19T13:07:56.141480Z",
+ "shell.execute_reply": "2024-01-19T13:07:56.140863Z"
},
"nbsphinx": "hidden"
},
@@ -93,7 +93,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -118,10 +118,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:34.468098Z",
- "iopub.status.busy": "2024-01-19T12:50:34.467632Z",
- "iopub.status.idle": "2024-01-19T12:50:34.470774Z",
- "shell.execute_reply": "2024-01-19T12:50:34.470243Z"
+ "iopub.execute_input": "2024-01-19T13:07:56.144463Z",
+ "iopub.status.busy": "2024-01-19T13:07:56.143966Z",
+ "iopub.status.idle": "2024-01-19T13:07:56.147168Z",
+ "shell.execute_reply": "2024-01-19T13:07:56.146583Z"
}
},
"outputs": [],
@@ -252,10 +252,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:34.473275Z",
- "iopub.status.busy": "2024-01-19T12:50:34.472927Z",
- "iopub.status.idle": "2024-01-19T12:50:34.482178Z",
- "shell.execute_reply": "2024-01-19T12:50:34.481663Z"
+ "iopub.execute_input": "2024-01-19T13:07:56.149590Z",
+ "iopub.status.busy": "2024-01-19T13:07:56.149288Z",
+ "iopub.status.idle": "2024-01-19T13:07:56.158838Z",
+ "shell.execute_reply": "2024-01-19T13:07:56.158279Z"
},
"nbsphinx": "hidden"
},
@@ -353,10 +353,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:34.484458Z",
- "iopub.status.busy": "2024-01-19T12:50:34.484072Z",
- "iopub.status.idle": "2024-01-19T12:50:34.488699Z",
- "shell.execute_reply": "2024-01-19T12:50:34.488226Z"
+ "iopub.execute_input": "2024-01-19T13:07:56.161126Z",
+ "iopub.status.busy": "2024-01-19T13:07:56.160751Z",
+ "iopub.status.idle": "2024-01-19T13:07:56.165417Z",
+ "shell.execute_reply": "2024-01-19T13:07:56.164928Z"
}
},
"outputs": [],
@@ -445,10 +445,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:34.491275Z",
- "iopub.status.busy": "2024-01-19T12:50:34.490815Z",
- "iopub.status.idle": "2024-01-19T12:50:34.760521Z",
- "shell.execute_reply": "2024-01-19T12:50:34.759795Z"
+ "iopub.execute_input": "2024-01-19T13:07:56.167885Z",
+ "iopub.status.busy": "2024-01-19T13:07:56.167516Z",
+ "iopub.status.idle": "2024-01-19T13:07:56.443431Z",
+ "shell.execute_reply": "2024-01-19T13:07:56.442803Z"
},
"nbsphinx": "hidden"
},
@@ -517,10 +517,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:34.763176Z",
- "iopub.status.busy": "2024-01-19T12:50:34.762969Z",
- "iopub.status.idle": "2024-01-19T12:50:35.071716Z",
- "shell.execute_reply": "2024-01-19T12:50:35.071065Z"
+ "iopub.execute_input": "2024-01-19T13:07:56.446245Z",
+ "iopub.status.busy": "2024-01-19T13:07:56.445843Z",
+ "iopub.status.idle": "2024-01-19T13:07:56.820306Z",
+ "shell.execute_reply": "2024-01-19T13:07:56.819638Z"
}
},
"outputs": [
@@ -568,10 +568,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:35.074665Z",
- "iopub.status.busy": "2024-01-19T12:50:35.074276Z",
- "iopub.status.idle": "2024-01-19T12:50:35.098964Z",
- "shell.execute_reply": "2024-01-19T12:50:35.098470Z"
+ "iopub.execute_input": "2024-01-19T13:07:56.823345Z",
+ "iopub.status.busy": "2024-01-19T13:07:56.822983Z",
+ "iopub.status.idle": "2024-01-19T13:07:56.847904Z",
+ "shell.execute_reply": "2024-01-19T13:07:56.847382Z"
}
},
"outputs": [],
@@ -607,10 +607,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:35.101410Z",
- "iopub.status.busy": "2024-01-19T12:50:35.101036Z",
- "iopub.status.idle": "2024-01-19T12:50:35.112417Z",
- "shell.execute_reply": "2024-01-19T12:50:35.111889Z"
+ "iopub.execute_input": "2024-01-19T13:07:56.850546Z",
+ "iopub.status.busy": "2024-01-19T13:07:56.850151Z",
+ "iopub.status.idle": "2024-01-19T13:07:56.861887Z",
+ "shell.execute_reply": "2024-01-19T13:07:56.861360Z"
}
},
"outputs": [],
@@ -641,10 +641,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:35.114902Z",
- "iopub.status.busy": "2024-01-19T12:50:35.114497Z",
- "iopub.status.idle": "2024-01-19T12:50:36.376286Z",
- "shell.execute_reply": "2024-01-19T12:50:36.375595Z"
+ "iopub.execute_input": "2024-01-19T13:07:56.864506Z",
+ "iopub.status.busy": "2024-01-19T13:07:56.864122Z",
+ "iopub.status.idle": "2024-01-19T13:07:58.188369Z",
+ "shell.execute_reply": "2024-01-19T13:07:58.187643Z"
}
},
"outputs": [
@@ -708,10 +708,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:36.379334Z",
- "iopub.status.busy": "2024-01-19T12:50:36.378786Z",
- "iopub.status.idle": "2024-01-19T12:50:36.401751Z",
- "shell.execute_reply": "2024-01-19T12:50:36.401205Z"
+ "iopub.execute_input": "2024-01-19T13:07:58.191481Z",
+ "iopub.status.busy": "2024-01-19T13:07:58.190938Z",
+ "iopub.status.idle": "2024-01-19T13:07:58.213481Z",
+ "shell.execute_reply": "2024-01-19T13:07:58.212833Z"
}
},
"outputs": [
@@ -820,10 +820,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:36.404335Z",
- "iopub.status.busy": "2024-01-19T12:50:36.403911Z",
- "iopub.status.idle": "2024-01-19T12:50:36.425254Z",
- "shell.execute_reply": "2024-01-19T12:50:36.424593Z"
+ "iopub.execute_input": "2024-01-19T13:07:58.216060Z",
+ "iopub.status.busy": "2024-01-19T13:07:58.215612Z",
+ "iopub.status.idle": "2024-01-19T13:07:58.237274Z",
+ "shell.execute_reply": "2024-01-19T13:07:58.236633Z"
}
},
"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 ['is_outlier_issue', 'outlier_score'] 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 ['outlier_score', 'is_outlier_issue'] 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 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:36.427826Z",
- "iopub.status.busy": "2024-01-19T12:50:36.427452Z",
- "iopub.status.idle": "2024-01-19T12:50:36.442202Z",
- "shell.execute_reply": "2024-01-19T12:50:36.441696Z"
+ "iopub.execute_input": "2024-01-19T13:07:58.239698Z",
+ "iopub.status.busy": "2024-01-19T13:07:58.239224Z",
+ "iopub.status.idle": "2024-01-19T13:07:58.254301Z",
+ "shell.execute_reply": "2024-01-19T13:07:58.253665Z"
}
},
"outputs": [
@@ -1068,17 +1068,17 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:36.444751Z",
- "iopub.status.busy": "2024-01-19T12:50:36.444360Z",
- "iopub.status.idle": "2024-01-19T12:50:36.466953Z",
- "shell.execute_reply": "2024-01-19T12:50:36.466226Z"
+ "iopub.execute_input": "2024-01-19T13:07:58.257047Z",
+ "iopub.status.busy": "2024-01-19T13:07:58.256574Z",
+ "iopub.status.idle": "2024-01-19T13:07:58.278484Z",
+ "shell.execute_reply": "2024-01-19T13:07:58.277807Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "97f7e2f0d4f44de88511eddcbbb551ee",
+ "model_id": "a5df88ed0500431eb57146361f5e55d1",
"version_major": 2,
"version_minor": 0
},
@@ -1114,10 +1114,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:36.471250Z",
- "iopub.status.busy": "2024-01-19T12:50:36.470740Z",
- "iopub.status.idle": "2024-01-19T12:50:36.486118Z",
- "shell.execute_reply": "2024-01-19T12:50:36.485507Z"
+ "iopub.execute_input": "2024-01-19T13:07:58.281251Z",
+ "iopub.status.busy": "2024-01-19T13:07:58.280926Z",
+ "iopub.status.idle": "2024-01-19T13:07:58.296763Z",
+ "shell.execute_reply": "2024-01-19T13:07:58.296199Z"
}
},
"outputs": [
@@ -1235,10 +1235,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:36.488565Z",
- "iopub.status.busy": "2024-01-19T12:50:36.488174Z",
- "iopub.status.idle": "2024-01-19T12:50:36.494474Z",
- "shell.execute_reply": "2024-01-19T12:50:36.493925Z"
+ "iopub.execute_input": "2024-01-19T13:07:58.299472Z",
+ "iopub.status.busy": "2024-01-19T13:07:58.298975Z",
+ "iopub.status.idle": "2024-01-19T13:07:58.305465Z",
+ "shell.execute_reply": "2024-01-19T13:07:58.304831Z"
}
},
"outputs": [],
@@ -1295,10 +1295,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:36.496647Z",
- "iopub.status.busy": "2024-01-19T12:50:36.496450Z",
- "iopub.status.idle": "2024-01-19T12:50:36.514548Z",
- "shell.execute_reply": "2024-01-19T12:50:36.514021Z"
+ "iopub.execute_input": "2024-01-19T13:07:58.307952Z",
+ "iopub.status.busy": "2024-01-19T13:07:58.307481Z",
+ "iopub.status.idle": "2024-01-19T13:07:58.326330Z",
+ "shell.execute_reply": "2024-01-19T13:07:58.325686Z"
}
},
"outputs": [
@@ -1430,7 +1430,37 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "07deda77e8e64216bc6a96cfbada7f4a": {
+ "01c39871d4974680a760c63c366f8e25": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "38a4c6baf77640e494469f436cb16ec7": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "41f084936c544acc9f51632074b7ef5d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1482,7 +1512,28 @@
"width": null
}
},
- "44afdbf9805c45af98e7b10a06fafcbb": {
+ "468a42322a9a491c99624d63df537020": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_4ef465b49ada487ca9249905ac323187",
+ "placeholder": "",
+ "style": "IPY_MODEL_01c39871d4974680a760c63c366f8e25",
+ "value": " 132/132 [00:00<00:00, 10859.04 examples/s]"
+ }
+ },
+ "4ef465b49ada487ca9249905ac323187": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1534,7 +1585,69 @@
"width": null
}
},
- "66999e74de7f40198a9db8a0d8401dcb": {
+ "953dadb6038c4c92bc5971668510c744": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_41f084936c544acc9f51632074b7ef5d",
+ "max": 132.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_cc8b21c764fd42129d910f6ccc9e058e",
+ "value": 132.0
+ }
+ },
+ "a5df88ed0500431eb57146361f5e55d1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_d4f9fd39be9d4575b81c80416d1f79bb",
+ "IPY_MODEL_953dadb6038c4c92bc5971668510c744",
+ "IPY_MODEL_468a42322a9a491c99624d63df537020"
+ ],
+ "layout": "IPY_MODEL_cff19e4007ea4fe9be9083a68b4d19e4"
+ }
+ },
+ "cc8b21c764fd42129d910f6ccc9e058e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "cff19e4007ea4fe9be9083a68b4d19e4": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1586,60 +1699,7 @@
"width": null
}
},
- "727066d7fa9d437580af09e76f2cc8f3": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "9502c9dd31dd4dbfb0e1a52127eaf236": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "97f7e2f0d4f44de88511eddcbbb551ee": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_b7394613517e4da081aaabf701a863ab",
- "IPY_MODEL_bfc2b2f88bde43119e0afb5af9e068a2",
- "IPY_MODEL_da5f1c592555475a887a67d84a03f6db"
- ],
- "layout": "IPY_MODEL_07deda77e8e64216bc6a96cfbada7f4a"
- }
- },
- "b7394613517e4da081aaabf701a863ab": {
+ "d4f9fd39be9d4575b81c80416d1f79bb": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -1654,37 +1714,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_44afdbf9805c45af98e7b10a06fafcbb",
+ "layout": "IPY_MODEL_faa5ad4104814310a9b7a0e55ebdce01",
"placeholder": "",
- "style": "IPY_MODEL_9502c9dd31dd4dbfb0e1a52127eaf236",
+ "style": "IPY_MODEL_38a4c6baf77640e494469f436cb16ec7",
"value": "Saving the dataset (1/1 shards): 100%"
}
},
- "bfc2b2f88bde43119e0afb5af9e068a2": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_66999e74de7f40198a9db8a0d8401dcb",
- "max": 132.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_727066d7fa9d437580af09e76f2cc8f3",
- "value": 132.0
- }
- },
- "cdd7028cae6641aa9875cec9a411571b": {
+ "faa5ad4104814310a9b7a0e55ebdce01": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1735,42 +1771,6 @@
"visibility": null,
"width": null
}
- },
- "d858b4ff987d420199d8ac1ad21cc0f1": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "da5f1c592555475a887a67d84a03f6db": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_cdd7028cae6641aa9875cec9a411571b",
- "placeholder": "",
- "style": "IPY_MODEL_d858b4ff987d420199d8ac1ad21cc0f1",
- "value": " 132/132 [00:00<00:00, 10101.96 examples/s]"
- }
}
},
"version_major": 2,
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
index 416381466..aaa5b2222 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb
@@ -78,10 +78,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:41.578902Z",
- "iopub.status.busy": "2024-01-19T12:50:41.578368Z",
- "iopub.status.idle": "2024-01-19T12:50:42.638573Z",
- "shell.execute_reply": "2024-01-19T12:50:42.637890Z"
+ "iopub.execute_input": "2024-01-19T13:08:03.356317Z",
+ "iopub.status.busy": "2024-01-19T13:08:03.355787Z",
+ "iopub.status.idle": "2024-01-19T13:08:04.468183Z",
+ "shell.execute_reply": "2024-01-19T13:08:04.467561Z"
},
"nbsphinx": "hidden"
},
@@ -91,7 +91,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"] # TODO: make sure this list is updated\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -116,10 +116,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:42.641726Z",
- "iopub.status.busy": "2024-01-19T12:50:42.641355Z",
- "iopub.status.idle": "2024-01-19T12:50:42.644659Z",
- "shell.execute_reply": "2024-01-19T12:50:42.644081Z"
+ "iopub.execute_input": "2024-01-19T13:08:04.471172Z",
+ "iopub.status.busy": "2024-01-19T13:08:04.470662Z",
+ "iopub.status.idle": "2024-01-19T13:08:04.473943Z",
+ "shell.execute_reply": "2024-01-19T13:08:04.473416Z"
}
},
"outputs": [],
@@ -250,10 +250,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:42.647076Z",
- "iopub.status.busy": "2024-01-19T12:50:42.646865Z",
- "iopub.status.idle": "2024-01-19T12:50:42.656596Z",
- "shell.execute_reply": "2024-01-19T12:50:42.656067Z"
+ "iopub.execute_input": "2024-01-19T13:08:04.476671Z",
+ "iopub.status.busy": "2024-01-19T13:08:04.476148Z",
+ "iopub.status.idle": "2024-01-19T13:08:04.486231Z",
+ "shell.execute_reply": "2024-01-19T13:08:04.485595Z"
},
"nbsphinx": "hidden"
},
@@ -356,10 +356,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:42.659374Z",
- "iopub.status.busy": "2024-01-19T12:50:42.658840Z",
- "iopub.status.idle": "2024-01-19T12:50:42.663585Z",
- "shell.execute_reply": "2024-01-19T12:50:42.662990Z"
+ "iopub.execute_input": "2024-01-19T13:08:04.488670Z",
+ "iopub.status.busy": "2024-01-19T13:08:04.488277Z",
+ "iopub.status.idle": "2024-01-19T13:08:04.493215Z",
+ "shell.execute_reply": "2024-01-19T13:08:04.492672Z"
}
},
"outputs": [],
@@ -448,10 +448,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:42.666144Z",
- "iopub.status.busy": "2024-01-19T12:50:42.665781Z",
- "iopub.status.idle": "2024-01-19T12:50:42.932773Z",
- "shell.execute_reply": "2024-01-19T12:50:42.932162Z"
+ "iopub.execute_input": "2024-01-19T13:08:04.495760Z",
+ "iopub.status.busy": "2024-01-19T13:08:04.495387Z",
+ "iopub.status.idle": "2024-01-19T13:08:04.771804Z",
+ "shell.execute_reply": "2024-01-19T13:08:04.771185Z"
},
"nbsphinx": "hidden"
},
@@ -520,10 +520,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:42.935505Z",
- "iopub.status.busy": "2024-01-19T12:50:42.935239Z",
- "iopub.status.idle": "2024-01-19T12:50:43.300690Z",
- "shell.execute_reply": "2024-01-19T12:50:43.299990Z"
+ "iopub.execute_input": "2024-01-19T13:08:04.774579Z",
+ "iopub.status.busy": "2024-01-19T13:08:04.774275Z",
+ "iopub.status.idle": "2024-01-19T13:08:05.091318Z",
+ "shell.execute_reply": "2024-01-19T13:08:05.090658Z"
}
},
"outputs": [
@@ -559,10 +559,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:43.303239Z",
- "iopub.status.busy": "2024-01-19T12:50:43.302865Z",
- "iopub.status.idle": "2024-01-19T12:50:43.305886Z",
- "shell.execute_reply": "2024-01-19T12:50:43.305284Z"
+ "iopub.execute_input": "2024-01-19T13:08:05.094128Z",
+ "iopub.status.busy": "2024-01-19T13:08:05.093740Z",
+ "iopub.status.idle": "2024-01-19T13:08:05.096656Z",
+ "shell.execute_reply": "2024-01-19T13:08:05.096112Z"
}
},
"outputs": [],
@@ -601,10 +601,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:43.308338Z",
- "iopub.status.busy": "2024-01-19T12:50:43.307972Z",
- "iopub.status.idle": "2024-01-19T12:50:43.345269Z",
- "shell.execute_reply": "2024-01-19T12:50:43.344636Z"
+ "iopub.execute_input": "2024-01-19T13:08:05.099113Z",
+ "iopub.status.busy": "2024-01-19T13:08:05.098744Z",
+ "iopub.status.idle": "2024-01-19T13:08:05.136743Z",
+ "shell.execute_reply": "2024-01-19T13:08:05.136082Z"
}
},
"outputs": [
@@ -646,10 +646,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:43.347663Z",
- "iopub.status.busy": "2024-01-19T12:50:43.347314Z",
- "iopub.status.idle": "2024-01-19T12:50:44.613519Z",
- "shell.execute_reply": "2024-01-19T12:50:44.612901Z"
+ "iopub.execute_input": "2024-01-19T13:08:05.139743Z",
+ "iopub.status.busy": "2024-01-19T13:08:05.139139Z",
+ "iopub.status.idle": "2024-01-19T13:08:06.471063Z",
+ "shell.execute_reply": "2024-01-19T13:08:06.470314Z"
}
},
"outputs": [
@@ -701,10 +701,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:44.616280Z",
- "iopub.status.busy": "2024-01-19T12:50:44.615819Z",
- "iopub.status.idle": "2024-01-19T12:50:44.640248Z",
- "shell.execute_reply": "2024-01-19T12:50:44.639690Z"
+ "iopub.execute_input": "2024-01-19T13:08:06.474142Z",
+ "iopub.status.busy": "2024-01-19T13:08:06.473503Z",
+ "iopub.status.idle": "2024-01-19T13:08:06.498552Z",
+ "shell.execute_reply": "2024-01-19T13:08:06.497905Z"
}
},
"outputs": [
@@ -878,10 +878,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:44.642748Z",
- "iopub.status.busy": "2024-01-19T12:50:44.642379Z",
- "iopub.status.idle": "2024-01-19T12:50:44.648887Z",
- "shell.execute_reply": "2024-01-19T12:50:44.648218Z"
+ "iopub.execute_input": "2024-01-19T13:08:06.501208Z",
+ "iopub.status.busy": "2024-01-19T13:08:06.500759Z",
+ "iopub.status.idle": "2024-01-19T13:08:06.507751Z",
+ "shell.execute_reply": "2024-01-19T13:08:06.507229Z"
}
},
"outputs": [
@@ -985,10 +985,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:44.651263Z",
- "iopub.status.busy": "2024-01-19T12:50:44.651052Z",
- "iopub.status.idle": "2024-01-19T12:50:44.657411Z",
- "shell.execute_reply": "2024-01-19T12:50:44.656783Z"
+ "iopub.execute_input": "2024-01-19T13:08:06.510149Z",
+ "iopub.status.busy": "2024-01-19T13:08:06.509805Z",
+ "iopub.status.idle": "2024-01-19T13:08:06.516084Z",
+ "shell.execute_reply": "2024-01-19T13:08:06.515458Z"
}
},
"outputs": [
@@ -1055,10 +1055,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:44.659781Z",
- "iopub.status.busy": "2024-01-19T12:50:44.659431Z",
- "iopub.status.idle": "2024-01-19T12:50:44.669722Z",
- "shell.execute_reply": "2024-01-19T12:50:44.669198Z"
+ "iopub.execute_input": "2024-01-19T13:08:06.518365Z",
+ "iopub.status.busy": "2024-01-19T13:08:06.518026Z",
+ "iopub.status.idle": "2024-01-19T13:08:06.528512Z",
+ "shell.execute_reply": "2024-01-19T13:08:06.527879Z"
}
},
"outputs": [
@@ -1231,10 +1231,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:44.672010Z",
- "iopub.status.busy": "2024-01-19T12:50:44.671648Z",
- "iopub.status.idle": "2024-01-19T12:50:44.680745Z",
- "shell.execute_reply": "2024-01-19T12:50:44.680111Z"
+ "iopub.execute_input": "2024-01-19T13:08:06.530906Z",
+ "iopub.status.busy": "2024-01-19T13:08:06.530465Z",
+ "iopub.status.idle": "2024-01-19T13:08:06.539962Z",
+ "shell.execute_reply": "2024-01-19T13:08:06.539314Z"
}
},
"outputs": [
@@ -1350,10 +1350,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:44.683114Z",
- "iopub.status.busy": "2024-01-19T12:50:44.682761Z",
- "iopub.status.idle": "2024-01-19T12:50:44.690231Z",
- "shell.execute_reply": "2024-01-19T12:50:44.689627Z"
+ "iopub.execute_input": "2024-01-19T13:08:06.542419Z",
+ "iopub.status.busy": "2024-01-19T13:08:06.542027Z",
+ "iopub.status.idle": "2024-01-19T13:08:06.549658Z",
+ "shell.execute_reply": "2024-01-19T13:08:06.549023Z"
},
"scrolled": true
},
@@ -1478,10 +1478,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:44.692656Z",
- "iopub.status.busy": "2024-01-19T12:50:44.692313Z",
- "iopub.status.idle": "2024-01-19T12:50:44.702129Z",
- "shell.execute_reply": "2024-01-19T12:50:44.701501Z"
+ "iopub.execute_input": "2024-01-19T13:08:06.552059Z",
+ "iopub.status.busy": "2024-01-19T13:08:06.551691Z",
+ "iopub.status.idle": "2024-01-19T13:08:06.561446Z",
+ "shell.execute_reply": "2024-01-19T13:08:06.560827Z"
}
},
"outputs": [
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
index f11893234..f85faffeb 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb
@@ -74,10 +74,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:49.638480Z",
- "iopub.status.busy": "2024-01-19T12:50:49.638289Z",
- "iopub.status.idle": "2024-01-19T12:50:50.657962Z",
- "shell.execute_reply": "2024-01-19T12:50:50.657384Z"
+ "iopub.execute_input": "2024-01-19T13:08:11.258197Z",
+ "iopub.status.busy": "2024-01-19T13:08:11.258003Z",
+ "iopub.status.idle": "2024-01-19T13:08:12.289739Z",
+ "shell.execute_reply": "2024-01-19T13:08:12.289039Z"
},
"nbsphinx": "hidden"
},
@@ -87,7 +87,7 @@
"dependencies = [\"cleanlab\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -112,10 +112,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:50.660798Z",
- "iopub.status.busy": "2024-01-19T12:50:50.660430Z",
- "iopub.status.idle": "2024-01-19T12:50:50.676880Z",
- "shell.execute_reply": "2024-01-19T12:50:50.676382Z"
+ "iopub.execute_input": "2024-01-19T13:08:12.292733Z",
+ "iopub.status.busy": "2024-01-19T13:08:12.292247Z",
+ "iopub.status.idle": "2024-01-19T13:08:12.308975Z",
+ "shell.execute_reply": "2024-01-19T13:08:12.308461Z"
}
},
"outputs": [],
@@ -155,10 +155,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:50.679564Z",
- "iopub.status.busy": "2024-01-19T12:50:50.679073Z",
- "iopub.status.idle": "2024-01-19T12:50:50.929537Z",
- "shell.execute_reply": "2024-01-19T12:50:50.928908Z"
+ "iopub.execute_input": "2024-01-19T13:08:12.311628Z",
+ "iopub.status.busy": "2024-01-19T13:08:12.311146Z",
+ "iopub.status.idle": "2024-01-19T13:08:12.470482Z",
+ "shell.execute_reply": "2024-01-19T13:08:12.469839Z"
}
},
"outputs": [
@@ -265,10 +265,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:50.932093Z",
- "iopub.status.busy": "2024-01-19T12:50:50.931628Z",
- "iopub.status.idle": "2024-01-19T12:50:50.935450Z",
- "shell.execute_reply": "2024-01-19T12:50:50.934835Z"
+ "iopub.execute_input": "2024-01-19T13:08:12.473118Z",
+ "iopub.status.busy": "2024-01-19T13:08:12.472755Z",
+ "iopub.status.idle": "2024-01-19T13:08:12.476613Z",
+ "shell.execute_reply": "2024-01-19T13:08:12.476087Z"
}
},
"outputs": [],
@@ -289,10 +289,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:50.937615Z",
- "iopub.status.busy": "2024-01-19T12:50:50.937419Z",
- "iopub.status.idle": "2024-01-19T12:50:50.945643Z",
- "shell.execute_reply": "2024-01-19T12:50:50.945176Z"
+ "iopub.execute_input": "2024-01-19T13:08:12.478899Z",
+ "iopub.status.busy": "2024-01-19T13:08:12.478684Z",
+ "iopub.status.idle": "2024-01-19T13:08:12.486733Z",
+ "shell.execute_reply": "2024-01-19T13:08:12.486214Z"
}
},
"outputs": [],
@@ -337,10 +337,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:50.948118Z",
- "iopub.status.busy": "2024-01-19T12:50:50.947757Z",
- "iopub.status.idle": "2024-01-19T12:50:50.950456Z",
- "shell.execute_reply": "2024-01-19T12:50:50.949929Z"
+ "iopub.execute_input": "2024-01-19T13:08:12.489068Z",
+ "iopub.status.busy": "2024-01-19T13:08:12.488867Z",
+ "iopub.status.idle": "2024-01-19T13:08:12.491740Z",
+ "shell.execute_reply": "2024-01-19T13:08:12.491110Z"
}
},
"outputs": [],
@@ -362,10 +362,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:50.952871Z",
- "iopub.status.busy": "2024-01-19T12:50:50.952512Z",
- "iopub.status.idle": "2024-01-19T12:50:54.596578Z",
- "shell.execute_reply": "2024-01-19T12:50:54.595931Z"
+ "iopub.execute_input": "2024-01-19T13:08:12.494043Z",
+ "iopub.status.busy": "2024-01-19T13:08:12.493708Z",
+ "iopub.status.idle": "2024-01-19T13:08:16.176585Z",
+ "shell.execute_reply": "2024-01-19T13:08:16.175849Z"
}
},
"outputs": [],
@@ -401,10 +401,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:54.599807Z",
- "iopub.status.busy": "2024-01-19T12:50:54.599582Z",
- "iopub.status.idle": "2024-01-19T12:50:54.609080Z",
- "shell.execute_reply": "2024-01-19T12:50:54.608594Z"
+ "iopub.execute_input": "2024-01-19T13:08:16.180120Z",
+ "iopub.status.busy": "2024-01-19T13:08:16.179586Z",
+ "iopub.status.idle": "2024-01-19T13:08:16.189458Z",
+ "shell.execute_reply": "2024-01-19T13:08:16.188826Z"
}
},
"outputs": [],
@@ -436,10 +436,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:54.611415Z",
- "iopub.status.busy": "2024-01-19T12:50:54.611205Z",
- "iopub.status.idle": "2024-01-19T12:50:55.917644Z",
- "shell.execute_reply": "2024-01-19T12:50:55.916869Z"
+ "iopub.execute_input": "2024-01-19T13:08:16.192283Z",
+ "iopub.status.busy": "2024-01-19T13:08:16.191843Z",
+ "iopub.status.idle": "2024-01-19T13:08:17.566660Z",
+ "shell.execute_reply": "2024-01-19T13:08:17.565887Z"
}
},
"outputs": [
@@ -475,10 +475,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:55.921391Z",
- "iopub.status.busy": "2024-01-19T12:50:55.920737Z",
- "iopub.status.idle": "2024-01-19T12:50:55.946433Z",
- "shell.execute_reply": "2024-01-19T12:50:55.945810Z"
+ "iopub.execute_input": "2024-01-19T13:08:17.570210Z",
+ "iopub.status.busy": "2024-01-19T13:08:17.569532Z",
+ "iopub.status.idle": "2024-01-19T13:08:17.595532Z",
+ "shell.execute_reply": "2024-01-19T13:08:17.594912Z"
},
"scrolled": true
},
@@ -624,10 +624,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:55.949254Z",
- "iopub.status.busy": "2024-01-19T12:50:55.948796Z",
- "iopub.status.idle": "2024-01-19T12:50:55.959031Z",
- "shell.execute_reply": "2024-01-19T12:50:55.958425Z"
+ "iopub.execute_input": "2024-01-19T13:08:17.598585Z",
+ "iopub.status.busy": "2024-01-19T13:08:17.598126Z",
+ "iopub.status.idle": "2024-01-19T13:08:17.608208Z",
+ "shell.execute_reply": "2024-01-19T13:08:17.607609Z"
}
},
"outputs": [
@@ -731,10 +731,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:55.962734Z",
- "iopub.status.busy": "2024-01-19T12:50:55.961478Z",
- "iopub.status.idle": "2024-01-19T12:50:55.976018Z",
- "shell.execute_reply": "2024-01-19T12:50:55.975434Z"
+ "iopub.execute_input": "2024-01-19T13:08:17.611144Z",
+ "iopub.status.busy": "2024-01-19T13:08:17.610706Z",
+ "iopub.status.idle": "2024-01-19T13:08:17.622793Z",
+ "shell.execute_reply": "2024-01-19T13:08:17.622185Z"
}
},
"outputs": [
@@ -863,10 +863,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:55.980296Z",
- "iopub.status.busy": "2024-01-19T12:50:55.979169Z",
- "iopub.status.idle": "2024-01-19T12:50:55.991739Z",
- "shell.execute_reply": "2024-01-19T12:50:55.991162Z"
+ "iopub.execute_input": "2024-01-19T13:08:17.626780Z",
+ "iopub.status.busy": "2024-01-19T13:08:17.625618Z",
+ "iopub.status.idle": "2024-01-19T13:08:17.638538Z",
+ "shell.execute_reply": "2024-01-19T13:08:17.637920Z"
}
},
"outputs": [
@@ -980,10 +980,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:55.995985Z",
- "iopub.status.busy": "2024-01-19T12:50:55.994872Z",
- "iopub.status.idle": "2024-01-19T12:50:56.009540Z",
- "shell.execute_reply": "2024-01-19T12:50:56.009064Z"
+ "iopub.execute_input": "2024-01-19T13:08:17.642879Z",
+ "iopub.status.busy": "2024-01-19T13:08:17.641730Z",
+ "iopub.status.idle": "2024-01-19T13:08:17.657516Z",
+ "shell.execute_reply": "2024-01-19T13:08:17.656872Z"
}
},
"outputs": [
@@ -1094,10 +1094,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:56.012212Z",
- "iopub.status.busy": "2024-01-19T12:50:56.011742Z",
- "iopub.status.idle": "2024-01-19T12:50:56.018821Z",
- "shell.execute_reply": "2024-01-19T12:50:56.018275Z"
+ "iopub.execute_input": "2024-01-19T13:08:17.660414Z",
+ "iopub.status.busy": "2024-01-19T13:08:17.659924Z",
+ "iopub.status.idle": "2024-01-19T13:08:17.667229Z",
+ "shell.execute_reply": "2024-01-19T13:08:17.666577Z"
}
},
"outputs": [
@@ -1181,10 +1181,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:56.021388Z",
- "iopub.status.busy": "2024-01-19T12:50:56.021024Z",
- "iopub.status.idle": "2024-01-19T12:50:56.028115Z",
- "shell.execute_reply": "2024-01-19T12:50:56.027494Z"
+ "iopub.execute_input": "2024-01-19T13:08:17.669390Z",
+ "iopub.status.busy": "2024-01-19T13:08:17.669202Z",
+ "iopub.status.idle": "2024-01-19T13:08:17.676415Z",
+ "shell.execute_reply": "2024-01-19T13:08:17.675860Z"
}
},
"outputs": [
@@ -1277,10 +1277,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:50:56.030528Z",
- "iopub.status.busy": "2024-01-19T12:50:56.030179Z",
- "iopub.status.idle": "2024-01-19T12:50:56.037308Z",
- "shell.execute_reply": "2024-01-19T12:50:56.036675Z"
+ "iopub.execute_input": "2024-01-19T13:08:17.678767Z",
+ "iopub.status.busy": "2024-01-19T13:08:17.678397Z",
+ "iopub.status.idle": "2024-01-19T13:08:17.685609Z",
+ "shell.execute_reply": "2024-01-19T13:08:17.684962Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
index 6d0b89d88..fc580b8f3 100644
--- a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb
@@ -75,10 +75,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:00.632381Z",
- "iopub.status.busy": "2024-01-19T12:51:00.632168Z",
- "iopub.status.idle": "2024-01-19T12:51:02.984171Z",
- "shell.execute_reply": "2024-01-19T12:51:02.983605Z"
+ "iopub.execute_input": "2024-01-19T13:08:22.286771Z",
+ "iopub.status.busy": "2024-01-19T13:08:22.286590Z",
+ "iopub.status.idle": "2024-01-19T13:08:24.625209Z",
+ "shell.execute_reply": "2024-01-19T13:08:24.624517Z"
},
"nbsphinx": "hidden"
},
@@ -93,7 +93,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "494abb3fbfb34e7482a6a8a734f86cbe",
+ "model_id": "00dbaa0e717a40478f7d88a8e4c93f25",
"version_major": 2,
"version_minor": 0
},
@@ -118,7 +118,7 @@
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\" # disable parallelism to avoid deadlocks with huggingface\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -143,10 +143,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:02.987240Z",
- "iopub.status.busy": "2024-01-19T12:51:02.986674Z",
- "iopub.status.idle": "2024-01-19T12:51:02.990268Z",
- "shell.execute_reply": "2024-01-19T12:51:02.989756Z"
+ "iopub.execute_input": "2024-01-19T13:08:24.628660Z",
+ "iopub.status.busy": "2024-01-19T13:08:24.627938Z",
+ "iopub.status.idle": "2024-01-19T13:08:24.631904Z",
+ "shell.execute_reply": "2024-01-19T13:08:24.631282Z"
}
},
"outputs": [],
@@ -167,10 +167,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:02.992672Z",
- "iopub.status.busy": "2024-01-19T12:51:02.992321Z",
- "iopub.status.idle": "2024-01-19T12:51:02.995659Z",
- "shell.execute_reply": "2024-01-19T12:51:02.995065Z"
+ "iopub.execute_input": "2024-01-19T13:08:24.634364Z",
+ "iopub.status.busy": "2024-01-19T13:08:24.634163Z",
+ "iopub.status.idle": "2024-01-19T13:08:24.637658Z",
+ "shell.execute_reply": "2024-01-19T13:08:24.637137Z"
},
"nbsphinx": "hidden"
},
@@ -200,10 +200,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:02.997936Z",
- "iopub.status.busy": "2024-01-19T12:51:02.997573Z",
- "iopub.status.idle": "2024-01-19T12:51:03.117799Z",
- "shell.execute_reply": "2024-01-19T12:51:03.117207Z"
+ "iopub.execute_input": "2024-01-19T13:08:24.639856Z",
+ "iopub.status.busy": "2024-01-19T13:08:24.639659Z",
+ "iopub.status.idle": "2024-01-19T13:08:24.693228Z",
+ "shell.execute_reply": "2024-01-19T13:08:24.692579Z"
}
},
"outputs": [
@@ -293,10 +293,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:03.120467Z",
- "iopub.status.busy": "2024-01-19T12:51:03.119949Z",
- "iopub.status.idle": "2024-01-19T12:51:03.124472Z",
- "shell.execute_reply": "2024-01-19T12:51:03.123932Z"
+ "iopub.execute_input": "2024-01-19T13:08:24.695893Z",
+ "iopub.status.busy": "2024-01-19T13:08:24.695382Z",
+ "iopub.status.idle": "2024-01-19T13:08:24.699717Z",
+ "shell.execute_reply": "2024-01-19T13:08:24.699075Z"
}
},
"outputs": [
@@ -305,7 +305,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'card_about_to_expire', 'getting_spare_card', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'change_pin', 'supported_cards_and_currencies', 'beneficiary_not_allowed', 'visa_or_mastercard', 'cancel_transfer', 'card_payment_fee_charged'}\n"
+ "Classes: {'card_payment_fee_charged', 'beneficiary_not_allowed', 'card_about_to_expire', 'apple_pay_or_google_pay', 'lost_or_stolen_phone', 'cancel_transfer', 'supported_cards_and_currencies', 'visa_or_mastercard', 'change_pin', 'getting_spare_card'}\n"
]
}
],
@@ -329,10 +329,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:03.126799Z",
- "iopub.status.busy": "2024-01-19T12:51:03.126431Z",
- "iopub.status.idle": "2024-01-19T12:51:03.130158Z",
- "shell.execute_reply": "2024-01-19T12:51:03.129646Z"
+ "iopub.execute_input": "2024-01-19T13:08:24.702105Z",
+ "iopub.status.busy": "2024-01-19T13:08:24.701802Z",
+ "iopub.status.idle": "2024-01-19T13:08:24.705567Z",
+ "shell.execute_reply": "2024-01-19T13:08:24.704959Z"
}
},
"outputs": [
@@ -387,17 +387,17 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:03.132507Z",
- "iopub.status.busy": "2024-01-19T12:51:03.132119Z",
- "iopub.status.idle": "2024-01-19T12:51:12.792398Z",
- "shell.execute_reply": "2024-01-19T12:51:12.791652Z"
+ "iopub.execute_input": "2024-01-19T13:08:24.708186Z",
+ "iopub.status.busy": "2024-01-19T13:08:24.707815Z",
+ "iopub.status.idle": "2024-01-19T13:08:33.805711Z",
+ "shell.execute_reply": "2024-01-19T13:08:33.805085Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "be8a4db363594d0394d6859a433dc337",
+ "model_id": "5633d788c61242bc9166b2492e7fddd9",
"version_major": 2,
"version_minor": 0
},
@@ -411,7 +411,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "68bfbaea9fc54dbb9c0e6b855fcffe04",
+ "model_id": "2606e76e7b5742e995352eeb03e9ed9c",
"version_major": 2,
"version_minor": 0
},
@@ -425,7 +425,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "93ce611b900343a3b6d41cc1e2425fc5",
+ "model_id": "47ba3fd8657740fcb69c0d02a6dcd702",
"version_major": 2,
"version_minor": 0
},
@@ -439,7 +439,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a5e3add4dd824dc5bd498fe3420c356a",
+ "model_id": "28e610f9b12147bba855319b4e56a618",
"version_major": 2,
"version_minor": 0
},
@@ -453,7 +453,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "604f8250401046a7ab096102d5c94b12",
+ "model_id": "9057764f51a3438a96690d81c91cc5bf",
"version_major": 2,
"version_minor": 0
},
@@ -467,7 +467,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "23a9d436761d44938f6b6d9595b9f79b",
+ "model_id": "7629354e5548440399aa24d33fbd4e07",
"version_major": 2,
"version_minor": 0
},
@@ -481,7 +481,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "1fe3687fd5a04913aa491b0b95089514",
+ "model_id": "4c5045d484604e16aa565dcd9c19eb9b",
"version_major": 2,
"version_minor": 0
},
@@ -535,10 +535,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:12.795738Z",
- "iopub.status.busy": "2024-01-19T12:51:12.795525Z",
- "iopub.status.idle": "2024-01-19T12:51:13.993596Z",
- "shell.execute_reply": "2024-01-19T12:51:13.992915Z"
+ "iopub.execute_input": "2024-01-19T13:08:33.808855Z",
+ "iopub.status.busy": "2024-01-19T13:08:33.808423Z",
+ "iopub.status.idle": "2024-01-19T13:08:34.981986Z",
+ "shell.execute_reply": "2024-01-19T13:08:34.981287Z"
},
"scrolled": true
},
@@ -570,10 +570,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:13.996903Z",
- "iopub.status.busy": "2024-01-19T12:51:13.996467Z",
- "iopub.status.idle": "2024-01-19T12:51:13.999740Z",
- "shell.execute_reply": "2024-01-19T12:51:13.999175Z"
+ "iopub.execute_input": "2024-01-19T13:08:34.985649Z",
+ "iopub.status.busy": "2024-01-19T13:08:34.985182Z",
+ "iopub.status.idle": "2024-01-19T13:08:34.988357Z",
+ "shell.execute_reply": "2024-01-19T13:08:34.987792Z"
}
},
"outputs": [],
@@ -593,10 +593,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:14.002532Z",
- "iopub.status.busy": "2024-01-19T12:51:14.002091Z",
- "iopub.status.idle": "2024-01-19T12:51:15.317325Z",
- "shell.execute_reply": "2024-01-19T12:51:15.316519Z"
+ "iopub.execute_input": "2024-01-19T13:08:34.991281Z",
+ "iopub.status.busy": "2024-01-19T13:08:34.990852Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.349531Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.348773Z"
},
"scrolled": true
},
@@ -640,10 +640,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:15.320768Z",
- "iopub.status.busy": "2024-01-19T12:51:15.320027Z",
- "iopub.status.idle": "2024-01-19T12:51:15.354502Z",
- "shell.execute_reply": "2024-01-19T12:51:15.353881Z"
+ "iopub.execute_input": "2024-01-19T13:08:36.353233Z",
+ "iopub.status.busy": "2024-01-19T13:08:36.352588Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.386782Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.386170Z"
},
"scrolled": true
},
@@ -808,10 +808,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:15.357374Z",
- "iopub.status.busy": "2024-01-19T12:51:15.356919Z",
- "iopub.status.idle": "2024-01-19T12:51:15.367686Z",
- "shell.execute_reply": "2024-01-19T12:51:15.367068Z"
+ "iopub.execute_input": "2024-01-19T13:08:36.390090Z",
+ "iopub.status.busy": "2024-01-19T13:08:36.389650Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.400032Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.399452Z"
},
"scrolled": true
},
@@ -921,10 +921,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:15.370591Z",
- "iopub.status.busy": "2024-01-19T12:51:15.370135Z",
- "iopub.status.idle": "2024-01-19T12:51:15.375814Z",
- "shell.execute_reply": "2024-01-19T12:51:15.375089Z"
+ "iopub.execute_input": "2024-01-19T13:08:36.402971Z",
+ "iopub.status.busy": "2024-01-19T13:08:36.402539Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.407866Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.407170Z"
}
},
"outputs": [
@@ -962,10 +962,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:15.378010Z",
- "iopub.status.busy": "2024-01-19T12:51:15.377811Z",
- "iopub.status.idle": "2024-01-19T12:51:15.385096Z",
- "shell.execute_reply": "2024-01-19T12:51:15.384354Z"
+ "iopub.execute_input": "2024-01-19T13:08:36.410045Z",
+ "iopub.status.busy": "2024-01-19T13:08:36.409849Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.416620Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.416007Z"
}
},
"outputs": [
@@ -1082,10 +1082,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:15.387676Z",
- "iopub.status.busy": "2024-01-19T12:51:15.387303Z",
- "iopub.status.idle": "2024-01-19T12:51:15.394285Z",
- "shell.execute_reply": "2024-01-19T12:51:15.393666Z"
+ "iopub.execute_input": "2024-01-19T13:08:36.418739Z",
+ "iopub.status.busy": "2024-01-19T13:08:36.418541Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.425248Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.424636Z"
}
},
"outputs": [
@@ -1168,10 +1168,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:15.396454Z",
- "iopub.status.busy": "2024-01-19T12:51:15.396255Z",
- "iopub.status.idle": "2024-01-19T12:51:15.402545Z",
- "shell.execute_reply": "2024-01-19T12:51:15.401924Z"
+ "iopub.execute_input": "2024-01-19T13:08:36.427399Z",
+ "iopub.status.busy": "2024-01-19T13:08:36.427191Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.433331Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.432721Z"
}
},
"outputs": [
@@ -1279,10 +1279,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:15.404706Z",
- "iopub.status.busy": "2024-01-19T12:51:15.404509Z",
- "iopub.status.idle": "2024-01-19T12:51:15.413816Z",
- "shell.execute_reply": "2024-01-19T12:51:15.413295Z"
+ "iopub.execute_input": "2024-01-19T13:08:36.435472Z",
+ "iopub.status.busy": "2024-01-19T13:08:36.435278Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.444505Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.443882Z"
}
},
"outputs": [
@@ -1393,10 +1393,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:15.415888Z",
- "iopub.status.busy": "2024-01-19T12:51:15.415691Z",
- "iopub.status.idle": "2024-01-19T12:51:15.583754Z",
- "shell.execute_reply": "2024-01-19T12:51:15.583102Z"
+ "iopub.execute_input": "2024-01-19T13:08:36.446777Z",
+ "iopub.status.busy": "2024-01-19T13:08:36.446426Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.452182Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.451568Z"
}
},
"outputs": [
@@ -1464,10 +1464,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:15.586178Z",
- "iopub.status.busy": "2024-01-19T12:51:15.585955Z",
- "iopub.status.idle": "2024-01-19T12:51:15.592260Z",
- "shell.execute_reply": "2024-01-19T12:51:15.591723Z"
+ "iopub.execute_input": "2024-01-19T13:08:36.454628Z",
+ "iopub.status.busy": "2024-01-19T13:08:36.454188Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.631673Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.630998Z"
}
},
"outputs": [
@@ -1546,10 +1546,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:15.594873Z",
- "iopub.status.busy": "2024-01-19T12:51:15.594406Z",
- "iopub.status.idle": "2024-01-19T12:51:15.598577Z",
- "shell.execute_reply": "2024-01-19T12:51:15.598062Z"
+ "iopub.execute_input": "2024-01-19T13:08:36.634282Z",
+ "iopub.status.busy": "2024-01-19T13:08:36.633916Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.637895Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.637396Z"
}
},
"outputs": [
@@ -1597,10 +1597,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:15.601183Z",
- "iopub.status.busy": "2024-01-19T12:51:15.600697Z",
- "iopub.status.idle": "2024-01-19T12:51:15.606229Z",
- "shell.execute_reply": "2024-01-19T12:51:15.605715Z"
+ "iopub.execute_input": "2024-01-19T13:08:36.640363Z",
+ "iopub.status.busy": "2024-01-19T13:08:36.640008Z",
+ "iopub.status.idle": "2024-01-19T13:08:36.645806Z",
+ "shell.execute_reply": "2024-01-19T13:08:36.645178Z"
},
"nbsphinx": "hidden"
},
@@ -1650,28 +1650,45 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "03313b667c664b9280bf091e61f0143a": {
+ "0028a63ae73147de9cd8e114a2a0ed90": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HTMLModel",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "00dbaa0e717a40478f7d88a8e4c93f25": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_91add70c869d4150bf1cad7fad3843bc",
- "placeholder": "",
- "style": "IPY_MODEL_13a2721927474a13828baa4df4a78e68",
- "value": " 2.21k/2.21k [00:00<00:00, 285kB/s]"
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_9b2e31429dfd4ac4b76fa687b99b5a4e",
+ "IPY_MODEL_50d06e3cbad04b0ebe181911a298f4f1",
+ "IPY_MODEL_0c23d24209ec438e91804dfe9d4be449"
+ ],
+ "layout": "IPY_MODEL_f09404a56571435cb6aac14140f9055c"
}
},
- "049f76cfcd3e4cff9ab3a475579f049c": {
+ "02e15e51450e48008eab3948566a0e3d": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
@@ -1687,15 +1704,15 @@
"bar_style": "success",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_346e8ed61adc4e4d83f183ada44207fa",
- "max": 466062.0,
+ "layout": "IPY_MODEL_4ebb8cbc14d84c08afcaab9e640c721a",
+ "max": 665.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_81ae714c261d4460889aaa95a5436f8d",
- "value": 466062.0
+ "style": "IPY_MODEL_314de3e3a82c472d9d2929bbccda5a49",
+ "value": 665.0
}
},
- "07cf8200b7e24fe28a0e477fd54e3495": {
+ "0815f8dbceb24a5790ba3a46b4c55bfb": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1747,7 +1764,7 @@
"width": null
}
},
- "098c1de7b5bb477592d5678574d93a00": {
+ "090cc46bfbb9461a8e039860aa05b75a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1799,7 +1816,125 @@
"width": null
}
},
- "0f6e70ca97df40b6863cdd8ee298b363": {
+ "0c23d24209ec438e91804dfe9d4be449": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_6eb36e6800bc4db98d76a081cf3998dd",
+ "placeholder": "",
+ "style": "IPY_MODEL_c296273218ec46c5a870f37cc1ad98ca",
+ "value": " 0/0 [00:00<?, ?it/s]"
+ }
+ },
+ "0e9897e263d34e909b92e7b32e980d5b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_ceccc1659776491483112f5cf8d19cf6",
+ "placeholder": "",
+ "style": "IPY_MODEL_4e7356e329564b248fe98e7473d61d90",
+ "value": " 54.2M/54.2M [00:00<00:00, 275MB/s]"
+ }
+ },
+ "0f79ffacb6904be8ba16a3fd288b5c09": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "1d958ade3d024495abb98900a959d57b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_f97a4a70f7cb49d4975c6405e175b54d",
+ "max": 2211.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_0f79ffacb6904be8ba16a3fd288b5c09",
+ "value": 2211.0
+ }
+ },
+ "1dd7c745749c40e496280d0dff983004": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "1f463d53ef0e450c9319438d02f1dbd5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_090cc46bfbb9461a8e039860aa05b75a",
+ "placeholder": "",
+ "style": "IPY_MODEL_fc4be386257044c59a7fecf1dbb6ad7a",
+ "value": " 232k/232k [00:00<00:00, 25.6MB/s]"
+ }
+ },
+ "205211107e7c416ea5b8825ce936ff3b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1851,31 +1986,67 @@
"width": null
}
},
- "12d316792f754d1c82bd960817220658": {
+ "220429677e1d4453b511a341990ffb67": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "2606e76e7b5742e995352eeb03e9ed9c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_405f8c98741f484189001e8a209ff2b8",
- "max": 1.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_e5b429accdc143e5bf67101827800ca0",
- "value": 0.0
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_b85af5aadb6f4c1e932ea165db73914b",
+ "IPY_MODEL_1d958ade3d024495abb98900a959d57b",
+ "IPY_MODEL_58f9fe9b062941359be067ca97400c63"
+ ],
+ "layout": "IPY_MODEL_7573692a66a64da6af73f93e249aee99"
+ }
+ },
+ "28e610f9b12147bba855319b4e56a618": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_88cb3b7c89f642b3b8b7011ab06b1b38",
+ "IPY_MODEL_766ee597c8684e9d97a61493ded7d122",
+ "IPY_MODEL_0e9897e263d34e909b92e7b32e980d5b"
+ ],
+ "layout": "IPY_MODEL_0815f8dbceb24a5790ba3a46b4c55bfb"
}
},
- "13a2721927474a13828baa4df4a78e68": {
+ "2a3a4a5251dc4fd9be8e8b4772344953": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -1890,7 +2061,7 @@
"description_width": ""
}
},
- "1554949af6264b12822019201e8fd4c7": {
+ "2e30262882cd460b9f7246a3b861824d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1942,7 +2113,53 @@
"width": null
}
},
- "15679da2267a441082823a7e94d2c318": {
+ "2fd388de8f674781bb3cd043e9cf3621": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "314de3e3a82c472d9d2929bbccda5a49": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "38228851302a402089fbe65cb22a64a6": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "398860b0e6ca41b6a585dfdf735b7ca6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1994,7 +2211,7 @@
"width": null
}
},
- "193d2ab0337645828a947bd6f1fb0323": {
+ "3d14d26c9c2e47469e94e4ef1638318c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2046,60 +2263,14 @@
"width": null
}
},
- "1e9d41c4cb86463e86da6c98c0c94c28": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
+ "3d8c9f91d4c642ef86738156e13da58a": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "1.2.0",
+ "model_name": "LayoutModel",
"state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_1554949af6264b12822019201e8fd4c7",
- "max": 54245363.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_5c0a4f31cccf414b84bcf979a6139035",
- "value": 54245363.0
- }
- },
- "1fe3687fd5a04913aa491b0b95089514": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_4244aede57534a3ca53108f28c896864",
- "IPY_MODEL_7f67507354494bfd8bc2409aa7c43e53",
- "IPY_MODEL_c7b6861f25c0481ca45185f6a64fc674"
- ],
- "layout": "IPY_MODEL_96c13439fc8944e78181bc287fad8b2a"
- }
- },
- "225d90b52b194065b0b357dccaf2da6d": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
- "state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "1.2.0",
- "_model_name": "LayoutModel",
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
@@ -2144,29 +2315,7 @@
"width": null
}
},
- "23a9d436761d44938f6b6d9595b9f79b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_758b34991fe242c98f93830209ed80e4",
- "IPY_MODEL_8d419a641c9d4f09ab9b14fd689798be",
- "IPY_MODEL_536be4a9f4eb41ae93529f149953c7e8"
- ],
- "layout": "IPY_MODEL_15679da2267a441082823a7e94d2c318"
- }
- },
- "257791cb293c4227a03e73677dee2583": {
+ "403607d9607142e59199d89e3f53ec8c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2218,7 +2367,120 @@
"width": null
}
},
- "266fa05ddee548e8a63cde013f285ec5": {
+ "43b5d2ace9b74316a61c8e8779248973": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "47ba3fd8657740fcb69c0d02a6dcd702": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_c9a9cdc98f414c5c8f7eabc1fb4b8b31",
+ "IPY_MODEL_02e15e51450e48008eab3948566a0e3d",
+ "IPY_MODEL_a8478f2e04f54c59b01db0ee4b491a22"
+ ],
+ "layout": "IPY_MODEL_e7312bcbf87c414bb9bb55706f6320d0"
+ }
+ },
+ "4933b70a7dbd40edaef83ff25f28e7f7": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "49a596baffc9471b87d64fc90881b38c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_d95e8b60192d43a0b6028ae743cbe2b8",
+ "max": 391.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_7bd61d6037204fdc8b7ab7df07841c51",
+ "value": 391.0
+ }
+ },
+ "4c5045d484604e16aa565dcd9c19eb9b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_82972f8cd1cd49fbb3b85fc3e9e1aa93",
+ "IPY_MODEL_fa97ff0a0234428398016df6e6cb35dd",
+ "IPY_MODEL_1f463d53ef0e450c9319438d02f1dbd5"
+ ],
+ "layout": "IPY_MODEL_3d14d26c9c2e47469e94e4ef1638318c"
+ }
+ },
+ "4e7356e329564b248fe98e7473d61d90": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "4ebb8cbc14d84c08afcaab9e640c721a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2270,7 +2532,116 @@
"width": null
}
},
- "29bb247c682b433f969bed5121c89f56": {
+ "50d06e3cbad04b0ebe181911a298f4f1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_fd1ed2a7574f4ed28fa2e80eae3203a4",
+ "max": 1.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_0028a63ae73147de9cd8e114a2a0ed90",
+ "value": 0.0
+ }
+ },
+ "5633d788c61242bc9166b2492e7fddd9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_e33dc17a75b3454f8cd46f1e5c076432",
+ "IPY_MODEL_49a596baffc9471b87d64fc90881b38c",
+ "IPY_MODEL_79e266cce29848f19c4e98eb8cadcb34"
+ ],
+ "layout": "IPY_MODEL_b9560547a8c94219b037ef0852bb8ed2"
+ }
+ },
+ "58f9fe9b062941359be067ca97400c63": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_ce6aa908c3f74e4eaf010b03460fbf71",
+ "placeholder": "",
+ "style": "IPY_MODEL_2fd388de8f674781bb3cd043e9cf3621",
+ "value": " 2.21k/2.21k [00:00<00:00, 286kB/s]"
+ }
+ },
+ "5a4cb59363e9444cb1c8b724a73d99f9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_f5667d7916734e0ba7f74ad0a9373cb6",
+ "placeholder": "",
+ "style": "IPY_MODEL_8ce96494db0d4715858b1f728aad4e5e",
+ "value": " 29.0/29.0 [00:00<00:00, 3.58kB/s]"
+ }
+ },
+ "5da26a69b5aa4c11afb55fb6fbb9a44a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_fc32c3974d604564807d1fde4f8746c2",
+ "placeholder": "",
+ "style": "IPY_MODEL_4933b70a7dbd40edaef83ff25f28e7f7",
+ "value": "tokenizer.json: 100%"
+ }
+ },
+ "6c513a7554d14fd1b35ff32249af8b41": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2322,31 +2693,7 @@
"width": null
}
},
- "2ddf0c5b613349199603ac2d7acd1313": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_3213a902744347dfb1dc9d58f7056e73",
- "max": 665.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_efed3c4236d8470d90c4f475c9ed2ee5",
- "value": 665.0
- }
- },
- "3213a902744347dfb1dc9d58f7056e73": {
+ "6eb36e6800bc4db98d76a081cf3998dd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2398,7 +2745,7 @@
"width": null
}
},
- "346e8ed61adc4e4d83f183ada44207fa": {
+ "704cfa8457c3477cab9c170ca76e1e63": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2450,7 +2797,7 @@
"width": null
}
},
- "3962477e7ac548e48eb0b27d1cb9bb2d": {
+ "7573692a66a64da6af73f93e249aee99": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2502,31 +2849,56 @@
"width": null
}
},
- "3b474e37a3cb4f75aa991708ad89d2c4": {
+ "7629354e5548440399aa24d33fbd4e07": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HTMLModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_52cedb587388480391d707b956912bc0",
- "placeholder": "",
- "style": "IPY_MODEL_42974d8c5b6e4531b43a314e92b4ba58",
- "value": " 391/391 [00:00<00:00, 39.7kB/s]"
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_ae72eeb3544641eab74cc0ee10f92b6e",
+ "IPY_MODEL_c92d40ea9a1142789cad56bcf90ce48d",
+ "IPY_MODEL_5a4cb59363e9444cb1c8b724a73d99f9"
+ ],
+ "layout": "IPY_MODEL_c5e3b82d2a754825bb86d27a9c6dc7fd"
}
},
- "3d5ebe16ec7447049b0cb316af8cf53c": {
+ "766ee597c8684e9d97a61493ded7d122": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HTMLModel",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_3d8c9f91d4c642ef86738156e13da58a",
+ "max": 54245363.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_220429677e1d4453b511a341990ffb67",
+ "value": 54245363.0
+ }
+ },
+ "79cff9211b7145d59e6afa1ab9e34e34": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
@@ -2538,13 +2910,66 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_225d90b52b194065b0b357dccaf2da6d",
+ "layout": "IPY_MODEL_dd36bba1a42c487d8de2dc8937ac1a2c",
"placeholder": "",
- "style": "IPY_MODEL_d29c8a78ed6a4a17a02bc5261eacd5fc",
- "value": "README.md: 100%"
+ "style": "IPY_MODEL_ca4e48071ad34e39b28187eab4f36e3c",
+ "value": " 466k/466k [00:00<00:00, 14.6MB/s]"
+ }
+ },
+ "79e266cce29848f19c4e98eb8cadcb34": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_aced6a9d36b54d73b886bd3845ea2d9a",
+ "placeholder": "",
+ "style": "IPY_MODEL_bd18a56daa4545a490f8e68dd2c7fa52",
+ "value": " 391/391 [00:00<00:00, 48.8kB/s]"
+ }
+ },
+ "7a163259881547b5b1e3f8b179e8d594": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "7bd61d6037204fdc8b7ab7df07841c51": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "405f8c98741f484189001e8a209ff2b8": {
+ "7d3db066da974ff58ab6b530943e0cec": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2593,26 +3018,10 @@
"right": null,
"top": null,
"visibility": null,
- "width": "20px"
- }
- },
- "4094b8a244764ec2b209b47b9d56fb6b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "width": null
}
},
- "418c619f0a5f41119950b24220999c0f": {
+ "8069a75862c04ab29242d5a0d7a58a00": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2664,7 +3073,7 @@
"width": null
}
},
- "4244aede57534a3ca53108f28c896864": {
+ "82972f8cd1cd49fbb3b85fc3e9e1aa93": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -2679,28 +3088,37 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_9a4074b5990e416f98b2fc92cf88d431",
+ "layout": "IPY_MODEL_b21fa98fc3574e6292c0bdbb77c9c00d",
"placeholder": "",
- "style": "IPY_MODEL_c9120aae1085452fb3a2d5b59c8c9471",
+ "style": "IPY_MODEL_38228851302a402089fbe65cb22a64a6",
"value": "vocab.txt: 100%"
}
},
- "42974d8c5b6e4531b43a314e92b4ba58": {
+ "84080c8e981841f9b3c7ce53744d8f60": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_9d659a42eebb41e1955016b780963a69",
+ "max": 466062.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_9ec1b04bb78640eea8ee760fc982ab62",
+ "value": 466062.0
}
},
- "42f8218f39a2453c9d91f514de4d9ca8": {
+ "877021429b694ed8ae2dbf34f6a522f5": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2752,7 +3170,7 @@
"width": null
}
},
- "46ba31a7baf343739956cd2bf0656869": {
+ "88cb3b7c89f642b3b8b7011ab06b1b38": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -2767,13 +3185,28 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_098c1de7b5bb477592d5678574d93a00",
+ "layout": "IPY_MODEL_7d3db066da974ff58ab6b530943e0cec",
"placeholder": "",
- "style": "IPY_MODEL_b83c4ec1ec0c47e5b909c6ddeba1d236",
- "value": "tokenizer.json: 100%"
+ "style": "IPY_MODEL_fe76896b0666432e81615f7d4ef0d334",
+ "value": "pytorch_model.bin: 100%"
+ }
+ },
+ "8ce96494db0d4715858b1f728aad4e5e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
}
},
- "494abb3fbfb34e7482a6a8a734f86cbe": {
+ "9057764f51a3438a96690d81c91cc5bf": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
@@ -2788,14 +3221,35 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_a65f253506f2456fb4376e99f7a6da1f",
- "IPY_MODEL_12d316792f754d1c82bd960817220658",
- "IPY_MODEL_7c522967032142a6b745223819f76811"
+ "IPY_MODEL_5da26a69b5aa4c11afb55fb6fbb9a44a",
+ "IPY_MODEL_84080c8e981841f9b3c7ce53744d8f60",
+ "IPY_MODEL_79cff9211b7145d59e6afa1ab9e34e34"
],
- "layout": "IPY_MODEL_d7efb5dd8a0d45409c31a3353a95f9a9"
+ "layout": "IPY_MODEL_403607d9607142e59199d89e3f53ec8c"
+ }
+ },
+ "9b2e31429dfd4ac4b76fa687b99b5a4e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_2e30262882cd460b9f7246a3b861824d",
+ "placeholder": "",
+ "style": "IPY_MODEL_ef63569751cf4648ab115fb83a8ac438",
+ "value": ""
}
},
- "52cedb587388480391d707b956912bc0": {
+ "9d659a42eebb41e1955016b780963a69": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2847,7 +3301,23 @@
"width": null
}
},
- "536be4a9f4eb41ae93529f149953c7e8": {
+ "9ec1b04bb78640eea8ee760fc982ab62": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "a8478f2e04f54c59b01db0ee4b491a22": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -2862,13 +3332,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_698e9d1a578d4de59e7e4dbc4846e8e1",
+ "layout": "IPY_MODEL_d0b557b38c0f4841acefb4e2b5ffb373",
"placeholder": "",
- "style": "IPY_MODEL_ff589dd8da954febb096b439d92a32d6",
- "value": " 29.0/29.0 [00:00<00:00, 3.96kB/s]"
+ "style": "IPY_MODEL_1dd7c745749c40e496280d0dff983004",
+ "value": " 665/665 [00:00<00:00, 82.2kB/s]"
}
},
- "591afa6717e14cd69636050c63619a4c": {
+ "aced6a9d36b54d73b886bd3845ea2d9a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -2920,7 +3390,7 @@
"width": null
}
},
- "5bc6e206f2f143a3b5aacff6afbcc1ff": {
+ "ae72eeb3544641eab74cc0ee10f92b6e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -2935,51 +3405,28 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_8ee8952a8400462e90dfb170da4ec35d",
+ "layout": "IPY_MODEL_6c513a7554d14fd1b35ff32249af8b41",
"placeholder": "",
- "style": "IPY_MODEL_b4ca02cc9611427d8dbce87b08c3964e",
- "value": "pytorch_model.bin: 100%"
+ "style": "IPY_MODEL_e459f209cb80409f90693db698f30cd4",
+ "value": "tokenizer_config.json: 100%"
}
},
- "5c0a4f31cccf414b84bcf979a6139035": {
+ "b20fbd8e1a884e9a880fbdc021aef1da": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
- "bar_color": null,
"description_width": ""
}
},
- "604f8250401046a7ab096102d5c94b12": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_46ba31a7baf343739956cd2bf0656869",
- "IPY_MODEL_049f76cfcd3e4cff9ab3a475579f049c",
- "IPY_MODEL_7cf3c75284884068a73ff22b60f6a57c"
- ],
- "layout": "IPY_MODEL_632d9a34ffdd4e2697a52dc5f737526a"
- }
- },
- "632d9a34ffdd4e2697a52dc5f737526a": {
+ "b21fa98fc3574e6292c0bdbb77c9c00d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3031,7 +3478,28 @@
"width": null
}
},
- "686c9d8ba0824e15846b2cbaa3f9f7d5": {
+ "b85af5aadb6f4c1e932ea165db73914b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_205211107e7c416ea5b8825ce936ff3b",
+ "placeholder": "",
+ "style": "IPY_MODEL_43b5d2ace9b74316a61c8e8779248973",
+ "value": "README.md: 100%"
+ }
+ },
+ "b9560547a8c94219b037ef0852bb8ed2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3083,7 +3551,7 @@
"width": null
}
},
- "68837b8bde5c4ad898daca1edff32383": {
+ "bd18a56daa4545a490f8e68dd2c7fa52": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -3098,29 +3566,22 @@
"description_width": ""
}
},
- "68bfbaea9fc54dbb9c0e6b855fcffe04": {
+ "c296273218ec46c5a870f37cc1ad98ca": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HBoxModel",
+ "model_name": "DescriptionStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
+ "_model_name": "DescriptionStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_3d5ebe16ec7447049b0cb316af8cf53c",
- "IPY_MODEL_faf1d8186eff4c78b3a4a91e97d62ecf",
- "IPY_MODEL_03313b667c664b9280bf091e61f0143a"
- ],
- "layout": "IPY_MODEL_686c9d8ba0824e15846b2cbaa3f9f7d5"
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
}
},
- "698e9d1a578d4de59e7e4dbc4846e8e1": {
+ "c5e3b82d2a754825bb86d27a9c6dc7fd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3172,132 +3633,31 @@
"width": null
}
},
- "6a6795a859414bf29d6de30d8d4ea375": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
+ "c92d40ea9a1142789cad56bcf90ce48d": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "1.2.0",
- "_model_name": "LayoutModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "overflow_x": null,
- "overflow_y": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
- }
- },
- "74ef1fded25d45279968cd27406d7ef8": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "758b34991fe242c98f93830209ed80e4": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_bccfd6d5006e4e6f9d8792d885fd01a2",
- "placeholder": "",
- "style": "IPY_MODEL_68837b8bde5c4ad898daca1edff32383",
- "value": "tokenizer_config.json: 100%"
- }
- },
- "7bf3d4989e804b3e98da4f19065ed878": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "7c522967032142a6b745223819f76811": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_3962477e7ac548e48eb0b27d1cb9bb2d",
- "placeholder": "",
- "style": "IPY_MODEL_84fed4b462ea426ea0ddbe7f8d0c318a",
- "value": " 0/0 [00:00<?, ?it/s]"
+ "layout": "IPY_MODEL_398860b0e6ca41b6a585dfdf735b7ca6",
+ "max": 29.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_e346e0c4ed5349b5b38784144d338526",
+ "value": 29.0
}
},
- "7cf3c75284884068a73ff22b60f6a57c": {
+ "c9a9cdc98f414c5c8f7eabc1fb4b8b31": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -3312,68 +3672,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_42f8218f39a2453c9d91f514de4d9ca8",
+ "layout": "IPY_MODEL_877021429b694ed8ae2dbf34f6a522f5",
"placeholder": "",
- "style": "IPY_MODEL_ae9134a8b5b34063b71ed5d492e7f9e4",
- "value": " 466k/466k [00:00<00:00, 20.7MB/s]"
- }
- },
- "7f67507354494bfd8bc2409aa7c43e53": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_257791cb293c4227a03e73677dee2583",
- "max": 231508.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_4094b8a244764ec2b209b47b9d56fb6b",
- "value": 231508.0
- }
- },
- "81ae714c261d4460889aaa95a5436f8d": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "81b50031e3604819bd8b126ed55f3342": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "style": "IPY_MODEL_b20fbd8e1a884e9a880fbdc021aef1da",
+ "value": "config.json: 100%"
}
},
- "84fed4b462ea426ea0ddbe7f8d0c318a": {
+ "ca4e48071ad34e39b28187eab4f36e3c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -3388,47 +3693,7 @@
"description_width": ""
}
},
- "8d419a641c9d4f09ab9b14fd689798be": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_d87c7e9ec81e49208018eedb6f2a340f",
- "max": 29.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_74ef1fded25d45279968cd27406d7ef8",
- "value": 29.0
- }
- },
- "8d6093445f274f88a03304f9c8de0d95": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "8ee8952a8400462e90dfb170da4ec35d": {
+ "ce6aa908c3f74e4eaf010b03460fbf71": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3480,7 +3745,7 @@
"width": null
}
},
- "91add70c869d4150bf1cad7fad3843bc": {
+ "ceccc1659776491483112f5cf8d19cf6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3532,59 +3797,7 @@
"width": null
}
},
- "934b9886fa46472f9b7a8fdfb1e0cd6b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "93ce611b900343a3b6d41cc1e2425fc5": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_fe57ef1db6ca48c8816787b755a7a793",
- "IPY_MODEL_2ddf0c5b613349199603ac2d7acd1313",
- "IPY_MODEL_d129da2ec32d4c839724265f44a9a870"
- ],
- "layout": "IPY_MODEL_0f6e70ca97df40b6863cdd8ee298b363"
- }
- },
- "950246aac16e495cb2d5670c65144bb8": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "96c13439fc8944e78181bc287fad8b2a": {
+ "d0b557b38c0f4841acefb4e2b5ffb373": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3636,7 +3849,7 @@
"width": null
}
},
- "9a4074b5990e416f98b2fc92cf88d431": {
+ "d95e8b60192d43a0b6028ae743cbe2b8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3688,7 +3901,7 @@
"width": null
}
},
- "a528a0291806489b99868ef7ef38c722": {
+ "dd36bba1a42c487d8de2dc8937ac1a2c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3740,29 +3953,7 @@
"width": null
}
},
- "a5e3add4dd824dc5bd498fe3420c356a": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_5bc6e206f2f143a3b5aacff6afbcc1ff",
- "IPY_MODEL_1e9d41c4cb86463e86da6c98c0c94c28",
- "IPY_MODEL_cd18d677d64843ada3d69fe0bd3b1708"
- ],
- "layout": "IPY_MODEL_418c619f0a5f41119950b24220999c0f"
- }
- },
- "a65f253506f2456fb4376e99f7a6da1f": {
+ "e33dc17a75b3454f8cd46f1e5c076432": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -3777,58 +3968,29 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_e78ad99a929445f6b0bc1b6d7aa8651a",
+ "layout": "IPY_MODEL_704cfa8457c3477cab9c170ca76e1e63",
"placeholder": "",
- "style": "IPY_MODEL_be061f0efa0c424a851070b5a07a8b85",
- "value": ""
- }
- },
- "ae9134a8b5b34063b71ed5d492e7f9e4": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "b4ca02cc9611427d8dbce87b08c3964e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "style": "IPY_MODEL_2a3a4a5251dc4fd9be8e8b4772344953",
+ "value": ".gitattributes: 100%"
}
},
- "b83c4ec1ec0c47e5b909c6ddeba1d236": {
+ "e346e0c4ed5349b5b38784144d338526": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
+ "bar_color": null,
"description_width": ""
}
},
- "b8415796ce1d48978de27b180914a509": {
+ "e459f209cb80409f90693db698f30cd4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -3843,7 +4005,7 @@
"description_width": ""
}
},
- "bccfd6d5006e4e6f9d8792d885fd01a2": {
+ "e7312bcbf87c414bb9bb55706f6320d0": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3895,65 +4057,7 @@
"width": null
}
},
- "be061f0efa0c424a851070b5a07a8b85": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "be8a4db363594d0394d6859a433dc337": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_db32ad127425453f8d0c14d6b7e1682c",
- "IPY_MODEL_e2f29db855374b3397867736eefe9cc1",
- "IPY_MODEL_3b474e37a3cb4f75aa991708ad89d2c4"
- ],
- "layout": "IPY_MODEL_591afa6717e14cd69636050c63619a4c"
- }
- },
- "c7b6861f25c0481ca45185f6a64fc674": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_266fa05ddee548e8a63cde013f285ec5",
- "placeholder": "",
- "style": "IPY_MODEL_81b50031e3604819bd8b126ed55f3342",
- "value": " 232k/232k [00:00<00:00, 26.6MB/s]"
- }
- },
- "c9120aae1085452fb3a2d5b59c8c9471": {
+ "ef63569751cf4648ab115fb83a8ac438": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -3968,64 +4072,59 @@
"description_width": ""
}
},
- "cd18d677d64843ada3d69fe0bd3b1708": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_07cf8200b7e24fe28a0e477fd54e3495",
- "placeholder": "",
- "style": "IPY_MODEL_950246aac16e495cb2d5670c65144bb8",
- "value": " 54.2M/54.2M [00:00<00:00, 294MB/s]"
- }
- },
- "d129da2ec32d4c839724265f44a9a870": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_6a6795a859414bf29d6de30d8d4ea375",
- "placeholder": "",
- "style": "IPY_MODEL_934b9886fa46472f9b7a8fdfb1e0cd6b",
- "value": " 665/665 [00:00<00:00, 84.0kB/s]"
- }
- },
- "d29c8a78ed6a4a17a02bc5261eacd5fc": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "f09404a56571435cb6aac14140f9055c": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "1.2.0",
+ "model_name": "LayoutModel",
"state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
}
},
- "d7efb5dd8a0d45409c31a3353a95f9a9": {
+ "f5667d7916734e0ba7f74ad0a9373cb6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4077,7 +4176,7 @@
"width": null
}
},
- "d87c7e9ec81e49208018eedb6f2a340f": {
+ "f97a4a70f7cb49d4975c6405e175b54d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4129,28 +4228,7 @@
"width": null
}
},
- "db32ad127425453f8d0c14d6b7e1682c": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_a528a0291806489b99868ef7ef38c722",
- "placeholder": "",
- "style": "IPY_MODEL_b8415796ce1d48978de27b180914a509",
- "value": ".gitattributes: 100%"
- }
- },
- "e2f29db855374b3397867736eefe9cc1": {
+ "fa97ff0a0234428398016df6e6cb35dd": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
@@ -4166,31 +4244,15 @@
"bar_style": "success",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_29bb247c682b433f969bed5121c89f56",
- "max": 391.0,
+ "layout": "IPY_MODEL_8069a75862c04ab29242d5a0d7a58a00",
+ "max": 231508.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_f0b616a4a5714832a10a15aaf952ae7c",
- "value": 391.0
- }
- },
- "e5b429accdc143e5bf67101827800ca0": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "style": "IPY_MODEL_7a163259881547b5b1e3f8b179e8d594",
+ "value": 231508.0
}
},
- "e78ad99a929445f6b0bc1b6d7aa8651a": {
+ "fc32c3974d604564807d1fde4f8746c2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4242,23 +4304,22 @@
"width": null
}
},
- "efed3c4236d8470d90c4f475c9ed2ee5": {
+ "fc4be386257044c59a7fecf1dbb6ad7a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "DescriptionStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "DescriptionStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
- "bar_color": null,
"description_width": ""
}
},
- "f09e7a7e214741e28e63d29f32e19bdd": {
+ "fd1ed2a7574f4ed28fa2e80eae3203a4": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4307,71 +4368,10 @@
"right": null,
"top": null,
"visibility": null,
- "width": null
- }
- },
- "f0b616a4a5714832a10a15aaf952ae7c": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "faf1d8186eff4c78b3a4a91e97d62ecf": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_193d2ab0337645828a947bd6f1fb0323",
- "max": 2211.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_8d6093445f274f88a03304f9c8de0d95",
- "value": 2211.0
- }
- },
- "fe57ef1db6ca48c8816787b755a7a793": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_f09e7a7e214741e28e63d29f32e19bdd",
- "placeholder": "",
- "style": "IPY_MODEL_7bf3d4989e804b3e98da4f19065ed878",
- "value": "config.json: 100%"
+ "width": "20px"
}
},
- "ff589dd8da954febb096b439d92a32d6": {
+ "fe76896b0666432e81615f7d4ef0d334": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
index f45020da1..32a59e201 100644
--- a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb
@@ -68,10 +68,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:21.038889Z",
- "iopub.status.busy": "2024-01-19T12:51:21.038691Z",
- "iopub.status.idle": "2024-01-19T12:51:22.056327Z",
- "shell.execute_reply": "2024-01-19T12:51:22.055700Z"
+ "iopub.execute_input": "2024-01-19T13:08:41.665770Z",
+ "iopub.status.busy": "2024-01-19T13:08:41.665320Z",
+ "iopub.status.idle": "2024-01-19T13:08:42.688948Z",
+ "shell.execute_reply": "2024-01-19T13:08:42.688323Z"
},
"nbsphinx": "hidden"
},
@@ -83,7 +83,7 @@
"dependencies = [\"cleanlab\", \"requests\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -108,10 +108,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:22.059125Z",
- "iopub.status.busy": "2024-01-19T12:51:22.058706Z",
- "iopub.status.idle": "2024-01-19T12:51:22.061726Z",
- "shell.execute_reply": "2024-01-19T12:51:22.061218Z"
+ "iopub.execute_input": "2024-01-19T13:08:42.692066Z",
+ "iopub.status.busy": "2024-01-19T13:08:42.691574Z",
+ "iopub.status.idle": "2024-01-19T13:08:42.694641Z",
+ "shell.execute_reply": "2024-01-19T13:08:42.694003Z"
},
"id": "_UvI80l42iyi"
},
@@ -201,10 +201,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:22.064116Z",
- "iopub.status.busy": "2024-01-19T12:51:22.063750Z",
- "iopub.status.idle": "2024-01-19T12:51:22.076650Z",
- "shell.execute_reply": "2024-01-19T12:51:22.076110Z"
+ "iopub.execute_input": "2024-01-19T13:08:42.697185Z",
+ "iopub.status.busy": "2024-01-19T13:08:42.696855Z",
+ "iopub.status.idle": "2024-01-19T13:08:42.709683Z",
+ "shell.execute_reply": "2024-01-19T13:08:42.709179Z"
},
"nbsphinx": "hidden"
},
@@ -283,10 +283,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:22.078904Z",
- "iopub.status.busy": "2024-01-19T12:51:22.078604Z",
- "iopub.status.idle": "2024-01-19T12:51:28.581817Z",
- "shell.execute_reply": "2024-01-19T12:51:28.581270Z"
+ "iopub.execute_input": "2024-01-19T13:08:42.712187Z",
+ "iopub.status.busy": "2024-01-19T13:08:42.711821Z",
+ "iopub.status.idle": "2024-01-19T13:08:47.358720Z",
+ "shell.execute_reply": "2024-01-19T13:08:47.358119Z"
},
"id": "dhTHOg8Pyv5G"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
index 22bfe663a..377ab754e 100644
--- a/master/.doctrees/nbsphinx/tutorials/faq.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/faq.ipynb
@@ -18,10 +18,10 @@
"id": "2a4efdde",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:33.656755Z",
- "iopub.status.busy": "2024-01-19T12:51:33.656561Z",
- "iopub.status.idle": "2024-01-19T12:51:34.671485Z",
- "shell.execute_reply": "2024-01-19T12:51:34.670792Z"
+ "iopub.execute_input": "2024-01-19T13:08:52.318537Z",
+ "iopub.status.busy": "2024-01-19T13:08:52.318153Z",
+ "iopub.status.idle": "2024-01-19T13:08:53.347490Z",
+ "shell.execute_reply": "2024-01-19T13:08:53.346904Z"
},
"nbsphinx": "hidden"
},
@@ -97,10 +97,10 @@
"id": "239d5ee7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:34.674658Z",
- "iopub.status.busy": "2024-01-19T12:51:34.674272Z",
- "iopub.status.idle": "2024-01-19T12:51:34.678064Z",
- "shell.execute_reply": "2024-01-19T12:51:34.677452Z"
+ "iopub.execute_input": "2024-01-19T13:08:53.350559Z",
+ "iopub.status.busy": "2024-01-19T13:08:53.350027Z",
+ "iopub.status.idle": "2024-01-19T13:08:53.353618Z",
+ "shell.execute_reply": "2024-01-19T13:08:53.353050Z"
}
},
"outputs": [],
@@ -136,10 +136,10 @@
"id": "28b324aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:34.680354Z",
- "iopub.status.busy": "2024-01-19T12:51:34.679986Z",
- "iopub.status.idle": "2024-01-19T12:51:36.646346Z",
- "shell.execute_reply": "2024-01-19T12:51:36.645544Z"
+ "iopub.execute_input": "2024-01-19T13:08:53.355980Z",
+ "iopub.status.busy": "2024-01-19T13:08:53.355778Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.393750Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.393044Z"
}
},
"outputs": [],
@@ -162,10 +162,10 @@
"id": "28b324ab",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.649897Z",
- "iopub.status.busy": "2024-01-19T12:51:36.649194Z",
- "iopub.status.idle": "2024-01-19T12:51:36.685131Z",
- "shell.execute_reply": "2024-01-19T12:51:36.684462Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.397150Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.396573Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.435816Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.435008Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "90c10e18",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.688079Z",
- "iopub.status.busy": "2024-01-19T12:51:36.687755Z",
- "iopub.status.idle": "2024-01-19T12:51:36.721309Z",
- "shell.execute_reply": "2024-01-19T12:51:36.720637Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.438790Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.438279Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.474322Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.473638Z"
}
},
"outputs": [],
@@ -213,10 +213,10 @@
"id": "88839519",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.724748Z",
- "iopub.status.busy": "2024-01-19T12:51:36.724001Z",
- "iopub.status.idle": "2024-01-19T12:51:36.727409Z",
- "shell.execute_reply": "2024-01-19T12:51:36.726890Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.477312Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.476975Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.480353Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.479795Z"
}
},
"outputs": [],
@@ -238,10 +238,10 @@
"id": "558490c2",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.729706Z",
- "iopub.status.busy": "2024-01-19T12:51:36.729343Z",
- "iopub.status.idle": "2024-01-19T12:51:36.732158Z",
- "shell.execute_reply": "2024-01-19T12:51:36.731642Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.482828Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.482340Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.485321Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.484706Z"
}
},
"outputs": [],
@@ -298,10 +298,10 @@
"id": "41714b51",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.734746Z",
- "iopub.status.busy": "2024-01-19T12:51:36.734368Z",
- "iopub.status.idle": "2024-01-19T12:51:36.762744Z",
- "shell.execute_reply": "2024-01-19T12:51:36.762080Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.487932Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.487445Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.515422Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.514772Z"
}
},
"outputs": [
@@ -315,7 +315,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "0cc6553486ca463cab243cf82fe98373",
+ "model_id": "c0cc2e5a396147278dac6b2a7e9e1379",
"version_major": 2,
"version_minor": 0
},
@@ -329,7 +329,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "9b3c874c41894580afaefb95a7c67d9c",
+ "model_id": "88b234f0d0394aa9bb8114bf220dd7e9",
"version_major": 2,
"version_minor": 0
},
@@ -387,10 +387,10 @@
"id": "20476c70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.771251Z",
- "iopub.status.busy": "2024-01-19T12:51:36.770900Z",
- "iopub.status.idle": "2024-01-19T12:51:36.777801Z",
- "shell.execute_reply": "2024-01-19T12:51:36.777245Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.522539Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.522006Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.529356Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.528725Z"
},
"nbsphinx": "hidden"
},
@@ -421,10 +421,10 @@
"id": "6983cdad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.780282Z",
- "iopub.status.busy": "2024-01-19T12:51:36.779909Z",
- "iopub.status.idle": "2024-01-19T12:51:36.783685Z",
- "shell.execute_reply": "2024-01-19T12:51:36.783039Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.531773Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.531399Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.535370Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.534718Z"
},
"nbsphinx": "hidden"
},
@@ -447,10 +447,10 @@
"id": "9092b8a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.786072Z",
- "iopub.status.busy": "2024-01-19T12:51:36.785707Z",
- "iopub.status.idle": "2024-01-19T12:51:36.792564Z",
- "shell.execute_reply": "2024-01-19T12:51:36.792017Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.537749Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.537295Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.544237Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.543652Z"
}
},
"outputs": [],
@@ -500,10 +500,10 @@
"id": "b0a01109",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.794872Z",
- "iopub.status.busy": "2024-01-19T12:51:36.794508Z",
- "iopub.status.idle": "2024-01-19T12:51:36.831474Z",
- "shell.execute_reply": "2024-01-19T12:51:36.830806Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.546877Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.546266Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.588199Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.587497Z"
}
},
"outputs": [],
@@ -520,10 +520,10 @@
"id": "8b1da032",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.834512Z",
- "iopub.status.busy": "2024-01-19T12:51:36.833999Z",
- "iopub.status.idle": "2024-01-19T12:51:36.871010Z",
- "shell.execute_reply": "2024-01-19T12:51:36.870219Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.591254Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.590907Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.633222Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.632525Z"
},
"nbsphinx": "hidden"
},
@@ -602,10 +602,10 @@
"id": "4c9e9030",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.874505Z",
- "iopub.status.busy": "2024-01-19T12:51:36.873963Z",
- "iopub.status.idle": "2024-01-19T12:51:36.992210Z",
- "shell.execute_reply": "2024-01-19T12:51:36.991574Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.636522Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.636121Z",
+ "iopub.status.idle": "2024-01-19T13:08:55.758163Z",
+ "shell.execute_reply": "2024-01-19T13:08:55.757472Z"
}
},
"outputs": [
@@ -672,10 +672,10 @@
"id": "8751619e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:36.994776Z",
- "iopub.status.busy": "2024-01-19T12:51:36.994559Z",
- "iopub.status.idle": "2024-01-19T12:51:39.469813Z",
- "shell.execute_reply": "2024-01-19T12:51:39.469073Z"
+ "iopub.execute_input": "2024-01-19T13:08:55.761039Z",
+ "iopub.status.busy": "2024-01-19T13:08:55.760630Z",
+ "iopub.status.idle": "2024-01-19T13:08:58.254939Z",
+ "shell.execute_reply": "2024-01-19T13:08:58.254244Z"
}
},
"outputs": [
@@ -761,10 +761,10 @@
"id": "623df36d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:39.472356Z",
- "iopub.status.busy": "2024-01-19T12:51:39.472118Z",
- "iopub.status.idle": "2024-01-19T12:51:39.531985Z",
- "shell.execute_reply": "2024-01-19T12:51:39.531301Z"
+ "iopub.execute_input": "2024-01-19T13:08:58.257586Z",
+ "iopub.status.busy": "2024-01-19T13:08:58.257369Z",
+ "iopub.status.idle": "2024-01-19T13:08:58.318446Z",
+ "shell.execute_reply": "2024-01-19T13:08:58.317764Z"
}
},
"outputs": [
@@ -802,7 +802,7 @@
},
{
"cell_type": "markdown",
- "id": "f6b7e362",
+ "id": "4bda542c",
"metadata": {},
"source": [
"### How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?"
@@ -810,7 +810,7 @@
},
{
"cell_type": "markdown",
- "id": "404e7531",
+ "id": "fcf8a1e4",
"metadata": {},
"source": [
"When detecting underperforming groups in a dataset, Datalab provides the option for passing pre-computed\n",
@@ -823,13 +823,13 @@
{
"cell_type": "code",
"execution_count": 17,
- "id": "2111e6af",
+ "id": "4580a09d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:39.534579Z",
- "iopub.status.busy": "2024-01-19T12:51:39.534234Z",
- "iopub.status.idle": "2024-01-19T12:51:39.640879Z",
- "shell.execute_reply": "2024-01-19T12:51:39.640205Z"
+ "iopub.execute_input": "2024-01-19T13:08:58.320950Z",
+ "iopub.status.busy": "2024-01-19T13:08:58.320599Z",
+ "iopub.status.idle": "2024-01-19T13:08:58.421856Z",
+ "shell.execute_reply": "2024-01-19T13:08:58.421183Z"
}
},
"outputs": [
@@ -870,7 +870,7 @@
},
{
"cell_type": "markdown",
- "id": "fde22aa7",
+ "id": "bf50f26c",
"metadata": {},
"source": [
"For a tabular dataset, you can alternatively use a categorical column's values as cluster IDs:"
@@ -879,13 +879,13 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "a7ae0f22",
+ "id": "f5e046ee",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:39.644396Z",
- "iopub.status.busy": "2024-01-19T12:51:39.643761Z",
- "iopub.status.idle": "2024-01-19T12:51:39.722376Z",
- "shell.execute_reply": "2024-01-19T12:51:39.721730Z"
+ "iopub.execute_input": "2024-01-19T13:08:58.425767Z",
+ "iopub.status.busy": "2024-01-19T13:08:58.425502Z",
+ "iopub.status.idle": "2024-01-19T13:08:58.507633Z",
+ "shell.execute_reply": "2024-01-19T13:08:58.507017Z"
}
},
"outputs": [
@@ -921,7 +921,7 @@
},
{
"cell_type": "markdown",
- "id": "b848701d",
+ "id": "5085bf55",
"metadata": {},
"source": [
"### How to handle near-duplicate data identified by cleanlab?\n",
@@ -932,13 +932,13 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "1fc2d087",
+ "id": "e6a28c6c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:39.724769Z",
- "iopub.status.busy": "2024-01-19T12:51:39.724425Z",
- "iopub.status.idle": "2024-01-19T12:51:39.732802Z",
- "shell.execute_reply": "2024-01-19T12:51:39.732160Z"
+ "iopub.execute_input": "2024-01-19T13:08:58.510210Z",
+ "iopub.status.busy": "2024-01-19T13:08:58.510000Z",
+ "iopub.status.idle": "2024-01-19T13:08:58.518372Z",
+ "shell.execute_reply": "2024-01-19T13:08:58.517756Z"
}
},
"outputs": [],
@@ -1040,7 +1040,7 @@
},
{
"cell_type": "markdown",
- "id": "1084e931",
+ "id": "6e841a98",
"metadata": {},
"source": [
"The functions above collect sets of near-duplicate examples. Within each\n",
@@ -1055,13 +1055,13 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "cf023c89",
+ "id": "3a9c9ad2",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:39.735198Z",
- "iopub.status.busy": "2024-01-19T12:51:39.734760Z",
- "iopub.status.idle": "2024-01-19T12:51:39.754320Z",
- "shell.execute_reply": "2024-01-19T12:51:39.753787Z"
+ "iopub.execute_input": "2024-01-19T13:08:58.520776Z",
+ "iopub.status.busy": "2024-01-19T13:08:58.520565Z",
+ "iopub.status.idle": "2024-01-19T13:08:58.538850Z",
+ "shell.execute_reply": "2024-01-19T13:08:58.538299Z"
}
},
"outputs": [
@@ -1104,13 +1104,13 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "3dc8d8dc",
+ "id": "661a4e0e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:39.756635Z",
- "iopub.status.busy": "2024-01-19T12:51:39.756261Z",
- "iopub.status.idle": "2024-01-19T12:51:39.760643Z",
- "shell.execute_reply": "2024-01-19T12:51:39.760108Z"
+ "iopub.execute_input": "2024-01-19T13:08:58.541135Z",
+ "iopub.status.busy": "2024-01-19T13:08:58.540787Z",
+ "iopub.status.idle": "2024-01-19T13:08:58.545036Z",
+ "shell.execute_reply": "2024-01-19T13:08:58.544410Z"
}
},
"outputs": [
@@ -1205,29 +1205,7 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0cc6553486ca463cab243cf82fe98373": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_3f808e0bac414818a955acd5a02d5021",
- "IPY_MODEL_6fa9a8bcddc34a89a2ae37d278b21e06",
- "IPY_MODEL_35fd47f4d1ad464f81638dd41a795590"
- ],
- "layout": "IPY_MODEL_9d3fbd9a2bfc48f494db3e3b0435717d"
- }
- },
- "240a3a84b646435ca510588549e37f79": {
+ "11398fbc74ed4a3d9368c83b4782efe4": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -1242,7 +1220,7 @@
"description_width": ""
}
},
- "35fd47f4d1ad464f81638dd41a795590": {
+ "26dac23ca8fa4e40b3fe1c27d517624e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -1257,73 +1235,29 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_9d34a5337e434407a1e45cacf35b7d5d",
+ "layout": "IPY_MODEL_9b709e41beb84f0e8ef3926484b0d937",
"placeholder": "",
- "style": "IPY_MODEL_240a3a84b646435ca510588549e37f79",
- "value": " 10000/? [00:00<00:00, 959224.26it/s]"
+ "style": "IPY_MODEL_4a54b2f55c5b4bd98f19c51ac00cc5b3",
+ "value": " 10000/? [00:00<00:00, 1169763.50it/s]"
}
},
- "3f808e0bac414818a955acd5a02d5021": {
+ "29cc8365c9f843ffa5dc6286ea3434bc": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_fb1ed6ea66534d82ad588a8f3f173999",
- "placeholder": "",
- "style": "IPY_MODEL_a9b2e7b868f04e06b29a903279acd603",
- "value": "number of examples processed for estimating thresholds: "
- }
- },
- "4c89d7da3c2845b79c934708ae88a9db": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_cc537b1966cb471db1549bae01c23dbf",
- "max": 50.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_e3e57b99806043f5846546a1b9e97e97",
- "value": 50.0
- }
- },
- "503d904a8aa148ae827739fe08fc3edc": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "model_name": "ProgressStyleModel",
"state": {
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
"_view_name": "StyleView",
+ "bar_color": null,
"description_width": ""
}
},
- "55c44583c93343eb9e025cd06541b4e6": {
+ "3247a39304794ad6912526d5f0aee9a5": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1375,7 +1309,61 @@
"width": null
}
},
- "566218841f6b4810945446cfe5723afa": {
+ "4a54b2f55c5b4bd98f19c51ac00cc5b3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "627241e902f644e0bb78367cc07a59ee": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "65410082f0ea46de93edc8e61326019f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_f8a4a2ee08da41de98e42cd973b88972",
+ "max": 50.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_29cc8365c9f843ffa5dc6286ea3434bc",
+ "value": 50.0
+ }
+ },
+ "666fce23be1947128bf3037fb6389b31": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1427,62 +1415,29 @@
"width": null
}
},
- "5b4b80ddbf114ec4bcc999494b2b288c": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "6fa9a8bcddc34a89a2ae37d278b21e06": {
+ "88b234f0d0394aa9bb8114bf220dd7e9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_e89f34674a85404387b6a6d21d33d6e1",
- "max": 50.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_5b4b80ddbf114ec4bcc999494b2b288c",
- "value": 50.0
- }
- },
- "88a90471972a486d92178660b1cb4075": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_d37d5bad43694ca18319b1dff1e53590",
+ "IPY_MODEL_c27567659ef74bfa82f6ec40bf0afe1b",
+ "IPY_MODEL_26dac23ca8fa4e40b3fe1c27d517624e"
+ ],
+ "layout": "IPY_MODEL_cf2fa67e161a4950a646501712e6773e"
}
},
- "93dc0c74fbf5465b94c66e4f996175fe": {
+ "9b709e41beb84f0e8ef3926484b0d937": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1534,7 +1489,7 @@
"width": null
}
},
- "9b3c874c41894580afaefb95a7c67d9c": {
+ "c0cc2e5a396147278dac6b2a7e9e1379": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
@@ -1549,14 +1504,38 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_9c80ede3ed834279bcfb70af835bb36c",
- "IPY_MODEL_4c89d7da3c2845b79c934708ae88a9db",
- "IPY_MODEL_fbf04abd16a74395bcd8ef83a8d8228e"
+ "IPY_MODEL_e3586ba15f454acfb2b06d13ff9dbb1b",
+ "IPY_MODEL_65410082f0ea46de93edc8e61326019f",
+ "IPY_MODEL_c4d9ba0c8ab84182ad112e43549a738e"
],
- "layout": "IPY_MODEL_55c44583c93343eb9e025cd06541b4e6"
+ "layout": "IPY_MODEL_f99b592a30b04505972e18ba80c11de2"
}
},
- "9c80ede3ed834279bcfb70af835bb36c": {
+ "c27567659ef74bfa82f6ec40bf0afe1b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_3247a39304794ad6912526d5f0aee9a5",
+ "max": 50.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_cc95ef5ffb594bcd9af00e0e235689f7",
+ "value": 50.0
+ }
+ },
+ "c4d9ba0c8ab84182ad112e43549a738e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -1571,13 +1550,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_566218841f6b4810945446cfe5723afa",
+ "layout": "IPY_MODEL_fa0442a925864b2dba2c5e859b8bd72e",
"placeholder": "",
- "style": "IPY_MODEL_88a90471972a486d92178660b1cb4075",
- "value": "number of examples processed for checking labels: "
+ "style": "IPY_MODEL_11398fbc74ed4a3d9368c83b4782efe4",
+ "value": " 10000/? [00:00<00:00, 951024.65it/s]"
}
},
- "9d34a5337e434407a1e45cacf35b7d5d": {
+ "cb6593032f414a19a38d8ea6020f014c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1629,7 +1608,23 @@
"width": null
}
},
- "9d3fbd9a2bfc48f494db3e3b0435717d": {
+ "cc95ef5ffb594bcd9af00e0e235689f7": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "cf2fa67e161a4950a646501712e6773e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1681,7 +1676,7 @@
"width": null
}
},
- "a9b2e7b868f04e06b29a903279acd603": {
+ "d120785d402c4bb8a29336446fc8fedc": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -1696,7 +1691,49 @@
"description_width": ""
}
},
- "cc537b1966cb471db1549bae01c23dbf": {
+ "d37d5bad43694ca18319b1dff1e53590": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_cb6593032f414a19a38d8ea6020f014c",
+ "placeholder": "",
+ "style": "IPY_MODEL_627241e902f644e0bb78367cc07a59ee",
+ "value": "number of examples processed for checking labels: "
+ }
+ },
+ "e3586ba15f454acfb2b06d13ff9dbb1b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_666fce23be1947128bf3037fb6389b31",
+ "placeholder": "",
+ "style": "IPY_MODEL_d120785d402c4bb8a29336446fc8fedc",
+ "value": "number of examples processed for estimating thresholds: "
+ }
+ },
+ "f8a4a2ee08da41de98e42cd973b88972": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1748,23 +1785,7 @@
"width": null
}
},
- "e3e57b99806043f5846546a1b9e97e97": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "e89f34674a85404387b6a6d21d33d6e1": {
+ "f99b592a30b04505972e18ba80c11de2": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1816,7 +1837,7 @@
"width": null
}
},
- "fb1ed6ea66534d82ad588a8f3f173999": {
+ "fa0442a925864b2dba2c5e859b8bd72e": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1867,27 +1888,6 @@
"visibility": null,
"width": null
}
- },
- "fbf04abd16a74395bcd8ef83a8d8228e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_93dc0c74fbf5465b94c66e4f996175fe",
- "placeholder": "",
- "style": "IPY_MODEL_503d904a8aa148ae827739fe08fc3edc",
- "value": " 10000/? [00:00<00:00, 1147426.82it/s]"
- }
}
},
"version_major": 2,
diff --git a/master/.doctrees/nbsphinx/tutorials/image.ipynb b/master/.doctrees/nbsphinx/tutorials/image.ipynb
index 90e53184d..8d692a9fb 100644
--- a/master/.doctrees/nbsphinx/tutorials/image.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/image.ipynb
@@ -71,10 +71,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:44.986826Z",
- "iopub.status.busy": "2024-01-19T12:51:44.986377Z",
- "iopub.status.idle": "2024-01-19T12:51:47.094224Z",
- "shell.execute_reply": "2024-01-19T12:51:47.093602Z"
+ "iopub.execute_input": "2024-01-19T13:09:03.670375Z",
+ "iopub.status.busy": "2024-01-19T13:09:03.669881Z",
+ "iopub.status.idle": "2024-01-19T13:09:05.902048Z",
+ "shell.execute_reply": "2024-01-19T13:09:05.901419Z"
},
"nbsphinx": "hidden"
},
@@ -112,10 +112,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:47.096994Z",
- "iopub.status.busy": "2024-01-19T12:51:47.096518Z",
- "iopub.status.idle": "2024-01-19T12:51:47.100399Z",
- "shell.execute_reply": "2024-01-19T12:51:47.099846Z"
+ "iopub.execute_input": "2024-01-19T13:09:05.904792Z",
+ "iopub.status.busy": "2024-01-19T13:09:05.904464Z",
+ "iopub.status.idle": "2024-01-19T13:09:05.908327Z",
+ "shell.execute_reply": "2024-01-19T13:09:05.907796Z"
}
},
"outputs": [],
@@ -152,17 +152,17 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:47.102833Z",
- "iopub.status.busy": "2024-01-19T12:51:47.102474Z",
- "iopub.status.idle": "2024-01-19T12:51:51.547028Z",
- "shell.execute_reply": "2024-01-19T12:51:51.546351Z"
+ "iopub.execute_input": "2024-01-19T13:09:05.910646Z",
+ "iopub.status.busy": "2024-01-19T13:09:05.910250Z",
+ "iopub.status.idle": "2024-01-19T13:09:07.396028Z",
+ "shell.execute_reply": "2024-01-19T13:09:07.395431Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a559be98a22e47de9dabbc3aa7e44f41",
+ "model_id": "18236cfb50484a5996293f537c5b5a7f",
"version_major": 2,
"version_minor": 0
},
@@ -176,7 +176,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "737f0fe7c43f4d05adfd2b7611f1592b",
+ "model_id": "54cfbb8e123c4280a44b9504ee28b400",
"version_major": 2,
"version_minor": 0
},
@@ -190,7 +190,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "60fa8eab68b64dfe985668e0543289eb",
+ "model_id": "1a362abf29a247599ed238a4bdde333f",
"version_major": 2,
"version_minor": 0
},
@@ -204,7 +204,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "9ddf9612ae3e4345bb7d9ac84c3a595a",
+ "model_id": "a0a73bbc41b24507bedf88c7673932ac",
"version_major": 2,
"version_minor": 0
},
@@ -246,10 +246,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:51.549691Z",
- "iopub.status.busy": "2024-01-19T12:51:51.549220Z",
- "iopub.status.idle": "2024-01-19T12:51:51.553234Z",
- "shell.execute_reply": "2024-01-19T12:51:51.552742Z"
+ "iopub.execute_input": "2024-01-19T13:09:07.398615Z",
+ "iopub.status.busy": "2024-01-19T13:09:07.398210Z",
+ "iopub.status.idle": "2024-01-19T13:09:07.402346Z",
+ "shell.execute_reply": "2024-01-19T13:09:07.401766Z"
}
},
"outputs": [
@@ -274,17 +274,17 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:51:51.555529Z",
- "iopub.status.busy": "2024-01-19T12:51:51.555185Z",
- "iopub.status.idle": "2024-01-19T12:52:03.642560Z",
- "shell.execute_reply": "2024-01-19T12:52:03.641837Z"
+ "iopub.execute_input": "2024-01-19T13:09:07.404930Z",
+ "iopub.status.busy": "2024-01-19T13:09:07.404515Z",
+ "iopub.status.idle": "2024-01-19T13:09:19.802120Z",
+ "shell.execute_reply": "2024-01-19T13:09:19.801505Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a2cafedc9a35438c932c6cf48ffeb5eb",
+ "model_id": "15abb7b381a94181ba661286d20518c2",
"version_major": 2,
"version_minor": 0
},
@@ -322,10 +322,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:52:03.645429Z",
- "iopub.status.busy": "2024-01-19T12:52:03.645178Z",
- "iopub.status.idle": "2024-01-19T12:52:25.488092Z",
- "shell.execute_reply": "2024-01-19T12:52:25.487411Z"
+ "iopub.execute_input": "2024-01-19T13:09:19.805056Z",
+ "iopub.status.busy": "2024-01-19T13:09:19.804732Z",
+ "iopub.status.idle": "2024-01-19T13:09:40.700017Z",
+ "shell.execute_reply": "2024-01-19T13:09:40.699393Z"
}
},
"outputs": [],
@@ -358,10 +358,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:52:25.491266Z",
- "iopub.status.busy": "2024-01-19T12:52:25.490863Z",
- "iopub.status.idle": "2024-01-19T12:52:25.496162Z",
- "shell.execute_reply": "2024-01-19T12:52:25.495644Z"
+ "iopub.execute_input": "2024-01-19T13:09:40.703039Z",
+ "iopub.status.busy": "2024-01-19T13:09:40.702827Z",
+ "iopub.status.idle": "2024-01-19T13:09:40.708033Z",
+ "shell.execute_reply": "2024-01-19T13:09:40.707498Z"
}
},
"outputs": [],
@@ -399,10 +399,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:52:25.498577Z",
- "iopub.status.busy": "2024-01-19T12:52:25.498104Z",
- "iopub.status.idle": "2024-01-19T12:52:25.502293Z",
- "shell.execute_reply": "2024-01-19T12:52:25.501704Z"
+ "iopub.execute_input": "2024-01-19T13:09:40.710363Z",
+ "iopub.status.busy": "2024-01-19T13:09:40.710019Z",
+ "iopub.status.idle": "2024-01-19T13:09:40.714235Z",
+ "shell.execute_reply": "2024-01-19T13:09:40.713759Z"
},
"nbsphinx": "hidden"
},
@@ -539,10 +539,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:52:25.504853Z",
- "iopub.status.busy": "2024-01-19T12:52:25.504482Z",
- "iopub.status.idle": "2024-01-19T12:52:25.514250Z",
- "shell.execute_reply": "2024-01-19T12:52:25.513726Z"
+ "iopub.execute_input": "2024-01-19T13:09:40.716772Z",
+ "iopub.status.busy": "2024-01-19T13:09:40.716314Z",
+ "iopub.status.idle": "2024-01-19T13:09:40.726320Z",
+ "shell.execute_reply": "2024-01-19T13:09:40.725790Z"
},
"nbsphinx": "hidden"
},
@@ -667,10 +667,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:52:25.516581Z",
- "iopub.status.busy": "2024-01-19T12:52:25.516206Z",
- "iopub.status.idle": "2024-01-19T12:52:25.543648Z",
- "shell.execute_reply": "2024-01-19T12:52:25.543167Z"
+ "iopub.execute_input": "2024-01-19T13:09:40.728501Z",
+ "iopub.status.busy": "2024-01-19T13:09:40.728299Z",
+ "iopub.status.idle": "2024-01-19T13:09:40.758295Z",
+ "shell.execute_reply": "2024-01-19T13:09:40.757755Z"
}
},
"outputs": [],
@@ -707,10 +707,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:52:25.546143Z",
- "iopub.status.busy": "2024-01-19T12:52:25.545782Z",
- "iopub.status.idle": "2024-01-19T12:52:56.238587Z",
- "shell.execute_reply": "2024-01-19T12:52:56.237865Z"
+ "iopub.execute_input": "2024-01-19T13:09:40.760622Z",
+ "iopub.status.busy": "2024-01-19T13:09:40.760416Z",
+ "iopub.status.idle": "2024-01-19T13:10:11.702233Z",
+ "shell.execute_reply": "2024-01-19T13:10:11.701476Z"
}
},
"outputs": [
@@ -726,14 +726,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.643\n"
+ "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.725\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.363\n",
+ "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.378\n",
"Computing feature embeddings ...\n"
]
},
@@ -750,7 +750,7 @@
"output_type": "stream",
"text": [
"\r",
- " 2%|▎ | 1/40 [00:00<00:04, 9.30it/s]"
+ " 5%|▌ | 2/40 [00:00<00:01, 19.53it/s]"
]
},
{
@@ -758,7 +758,7 @@
"output_type": "stream",
"text": [
"\r",
- " 20%|██ | 8/40 [00:00<00:00, 42.55it/s]"
+ " 22%|██▎ | 9/40 [00:00<00:00, 45.61it/s]"
]
},
{
@@ -766,7 +766,7 @@
"output_type": "stream",
"text": [
"\r",
- " 40%|████ | 16/40 [00:00<00:00, 57.40it/s]"
+ " 42%|████▎ | 17/40 [00:00<00:00, 58.10it/s]"
]
},
{
@@ -774,7 +774,7 @@
"output_type": "stream",
"text": [
"\r",
- " 60%|██████ | 24/40 [00:00<00:00, 64.39it/s]"
+ " 62%|██████▎ | 25/40 [00:00<00:00, 63.58it/s]"
]
},
{
@@ -782,7 +782,7 @@
"output_type": "stream",
"text": [
"\r",
- " 80%|████████ | 32/40 [00:00<00:00, 67.90it/s]"
+ " 82%|████████▎ | 33/40 [00:00<00:00, 67.04it/s]"
]
},
{
@@ -790,7 +790,7 @@
"output_type": "stream",
"text": [
"\r",
- "100%|██████████| 40/40 [00:00<00:00, 63.24it/s]"
+ "100%|██████████| 40/40 [00:00<00:00, 62.85it/s]"
]
},
{
@@ -820,7 +820,7 @@
"output_type": "stream",
"text": [
"\r",
- " 5%|▌ | 2/40 [00:00<00:02, 18.69it/s]"
+ " 2%|▎ | 1/40 [00:00<00:04, 9.65it/s]"
]
},
{
@@ -828,7 +828,7 @@
"output_type": "stream",
"text": [
"\r",
- " 25%|██▌ | 10/40 [00:00<00:00, 50.91it/s]"
+ " 22%|██▎ | 9/40 [00:00<00:00, 48.20it/s]"
]
},
{
@@ -836,7 +836,7 @@
"output_type": "stream",
"text": [
"\r",
- " 45%|████▌ | 18/40 [00:00<00:00, 62.34it/s]"
+ " 42%|████▎ | 17/40 [00:00<00:00, 60.58it/s]"
]
},
{
@@ -844,7 +844,7 @@
"output_type": "stream",
"text": [
"\r",
- " 65%|██████▌ | 26/40 [00:00<00:00, 67.86it/s]"
+ " 62%|██████▎ | 25/40 [00:00<00:00, 65.50it/s]"
]
},
{
@@ -852,7 +852,7 @@
"output_type": "stream",
"text": [
"\r",
- " 88%|████████▊ | 35/40 [00:00<00:00, 72.86it/s]"
+ " 82%|████████▎ | 33/40 [00:00<00:00, 69.42it/s]"
]
},
{
@@ -860,7 +860,7 @@
"output_type": "stream",
"text": [
"\r",
- "100%|██████████| 40/40 [00:00<00:00, 66.32it/s]"
+ "100%|██████████| 40/40 [00:00<00:00, 64.21it/s]"
]
},
{
@@ -882,14 +882,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.550\n"
+ "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.585\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.573\n",
+ "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.446\n",
"Computing feature embeddings ...\n"
]
},
@@ -906,7 +906,7 @@
"output_type": "stream",
"text": [
"\r",
- " 2%|▎ | 1/40 [00:00<00:04, 9.49it/s]"
+ " 2%|▎ | 1/40 [00:00<00:04, 9.53it/s]"
]
},
{
@@ -914,7 +914,7 @@
"output_type": "stream",
"text": [
"\r",
- " 22%|██▎ | 9/40 [00:00<00:00, 47.97it/s]"
+ " 22%|██▎ | 9/40 [00:00<00:00, 47.06it/s]"
]
},
{
@@ -922,7 +922,7 @@
"output_type": "stream",
"text": [
"\r",
- " 42%|████▎ | 17/40 [00:00<00:00, 60.64it/s]"
+ " 42%|████▎ | 17/40 [00:00<00:00, 59.50it/s]"
]
},
{
@@ -930,7 +930,7 @@
"output_type": "stream",
"text": [
"\r",
- " 62%|██████▎ | 25/40 [00:00<00:00, 65.05it/s]"
+ " 62%|██████▎ | 25/40 [00:00<00:00, 64.84it/s]"
]
},
{
@@ -938,7 +938,7 @@
"output_type": "stream",
"text": [
"\r",
- " 82%|████████▎ | 33/40 [00:00<00:00, 70.01it/s]"
+ " 82%|████████▎ | 33/40 [00:00<00:00, 68.46it/s]"
]
},
{
@@ -946,7 +946,7 @@
"output_type": "stream",
"text": [
"\r",
- "100%|██████████| 40/40 [00:00<00:00, 64.60it/s]"
+ "100%|██████████| 40/40 [00:00<00:00, 63.55it/s]"
]
},
{
@@ -976,7 +976,7 @@
"output_type": "stream",
"text": [
"\r",
- " 5%|▌ | 2/40 [00:00<00:01, 19.38it/s]"
+ " 2%|▎ | 1/40 [00:00<00:04, 9.58it/s]"
]
},
{
@@ -984,7 +984,7 @@
"output_type": "stream",
"text": [
"\r",
- " 25%|██▌ | 10/40 [00:00<00:00, 51.98it/s]"
+ " 22%|██▎ | 9/40 [00:00<00:00, 47.76it/s]"
]
},
{
@@ -992,7 +992,7 @@
"output_type": "stream",
"text": [
"\r",
- " 45%|████▌ | 18/40 [00:00<00:00, 62.64it/s]"
+ " 42%|████▎ | 17/40 [00:00<00:00, 60.27it/s]"
]
},
{
@@ -1000,7 +1000,7 @@
"output_type": "stream",
"text": [
"\r",
- " 65%|██████▌ | 26/40 [00:00<00:00, 68.15it/s]"
+ " 62%|██████▎ | 25/40 [00:00<00:00, 65.04it/s]"
]
},
{
@@ -1008,7 +1008,7 @@
"output_type": "stream",
"text": [
"\r",
- " 88%|████████▊ | 35/40 [00:00<00:00, 73.37it/s]"
+ " 82%|████████▎ | 33/40 [00:00<00:00, 69.72it/s]"
]
},
{
@@ -1016,7 +1016,7 @@
"output_type": "stream",
"text": [
"\r",
- "100%|██████████| 40/40 [00:00<00:00, 67.01it/s]"
+ "100%|██████████| 40/40 [00:00<00:00, 64.32it/s]"
]
},
{
@@ -1038,14 +1038,14 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.625\n"
+ "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.577\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.235\n",
+ "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.419\n",
"Computing feature embeddings ...\n"
]
},
@@ -1062,7 +1062,7 @@
"output_type": "stream",
"text": [
"\r",
- " 5%|▌ | 2/40 [00:00<00:02, 17.42it/s]"
+ " 2%|▎ | 1/40 [00:00<00:04, 9.11it/s]"
]
},
{
@@ -1070,7 +1070,7 @@
"output_type": "stream",
"text": [
"\r",
- " 25%|██▌ | 10/40 [00:00<00:00, 50.62it/s]"
+ " 22%|██▎ | 9/40 [00:00<00:00, 46.41it/s]"
]
},
{
@@ -1078,7 +1078,7 @@
"output_type": "stream",
"text": [
"\r",
- " 45%|████▌ | 18/40 [00:00<00:00, 61.79it/s]"
+ " 42%|████▎ | 17/40 [00:00<00:00, 58.35it/s]"
]
},
{
@@ -1086,7 +1086,7 @@
"output_type": "stream",
"text": [
"\r",
- " 65%|██████▌ | 26/40 [00:00<00:00, 67.65it/s]"
+ " 62%|██████▎ | 25/40 [00:00<00:00, 63.62it/s]"
]
},
{
@@ -1094,7 +1094,7 @@
"output_type": "stream",
"text": [
"\r",
- " 85%|████████▌ | 34/40 [00:00<00:00, 70.57it/s]"
+ " 82%|████████▎ | 33/40 [00:00<00:00, 67.79it/s]"
]
},
{
@@ -1102,7 +1102,7 @@
"output_type": "stream",
"text": [
"\r",
- "100%|██████████| 40/40 [00:00<00:00, 65.00it/s]"
+ "100%|██████████| 40/40 [00:00<00:00, 62.64it/s]"
]
},
{
@@ -1132,7 +1132,7 @@
"output_type": "stream",
"text": [
"\r",
- " 2%|▎ | 1/40 [00:00<00:04, 9.54it/s]"
+ " 8%|▊ | 3/40 [00:00<00:01, 26.01it/s]"
]
},
{
@@ -1140,7 +1140,7 @@
"output_type": "stream",
"text": [
"\r",
- " 22%|██▎ | 9/40 [00:00<00:00, 49.21it/s]"
+ " 25%|██▌ | 10/40 [00:00<00:00, 48.70it/s]"
]
},
{
@@ -1148,7 +1148,7 @@
"output_type": "stream",
"text": [
"\r",
- " 42%|████▎ | 17/40 [00:00<00:00, 62.16it/s]"
+ " 45%|████▌ | 18/40 [00:00<00:00, 60.26it/s]"
]
},
{
@@ -1156,7 +1156,7 @@
"output_type": "stream",
"text": [
"\r",
- " 62%|██████▎ | 25/40 [00:00<00:00, 68.34it/s]"
+ " 62%|██████▎ | 25/40 [00:00<00:00, 63.50it/s]"
]
},
{
@@ -1164,7 +1164,7 @@
"output_type": "stream",
"text": [
"\r",
- " 85%|████████▌ | 34/40 [00:00<00:00, 73.36it/s]"
+ " 82%|████████▎ | 33/40 [00:00<00:00, 68.77it/s]"
]
},
{
@@ -1172,7 +1172,7 @@
"output_type": "stream",
"text": [
"\r",
- "100%|██████████| 40/40 [00:00<00:00, 67.02it/s]"
+ "100%|██████████| 40/40 [00:00<00:00, 64.40it/s]"
]
},
{
@@ -1249,10 +1249,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:52:56.241382Z",
- "iopub.status.busy": "2024-01-19T12:52:56.241121Z",
- "iopub.status.idle": "2024-01-19T12:52:56.256297Z",
- "shell.execute_reply": "2024-01-19T12:52:56.255776Z"
+ "iopub.execute_input": "2024-01-19T13:10:11.705171Z",
+ "iopub.status.busy": "2024-01-19T13:10:11.704899Z",
+ "iopub.status.idle": "2024-01-19T13:10:11.720628Z",
+ "shell.execute_reply": "2024-01-19T13:10:11.719987Z"
}
},
"outputs": [],
@@ -1277,10 +1277,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:52:56.258699Z",
- "iopub.status.busy": "2024-01-19T12:52:56.258352Z",
- "iopub.status.idle": "2024-01-19T12:52:56.697951Z",
- "shell.execute_reply": "2024-01-19T12:52:56.697348Z"
+ "iopub.execute_input": "2024-01-19T13:10:11.723515Z",
+ "iopub.status.busy": "2024-01-19T13:10:11.722974Z",
+ "iopub.status.idle": "2024-01-19T13:10:12.175264Z",
+ "shell.execute_reply": "2024-01-19T13:10:12.174533Z"
}
},
"outputs": [],
@@ -1300,10 +1300,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:52:56.700989Z",
- "iopub.status.busy": "2024-01-19T12:52:56.700435Z",
- "iopub.status.idle": "2024-01-19T12:56:16.767282Z",
- "shell.execute_reply": "2024-01-19T12:56:16.766650Z"
+ "iopub.execute_input": "2024-01-19T13:10:12.178202Z",
+ "iopub.status.busy": "2024-01-19T13:10:12.177936Z",
+ "iopub.status.idle": "2024-01-19T13:13:32.295952Z",
+ "shell.execute_reply": "2024-01-19T13:13:32.295101Z"
}
},
"outputs": [
@@ -1342,7 +1342,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "276be56803c6481cb7f67fd91e4f6583",
+ "model_id": "5434c58283dd404eace23a364feb33e5",
"version_major": 2,
"version_minor": 0
},
@@ -1381,10 +1381,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:16.770240Z",
- "iopub.status.busy": "2024-01-19T12:56:16.769679Z",
- "iopub.status.idle": "2024-01-19T12:56:17.301531Z",
- "shell.execute_reply": "2024-01-19T12:56:17.300863Z"
+ "iopub.execute_input": "2024-01-19T13:13:32.299107Z",
+ "iopub.status.busy": "2024-01-19T13:13:32.298416Z",
+ "iopub.status.idle": "2024-01-19T13:13:32.822312Z",
+ "shell.execute_reply": "2024-01-19T13:13:32.821658Z"
}
},
"outputs": [
@@ -1596,10 +1596,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:17.304988Z",
- "iopub.status.busy": "2024-01-19T12:56:17.304420Z",
- "iopub.status.idle": "2024-01-19T12:56:17.367975Z",
- "shell.execute_reply": "2024-01-19T12:56:17.367334Z"
+ "iopub.execute_input": "2024-01-19T13:13:32.825748Z",
+ "iopub.status.busy": "2024-01-19T13:13:32.825171Z",
+ "iopub.status.idle": "2024-01-19T13:13:32.889011Z",
+ "shell.execute_reply": "2024-01-19T13:13:32.888370Z"
}
},
"outputs": [
@@ -1703,10 +1703,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:17.370726Z",
- "iopub.status.busy": "2024-01-19T12:56:17.370213Z",
- "iopub.status.idle": "2024-01-19T12:56:17.379378Z",
- "shell.execute_reply": "2024-01-19T12:56:17.378759Z"
+ "iopub.execute_input": "2024-01-19T13:13:32.891599Z",
+ "iopub.status.busy": "2024-01-19T13:13:32.891265Z",
+ "iopub.status.idle": "2024-01-19T13:13:32.900698Z",
+ "shell.execute_reply": "2024-01-19T13:13:32.900059Z"
}
},
"outputs": [
@@ -1836,10 +1836,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:17.381946Z",
- "iopub.status.busy": "2024-01-19T12:56:17.381461Z",
- "iopub.status.idle": "2024-01-19T12:56:17.386473Z",
- "shell.execute_reply": "2024-01-19T12:56:17.385882Z"
+ "iopub.execute_input": "2024-01-19T13:13:32.903424Z",
+ "iopub.status.busy": "2024-01-19T13:13:32.902980Z",
+ "iopub.status.idle": "2024-01-19T13:13:32.909166Z",
+ "shell.execute_reply": "2024-01-19T13:13:32.908544Z"
},
"nbsphinx": "hidden"
},
@@ -1885,10 +1885,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:17.388884Z",
- "iopub.status.busy": "2024-01-19T12:56:17.388412Z",
- "iopub.status.idle": "2024-01-19T12:56:17.879759Z",
- "shell.execute_reply": "2024-01-19T12:56:17.879068Z"
+ "iopub.execute_input": "2024-01-19T13:13:32.911571Z",
+ "iopub.status.busy": "2024-01-19T13:13:32.911364Z",
+ "iopub.status.idle": "2024-01-19T13:13:33.401299Z",
+ "shell.execute_reply": "2024-01-19T13:13:33.400642Z"
}
},
"outputs": [
@@ -1923,10 +1923,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:17.882485Z",
- "iopub.status.busy": "2024-01-19T12:56:17.882012Z",
- "iopub.status.idle": "2024-01-19T12:56:17.891231Z",
- "shell.execute_reply": "2024-01-19T12:56:17.890617Z"
+ "iopub.execute_input": "2024-01-19T13:13:33.404052Z",
+ "iopub.status.busy": "2024-01-19T13:13:33.403677Z",
+ "iopub.status.idle": "2024-01-19T13:13:33.412882Z",
+ "shell.execute_reply": "2024-01-19T13:13:33.412358Z"
}
},
"outputs": [
@@ -2093,10 +2093,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:17.893746Z",
- "iopub.status.busy": "2024-01-19T12:56:17.893276Z",
- "iopub.status.idle": "2024-01-19T12:56:17.902071Z",
- "shell.execute_reply": "2024-01-19T12:56:17.901459Z"
+ "iopub.execute_input": "2024-01-19T13:13:33.415544Z",
+ "iopub.status.busy": "2024-01-19T13:13:33.415075Z",
+ "iopub.status.idle": "2024-01-19T13:13:33.422924Z",
+ "shell.execute_reply": "2024-01-19T13:13:33.422424Z"
},
"nbsphinx": "hidden"
},
@@ -2172,10 +2172,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:17.904614Z",
- "iopub.status.busy": "2024-01-19T12:56:17.904217Z",
- "iopub.status.idle": "2024-01-19T12:56:18.375394Z",
- "shell.execute_reply": "2024-01-19T12:56:18.374724Z"
+ "iopub.execute_input": "2024-01-19T13:13:33.425284Z",
+ "iopub.status.busy": "2024-01-19T13:13:33.424950Z",
+ "iopub.status.idle": "2024-01-19T13:13:33.895167Z",
+ "shell.execute_reply": "2024-01-19T13:13:33.894529Z"
}
},
"outputs": [
@@ -2212,10 +2212,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:18.378080Z",
- "iopub.status.busy": "2024-01-19T12:56:18.377676Z",
- "iopub.status.idle": "2024-01-19T12:56:18.394347Z",
- "shell.execute_reply": "2024-01-19T12:56:18.393762Z"
+ "iopub.execute_input": "2024-01-19T13:13:33.897799Z",
+ "iopub.status.busy": "2024-01-19T13:13:33.897402Z",
+ "iopub.status.idle": "2024-01-19T13:13:33.913969Z",
+ "shell.execute_reply": "2024-01-19T13:13:33.913307Z"
}
},
"outputs": [
@@ -2372,10 +2372,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:18.396954Z",
- "iopub.status.busy": "2024-01-19T12:56:18.396573Z",
- "iopub.status.idle": "2024-01-19T12:56:18.402501Z",
- "shell.execute_reply": "2024-01-19T12:56:18.401968Z"
+ "iopub.execute_input": "2024-01-19T13:13:33.916700Z",
+ "iopub.status.busy": "2024-01-19T13:13:33.916304Z",
+ "iopub.status.idle": "2024-01-19T13:13:33.922508Z",
+ "shell.execute_reply": "2024-01-19T13:13:33.921880Z"
},
"nbsphinx": "hidden"
},
@@ -2420,10 +2420,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:18.404852Z",
- "iopub.status.busy": "2024-01-19T12:56:18.404489Z",
- "iopub.status.idle": "2024-01-19T12:56:19.074668Z",
- "shell.execute_reply": "2024-01-19T12:56:19.074028Z"
+ "iopub.execute_input": "2024-01-19T13:13:33.924836Z",
+ "iopub.status.busy": "2024-01-19T13:13:33.924490Z",
+ "iopub.status.idle": "2024-01-19T13:13:34.522414Z",
+ "shell.execute_reply": "2024-01-19T13:13:34.521732Z"
}
},
"outputs": [
@@ -2505,10 +2505,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:19.077766Z",
- "iopub.status.busy": "2024-01-19T12:56:19.077304Z",
- "iopub.status.idle": "2024-01-19T12:56:19.088795Z",
- "shell.execute_reply": "2024-01-19T12:56:19.088147Z"
+ "iopub.execute_input": "2024-01-19T13:13:34.525382Z",
+ "iopub.status.busy": "2024-01-19T13:13:34.525134Z",
+ "iopub.status.idle": "2024-01-19T13:13:34.534006Z",
+ "shell.execute_reply": "2024-01-19T13:13:34.533373Z"
}
},
"outputs": [
@@ -2533,47 +2533,47 @@
" \n",
" \n",
" | \n",
- " dark_score | \n",
" is_dark_issue | \n",
+ " dark_score | \n",
"
\n",
" \n",
"
\n",
" \n",
" 34848 | \n",
- " 0.203922 | \n",
" True | \n",
+ " 0.203922 | \n",
"
\n",
" \n",
" 50270 | \n",
- " 0.204588 | \n",
" True | \n",
+ " 0.204588 | \n",
"
\n",
" \n",
" 3936 | \n",
- " 0.213098 | \n",
" True | \n",
+ " 0.213098 | \n",
"
\n",
" \n",
" 733 | \n",
- " 0.217686 | \n",
" True | \n",
+ " 0.217686 | \n",
"
\n",
" \n",
" 8094 | \n",
- " 0.230118 | \n",
" True | \n",
+ " 0.230118 | \n",
"
\n",
" \n",
"\n",
""
],
"text/plain": [
- " dark_score is_dark_issue\n",
- "34848 0.203922 True\n",
- "50270 0.204588 True\n",
- "3936 0.213098 True\n",
- "733 0.217686 True\n",
- "8094 0.230118 True"
+ " is_dark_issue dark_score\n",
+ "34848 True 0.203922\n",
+ "50270 True 0.204588\n",
+ "3936 True 0.213098\n",
+ "733 True 0.217686\n",
+ "8094 True 0.230118"
]
},
"execution_count": 26,
@@ -2636,10 +2636,10 @@
"execution_count": 27,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:19.091685Z",
- "iopub.status.busy": "2024-01-19T12:56:19.091443Z",
- "iopub.status.idle": "2024-01-19T12:56:19.097837Z",
- "shell.execute_reply": "2024-01-19T12:56:19.097169Z"
+ "iopub.execute_input": "2024-01-19T13:13:34.536705Z",
+ "iopub.status.busy": "2024-01-19T13:13:34.536507Z",
+ "iopub.status.idle": "2024-01-19T13:13:34.541531Z",
+ "shell.execute_reply": "2024-01-19T13:13:34.540917Z"
},
"nbsphinx": "hidden"
},
@@ -2676,10 +2676,10 @@
"execution_count": 28,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:19.100690Z",
- "iopub.status.busy": "2024-01-19T12:56:19.100452Z",
- "iopub.status.idle": "2024-01-19T12:56:19.304161Z",
- "shell.execute_reply": "2024-01-19T12:56:19.303573Z"
+ "iopub.execute_input": "2024-01-19T13:13:34.543925Z",
+ "iopub.status.busy": "2024-01-19T13:13:34.543727Z",
+ "iopub.status.idle": "2024-01-19T13:13:34.719324Z",
+ "shell.execute_reply": "2024-01-19T13:13:34.718636Z"
}
},
"outputs": [
@@ -2721,10 +2721,10 @@
"execution_count": 29,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:19.307056Z",
- "iopub.status.busy": "2024-01-19T12:56:19.306572Z",
- "iopub.status.idle": "2024-01-19T12:56:19.315207Z",
- "shell.execute_reply": "2024-01-19T12:56:19.314577Z"
+ "iopub.execute_input": "2024-01-19T13:13:34.722002Z",
+ "iopub.status.busy": "2024-01-19T13:13:34.721786Z",
+ "iopub.status.idle": "2024-01-19T13:13:34.730892Z",
+ "shell.execute_reply": "2024-01-19T13:13:34.730376Z"
}
},
"outputs": [
@@ -2810,10 +2810,10 @@
"execution_count": 30,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:19.317590Z",
- "iopub.status.busy": "2024-01-19T12:56:19.317158Z",
- "iopub.status.idle": "2024-01-19T12:56:19.516071Z",
- "shell.execute_reply": "2024-01-19T12:56:19.515380Z"
+ "iopub.execute_input": "2024-01-19T13:13:34.733406Z",
+ "iopub.status.busy": "2024-01-19T13:13:34.733006Z",
+ "iopub.status.idle": "2024-01-19T13:13:34.901341Z",
+ "shell.execute_reply": "2024-01-19T13:13:34.900645Z"
}
},
"outputs": [
@@ -2853,10 +2853,10 @@
"execution_count": 31,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:19.518876Z",
- "iopub.status.busy": "2024-01-19T12:56:19.518453Z",
- "iopub.status.idle": "2024-01-19T12:56:19.523479Z",
- "shell.execute_reply": "2024-01-19T12:56:19.522831Z"
+ "iopub.execute_input": "2024-01-19T13:13:34.903982Z",
+ "iopub.status.busy": "2024-01-19T13:13:34.903619Z",
+ "iopub.status.idle": "2024-01-19T13:13:34.908442Z",
+ "shell.execute_reply": "2024-01-19T13:13:34.907827Z"
},
"nbsphinx": "hidden"
},
@@ -2893,67 +2893,7 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "00a586c54fd34c94af3645649715a16a": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_c7a28edc887849d6be529bd6bf1d23fa",
- "max": 30931277.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_855ae8a4cc8d4a64b345bc506041bdce",
- "value": 30931277.0
- }
- },
- "03e0abdc7ec44be48696dc841170b4a1": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_5842077a46554f5e8a53e42e41b4351d",
- "placeholder": "",
- "style": "IPY_MODEL_bf0366474b534f0584e87e557c9d5c4f",
- "value": " 60000/60000 [00:12<00:00, 4038.11 examples/s]"
- }
- },
- "0438ed5a10574f5ca717d22d574adaf5": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "088a027305824a299ef6979f816f5e0a": {
+ "00214dfda36a4348859a556b0a2b67dc": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3005,59 +2945,28 @@
"width": null
}
},
- "0e0e297c6b2948f986553e7bfdf89435": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
+ "014db40c720b489f95298d366da076c5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "1.2.0",
- "_model_name": "LayoutModel",
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "overflow_x": null,
- "overflow_y": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_68fd8915cc094cc8ae24efd691c1237a",
+ "placeholder": "",
+ "style": "IPY_MODEL_1232c539f1d4404eb897117eb480278e",
+ "value": " 60000/60000 [00:12<00:00, 4241.60 examples/s]"
}
},
- "0f2c52c35ad648b1abd16ac89da80f78": {
+ "0237bd79dc044450834b8c166b26c10e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -3072,28 +2981,79 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_088a027305824a299ef6979f816f5e0a",
+ "layout": "IPY_MODEL_cdd7c60e365844f5a76d814939d5b32b",
"placeholder": "",
- "style": "IPY_MODEL_616bb20104f14d74860774e772f1a8fe",
- "value": " 30.9M/30.9M [00:01<00:00, 25.1MB/s]"
+ "style": "IPY_MODEL_50ef2b3127304847a5437d31c4f6ad60",
+ "value": " 10000/0 [00:00<00:00, 543444.42 examples/s]"
}
},
- "15c2e8c6d0ec48ae930b04ebc62f7e1b": {
+ "061dbe235abe4c2eb7692055818ebc44": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "model_name": "HTMLModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_32b4b8ae2adf42be997bc63f0c220a30",
+ "placeholder": "",
+ "style": "IPY_MODEL_4bdc0d205d5943aebc2f75153a306915",
+ "value": "Computing checksums: 100%"
+ }
+ },
+ "083f8a50f47c4ba898661e1602fe3aa4": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_3517e64a7bfe4cfe8ab14a56c7c352db",
+ "max": 1.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_3f7f9d6e94244665bdd43fbff826c300",
+ "value": 1.0
+ }
+ },
+ "0a9b41f1737843e78f020c0990c4cc49": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_7e03352280bf48bb9154b0e06367bd00",
+ "placeholder": "",
+ "style": "IPY_MODEL_2bf7e1c65c4d43209d0c278062fbfe6e",
+ "value": " 60000/0 [00:00<00:00, 815999.22 examples/s]"
}
},
- "1bfb856e456540a69728866864b5932b": {
+ "0c17cdebb6f34df98b73bd3425115510": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3142,41 +3102,10 @@
"right": null,
"top": null,
"visibility": null,
- "width": "20px"
- }
- },
- "20552962b6f041c79bb6d65a70fe96b5": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "21fb130c4b5149cf8ab85d85f3711d7d": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "width": null
}
},
- "22ea0aaa2e6b43d385fda375f2598e43": {
+ "0df9707d07b04b3d945eb68e1ab423cf": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -3191,13 +3120,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_4b9b5a0172ba477fa7b315c779c3a907",
+ "layout": "IPY_MODEL_ed0a241713934b3a9b6f1ea6140617f9",
"placeholder": "",
- "style": "IPY_MODEL_20552962b6f041c79bb6d65a70fe96b5",
- "value": "Downloading data: 100%"
+ "style": "IPY_MODEL_907a310ee53d4b9b931eaddaf2ba8bfb",
+ "value": "Map (num_proc=4): 100%"
}
},
- "2523e879f10045f2b7252e651f1d4967": {
+ "1232c539f1d4404eb897117eb480278e": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -3212,28 +3141,29 @@
"description_width": ""
}
},
- "25e59a1b6e2d47f7abbd07a9b762867a": {
+ "15abb7b381a94181ba661286d20518c2": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HTMLModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_352812ca5c8d4c67be201508e060b945",
- "placeholder": "",
- "style": "IPY_MODEL_fa239a377b664ced82ae301dfeb00a05",
- "value": "Downloading data: 100%"
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_0df9707d07b04b3d945eb68e1ab423cf",
+ "IPY_MODEL_f246e7a3b6f649d3af6d4bf9d10d5ee4",
+ "IPY_MODEL_014db40c720b489f95298d366da076c5"
+ ],
+ "layout": "IPY_MODEL_d3c6d8bc2e3e4c638c982649dcc4121a"
}
},
- "276be56803c6481cb7f67fd91e4f6583": {
+ "18236cfb50484a5996293f537c5b5a7f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
@@ -3248,14 +3178,36 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_4cb1766ae96a48c1a3e7a3f197edce50",
- "IPY_MODEL_437c3f3128794f4ca96cc25e4d0c90e8",
- "IPY_MODEL_7cb7f36c41ea4a6790310ad146927c2e"
+ "IPY_MODEL_337d430a436d4a70be3d1676c0139a01",
+ "IPY_MODEL_acfb56183cef4116887aa6c986d67fca",
+ "IPY_MODEL_ff4c9cc0bf2442178d1b4be2d4526897"
],
- "layout": "IPY_MODEL_69cb1ca9bcd04423a4e5a148a6db231f"
+ "layout": "IPY_MODEL_a5b4ece8f3894162be9db2df72e01a01"
}
},
- "2825f20ea57f4e57b40af57a3f274ad9": {
+ "1a362abf29a247599ed238a4bdde333f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_38ee0f3967674ab2916bc2ac8f00c44b",
+ "IPY_MODEL_083f8a50f47c4ba898661e1602fe3aa4",
+ "IPY_MODEL_0a9b41f1737843e78f020c0990c4cc49"
+ ],
+ "layout": "IPY_MODEL_59170595b2844699a1cc216efcf5a5b9"
+ }
+ },
+ "24b674d4f0d04eeab6519b95ff2c7758": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -3270,17 +3222,53 @@
"description_width": ""
}
},
- "293a04acf12e476c8f240f7bb971e450": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
+ "26a0536582314e2689d82852c5b8038b": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "1.2.0",
- "_model_name": "LayoutModel",
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_00214dfda36a4348859a556b0a2b67dc",
+ "placeholder": "",
+ "style": "IPY_MODEL_d31c46fcccf24a1d83404ecf88e01cab",
+ "value": "Downloading data: 100%"
+ }
+ },
+ "2bf7e1c65c4d43209d0c278062fbfe6e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "2f21f3fbdf4c4e06ba3d123a93dcb1fe": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "1.2.0",
+ "model_name": "LayoutModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
"_view_name": "LayoutView",
"align_content": null,
"align_items": null,
@@ -3322,47 +3310,7 @@
"width": null
}
},
- "2bd60d94821a46edaec81182d5f78975": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "3086ae0bb0714cd9b40c4afc7cdf7946": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_86e87264ef5045bca87730c3936522bd",
- "max": 1.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_3e349f3ac36941c3828490432d7f596b",
- "value": 1.0
- }
- },
- "352812ca5c8d4c67be201508e060b945": {
+ "32b4b8ae2adf42be997bc63f0c220a30": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3414,63 +3362,7 @@
"width": null
}
},
- "39ea8de010b24f29b04c7e322202ea08": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "3e349f3ac36941c3828490432d7f596b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "437c3f3128794f4ca96cc25e4d0c90e8": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_55068c5ecdfa4b6e89c90196953b7797",
- "max": 60000.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_ce54d6af5010490f87aa374dc7281c57",
- "value": 60000.0
- }
- },
- "438fd2e6e610431baabfa4256496e33c": {
+ "337d430a436d4a70be3d1676c0139a01": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -3485,13 +3377,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_0e0e297c6b2948f986553e7bfdf89435",
+ "layout": "IPY_MODEL_0c17cdebb6f34df98b73bd3425115510",
"placeholder": "",
- "style": "IPY_MODEL_2523e879f10045f2b7252e651f1d4967",
- "value": " 60000/0 [00:00<00:00, 936326.11 examples/s]"
+ "style": "IPY_MODEL_f30a26b9bf2e435991ce4af697c8d3cc",
+ "value": "Downloading data: 100%"
}
},
- "4b9b5a0172ba477fa7b315c779c3a907": {
+ "3453ac0fb3ea435bae7934c25725123c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3543,22 +3435,59 @@
"width": null
}
},
- "4be89372c35545a0b20170055b1b4449": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "3517e64a7bfe4cfe8ab14a56c7c352db": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "1.2.0",
+ "model_name": "LayoutModel",
"state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": "20px"
}
},
- "4cb1766ae96a48c1a3e7a3f197edce50": {
+ "37debcc2b436401ca6097ab0c8d33055": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -3573,13 +3502,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_d19616be18bf475c925f56776c6ff129",
+ "layout": "IPY_MODEL_ad3935ce1e6d475ea9b10b923ca30d9d",
"placeholder": "",
- "style": "IPY_MODEL_4be89372c35545a0b20170055b1b4449",
- "value": "100%"
+ "style": "IPY_MODEL_c51cdac0487942c5b7396177e0296c48",
+ "value": " 5.18M/5.18M [00:00<00:00, 90.7MB/s]"
}
},
- "4f5b6456b42d4ff8b1b9874da7f3bf79": {
+ "38ee0f3967674ab2916bc2ac8f00c44b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -3594,89 +3523,75 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_776bf9880b1948b09b58ad1e1b8d4b13",
+ "layout": "IPY_MODEL_d94d320229fa4e7494889831b5aa801d",
"placeholder": "",
- "style": "IPY_MODEL_15c2e8c6d0ec48ae930b04ebc62f7e1b",
- "value": " 5.18M/5.18M [00:00<00:00, 9.88MB/s]"
+ "style": "IPY_MODEL_24b674d4f0d04eeab6519b95ff2c7758",
+ "value": "Generating train split: "
}
},
- "52c5a37ef541462fb52190fbe8e6ed03": {
+ "3a21c5448f0940f6bb6f8938aa586ed2": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
+ "model_name": "DescriptionStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "DescriptionStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_ec772fec88cd48e7a13b13a9e964a5e6",
- "max": 2.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_21fb130c4b5149cf8ab85d85f3711d7d",
- "value": 2.0
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
}
},
- "55068c5ecdfa4b6e89c90196953b7797": {
- "model_module": "@jupyter-widgets/base",
- "model_module_version": "1.2.0",
- "model_name": "LayoutModel",
+ "3ac23e294d98476f8561474f5c077ac5": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
"state": {
- "_model_module": "@jupyter-widgets/base",
- "_model_module_version": "1.2.0",
- "_model_name": "LayoutModel",
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
- "_view_name": "LayoutView",
- "align_content": null,
- "align_items": null,
- "align_self": null,
- "border": null,
- "bottom": null,
- "display": null,
- "flex": null,
- "flex_flow": null,
- "grid_area": null,
- "grid_auto_columns": null,
- "grid_auto_flow": null,
- "grid_auto_rows": null,
- "grid_column": null,
- "grid_gap": null,
- "grid_row": null,
- "grid_template_areas": null,
- "grid_template_columns": null,
- "grid_template_rows": null,
- "height": null,
- "justify_content": null,
- "justify_items": null,
- "left": null,
- "margin": null,
- "max_height": null,
- "max_width": null,
- "min_height": null,
- "min_width": null,
- "object_fit": null,
- "object_position": null,
- "order": null,
- "overflow": null,
- "overflow_x": null,
- "overflow_y": null,
- "padding": null,
- "right": null,
- "top": null,
- "visibility": null,
- "width": null
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "3f7f9d6e94244665bdd43fbff826c300": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "4383548f83a147c5beec7cb3797143dc": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
}
},
- "555490e32f6c45b2b699bb774fa4d22e": {
+ "46dba87f34ce4783a9845b38e2dc950d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3728,7 +3643,7 @@
"width": null
}
},
- "5842077a46554f5e8a53e42e41b4351d": {
+ "4855055064564a7ca2718f30f02d9644": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3777,10 +3692,32 @@
"right": null,
"top": null,
"visibility": null,
- "width": null
+ "width": "20px"
+ }
+ },
+ "4867b0380f70419ea032a4fff943af78": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_061dbe235abe4c2eb7692055818ebc44",
+ "IPY_MODEL_5140babee9b445f0ba0ac429040535ff",
+ "IPY_MODEL_794d2c2a6e9d40b9b2910b3e33b6eb0b"
+ ],
+ "layout": "IPY_MODEL_82c98279ba4c4788b118af96547dcb79"
}
},
- "5b8a5a84b6d74705b2a534bcd091f8ac": {
+ "4954e3b6b2cc47299c3a789671da8498": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3832,29 +3769,22 @@
"width": null
}
},
- "5d841c13a19a439bb2900021390d1cf0": {
+ "4bdc0d205d5943aebc2f75153a306915": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HBoxModel",
+ "model_name": "DescriptionStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
+ "_model_name": "DescriptionStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_eb587f36ffe24b5ba8f1d6510bd6e16e",
- "IPY_MODEL_52c5a37ef541462fb52190fbe8e6ed03",
- "IPY_MODEL_cd24308a065f4f5fba2af3e426380be6"
- ],
- "layout": "IPY_MODEL_293a04acf12e476c8f240f7bb971e450"
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
}
},
- "5e8b0d584bb84cfb9464792906122eb5": {
+ "4c088f7d66f2430d806cc86030293bda": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
@@ -3870,7 +3800,46 @@
"description_width": ""
}
},
- "60fa8eab68b64dfe985668e0543289eb": {
+ "50ef2b3127304847a5437d31c4f6ad60": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "5140babee9b445f0ba0ac429040535ff": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_c5a01277798c4560b59b3b6f6172f892",
+ "max": 2.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_b1130c441a2248d9b762392d19373dcf",
+ "value": 2.0
+ }
+ },
+ "5434c58283dd404eace23a364feb33e5": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HBoxModel",
@@ -3885,29 +3854,36 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_c0b7af83aa3f403d870c1677b79c884a",
- "IPY_MODEL_d1fb956b9b4149a3896950bbe3f7e799",
- "IPY_MODEL_438fd2e6e610431baabfa4256496e33c"
+ "IPY_MODEL_cebe883ff6184e2bb26de12b8489f321",
+ "IPY_MODEL_f87f6d7aabf34f92bacedcefa2037ec0",
+ "IPY_MODEL_e7da21682f8746bebd73e4f6c9639b79"
],
- "layout": "IPY_MODEL_b12c2cf5803c4424b3242d3d06a0fb2a"
+ "layout": "IPY_MODEL_3453ac0fb3ea435bae7934c25725123c"
}
},
- "616bb20104f14d74860774e772f1a8fe": {
+ "54cfbb8e123c4280a44b9504ee28b400": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "model_name": "HBoxModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_26a0536582314e2689d82852c5b8038b",
+ "IPY_MODEL_ee2dd904eb764f48a188f1674cc98415",
+ "IPY_MODEL_37debcc2b436401ca6097ab0c8d33055"
+ ],
+ "layout": "IPY_MODEL_a2a3863dcbfe4ad7a6dce5fde38c41ff"
}
},
- "69cb1ca9bcd04423a4e5a148a6db231f": {
+ "59170595b2844699a1cc216efcf5a5b9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3959,22 +3935,7 @@
"width": null
}
},
- "6f645da637b840a78bb8cc51620043f4": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "70e264293a9a4064918830b893b507d9": {
+ "6517caeb581942c89926382a2b7ba1dd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4026,50 +3987,7 @@
"width": null
}
},
- "736992f301e04017bd531dcfe83da58b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_b68a8436a60a459b86067950036f75b5",
- "placeholder": "",
- "style": "IPY_MODEL_bbc586b317ec4c25a92b1abad2d35e19",
- "value": "Map (num_proc=4): 100%"
- }
- },
- "737f0fe7c43f4d05adfd2b7611f1592b": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_25e59a1b6e2d47f7abbd07a9b762867a",
- "IPY_MODEL_7c78e5baf98d44ff9a60616e7d0a6731",
- "IPY_MODEL_4f5b6456b42d4ff8b1b9874da7f3bf79"
- ],
- "layout": "IPY_MODEL_555490e32f6c45b2b699bb774fa4d22e"
- }
- },
- "776bf9880b1948b09b58ad1e1b8d4b13": {
+ "68fd8915cc094cc8ae24efd691c1237a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4121,28 +4039,7 @@
"width": null
}
},
- "781303e8c55b4a4da01446f7c3593cd5": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_70e264293a9a4064918830b893b507d9",
- "placeholder": "",
- "style": "IPY_MODEL_8af29d536d25415bb1e6b5cd82c45413",
- "value": "Generating test split: "
- }
- },
- "7bbace17daaa4056a5938bf1bf6944a8": {
+ "6da75cd8a1ff441ab678bd5d3d5cead9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -4157,31 +4054,23 @@
"description_width": ""
}
},
- "7c78e5baf98d44ff9a60616e7d0a6731": {
+ "76375ec779794d10bce65b397d71f22f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_ab52cb0c45ec459f9e70da6b45c7752d",
- "max": 5175617.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_39ea8de010b24f29b04c7e322202ea08",
- "value": 5175617.0
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "7cb7f36c41ea4a6790310ad146927c2e": {
+ "794d2c2a6e9d40b9b2910b3e33b6eb0b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -4196,13 +4085,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_e405220caaa744f0b3ea20aa635bc684",
+ "layout": "IPY_MODEL_e1f10adcd76943fda7a1ea4e5b81a544",
"placeholder": "",
- "style": "IPY_MODEL_7bbace17daaa4056a5938bf1bf6944a8",
- "value": " 60000/60000 [00:34<00:00, 1753.74it/s]"
+ "style": "IPY_MODEL_90840e8d56d74247902993bbd789591c",
+ "value": " 2/2 [00:00<00:00, 331.15it/s]"
}
},
- "81375265a9fd4a4aad0b6eabf98b56cc": {
+ "7d2e2a5f88ac4a0ba75e07edaba34695": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4254,23 +4143,7 @@
"width": null
}
},
- "855ae8a4cc8d4a64b345bc506041bdce": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "86e87264ef5045bca87730c3936522bd": {
+ "7e03352280bf48bb9154b0e06367bd00": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4319,112 +4192,10 @@
"right": null,
"top": null,
"visibility": null,
- "width": "20px"
- }
- },
- "8af29d536d25415bb1e6b5cd82c45413": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "8d8b5d28988247138f45c577981f7882": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_81375265a9fd4a4aad0b6eabf98b56cc",
- "placeholder": "",
- "style": "IPY_MODEL_fd63f072f1b24812a161bfc0540e230a",
- "value": " 10000/0 [00:00<00:00, 464177.07 examples/s]"
- }
- },
- "9ddf9612ae3e4345bb7d9ac84c3a595a": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_781303e8c55b4a4da01446f7c3593cd5",
- "IPY_MODEL_3086ae0bb0714cd9b40c4afc7cdf7946",
- "IPY_MODEL_8d8b5d28988247138f45c577981f7882"
- ],
- "layout": "IPY_MODEL_fc667583764344ca8216f791cf59951c"
- }
- },
- "a2cafedc9a35438c932c6cf48ffeb5eb": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_736992f301e04017bd531dcfe83da58b",
- "IPY_MODEL_dfb6b0a08f784ec397fdad1285b8f37d",
- "IPY_MODEL_03e0abdc7ec44be48696dc841170b4a1"
- ],
- "layout": "IPY_MODEL_e2eec0d260f94d5d91e6c57f7f021ac5"
- }
- },
- "a559be98a22e47de9dabbc3aa7e44f41": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_22ea0aaa2e6b43d385fda375f2598e43",
- "IPY_MODEL_00a586c54fd34c94af3645649715a16a",
- "IPY_MODEL_0f2c52c35ad648b1abd16ac89da80f78"
- ],
- "layout": "IPY_MODEL_f4cb2707fa0547349b86e7bb64e841f2"
+ "width": null
}
},
- "a8a718fe2f7340268da3bdfeadd54ad9": {
+ "82c98279ba4c4788b118af96547dcb79": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4476,7 +4247,7 @@
"width": null
}
},
- "ab52cb0c45ec459f9e70da6b45c7752d": {
+ "86d6a0c587cb450ab303690c6a4d10ae": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4528,7 +4299,7 @@
"width": null
}
},
- "b12c2cf5803c4424b3242d3d06a0fb2a": {
+ "8993769613d84b06b679c0056c2d3c0f": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4580,7 +4351,90 @@
"width": null
}
},
- "b68a8436a60a459b86067950036f75b5": {
+ "907a310ee53d4b9b931eaddaf2ba8bfb": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "90840e8d56d74247902993bbd789591c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "9673bce83a684b31880934b31dd2e9cd": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "9ff96418b33246e8be1f14d63be503e1": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "a0a73bbc41b24507bedf88c7673932ac": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_dd84b5ba5e8f4a938480a9a0cea37e57",
+ "IPY_MODEL_a6d4c4cf343246609343b49a3a4df8c9",
+ "IPY_MODEL_0237bd79dc044450834b8c166b26c10e"
+ ],
+ "layout": "IPY_MODEL_6517caeb581942c89926382a2b7ba1dd"
+ }
+ },
+ "a2a3863dcbfe4ad7a6dce5fde38c41ff": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4632,58 +4486,107 @@
"width": null
}
},
- "bbc586b317ec4c25a92b1abad2d35e19": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "a5b4ece8f3894162be9db2df72e01a01": {
+ "model_module": "@jupyter-widgets/base",
+ "model_module_version": "1.2.0",
+ "model_name": "LayoutModel",
"state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_module": "@jupyter-widgets/base",
+ "_model_module_version": "1.2.0",
+ "_model_name": "LayoutModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/base",
"_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_name": "LayoutView",
+ "align_content": null,
+ "align_items": null,
+ "align_self": null,
+ "border": null,
+ "bottom": null,
+ "display": null,
+ "flex": null,
+ "flex_flow": null,
+ "grid_area": null,
+ "grid_auto_columns": null,
+ "grid_auto_flow": null,
+ "grid_auto_rows": null,
+ "grid_column": null,
+ "grid_gap": null,
+ "grid_row": null,
+ "grid_template_areas": null,
+ "grid_template_columns": null,
+ "grid_template_rows": null,
+ "height": null,
+ "justify_content": null,
+ "justify_items": null,
+ "left": null,
+ "margin": null,
+ "max_height": null,
+ "max_width": null,
+ "min_height": null,
+ "min_width": null,
+ "object_fit": null,
+ "object_position": null,
+ "order": null,
+ "overflow": null,
+ "overflow_x": null,
+ "overflow_y": null,
+ "padding": null,
+ "right": null,
+ "top": null,
+ "visibility": null,
+ "width": null
}
},
- "bf0366474b534f0584e87e557c9d5c4f": {
+ "a6d4c4cf343246609343b49a3a4df8c9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_4855055064564a7ca2718f30f02d9644",
+ "max": 1.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_9673bce83a684b31880934b31dd2e9cd",
+ "value": 1.0
}
},
- "c0b7af83aa3f403d870c1677b79c884a": {
+ "acfb56183cef4116887aa6c986d67fca": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HTMLModel",
+ "model_name": "FloatProgressModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_f877f15eaeb34c9dacb89ed045d99dc7",
- "placeholder": "",
- "style": "IPY_MODEL_0438ed5a10574f5ca717d22d574adaf5",
- "value": "Generating train split: "
+ "layout": "IPY_MODEL_8993769613d84b06b679c0056c2d3c0f",
+ "max": 30931277.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_4c088f7d66f2430d806cc86030293bda",
+ "value": 30931277.0
}
},
- "c7a28edc887849d6be529bd6bf1d23fa": {
+ "ad3935ce1e6d475ea9b10b923ca30d9d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4735,28 +4638,23 @@
"width": null
}
},
- "cd24308a065f4f5fba2af3e426380be6": {
+ "b1130c441a2248d9b762392d19373dcf": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HTMLModel",
+ "model_name": "ProgressStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
+ "_model_name": "ProgressStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_e198e4a9f01b4924a654c1bdf380f24f",
- "placeholder": "",
- "style": "IPY_MODEL_2825f20ea57f4e57b40af57a3f274ad9",
- "value": " 2/2 [00:00<00:00, 349.69it/s]"
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
}
},
- "ce54d6af5010490f87aa374dc7281c57": {
+ "bd2eac4ba27f4a1aac97f8fc9706932a": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
@@ -4772,7 +4670,22 @@
"description_width": ""
}
},
- "d19616be18bf475c925f56776c6ff129": {
+ "c51cdac0487942c5b7396177e0296c48": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "c5a01277798c4560b59b3b6f6172f892": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4824,55 +4737,7 @@
"width": null
}
},
- "d1fb956b9b4149a3896950bbe3f7e799": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_1bfb856e456540a69728866864b5932b",
- "max": 1.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_2bd60d94821a46edaec81182d5f78975",
- "value": 1.0
- }
- },
- "dfb6b0a08f784ec397fdad1285b8f37d": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_a8a718fe2f7340268da3bdfeadd54ad9",
- "max": 60000.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_5e8b0d584bb84cfb9464792906122eb5",
- "value": 60000.0
- }
- },
- "e198e4a9f01b4924a654c1bdf380f24f": {
+ "cdd7c60e365844f5a76d814939d5b32b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4924,7 +4789,7 @@
"width": null
}
},
- "e2eec0d260f94d5d91e6c57f7f021ac5": {
+ "ce5106024ba54117afb907facdba8c0d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4976,7 +4841,43 @@
"width": null
}
},
- "e405220caaa744f0b3ea20aa635bc684": {
+ "cebe883ff6184e2bb26de12b8489f321": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_2f21f3fbdf4c4e06ba3d123a93dcb1fe",
+ "placeholder": "",
+ "style": "IPY_MODEL_9ff96418b33246e8be1f14d63be503e1",
+ "value": "100%"
+ }
+ },
+ "d31c46fcccf24a1d83404ecf88e01cab": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "d3c6d8bc2e3e4c638c982649dcc4121a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -5028,28 +4929,7 @@
"width": null
}
},
- "eb587f36ffe24b5ba8f1d6510bd6e16e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_5b8a5a84b6d74705b2a534bcd091f8ac",
- "placeholder": "",
- "style": "IPY_MODEL_6f645da637b840a78bb8cc51620043f4",
- "value": "Computing checksums: 100%"
- }
- },
- "ec772fec88cd48e7a13b13a9e964a5e6": {
+ "d94d320229fa4e7494889831b5aa801d": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -5101,7 +4981,28 @@
"width": null
}
},
- "f4cb2707fa0547349b86e7bb64e841f2": {
+ "dd84b5ba5e8f4a938480a9a0cea37e57": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_ce5106024ba54117afb907facdba8c0d",
+ "placeholder": "",
+ "style": "IPY_MODEL_4383548f83a147c5beec7cb3797143dc",
+ "value": "Generating test split: "
+ }
+ },
+ "e1f10adcd76943fda7a1ea4e5b81a544": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -5153,7 +5054,28 @@
"width": null
}
},
- "f877f15eaeb34c9dacb89ed045d99dc7": {
+ "e7da21682f8746bebd73e4f6c9639b79": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_86d6a0c587cb450ab303690c6a4d10ae",
+ "placeholder": "",
+ "style": "IPY_MODEL_3a21c5448f0940f6bb6f8938aa586ed2",
+ "value": " 60000/60000 [00:34<00:00, 1833.91it/s]"
+ }
+ },
+ "ed0a241713934b3a9b6f1ea6140617f9": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -5205,22 +5127,31 @@
"width": null
}
},
- "fa239a377b664ced82ae301dfeb00a05": {
+ "ee2dd904eb764f48a188f1674cc98415": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
+ "model_name": "FloatProgressModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
+ "_model_name": "FloatProgressModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_f0a9ff5145bb4952a4c6cc0a922c9e12",
+ "max": 5175617.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_3ac23e294d98476f8561474f5c077ac5",
+ "value": 5175617.0
}
},
- "fc667583764344ca8216f791cf59951c": {
+ "f0a9ff5145bb4952a4c6cc0a922c9e12": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -5272,7 +5203,31 @@
"width": null
}
},
- "fd63f072f1b24812a161bfc0540e230a": {
+ "f246e7a3b6f649d3af6d4bf9d10d5ee4": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_46dba87f34ce4783a9845b38e2dc950d",
+ "max": 60000.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_bd2eac4ba27f4a1aac97f8fc9706932a",
+ "value": 60000.0
+ }
+ },
+ "f30a26b9bf2e435991ce4af697c8d3cc": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -5286,6 +5241,51 @@
"_view_name": "StyleView",
"description_width": ""
}
+ },
+ "f87f6d7aabf34f92bacedcefa2037ec0": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_7d2e2a5f88ac4a0ba75e07edaba34695",
+ "max": 60000.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_76375ec779794d10bce65b397d71f22f",
+ "value": 60000.0
+ }
+ },
+ "ff4c9cc0bf2442178d1b4be2d4526897": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_4954e3b6b2cc47299c3a789671da8498",
+ "placeholder": "",
+ "style": "IPY_MODEL_6da75cd8a1ff441ab678bd5d3d5cead9",
+ "value": " 30.9M/30.9M [00:00<00:00, 97.4MB/s]"
+ }
}
},
"version_major": 2,
diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
index ba4cf51f0..73fbd55fb 100644
--- a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb
@@ -53,10 +53,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:24.709231Z",
- "iopub.status.busy": "2024-01-19T12:56:24.708785Z",
- "iopub.status.idle": "2024-01-19T12:56:25.791978Z",
- "shell.execute_reply": "2024-01-19T12:56:25.791363Z"
+ "iopub.execute_input": "2024-01-19T13:13:40.366774Z",
+ "iopub.status.busy": "2024-01-19T13:13:40.366239Z",
+ "iopub.status.idle": "2024-01-19T13:13:41.457112Z",
+ "shell.execute_reply": "2024-01-19T13:13:41.456496Z"
},
"nbsphinx": "hidden"
},
@@ -68,7 +68,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\", \"datasets\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -95,10 +95,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:25.794937Z",
- "iopub.status.busy": "2024-01-19T12:56:25.794332Z",
- "iopub.status.idle": "2024-01-19T12:56:26.066084Z",
- "shell.execute_reply": "2024-01-19T12:56:26.065383Z"
+ "iopub.execute_input": "2024-01-19T13:13:41.460120Z",
+ "iopub.status.busy": "2024-01-19T13:13:41.459669Z",
+ "iopub.status.idle": "2024-01-19T13:13:41.732370Z",
+ "shell.execute_reply": "2024-01-19T13:13:41.731753Z"
},
"id": "avXlHJcXjruP"
},
@@ -234,10 +234,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:26.069176Z",
- "iopub.status.busy": "2024-01-19T12:56:26.068758Z",
- "iopub.status.idle": "2024-01-19T12:56:26.080951Z",
- "shell.execute_reply": "2024-01-19T12:56:26.080313Z"
+ "iopub.execute_input": "2024-01-19T13:13:41.735361Z",
+ "iopub.status.busy": "2024-01-19T13:13:41.734964Z",
+ "iopub.status.idle": "2024-01-19T13:13:41.747480Z",
+ "shell.execute_reply": "2024-01-19T13:13:41.746971Z"
},
"nbsphinx": "hidden"
},
@@ -340,10 +340,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:26.083402Z",
- "iopub.status.busy": "2024-01-19T12:56:26.083092Z",
- "iopub.status.idle": "2024-01-19T12:56:26.316325Z",
- "shell.execute_reply": "2024-01-19T12:56:26.315668Z"
+ "iopub.execute_input": "2024-01-19T13:13:41.749786Z",
+ "iopub.status.busy": "2024-01-19T13:13:41.749406Z",
+ "iopub.status.idle": "2024-01-19T13:13:41.982126Z",
+ "shell.execute_reply": "2024-01-19T13:13:41.981470Z"
}
},
"outputs": [
@@ -393,10 +393,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:26.319030Z",
- "iopub.status.busy": "2024-01-19T12:56:26.318637Z",
- "iopub.status.idle": "2024-01-19T12:56:26.345099Z",
- "shell.execute_reply": "2024-01-19T12:56:26.344588Z"
+ "iopub.execute_input": "2024-01-19T13:13:41.984824Z",
+ "iopub.status.busy": "2024-01-19T13:13:41.984422Z",
+ "iopub.status.idle": "2024-01-19T13:13:42.010942Z",
+ "shell.execute_reply": "2024-01-19T13:13:42.010429Z"
}
},
"outputs": [],
@@ -427,10 +427,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:26.347554Z",
- "iopub.status.busy": "2024-01-19T12:56:26.347198Z",
- "iopub.status.idle": "2024-01-19T12:56:27.653201Z",
- "shell.execute_reply": "2024-01-19T12:56:27.652447Z"
+ "iopub.execute_input": "2024-01-19T13:13:42.013621Z",
+ "iopub.status.busy": "2024-01-19T13:13:42.013269Z",
+ "iopub.status.idle": "2024-01-19T13:13:43.334262Z",
+ "shell.execute_reply": "2024-01-19T13:13:43.333487Z"
}
},
"outputs": [
@@ -473,10 +473,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:27.656925Z",
- "iopub.status.busy": "2024-01-19T12:56:27.656133Z",
- "iopub.status.idle": "2024-01-19T12:56:27.680983Z",
- "shell.execute_reply": "2024-01-19T12:56:27.680424Z"
+ "iopub.execute_input": "2024-01-19T13:13:43.337584Z",
+ "iopub.status.busy": "2024-01-19T13:13:43.336993Z",
+ "iopub.status.idle": "2024-01-19T13:13:43.361516Z",
+ "shell.execute_reply": "2024-01-19T13:13:43.360955Z"
},
"scrolled": true
},
@@ -641,10 +641,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:27.683316Z",
- "iopub.status.busy": "2024-01-19T12:56:27.683114Z",
- "iopub.status.idle": "2024-01-19T12:56:28.551854Z",
- "shell.execute_reply": "2024-01-19T12:56:28.551140Z"
+ "iopub.execute_input": "2024-01-19T13:13:43.364055Z",
+ "iopub.status.busy": "2024-01-19T13:13:43.363673Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.252306Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.251584Z"
},
"id": "AaHC5MRKjruT"
},
@@ -763,10 +763,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:28.554360Z",
- "iopub.status.busy": "2024-01-19T12:56:28.554090Z",
- "iopub.status.idle": "2024-01-19T12:56:28.568744Z",
- "shell.execute_reply": "2024-01-19T12:56:28.568210Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.255161Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.254743Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.269276Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.268628Z"
},
"id": "Wy27rvyhjruU"
},
@@ -815,10 +815,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:28.571028Z",
- "iopub.status.busy": "2024-01-19T12:56:28.570723Z",
- "iopub.status.idle": "2024-01-19T12:56:28.660917Z",
- "shell.execute_reply": "2024-01-19T12:56:28.660137Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.272046Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.271656Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.359058Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.358303Z"
},
"id": "Db8YHnyVjruU"
},
@@ -925,10 +925,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:28.663642Z",
- "iopub.status.busy": "2024-01-19T12:56:28.663218Z",
- "iopub.status.idle": "2024-01-19T12:56:28.865034Z",
- "shell.execute_reply": "2024-01-19T12:56:28.864228Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.361846Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.361349Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.565345Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.564630Z"
},
"id": "iJqAHuS2jruV"
},
@@ -965,10 +965,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:28.867918Z",
- "iopub.status.busy": "2024-01-19T12:56:28.867488Z",
- "iopub.status.idle": "2024-01-19T12:56:28.885280Z",
- "shell.execute_reply": "2024-01-19T12:56:28.884675Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.568108Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.567641Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.585488Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.584873Z"
},
"id": "PcPTZ_JJG3Cx"
},
@@ -1030,10 +1030,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:28.887938Z",
- "iopub.status.busy": "2024-01-19T12:56:28.887426Z",
- "iopub.status.idle": "2024-01-19T12:56:28.897744Z",
- "shell.execute_reply": "2024-01-19T12:56:28.897234Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.588291Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.587785Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.598125Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.597602Z"
},
"id": "0lonvOYvjruV"
},
@@ -1180,10 +1180,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:28.900172Z",
- "iopub.status.busy": "2024-01-19T12:56:28.899823Z",
- "iopub.status.idle": "2024-01-19T12:56:29.000447Z",
- "shell.execute_reply": "2024-01-19T12:56:28.999556Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.600660Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.600218Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.699273Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.698526Z"
},
"id": "MfqTCa3kjruV"
},
@@ -1264,10 +1264,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:29.003225Z",
- "iopub.status.busy": "2024-01-19T12:56:29.002885Z",
- "iopub.status.idle": "2024-01-19T12:56:29.160269Z",
- "shell.execute_reply": "2024-01-19T12:56:29.159541Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.702214Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.701708Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.852569Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.851877Z"
},
"id": "9ZtWAYXqMAPL"
},
@@ -1327,10 +1327,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:29.163115Z",
- "iopub.status.busy": "2024-01-19T12:56:29.162766Z",
- "iopub.status.idle": "2024-01-19T12:56:29.167250Z",
- "shell.execute_reply": "2024-01-19T12:56:29.166612Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.855380Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.855047Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.859153Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.858540Z"
},
"id": "0rXP3ZPWjruW"
},
@@ -1368,10 +1368,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:29.169723Z",
- "iopub.status.busy": "2024-01-19T12:56:29.169434Z",
- "iopub.status.idle": "2024-01-19T12:56:29.174579Z",
- "shell.execute_reply": "2024-01-19T12:56:29.174065Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.861483Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.861180Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.866081Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.865457Z"
},
"id": "-iRPe8KXjruW"
},
@@ -1426,10 +1426,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:29.176805Z",
- "iopub.status.busy": "2024-01-19T12:56:29.176594Z",
- "iopub.status.idle": "2024-01-19T12:56:29.216411Z",
- "shell.execute_reply": "2024-01-19T12:56:29.215739Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.868505Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.868066Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.908189Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.907523Z"
},
"id": "ZpipUliyjruW"
},
@@ -1480,10 +1480,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:29.218881Z",
- "iopub.status.busy": "2024-01-19T12:56:29.218511Z",
- "iopub.status.idle": "2024-01-19T12:56:29.264948Z",
- "shell.execute_reply": "2024-01-19T12:56:29.264367Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.910674Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.910277Z",
+ "iopub.status.idle": "2024-01-19T13:13:44.958083Z",
+ "shell.execute_reply": "2024-01-19T13:13:44.957403Z"
},
"id": "SLq-3q4xjruX"
},
@@ -1552,10 +1552,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:29.267485Z",
- "iopub.status.busy": "2024-01-19T12:56:29.267094Z",
- "iopub.status.idle": "2024-01-19T12:56:29.378921Z",
- "shell.execute_reply": "2024-01-19T12:56:29.378260Z"
+ "iopub.execute_input": "2024-01-19T13:13:44.960811Z",
+ "iopub.status.busy": "2024-01-19T13:13:44.960342Z",
+ "iopub.status.idle": "2024-01-19T13:13:45.063229Z",
+ "shell.execute_reply": "2024-01-19T13:13:45.062241Z"
},
"id": "g5LHhhuqFbXK"
},
@@ -1587,10 +1587,10 @@
"execution_count": 21,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:29.381949Z",
- "iopub.status.busy": "2024-01-19T12:56:29.381685Z",
- "iopub.status.idle": "2024-01-19T12:56:29.488080Z",
- "shell.execute_reply": "2024-01-19T12:56:29.487466Z"
+ "iopub.execute_input": "2024-01-19T13:13:45.066116Z",
+ "iopub.status.busy": "2024-01-19T13:13:45.065854Z",
+ "iopub.status.idle": "2024-01-19T13:13:45.181672Z",
+ "shell.execute_reply": "2024-01-19T13:13:45.180942Z"
},
"id": "p7w8F8ezBcet"
},
@@ -1647,10 +1647,10 @@
"execution_count": 22,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:29.490724Z",
- "iopub.status.busy": "2024-01-19T12:56:29.490388Z",
- "iopub.status.idle": "2024-01-19T12:56:29.692198Z",
- "shell.execute_reply": "2024-01-19T12:56:29.691632Z"
+ "iopub.execute_input": "2024-01-19T13:13:45.184299Z",
+ "iopub.status.busy": "2024-01-19T13:13:45.184031Z",
+ "iopub.status.idle": "2024-01-19T13:13:45.390649Z",
+ "shell.execute_reply": "2024-01-19T13:13:45.390061Z"
},
"id": "WETRL74tE_sU"
},
@@ -1685,10 +1685,10 @@
"execution_count": 23,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:29.694825Z",
- "iopub.status.busy": "2024-01-19T12:56:29.694430Z",
- "iopub.status.idle": "2024-01-19T12:56:29.918535Z",
- "shell.execute_reply": "2024-01-19T12:56:29.917846Z"
+ "iopub.execute_input": "2024-01-19T13:13:45.393315Z",
+ "iopub.status.busy": "2024-01-19T13:13:45.392932Z",
+ "iopub.status.idle": "2024-01-19T13:13:45.623609Z",
+ "shell.execute_reply": "2024-01-19T13:13:45.622866Z"
},
"id": "kCfdx2gOLmXS"
},
@@ -1850,10 +1850,10 @@
"execution_count": 24,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:29.921474Z",
- "iopub.status.busy": "2024-01-19T12:56:29.921063Z",
- "iopub.status.idle": "2024-01-19T12:56:29.927718Z",
- "shell.execute_reply": "2024-01-19T12:56:29.927214Z"
+ "iopub.execute_input": "2024-01-19T13:13:45.626484Z",
+ "iopub.status.busy": "2024-01-19T13:13:45.626166Z",
+ "iopub.status.idle": "2024-01-19T13:13:45.632662Z",
+ "shell.execute_reply": "2024-01-19T13:13:45.632119Z"
},
"id": "-uogYRWFYnuu"
},
@@ -1907,10 +1907,10 @@
"execution_count": 25,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:29.930206Z",
- "iopub.status.busy": "2024-01-19T12:56:29.929837Z",
- "iopub.status.idle": "2024-01-19T12:56:30.137558Z",
- "shell.execute_reply": "2024-01-19T12:56:30.136916Z"
+ "iopub.execute_input": "2024-01-19T13:13:45.634871Z",
+ "iopub.status.busy": "2024-01-19T13:13:45.634668Z",
+ "iopub.status.idle": "2024-01-19T13:13:45.841709Z",
+ "shell.execute_reply": "2024-01-19T13:13:45.841180Z"
},
"id": "pG-ljrmcYp9Q"
},
@@ -1957,10 +1957,10 @@
"execution_count": 26,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:30.140269Z",
- "iopub.status.busy": "2024-01-19T12:56:30.139990Z",
- "iopub.status.idle": "2024-01-19T12:56:31.202636Z",
- "shell.execute_reply": "2024-01-19T12:56:31.201959Z"
+ "iopub.execute_input": "2024-01-19T13:13:45.844509Z",
+ "iopub.status.busy": "2024-01-19T13:13:45.844028Z",
+ "iopub.status.idle": "2024-01-19T13:13:46.910155Z",
+ "shell.execute_reply": "2024-01-19T13:13:46.909436Z"
},
"id": "wL3ngCnuLEWd"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
index 0e51ccc4c..9701873b3 100644
--- a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb
@@ -89,10 +89,10 @@
"id": "a3ddc95f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:36.940506Z",
- "iopub.status.busy": "2024-01-19T12:56:36.940286Z",
- "iopub.status.idle": "2024-01-19T12:56:37.979068Z",
- "shell.execute_reply": "2024-01-19T12:56:37.978350Z"
+ "iopub.execute_input": "2024-01-19T13:13:52.733346Z",
+ "iopub.status.busy": "2024-01-19T13:13:52.733153Z",
+ "iopub.status.idle": "2024-01-19T13:13:53.767671Z",
+ "shell.execute_reply": "2024-01-19T13:13:53.767046Z"
},
"nbsphinx": "hidden"
},
@@ -102,7 +102,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -136,10 +136,10 @@
"id": "c4efd119",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:37.982157Z",
- "iopub.status.busy": "2024-01-19T12:56:37.981822Z",
- "iopub.status.idle": "2024-01-19T12:56:37.985349Z",
- "shell.execute_reply": "2024-01-19T12:56:37.984818Z"
+ "iopub.execute_input": "2024-01-19T13:13:53.770651Z",
+ "iopub.status.busy": "2024-01-19T13:13:53.770181Z",
+ "iopub.status.idle": "2024-01-19T13:13:53.773511Z",
+ "shell.execute_reply": "2024-01-19T13:13:53.772910Z"
}
},
"outputs": [],
@@ -264,10 +264,10 @@
"id": "c37c0a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:37.987922Z",
- "iopub.status.busy": "2024-01-19T12:56:37.987547Z",
- "iopub.status.idle": "2024-01-19T12:56:37.996017Z",
- "shell.execute_reply": "2024-01-19T12:56:37.995487Z"
+ "iopub.execute_input": "2024-01-19T13:13:53.776182Z",
+ "iopub.status.busy": "2024-01-19T13:13:53.775754Z",
+ "iopub.status.idle": "2024-01-19T13:13:53.784224Z",
+ "shell.execute_reply": "2024-01-19T13:13:53.783629Z"
},
"nbsphinx": "hidden"
},
@@ -351,10 +351,10 @@
"id": "99f69523",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:37.998354Z",
- "iopub.status.busy": "2024-01-19T12:56:37.997989Z",
- "iopub.status.idle": "2024-01-19T12:56:38.047123Z",
- "shell.execute_reply": "2024-01-19T12:56:38.046572Z"
+ "iopub.execute_input": "2024-01-19T13:13:53.786448Z",
+ "iopub.status.busy": "2024-01-19T13:13:53.786086Z",
+ "iopub.status.idle": "2024-01-19T13:13:53.835119Z",
+ "shell.execute_reply": "2024-01-19T13:13:53.834424Z"
}
},
"outputs": [],
@@ -380,10 +380,10 @@
"id": "8f241c16",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:38.050006Z",
- "iopub.status.busy": "2024-01-19T12:56:38.049574Z",
- "iopub.status.idle": "2024-01-19T12:56:38.069609Z",
- "shell.execute_reply": "2024-01-19T12:56:38.069050Z"
+ "iopub.execute_input": "2024-01-19T13:13:53.837927Z",
+ "iopub.status.busy": "2024-01-19T13:13:53.837475Z",
+ "iopub.status.idle": "2024-01-19T13:13:53.857080Z",
+ "shell.execute_reply": "2024-01-19T13:13:53.856540Z"
}
},
"outputs": [
@@ -598,10 +598,10 @@
"id": "4f0819ba",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:38.072087Z",
- "iopub.status.busy": "2024-01-19T12:56:38.071769Z",
- "iopub.status.idle": "2024-01-19T12:56:38.075952Z",
- "shell.execute_reply": "2024-01-19T12:56:38.075359Z"
+ "iopub.execute_input": "2024-01-19T13:13:53.859506Z",
+ "iopub.status.busy": "2024-01-19T13:13:53.859127Z",
+ "iopub.status.idle": "2024-01-19T13:13:53.863252Z",
+ "shell.execute_reply": "2024-01-19T13:13:53.862647Z"
}
},
"outputs": [
@@ -672,10 +672,10 @@
"id": "d009f347",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:38.078514Z",
- "iopub.status.busy": "2024-01-19T12:56:38.078141Z",
- "iopub.status.idle": "2024-01-19T12:56:38.105321Z",
- "shell.execute_reply": "2024-01-19T12:56:38.104816Z"
+ "iopub.execute_input": "2024-01-19T13:13:53.865786Z",
+ "iopub.status.busy": "2024-01-19T13:13:53.865409Z",
+ "iopub.status.idle": "2024-01-19T13:13:53.892918Z",
+ "shell.execute_reply": "2024-01-19T13:13:53.892386Z"
}
},
"outputs": [],
@@ -699,10 +699,10 @@
"id": "cbd1e415",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:38.107842Z",
- "iopub.status.busy": "2024-01-19T12:56:38.107476Z",
- "iopub.status.idle": "2024-01-19T12:56:38.134883Z",
- "shell.execute_reply": "2024-01-19T12:56:38.134221Z"
+ "iopub.execute_input": "2024-01-19T13:13:53.895558Z",
+ "iopub.status.busy": "2024-01-19T13:13:53.895099Z",
+ "iopub.status.idle": "2024-01-19T13:13:53.922924Z",
+ "shell.execute_reply": "2024-01-19T13:13:53.922400Z"
}
},
"outputs": [],
@@ -739,10 +739,10 @@
"id": "6ca92617",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:38.137859Z",
- "iopub.status.busy": "2024-01-19T12:56:38.137488Z",
- "iopub.status.idle": "2024-01-19T12:56:39.465282Z",
- "shell.execute_reply": "2024-01-19T12:56:39.464635Z"
+ "iopub.execute_input": "2024-01-19T13:13:53.925451Z",
+ "iopub.status.busy": "2024-01-19T13:13:53.925096Z",
+ "iopub.status.idle": "2024-01-19T13:13:55.261031Z",
+ "shell.execute_reply": "2024-01-19T13:13:55.260289Z"
}
},
"outputs": [],
@@ -772,10 +772,10 @@
"id": "bf945113",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:39.468399Z",
- "iopub.status.busy": "2024-01-19T12:56:39.467809Z",
- "iopub.status.idle": "2024-01-19T12:56:39.475373Z",
- "shell.execute_reply": "2024-01-19T12:56:39.474841Z"
+ "iopub.execute_input": "2024-01-19T13:13:55.264184Z",
+ "iopub.status.busy": "2024-01-19T13:13:55.263807Z",
+ "iopub.status.idle": "2024-01-19T13:13:55.271151Z",
+ "shell.execute_reply": "2024-01-19T13:13:55.270592Z"
},
"scrolled": true
},
@@ -886,10 +886,10 @@
"id": "14251ee0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:39.477904Z",
- "iopub.status.busy": "2024-01-19T12:56:39.477522Z",
- "iopub.status.idle": "2024-01-19T12:56:39.491533Z",
- "shell.execute_reply": "2024-01-19T12:56:39.490963Z"
+ "iopub.execute_input": "2024-01-19T13:13:55.273591Z",
+ "iopub.status.busy": "2024-01-19T13:13:55.273206Z",
+ "iopub.status.idle": "2024-01-19T13:13:55.286939Z",
+ "shell.execute_reply": "2024-01-19T13:13:55.286324Z"
}
},
"outputs": [
@@ -1139,10 +1139,10 @@
"id": "efe16638",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:39.493969Z",
- "iopub.status.busy": "2024-01-19T12:56:39.493605Z",
- "iopub.status.idle": "2024-01-19T12:56:39.500436Z",
- "shell.execute_reply": "2024-01-19T12:56:39.499876Z"
+ "iopub.execute_input": "2024-01-19T13:13:55.289354Z",
+ "iopub.status.busy": "2024-01-19T13:13:55.288991Z",
+ "iopub.status.idle": "2024-01-19T13:13:55.295852Z",
+ "shell.execute_reply": "2024-01-19T13:13:55.295299Z"
},
"scrolled": true
},
@@ -1316,10 +1316,10 @@
"id": "abd0fb0b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:39.503013Z",
- "iopub.status.busy": "2024-01-19T12:56:39.502528Z",
- "iopub.status.idle": "2024-01-19T12:56:39.505515Z",
- "shell.execute_reply": "2024-01-19T12:56:39.504993Z"
+ "iopub.execute_input": "2024-01-19T13:13:55.298225Z",
+ "iopub.status.busy": "2024-01-19T13:13:55.297855Z",
+ "iopub.status.idle": "2024-01-19T13:13:55.300664Z",
+ "shell.execute_reply": "2024-01-19T13:13:55.300117Z"
}
},
"outputs": [],
@@ -1341,10 +1341,10 @@
"id": "cdf061df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:39.507675Z",
- "iopub.status.busy": "2024-01-19T12:56:39.507478Z",
- "iopub.status.idle": "2024-01-19T12:56:39.511792Z",
- "shell.execute_reply": "2024-01-19T12:56:39.511268Z"
+ "iopub.execute_input": "2024-01-19T13:13:55.302966Z",
+ "iopub.status.busy": "2024-01-19T13:13:55.302596Z",
+ "iopub.status.idle": "2024-01-19T13:13:55.306865Z",
+ "shell.execute_reply": "2024-01-19T13:13:55.306323Z"
},
"scrolled": true
},
@@ -1396,10 +1396,10 @@
"id": "08949890",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:39.514037Z",
- "iopub.status.busy": "2024-01-19T12:56:39.513839Z",
- "iopub.status.idle": "2024-01-19T12:56:39.516764Z",
- "shell.execute_reply": "2024-01-19T12:56:39.516235Z"
+ "iopub.execute_input": "2024-01-19T13:13:55.309265Z",
+ "iopub.status.busy": "2024-01-19T13:13:55.308892Z",
+ "iopub.status.idle": "2024-01-19T13:13:55.311730Z",
+ "shell.execute_reply": "2024-01-19T13:13:55.311187Z"
}
},
"outputs": [],
@@ -1423,10 +1423,10 @@
"id": "6948b073",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:39.519078Z",
- "iopub.status.busy": "2024-01-19T12:56:39.518880Z",
- "iopub.status.idle": "2024-01-19T12:56:39.523434Z",
- "shell.execute_reply": "2024-01-19T12:56:39.522796Z"
+ "iopub.execute_input": "2024-01-19T13:13:55.314047Z",
+ "iopub.status.busy": "2024-01-19T13:13:55.313677Z",
+ "iopub.status.idle": "2024-01-19T13:13:55.319748Z",
+ "shell.execute_reply": "2024-01-19T13:13:55.319218Z"
}
},
"outputs": [
@@ -1481,10 +1481,10 @@
"id": "6f8e6914",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:39.525659Z",
- "iopub.status.busy": "2024-01-19T12:56:39.525459Z",
- "iopub.status.idle": "2024-01-19T12:56:39.558825Z",
- "shell.execute_reply": "2024-01-19T12:56:39.558295Z"
+ "iopub.execute_input": "2024-01-19T13:13:55.322036Z",
+ "iopub.status.busy": "2024-01-19T13:13:55.321834Z",
+ "iopub.status.idle": "2024-01-19T13:13:55.355568Z",
+ "shell.execute_reply": "2024-01-19T13:13:55.355029Z"
}
},
"outputs": [],
@@ -1527,10 +1527,10 @@
"id": "b806d2ea",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:39.561231Z",
- "iopub.status.busy": "2024-01-19T12:56:39.561021Z",
- "iopub.status.idle": "2024-01-19T12:56:39.566195Z",
- "shell.execute_reply": "2024-01-19T12:56:39.565674Z"
+ "iopub.execute_input": "2024-01-19T13:13:55.358209Z",
+ "iopub.status.busy": "2024-01-19T13:13:55.357824Z",
+ "iopub.status.idle": "2024-01-19T13:13:55.362831Z",
+ "shell.execute_reply": "2024-01-19T13:13:55.362239Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
index fafa01ab2..d5809d88c 100644
--- a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb
@@ -63,10 +63,10 @@
"id": "7383d024-8273-4039-bccd-aab3020d331f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:44.296076Z",
- "iopub.status.busy": "2024-01-19T12:56:44.295880Z",
- "iopub.status.idle": "2024-01-19T12:56:45.370550Z",
- "shell.execute_reply": "2024-01-19T12:56:45.369876Z"
+ "iopub.execute_input": "2024-01-19T13:14:01.054490Z",
+ "iopub.status.busy": "2024-01-19T13:14:01.054253Z",
+ "iopub.status.idle": "2024-01-19T13:14:02.143973Z",
+ "shell.execute_reply": "2024-01-19T13:14:02.143360Z"
},
"nbsphinx": "hidden"
},
@@ -78,7 +78,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -104,10 +104,10 @@
"id": "bf9101d8-b1a9-4305-b853-45aaf3d67a69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:45.373476Z",
- "iopub.status.busy": "2024-01-19T12:56:45.373194Z",
- "iopub.status.idle": "2024-01-19T12:56:45.661821Z",
- "shell.execute_reply": "2024-01-19T12:56:45.661145Z"
+ "iopub.execute_input": "2024-01-19T13:14:02.147132Z",
+ "iopub.status.busy": "2024-01-19T13:14:02.146506Z",
+ "iopub.status.idle": "2024-01-19T13:14:02.436404Z",
+ "shell.execute_reply": "2024-01-19T13:14:02.435665Z"
}
},
"outputs": [],
@@ -269,10 +269,10 @@
"id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:45.664618Z",
- "iopub.status.busy": "2024-01-19T12:56:45.664398Z",
- "iopub.status.idle": "2024-01-19T12:56:45.678396Z",
- "shell.execute_reply": "2024-01-19T12:56:45.677755Z"
+ "iopub.execute_input": "2024-01-19T13:14:02.439649Z",
+ "iopub.status.busy": "2024-01-19T13:14:02.439218Z",
+ "iopub.status.idle": "2024-01-19T13:14:02.454218Z",
+ "shell.execute_reply": "2024-01-19T13:14:02.453660Z"
},
"nbsphinx": "hidden"
},
@@ -408,10 +408,10 @@
"id": "dac65d3b-51e8-4682-b829-beab610b56d6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:45.680992Z",
- "iopub.status.busy": "2024-01-19T12:56:45.680617Z",
- "iopub.status.idle": "2024-01-19T12:56:48.347014Z",
- "shell.execute_reply": "2024-01-19T12:56:48.346362Z"
+ "iopub.execute_input": "2024-01-19T13:14:02.456729Z",
+ "iopub.status.busy": "2024-01-19T13:14:02.456372Z",
+ "iopub.status.idle": "2024-01-19T13:14:05.085807Z",
+ "shell.execute_reply": "2024-01-19T13:14:05.085123Z"
}
},
"outputs": [
@@ -453,10 +453,10 @@
"id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:48.349400Z",
- "iopub.status.busy": "2024-01-19T12:56:48.349192Z",
- "iopub.status.idle": "2024-01-19T12:56:49.916573Z",
- "shell.execute_reply": "2024-01-19T12:56:49.915925Z"
+ "iopub.execute_input": "2024-01-19T13:14:05.088502Z",
+ "iopub.status.busy": "2024-01-19T13:14:05.088029Z",
+ "iopub.status.idle": "2024-01-19T13:14:06.659272Z",
+ "shell.execute_reply": "2024-01-19T13:14:06.658643Z"
}
},
"outputs": [],
@@ -498,10 +498,10 @@
"id": "ac1a60df",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:49.919340Z",
- "iopub.status.busy": "2024-01-19T12:56:49.919074Z",
- "iopub.status.idle": "2024-01-19T12:56:49.924277Z",
- "shell.execute_reply": "2024-01-19T12:56:49.923758Z"
+ "iopub.execute_input": "2024-01-19T13:14:06.662042Z",
+ "iopub.status.busy": "2024-01-19T13:14:06.661769Z",
+ "iopub.status.idle": "2024-01-19T13:14:06.667112Z",
+ "shell.execute_reply": "2024-01-19T13:14:06.666570Z"
}
},
"outputs": [
@@ -543,10 +543,10 @@
"id": "d09115b6-ad44-474f-9c8a-85a459586439",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:49.926581Z",
- "iopub.status.busy": "2024-01-19T12:56:49.926373Z",
- "iopub.status.idle": "2024-01-19T12:56:51.267051Z",
- "shell.execute_reply": "2024-01-19T12:56:51.266313Z"
+ "iopub.execute_input": "2024-01-19T13:14:06.669642Z",
+ "iopub.status.busy": "2024-01-19T13:14:06.669150Z",
+ "iopub.status.idle": "2024-01-19T13:14:08.030098Z",
+ "shell.execute_reply": "2024-01-19T13:14:08.029317Z"
}
},
"outputs": [
@@ -584,10 +584,10 @@
"id": "fffa88f6-84d7-45fe-8214-0e22079a06d1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:51.269990Z",
- "iopub.status.busy": "2024-01-19T12:56:51.269403Z",
- "iopub.status.idle": "2024-01-19T12:56:54.073371Z",
- "shell.execute_reply": "2024-01-19T12:56:54.072671Z"
+ "iopub.execute_input": "2024-01-19T13:14:08.033291Z",
+ "iopub.status.busy": "2024-01-19T13:14:08.032438Z",
+ "iopub.status.idle": "2024-01-19T13:14:10.835031Z",
+ "shell.execute_reply": "2024-01-19T13:14:10.834300Z"
}
},
"outputs": [
@@ -622,10 +622,10 @@
"id": "c1198575",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:54.076151Z",
- "iopub.status.busy": "2024-01-19T12:56:54.075650Z",
- "iopub.status.idle": "2024-01-19T12:56:54.080863Z",
- "shell.execute_reply": "2024-01-19T12:56:54.080225Z"
+ "iopub.execute_input": "2024-01-19T13:14:10.837501Z",
+ "iopub.status.busy": "2024-01-19T13:14:10.837286Z",
+ "iopub.status.idle": "2024-01-19T13:14:10.842407Z",
+ "shell.execute_reply": "2024-01-19T13:14:10.841758Z"
}
},
"outputs": [
@@ -662,10 +662,10 @@
"id": "49161b19-7625-4fb7-add9-607d91a7eca1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:54.083092Z",
- "iopub.status.busy": "2024-01-19T12:56:54.082892Z",
- "iopub.status.idle": "2024-01-19T12:56:54.087291Z",
- "shell.execute_reply": "2024-01-19T12:56:54.086648Z"
+ "iopub.execute_input": "2024-01-19T13:14:10.844714Z",
+ "iopub.status.busy": "2024-01-19T13:14:10.844373Z",
+ "iopub.status.idle": "2024-01-19T13:14:10.848475Z",
+ "shell.execute_reply": "2024-01-19T13:14:10.847942Z"
}
},
"outputs": [],
@@ -688,10 +688,10 @@
"id": "d1a2c008",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:54.089672Z",
- "iopub.status.busy": "2024-01-19T12:56:54.089291Z",
- "iopub.status.idle": "2024-01-19T12:56:54.092626Z",
- "shell.execute_reply": "2024-01-19T12:56:54.092056Z"
+ "iopub.execute_input": "2024-01-19T13:14:10.850682Z",
+ "iopub.status.busy": "2024-01-19T13:14:10.850483Z",
+ "iopub.status.idle": "2024-01-19T13:14:10.854036Z",
+ "shell.execute_reply": "2024-01-19T13:14:10.853511Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
index d3c4636fc..c0e5bf4cf 100644
--- a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb
@@ -70,10 +70,10 @@
"id": "0ba0dc70",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:56:59.162349Z",
- "iopub.status.busy": "2024-01-19T12:56:59.162148Z",
- "iopub.status.idle": "2024-01-19T12:57:00.251984Z",
- "shell.execute_reply": "2024-01-19T12:57:00.251377Z"
+ "iopub.execute_input": "2024-01-19T13:14:15.654143Z",
+ "iopub.status.busy": "2024-01-19T13:14:15.653951Z",
+ "iopub.status.idle": "2024-01-19T13:14:16.732658Z",
+ "shell.execute_reply": "2024-01-19T13:14:16.732026Z"
},
"nbsphinx": "hidden"
},
@@ -83,7 +83,7 @@
"dependencies = [\"cleanlab\", \"matplotlib\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -109,10 +109,10 @@
"id": "c90449c8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:00.254661Z",
- "iopub.status.busy": "2024-01-19T12:57:00.254363Z",
- "iopub.status.idle": "2024-01-19T12:57:02.963401Z",
- "shell.execute_reply": "2024-01-19T12:57:02.962626Z"
+ "iopub.execute_input": "2024-01-19T13:14:16.735690Z",
+ "iopub.status.busy": "2024-01-19T13:14:16.735123Z",
+ "iopub.status.idle": "2024-01-19T13:14:18.049758Z",
+ "shell.execute_reply": "2024-01-19T13:14:18.048985Z"
}
},
"outputs": [],
@@ -130,10 +130,10 @@
"id": "df8be4c6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:02.966356Z",
- "iopub.status.busy": "2024-01-19T12:57:02.965929Z",
- "iopub.status.idle": "2024-01-19T12:57:02.969226Z",
- "shell.execute_reply": "2024-01-19T12:57:02.968718Z"
+ "iopub.execute_input": "2024-01-19T13:14:18.052739Z",
+ "iopub.status.busy": "2024-01-19T13:14:18.052343Z",
+ "iopub.status.idle": "2024-01-19T13:14:18.055751Z",
+ "shell.execute_reply": "2024-01-19T13:14:18.055112Z"
}
},
"outputs": [],
@@ -165,10 +165,10 @@
"id": "2e9ffd6f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:02.971577Z",
- "iopub.status.busy": "2024-01-19T12:57:02.971211Z",
- "iopub.status.idle": "2024-01-19T12:57:02.976682Z",
- "shell.execute_reply": "2024-01-19T12:57:02.976078Z"
+ "iopub.execute_input": "2024-01-19T13:14:18.058191Z",
+ "iopub.status.busy": "2024-01-19T13:14:18.057826Z",
+ "iopub.status.idle": "2024-01-19T13:14:18.063362Z",
+ "shell.execute_reply": "2024-01-19T13:14:18.062772Z"
}
},
"outputs": [],
@@ -194,10 +194,10 @@
"id": "56705562",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:02.979048Z",
- "iopub.status.busy": "2024-01-19T12:57:02.978613Z",
- "iopub.status.idle": "2024-01-19T12:57:03.577752Z",
- "shell.execute_reply": "2024-01-19T12:57:03.577113Z"
+ "iopub.execute_input": "2024-01-19T13:14:18.065877Z",
+ "iopub.status.busy": "2024-01-19T13:14:18.065503Z",
+ "iopub.status.idle": "2024-01-19T13:14:18.663282Z",
+ "shell.execute_reply": "2024-01-19T13:14:18.662620Z"
},
"scrolled": true
},
@@ -237,10 +237,10 @@
"id": "b08144d7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:03.580655Z",
- "iopub.status.busy": "2024-01-19T12:57:03.580385Z",
- "iopub.status.idle": "2024-01-19T12:57:03.586713Z",
- "shell.execute_reply": "2024-01-19T12:57:03.586082Z"
+ "iopub.execute_input": "2024-01-19T13:14:18.666470Z",
+ "iopub.status.busy": "2024-01-19T13:14:18.665995Z",
+ "iopub.status.idle": "2024-01-19T13:14:18.672076Z",
+ "shell.execute_reply": "2024-01-19T13:14:18.671455Z"
}
},
"outputs": [
@@ -492,10 +492,10 @@
"id": "3d70bec6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:03.589295Z",
- "iopub.status.busy": "2024-01-19T12:57:03.588906Z",
- "iopub.status.idle": "2024-01-19T12:57:03.593163Z",
- "shell.execute_reply": "2024-01-19T12:57:03.592563Z"
+ "iopub.execute_input": "2024-01-19T13:14:18.674686Z",
+ "iopub.status.busy": "2024-01-19T13:14:18.674288Z",
+ "iopub.status.idle": "2024-01-19T13:14:18.678652Z",
+ "shell.execute_reply": "2024-01-19T13:14:18.678119Z"
}
},
"outputs": [
@@ -552,10 +552,10 @@
"id": "4caa635d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:03.595373Z",
- "iopub.status.busy": "2024-01-19T12:57:03.595168Z",
- "iopub.status.idle": "2024-01-19T12:57:04.219392Z",
- "shell.execute_reply": "2024-01-19T12:57:04.218653Z"
+ "iopub.execute_input": "2024-01-19T13:14:18.681235Z",
+ "iopub.status.busy": "2024-01-19T13:14:18.680752Z",
+ "iopub.status.idle": "2024-01-19T13:14:19.339559Z",
+ "shell.execute_reply": "2024-01-19T13:14:19.338911Z"
}
},
"outputs": [
@@ -611,10 +611,10 @@
"id": "a9b4c590",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:04.222086Z",
- "iopub.status.busy": "2024-01-19T12:57:04.221852Z",
- "iopub.status.idle": "2024-01-19T12:57:04.310543Z",
- "shell.execute_reply": "2024-01-19T12:57:04.309850Z"
+ "iopub.execute_input": "2024-01-19T13:14:19.342458Z",
+ "iopub.status.busy": "2024-01-19T13:14:19.341885Z",
+ "iopub.status.idle": "2024-01-19T13:14:19.460158Z",
+ "shell.execute_reply": "2024-01-19T13:14:19.459565Z"
}
},
"outputs": [
@@ -655,10 +655,10 @@
"id": "ffd9ebcc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:04.313377Z",
- "iopub.status.busy": "2024-01-19T12:57:04.312786Z",
- "iopub.status.idle": "2024-01-19T12:57:04.317904Z",
- "shell.execute_reply": "2024-01-19T12:57:04.317387Z"
+ "iopub.execute_input": "2024-01-19T13:14:19.462760Z",
+ "iopub.status.busy": "2024-01-19T13:14:19.462351Z",
+ "iopub.status.idle": "2024-01-19T13:14:19.467093Z",
+ "shell.execute_reply": "2024-01-19T13:14:19.466568Z"
}
},
"outputs": [
@@ -695,10 +695,10 @@
"id": "4dd46d67",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:04.320284Z",
- "iopub.status.busy": "2024-01-19T12:57:04.319895Z",
- "iopub.status.idle": "2024-01-19T12:57:04.698331Z",
- "shell.execute_reply": "2024-01-19T12:57:04.697610Z"
+ "iopub.execute_input": "2024-01-19T13:14:19.469597Z",
+ "iopub.status.busy": "2024-01-19T13:14:19.469224Z",
+ "iopub.status.idle": "2024-01-19T13:14:19.848671Z",
+ "shell.execute_reply": "2024-01-19T13:14:19.847985Z"
}
},
"outputs": [
@@ -757,10 +757,10 @@
"id": "ceec2394",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:04.701665Z",
- "iopub.status.busy": "2024-01-19T12:57:04.701140Z",
- "iopub.status.idle": "2024-01-19T12:57:05.040985Z",
- "shell.execute_reply": "2024-01-19T12:57:05.040290Z"
+ "iopub.execute_input": "2024-01-19T13:14:19.852080Z",
+ "iopub.status.busy": "2024-01-19T13:14:19.851605Z",
+ "iopub.status.idle": "2024-01-19T13:14:20.161821Z",
+ "shell.execute_reply": "2024-01-19T13:14:20.161158Z"
}
},
"outputs": [
@@ -807,10 +807,10 @@
"id": "94f82b0d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:05.044322Z",
- "iopub.status.busy": "2024-01-19T12:57:05.043915Z",
- "iopub.status.idle": "2024-01-19T12:57:05.401145Z",
- "shell.execute_reply": "2024-01-19T12:57:05.400398Z"
+ "iopub.execute_input": "2024-01-19T13:14:20.165233Z",
+ "iopub.status.busy": "2024-01-19T13:14:20.164821Z",
+ "iopub.status.idle": "2024-01-19T13:14:20.523643Z",
+ "shell.execute_reply": "2024-01-19T13:14:20.522926Z"
}
},
"outputs": [
@@ -857,10 +857,10 @@
"id": "1ea18c5d",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:05.404146Z",
- "iopub.status.busy": "2024-01-19T12:57:05.403869Z",
- "iopub.status.idle": "2024-01-19T12:57:05.870011Z",
- "shell.execute_reply": "2024-01-19T12:57:05.869305Z"
+ "iopub.execute_input": "2024-01-19T13:14:20.526869Z",
+ "iopub.status.busy": "2024-01-19T13:14:20.526366Z",
+ "iopub.status.idle": "2024-01-19T13:14:20.964829Z",
+ "shell.execute_reply": "2024-01-19T13:14:20.964168Z"
}
},
"outputs": [
@@ -920,10 +920,10 @@
"id": "7e770d23",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:05.874835Z",
- "iopub.status.busy": "2024-01-19T12:57:05.874423Z",
- "iopub.status.idle": "2024-01-19T12:57:06.304629Z",
- "shell.execute_reply": "2024-01-19T12:57:06.303894Z"
+ "iopub.execute_input": "2024-01-19T13:14:20.969428Z",
+ "iopub.status.busy": "2024-01-19T13:14:20.969198Z",
+ "iopub.status.idle": "2024-01-19T13:14:21.424607Z",
+ "shell.execute_reply": "2024-01-19T13:14:21.423904Z"
}
},
"outputs": [
@@ -966,10 +966,10 @@
"id": "57e84a27",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:06.308441Z",
- "iopub.status.busy": "2024-01-19T12:57:06.307985Z",
- "iopub.status.idle": "2024-01-19T12:57:06.615518Z",
- "shell.execute_reply": "2024-01-19T12:57:06.614798Z"
+ "iopub.execute_input": "2024-01-19T13:14:21.428182Z",
+ "iopub.status.busy": "2024-01-19T13:14:21.427945Z",
+ "iopub.status.idle": "2024-01-19T13:14:21.770310Z",
+ "shell.execute_reply": "2024-01-19T13:14:21.769661Z"
}
},
"outputs": [
@@ -1012,10 +1012,10 @@
"id": "0302818a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:06.618609Z",
- "iopub.status.busy": "2024-01-19T12:57:06.618198Z",
- "iopub.status.idle": "2024-01-19T12:57:06.798486Z",
- "shell.execute_reply": "2024-01-19T12:57:06.797851Z"
+ "iopub.execute_input": "2024-01-19T13:14:21.773017Z",
+ "iopub.status.busy": "2024-01-19T13:14:21.772598Z",
+ "iopub.status.idle": "2024-01-19T13:14:21.953150Z",
+ "shell.execute_reply": "2024-01-19T13:14:21.952450Z"
}
},
"outputs": [
@@ -1050,10 +1050,10 @@
"id": "8ce74938",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:06.800992Z",
- "iopub.status.busy": "2024-01-19T12:57:06.800787Z",
- "iopub.status.idle": "2024-01-19T12:57:06.804722Z",
- "shell.execute_reply": "2024-01-19T12:57:06.804194Z"
+ "iopub.execute_input": "2024-01-19T13:14:21.955755Z",
+ "iopub.status.busy": "2024-01-19T13:14:21.955370Z",
+ "iopub.status.idle": "2024-01-19T13:14:21.959181Z",
+ "shell.execute_reply": "2024-01-19T13:14:21.958604Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
index 9beee3bfb..8a11b39c7 100644
--- a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb
@@ -109,10 +109,10 @@
"id": "2bbebfc8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:08.989257Z",
- "iopub.status.busy": "2024-01-19T12:57:08.989066Z",
- "iopub.status.idle": "2024-01-19T12:57:10.935592Z",
- "shell.execute_reply": "2024-01-19T12:57:10.934906Z"
+ "iopub.execute_input": "2024-01-19T13:14:24.126917Z",
+ "iopub.status.busy": "2024-01-19T13:14:24.126720Z",
+ "iopub.status.idle": "2024-01-19T13:14:26.086659Z",
+ "shell.execute_reply": "2024-01-19T13:14:26.085909Z"
},
"nbsphinx": "hidden"
},
@@ -125,7 +125,7 @@
"dependencies = [\"matplotlib\", \"torch\", \"torchvision\", \"timm\", \"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -159,10 +159,10 @@
"id": "4396f544",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:10.938711Z",
- "iopub.status.busy": "2024-01-19T12:57:10.938123Z",
- "iopub.status.idle": "2024-01-19T12:57:11.254935Z",
- "shell.execute_reply": "2024-01-19T12:57:11.254320Z"
+ "iopub.execute_input": "2024-01-19T13:14:26.089823Z",
+ "iopub.status.busy": "2024-01-19T13:14:26.089459Z",
+ "iopub.status.idle": "2024-01-19T13:14:26.406127Z",
+ "shell.execute_reply": "2024-01-19T13:14:26.405439Z"
}
},
"outputs": [],
@@ -188,10 +188,10 @@
"id": "3792f82e",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:11.257767Z",
- "iopub.status.busy": "2024-01-19T12:57:11.257541Z",
- "iopub.status.idle": "2024-01-19T12:57:11.261909Z",
- "shell.execute_reply": "2024-01-19T12:57:11.261420Z"
+ "iopub.execute_input": "2024-01-19T13:14:26.409046Z",
+ "iopub.status.busy": "2024-01-19T13:14:26.408824Z",
+ "iopub.status.idle": "2024-01-19T13:14:26.413360Z",
+ "shell.execute_reply": "2024-01-19T13:14:26.412877Z"
},
"nbsphinx": "hidden"
},
@@ -225,10 +225,10 @@
"id": "fd853a54",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:11.264439Z",
- "iopub.status.busy": "2024-01-19T12:57:11.264048Z",
- "iopub.status.idle": "2024-01-19T12:57:18.356066Z",
- "shell.execute_reply": "2024-01-19T12:57:18.355403Z"
+ "iopub.execute_input": "2024-01-19T13:14:26.415764Z",
+ "iopub.status.busy": "2024-01-19T13:14:26.415395Z",
+ "iopub.status.idle": "2024-01-19T13:14:30.868382Z",
+ "shell.execute_reply": "2024-01-19T13:14:30.867705Z"
}
},
"outputs": [
@@ -242,7 +242,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e8084a26e1dd4ab7b55855153c465192",
+ "model_id": "8cdf40e74d564639b00dec130489c5a3",
"version_major": 2,
"version_minor": 0
},
@@ -361,10 +361,10 @@
"id": "9b64e0aa",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:18.358542Z",
- "iopub.status.busy": "2024-01-19T12:57:18.358324Z",
- "iopub.status.idle": "2024-01-19T12:57:18.363477Z",
- "shell.execute_reply": "2024-01-19T12:57:18.362876Z"
+ "iopub.execute_input": "2024-01-19T13:14:30.870924Z",
+ "iopub.status.busy": "2024-01-19T13:14:30.870715Z",
+ "iopub.status.idle": "2024-01-19T13:14:30.875895Z",
+ "shell.execute_reply": "2024-01-19T13:14:30.875359Z"
},
"nbsphinx": "hidden"
},
@@ -415,10 +415,10 @@
"id": "a00aa3ed",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:18.365966Z",
- "iopub.status.busy": "2024-01-19T12:57:18.365627Z",
- "iopub.status.idle": "2024-01-19T12:57:18.881372Z",
- "shell.execute_reply": "2024-01-19T12:57:18.880628Z"
+ "iopub.execute_input": "2024-01-19T13:14:30.878039Z",
+ "iopub.status.busy": "2024-01-19T13:14:30.877847Z",
+ "iopub.status.idle": "2024-01-19T13:14:31.422792Z",
+ "shell.execute_reply": "2024-01-19T13:14:31.422086Z"
}
},
"outputs": [
@@ -451,10 +451,10 @@
"id": "41e5cb6b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:18.884108Z",
- "iopub.status.busy": "2024-01-19T12:57:18.883586Z",
- "iopub.status.idle": "2024-01-19T12:57:19.523212Z",
- "shell.execute_reply": "2024-01-19T12:57:19.522614Z"
+ "iopub.execute_input": "2024-01-19T13:14:31.425651Z",
+ "iopub.status.busy": "2024-01-19T13:14:31.425146Z",
+ "iopub.status.idle": "2024-01-19T13:14:32.078441Z",
+ "shell.execute_reply": "2024-01-19T13:14:32.077862Z"
}
},
"outputs": [
@@ -492,10 +492,10 @@
"id": "1cf25354",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:19.525713Z",
- "iopub.status.busy": "2024-01-19T12:57:19.525485Z",
- "iopub.status.idle": "2024-01-19T12:57:19.529278Z",
- "shell.execute_reply": "2024-01-19T12:57:19.528717Z"
+ "iopub.execute_input": "2024-01-19T13:14:32.080974Z",
+ "iopub.status.busy": "2024-01-19T13:14:32.080744Z",
+ "iopub.status.idle": "2024-01-19T13:14:32.084749Z",
+ "shell.execute_reply": "2024-01-19T13:14:32.084224Z"
}
},
"outputs": [],
@@ -518,10 +518,10 @@
"id": "85a58d41",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:19.531340Z",
- "iopub.status.busy": "2024-01-19T12:57:19.531137Z",
- "iopub.status.idle": "2024-01-19T12:57:33.183812Z",
- "shell.execute_reply": "2024-01-19T12:57:33.182994Z"
+ "iopub.execute_input": "2024-01-19T13:14:32.087063Z",
+ "iopub.status.busy": "2024-01-19T13:14:32.086691Z",
+ "iopub.status.idle": "2024-01-19T13:14:44.227528Z",
+ "shell.execute_reply": "2024-01-19T13:14:44.226793Z"
}
},
"outputs": [
@@ -580,10 +580,10 @@
"id": "feb0f519",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:33.186611Z",
- "iopub.status.busy": "2024-01-19T12:57:33.186340Z",
- "iopub.status.idle": "2024-01-19T12:57:34.741472Z",
- "shell.execute_reply": "2024-01-19T12:57:34.740730Z"
+ "iopub.execute_input": "2024-01-19T13:14:44.230643Z",
+ "iopub.status.busy": "2024-01-19T13:14:44.230121Z",
+ "iopub.status.idle": "2024-01-19T13:14:45.819720Z",
+ "shell.execute_reply": "2024-01-19T13:14:45.819011Z"
}
},
"outputs": [
@@ -627,10 +627,10 @@
"id": "089d5860",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:34.744894Z",
- "iopub.status.busy": "2024-01-19T12:57:34.744369Z",
- "iopub.status.idle": "2024-01-19T12:57:35.010750Z",
- "shell.execute_reply": "2024-01-19T12:57:35.010054Z"
+ "iopub.execute_input": "2024-01-19T13:14:45.822635Z",
+ "iopub.status.busy": "2024-01-19T13:14:45.822420Z",
+ "iopub.status.idle": "2024-01-19T13:14:46.065887Z",
+ "shell.execute_reply": "2024-01-19T13:14:46.063765Z"
}
},
"outputs": [
@@ -666,10 +666,10 @@
"id": "78b1951c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:35.014411Z",
- "iopub.status.busy": "2024-01-19T12:57:35.014164Z",
- "iopub.status.idle": "2024-01-19T12:57:35.683162Z",
- "shell.execute_reply": "2024-01-19T12:57:35.682471Z"
+ "iopub.execute_input": "2024-01-19T13:14:46.068680Z",
+ "iopub.status.busy": "2024-01-19T13:14:46.068438Z",
+ "iopub.status.idle": "2024-01-19T13:14:46.720059Z",
+ "shell.execute_reply": "2024-01-19T13:14:46.719510Z"
}
},
"outputs": [
@@ -719,10 +719,10 @@
"id": "e9dff81b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:35.686307Z",
- "iopub.status.busy": "2024-01-19T12:57:35.686041Z",
- "iopub.status.idle": "2024-01-19T12:57:36.184663Z",
- "shell.execute_reply": "2024-01-19T12:57:36.184008Z"
+ "iopub.execute_input": "2024-01-19T13:14:46.723088Z",
+ "iopub.status.busy": "2024-01-19T13:14:46.722644Z",
+ "iopub.status.idle": "2024-01-19T13:14:47.182095Z",
+ "shell.execute_reply": "2024-01-19T13:14:47.181486Z"
}
},
"outputs": [
@@ -770,10 +770,10 @@
"id": "616769f8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:36.187455Z",
- "iopub.status.busy": "2024-01-19T12:57:36.186928Z",
- "iopub.status.idle": "2024-01-19T12:57:36.436404Z",
- "shell.execute_reply": "2024-01-19T12:57:36.435673Z"
+ "iopub.execute_input": "2024-01-19T13:14:47.184894Z",
+ "iopub.status.busy": "2024-01-19T13:14:47.184437Z",
+ "iopub.status.idle": "2024-01-19T13:14:47.433903Z",
+ "shell.execute_reply": "2024-01-19T13:14:47.433162Z"
}
},
"outputs": [
@@ -829,10 +829,10 @@
"id": "40fed4ef",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:36.439654Z",
- "iopub.status.busy": "2024-01-19T12:57:36.439108Z",
- "iopub.status.idle": "2024-01-19T12:57:36.523965Z",
- "shell.execute_reply": "2024-01-19T12:57:36.523390Z"
+ "iopub.execute_input": "2024-01-19T13:14:47.437217Z",
+ "iopub.status.busy": "2024-01-19T13:14:47.436677Z",
+ "iopub.status.idle": "2024-01-19T13:14:47.525071Z",
+ "shell.execute_reply": "2024-01-19T13:14:47.524497Z"
}
},
"outputs": [],
@@ -853,10 +853,10 @@
"id": "89f9db72",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:57:36.526740Z",
- "iopub.status.busy": "2024-01-19T12:57:36.526389Z",
- "iopub.status.idle": "2024-01-19T12:58:14.390703Z",
- "shell.execute_reply": "2024-01-19T12:58:14.390042Z"
+ "iopub.execute_input": "2024-01-19T13:14:47.528096Z",
+ "iopub.status.busy": "2024-01-19T13:14:47.527639Z",
+ "iopub.status.idle": "2024-01-19T13:15:25.547141Z",
+ "shell.execute_reply": "2024-01-19T13:15:25.546409Z"
}
},
"outputs": [
@@ -893,10 +893,10 @@
"id": "874c885a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:14.393454Z",
- "iopub.status.busy": "2024-01-19T12:58:14.393043Z",
- "iopub.status.idle": "2024-01-19T12:58:15.563828Z",
- "shell.execute_reply": "2024-01-19T12:58:15.563133Z"
+ "iopub.execute_input": "2024-01-19T13:15:25.550087Z",
+ "iopub.status.busy": "2024-01-19T13:15:25.549648Z",
+ "iopub.status.idle": "2024-01-19T13:15:26.747021Z",
+ "shell.execute_reply": "2024-01-19T13:15:26.746429Z"
}
},
"outputs": [
@@ -927,10 +927,10 @@
"id": "e110fc4b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:15.567191Z",
- "iopub.status.busy": "2024-01-19T12:58:15.566526Z",
- "iopub.status.idle": "2024-01-19T12:58:15.752535Z",
- "shell.execute_reply": "2024-01-19T12:58:15.751944Z"
+ "iopub.execute_input": "2024-01-19T13:15:26.750303Z",
+ "iopub.status.busy": "2024-01-19T13:15:26.749564Z",
+ "iopub.status.idle": "2024-01-19T13:15:26.947879Z",
+ "shell.execute_reply": "2024-01-19T13:15:26.947123Z"
}
},
"outputs": [],
@@ -944,10 +944,10 @@
"id": "85b60cbf",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:15.755557Z",
- "iopub.status.busy": "2024-01-19T12:58:15.755145Z",
- "iopub.status.idle": "2024-01-19T12:58:15.758689Z",
- "shell.execute_reply": "2024-01-19T12:58:15.758163Z"
+ "iopub.execute_input": "2024-01-19T13:15:26.950704Z",
+ "iopub.status.busy": "2024-01-19T13:15:26.950490Z",
+ "iopub.status.idle": "2024-01-19T13:15:26.953958Z",
+ "shell.execute_reply": "2024-01-19T13:15:26.953433Z"
}
},
"outputs": [],
@@ -969,10 +969,10 @@
"id": "17f96fa6",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:15.761016Z",
- "iopub.status.busy": "2024-01-19T12:58:15.760815Z",
- "iopub.status.idle": "2024-01-19T12:58:15.769564Z",
- "shell.execute_reply": "2024-01-19T12:58:15.768942Z"
+ "iopub.execute_input": "2024-01-19T13:15:26.956522Z",
+ "iopub.status.busy": "2024-01-19T13:15:26.956081Z",
+ "iopub.status.idle": "2024-01-19T13:15:26.965068Z",
+ "shell.execute_reply": "2024-01-19T13:15:26.964571Z"
},
"nbsphinx": "hidden"
},
@@ -1017,83 +1017,7 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0e993a49bdf8462da07abda0ddc32499": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "1409a38738454957ada643fa9b266eb4": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_fe4b86168b0b4e768475ba3fffb0e5f5",
- "max": 170498071.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_5e2d19d279244d568f678aac44796df2",
- "value": 170498071.0
- }
- },
- "15f08e3e6f214ff9a1e75259c8495005": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_f9ccb7a5b44043c187af6c4eabf845a3",
- "placeholder": "",
- "style": "IPY_MODEL_c1a2789870cb4d02995963bc02ad1575",
- "value": "100%"
- }
- },
- "5e2d19d279244d568f678aac44796df2": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
- }
- },
- "65e4beb663ca4724b98dcaf08e6fe200": {
+ "0495739ff5494676b70f17498d6bf591": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1145,7 +1069,7 @@
"width": null
}
},
- "8f8a74e6aa6c48f497c4f44c5c2f056e": {
+ "5b8932e3eb014308943c1433237125b5": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1197,7 +1121,7 @@
"width": null
}
},
- "abec7c25ac6a42d78f15b03e405dd7a0": {
+ "612c1541da9745058471b861b1db710f": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -1212,13 +1136,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_65e4beb663ca4724b98dcaf08e6fe200",
+ "layout": "IPY_MODEL_895a622d781d41bf922dc812453bbdf4",
"placeholder": "",
- "style": "IPY_MODEL_0e993a49bdf8462da07abda0ddc32499",
- "value": " 170498071/170498071 [00:03<00:00, 48564193.64it/s]"
+ "style": "IPY_MODEL_c9da0d59c1404ee59bd92a6b427e518a",
+ "value": "100%"
}
},
- "c1a2789870cb4d02995963bc02ad1575": {
+ "76813cd6dac044a893936c59c63b1cae": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
@@ -1233,29 +1157,7 @@
"description_width": ""
}
},
- "e8084a26e1dd4ab7b55855153c465192": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_15f08e3e6f214ff9a1e75259c8495005",
- "IPY_MODEL_1409a38738454957ada643fa9b266eb4",
- "IPY_MODEL_abec7c25ac6a42d78f15b03e405dd7a0"
- ],
- "layout": "IPY_MODEL_8f8a74e6aa6c48f497c4f44c5c2f056e"
- }
- },
- "f9ccb7a5b44043c187af6c4eabf845a3": {
+ "895a622d781d41bf922dc812453bbdf4": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -1307,7 +1209,105 @@
"width": null
}
},
- "fe4b86168b0b4e768475ba3fffb0e5f5": {
+ "8cdf40e74d564639b00dec130489c5a3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_612c1541da9745058471b861b1db710f",
+ "IPY_MODEL_a7bdc8b56d0c48e790cc06cecba479fd",
+ "IPY_MODEL_c14e2c968b3e43abb05bb21d295b52d7"
+ ],
+ "layout": "IPY_MODEL_5b8932e3eb014308943c1433237125b5"
+ }
+ },
+ "a7bdc8b56d0c48e790cc06cecba479fd": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_f87f876c755b4a24b2fb037e16166ce0",
+ "max": 170498071.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_cd19ceb14c60455488a708c6d7be8ba2",
+ "value": 170498071.0
+ }
+ },
+ "c14e2c968b3e43abb05bb21d295b52d7": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_0495739ff5494676b70f17498d6bf591",
+ "placeholder": "",
+ "style": "IPY_MODEL_76813cd6dac044a893936c59c63b1cae",
+ "value": " 170498071/170498071 [00:01<00:00, 108293299.85it/s]"
+ }
+ },
+ "c9da0d59c1404ee59bd92a6b427e518a": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "cd19ceb14c60455488a708c6d7be8ba2": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "f87f876c755b4a24b2fb037e16166ce0": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
index 0bff0476d..fad612e0c 100644
--- a/master/.doctrees/nbsphinx/tutorials/regression.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/regression.ipynb
@@ -94,10 +94,10 @@
"id": "2e1af7d8",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:21.001062Z",
- "iopub.status.busy": "2024-01-19T12:58:21.000612Z",
- "iopub.status.idle": "2024-01-19T12:58:22.064457Z",
- "shell.execute_reply": "2024-01-19T12:58:22.063755Z"
+ "iopub.execute_input": "2024-01-19T13:15:32.249696Z",
+ "iopub.status.busy": "2024-01-19T13:15:32.249501Z",
+ "iopub.status.idle": "2024-01-19T13:15:33.330970Z",
+ "shell.execute_reply": "2024-01-19T13:15:33.330314Z"
},
"nbsphinx": "hidden"
},
@@ -109,7 +109,7 @@
"dependencies = [\"cleanlab\", \"matplotlib>=3.6.0\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = \" \".join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -135,10 +135,10 @@
"id": "4fb10b8f",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:22.067163Z",
- "iopub.status.busy": "2024-01-19T12:58:22.066893Z",
- "iopub.status.idle": "2024-01-19T12:58:22.082508Z",
- "shell.execute_reply": "2024-01-19T12:58:22.082033Z"
+ "iopub.execute_input": "2024-01-19T13:15:33.333669Z",
+ "iopub.status.busy": "2024-01-19T13:15:33.333381Z",
+ "iopub.status.idle": "2024-01-19T13:15:33.349521Z",
+ "shell.execute_reply": "2024-01-19T13:15:33.348985Z"
}
},
"outputs": [],
@@ -157,10 +157,10 @@
"id": "284dc264",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:22.084695Z",
- "iopub.status.busy": "2024-01-19T12:58:22.084497Z",
- "iopub.status.idle": "2024-01-19T12:58:22.088420Z",
- "shell.execute_reply": "2024-01-19T12:58:22.087789Z"
+ "iopub.execute_input": "2024-01-19T13:15:33.352057Z",
+ "iopub.status.busy": "2024-01-19T13:15:33.351615Z",
+ "iopub.status.idle": "2024-01-19T13:15:33.354870Z",
+ "shell.execute_reply": "2024-01-19T13:15:33.354322Z"
},
"nbsphinx": "hidden"
},
@@ -191,10 +191,10 @@
"id": "0f7450db",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:22.090542Z",
- "iopub.status.busy": "2024-01-19T12:58:22.090340Z",
- "iopub.status.idle": "2024-01-19T12:58:22.316020Z",
- "shell.execute_reply": "2024-01-19T12:58:22.315376Z"
+ "iopub.execute_input": "2024-01-19T13:15:33.357419Z",
+ "iopub.status.busy": "2024-01-19T13:15:33.356955Z",
+ "iopub.status.idle": "2024-01-19T13:15:33.438699Z",
+ "shell.execute_reply": "2024-01-19T13:15:33.438059Z"
}
},
"outputs": [
@@ -367,10 +367,10 @@
"id": "55513fed",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:22.318511Z",
- "iopub.status.busy": "2024-01-19T12:58:22.318305Z",
- "iopub.status.idle": "2024-01-19T12:58:22.582802Z",
- "shell.execute_reply": "2024-01-19T12:58:22.582094Z"
+ "iopub.execute_input": "2024-01-19T13:15:33.441568Z",
+ "iopub.status.busy": "2024-01-19T13:15:33.441070Z",
+ "iopub.status.idle": "2024-01-19T13:15:33.714034Z",
+ "shell.execute_reply": "2024-01-19T13:15:33.713264Z"
},
"nbsphinx": "hidden"
},
@@ -410,10 +410,10 @@
"id": "df5a0f59",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:22.585568Z",
- "iopub.status.busy": "2024-01-19T12:58:22.585375Z",
- "iopub.status.idle": "2024-01-19T12:58:22.839267Z",
- "shell.execute_reply": "2024-01-19T12:58:22.838553Z"
+ "iopub.execute_input": "2024-01-19T13:15:33.717156Z",
+ "iopub.status.busy": "2024-01-19T13:15:33.716682Z",
+ "iopub.status.idle": "2024-01-19T13:15:33.974651Z",
+ "shell.execute_reply": "2024-01-19T13:15:33.973940Z"
}
},
"outputs": [
@@ -449,10 +449,10 @@
"id": "7af78a8a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:22.841758Z",
- "iopub.status.busy": "2024-01-19T12:58:22.841532Z",
- "iopub.status.idle": "2024-01-19T12:58:22.846327Z",
- "shell.execute_reply": "2024-01-19T12:58:22.845816Z"
+ "iopub.execute_input": "2024-01-19T13:15:33.977068Z",
+ "iopub.status.busy": "2024-01-19T13:15:33.976854Z",
+ "iopub.status.idle": "2024-01-19T13:15:33.981765Z",
+ "shell.execute_reply": "2024-01-19T13:15:33.981252Z"
}
},
"outputs": [],
@@ -470,10 +470,10 @@
"id": "9556c624",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:22.848663Z",
- "iopub.status.busy": "2024-01-19T12:58:22.848299Z",
- "iopub.status.idle": "2024-01-19T12:58:22.854534Z",
- "shell.execute_reply": "2024-01-19T12:58:22.854039Z"
+ "iopub.execute_input": "2024-01-19T13:15:33.984121Z",
+ "iopub.status.busy": "2024-01-19T13:15:33.983914Z",
+ "iopub.status.idle": "2024-01-19T13:15:33.990156Z",
+ "shell.execute_reply": "2024-01-19T13:15:33.989646Z"
}
},
"outputs": [],
@@ -520,10 +520,10 @@
"id": "3c2f1ccc",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:22.857083Z",
- "iopub.status.busy": "2024-01-19T12:58:22.856681Z",
- "iopub.status.idle": "2024-01-19T12:58:22.859401Z",
- "shell.execute_reply": "2024-01-19T12:58:22.858861Z"
+ "iopub.execute_input": "2024-01-19T13:15:33.992394Z",
+ "iopub.status.busy": "2024-01-19T13:15:33.992192Z",
+ "iopub.status.idle": "2024-01-19T13:15:33.995069Z",
+ "shell.execute_reply": "2024-01-19T13:15:33.994537Z"
}
},
"outputs": [],
@@ -538,10 +538,10 @@
"id": "7e1b7860",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:22.861749Z",
- "iopub.status.busy": "2024-01-19T12:58:22.861387Z",
- "iopub.status.idle": "2024-01-19T12:58:32.973934Z",
- "shell.execute_reply": "2024-01-19T12:58:32.973301Z"
+ "iopub.execute_input": "2024-01-19T13:15:33.997231Z",
+ "iopub.status.busy": "2024-01-19T13:15:33.997025Z",
+ "iopub.status.idle": "2024-01-19T13:15:44.385663Z",
+ "shell.execute_reply": "2024-01-19T13:15:44.384914Z"
}
},
"outputs": [],
@@ -565,10 +565,10 @@
"id": "f407bd69",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:32.977432Z",
- "iopub.status.busy": "2024-01-19T12:58:32.976686Z",
- "iopub.status.idle": "2024-01-19T12:58:32.984320Z",
- "shell.execute_reply": "2024-01-19T12:58:32.983702Z"
+ "iopub.execute_input": "2024-01-19T13:15:44.389040Z",
+ "iopub.status.busy": "2024-01-19T13:15:44.388379Z",
+ "iopub.status.idle": "2024-01-19T13:15:44.396242Z",
+ "shell.execute_reply": "2024-01-19T13:15:44.395598Z"
}
},
"outputs": [
@@ -671,10 +671,10 @@
"id": "f7385336",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:32.986712Z",
- "iopub.status.busy": "2024-01-19T12:58:32.986463Z",
- "iopub.status.idle": "2024-01-19T12:58:32.990646Z",
- "shell.execute_reply": "2024-01-19T12:58:32.990134Z"
+ "iopub.execute_input": "2024-01-19T13:15:44.398601Z",
+ "iopub.status.busy": "2024-01-19T13:15:44.398236Z",
+ "iopub.status.idle": "2024-01-19T13:15:44.402168Z",
+ "shell.execute_reply": "2024-01-19T13:15:44.401544Z"
}
},
"outputs": [],
@@ -689,10 +689,10 @@
"id": "59fc3091",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:32.992855Z",
- "iopub.status.busy": "2024-01-19T12:58:32.992653Z",
- "iopub.status.idle": "2024-01-19T12:58:32.996134Z",
- "shell.execute_reply": "2024-01-19T12:58:32.995504Z"
+ "iopub.execute_input": "2024-01-19T13:15:44.404540Z",
+ "iopub.status.busy": "2024-01-19T13:15:44.404166Z",
+ "iopub.status.idle": "2024-01-19T13:15:44.407994Z",
+ "shell.execute_reply": "2024-01-19T13:15:44.407451Z"
}
},
"outputs": [
@@ -727,10 +727,10 @@
"id": "00949977",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:32.998411Z",
- "iopub.status.busy": "2024-01-19T12:58:32.998211Z",
- "iopub.status.idle": "2024-01-19T12:58:33.001368Z",
- "shell.execute_reply": "2024-01-19T12:58:33.000849Z"
+ "iopub.execute_input": "2024-01-19T13:15:44.410314Z",
+ "iopub.status.busy": "2024-01-19T13:15:44.409944Z",
+ "iopub.status.idle": "2024-01-19T13:15:44.413255Z",
+ "shell.execute_reply": "2024-01-19T13:15:44.412719Z"
}
},
"outputs": [],
@@ -749,10 +749,10 @@
"id": "b6c1ae3a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:33.003742Z",
- "iopub.status.busy": "2024-01-19T12:58:33.003298Z",
- "iopub.status.idle": "2024-01-19T12:58:33.011980Z",
- "shell.execute_reply": "2024-01-19T12:58:33.011356Z"
+ "iopub.execute_input": "2024-01-19T13:15:44.415697Z",
+ "iopub.status.busy": "2024-01-19T13:15:44.415291Z",
+ "iopub.status.idle": "2024-01-19T13:15:44.424268Z",
+ "shell.execute_reply": "2024-01-19T13:15:44.423746Z"
}
},
"outputs": [
@@ -894,10 +894,10 @@
"id": "31c704e7",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:33.014407Z",
- "iopub.status.busy": "2024-01-19T12:58:33.013949Z",
- "iopub.status.idle": "2024-01-19T12:58:33.159031Z",
- "shell.execute_reply": "2024-01-19T12:58:33.158357Z"
+ "iopub.execute_input": "2024-01-19T13:15:44.426890Z",
+ "iopub.status.busy": "2024-01-19T13:15:44.426509Z",
+ "iopub.status.idle": "2024-01-19T13:15:44.578180Z",
+ "shell.execute_reply": "2024-01-19T13:15:44.577458Z"
}
},
"outputs": [
@@ -936,10 +936,10 @@
"id": "0bcc43db",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:33.161962Z",
- "iopub.status.busy": "2024-01-19T12:58:33.161478Z",
- "iopub.status.idle": "2024-01-19T12:58:33.292300Z",
- "shell.execute_reply": "2024-01-19T12:58:33.291597Z"
+ "iopub.execute_input": "2024-01-19T13:15:44.580910Z",
+ "iopub.status.busy": "2024-01-19T13:15:44.580660Z",
+ "iopub.status.idle": "2024-01-19T13:15:44.715793Z",
+ "shell.execute_reply": "2024-01-19T13:15:44.715089Z"
}
},
"outputs": [
@@ -995,10 +995,10 @@
"id": "7021bd68",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:33.295465Z",
- "iopub.status.busy": "2024-01-19T12:58:33.294870Z",
- "iopub.status.idle": "2024-01-19T12:58:33.876756Z",
- "shell.execute_reply": "2024-01-19T12:58:33.875928Z"
+ "iopub.execute_input": "2024-01-19T13:15:44.718691Z",
+ "iopub.status.busy": "2024-01-19T13:15:44.718237Z",
+ "iopub.status.idle": "2024-01-19T13:15:45.334415Z",
+ "shell.execute_reply": "2024-01-19T13:15:45.333771Z"
}
},
"outputs": [],
@@ -1014,10 +1014,10 @@
"id": "d49c990b",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:33.880375Z",
- "iopub.status.busy": "2024-01-19T12:58:33.879802Z",
- "iopub.status.idle": "2024-01-19T12:58:33.967154Z",
- "shell.execute_reply": "2024-01-19T12:58:33.966562Z"
+ "iopub.execute_input": "2024-01-19T13:15:45.337506Z",
+ "iopub.status.busy": "2024-01-19T13:15:45.337248Z",
+ "iopub.status.idle": "2024-01-19T13:15:45.419871Z",
+ "shell.execute_reply": "2024-01-19T13:15:45.419276Z"
}
},
"outputs": [
@@ -1055,10 +1055,10 @@
"id": "95531cda",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:33.969884Z",
- "iopub.status.busy": "2024-01-19T12:58:33.969636Z",
- "iopub.status.idle": "2024-01-19T12:58:33.979670Z",
- "shell.execute_reply": "2024-01-19T12:58:33.979066Z"
+ "iopub.execute_input": "2024-01-19T13:15:45.422696Z",
+ "iopub.status.busy": "2024-01-19T13:15:45.422445Z",
+ "iopub.status.idle": "2024-01-19T13:15:45.432686Z",
+ "shell.execute_reply": "2024-01-19T13:15:45.432206Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
index 543290131..1cea2ed7e 100644
--- a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb
@@ -61,10 +61,10 @@
"id": "ae8a08e0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:39.214241Z",
- "iopub.status.busy": "2024-01-19T12:58:39.214051Z",
- "iopub.status.idle": "2024-01-19T12:58:41.801622Z",
- "shell.execute_reply": "2024-01-19T12:58:41.800823Z"
+ "iopub.execute_input": "2024-01-19T13:15:50.310822Z",
+ "iopub.status.busy": "2024-01-19T13:15:50.310269Z",
+ "iopub.status.idle": "2024-01-19T13:15:51.852457Z",
+ "shell.execute_reply": "2024-01-19T13:15:51.851699Z"
}
},
"outputs": [],
@@ -79,10 +79,10 @@
"id": "58fd4c55",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:58:41.804668Z",
- "iopub.status.busy": "2024-01-19T12:58:41.804336Z",
- "iopub.status.idle": "2024-01-19T12:59:42.913681Z",
- "shell.execute_reply": "2024-01-19T12:59:42.912945Z"
+ "iopub.execute_input": "2024-01-19T13:15:51.855203Z",
+ "iopub.status.busy": "2024-01-19T13:15:51.854999Z",
+ "iopub.status.idle": "2024-01-19T13:16:41.092406Z",
+ "shell.execute_reply": "2024-01-19T13:16:41.091588Z"
}
},
"outputs": [],
@@ -97,10 +97,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:59:42.916418Z",
- "iopub.status.busy": "2024-01-19T12:59:42.916196Z",
- "iopub.status.idle": "2024-01-19T12:59:43.946591Z",
- "shell.execute_reply": "2024-01-19T12:59:43.945972Z"
+ "iopub.execute_input": "2024-01-19T13:16:41.095722Z",
+ "iopub.status.busy": "2024-01-19T13:16:41.095295Z",
+ "iopub.status.idle": "2024-01-19T13:16:42.137799Z",
+ "shell.execute_reply": "2024-01-19T13:16:42.137163Z"
},
"nbsphinx": "hidden"
},
@@ -111,7 +111,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -137,10 +137,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:59:43.949661Z",
- "iopub.status.busy": "2024-01-19T12:59:43.949039Z",
- "iopub.status.idle": "2024-01-19T12:59:43.952664Z",
- "shell.execute_reply": "2024-01-19T12:59:43.952033Z"
+ "iopub.execute_input": "2024-01-19T13:16:42.140892Z",
+ "iopub.status.busy": "2024-01-19T13:16:42.140352Z",
+ "iopub.status.idle": "2024-01-19T13:16:42.144028Z",
+ "shell.execute_reply": "2024-01-19T13:16:42.143464Z"
}
},
"outputs": [],
@@ -203,10 +203,10 @@
"id": "07dc5678",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:59:43.955147Z",
- "iopub.status.busy": "2024-01-19T12:59:43.954784Z",
- "iopub.status.idle": "2024-01-19T12:59:43.958644Z",
- "shell.execute_reply": "2024-01-19T12:59:43.958134Z"
+ "iopub.execute_input": "2024-01-19T13:16:42.146497Z",
+ "iopub.status.busy": "2024-01-19T13:16:42.146103Z",
+ "iopub.status.idle": "2024-01-19T13:16:42.150081Z",
+ "shell.execute_reply": "2024-01-19T13:16:42.149568Z"
}
},
"outputs": [
@@ -247,10 +247,10 @@
"id": "25ebe22a",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:59:43.960980Z",
- "iopub.status.busy": "2024-01-19T12:59:43.960642Z",
- "iopub.status.idle": "2024-01-19T12:59:43.964591Z",
- "shell.execute_reply": "2024-01-19T12:59:43.964050Z"
+ "iopub.execute_input": "2024-01-19T13:16:42.152329Z",
+ "iopub.status.busy": "2024-01-19T13:16:42.152134Z",
+ "iopub.status.idle": "2024-01-19T13:16:42.156167Z",
+ "shell.execute_reply": "2024-01-19T13:16:42.155632Z"
}
},
"outputs": [
@@ -290,10 +290,10 @@
"id": "3faedea9",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:59:43.966860Z",
- "iopub.status.busy": "2024-01-19T12:59:43.966516Z",
- "iopub.status.idle": "2024-01-19T12:59:43.969557Z",
- "shell.execute_reply": "2024-01-19T12:59:43.969047Z"
+ "iopub.execute_input": "2024-01-19T13:16:42.158478Z",
+ "iopub.status.busy": "2024-01-19T13:16:42.158091Z",
+ "iopub.status.idle": "2024-01-19T13:16:42.161167Z",
+ "shell.execute_reply": "2024-01-19T13:16:42.160660Z"
}
},
"outputs": [],
@@ -333,10 +333,10 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T12:59:43.971728Z",
- "iopub.status.busy": "2024-01-19T12:59:43.971377Z",
- "iopub.status.idle": "2024-01-19T13:01:10.049754Z",
- "shell.execute_reply": "2024-01-19T13:01:10.048962Z"
+ "iopub.execute_input": "2024-01-19T13:16:42.163518Z",
+ "iopub.status.busy": "2024-01-19T13:16:42.163148Z",
+ "iopub.status.idle": "2024-01-19T13:18:07.406341Z",
+ "shell.execute_reply": "2024-01-19T13:18:07.405631Z"
}
},
"outputs": [
@@ -350,7 +350,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d2996cb658054f4098f2be3d348c4551",
+ "model_id": "5a983a51c0c24c4a9b8e710d3f3f0b48",
"version_major": 2,
"version_minor": 0
},
@@ -364,7 +364,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "acf781e3ba0a40a98db989b3f5b4de56",
+ "model_id": "52103b628c524e138dda90ac2442416c",
"version_major": 2,
"version_minor": 0
},
@@ -407,10 +407,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:01:10.052831Z",
- "iopub.status.busy": "2024-01-19T13:01:10.052600Z",
- "iopub.status.idle": "2024-01-19T13:01:10.804998Z",
- "shell.execute_reply": "2024-01-19T13:01:10.804324Z"
+ "iopub.execute_input": "2024-01-19T13:18:07.409341Z",
+ "iopub.status.busy": "2024-01-19T13:18:07.409046Z",
+ "iopub.status.idle": "2024-01-19T13:18:08.176817Z",
+ "shell.execute_reply": "2024-01-19T13:18:08.176116Z"
}
},
"outputs": [
@@ -453,10 +453,10 @@
"id": "57fed473",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:01:10.807826Z",
- "iopub.status.busy": "2024-01-19T13:01:10.807250Z",
- "iopub.status.idle": "2024-01-19T13:01:12.910252Z",
- "shell.execute_reply": "2024-01-19T13:01:12.909551Z"
+ "iopub.execute_input": "2024-01-19T13:18:08.179891Z",
+ "iopub.status.busy": "2024-01-19T13:18:08.179271Z",
+ "iopub.status.idle": "2024-01-19T13:18:10.289782Z",
+ "shell.execute_reply": "2024-01-19T13:18:10.289167Z"
}
},
"outputs": [
@@ -526,10 +526,10 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:01:12.913031Z",
- "iopub.status.busy": "2024-01-19T13:01:12.912621Z",
- "iopub.status.idle": "2024-01-19T13:01:42.575681Z",
- "shell.execute_reply": "2024-01-19T13:01:42.575012Z"
+ "iopub.execute_input": "2024-01-19T13:18:10.292563Z",
+ "iopub.status.busy": "2024-01-19T13:18:10.292133Z",
+ "iopub.status.idle": "2024-01-19T13:18:39.687106Z",
+ "shell.execute_reply": "2024-01-19T13:18:39.686429Z"
}
},
"outputs": [
@@ -546,7 +546,7 @@
"output_type": "stream",
"text": [
"\r",
- " 0%| | 16894/4997817 [00:00<00:29, 168930.09it/s]"
+ " 0%| | 17086/4997817 [00:00<00:29, 170847.54it/s]"
]
},
{
@@ -554,7 +554,7 @@
"output_type": "stream",
"text": [
"\r",
- " 1%| | 33840/4997817 [00:00<00:29, 169232.87it/s]"
+ " 1%| | 34440/4997817 [00:00<00:28, 172423.89it/s]"
]
},
{
@@ -562,7 +562,7 @@
"output_type": "stream",
"text": [
"\r",
- " 1%| | 50764/4997817 [00:00<00:29, 168912.38it/s]"
+ " 1%| | 51683/4997817 [00:00<00:28, 172348.70it/s]"
]
},
{
@@ -570,7 +570,7 @@
"output_type": "stream",
"text": [
"\r",
- " 1%|▏ | 67681/4997817 [00:00<00:29, 169008.95it/s]"
+ " 1%|▏ | 69010/4997817 [00:00<00:28, 172708.80it/s]"
]
},
{
@@ -578,7 +578,7 @@
"output_type": "stream",
"text": [
"\r",
- " 2%|▏ | 84582/4997817 [00:00<00:29, 168597.17it/s]"
+ " 2%|▏ | 86281/4997817 [00:00<00:28, 172699.35it/s]"
]
},
{
@@ -586,7 +586,7 @@
"output_type": "stream",
"text": [
"\r",
- " 2%|▏ | 101562/4997817 [00:00<00:28, 169001.77it/s]"
+ " 2%|▏ | 103551/4997817 [00:00<00:28, 172696.13it/s]"
]
},
{
@@ -594,7 +594,7 @@
"output_type": "stream",
"text": [
"\r",
- " 2%|▏ | 118463/4997817 [00:00<00:28, 168946.91it/s]"
+ " 2%|▏ | 120836/4997817 [00:00<00:28, 172742.57it/s]"
]
},
{
@@ -602,7 +602,7 @@
"output_type": "stream",
"text": [
"\r",
- " 3%|▎ | 135439/4997817 [00:00<00:28, 169203.06it/s]"
+ " 3%|▎ | 138126/4997817 [00:00<00:28, 172788.54it/s]"
]
},
{
@@ -610,7 +610,7 @@
"output_type": "stream",
"text": [
"\r",
- " 3%|▎ | 152375/4997817 [00:00<00:28, 169249.23it/s]"
+ " 3%|▎ | 155405/4997817 [00:00<00:28, 172451.81it/s]"
]
},
{
@@ -618,7 +618,7 @@
"output_type": "stream",
"text": [
"\r",
- " 3%|▎ | 169316/4997817 [00:01<00:28, 169296.89it/s]"
+ " 3%|▎ | 172651/4997817 [00:01<00:28, 172202.76it/s]"
]
},
{
@@ -626,7 +626,7 @@
"output_type": "stream",
"text": [
"\r",
- " 4%|▎ | 186292/4997817 [00:01<00:28, 169436.64it/s]"
+ " 4%|▍ | 189891/4997817 [00:01<00:27, 172258.29it/s]"
]
},
{
@@ -634,7 +634,7 @@
"output_type": "stream",
"text": [
"\r",
- " 4%|▍ | 203347/4997817 [00:01<00:28, 169773.68it/s]"
+ " 4%|▍ | 207171/4997817 [00:01<00:27, 172419.78it/s]"
]
},
{
@@ -642,7 +642,7 @@
"output_type": "stream",
"text": [
"\r",
- " 4%|▍ | 220405/4997817 [00:01<00:28, 170016.96it/s]"
+ " 4%|▍ | 224449/4997817 [00:01<00:27, 172523.62it/s]"
]
},
{
@@ -650,7 +650,7 @@
"output_type": "stream",
"text": [
"\r",
- " 5%|▍ | 237407/4997817 [00:01<00:28, 169928.36it/s]"
+ " 5%|▍ | 241702/4997817 [00:01<00:27, 172233.70it/s]"
]
},
{
@@ -658,7 +658,7 @@
"output_type": "stream",
"text": [
"\r",
- " 5%|▌ | 254477/4997817 [00:01<00:27, 170158.45it/s]"
+ " 5%|▌ | 259049/4997817 [00:01<00:27, 172601.58it/s]"
]
},
{
@@ -666,7 +666,7 @@
"output_type": "stream",
"text": [
"\r",
- " 5%|▌ | 271493/4997817 [00:01<00:28, 163317.46it/s]"
+ " 6%|▌ | 276310/4997817 [00:01<00:27, 172489.42it/s]"
]
},
{
@@ -674,7 +674,7 @@
"output_type": "stream",
"text": [
"\r",
- " 6%|▌ | 288574/4997817 [00:01<00:28, 165502.17it/s]"
+ " 6%|▌ | 293563/4997817 [00:01<00:27, 172497.33it/s]"
]
},
{
@@ -682,7 +682,7 @@
"output_type": "stream",
"text": [
"\r",
- " 6%|▌ | 305691/4997817 [00:01<00:28, 167167.31it/s]"
+ " 6%|▌ | 310813/4997817 [00:01<00:27, 172337.95it/s]"
]
},
{
@@ -690,7 +690,7 @@
"output_type": "stream",
"text": [
"\r",
- " 6%|▋ | 322684/4997817 [00:01<00:27, 167981.23it/s]"
+ " 7%|▋ | 328047/4997817 [00:01<00:27, 172170.55it/s]"
]
},
{
@@ -698,7 +698,7 @@
"output_type": "stream",
"text": [
"\r",
- " 7%|▋ | 339600/4997817 [00:02<00:27, 168329.84it/s]"
+ " 7%|▋ | 345265/4997817 [00:02<00:27, 171966.46it/s]"
]
},
{
@@ -706,7 +706,7 @@
"output_type": "stream",
"text": [
"\r",
- " 7%|▋ | 356742/4997817 [00:02<00:27, 169247.54it/s]"
+ " 7%|▋ | 362537/4997817 [00:02<00:26, 172189.92it/s]"
]
},
{
@@ -714,7 +714,7 @@
"output_type": "stream",
"text": [
"\r",
- " 7%|▋ | 373883/4997817 [00:02<00:27, 169890.33it/s]"
+ " 8%|▊ | 379879/4997817 [00:02<00:26, 172555.05it/s]"
]
},
{
@@ -722,7 +722,7 @@
"output_type": "stream",
"text": [
"\r",
- " 8%|▊ | 390996/4997817 [00:02<00:27, 170256.83it/s]"
+ " 8%|▊ | 397213/4997817 [00:02<00:26, 172785.48it/s]"
]
},
{
@@ -730,7 +730,7 @@
"output_type": "stream",
"text": [
"\r",
- " 8%|▊ | 408029/4997817 [00:02<00:26, 170233.59it/s]"
+ " 8%|▊ | 414506/4997817 [00:02<00:26, 172826.02it/s]"
]
},
{
@@ -738,7 +738,7 @@
"output_type": "stream",
"text": [
"\r",
- " 9%|▊ | 425064/4997817 [00:02<00:26, 170265.58it/s]"
+ " 9%|▊ | 431820/4997817 [00:02<00:26, 172915.66it/s]"
]
},
{
@@ -746,7 +746,7 @@
"output_type": "stream",
"text": [
"\r",
- " 9%|▉ | 442095/4997817 [00:02<00:26, 170175.97it/s]"
+ " 9%|▉ | 449112/4997817 [00:02<00:26, 172764.44it/s]"
]
},
{
@@ -754,7 +754,7 @@
"output_type": "stream",
"text": [
"\r",
- " 9%|▉ | 459116/4997817 [00:02<00:26, 169906.95it/s]"
+ " 9%|▉ | 466414/4997817 [00:02<00:26, 172836.90it/s]"
]
},
{
@@ -762,7 +762,7 @@
"output_type": "stream",
"text": [
"\r",
- " 10%|▉ | 476109/4997817 [00:02<00:26, 169776.11it/s]"
+ " 10%|▉ | 483698/4997817 [00:02<00:26, 172730.45it/s]"
]
},
{
@@ -770,7 +770,7 @@
"output_type": "stream",
"text": [
"\r",
- " 10%|▉ | 493088/4997817 [00:02<00:26, 169632.96it/s]"
+ " 10%|█ | 500972/4997817 [00:02<00:26, 172466.77it/s]"
]
},
{
@@ -778,7 +778,7 @@
"output_type": "stream",
"text": [
"\r",
- " 10%|█ | 510053/4997817 [00:03<00:26, 169530.04it/s]"
+ " 10%|█ | 518219/4997817 [00:03<00:25, 172446.24it/s]"
]
},
{
@@ -786,7 +786,7 @@
"output_type": "stream",
"text": [
"\r",
- " 11%|█ | 527116/4997817 [00:03<00:26, 169855.10it/s]"
+ " 11%|█ | 535464/4997817 [00:03<00:25, 172438.42it/s]"
]
},
{
@@ -794,7 +794,7 @@
"output_type": "stream",
"text": [
"\r",
- " 11%|█ | 544102/4997817 [00:03<00:26, 169441.47it/s]"
+ " 11%|█ | 552708/4997817 [00:03<00:25, 172043.07it/s]"
]
},
{
@@ -802,7 +802,7 @@
"output_type": "stream",
"text": [
"\r",
- " 11%|█ | 561135/4997817 [00:03<00:26, 169705.92it/s]"
+ " 11%|█▏ | 569913/4997817 [00:03<00:25, 172004.81it/s]"
]
},
{
@@ -810,7 +810,7 @@
"output_type": "stream",
"text": [
"\r",
- " 12%|█▏ | 578144/4997817 [00:03<00:26, 169819.40it/s]"
+ " 12%|█▏ | 587114/4997817 [00:03<00:25, 171923.43it/s]"
]
},
{
@@ -818,7 +818,7 @@
"output_type": "stream",
"text": [
"\r",
- " 12%|█▏ | 595127/4997817 [00:03<00:26, 169307.78it/s]"
+ " 12%|█▏ | 604307/4997817 [00:03<00:25, 171654.53it/s]"
]
},
{
@@ -826,7 +826,7 @@
"output_type": "stream",
"text": [
"\r",
- " 12%|█▏ | 612059/4997817 [00:03<00:25, 168901.50it/s]"
+ " 12%|█▏ | 621473/4997817 [00:03<00:25, 171589.48it/s]"
]
},
{
@@ -834,7 +834,7 @@
"output_type": "stream",
"text": [
"\r",
- " 13%|█▎ | 628950/4997817 [00:03<00:25, 168898.92it/s]"
+ " 13%|█▎ | 638633/4997817 [00:03<00:25, 171509.65it/s]"
]
},
{
@@ -842,7 +842,7 @@
"output_type": "stream",
"text": [
"\r",
- " 13%|█▎ | 645884/4997817 [00:03<00:25, 169026.35it/s]"
+ " 13%|█▎ | 655785/4997817 [00:03<00:25, 171228.88it/s]"
]
},
{
@@ -850,7 +850,7 @@
"output_type": "stream",
"text": [
"\r",
- " 13%|█▎ | 662787/4997817 [00:03<00:25, 168976.71it/s]"
+ " 13%|█▎ | 672908/4997817 [00:03<00:25, 170161.40it/s]"
]
},
{
@@ -858,7 +858,7 @@
"output_type": "stream",
"text": [
"\r",
- " 14%|█▎ | 679685/4997817 [00:04<00:25, 168905.61it/s]"
+ " 14%|█▍ | 690003/4997817 [00:04<00:25, 170392.25it/s]"
]
},
{
@@ -866,7 +866,7 @@
"output_type": "stream",
"text": [
"\r",
- " 14%|█▍ | 696576/4997817 [00:04<00:25, 168804.61it/s]"
+ " 14%|█▍ | 707173/4997817 [00:04<00:25, 170780.06it/s]"
]
},
{
@@ -874,7 +874,7 @@
"output_type": "stream",
"text": [
"\r",
- " 14%|█▍ | 713464/4997817 [00:04<00:25, 168825.31it/s]"
+ " 14%|█▍ | 724317/4997817 [00:04<00:24, 170972.99it/s]"
]
},
{
@@ -882,7 +882,7 @@
"output_type": "stream",
"text": [
"\r",
- " 15%|█▍ | 730347/4997817 [00:04<00:25, 168773.94it/s]"
+ " 15%|█▍ | 741430/4997817 [00:04<00:24, 171016.38it/s]"
]
},
{
@@ -890,7 +890,7 @@
"output_type": "stream",
"text": [
"\r",
- " 15%|█▍ | 747247/4997817 [00:04<00:25, 168838.49it/s]"
+ " 15%|█▌ | 758533/4997817 [00:04<00:25, 167182.47it/s]"
]
},
{
@@ -898,7 +898,7 @@
"output_type": "stream",
"text": [
"\r",
- " 15%|█▌ | 764233/4997817 [00:04<00:25, 169140.44it/s]"
+ " 16%|█▌ | 776036/4997817 [00:04<00:24, 169494.74it/s]"
]
},
{
@@ -906,7 +906,7 @@
"output_type": "stream",
"text": [
"\r",
- " 16%|█▌ | 781186/4997817 [00:04<00:24, 169252.22it/s]"
+ " 16%|█▌ | 793560/4997817 [00:04<00:24, 171193.69it/s]"
]
},
{
@@ -914,7 +914,7 @@
"output_type": "stream",
"text": [
"\r",
- " 16%|█▌ | 798112/4997817 [00:04<00:25, 167898.89it/s]"
+ " 16%|█▌ | 810976/4997817 [00:04<00:24, 172071.57it/s]"
]
},
{
@@ -922,7 +922,7 @@
"output_type": "stream",
"text": [
"\r",
- " 16%|█▋ | 815132/4997817 [00:04<00:24, 168581.63it/s]"
+ " 17%|█▋ | 828224/4997817 [00:04<00:24, 172188.90it/s]"
]
},
{
@@ -930,7 +930,7 @@
"output_type": "stream",
"text": [
"\r",
- " 17%|█▋ | 832361/4997817 [00:04<00:24, 169686.21it/s]"
+ " 17%|█▋ | 845496/4997817 [00:04<00:24, 172343.08it/s]"
]
},
{
@@ -938,7 +938,7 @@
"output_type": "stream",
"text": [
"\r",
- " 17%|█▋ | 849444/4997817 [00:05<00:24, 170023.43it/s]"
+ " 17%|█▋ | 862736/4997817 [00:05<00:24, 172024.98it/s]"
]
},
{
@@ -946,7 +946,7 @@
"output_type": "stream",
"text": [
"\r",
- " 17%|█▋ | 866543/4997817 [00:05<00:24, 170309.45it/s]"
+ " 18%|█▊ | 879997/4997817 [00:05<00:23, 172194.78it/s]"
]
},
{
@@ -954,7 +954,7 @@
"output_type": "stream",
"text": [
"\r",
- " 18%|█▊ | 883576/4997817 [00:05<00:24, 170166.04it/s]"
+ " 18%|█▊ | 897274/4997817 [00:05<00:23, 172364.52it/s]"
]
},
{
@@ -962,7 +962,7 @@
"output_type": "stream",
"text": [
"\r",
- " 18%|█▊ | 900734/4997817 [00:05<00:24, 170585.69it/s]"
+ " 18%|█▊ | 914513/4997817 [00:05<00:23, 172254.49it/s]"
]
},
{
@@ -970,7 +970,7 @@
"output_type": "stream",
"text": [
"\r",
- " 18%|█▊ | 917906/4997817 [00:05<00:23, 170923.19it/s]"
+ " 19%|█▊ | 931740/4997817 [00:05<00:23, 169929.21it/s]"
]
},
{
@@ -978,7 +978,7 @@
"output_type": "stream",
"text": [
"\r",
- " 19%|█▊ | 935029/4997817 [00:05<00:23, 171010.93it/s]"
+ " 19%|█▉ | 949055/4997817 [00:05<00:23, 170883.37it/s]"
]
},
{
@@ -986,7 +986,7 @@
"output_type": "stream",
"text": [
"\r",
- " 19%|█▉ | 952131/4997817 [00:05<00:23, 170707.90it/s]"
+ " 19%|█▉ | 966340/4997817 [00:05<00:23, 171465.82it/s]"
]
},
{
@@ -994,7 +994,7 @@
"output_type": "stream",
"text": [
"\r",
- " 19%|█▉ | 969203/4997817 [00:05<00:24, 163872.60it/s]"
+ " 20%|█▉ | 983583/4997817 [00:05<00:23, 171749.93it/s]"
]
},
{
@@ -1002,7 +1002,7 @@
"output_type": "stream",
"text": [
"\r",
- " 20%|█▉ | 986256/4997817 [00:05<00:24, 165809.50it/s]"
+ " 20%|██ | 1000769/4997817 [00:05<00:23, 171780.19it/s]"
]
},
{
@@ -1010,7 +1010,7 @@
"output_type": "stream",
"text": [
"\r",
- " 20%|██ | 1003150/4997817 [00:05<00:23, 166727.47it/s]"
+ " 20%|██ | 1017998/4997817 [00:05<00:23, 171928.73it/s]"
]
},
{
@@ -1018,7 +1018,7 @@
"output_type": "stream",
"text": [
"\r",
- " 20%|██ | 1020118/4997817 [00:06<00:23, 167597.66it/s]"
+ " 21%|██ | 1035235/4997817 [00:06<00:23, 172056.86it/s]"
]
},
{
@@ -1026,7 +1026,7 @@
"output_type": "stream",
"text": [
"\r",
- " 21%|██ | 1037173/4997817 [00:06<00:23, 168470.66it/s]"
+ " 21%|██ | 1052442/4997817 [00:06<00:22, 172014.40it/s]"
]
},
{
@@ -1034,7 +1034,7 @@
"output_type": "stream",
"text": [
"\r",
- " 21%|██ | 1054270/4997817 [00:06<00:23, 169210.25it/s]"
+ " 21%|██▏ | 1069690/4997817 [00:06<00:22, 172149.67it/s]"
]
},
{
@@ -1042,7 +1042,7 @@
"output_type": "stream",
"text": [
"\r",
- " 21%|██▏ | 1071205/4997817 [00:06<00:23, 168701.48it/s]"
+ " 22%|██▏ | 1086906/4997817 [00:06<00:22, 172109.52it/s]"
]
},
{
@@ -1050,7 +1050,7 @@
"output_type": "stream",
"text": [
"\r",
- " 22%|██▏ | 1088132/4997817 [00:06<00:23, 168870.00it/s]"
+ " 22%|██▏ | 1104148/4997817 [00:06<00:22, 172198.83it/s]"
]
},
{
@@ -1058,7 +1058,7 @@
"output_type": "stream",
"text": [
"\r",
- " 22%|██▏ | 1105026/4997817 [00:06<00:23, 167979.68it/s]"
+ " 22%|██▏ | 1121369/4997817 [00:06<00:23, 165344.76it/s]"
]
},
{
@@ -1066,7 +1066,7 @@
"output_type": "stream",
"text": [
"\r",
- " 22%|██▏ | 1122032/4997817 [00:06<00:22, 168597.03it/s]"
+ " 23%|██▎ | 1138453/4997817 [00:06<00:23, 166943.26it/s]"
]
},
{
@@ -1074,7 +1074,7 @@
"output_type": "stream",
"text": [
"\r",
- " 23%|██▎ | 1139097/4997817 [00:06<00:22, 169207.19it/s]"
+ " 23%|██▎ | 1155552/4997817 [00:06<00:22, 168130.23it/s]"
]
},
{
@@ -1082,7 +1082,7 @@
"output_type": "stream",
"text": [
"\r",
- " 23%|██▎ | 1156205/4997817 [00:06<00:22, 169765.42it/s]"
+ " 23%|██▎ | 1172616/4997817 [00:06<00:22, 168868.80it/s]"
]
},
{
@@ -1090,7 +1090,7 @@
"output_type": "stream",
"text": [
"\r",
- " 23%|██▎ | 1173249/4997817 [00:06<00:22, 169965.31it/s]"
+ " 24%|██▍ | 1189678/4997817 [00:06<00:22, 169384.87it/s]"
]
},
{
@@ -1098,7 +1098,7 @@
"output_type": "stream",
"text": [
"\r",
- " 24%|██▍ | 1190325/4997817 [00:07<00:22, 170201.55it/s]"
+ " 24%|██▍ | 1206756/4997817 [00:07<00:22, 169795.56it/s]"
]
},
{
@@ -1106,7 +1106,7 @@
"output_type": "stream",
"text": [
"\r",
- " 24%|██▍ | 1207355/4997817 [00:07<00:22, 170227.21it/s]"
+ " 24%|██▍ | 1223863/4997817 [00:07<00:22, 170171.08it/s]"
]
},
{
@@ -1114,7 +1114,7 @@
"output_type": "stream",
"text": [
"\r",
- " 25%|██▍ | 1224496/4997817 [00:07<00:22, 170578.51it/s]"
+ " 25%|██▍ | 1240930/4997817 [00:07<00:22, 170314.82it/s]"
]
},
{
@@ -1122,7 +1122,7 @@
"output_type": "stream",
"text": [
"\r",
- " 25%|██▍ | 1241555/4997817 [00:07<00:22, 170573.23it/s]"
+ " 25%|██▌ | 1258042/4997817 [00:07<00:21, 170553.51it/s]"
]
},
{
@@ -1130,7 +1130,7 @@
"output_type": "stream",
"text": [
"\r",
- " 25%|██▌ | 1258641/4997817 [00:07<00:21, 170656.22it/s]"
+ " 26%|██▌ | 1275102/4997817 [00:07<00:21, 170438.61it/s]"
]
},
{
@@ -1138,7 +1138,7 @@
"output_type": "stream",
"text": [
"\r",
- " 26%|██▌ | 1275707/4997817 [00:07<00:21, 169747.59it/s]"
+ " 26%|██▌ | 1292201/4997817 [00:07<00:21, 170599.16it/s]"
]
},
{
@@ -1146,7 +1146,7 @@
"output_type": "stream",
"text": [
"\r",
- " 26%|██▌ | 1292837/4997817 [00:07<00:21, 170209.33it/s]"
+ " 26%|██▌ | 1309373/4997817 [00:07<00:21, 170931.50it/s]"
]
},
{
@@ -1154,7 +1154,7 @@
"output_type": "stream",
"text": [
"\r",
- " 26%|██▌ | 1309860/4997817 [00:07<00:22, 166504.62it/s]"
+ " 27%|██▋ | 1326571/4997817 [00:07<00:21, 171243.74it/s]"
]
},
{
@@ -1162,7 +1162,7 @@
"output_type": "stream",
"text": [
"\r",
- " 27%|██▋ | 1327006/4997817 [00:07<00:21, 167961.20it/s]"
+ " 27%|██▋ | 1343697/4997817 [00:07<00:21, 170805.83it/s]"
]
},
{
@@ -1170,7 +1170,7 @@
"output_type": "stream",
"text": [
"\r",
- " 27%|██▋ | 1344103/4997817 [00:07<00:21, 168850.38it/s]"
+ " 27%|██▋ | 1360802/4997817 [00:07<00:21, 170874.70it/s]"
]
},
{
@@ -1178,7 +1178,7 @@
"output_type": "stream",
"text": [
"\r",
- " 27%|██▋ | 1361223/4997817 [00:08<00:21, 169546.87it/s]"
+ " 28%|██▊ | 1377979/4997817 [00:08<00:21, 171139.24it/s]"
]
},
{
@@ -1186,7 +1186,7 @@
"output_type": "stream",
"text": [
"\r",
- " 28%|██▊ | 1378268/4997817 [00:08<00:21, 169813.20it/s]"
+ " 28%|██▊ | 1395094/4997817 [00:08<00:21, 170088.34it/s]"
]
},
{
@@ -1194,7 +1194,7 @@
"output_type": "stream",
"text": [
"\r",
- " 28%|██▊ | 1395358/4997817 [00:08<00:21, 170134.64it/s]"
+ " 28%|██▊ | 1412105/4997817 [00:08<00:21, 169658.17it/s]"
]
},
{
@@ -1202,7 +1202,7 @@
"output_type": "stream",
"text": [
"\r",
- " 28%|██▊ | 1412443/4997817 [00:08<00:21, 170344.40it/s]"
+ " 29%|██▊ | 1429228/4997817 [00:08<00:20, 170122.64it/s]"
]
},
{
@@ -1210,7 +1210,7 @@
"output_type": "stream",
"text": [
"\r",
- " 29%|██▊ | 1429592/4997817 [00:08<00:20, 170683.51it/s]"
+ " 29%|██▉ | 1446318/4997817 [00:08<00:20, 170351.98it/s]"
]
},
{
@@ -1218,7 +1218,7 @@
"output_type": "stream",
"text": [
"\r",
- " 29%|██▉ | 1446663/4997817 [00:08<00:20, 170624.96it/s]"
+ " 29%|██▉ | 1463355/4997817 [00:08<00:21, 164217.42it/s]"
]
},
{
@@ -1226,7 +1226,7 @@
"output_type": "stream",
"text": [
"\r",
- " 29%|██▉ | 1463728/4997817 [00:08<00:20, 170574.15it/s]"
+ " 30%|██▉ | 1480456/4997817 [00:08<00:21, 166196.86it/s]"
]
},
{
@@ -1234,7 +1234,7 @@
"output_type": "stream",
"text": [
"\r",
- " 30%|██▉ | 1480787/4997817 [00:08<00:20, 170117.96it/s]"
+ " 30%|██▉ | 1497358/4997817 [00:08<00:20, 167024.21it/s]"
]
},
{
@@ -1242,7 +1242,7 @@
"output_type": "stream",
"text": [
"\r",
- " 30%|██▉ | 1497806/4997817 [00:08<00:20, 170137.62it/s]"
+ " 30%|███ | 1514556/4997817 [00:08<00:20, 168486.84it/s]"
]
},
{
@@ -1250,7 +1250,7 @@
"output_type": "stream",
"text": [
"\r",
- " 30%|███ | 1514821/4997817 [00:08<00:20, 170012.11it/s]"
+ " 31%|███ | 1531721/4997817 [00:08<00:20, 169424.10it/s]"
]
},
{
@@ -1258,7 +1258,7 @@
"output_type": "stream",
"text": [
"\r",
- " 31%|███ | 1531832/4997817 [00:09<00:20, 170038.44it/s]"
+ " 31%|███ | 1548911/4997817 [00:09<00:20, 170159.13it/s]"
]
},
{
@@ -1266,7 +1266,7 @@
"output_type": "stream",
"text": [
"\r",
- " 31%|███ | 1548896/4997817 [00:09<00:20, 170215.19it/s]"
+ " 31%|███▏ | 1566094/4997817 [00:09<00:20, 170655.03it/s]"
]
},
{
@@ -1274,7 +1274,7 @@
"output_type": "stream",
"text": [
"\r",
- " 31%|███▏ | 1566000/4997817 [00:09<00:20, 170459.41it/s]"
+ " 32%|███▏ | 1583242/4997817 [00:09<00:19, 170897.97it/s]"
]
},
{
@@ -1282,7 +1282,7 @@
"output_type": "stream",
"text": [
"\r",
- " 32%|███▏ | 1583047/4997817 [00:09<00:20, 170272.52it/s]"
+ " 32%|███▏ | 1600338/4997817 [00:09<00:19, 170597.76it/s]"
]
},
{
@@ -1290,7 +1290,7 @@
"output_type": "stream",
"text": [
"\r",
- " 32%|███▏ | 1600075/4997817 [00:09<00:19, 170017.21it/s]"
+ " 32%|███▏ | 1617403/4997817 [00:09<00:19, 170590.81it/s]"
]
},
{
@@ -1298,7 +1298,7 @@
"output_type": "stream",
"text": [
"\r",
- " 32%|███▏ | 1617077/4997817 [00:09<00:19, 169842.89it/s]"
+ " 33%|███▎ | 1634553/4997817 [00:09<00:19, 170859.49it/s]"
]
},
{
@@ -1306,7 +1306,7 @@
"output_type": "stream",
"text": [
"\r",
- " 33%|███▎ | 1634062/4997817 [00:09<00:19, 169498.93it/s]"
+ " 33%|███▎ | 1651677/4997817 [00:09<00:19, 170970.83it/s]"
]
},
{
@@ -1314,7 +1314,7 @@
"output_type": "stream",
"text": [
"\r",
- " 33%|███▎ | 1651013/4997817 [00:09<00:19, 168545.09it/s]"
+ " 33%|███▎ | 1668835/4997817 [00:09<00:19, 171148.17it/s]"
]
},
{
@@ -1322,7 +1322,7 @@
"output_type": "stream",
"text": [
"\r",
- " 33%|███▎ | 1668143/4997817 [00:09<00:19, 169363.67it/s]"
+ " 34%|███▎ | 1686094/4997817 [00:09<00:19, 171576.76it/s]"
]
},
{
@@ -1330,7 +1330,7 @@
"output_type": "stream",
"text": [
"\r",
- " 34%|███▎ | 1685225/4997817 [00:09<00:19, 169793.94it/s]"
+ " 34%|███▍ | 1703331/4997817 [00:09<00:19, 171811.94it/s]"
]
},
{
@@ -1338,7 +1338,7 @@
"output_type": "stream",
"text": [
"\r",
- " 34%|███▍ | 1702217/4997817 [00:10<00:19, 169829.27it/s]"
+ " 34%|███▍ | 1720557/4997817 [00:10<00:19, 171943.04it/s]"
]
},
{
@@ -1346,7 +1346,7 @@
"output_type": "stream",
"text": [
"\r",
- " 34%|███▍ | 1719201/4997817 [00:10<00:19, 169735.37it/s]"
+ " 35%|███▍ | 1737819/4997817 [00:10<00:18, 172141.68it/s]"
]
},
{
@@ -1354,7 +1354,7 @@
"output_type": "stream",
"text": [
"\r",
- " 35%|███▍ | 1736176/4997817 [00:10<00:19, 169526.91it/s]"
+ " 35%|███▌ | 1755034/4997817 [00:10<00:18, 172091.31it/s]"
]
},
{
@@ -1362,7 +1362,7 @@
"output_type": "stream",
"text": [
"\r",
- " 35%|███▌ | 1753215/4997817 [00:10<00:19, 169781.56it/s]"
+ " 35%|███▌ | 1772420/4997817 [00:10<00:18, 172616.46it/s]"
]
},
{
@@ -1370,7 +1370,7 @@
"output_type": "stream",
"text": [
"\r",
- " 35%|███▌ | 1770194/4997817 [00:10<00:19, 169779.29it/s]"
+ " 36%|███▌ | 1789723/4997817 [00:10<00:18, 172735.23it/s]"
]
},
{
@@ -1378,7 +1378,7 @@
"output_type": "stream",
"text": [
"\r",
- " 36%|███▌ | 1787173/4997817 [00:10<00:18, 169599.94it/s]"
+ " 36%|███▌ | 1806997/4997817 [00:10<00:18, 169044.08it/s]"
]
},
{
@@ -1386,7 +1386,7 @@
"output_type": "stream",
"text": [
"\r",
- " 36%|███▌ | 1804134/4997817 [00:10<00:18, 169303.75it/s]"
+ " 37%|███▋ | 1824418/4997817 [00:10<00:18, 170567.09it/s]"
]
},
{
@@ -1394,7 +1394,7 @@
"output_type": "stream",
"text": [
"\r",
- " 36%|███▋ | 1821065/4997817 [00:10<00:19, 162204.92it/s]"
+ " 37%|███▋ | 1841773/4997817 [00:10<00:18, 171448.30it/s]"
]
},
{
@@ -1402,7 +1402,7 @@
"output_type": "stream",
"text": [
"\r",
- " 37%|███▋ | 1838101/4997817 [00:10<00:19, 164570.88it/s]"
+ " 37%|███▋ | 1859140/4997817 [00:10<00:18, 172107.03it/s]"
]
},
{
@@ -1410,7 +1410,7 @@
"output_type": "stream",
"text": [
"\r",
- " 37%|███▋ | 1855142/4997817 [00:10<00:18, 166280.50it/s]"
+ " 38%|███▊ | 1876360/4997817 [00:10<00:18, 172090.62it/s]"
]
},
{
@@ -1418,7 +1418,7 @@
"output_type": "stream",
"text": [
"\r",
- " 37%|███▋ | 1872038/4997817 [00:11<00:18, 167067.61it/s]"
+ " 38%|███▊ | 1893635/4997817 [00:11<00:18, 172285.13it/s]"
]
},
{
@@ -1426,7 +1426,7 @@
"output_type": "stream",
"text": [
"\r",
- " 38%|███▊ | 1889187/4997817 [00:11<00:18, 168375.49it/s]"
+ " 38%|███▊ | 1910941/4997817 [00:11<00:17, 172514.18it/s]"
]
},
{
@@ -1434,7 +1434,7 @@
"output_type": "stream",
"text": [
"\r",
- " 38%|███▊ | 1906283/4997817 [00:11<00:18, 169143.20it/s]"
+ " 39%|███▊ | 1928231/4997817 [00:11<00:17, 172626.30it/s]"
]
},
{
@@ -1442,7 +1442,7 @@
"output_type": "stream",
"text": [
"\r",
- " 38%|███▊ | 1923326/4997817 [00:11<00:18, 169524.17it/s]"
+ " 39%|███▉ | 1945536/4997817 [00:11<00:17, 172751.19it/s]"
]
},
{
@@ -1450,7 +1450,7 @@
"output_type": "stream",
"text": [
"\r",
- " 39%|███▉ | 1940290/4997817 [00:11<00:18, 169381.43it/s]"
+ " 39%|███▉ | 1962813/4997817 [00:11<00:17, 172740.95it/s]"
]
},
{
@@ -1458,7 +1458,7 @@
"output_type": "stream",
"text": [
"\r",
- " 39%|███▉ | 1957260/4997817 [00:11<00:17, 169473.34it/s]"
+ " 40%|███▉ | 1980089/4997817 [00:11<00:17, 172371.69it/s]"
]
},
{
@@ -1466,7 +1466,7 @@
"output_type": "stream",
"text": [
"\r",
- " 40%|███▉ | 1974242/4997817 [00:11<00:17, 169572.90it/s]"
+ " 40%|███▉ | 1997328/4997817 [00:11<00:17, 171941.16it/s]"
]
},
{
@@ -1474,7 +1474,7 @@
"output_type": "stream",
"text": [
"\r",
- " 40%|███▉ | 1991316/4997817 [00:11<00:17, 169920.66it/s]"
+ " 40%|████ | 2014523/4997817 [00:11<00:17, 171574.06it/s]"
]
},
{
@@ -1482,7 +1482,7 @@
"output_type": "stream",
"text": [
"\r",
- " 40%|████ | 2008311/4997817 [00:11<00:17, 169715.14it/s]"
+ " 41%|████ | 2031682/4997817 [00:11<00:17, 171369.81it/s]"
]
},
{
@@ -1490,7 +1490,7 @@
"output_type": "stream",
"text": [
"\r",
- " 41%|████ | 2025285/4997817 [00:11<00:17, 169583.02it/s]"
+ " 41%|████ | 2048829/4997817 [00:11<00:17, 171395.17it/s]"
]
},
{
@@ -1498,7 +1498,7 @@
"output_type": "stream",
"text": [
"\r",
- " 41%|████ | 2042256/4997817 [00:12<00:17, 169618.31it/s]"
+ " 41%|████▏ | 2065969/4997817 [00:12<00:17, 171282.40it/s]"
]
},
{
@@ -1506,7 +1506,7 @@
"output_type": "stream",
"text": [
"\r",
- " 41%|████ | 2059371/4997817 [00:12<00:17, 170075.34it/s]"
+ " 42%|████▏ | 2083130/4997817 [00:12<00:17, 171378.07it/s]"
]
},
{
@@ -1514,7 +1514,7 @@
"output_type": "stream",
"text": [
"\r",
- " 42%|████▏ | 2076380/4997817 [00:12<00:17, 169403.91it/s]"
+ " 42%|████▏ | 2100431/4997817 [00:12<00:16, 171863.75it/s]"
]
},
{
@@ -1522,7 +1522,7 @@
"output_type": "stream",
"text": [
"\r",
- " 42%|████▏ | 2093428/4997817 [00:12<00:17, 169721.33it/s]"
+ " 42%|████▏ | 2117705/4997817 [00:12<00:16, 172124.17it/s]"
]
},
{
@@ -1530,7 +1530,7 @@
"output_type": "stream",
"text": [
"\r",
- " 42%|████▏ | 2110449/4997817 [00:12<00:16, 169865.20it/s]"
+ " 43%|████▎ | 2134918/4997817 [00:12<00:16, 172104.69it/s]"
]
},
{
@@ -1538,7 +1538,7 @@
"output_type": "stream",
"text": [
"\r",
- " 43%|████▎ | 2127507/4997817 [00:12<00:16, 170076.05it/s]"
+ " 43%|████▎ | 2152129/4997817 [00:12<00:16, 171791.60it/s]"
]
},
{
@@ -1546,7 +1546,7 @@
"output_type": "stream",
"text": [
"\r",
- " 43%|████▎ | 2144516/4997817 [00:12<00:16, 169981.79it/s]"
+ " 43%|████▎ | 2169309/4997817 [00:12<00:17, 164877.71it/s]"
]
},
{
@@ -1554,7 +1554,7 @@
"output_type": "stream",
"text": [
"\r",
- " 43%|████▎ | 2161515/4997817 [00:12<00:16, 169803.32it/s]"
+ " 44%|████▎ | 2186442/4997817 [00:12<00:16, 166753.50it/s]"
]
},
{
@@ -1562,7 +1562,7 @@
"output_type": "stream",
"text": [
"\r",
- " 44%|████▎ | 2178496/4997817 [00:12<00:16, 169117.72it/s]"
+ " 44%|████▍ | 2203549/4997817 [00:12<00:16, 168018.49it/s]"
]
},
{
@@ -1570,7 +1570,7 @@
"output_type": "stream",
"text": [
"\r",
- " 44%|████▍ | 2195409/4997817 [00:12<00:16, 168534.31it/s]"
+ " 44%|████▍ | 2220689/4997817 [00:12<00:16, 169015.83it/s]"
]
},
{
@@ -1578,7 +1578,7 @@
"output_type": "stream",
"text": [
"\r",
- " 44%|████▍ | 2212388/4997817 [00:13<00:16, 168906.22it/s]"
+ " 45%|████▍ | 2237815/4997817 [00:13<00:16, 169679.11it/s]"
]
},
{
@@ -1586,7 +1586,7 @@
"output_type": "stream",
"text": [
"\r",
- " 45%|████▍ | 2229280/4997817 [00:13<00:16, 168637.59it/s]"
+ " 45%|████▌ | 2254844/4997817 [00:13<00:16, 169856.74it/s]"
]
},
{
@@ -1594,7 +1594,7 @@
"output_type": "stream",
"text": [
"\r",
- " 45%|████▍ | 2246219/4997817 [00:13<00:16, 168857.59it/s]"
+ " 45%|████▌ | 2271879/4997817 [00:13<00:16, 170000.74it/s]"
]
},
{
@@ -1602,7 +1602,7 @@
"output_type": "stream",
"text": [
"\r",
- " 45%|████▌ | 2263106/4997817 [00:13<00:16, 168820.16it/s]"
+ " 46%|████▌ | 2288907/4997817 [00:13<00:15, 170080.26it/s]"
]
},
{
@@ -1610,7 +1610,7 @@
"output_type": "stream",
"text": [
"\r",
- " 46%|████▌ | 2279989/4997817 [00:13<00:16, 168568.78it/s]"
+ " 46%|████▌ | 2305964/4997817 [00:13<00:15, 170224.06it/s]"
]
},
{
@@ -1618,7 +1618,7 @@
"output_type": "stream",
"text": [
"\r",
- " 46%|████▌ | 2296887/4997817 [00:13<00:16, 168651.70it/s]"
+ " 46%|████▋ | 2323041/4997817 [00:13<00:15, 170385.04it/s]"
]
},
{
@@ -1626,7 +1626,7 @@
"output_type": "stream",
"text": [
"\r",
- " 46%|████▋ | 2313821/4997817 [00:13<00:15, 168856.04it/s]"
+ " 47%|████▋ | 2340083/4997817 [00:13<00:15, 170382.06it/s]"
]
},
{
@@ -1634,7 +1634,7 @@
"output_type": "stream",
"text": [
"\r",
- " 47%|████▋ | 2330819/4997817 [00:13<00:15, 169189.45it/s]"
+ " 47%|████▋ | 2357190/4997817 [00:13<00:15, 170585.98it/s]"
]
},
{
@@ -1642,7 +1642,7 @@
"output_type": "stream",
"text": [
"\r",
- " 47%|████▋ | 2347801/4997817 [00:13<00:15, 169374.61it/s]"
+ " 48%|████▊ | 2374382/4997817 [00:13<00:15, 170982.79it/s]"
]
},
{
@@ -1650,7 +1650,7 @@
"output_type": "stream",
"text": [
"\r",
- " 47%|████▋ | 2364749/4997817 [00:13<00:15, 169403.59it/s]"
+ " 48%|████▊ | 2391535/4997817 [00:13<00:15, 171143.49it/s]"
]
},
{
@@ -1658,7 +1658,7 @@
"output_type": "stream",
"text": [
"\r",
- " 48%|████▊ | 2381869/4997817 [00:14<00:15, 169939.63it/s]"
+ " 48%|████▊ | 2408651/4997817 [00:14<00:15, 171036.96it/s]"
]
},
{
@@ -1666,7 +1666,7 @@
"output_type": "stream",
"text": [
"\r",
- " 48%|████▊ | 2398864/4997817 [00:14<00:15, 169919.22it/s]"
+ " 49%|████▊ | 2425756/4997817 [00:14<00:15, 170904.60it/s]"
]
},
{
@@ -1674,7 +1674,7 @@
"output_type": "stream",
"text": [
"\r",
- " 48%|████▊ | 2415940/4997817 [00:14<00:15, 170168.49it/s]"
+ " 49%|████▉ | 2442871/4997817 [00:14<00:14, 170975.01it/s]"
]
},
{
@@ -1682,7 +1682,7 @@
"output_type": "stream",
"text": [
"\r",
- " 49%|████▊ | 2432957/4997817 [00:14<00:15, 170055.42it/s]"
+ " 49%|████▉ | 2460006/4997817 [00:14<00:14, 171084.91it/s]"
]
},
{
@@ -1690,7 +1690,7 @@
"output_type": "stream",
"text": [
"\r",
- " 49%|████▉ | 2449963/4997817 [00:14<00:14, 170008.23it/s]"
+ " 50%|████▉ | 2477172/4997817 [00:14<00:14, 171255.80it/s]"
]
},
{
@@ -1698,7 +1698,7 @@
"output_type": "stream",
"text": [
"\r",
- " 49%|████▉ | 2466964/4997817 [00:14<00:14, 169952.56it/s]"
+ " 50%|████▉ | 2494379/4997817 [00:14<00:14, 171495.48it/s]"
]
},
{
@@ -1706,7 +1706,7 @@
"output_type": "stream",
"text": [
"\r",
- " 50%|████▉ | 2483960/4997817 [00:14<00:14, 169636.32it/s]"
+ " 50%|█████ | 2511529/4997817 [00:14<00:14, 170654.77it/s]"
]
},
{
@@ -1714,7 +1714,7 @@
"output_type": "stream",
"text": [
"\r",
- " 50%|█████ | 2500924/4997817 [00:14<00:14, 169525.39it/s]"
+ " 51%|█████ | 2528643/4997817 [00:14<00:14, 170797.06it/s]"
]
},
{
@@ -1722,7 +1722,7 @@
"output_type": "stream",
"text": [
"\r",
- " 50%|█████ | 2517877/4997817 [00:14<00:14, 169246.09it/s]"
+ " 51%|█████ | 2545739/4997817 [00:14<00:14, 170841.60it/s]"
]
},
{
@@ -1730,7 +1730,7 @@
"output_type": "stream",
"text": [
"\r",
- " 51%|█████ | 2534802/4997817 [00:14<00:14, 168648.25it/s]"
+ " 51%|█████▏ | 2562824/4997817 [00:14<00:14, 170493.84it/s]"
]
},
{
@@ -1738,7 +1738,7 @@
"output_type": "stream",
"text": [
"\r",
- " 51%|█████ | 2551859/4997817 [00:15<00:14, 169220.39it/s]"
+ " 52%|█████▏ | 2579953/4997817 [00:15<00:14, 170728.07it/s]"
]
},
{
@@ -1746,7 +1746,7 @@
"output_type": "stream",
"text": [
"\r",
- " 51%|█████▏ | 2568790/4997817 [00:15<00:14, 169243.74it/s]"
+ " 52%|█████▏ | 2597105/4997817 [00:15<00:14, 170961.83it/s]"
]
},
{
@@ -1754,7 +1754,7 @@
"output_type": "stream",
"text": [
"\r",
- " 52%|█████▏ | 2585772/4997817 [00:15<00:14, 169413.90it/s]"
+ " 52%|█████▏ | 2614202/4997817 [00:15<00:13, 170940.27it/s]"
]
},
{
@@ -1762,7 +1762,7 @@
"output_type": "stream",
"text": [
"\r",
- " 52%|█████▏ | 2602736/4997817 [00:15<00:14, 169477.56it/s]"
+ " 53%|█████▎ | 2631309/4997817 [00:15<00:13, 170974.92it/s]"
]
},
{
@@ -1770,7 +1770,7 @@
"output_type": "stream",
"text": [
"\r",
- " 52%|█████▏ | 2619684/4997817 [00:15<00:14, 169184.83it/s]"
+ " 53%|█████▎ | 2648407/4997817 [00:15<00:13, 170962.69it/s]"
]
},
{
@@ -1778,7 +1778,7 @@
"output_type": "stream",
"text": [
"\r",
- " 53%|█████▎ | 2636634/4997817 [00:15<00:13, 169276.10it/s]"
+ " 53%|█████▎ | 2665526/4997817 [00:15<00:13, 171027.34it/s]"
]
},
{
@@ -1786,7 +1786,7 @@
"output_type": "stream",
"text": [
"\r",
- " 53%|█████▎ | 2653622/4997817 [00:15<00:13, 169453.20it/s]"
+ " 54%|█████▎ | 2682658/4997817 [00:15<00:13, 171111.48it/s]"
]
},
{
@@ -1794,7 +1794,7 @@
"output_type": "stream",
"text": [
"\r",
- " 53%|█████▎ | 2670568/4997817 [00:15<00:13, 169322.80it/s]"
+ " 54%|█████▍ | 2699770/4997817 [00:15<00:13, 170975.45it/s]"
]
},
{
@@ -1802,7 +1802,7 @@
"output_type": "stream",
"text": [
"\r",
- " 54%|█████▍ | 2687501/4997817 [00:15<00:13, 169134.10it/s]"
+ " 54%|█████▍ | 2716873/4997817 [00:15<00:13, 170989.41it/s]"
]
},
{
@@ -1810,7 +1810,7 @@
"output_type": "stream",
"text": [
"\r",
- " 54%|█████▍ | 2704449/4997817 [00:15<00:13, 169234.73it/s]"
+ " 55%|█████▍ | 2734011/4997817 [00:15<00:13, 171103.39it/s]"
]
},
{
@@ -1818,7 +1818,7 @@
"output_type": "stream",
"text": [
"\r",
- " 54%|█████▍ | 2721441/4997817 [00:16<00:13, 169438.06it/s]"
+ " 55%|█████▌ | 2751181/4997817 [00:16<00:13, 171278.19it/s]"
]
},
{
@@ -1826,7 +1826,7 @@
"output_type": "stream",
"text": [
"\r",
- " 55%|█████▍ | 2738385/4997817 [00:16<00:13, 169363.89it/s]"
+ " 55%|█████▌ | 2768309/4997817 [00:16<00:13, 171162.63it/s]"
]
},
{
@@ -1834,7 +1834,7 @@
"output_type": "stream",
"text": [
"\r",
- " 55%|█████▌ | 2755322/4997817 [00:16<00:13, 168970.19it/s]"
+ " 56%|█████▌ | 2785426/4997817 [00:16<00:12, 171065.60it/s]"
]
},
{
@@ -1842,7 +1842,7 @@
"output_type": "stream",
"text": [
"\r",
- " 55%|█████▌ | 2772343/4997817 [00:16<00:13, 169339.77it/s]"
+ " 56%|█████▌ | 2802570/4997817 [00:16<00:12, 171176.28it/s]"
]
},
{
@@ -1850,7 +1850,7 @@
"output_type": "stream",
"text": [
"\r",
- " 56%|█████▌ | 2789278/4997817 [00:16<00:13, 169325.35it/s]"
+ " 56%|█████▋ | 2819713/4997817 [00:16<00:12, 171250.56it/s]"
]
},
{
@@ -1858,7 +1858,7 @@
"output_type": "stream",
"text": [
"\r",
- " 56%|█████▌ | 2806348/4997817 [00:16<00:12, 169733.91it/s]"
+ " 57%|█████▋ | 2836839/4997817 [00:16<00:12, 171176.46it/s]"
]
},
{
@@ -1866,7 +1866,7 @@
"output_type": "stream",
"text": [
"\r",
- " 56%|█████▋ | 2823429/4997817 [00:16<00:12, 170053.72it/s]"
+ " 57%|█████▋ | 2853957/4997817 [00:16<00:12, 171024.52it/s]"
]
},
{
@@ -1874,7 +1874,7 @@
"output_type": "stream",
"text": [
"\r",
- " 57%|█████▋ | 2840435/4997817 [00:16<00:12, 169836.15it/s]"
+ " 57%|█████▋ | 2871241/4997817 [00:16<00:12, 171565.44it/s]"
]
},
{
@@ -1882,7 +1882,7 @@
"output_type": "stream",
"text": [
"\r",
- " 57%|█████▋ | 2857419/4997817 [00:16<00:12, 169579.34it/s]"
+ " 58%|█████▊ | 2888648/4997817 [00:16<00:12, 172312.72it/s]"
]
},
{
@@ -1890,7 +1890,7 @@
"output_type": "stream",
"text": [
"\r",
- " 58%|█████▊ | 2874445/4997817 [00:16<00:12, 169779.55it/s]"
+ " 58%|█████▊ | 2905883/4997817 [00:16<00:12, 172319.39it/s]"
]
},
{
@@ -1898,7 +1898,7 @@
"output_type": "stream",
"text": [
"\r",
- " 58%|█████▊ | 2891517/4997817 [00:17<00:12, 170059.03it/s]"
+ " 58%|█████▊ | 2923116/4997817 [00:17<00:12, 172179.82it/s]"
]
},
{
@@ -1906,7 +1906,7 @@
"output_type": "stream",
"text": [
"\r",
- " 58%|█████▊ | 2908868/4997817 [00:17<00:12, 171088.81it/s]"
+ " 59%|█████▉ | 2940335/4997817 [00:17<00:11, 171700.69it/s]"
]
},
{
@@ -1914,7 +1914,7 @@
"output_type": "stream",
"text": [
"\r",
- " 59%|█████▊ | 2925996/4997817 [00:17<00:12, 171143.24it/s]"
+ " 59%|█████▉ | 2957506/4997817 [00:17<00:11, 171411.62it/s]"
]
},
{
@@ -1922,7 +1922,7 @@
"output_type": "stream",
"text": [
"\r",
- " 59%|█████▉ | 2943183/4997817 [00:17<00:11, 171359.37it/s]"
+ " 60%|█████▉ | 2974693/4997817 [00:17<00:11, 171544.25it/s]"
]
},
{
@@ -1930,7 +1930,7 @@
"output_type": "stream",
"text": [
"\r",
- " 59%|█████▉ | 2960320/4997817 [00:17<00:11, 171311.41it/s]"
+ " 60%|█████▉ | 2991852/4997817 [00:17<00:11, 171555.30it/s]"
]
},
{
@@ -1938,7 +1938,7 @@
"output_type": "stream",
"text": [
"\r",
- " 60%|█████▉ | 2977540/4997817 [00:17<00:11, 171575.21it/s]"
+ " 60%|██████ | 3009024/4997817 [00:17<00:11, 171601.74it/s]"
]
},
{
@@ -1946,7 +1946,7 @@
"output_type": "stream",
"text": [
"\r",
- " 60%|█████▉ | 2994733/4997817 [00:17<00:11, 171677.61it/s]"
+ " 61%|██████ | 3026185/4997817 [00:17<00:11, 171560.51it/s]"
]
},
{
@@ -1954,7 +1954,7 @@
"output_type": "stream",
"text": [
"\r",
- " 60%|██████ | 3011937/4997817 [00:17<00:11, 171783.03it/s]"
+ " 61%|██████ | 3043342/4997817 [00:17<00:11, 171322.83it/s]"
]
},
{
@@ -1962,7 +1962,7 @@
"output_type": "stream",
"text": [
"\r",
- " 61%|██████ | 3029257/4997817 [00:17<00:11, 172203.66it/s]"
+ " 61%|██████ | 3060482/4997817 [00:17<00:11, 171342.32it/s]"
]
},
{
@@ -1970,7 +1970,7 @@
"output_type": "stream",
"text": [
"\r",
- " 61%|██████ | 3046478/4997817 [00:17<00:11, 169427.04it/s]"
+ " 62%|██████▏ | 3077630/4997817 [00:17<00:11, 171380.57it/s]"
]
},
{
@@ -1978,7 +1978,7 @@
"output_type": "stream",
"text": [
"\r",
- " 61%|██████▏ | 3063586/4997817 [00:18<00:11, 169913.81it/s]"
+ " 62%|██████▏ | 3094806/4997817 [00:18<00:11, 171490.92it/s]"
]
},
{
@@ -1986,7 +1986,7 @@
"output_type": "stream",
"text": [
"\r",
- " 62%|██████▏ | 3080733/4997817 [00:18<00:11, 170374.30it/s]"
+ " 62%|██████▏ | 3112003/4997817 [00:18<00:10, 171629.09it/s]"
]
},
{
@@ -1994,7 +1994,7 @@
"output_type": "stream",
"text": [
"\r",
- " 62%|██████▏ | 3097777/4997817 [00:18<00:11, 170379.04it/s]"
+ " 63%|██████▎ | 3129202/4997817 [00:18<00:10, 171732.33it/s]"
]
},
{
@@ -2002,7 +2002,7 @@
"output_type": "stream",
"text": [
"\r",
- " 62%|██████▏ | 3114863/4997817 [00:18<00:11, 170521.22it/s]"
+ " 63%|██████▎ | 3146417/4997817 [00:18<00:10, 171853.71it/s]"
]
},
{
@@ -2010,7 +2010,7 @@
"output_type": "stream",
"text": [
"\r",
- " 63%|██████▎ | 3131918/4997817 [00:18<00:10, 170371.91it/s]"
+ " 63%|██████▎ | 3163656/4997817 [00:18<00:10, 172010.55it/s]"
]
},
{
@@ -2018,7 +2018,7 @@
"output_type": "stream",
"text": [
"\r",
- " 63%|██████▎ | 3148958/4997817 [00:18<00:10, 169599.75it/s]"
+ " 64%|██████▎ | 3180858/4997817 [00:18<00:10, 171988.09it/s]"
]
},
{
@@ -2026,7 +2026,7 @@
"output_type": "stream",
"text": [
"\r",
- " 63%|██████▎ | 3166085/4997817 [00:18<00:10, 170095.19it/s]"
+ " 64%|██████▍ | 3198078/4997817 [00:18<00:10, 172048.62it/s]"
]
},
{
@@ -2034,7 +2034,7 @@
"output_type": "stream",
"text": [
"\r",
- " 64%|██████▎ | 3183249/4997817 [00:18<00:10, 170553.27it/s]"
+ " 64%|██████▍ | 3215283/4997817 [00:18<00:10, 169778.53it/s]"
]
},
{
@@ -2042,7 +2042,7 @@
"output_type": "stream",
"text": [
"\r",
- " 64%|██████▍ | 3200429/4997817 [00:18<00:10, 170922.50it/s]"
+ " 65%|██████▍ | 3232268/4997817 [00:18<00:10, 169721.89it/s]"
]
},
{
@@ -2050,7 +2050,7 @@
"output_type": "stream",
"text": [
"\r",
- " 64%|██████▍ | 3217523/4997817 [00:18<00:10, 170624.69it/s]"
+ " 65%|██████▌ | 3249541/4997817 [00:18<00:10, 170613.89it/s]"
]
},
{
@@ -2058,7 +2058,7 @@
"output_type": "stream",
"text": [
"\r",
- " 65%|██████▍ | 3234631/4997817 [00:19<00:10, 170758.43it/s]"
+ " 65%|██████▌ | 3266864/4997817 [00:19<00:10, 171390.56it/s]"
]
},
{
@@ -2066,7 +2066,7 @@
"output_type": "stream",
"text": [
"\r",
- " 65%|██████▌ | 3251708/4997817 [00:19<00:10, 170664.58it/s]"
+ " 66%|██████▌ | 3284229/4997817 [00:19<00:09, 172060.47it/s]"
]
},
{
@@ -2074,7 +2074,7 @@
"output_type": "stream",
"text": [
"\r",
- " 65%|██████▌ | 3268775/4997817 [00:19<00:10, 170027.00it/s]"
+ " 66%|██████▌ | 3301452/4997817 [00:19<00:09, 172109.17it/s]"
]
},
{
@@ -2082,7 +2082,7 @@
"output_type": "stream",
"text": [
"\r",
- " 66%|██████▌ | 3285907/4997817 [00:19<00:10, 170384.72it/s]"
+ " 66%|██████▋ | 3318696/4997817 [00:19<00:09, 172205.72it/s]"
]
},
{
@@ -2090,7 +2090,7 @@
"output_type": "stream",
"text": [
"\r",
- " 66%|██████▌ | 3302958/4997817 [00:19<00:09, 170419.09it/s]"
+ " 67%|██████▋ | 3336026/4997817 [00:19<00:09, 172528.23it/s]"
]
},
{
@@ -2098,7 +2098,7 @@
"output_type": "stream",
"text": [
"\r",
- " 66%|██████▋ | 3320005/4997817 [00:19<00:09, 170431.70it/s]"
+ " 67%|██████▋ | 3353385/4997817 [00:19<00:09, 172841.84it/s]"
]
},
{
@@ -2106,7 +2106,7 @@
"output_type": "stream",
"text": [
"\r",
- " 67%|██████▋ | 3337049/4997817 [00:19<00:09, 170277.71it/s]"
+ " 67%|██████▋ | 3370761/4997817 [00:19<00:09, 173114.69it/s]"
]
},
{
@@ -2114,7 +2114,7 @@
"output_type": "stream",
"text": [
"\r",
- " 67%|██████▋ | 3354101/4997817 [00:19<00:09, 170345.83it/s]"
+ " 68%|██████▊ | 3388087/4997817 [00:19<00:09, 173153.98it/s]"
]
},
{
@@ -2122,7 +2122,7 @@
"output_type": "stream",
"text": [
"\r",
- " 67%|██████▋ | 3371136/4997817 [00:19<00:09, 170228.79it/s]"
+ " 68%|██████▊ | 3405403/4997817 [00:19<00:09, 172969.46it/s]"
]
},
{
@@ -2130,7 +2130,7 @@
"output_type": "stream",
"text": [
"\r",
- " 68%|██████▊ | 3388160/4997817 [00:19<00:09, 169938.30it/s]"
+ " 68%|██████▊ | 3422701/4997817 [00:19<00:09, 172546.69it/s]"
]
},
{
@@ -2138,7 +2138,7 @@
"output_type": "stream",
"text": [
"\r",
- " 68%|██████▊ | 3405154/4997817 [00:20<00:09, 169827.83it/s]"
+ " 69%|██████▉ | 3439957/4997817 [00:20<00:09, 172327.55it/s]"
]
},
{
@@ -2146,7 +2146,7 @@
"output_type": "stream",
"text": [
"\r",
- " 68%|██████▊ | 3422211/4997817 [00:20<00:09, 170045.36it/s]"
+ " 69%|██████▉ | 3457191/4997817 [00:20<00:08, 172085.05it/s]"
]
},
{
@@ -2154,7 +2154,7 @@
"output_type": "stream",
"text": [
"\r",
- " 69%|██████▉ | 3439365/4997817 [00:20<00:09, 170489.66it/s]"
+ " 70%|██████▉ | 3474400/4997817 [00:20<00:08, 171804.75it/s]"
]
},
{
@@ -2162,7 +2162,7 @@
"output_type": "stream",
"text": [
"\r",
- " 69%|██████▉ | 3456541/4997817 [00:20<00:09, 170867.26it/s]"
+ " 70%|██████▉ | 3491581/4997817 [00:20<00:08, 171606.43it/s]"
]
},
{
@@ -2170,7 +2170,7 @@
"output_type": "stream",
"text": [
"\r",
- " 70%|██████▉ | 3473744/4997817 [00:20<00:08, 171210.87it/s]"
+ " 70%|███████ | 3508742/4997817 [00:20<00:08, 171311.42it/s]"
]
},
{
@@ -2178,7 +2178,7 @@
"output_type": "stream",
"text": [
"\r",
- " 70%|██████▉ | 3491131/4997817 [00:20<00:08, 172003.95it/s]"
+ " 71%|███████ | 3525874/4997817 [00:20<00:08, 170939.31it/s]"
]
},
{
@@ -2186,7 +2186,7 @@
"output_type": "stream",
"text": [
"\r",
- " 70%|███████ | 3508380/4997817 [00:20<00:08, 172145.58it/s]"
+ " 71%|███████ | 3543043/4997817 [00:20<00:08, 171160.16it/s]"
]
},
{
@@ -2194,7 +2194,7 @@
"output_type": "stream",
"text": [
"\r",
- " 71%|███████ | 3525653/4997817 [00:20<00:08, 172317.82it/s]"
+ " 71%|███████ | 3560160/4997817 [00:20<00:08, 171067.24it/s]"
]
},
{
@@ -2202,7 +2202,7 @@
"output_type": "stream",
"text": [
"\r",
- " 71%|███████ | 3543024/4997817 [00:20<00:08, 172730.50it/s]"
+ " 72%|███████▏ | 3577419/4997817 [00:20<00:08, 171518.42it/s]"
]
},
{
@@ -2210,7 +2210,7 @@
"output_type": "stream",
"text": [
"\r",
- " 71%|███████ | 3560298/4997817 [00:20<00:08, 172529.20it/s]"
+ " 72%|███████▏ | 3594624/4997817 [00:20<00:08, 171675.23it/s]"
]
},
{
@@ -2218,7 +2218,7 @@
"output_type": "stream",
"text": [
"\r",
- " 72%|███████▏ | 3577646/4997817 [00:21<00:08, 172812.12it/s]"
+ " 72%|███████▏ | 3611792/4997817 [00:21<00:08, 171544.10it/s]"
]
},
{
@@ -2226,7 +2226,7 @@
"output_type": "stream",
"text": [
"\r",
- " 72%|███████▏ | 3594928/4997817 [00:21<00:08, 172575.84it/s]"
+ " 73%|███████▎ | 3629022/4997817 [00:21<00:07, 171767.30it/s]"
]
},
{
@@ -2234,7 +2234,7 @@
"output_type": "stream",
"text": [
"\r",
- " 72%|███████▏ | 3612186/4997817 [00:21<00:08, 172357.15it/s]"
+ " 73%|███████▎ | 3646274/4997817 [00:21<00:07, 171989.26it/s]"
]
},
{
@@ -2242,7 +2242,7 @@
"output_type": "stream",
"text": [
"\r",
- " 73%|███████▎ | 3629560/4997817 [00:21<00:07, 172769.91it/s]"
+ " 73%|███████▎ | 3663474/4997817 [00:21<00:07, 171964.63it/s]"
]
},
{
@@ -2250,7 +2250,7 @@
"output_type": "stream",
"text": [
"\r",
- " 73%|███████▎ | 3647045/4997817 [00:21<00:07, 173390.18it/s]"
+ " 74%|███████▎ | 3680719/4997817 [00:21<00:07, 172105.90it/s]"
]
},
{
@@ -2258,7 +2258,7 @@
"output_type": "stream",
"text": [
"\r",
- " 73%|███████▎ | 3664444/4997817 [00:21<00:07, 173567.90it/s]"
+ " 74%|███████▍ | 3697930/4997817 [00:21<00:07, 172046.17it/s]"
]
},
{
@@ -2266,7 +2266,7 @@
"output_type": "stream",
"text": [
"\r",
- " 74%|███████▎ | 3681801/4997817 [00:21<00:07, 173545.26it/s]"
+ " 74%|███████▍ | 3715230/4997817 [00:21<00:07, 172328.64it/s]"
]
},
{
@@ -2274,7 +2274,7 @@
"output_type": "stream",
"text": [
"\r",
- " 74%|███████▍ | 3699161/4997817 [00:21<00:07, 173557.75it/s]"
+ " 75%|███████▍ | 3732463/4997817 [00:21<00:07, 172130.09it/s]"
]
},
{
@@ -2282,7 +2282,7 @@
"output_type": "stream",
"text": [
"\r",
- " 74%|███████▍ | 3716548/4997817 [00:21<00:07, 173647.59it/s]"
+ " 75%|███████▌ | 3749677/4997817 [00:21<00:07, 171467.15it/s]"
]
},
{
@@ -2290,7 +2290,7 @@
"output_type": "stream",
"text": [
"\r",
- " 75%|███████▍ | 3733913/4997817 [00:21<00:07, 173069.45it/s]"
+ " 75%|███████▌ | 3766854/4997817 [00:21<00:07, 171555.66it/s]"
]
},
{
@@ -2298,7 +2298,7 @@
"output_type": "stream",
"text": [
"\r",
- " 75%|███████▌ | 3751244/4997817 [00:22<00:07, 173136.91it/s]"
+ " 76%|███████▌ | 3784058/4997817 [00:22<00:07, 171695.63it/s]"
]
},
{
@@ -2306,7 +2306,7 @@
"output_type": "stream",
"text": [
"\r",
- " 75%|███████▌ | 3768646/4997817 [00:22<00:07, 173398.81it/s]"
+ " 76%|███████▌ | 3801268/4997817 [00:22<00:06, 171812.49it/s]"
]
},
{
@@ -2314,7 +2314,7 @@
"output_type": "stream",
"text": [
"\r",
- " 76%|███████▌ | 3786035/4997817 [00:22<00:06, 173543.43it/s]"
+ " 76%|███████▋ | 3818508/4997817 [00:22<00:06, 171985.38it/s]"
]
},
{
@@ -2322,7 +2322,7 @@
"output_type": "stream",
"text": [
"\r",
- " 76%|███████▌ | 3803496/4997817 [00:22<00:06, 173859.68it/s]"
+ " 77%|███████▋ | 3835708/4997817 [00:22<00:06, 171988.09it/s]"
]
},
{
@@ -2330,7 +2330,7 @@
"output_type": "stream",
"text": [
"\r",
- " 76%|███████▋ | 3820883/4997817 [00:22<00:06, 173726.10it/s]"
+ " 77%|███████▋ | 3852981/4997817 [00:22<00:06, 172207.50it/s]"
]
},
{
@@ -2338,7 +2338,7 @@
"output_type": "stream",
"text": [
"\r",
- " 77%|███████▋ | 3838320/4997817 [00:22<00:06, 173916.89it/s]"
+ " 77%|███████▋ | 3870202/4997817 [00:22<00:06, 172135.93it/s]"
]
},
{
@@ -2346,7 +2346,7 @@
"output_type": "stream",
"text": [
"\r",
- " 77%|███████▋ | 3855712/4997817 [00:22<00:06, 173459.27it/s]"
+ " 78%|███████▊ | 3887458/4997817 [00:22<00:06, 172260.05it/s]"
]
},
{
@@ -2354,7 +2354,7 @@
"output_type": "stream",
"text": [
"\r",
- " 77%|███████▋ | 3873059/4997817 [00:22<00:06, 172994.18it/s]"
+ " 78%|███████▊ | 3904685/4997817 [00:22<00:06, 171946.29it/s]"
]
},
{
@@ -2362,7 +2362,7 @@
"output_type": "stream",
"text": [
"\r",
- " 78%|███████▊ | 3890432/4997817 [00:22<00:06, 173205.80it/s]"
+ " 78%|███████▊ | 3921880/4997817 [00:22<00:06, 171690.22it/s]"
]
},
{
@@ -2370,7 +2370,7 @@
"output_type": "stream",
"text": [
"\r",
- " 78%|███████▊ | 3907753/4997817 [00:23<00:06, 173026.26it/s]"
+ " 79%|███████▉ | 3939050/4997817 [00:22<00:06, 171373.23it/s]"
]
},
{
@@ -2378,7 +2378,7 @@
"output_type": "stream",
"text": [
"\r",
- " 79%|███████▊ | 3925056/4997817 [00:23<00:06, 172923.17it/s]"
+ " 79%|███████▉ | 3956200/4997817 [00:23<00:06, 171406.44it/s]"
]
},
{
@@ -2386,7 +2386,7 @@
"output_type": "stream",
"text": [
"\r",
- " 79%|███████▉ | 3942349/4997817 [00:23<00:06, 172426.25it/s]"
+ " 80%|███████▉ | 3973341/4997817 [00:23<00:05, 171115.38it/s]"
]
},
{
@@ -2394,7 +2394,7 @@
"output_type": "stream",
"text": [
"\r",
- " 79%|███████▉ | 3959737/4997817 [00:23<00:06, 172857.80it/s]"
+ " 80%|███████▉ | 3990459/4997817 [00:23<00:05, 171130.98it/s]"
]
},
{
@@ -2402,7 +2402,7 @@
"output_type": "stream",
"text": [
"\r",
- " 80%|███████▉ | 3977024/4997817 [00:23<00:05, 172594.90it/s]"
+ " 80%|████████ | 4007594/4997817 [00:23<00:05, 171193.80it/s]"
]
},
{
@@ -2410,7 +2410,7 @@
"output_type": "stream",
"text": [
"\r",
- " 80%|███████▉ | 3994284/4997817 [00:23<00:05, 172302.64it/s]"
+ " 81%|████████ | 4024747/4997817 [00:23<00:05, 171289.44it/s]"
]
},
{
@@ -2418,7 +2418,7 @@
"output_type": "stream",
"text": [
"\r",
- " 80%|████████ | 4011554/4997817 [00:23<00:05, 172417.58it/s]"
+ " 81%|████████ | 4041877/4997817 [00:23<00:05, 171288.74it/s]"
]
},
{
@@ -2426,7 +2426,7 @@
"output_type": "stream",
"text": [
"\r",
- " 81%|████████ | 4028936/4997817 [00:23<00:05, 172835.07it/s]"
+ " 81%|████████ | 4059428/4997817 [00:23<00:05, 172552.23it/s]"
]
},
{
@@ -2434,7 +2434,7 @@
"output_type": "stream",
"text": [
"\r",
- " 81%|████████ | 4046220/4997817 [00:23<00:05, 172545.92it/s]"
+ " 82%|████████▏ | 4076757/4997817 [00:23<00:05, 172770.17it/s]"
]
},
{
@@ -2442,7 +2442,7 @@
"output_type": "stream",
"text": [
"\r",
- " 81%|████████▏ | 4063475/4997817 [00:23<00:05, 172273.78it/s]"
+ " 82%|████████▏ | 4094035/4997817 [00:23<00:05, 172749.45it/s]"
]
},
{
@@ -2450,7 +2450,7 @@
"output_type": "stream",
"text": [
"\r",
- " 82%|████████▏ | 4080783/4997817 [00:24<00:05, 172511.10it/s]"
+ " 82%|████████▏ | 4111311/4997817 [00:23<00:05, 172590.88it/s]"
]
},
{
@@ -2458,7 +2458,7 @@
"output_type": "stream",
"text": [
"\r",
- " 82%|████████▏ | 4098035/4997817 [00:24<00:05, 172243.42it/s]"
+ " 83%|████████▎ | 4128626/4997817 [00:24<00:05, 172755.18it/s]"
]
},
{
@@ -2466,7 +2466,7 @@
"output_type": "stream",
"text": [
"\r",
- " 82%|████████▏ | 4115260/4997817 [00:24<00:05, 171411.32it/s]"
+ " 83%|████████▎ | 4145965/4997817 [00:24<00:04, 172941.11it/s]"
]
},
{
@@ -2474,7 +2474,7 @@
"output_type": "stream",
"text": [
"\r",
- " 83%|████████▎ | 4132402/4997817 [00:24<00:05, 170566.51it/s]"
+ " 83%|████████▎ | 4163390/4997817 [00:24<00:04, 173331.16it/s]"
]
},
{
@@ -2482,7 +2482,7 @@
"output_type": "stream",
"text": [
"\r",
- " 83%|████████▎ | 4149460/4997817 [00:24<00:04, 169858.69it/s]"
+ " 84%|████████▎ | 4180724/4997817 [00:24<00:04, 173224.50it/s]"
]
},
{
@@ -2490,7 +2490,7 @@
"output_type": "stream",
"text": [
"\r",
- " 83%|████████▎ | 4166447/4997817 [00:24<00:04, 168976.19it/s]"
+ " 84%|████████▍ | 4198100/4997817 [00:24<00:04, 173382.98it/s]"
]
},
{
@@ -2498,7 +2498,7 @@
"output_type": "stream",
"text": [
"\r",
- " 84%|████████▎ | 4183346/4997817 [00:24<00:04, 168708.09it/s]"
+ " 84%|████████▍ | 4215467/4997817 [00:24<00:04, 173465.13it/s]"
]
},
{
@@ -2506,7 +2506,7 @@
"output_type": "stream",
"text": [
"\r",
- " 84%|████████▍ | 4200264/4997817 [00:24<00:04, 168846.61it/s]"
+ " 85%|████████▍ | 4232814/4997817 [00:24<00:04, 173187.90it/s]"
]
},
{
@@ -2514,7 +2514,7 @@
"output_type": "stream",
"text": [
"\r",
- " 84%|████████▍ | 4217215/4997817 [00:24<00:04, 169042.33it/s]"
+ " 85%|████████▌ | 4250164/4997817 [00:24<00:04, 173276.25it/s]"
]
},
{
@@ -2522,7 +2522,7 @@
"output_type": "stream",
"text": [
"\r",
- " 85%|████████▍ | 4234122/4997817 [00:24<00:04, 169046.80it/s]"
+ " 85%|████████▌ | 4267492/4997817 [00:24<00:04, 171087.42it/s]"
]
},
{
@@ -2530,7 +2530,7 @@
"output_type": "stream",
"text": [
"\r",
- " 85%|████████▌ | 4251190/4997817 [00:25<00:04, 169532.01it/s]"
+ " 86%|████████▌ | 4284847/4997817 [00:25<00:04, 171815.28it/s]"
]
},
{
@@ -2538,7 +2538,7 @@
"output_type": "stream",
"text": [
"\r",
- " 85%|████████▌ | 4268278/4997817 [00:25<00:04, 169933.04it/s]"
+ " 86%|████████▌ | 4302136/4997817 [00:25<00:04, 172133.13it/s]"
]
},
{
@@ -2546,7 +2546,7 @@
"output_type": "stream",
"text": [
"\r",
- " 86%|████████▌ | 4285272/4997817 [00:25<00:04, 169817.02it/s]"
+ " 86%|████████▋ | 4319480/4997817 [00:25<00:03, 172520.19it/s]"
]
},
{
@@ -2554,7 +2554,7 @@
"output_type": "stream",
"text": [
"\r",
- " 86%|████████▌ | 4302254/4997817 [00:25<00:04, 169255.34it/s]"
+ " 87%|████████▋ | 4336926/4997817 [00:25<00:03, 173095.56it/s]"
]
},
{
@@ -2562,7 +2562,7 @@
"output_type": "stream",
"text": [
"\r",
- " 86%|████████▋ | 4319210/4997817 [00:25<00:04, 169345.15it/s]"
+ " 87%|████████▋ | 4354434/4997817 [00:25<00:03, 173685.45it/s]"
]
},
{
@@ -2570,7 +2570,7 @@
"output_type": "stream",
"text": [
"\r",
- " 87%|████████▋ | 4336149/4997817 [00:25<00:03, 169354.52it/s]"
+ " 87%|████████▋ | 4371978/4997817 [00:25<00:03, 174208.63it/s]"
]
},
{
@@ -2578,7 +2578,7 @@
"output_type": "stream",
"text": [
"\r",
- " 87%|████████▋ | 4353220/4997817 [00:25<00:03, 169758.84it/s]"
+ " 88%|████████▊ | 4389480/4997817 [00:25<00:03, 174447.81it/s]"
]
},
{
@@ -2586,7 +2586,7 @@
"output_type": "stream",
"text": [
"\r",
- " 87%|████████▋ | 4370197/4997817 [00:25<00:03, 169598.78it/s]"
+ " 88%|████████▊ | 4406926/4997817 [00:25<00:03, 174264.33it/s]"
]
},
{
@@ -2594,7 +2594,7 @@
"output_type": "stream",
"text": [
"\r",
- " 88%|████████▊ | 4387158/4997817 [00:25<00:03, 169274.57it/s]"
+ " 89%|████████▊ | 4424354/4997817 [00:25<00:03, 173694.76it/s]"
]
},
{
@@ -2602,7 +2602,7 @@
"output_type": "stream",
"text": [
"\r",
- " 88%|████████▊ | 4404086/4997817 [00:25<00:03, 168827.02it/s]"
+ " 89%|████████▉ | 4441725/4997817 [00:25<00:03, 173476.87it/s]"
]
},
{
@@ -2610,7 +2610,7 @@
"output_type": "stream",
"text": [
"\r",
- " 88%|████████▊ | 4421002/4997817 [00:26<00:03, 168923.60it/s]"
+ " 89%|████████▉ | 4459108/4997817 [00:26<00:03, 173579.55it/s]"
]
},
{
@@ -2618,7 +2618,7 @@
"output_type": "stream",
"text": [
"\r",
- " 89%|████████▉ | 4437895/4997817 [00:26<00:03, 168606.30it/s]"
+ " 90%|████████▉ | 4476523/4997817 [00:26<00:03, 173746.26it/s]"
]
},
{
@@ -2626,7 +2626,7 @@
"output_type": "stream",
"text": [
"\r",
- " 89%|████████▉ | 4454904/4997817 [00:26<00:03, 169048.16it/s]"
+ " 90%|████████▉ | 4493902/4997817 [00:26<00:02, 173756.06it/s]"
]
},
{
@@ -2634,7 +2634,7 @@
"output_type": "stream",
"text": [
"\r",
- " 89%|████████▉ | 4471810/4997817 [00:26<00:03, 168624.03it/s]"
+ " 90%|█████████ | 4511278/4997817 [00:26<00:02, 173434.63it/s]"
]
},
{
@@ -2642,7 +2642,7 @@
"output_type": "stream",
"text": [
"\r",
- " 90%|████████▉ | 4488768/4997817 [00:26<00:03, 168907.16it/s]"
+ " 91%|█████████ | 4528660/4997817 [00:26<00:02, 173546.58it/s]"
]
},
{
@@ -2650,7 +2650,7 @@
"output_type": "stream",
"text": [
"\r",
- " 90%|█████████ | 4505693/4997817 [00:26<00:02, 169006.81it/s]"
+ " 91%|█████████ | 4546015/4997817 [00:26<00:02, 173215.74it/s]"
]
},
{
@@ -2658,7 +2658,7 @@
"output_type": "stream",
"text": [
"\r",
- " 90%|█████████ | 4522681/4997817 [00:26<00:02, 169265.97it/s]"
+ " 91%|█████████▏| 4563337/4997817 [00:26<00:02, 173046.29it/s]"
]
},
{
@@ -2666,7 +2666,7 @@
"output_type": "stream",
"text": [
"\r",
- " 91%|█████████ | 4539608/4997817 [00:26<00:02, 169250.06it/s]"
+ " 92%|█████████▏| 4580642/4997817 [00:26<00:02, 173036.71it/s]"
]
},
{
@@ -2674,7 +2674,7 @@
"output_type": "stream",
"text": [
"\r",
- " 91%|█████████ | 4556534/4997817 [00:26<00:02, 169120.26it/s]"
+ " 92%|█████████▏| 4597946/4997817 [00:26<00:02, 172707.12it/s]"
]
},
{
@@ -2682,7 +2682,7 @@
"output_type": "stream",
"text": [
"\r",
- " 92%|█████████▏| 4573478/4997817 [00:26<00:02, 169212.61it/s]"
+ " 92%|█████████▏| 4615217/4997817 [00:26<00:02, 171945.48it/s]"
]
},
{
@@ -2690,7 +2690,7 @@
"output_type": "stream",
"text": [
"\r",
- " 92%|█████████▏| 4590405/4997817 [00:27<00:02, 169225.99it/s]"
+ " 93%|█████████▎| 4632413/4997817 [00:27<00:02, 171736.56it/s]"
]
},
{
@@ -2698,7 +2698,7 @@
"output_type": "stream",
"text": [
"\r",
- " 92%|█████████▏| 4607328/4997817 [00:27<00:02, 168951.64it/s]"
+ " 93%|█████████▎| 4649630/4997817 [00:27<00:02, 171861.43it/s]"
]
},
{
@@ -2706,7 +2706,7 @@
"output_type": "stream",
"text": [
"\r",
- " 93%|█████████▎| 4624224/4997817 [00:27<00:02, 168422.93it/s]"
+ " 93%|█████████▎| 4666868/4997817 [00:27<00:01, 172014.01it/s]"
]
},
{
@@ -2714,7 +2714,7 @@
"output_type": "stream",
"text": [
"\r",
- " 93%|█████████▎| 4641067/4997817 [00:27<00:02, 168260.39it/s]"
+ " 94%|█████████▎| 4684070/4997817 [00:27<00:01, 171985.41it/s]"
]
},
{
@@ -2722,7 +2722,7 @@
"output_type": "stream",
"text": [
"\r",
- " 93%|█████████▎| 4657969/4997817 [00:27<00:02, 168483.80it/s]"
+ " 94%|█████████▍| 4701269/4997817 [00:27<00:01, 171722.45it/s]"
]
},
{
@@ -2730,7 +2730,7 @@
"output_type": "stream",
"text": [
"\r",
- " 94%|█████████▎| 4674900/4997817 [00:27<00:01, 168729.86it/s]"
+ " 94%|█████████▍| 4718442/4997817 [00:27<00:01, 171694.00it/s]"
]
},
{
@@ -2738,7 +2738,7 @@
"output_type": "stream",
"text": [
"\r",
- " 94%|█████████▍| 4691857/4997817 [00:27<00:01, 168977.11it/s]"
+ " 95%|█████████▍| 4735612/4997817 [00:27<00:01, 171538.33it/s]"
]
},
{
@@ -2746,7 +2746,7 @@
"output_type": "stream",
"text": [
"\r",
- " 94%|█████████▍| 4708755/4997817 [00:27<00:01, 168949.66it/s]"
+ " 95%|█████████▌| 4752766/4997817 [00:27<00:01, 171290.62it/s]"
]
},
{
@@ -2754,7 +2754,7 @@
"output_type": "stream",
"text": [
"\r",
- " 95%|█████████▍| 4725694/4997817 [00:27<00:01, 169078.07it/s]"
+ " 95%|█████████▌| 4769896/4997817 [00:27<00:01, 171233.96it/s]"
]
},
{
@@ -2762,7 +2762,7 @@
"output_type": "stream",
"text": [
"\r",
- " 95%|█████████▍| 4742602/4997817 [00:27<00:01, 168968.66it/s]"
+ " 96%|█████████▌| 4787020/4997817 [00:27<00:01, 171187.67it/s]"
]
},
{
@@ -2770,7 +2770,7 @@
"output_type": "stream",
"text": [
"\r",
- " 95%|█████████▌| 4759560/4997817 [00:28<00:01, 169149.53it/s]"
+ " 96%|█████████▌| 4804139/4997817 [00:28<00:01, 167381.22it/s]"
]
},
{
@@ -2778,7 +2778,7 @@
"output_type": "stream",
"text": [
"\r",
- " 96%|█████████▌| 4776476/4997817 [00:28<00:01, 163091.56it/s]"
+ " 96%|█████████▋| 4821271/4997817 [00:28<00:01, 168539.85it/s]"
]
},
{
@@ -2786,7 +2786,7 @@
"output_type": "stream",
"text": [
"\r",
- " 96%|█████████▌| 4792993/4997817 [00:28<00:01, 163697.23it/s]"
+ " 97%|█████████▋| 4838412/4997817 [00:28<00:00, 169387.22it/s]"
]
},
{
@@ -2794,7 +2794,7 @@
"output_type": "stream",
"text": [
"\r",
- " 96%|█████████▌| 4809801/4997817 [00:28<00:01, 164984.01it/s]"
+ " 97%|█████████▋| 4855548/4997817 [00:28<00:00, 169969.50it/s]"
]
},
{
@@ -2802,7 +2802,7 @@
"output_type": "stream",
"text": [
"\r",
- " 97%|█████████▋| 4826610/4997817 [00:28<00:01, 165895.93it/s]"
+ " 97%|█████████▋| 4872639/4997817 [00:28<00:00, 170246.54it/s]"
]
},
{
@@ -2810,7 +2810,7 @@
"output_type": "stream",
"text": [
"\r",
- " 97%|█████████▋| 4843220/4997817 [00:28<00:00, 165180.54it/s]"
+ " 98%|█████████▊| 4889670/4997817 [00:28<00:00, 169986.81it/s]"
]
},
{
@@ -2818,7 +2818,7 @@
"output_type": "stream",
"text": [
"\r",
- " 97%|█████████▋| 4860457/4997817 [00:28<00:00, 167314.20it/s]"
+ " 98%|█████████▊| 4906852/4997817 [00:28<00:00, 170529.63it/s]"
]
},
{
@@ -2826,7 +2826,7 @@
"output_type": "stream",
"text": [
"\r",
- " 98%|█████████▊| 4877808/4997817 [00:28<00:00, 169156.09it/s]"
+ " 99%|█████████▊| 4924033/4997817 [00:28<00:00, 170909.89it/s]"
]
},
{
@@ -2834,7 +2834,7 @@
"output_type": "stream",
"text": [
"\r",
- " 98%|█████████▊| 4895138/4997817 [00:28<00:00, 170391.82it/s]"
+ " 99%|█████████▉| 4941127/4997817 [00:28<00:00, 170913.59it/s]"
]
},
{
@@ -2842,7 +2842,7 @@
"output_type": "stream",
"text": [
"\r",
- " 98%|█████████▊| 4912284/4997817 [00:28<00:00, 170708.57it/s]"
+ " 99%|█████████▉| 4958234/4997817 [00:28<00:00, 170956.10it/s]"
]
},
{
@@ -2850,7 +2850,7 @@
"output_type": "stream",
"text": [
"\r",
- " 99%|█████████▊| 4929461/4997817 [00:29<00:00, 171024.23it/s]"
+ "100%|█████████▉| 4975331/4997817 [00:29<00:00, 170614.46it/s]"
]
},
{
@@ -2858,7 +2858,7 @@
"output_type": "stream",
"text": [
"\r",
- " 99%|█████████▉| 4946762/4997817 [00:29<00:00, 171616.32it/s]"
+ "100%|█████████▉| 4992485/4997817 [00:29<00:00, 170886.82it/s]"
]
},
{
@@ -2866,30 +2866,7 @@
"output_type": "stream",
"text": [
"\r",
- " 99%|█████████▉| 4963929/4997817 [00:29<00:00, 171628.66it/s]"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\r",
- "100%|█████████▉| 4981169/4997817 [00:29<00:00, 171855.98it/s]"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\r",
- "100%|██████████| 4997817/4997817 [00:29<00:00, 169820.90it/s]"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
+ "100%|██████████| 4997817/4997817 [00:29<00:00, 171429.56it/s]"
]
},
{
@@ -2918,6 +2895,13 @@
"Class 'traffic sign' is potentially mislabeled as class for 'building' 5011 pixels in the dataset\n"
]
},
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ },
{
"data": {
"text/html": [
@@ -3121,10 +3105,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:01:42.578076Z",
- "iopub.status.busy": "2024-01-19T13:01:42.577871Z",
- "iopub.status.idle": "2024-01-19T13:01:49.786292Z",
- "shell.execute_reply": "2024-01-19T13:01:49.785547Z"
+ "iopub.execute_input": "2024-01-19T13:18:39.689681Z",
+ "iopub.status.busy": "2024-01-19T13:18:39.689324Z",
+ "iopub.status.idle": "2024-01-19T13:18:46.929239Z",
+ "shell.execute_reply": "2024-01-19T13:18:46.928510Z"
}
},
"outputs": [],
@@ -3138,10 +3122,10 @@
"id": "716c74f3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:01:49.789220Z",
- "iopub.status.busy": "2024-01-19T13:01:49.788962Z",
- "iopub.status.idle": "2024-01-19T13:01:52.775376Z",
- "shell.execute_reply": "2024-01-19T13:01:52.774767Z"
+ "iopub.execute_input": "2024-01-19T13:18:46.932249Z",
+ "iopub.status.busy": "2024-01-19T13:18:46.932011Z",
+ "iopub.status.idle": "2024-01-19T13:18:50.010964Z",
+ "shell.execute_reply": "2024-01-19T13:18:50.010241Z"
}
},
"outputs": [
@@ -3210,17 +3194,17 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:01:52.777984Z",
- "iopub.status.busy": "2024-01-19T13:01:52.777520Z",
- "iopub.status.idle": "2024-01-19T13:01:54.080936Z",
- "shell.execute_reply": "2024-01-19T13:01:54.080225Z"
+ "iopub.execute_input": "2024-01-19T13:18:50.013688Z",
+ "iopub.status.busy": "2024-01-19T13:18:50.013189Z",
+ "iopub.status.idle": "2024-01-19T13:18:51.331395Z",
+ "shell.execute_reply": "2024-01-19T13:18:51.330682Z"
}
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4e313d5215ab41f78569b282d7e503d7",
+ "model_id": "ed18734f86dc42bda39e96384b2555d3",
"version_major": 2,
"version_minor": 0
},
@@ -3250,10 +3234,10 @@
"id": "390780a1",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:01:54.083883Z",
- "iopub.status.busy": "2024-01-19T13:01:54.083669Z",
- "iopub.status.idle": "2024-01-19T13:01:54.300926Z",
- "shell.execute_reply": "2024-01-19T13:01:54.300226Z"
+ "iopub.execute_input": "2024-01-19T13:18:51.334421Z",
+ "iopub.status.busy": "2024-01-19T13:18:51.334084Z",
+ "iopub.status.idle": "2024-01-19T13:18:51.551723Z",
+ "shell.execute_reply": "2024-01-19T13:18:51.551106Z"
}
},
"outputs": [],
@@ -3267,10 +3251,10 @@
"id": "933d6ef0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:01:54.303844Z",
- "iopub.status.busy": "2024-01-19T13:01:54.303592Z",
- "iopub.status.idle": "2024-01-19T13:01:58.843109Z",
- "shell.execute_reply": "2024-01-19T13:01:58.842413Z"
+ "iopub.execute_input": "2024-01-19T13:18:51.554797Z",
+ "iopub.status.busy": "2024-01-19T13:18:51.554173Z",
+ "iopub.status.idle": "2024-01-19T13:18:56.263686Z",
+ "shell.execute_reply": "2024-01-19T13:18:56.263006Z"
}
},
"outputs": [
@@ -3343,10 +3327,10 @@
"id": "86bac686",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:01:58.845706Z",
- "iopub.status.busy": "2024-01-19T13:01:58.845260Z",
- "iopub.status.idle": "2024-01-19T13:01:58.902078Z",
- "shell.execute_reply": "2024-01-19T13:01:58.901359Z"
+ "iopub.execute_input": "2024-01-19T13:18:56.266490Z",
+ "iopub.status.busy": "2024-01-19T13:18:56.266071Z",
+ "iopub.status.idle": "2024-01-19T13:18:56.322866Z",
+ "shell.execute_reply": "2024-01-19T13:18:56.322256Z"
},
"nbsphinx": "hidden"
},
@@ -3390,7 +3374,23 @@
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
- "0ce9acb8adf343c1b293df7e7bdeab64": {
+ "1074e695a6e5445d9062866716051da2": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "1141d0d175534189b6dce7d84bdf61ff": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3442,7 +3442,7 @@
"width": null
}
},
- "29629c4695e04ebd99572a31108a71b4": {
+ "200f18cb1a5240c3a4b72ab8071b3c6b": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3494,113 +3494,7 @@
"width": null
}
},
- "2b19aded834b481dad28af0237cdf6c8": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "FloatProgressModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "FloatProgressModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "ProgressView",
- "bar_style": "success",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_5ba48fd4254e4ca39c47c1524516d8e6",
- "max": 30.0,
- "min": 0.0,
- "orientation": "horizontal",
- "style": "IPY_MODEL_4e58c745e368414383fc086367ed3098",
- "value": 30.0
- }
- },
- "2c213871578c48a498ba0637dc6c9fe7": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "33399ee7aa994fc8b74dfdcdf4bda69d": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "3f1b791bb5824d9b80f4365cb8ad4390": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "48ffc52128b04defa3790651cc845c5a": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "4e313d5215ab41f78569b282d7e503d7": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_5945a17b13d84f8492e31ae094cbd9d6",
- "IPY_MODEL_cb60797b0f584c168462a201850e8b00",
- "IPY_MODEL_5ed2068ebc9e4bbb9967f274f37bdf39"
- ],
- "layout": "IPY_MODEL_dd749bae3cd444fbbf69e2b20f47068b"
- }
- },
- "4e58c745e368414383fc086367ed3098": {
+ "229d03c97ed448f1bb5324faf5b99aff": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
@@ -3616,28 +3510,7 @@
"description_width": ""
}
},
- "5945a17b13d84f8492e31ae094cbd9d6": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_608963552f3045f4b33b18e23cd2d4e2",
- "placeholder": "",
- "style": "IPY_MODEL_3f1b791bb5824d9b80f4365cb8ad4390",
- "value": "images processed using softmin: 100%"
- }
- },
- "5ba18c1ecd674dd7847ba87992ff31c1": {
+ "2da1df921bc141c5b56a3a0ac6518a79": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
@@ -3653,15 +3526,15 @@
"bar_style": "success",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_e6326305b1a649e7a338f20e5f360907",
+ "layout": "IPY_MODEL_1141d0d175534189b6dce7d84bdf61ff",
"max": 30.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_c9cc6d97698c4eec9f7d00fa7c158c31",
+ "style": "IPY_MODEL_34f9658e133949e69d12aaaec258fc9c",
"value": 30.0
}
},
- "5ba48fd4254e4ca39c47c1524516d8e6": {
+ "32a16eef4b8649f19a419cf7e3d3f9c8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3713,28 +3586,75 @@
"width": null
}
},
- "5ed2068ebc9e4bbb9967f274f37bdf39": {
+ "34f9658e133949e69d12aaaec258fc9c": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HTMLModel",
+ "model_name": "ProgressStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "ProgressStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "bar_color": null,
+ "description_width": ""
+ }
+ },
+ "3d34325de50f45e787a02d9788351f9e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "50e864a95b764c32a3f298207bb999a4": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "52103b628c524e138dda90ac2442416c": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_bf354a34833841bc8439d23a2acfbc98",
- "placeholder": "",
- "style": "IPY_MODEL_48ffc52128b04defa3790651cc845c5a",
- "value": " 30/30 [00:01<00:00, 23.51it/s]"
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_98ee5ed171fb4efda70fd920e0e77166",
+ "IPY_MODEL_9b71c7a548a64fda936112502bea36b9",
+ "IPY_MODEL_fcece4f7fe394886a285cf38e89aa102"
+ ],
+ "layout": "IPY_MODEL_96a24691878347fc8104beb2ea3628d8"
}
},
- "608963552f3045f4b33b18e23cd2d4e2": {
+ "559fa2257b6042869b3f5c1ed174e555": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3786,28 +3706,44 @@
"width": null
}
},
- "6c6d79b62d334e18b7277f0a67f357d1": {
+ "5a983a51c0c24c4a9b8e710d3f3f0b48": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HTMLModel",
+ "model_name": "HBoxModel",
"state": {
"_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
+ "_model_name": "HBoxModel",
"_view_count": null,
"_view_module": "@jupyter-widgets/controls",
"_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_6ffbf1f15b744354ac70b585977b6246",
- "placeholder": "",
- "style": "IPY_MODEL_a79fbf81945e4e0e8de867de5888014d",
- "value": "number of examples processed for checking labels: 100%"
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_c6ec629482934be0a10790dc9bbf0f3b",
+ "IPY_MODEL_d59a5c8c1ed54f4f81f259679db75c67",
+ "IPY_MODEL_fa1f563af9dc491688a92fceec4d757d"
+ ],
+ "layout": "IPY_MODEL_32a16eef4b8649f19a419cf7e3d3f9c8"
+ }
+ },
+ "74f00cd25a38441da6a3420c4e8426a9": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
}
},
- "6ffbf1f15b744354ac70b585977b6246": {
+ "77b3d99731164103838cc9c36604f3fd": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3859,7 +3795,7 @@
"width": null
}
},
- "823ed974c1fa4334955656562fbd23fc": {
+ "7bf99e71e46f4b8b9ca4dcf3ff04dc62": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -3911,7 +3847,7 @@
"width": null
}
},
- "8384c5f81c194d22bdb3ea977f428523": {
+ "88521867096e4395b839e6ddbe72c283": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -3926,65 +3862,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_29629c4695e04ebd99572a31108a71b4",
+ "layout": "IPY_MODEL_559fa2257b6042869b3f5c1ed174e555",
"placeholder": "",
- "style": "IPY_MODEL_33399ee7aa994fc8b74dfdcdf4bda69d",
- "value": " 30/30 [00:37<00:00, 1.25s/it]"
- }
- },
- "97b0043f80be4813bf4801d0749b1f64": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "a79fbf81945e4e0e8de867de5888014d": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "DescriptionStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "DescriptionStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "description_width": ""
- }
- },
- "acf781e3ba0a40a98db989b3f5b4de56": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HBoxModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_6c6d79b62d334e18b7277f0a67f357d1",
- "IPY_MODEL_5ba18c1ecd674dd7847ba87992ff31c1",
- "IPY_MODEL_8384c5f81c194d22bdb3ea977f428523"
- ],
- "layout": "IPY_MODEL_b9a3a773874448628402db787486f906"
+ "style": "IPY_MODEL_3d34325de50f45e787a02d9788351f9e",
+ "value": "images processed using softmin: 100%"
}
},
- "b9a3a773874448628402db787486f906": {
+ "8b3eccd24a6d4ca8b5a5be1411bb197a": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4036,28 +3920,7 @@
"width": null
}
},
- "be39d4166acb48f5ac6b964bf0f0618e": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "HTMLModel",
- "state": {
- "_dom_classes": [],
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "HTMLModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HTMLView",
- "description": "",
- "description_tooltip": null,
- "layout": "IPY_MODEL_823ed974c1fa4334955656562fbd23fc",
- "placeholder": "",
- "style": "IPY_MODEL_2c213871578c48a498ba0637dc6c9fe7",
- "value": " 30/30 [00:00<00:00, 426.97it/s]"
- }
- },
- "bf354a34833841bc8439d23a2acfbc98": {
+ "96a24691878347fc8104beb2ea3628d8": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4109,7 +3972,7 @@
"width": null
}
},
- "c51d707a1d7c4be0a55111ae99ad1e5c": {
+ "98ee5ed171fb4efda70fd920e0e77166": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
@@ -4124,29 +3987,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_0ce9acb8adf343c1b293df7e7bdeab64",
+ "layout": "IPY_MODEL_7bf99e71e46f4b8b9ca4dcf3ff04dc62",
"placeholder": "",
- "style": "IPY_MODEL_97b0043f80be4813bf4801d0749b1f64",
- "value": "number of examples processed for estimating thresholds: 100%"
- }
- },
- "c9cc6d97698c4eec9f7d00fa7c158c31": {
- "model_module": "@jupyter-widgets/controls",
- "model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
- "state": {
- "_model_module": "@jupyter-widgets/controls",
- "_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
- "_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "style": "IPY_MODEL_50e864a95b764c32a3f298207bb999a4",
+ "value": "number of examples processed for checking labels: 100%"
}
},
- "cb60797b0f584c168462a201850e8b00": {
+ "9b71c7a548a64fda936112502bea36b9": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
@@ -4162,37 +4009,30 @@
"bar_style": "success",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_e149f3fc3f8b41879e8dd459815341f4",
+ "layout": "IPY_MODEL_c669420e199a4aa69adcde3c18c3f21c",
"max": 30.0,
"min": 0.0,
"orientation": "horizontal",
- "style": "IPY_MODEL_e4ea07e610784054b37958e7b6c7a21e",
+ "style": "IPY_MODEL_229d03c97ed448f1bb5324faf5b99aff",
"value": 30.0
}
},
- "d2996cb658054f4098f2be3d348c4551": {
+ "9e8e9b2b493944e189477747236f2015": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "HBoxModel",
+ "model_name": "DescriptionStyleModel",
"state": {
- "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "HBoxModel",
+ "_model_name": "DescriptionStyleModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/controls",
- "_view_module_version": "1.5.0",
- "_view_name": "HBoxView",
- "box_style": "",
- "children": [
- "IPY_MODEL_c51d707a1d7c4be0a55111ae99ad1e5c",
- "IPY_MODEL_2b19aded834b481dad28af0237cdf6c8",
- "IPY_MODEL_be39d4166acb48f5ac6b964bf0f0618e"
- ],
- "layout": "IPY_MODEL_d3bceb985b0a4a5ab92109131bb6c945"
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
}
},
- "d3bceb985b0a4a5ab92109131bb6c945": {
+ "a862e27143de4d2ca0b30311f1c62869": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4244,7 +4084,7 @@
"width": null
}
},
- "dd749bae3cd444fbbf69e2b20f47068b": {
+ "a9c24cdddacb4175929de521414a3a84": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4296,7 +4136,37 @@
"width": null
}
},
- "e149f3fc3f8b41879e8dd459815341f4": {
+ "aa51bec0209f4711a55442080d3ee93f": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "adb5534d0cc7414f9e7e2a8d278e3c92": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "DescriptionStyleModel",
+ "state": {
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "DescriptionStyleModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/base",
+ "_view_module_version": "1.2.0",
+ "_view_name": "StyleView",
+ "description_width": ""
+ }
+ },
+ "c669420e199a4aa69adcde3c18c3f21c": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4348,23 +4218,52 @@
"width": null
}
},
- "e4ea07e610784054b37958e7b6c7a21e": {
+ "c6ec629482934be0a10790dc9bbf0f3b": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
- "model_name": "ProgressStyleModel",
+ "model_name": "HTMLModel",
"state": {
+ "_dom_classes": [],
"_model_module": "@jupyter-widgets/controls",
"_model_module_version": "1.5.0",
- "_model_name": "ProgressStyleModel",
+ "_model_name": "HTMLModel",
"_view_count": null,
- "_view_module": "@jupyter-widgets/base",
- "_view_module_version": "1.2.0",
- "_view_name": "StyleView",
- "bar_color": null,
- "description_width": ""
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_8b3eccd24a6d4ca8b5a5be1411bb197a",
+ "placeholder": "",
+ "style": "IPY_MODEL_adb5534d0cc7414f9e7e2a8d278e3c92",
+ "value": "number of examples processed for estimating thresholds: 100%"
+ }
+ },
+ "d59a5c8c1ed54f4f81f259679db75c67": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "FloatProgressModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "FloatProgressModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "ProgressView",
+ "bar_style": "success",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_d5d80d2226824679802f3f1820a68d56",
+ "max": 30.0,
+ "min": 0.0,
+ "orientation": "horizontal",
+ "style": "IPY_MODEL_1074e695a6e5445d9062866716051da2",
+ "value": 30.0
}
},
- "e6326305b1a649e7a338f20e5f360907": {
+ "d5d80d2226824679802f3f1820a68d56": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
@@ -4415,6 +4314,91 @@
"visibility": null,
"width": null
}
+ },
+ "e28b70ba60da48de9f6477d7762fdd9e": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_200f18cb1a5240c3a4b72ab8071b3c6b",
+ "placeholder": "",
+ "style": "IPY_MODEL_aa51bec0209f4711a55442080d3ee93f",
+ "value": " 30/30 [00:01<00:00, 23.30it/s]"
+ }
+ },
+ "ed18734f86dc42bda39e96384b2555d3": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HBoxModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HBoxModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HBoxView",
+ "box_style": "",
+ "children": [
+ "IPY_MODEL_88521867096e4395b839e6ddbe72c283",
+ "IPY_MODEL_2da1df921bc141c5b56a3a0ac6518a79",
+ "IPY_MODEL_e28b70ba60da48de9f6477d7762fdd9e"
+ ],
+ "layout": "IPY_MODEL_a9c24cdddacb4175929de521414a3a84"
+ }
+ },
+ "fa1f563af9dc491688a92fceec4d757d": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_77b3d99731164103838cc9c36604f3fd",
+ "placeholder": "",
+ "style": "IPY_MODEL_74f00cd25a38441da6a3420c4e8426a9",
+ "value": " 30/30 [00:00<00:00, 406.68it/s]"
+ }
+ },
+ "fcece4f7fe394886a285cf38e89aa102": {
+ "model_module": "@jupyter-widgets/controls",
+ "model_module_version": "1.5.0",
+ "model_name": "HTMLModel",
+ "state": {
+ "_dom_classes": [],
+ "_model_module": "@jupyter-widgets/controls",
+ "_model_module_version": "1.5.0",
+ "_model_name": "HTMLModel",
+ "_view_count": null,
+ "_view_module": "@jupyter-widgets/controls",
+ "_view_module_version": "1.5.0",
+ "_view_name": "HTMLView",
+ "description": "",
+ "description_tooltip": null,
+ "layout": "IPY_MODEL_a862e27143de4d2ca0b30311f1c62869",
+ "placeholder": "",
+ "style": "IPY_MODEL_9e8e9b2b493944e189477747236f2015",
+ "value": " 30/30 [00:36<00:00, 1.27s/it]"
+ }
}
},
"version_major": 2,
diff --git a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb
index dfbaab569..97fea6f48 100644
--- a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb
@@ -112,10 +112,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:03.646325Z",
- "iopub.status.busy": "2024-01-19T13:02:03.646132Z",
- "iopub.status.idle": "2024-01-19T13:02:04.672807Z",
- "shell.execute_reply": "2024-01-19T13:02:04.672197Z"
+ "iopub.execute_input": "2024-01-19T13:19:00.994417Z",
+ "iopub.status.busy": "2024-01-19T13:19:00.993867Z",
+ "iopub.status.idle": "2024-01-19T13:19:02.043914Z",
+ "shell.execute_reply": "2024-01-19T13:19:02.043289Z"
},
"nbsphinx": "hidden"
},
@@ -125,7 +125,7 @@
"dependencies = [\"cleanlab\"]\n",
"\n",
"if \"google.colab\" in str(get_ipython()): # Check if it's running in Google Colab\n",
- " %pip install git+https://github.com/cleanlab/cleanlab.git@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -150,10 +150,10 @@
"execution_count": 2,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:04.675567Z",
- "iopub.status.busy": "2024-01-19T13:02:04.675261Z",
- "iopub.status.idle": "2024-01-19T13:02:04.691663Z",
- "shell.execute_reply": "2024-01-19T13:02:04.691175Z"
+ "iopub.execute_input": "2024-01-19T13:19:02.047066Z",
+ "iopub.status.busy": "2024-01-19T13:19:02.046566Z",
+ "iopub.status.idle": "2024-01-19T13:19:02.063313Z",
+ "shell.execute_reply": "2024-01-19T13:19:02.062783Z"
}
},
"outputs": [],
@@ -194,10 +194,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:04.693951Z",
- "iopub.status.busy": "2024-01-19T13:02:04.693753Z",
- "iopub.status.idle": "2024-01-19T13:02:04.799328Z",
- "shell.execute_reply": "2024-01-19T13:02:04.798772Z"
+ "iopub.execute_input": "2024-01-19T13:19:02.065634Z",
+ "iopub.status.busy": "2024-01-19T13:19:02.065423Z",
+ "iopub.status.idle": "2024-01-19T13:19:02.119695Z",
+ "shell.execute_reply": "2024-01-19T13:19:02.119075Z"
}
},
"outputs": [
@@ -304,10 +304,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:04.801680Z",
- "iopub.status.busy": "2024-01-19T13:02:04.801476Z",
- "iopub.status.idle": "2024-01-19T13:02:04.805408Z",
- "shell.execute_reply": "2024-01-19T13:02:04.804892Z"
+ "iopub.execute_input": "2024-01-19T13:19:02.122273Z",
+ "iopub.status.busy": "2024-01-19T13:19:02.121896Z",
+ "iopub.status.idle": "2024-01-19T13:19:02.125726Z",
+ "shell.execute_reply": "2024-01-19T13:19:02.125095Z"
}
},
"outputs": [],
@@ -328,10 +328,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:04.807878Z",
- "iopub.status.busy": "2024-01-19T13:02:04.807450Z",
- "iopub.status.idle": "2024-01-19T13:02:04.815956Z",
- "shell.execute_reply": "2024-01-19T13:02:04.815473Z"
+ "iopub.execute_input": "2024-01-19T13:19:02.128193Z",
+ "iopub.status.busy": "2024-01-19T13:19:02.127851Z",
+ "iopub.status.idle": "2024-01-19T13:19:02.137323Z",
+ "shell.execute_reply": "2024-01-19T13:19:02.136826Z"
}
},
"outputs": [],
@@ -383,10 +383,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:04.818179Z",
- "iopub.status.busy": "2024-01-19T13:02:04.817986Z",
- "iopub.status.idle": "2024-01-19T13:02:04.820768Z",
- "shell.execute_reply": "2024-01-19T13:02:04.820280Z"
+ "iopub.execute_input": "2024-01-19T13:19:02.139880Z",
+ "iopub.status.busy": "2024-01-19T13:19:02.139675Z",
+ "iopub.status.idle": "2024-01-19T13:19:02.142465Z",
+ "shell.execute_reply": "2024-01-19T13:19:02.141895Z"
}
},
"outputs": [],
@@ -408,10 +408,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:04.823048Z",
- "iopub.status.busy": "2024-01-19T13:02:04.822853Z",
- "iopub.status.idle": "2024-01-19T13:02:05.407663Z",
- "shell.execute_reply": "2024-01-19T13:02:05.407051Z"
+ "iopub.execute_input": "2024-01-19T13:19:02.144777Z",
+ "iopub.status.busy": "2024-01-19T13:19:02.144576Z",
+ "iopub.status.idle": "2024-01-19T13:19:02.733236Z",
+ "shell.execute_reply": "2024-01-19T13:19:02.732614Z"
}
},
"outputs": [],
@@ -445,10 +445,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:05.410646Z",
- "iopub.status.busy": "2024-01-19T13:02:05.410221Z",
- "iopub.status.idle": "2024-01-19T13:02:06.644721Z",
- "shell.execute_reply": "2024-01-19T13:02:06.643930Z"
+ "iopub.execute_input": "2024-01-19T13:19:02.736443Z",
+ "iopub.status.busy": "2024-01-19T13:19:02.735884Z",
+ "iopub.status.idle": "2024-01-19T13:19:04.009771Z",
+ "shell.execute_reply": "2024-01-19T13:19:04.008986Z"
}
},
"outputs": [
@@ -480,10 +480,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:06.647879Z",
- "iopub.status.busy": "2024-01-19T13:02:06.647179Z",
- "iopub.status.idle": "2024-01-19T13:02:06.657632Z",
- "shell.execute_reply": "2024-01-19T13:02:06.657026Z"
+ "iopub.execute_input": "2024-01-19T13:19:04.012991Z",
+ "iopub.status.busy": "2024-01-19T13:19:04.012276Z",
+ "iopub.status.idle": "2024-01-19T13:19:04.022931Z",
+ "shell.execute_reply": "2024-01-19T13:19:04.022271Z"
}
},
"outputs": [
@@ -604,10 +604,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:06.660132Z",
- "iopub.status.busy": "2024-01-19T13:02:06.659702Z",
- "iopub.status.idle": "2024-01-19T13:02:06.664320Z",
- "shell.execute_reply": "2024-01-19T13:02:06.663782Z"
+ "iopub.execute_input": "2024-01-19T13:19:04.025490Z",
+ "iopub.status.busy": "2024-01-19T13:19:04.025188Z",
+ "iopub.status.idle": "2024-01-19T13:19:04.029629Z",
+ "shell.execute_reply": "2024-01-19T13:19:04.029114Z"
}
},
"outputs": [],
@@ -632,10 +632,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:06.666577Z",
- "iopub.status.busy": "2024-01-19T13:02:06.666250Z",
- "iopub.status.idle": "2024-01-19T13:02:06.674752Z",
- "shell.execute_reply": "2024-01-19T13:02:06.674155Z"
+ "iopub.execute_input": "2024-01-19T13:19:04.032137Z",
+ "iopub.status.busy": "2024-01-19T13:19:04.031753Z",
+ "iopub.status.idle": "2024-01-19T13:19:04.040621Z",
+ "shell.execute_reply": "2024-01-19T13:19:04.040119Z"
}
},
"outputs": [],
@@ -657,10 +657,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:06.677352Z",
- "iopub.status.busy": "2024-01-19T13:02:06.676901Z",
- "iopub.status.idle": "2024-01-19T13:02:06.803213Z",
- "shell.execute_reply": "2024-01-19T13:02:06.802643Z"
+ "iopub.execute_input": "2024-01-19T13:19:04.043062Z",
+ "iopub.status.busy": "2024-01-19T13:19:04.042690Z",
+ "iopub.status.idle": "2024-01-19T13:19:04.166738Z",
+ "shell.execute_reply": "2024-01-19T13:19:04.166129Z"
}
},
"outputs": [
@@ -690,10 +690,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:06.805829Z",
- "iopub.status.busy": "2024-01-19T13:02:06.805472Z",
- "iopub.status.idle": "2024-01-19T13:02:06.808435Z",
- "shell.execute_reply": "2024-01-19T13:02:06.807858Z"
+ "iopub.execute_input": "2024-01-19T13:19:04.169581Z",
+ "iopub.status.busy": "2024-01-19T13:19:04.168911Z",
+ "iopub.status.idle": "2024-01-19T13:19:04.172263Z",
+ "shell.execute_reply": "2024-01-19T13:19:04.171740Z"
}
},
"outputs": [],
@@ -714,10 +714,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:06.810885Z",
- "iopub.status.busy": "2024-01-19T13:02:06.810560Z",
- "iopub.status.idle": "2024-01-19T13:02:08.247308Z",
- "shell.execute_reply": "2024-01-19T13:02:08.246558Z"
+ "iopub.execute_input": "2024-01-19T13:19:04.174796Z",
+ "iopub.status.busy": "2024-01-19T13:19:04.174280Z",
+ "iopub.status.idle": "2024-01-19T13:19:05.616469Z",
+ "shell.execute_reply": "2024-01-19T13:19:05.614673Z"
}
},
"outputs": [],
@@ -737,10 +737,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:08.250402Z",
- "iopub.status.busy": "2024-01-19T13:02:08.250010Z",
- "iopub.status.idle": "2024-01-19T13:02:08.263981Z",
- "shell.execute_reply": "2024-01-19T13:02:08.263347Z"
+ "iopub.execute_input": "2024-01-19T13:19:05.620047Z",
+ "iopub.status.busy": "2024-01-19T13:19:05.619518Z",
+ "iopub.status.idle": "2024-01-19T13:19:05.634089Z",
+ "shell.execute_reply": "2024-01-19T13:19:05.633531Z"
}
},
"outputs": [
@@ -770,10 +770,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:08.266530Z",
- "iopub.status.busy": "2024-01-19T13:02:08.266175Z",
- "iopub.status.idle": "2024-01-19T13:02:08.361196Z",
- "shell.execute_reply": "2024-01-19T13:02:08.360492Z"
+ "iopub.execute_input": "2024-01-19T13:19:05.636644Z",
+ "iopub.status.busy": "2024-01-19T13:19:05.636255Z",
+ "iopub.status.idle": "2024-01-19T13:19:05.672883Z",
+ "shell.execute_reply": "2024-01-19T13:19:05.672315Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/text.ipynb b/master/.doctrees/nbsphinx/tutorials/text.ipynb
index c0f5d773e..46c6010d9 100644
--- a/master/.doctrees/nbsphinx/tutorials/text.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/text.ipynb
@@ -114,10 +114,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:13.645924Z",
- "iopub.status.busy": "2024-01-19T13:02:13.645727Z",
- "iopub.status.idle": "2024-01-19T13:02:15.732275Z",
- "shell.execute_reply": "2024-01-19T13:02:15.731557Z"
+ "iopub.execute_input": "2024-01-19T13:19:11.261011Z",
+ "iopub.status.busy": "2024-01-19T13:19:11.260812Z",
+ "iopub.status.idle": "2024-01-19T13:19:13.377133Z",
+ "shell.execute_reply": "2024-01-19T13:19:13.376486Z"
},
"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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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": "2024-01-19T13:02:15.735281Z",
- "iopub.status.busy": "2024-01-19T13:02:15.734966Z",
- "iopub.status.idle": "2024-01-19T13:02:15.739193Z",
- "shell.execute_reply": "2024-01-19T13:02:15.738704Z"
+ "iopub.execute_input": "2024-01-19T13:19:13.380324Z",
+ "iopub.status.busy": "2024-01-19T13:19:13.379847Z",
+ "iopub.status.idle": "2024-01-19T13:19:13.383506Z",
+ "shell.execute_reply": "2024-01-19T13:19:13.382883Z"
}
},
"outputs": [],
@@ -184,10 +184,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:15.741641Z",
- "iopub.status.busy": "2024-01-19T13:02:15.741273Z",
- "iopub.status.idle": "2024-01-19T13:02:15.744514Z",
- "shell.execute_reply": "2024-01-19T13:02:15.743954Z"
+ "iopub.execute_input": "2024-01-19T13:19:13.385849Z",
+ "iopub.status.busy": "2024-01-19T13:19:13.385422Z",
+ "iopub.status.idle": "2024-01-19T13:19:13.388713Z",
+ "shell.execute_reply": "2024-01-19T13:19:13.388215Z"
},
"nbsphinx": "hidden"
},
@@ -218,10 +218,10 @@
"execution_count": 4,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:15.746950Z",
- "iopub.status.busy": "2024-01-19T13:02:15.746595Z",
- "iopub.status.idle": "2024-01-19T13:02:15.854881Z",
- "shell.execute_reply": "2024-01-19T13:02:15.854251Z"
+ "iopub.execute_input": "2024-01-19T13:19:13.391235Z",
+ "iopub.status.busy": "2024-01-19T13:19:13.390759Z",
+ "iopub.status.idle": "2024-01-19T13:19:13.445441Z",
+ "shell.execute_reply": "2024-01-19T13:19:13.444803Z"
}
},
"outputs": [
@@ -311,10 +311,10 @@
"execution_count": 5,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:15.857468Z",
- "iopub.status.busy": "2024-01-19T13:02:15.857060Z",
- "iopub.status.idle": "2024-01-19T13:02:15.860861Z",
- "shell.execute_reply": "2024-01-19T13:02:15.860337Z"
+ "iopub.execute_input": "2024-01-19T13:19:13.448187Z",
+ "iopub.status.busy": "2024-01-19T13:19:13.447828Z",
+ "iopub.status.idle": "2024-01-19T13:19:13.451728Z",
+ "shell.execute_reply": "2024-01-19T13:19:13.451108Z"
}
},
"outputs": [],
@@ -329,10 +329,10 @@
"execution_count": 6,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:15.863126Z",
- "iopub.status.busy": "2024-01-19T13:02:15.862754Z",
- "iopub.status.idle": "2024-01-19T13:02:15.866444Z",
- "shell.execute_reply": "2024-01-19T13:02:15.865845Z"
+ "iopub.execute_input": "2024-01-19T13:19:13.454190Z",
+ "iopub.status.busy": "2024-01-19T13:19:13.453834Z",
+ "iopub.status.idle": "2024-01-19T13:19:13.457725Z",
+ "shell.execute_reply": "2024-01-19T13:19:13.457121Z"
}
},
"outputs": [
@@ -341,7 +341,7 @@
"output_type": "stream",
"text": [
"This dataset has 10 classes.\n",
- "Classes: {'beneficiary_not_allowed', 'getting_spare_card', 'supported_cards_and_currencies', 'lost_or_stolen_phone', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'cancel_transfer', 'card_payment_fee_charged', 'change_pin', 'card_about_to_expire'}\n"
+ "Classes: {'card_payment_fee_charged', 'supported_cards_and_currencies', 'getting_spare_card', 'cancel_transfer', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'card_about_to_expire', 'lost_or_stolen_phone', 'change_pin', 'beneficiary_not_allowed'}\n"
]
}
],
@@ -364,10 +364,10 @@
"execution_count": 7,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:15.868736Z",
- "iopub.status.busy": "2024-01-19T13:02:15.868493Z",
- "iopub.status.idle": "2024-01-19T13:02:15.872539Z",
- "shell.execute_reply": "2024-01-19T13:02:15.872005Z"
+ "iopub.execute_input": "2024-01-19T13:19:13.460153Z",
+ "iopub.status.busy": "2024-01-19T13:19:13.459958Z",
+ "iopub.status.idle": "2024-01-19T13:19:13.463935Z",
+ "shell.execute_reply": "2024-01-19T13:19:13.463403Z"
}
},
"outputs": [
@@ -408,10 +408,10 @@
"execution_count": 8,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:15.874949Z",
- "iopub.status.busy": "2024-01-19T13:02:15.874576Z",
- "iopub.status.idle": "2024-01-19T13:02:15.878021Z",
- "shell.execute_reply": "2024-01-19T13:02:15.877480Z"
+ "iopub.execute_input": "2024-01-19T13:19:13.466096Z",
+ "iopub.status.busy": "2024-01-19T13:19:13.465905Z",
+ "iopub.status.idle": "2024-01-19T13:19:13.469440Z",
+ "shell.execute_reply": "2024-01-19T13:19:13.468917Z"
}
},
"outputs": [],
@@ -452,10 +452,10 @@
"execution_count": 9,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:15.880610Z",
- "iopub.status.busy": "2024-01-19T13:02:15.880005Z",
- "iopub.status.idle": "2024-01-19T13:02:24.804860Z",
- "shell.execute_reply": "2024-01-19T13:02:24.804212Z"
+ "iopub.execute_input": "2024-01-19T13:19:13.471913Z",
+ "iopub.status.busy": "2024-01-19T13:19:13.471546Z",
+ "iopub.status.idle": "2024-01-19T13:19:22.127887Z",
+ "shell.execute_reply": "2024-01-19T13:19:22.127152Z"
}
},
"outputs": [
@@ -502,10 +502,10 @@
"execution_count": 10,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:24.807970Z",
- "iopub.status.busy": "2024-01-19T13:02:24.807716Z",
- "iopub.status.idle": "2024-01-19T13:02:24.810829Z",
- "shell.execute_reply": "2024-01-19T13:02:24.810197Z"
+ "iopub.execute_input": "2024-01-19T13:19:22.131266Z",
+ "iopub.status.busy": "2024-01-19T13:19:22.130823Z",
+ "iopub.status.idle": "2024-01-19T13:19:22.134087Z",
+ "shell.execute_reply": "2024-01-19T13:19:22.133557Z"
}
},
"outputs": [],
@@ -527,10 +527,10 @@
"execution_count": 11,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:24.813301Z",
- "iopub.status.busy": "2024-01-19T13:02:24.812867Z",
- "iopub.status.idle": "2024-01-19T13:02:24.815721Z",
- "shell.execute_reply": "2024-01-19T13:02:24.815169Z"
+ "iopub.execute_input": "2024-01-19T13:19:22.136628Z",
+ "iopub.status.busy": "2024-01-19T13:19:22.136251Z",
+ "iopub.status.idle": "2024-01-19T13:19:22.139080Z",
+ "shell.execute_reply": "2024-01-19T13:19:22.138518Z"
}
},
"outputs": [],
@@ -545,10 +545,10 @@
"execution_count": 12,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:24.818026Z",
- "iopub.status.busy": "2024-01-19T13:02:24.817665Z",
- "iopub.status.idle": "2024-01-19T13:02:27.010877Z",
- "shell.execute_reply": "2024-01-19T13:02:27.010025Z"
+ "iopub.execute_input": "2024-01-19T13:19:22.141276Z",
+ "iopub.status.busy": "2024-01-19T13:19:22.140970Z",
+ "iopub.status.idle": "2024-01-19T13:19:24.384370Z",
+ "shell.execute_reply": "2024-01-19T13:19:24.383504Z"
},
"scrolled": true
},
@@ -571,10 +571,10 @@
"execution_count": 13,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:27.014549Z",
- "iopub.status.busy": "2024-01-19T13:02:27.013857Z",
- "iopub.status.idle": "2024-01-19T13:02:27.022047Z",
- "shell.execute_reply": "2024-01-19T13:02:27.021442Z"
+ "iopub.execute_input": "2024-01-19T13:19:24.388106Z",
+ "iopub.status.busy": "2024-01-19T13:19:24.387253Z",
+ "iopub.status.idle": "2024-01-19T13:19:24.395657Z",
+ "shell.execute_reply": "2024-01-19T13:19:24.395085Z"
}
},
"outputs": [
@@ -675,10 +675,10 @@
"execution_count": 14,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:27.024503Z",
- "iopub.status.busy": "2024-01-19T13:02:27.024058Z",
- "iopub.status.idle": "2024-01-19T13:02:27.028311Z",
- "shell.execute_reply": "2024-01-19T13:02:27.027694Z"
+ "iopub.execute_input": "2024-01-19T13:19:24.398114Z",
+ "iopub.status.busy": "2024-01-19T13:19:24.397673Z",
+ "iopub.status.idle": "2024-01-19T13:19:24.401811Z",
+ "shell.execute_reply": "2024-01-19T13:19:24.401286Z"
}
},
"outputs": [],
@@ -692,10 +692,10 @@
"execution_count": 15,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:27.030637Z",
- "iopub.status.busy": "2024-01-19T13:02:27.030266Z",
- "iopub.status.idle": "2024-01-19T13:02:27.033747Z",
- "shell.execute_reply": "2024-01-19T13:02:27.033154Z"
+ "iopub.execute_input": "2024-01-19T13:19:24.404223Z",
+ "iopub.status.busy": "2024-01-19T13:19:24.403768Z",
+ "iopub.status.idle": "2024-01-19T13:19:24.407221Z",
+ "shell.execute_reply": "2024-01-19T13:19:24.406566Z"
}
},
"outputs": [
@@ -730,10 +730,10 @@
"execution_count": 16,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:27.036139Z",
- "iopub.status.busy": "2024-01-19T13:02:27.035761Z",
- "iopub.status.idle": "2024-01-19T13:02:27.039115Z",
- "shell.execute_reply": "2024-01-19T13:02:27.038605Z"
+ "iopub.execute_input": "2024-01-19T13:19:24.409805Z",
+ "iopub.status.busy": "2024-01-19T13:19:24.409363Z",
+ "iopub.status.idle": "2024-01-19T13:19:24.412607Z",
+ "shell.execute_reply": "2024-01-19T13:19:24.412076Z"
}
},
"outputs": [],
@@ -753,10 +753,10 @@
"execution_count": 17,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:27.041325Z",
- "iopub.status.busy": "2024-01-19T13:02:27.041119Z",
- "iopub.status.idle": "2024-01-19T13:02:27.048523Z",
- "shell.execute_reply": "2024-01-19T13:02:27.047909Z"
+ "iopub.execute_input": "2024-01-19T13:19:24.414823Z",
+ "iopub.status.busy": "2024-01-19T13:19:24.414619Z",
+ "iopub.status.idle": "2024-01-19T13:19:24.422015Z",
+ "shell.execute_reply": "2024-01-19T13:19:24.421515Z"
}
},
"outputs": [
@@ -881,10 +881,10 @@
"execution_count": 18,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:27.051035Z",
- "iopub.status.busy": "2024-01-19T13:02:27.050695Z",
- "iopub.status.idle": "2024-01-19T13:02:27.292104Z",
- "shell.execute_reply": "2024-01-19T13:02:27.291465Z"
+ "iopub.execute_input": "2024-01-19T13:19:24.424497Z",
+ "iopub.status.busy": "2024-01-19T13:19:24.424291Z",
+ "iopub.status.idle": "2024-01-19T13:19:24.666254Z",
+ "shell.execute_reply": "2024-01-19T13:19:24.665613Z"
},
"scrolled": true
},
@@ -923,10 +923,10 @@
"execution_count": 19,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:27.295269Z",
- "iopub.status.busy": "2024-01-19T13:02:27.294877Z",
- "iopub.status.idle": "2024-01-19T13:02:27.569928Z",
- "shell.execute_reply": "2024-01-19T13:02:27.569290Z"
+ "iopub.execute_input": "2024-01-19T13:19:24.669511Z",
+ "iopub.status.busy": "2024-01-19T13:19:24.668901Z",
+ "iopub.status.idle": "2024-01-19T13:19:24.946560Z",
+ "shell.execute_reply": "2024-01-19T13:19:24.945868Z"
},
"scrolled": true
},
@@ -959,10 +959,10 @@
"execution_count": 20,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:27.573085Z",
- "iopub.status.busy": "2024-01-19T13:02:27.572702Z",
- "iopub.status.idle": "2024-01-19T13:02:27.576733Z",
- "shell.execute_reply": "2024-01-19T13:02:27.576146Z"
+ "iopub.execute_input": "2024-01-19T13:19:24.949826Z",
+ "iopub.status.busy": "2024-01-19T13:19:24.949376Z",
+ "iopub.status.idle": "2024-01-19T13:19:24.953621Z",
+ "shell.execute_reply": "2024-01-19T13:19:24.953015Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
index e13515704..9cb0a7094 100644
--- a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
+++ b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb
@@ -75,10 +75,10 @@
"id": "ae8a08e0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:32.659971Z",
- "iopub.status.busy": "2024-01-19T13:02:32.659510Z",
- "iopub.status.idle": "2024-01-19T13:02:34.711893Z",
- "shell.execute_reply": "2024-01-19T13:02:34.711146Z"
+ "iopub.execute_input": "2024-01-19T13:19:30.057092Z",
+ "iopub.status.busy": "2024-01-19T13:19:30.056885Z",
+ "iopub.status.idle": "2024-01-19T13:19:31.354643Z",
+ "shell.execute_reply": "2024-01-19T13:19:31.353832Z"
}
},
"outputs": [
@@ -86,7 +86,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-01-19 13:02:32-- https://data.deepai.org/conll2003.zip\r\n",
+ "--2024-01-19 13:19:30-- https://data.deepai.org/conll2003.zip\r\n",
"Resolving data.deepai.org (data.deepai.org)... "
]
},
@@ -94,15 +94,29 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "143.244.49.179, 2400:52e0:1a01::996:1\r\n",
- "Connecting to data.deepai.org (data.deepai.org)|143.244.49.179|:443... connected.\r\n"
+ "185.93.1.251, 2400:52e0:1a00::845:1\r\n",
+ "Connecting to data.deepai.org (data.deepai.org)|185.93.1.251|:443... "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "HTTP request sent, awaiting response... 200 OK\r\n",
+ "connected.\r\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "HTTP request sent, awaiting response... "
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "200 OK\r\n",
"Length: 982975 (960K) [application/zip]\r\n",
"Saving to: ‘conll2003.zip’\r\n",
"\r\n",
@@ -115,9 +129,9 @@
"output_type": "stream",
"text": [
"\r",
- "conll2003.zip 100%[===================>] 959.94K 6.15MB/s in 0.2s \r\n",
+ "conll2003.zip 100%[===================>] 959.94K 5.68MB/s in 0.2s \r\n",
"\r\n",
- "2024-01-19 13:02:33 (6.15 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
+ "2024-01-19 13:19:30 (5.68 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n",
"\r\n",
"mkdir: cannot create directory ‘data’: File exists\r\n"
]
@@ -137,22 +151,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "--2024-01-19 13:02:33-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
- "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.49.121, 54.231.198.217, 52.217.129.57, ...\r\n",
- "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.49.121|:443... "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "connected.\r\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
+ "--2024-01-19 13:19:30-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n",
+ "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.216.90.28, 3.5.16.103, 52.217.17.188, ...\r\n",
+ "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.216.90.28|:443... connected.\r\n",
"HTTP request sent, awaiting response... "
]
},
@@ -173,34 +174,9 @@
"output_type": "stream",
"text": [
"\r",
- "pred_probs.npz 0%[ ] 126.64K 597KB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 6%[> ] 1.10M 2.60MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 45%[========> ] 7.40M 11.6MB/s "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "pred_probs.npz 99%[==================> ] 16.12M 19.0MB/s \r",
- "pred_probs.npz 100%[===================>] 16.26M 19.1MB/s in 0.9s \r\n",
+ "pred_probs.npz 100%[===================>] 16.26M 108MB/s in 0.2s \r\n",
"\r\n",
- "2024-01-19 13:02:34 (19.1 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
+ "2024-01-19 13:19:31 (108 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n",
"\r\n"
]
}
@@ -217,10 +193,10 @@
"id": "439b0305",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:34.714736Z",
- "iopub.status.busy": "2024-01-19T13:02:34.714520Z",
- "iopub.status.idle": "2024-01-19T13:02:35.734955Z",
- "shell.execute_reply": "2024-01-19T13:02:35.734335Z"
+ "iopub.execute_input": "2024-01-19T13:19:31.357667Z",
+ "iopub.status.busy": "2024-01-19T13:19:31.357409Z",
+ "iopub.status.idle": "2024-01-19T13:19:32.405194Z",
+ "shell.execute_reply": "2024-01-19T13:19:32.404633Z"
},
"nbsphinx": "hidden"
},
@@ -231,7 +207,7 @@
"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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
@@ -257,10 +233,10 @@
"id": "a1349304",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:35.737880Z",
- "iopub.status.busy": "2024-01-19T13:02:35.737368Z",
- "iopub.status.idle": "2024-01-19T13:02:35.740997Z",
- "shell.execute_reply": "2024-01-19T13:02:35.740468Z"
+ "iopub.execute_input": "2024-01-19T13:19:32.408331Z",
+ "iopub.status.busy": "2024-01-19T13:19:32.407774Z",
+ "iopub.status.idle": "2024-01-19T13:19:32.411644Z",
+ "shell.execute_reply": "2024-01-19T13:19:32.411025Z"
}
},
"outputs": [],
@@ -310,10 +286,10 @@
"id": "ab9d59a0",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:35.743286Z",
- "iopub.status.busy": "2024-01-19T13:02:35.742987Z",
- "iopub.status.idle": "2024-01-19T13:02:35.746099Z",
- "shell.execute_reply": "2024-01-19T13:02:35.745563Z"
+ "iopub.execute_input": "2024-01-19T13:19:32.414212Z",
+ "iopub.status.busy": "2024-01-19T13:19:32.413739Z",
+ "iopub.status.idle": "2024-01-19T13:19:32.417056Z",
+ "shell.execute_reply": "2024-01-19T13:19:32.416445Z"
},
"nbsphinx": "hidden"
},
@@ -331,10 +307,10 @@
"id": "519cb80c",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:35.748461Z",
- "iopub.status.busy": "2024-01-19T13:02:35.748086Z",
- "iopub.status.idle": "2024-01-19T13:02:43.711839Z",
- "shell.execute_reply": "2024-01-19T13:02:43.711157Z"
+ "iopub.execute_input": "2024-01-19T13:19:32.419393Z",
+ "iopub.status.busy": "2024-01-19T13:19:32.419024Z",
+ "iopub.status.idle": "2024-01-19T13:19:40.325412Z",
+ "shell.execute_reply": "2024-01-19T13:19:40.324750Z"
}
},
"outputs": [],
@@ -408,10 +384,10 @@
"id": "202f1526",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:43.714693Z",
- "iopub.status.busy": "2024-01-19T13:02:43.714344Z",
- "iopub.status.idle": "2024-01-19T13:02:43.720409Z",
- "shell.execute_reply": "2024-01-19T13:02:43.719838Z"
+ "iopub.execute_input": "2024-01-19T13:19:40.328257Z",
+ "iopub.status.busy": "2024-01-19T13:19:40.327935Z",
+ "iopub.status.idle": "2024-01-19T13:19:40.333869Z",
+ "shell.execute_reply": "2024-01-19T13:19:40.333355Z"
},
"nbsphinx": "hidden"
},
@@ -451,10 +427,10 @@
"id": "a4381f03",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:43.722795Z",
- "iopub.status.busy": "2024-01-19T13:02:43.722436Z",
- "iopub.status.idle": "2024-01-19T13:02:44.166537Z",
- "shell.execute_reply": "2024-01-19T13:02:44.165804Z"
+ "iopub.execute_input": "2024-01-19T13:19:40.336172Z",
+ "iopub.status.busy": "2024-01-19T13:19:40.335869Z",
+ "iopub.status.idle": "2024-01-19T13:19:40.774852Z",
+ "shell.execute_reply": "2024-01-19T13:19:40.774245Z"
}
},
"outputs": [],
@@ -491,10 +467,10 @@
"id": "7842e4a3",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:44.169614Z",
- "iopub.status.busy": "2024-01-19T13:02:44.169286Z",
- "iopub.status.idle": "2024-01-19T13:02:44.174511Z",
- "shell.execute_reply": "2024-01-19T13:02:44.173939Z"
+ "iopub.execute_input": "2024-01-19T13:19:40.777591Z",
+ "iopub.status.busy": "2024-01-19T13:19:40.777360Z",
+ "iopub.status.idle": "2024-01-19T13:19:40.783994Z",
+ "shell.execute_reply": "2024-01-19T13:19:40.783490Z"
}
},
"outputs": [
@@ -566,10 +542,10 @@
"id": "2c2ad9ad",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:44.176947Z",
- "iopub.status.busy": "2024-01-19T13:02:44.176582Z",
- "iopub.status.idle": "2024-01-19T13:02:46.142187Z",
- "shell.execute_reply": "2024-01-19T13:02:46.141290Z"
+ "iopub.execute_input": "2024-01-19T13:19:40.786852Z",
+ "iopub.status.busy": "2024-01-19T13:19:40.786325Z",
+ "iopub.status.idle": "2024-01-19T13:19:42.782330Z",
+ "shell.execute_reply": "2024-01-19T13:19:42.781424Z"
}
},
"outputs": [],
@@ -591,10 +567,10 @@
"id": "95dc7268",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:46.146092Z",
- "iopub.status.busy": "2024-01-19T13:02:46.145339Z",
- "iopub.status.idle": "2024-01-19T13:02:46.152014Z",
- "shell.execute_reply": "2024-01-19T13:02:46.151360Z"
+ "iopub.execute_input": "2024-01-19T13:19:42.788082Z",
+ "iopub.status.busy": "2024-01-19T13:19:42.785260Z",
+ "iopub.status.idle": "2024-01-19T13:19:42.792349Z",
+ "shell.execute_reply": "2024-01-19T13:19:42.791696Z"
}
},
"outputs": [
@@ -630,10 +606,10 @@
"id": "e13de188",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:46.154580Z",
- "iopub.status.busy": "2024-01-19T13:02:46.154182Z",
- "iopub.status.idle": "2024-01-19T13:02:46.179180Z",
- "shell.execute_reply": "2024-01-19T13:02:46.178553Z"
+ "iopub.execute_input": "2024-01-19T13:19:42.795225Z",
+ "iopub.status.busy": "2024-01-19T13:19:42.794689Z",
+ "iopub.status.idle": "2024-01-19T13:19:42.812998Z",
+ "shell.execute_reply": "2024-01-19T13:19:42.812498Z"
}
},
"outputs": [
@@ -811,10 +787,10 @@
"id": "e4a006bd",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:46.182049Z",
- "iopub.status.busy": "2024-01-19T13:02:46.181542Z",
- "iopub.status.idle": "2024-01-19T13:02:46.215060Z",
- "shell.execute_reply": "2024-01-19T13:02:46.214403Z"
+ "iopub.execute_input": "2024-01-19T13:19:42.815258Z",
+ "iopub.status.busy": "2024-01-19T13:19:42.815059Z",
+ "iopub.status.idle": "2024-01-19T13:19:42.850913Z",
+ "shell.execute_reply": "2024-01-19T13:19:42.850171Z"
}
},
"outputs": [
@@ -916,10 +892,10 @@
"id": "c8f4e163",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:46.217577Z",
- "iopub.status.busy": "2024-01-19T13:02:46.217369Z",
- "iopub.status.idle": "2024-01-19T13:02:46.225655Z",
- "shell.execute_reply": "2024-01-19T13:02:46.225150Z"
+ "iopub.execute_input": "2024-01-19T13:19:42.853941Z",
+ "iopub.status.busy": "2024-01-19T13:19:42.853435Z",
+ "iopub.status.idle": "2024-01-19T13:19:42.864008Z",
+ "shell.execute_reply": "2024-01-19T13:19:42.863458Z"
}
},
"outputs": [
@@ -993,10 +969,10 @@
"id": "db0b5179",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:46.227858Z",
- "iopub.status.busy": "2024-01-19T13:02:46.227660Z",
- "iopub.status.idle": "2024-01-19T13:02:48.102395Z",
- "shell.execute_reply": "2024-01-19T13:02:48.101748Z"
+ "iopub.execute_input": "2024-01-19T13:19:42.866546Z",
+ "iopub.status.busy": "2024-01-19T13:19:42.866086Z",
+ "iopub.status.idle": "2024-01-19T13:19:44.763356Z",
+ "shell.execute_reply": "2024-01-19T13:19:44.762674Z"
}
},
"outputs": [
@@ -1168,10 +1144,10 @@
"id": "a18795eb",
"metadata": {
"execution": {
- "iopub.execute_input": "2024-01-19T13:02:48.104921Z",
- "iopub.status.busy": "2024-01-19T13:02:48.104712Z",
- "iopub.status.idle": "2024-01-19T13:02:48.109010Z",
- "shell.execute_reply": "2024-01-19T13:02:48.108486Z"
+ "iopub.execute_input": "2024-01-19T13:19:44.765837Z",
+ "iopub.status.busy": "2024-01-19T13:19:44.765617Z",
+ "iopub.status.idle": "2024-01-19T13:19:44.770163Z",
+ "shell.execute_reply": "2024-01-19T13:19:44.769534Z"
},
"nbsphinx": "hidden"
},
diff --git a/master/.doctrees/tutorials/audio.doctree b/master/.doctrees/tutorials/audio.doctree
index 7e1e732cb..8feb16d09 100644
Binary files a/master/.doctrees/tutorials/audio.doctree and b/master/.doctrees/tutorials/audio.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree
index 4065a38e3..01765ed0a 100644
Binary files a/master/.doctrees/tutorials/datalab/datalab_advanced.doctree and b/master/.doctrees/tutorials/datalab/datalab_advanced.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree
index a9fb5d2bb..1f77bc6f8 100644
Binary files a/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree and b/master/.doctrees/tutorials/datalab/datalab_quickstart.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/index.doctree b/master/.doctrees/tutorials/datalab/index.doctree
index 98eff27e0..70009b42a 100644
Binary files a/master/.doctrees/tutorials/datalab/index.doctree and b/master/.doctrees/tutorials/datalab/index.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/tabular.doctree b/master/.doctrees/tutorials/datalab/tabular.doctree
index be421c7de..9305c5e70 100644
Binary files a/master/.doctrees/tutorials/datalab/tabular.doctree and b/master/.doctrees/tutorials/datalab/tabular.doctree differ
diff --git a/master/.doctrees/tutorials/datalab/text.doctree b/master/.doctrees/tutorials/datalab/text.doctree
index f28174091..09b226702 100644
Binary files a/master/.doctrees/tutorials/datalab/text.doctree and b/master/.doctrees/tutorials/datalab/text.doctree differ
diff --git a/master/.doctrees/tutorials/dataset_health.doctree b/master/.doctrees/tutorials/dataset_health.doctree
index dc6affc9e..49560423f 100644
Binary files a/master/.doctrees/tutorials/dataset_health.doctree and b/master/.doctrees/tutorials/dataset_health.doctree differ
diff --git a/master/.doctrees/tutorials/faq.doctree b/master/.doctrees/tutorials/faq.doctree
index 110e88eca..5949750f0 100644
Binary files a/master/.doctrees/tutorials/faq.doctree and b/master/.doctrees/tutorials/faq.doctree differ
diff --git a/master/.doctrees/tutorials/image.doctree b/master/.doctrees/tutorials/image.doctree
index eeda19b27..d3ccb5246 100644
Binary files a/master/.doctrees/tutorials/image.doctree and b/master/.doctrees/tutorials/image.doctree differ
diff --git a/master/.doctrees/tutorials/indepth_overview.doctree b/master/.doctrees/tutorials/indepth_overview.doctree
index 17a9a0e1d..d8c6e16af 100644
Binary files a/master/.doctrees/tutorials/indepth_overview.doctree and b/master/.doctrees/tutorials/indepth_overview.doctree differ
diff --git a/master/.doctrees/tutorials/index.doctree b/master/.doctrees/tutorials/index.doctree
index c75983790..4cdd99cb2 100644
Binary files a/master/.doctrees/tutorials/index.doctree and b/master/.doctrees/tutorials/index.doctree differ
diff --git a/master/.doctrees/tutorials/multiannotator.doctree b/master/.doctrees/tutorials/multiannotator.doctree
index aa5e1fa18..5bc8f2d1b 100644
Binary files a/master/.doctrees/tutorials/multiannotator.doctree and b/master/.doctrees/tutorials/multiannotator.doctree differ
diff --git a/master/.doctrees/tutorials/multilabel_classification.doctree b/master/.doctrees/tutorials/multilabel_classification.doctree
index 688c516a1..7c7cbd678 100644
Binary files a/master/.doctrees/tutorials/multilabel_classification.doctree and b/master/.doctrees/tutorials/multilabel_classification.doctree differ
diff --git a/master/.doctrees/tutorials/object_detection.doctree b/master/.doctrees/tutorials/object_detection.doctree
index e8813262d..28ed286ed 100644
Binary files a/master/.doctrees/tutorials/object_detection.doctree and b/master/.doctrees/tutorials/object_detection.doctree differ
diff --git a/master/.doctrees/tutorials/outliers.doctree b/master/.doctrees/tutorials/outliers.doctree
index 7b4d89494..a519fa718 100644
Binary files a/master/.doctrees/tutorials/outliers.doctree and b/master/.doctrees/tutorials/outliers.doctree differ
diff --git a/master/.doctrees/tutorials/pred_probs_cross_val.doctree b/master/.doctrees/tutorials/pred_probs_cross_val.doctree
index ccc6c8916..6a2485a8e 100644
Binary files a/master/.doctrees/tutorials/pred_probs_cross_val.doctree and b/master/.doctrees/tutorials/pred_probs_cross_val.doctree differ
diff --git a/master/.doctrees/tutorials/regression.doctree b/master/.doctrees/tutorials/regression.doctree
index e9d5efe9a..182cceb18 100644
Binary files a/master/.doctrees/tutorials/regression.doctree and b/master/.doctrees/tutorials/regression.doctree differ
diff --git a/master/.doctrees/tutorials/segmentation.doctree b/master/.doctrees/tutorials/segmentation.doctree
index 45f02f389..3efde844d 100644
Binary files a/master/.doctrees/tutorials/segmentation.doctree and b/master/.doctrees/tutorials/segmentation.doctree differ
diff --git a/master/.doctrees/tutorials/tabular.doctree b/master/.doctrees/tutorials/tabular.doctree
index 91a5c1e98..99ce42c7f 100644
Binary files a/master/.doctrees/tutorials/tabular.doctree and b/master/.doctrees/tutorials/tabular.doctree differ
diff --git a/master/.doctrees/tutorials/text.doctree b/master/.doctrees/tutorials/text.doctree
index 1a70b7b61..fd1e257a8 100644
Binary files a/master/.doctrees/tutorials/text.doctree and b/master/.doctrees/tutorials/text.doctree differ
diff --git a/master/.doctrees/tutorials/token_classification.doctree b/master/.doctrees/tutorials/token_classification.doctree
index 1dc46d7bd..114987adc 100644
Binary files a/master/.doctrees/tutorials/token_classification.doctree and b/master/.doctrees/tutorials/token_classification.doctree differ
diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb
index 912f8f789..b6af7df46 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 7c1b3eeb8..effbbc9d0 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 6837dd9a0..acc73193a 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 9d2f87be8..ed568e5ec 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 e9cfc693c..c26cb64c8 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 9cb11128f..36a6cc96f 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 87960eb85..bfdfac36f 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 7b27d6d92..2e6a162c6 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 4147a79d0..8bc6e9a6e 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 38b79f723..e28e8816a 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 520ab035e..e3de7543a 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 e14c6ac56..fbfea9511 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 be8c390cd..38b091f65 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 5b353c87f..29f688668 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 7fcfe8251..b28f065c8 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\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 16691f2a3..d3827c31e 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@67fe249386f3dd0ecbf0482ad7a6e41dd363aa83\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@437d3f3f545eeb476ba8877b42bafa45ef585321\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/cleanlab/benchmarking/index.html b/master/cleanlab/benchmarking/index.html
index 6e274950e..3abf3e6c2 100644
--- a/master/cleanlab/benchmarking/index.html
+++ b/master/cleanlab/benchmarking/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/benchmarking/index.html" />
diff --git a/master/cleanlab/benchmarking/noise_generation.html b/master/cleanlab/benchmarking/noise_generation.html
index c632488b5..9f0549464 100644
--- a/master/cleanlab/benchmarking/noise_generation.html
+++ b/master/cleanlab/benchmarking/noise_generation.html
@@ -15,7 +15,7 @@
-
+/cleanlab/benchmarking/noise_generation.html" />
diff --git a/master/cleanlab/classification.html b/master/cleanlab/classification.html
index 318bef5a4..dddda2cc7 100644
--- a/master/cleanlab/classification.html
+++ b/master/cleanlab/classification.html
@@ -15,7 +15,7 @@
-
+/cleanlab/classification.html" />
diff --git a/master/cleanlab/count.html b/master/cleanlab/count.html
index e1a162dc7..fd9459ae2 100644
--- a/master/cleanlab/count.html
+++ b/master/cleanlab/count.html
@@ -15,7 +15,7 @@
-
+/cleanlab/count.html" />
diff --git a/master/cleanlab/datalab/datalab.html b/master/cleanlab/datalab/datalab.html
index 5e465df8e..fe84d9f90 100644
--- a/master/cleanlab/datalab/datalab.html
+++ b/master/cleanlab/datalab/datalab.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/datalab.html" />
diff --git a/master/cleanlab/datalab/guide/custom_issue_manager.html b/master/cleanlab/datalab/guide/custom_issue_manager.html
index 40203d630..78c198e88 100644
--- a/master/cleanlab/datalab/guide/custom_issue_manager.html
+++ b/master/cleanlab/datalab/guide/custom_issue_manager.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/guide/custom_issue_manager.html" />
diff --git a/master/cleanlab/datalab/guide/generating_cluster_ids.html b/master/cleanlab/datalab/guide/generating_cluster_ids.html
index 4215c4d46..55dfecae7 100644
--- a/master/cleanlab/datalab/guide/generating_cluster_ids.html
+++ b/master/cleanlab/datalab/guide/generating_cluster_ids.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/guide/generating_cluster_ids.html" />
diff --git a/master/cleanlab/datalab/guide/index.html b/master/cleanlab/datalab/guide/index.html
index c0822887c..828f4c55e 100644
--- a/master/cleanlab/datalab/guide/index.html
+++ b/master/cleanlab/datalab/guide/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/guide/index.html" />
diff --git a/master/cleanlab/datalab/guide/issue_type_description.html b/master/cleanlab/datalab/guide/issue_type_description.html
index 6a86081ef..303981b89 100644
--- a/master/cleanlab/datalab/guide/issue_type_description.html
+++ b/master/cleanlab/datalab/guide/issue_type_description.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/guide/issue_type_description.html" />
diff --git a/master/cleanlab/datalab/index.html b/master/cleanlab/datalab/index.html
index 04608f193..3588fefc1 100644
--- a/master/cleanlab/datalab/index.html
+++ b/master/cleanlab/datalab/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/index.html" />
diff --git a/master/cleanlab/datalab/internal/data.html b/master/cleanlab/datalab/internal/data.html
index 4696a1a40..8b773bfc8 100644
--- a/master/cleanlab/datalab/internal/data.html
+++ b/master/cleanlab/datalab/internal/data.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/data.html" />
diff --git a/master/cleanlab/datalab/internal/data_issues.html b/master/cleanlab/datalab/internal/data_issues.html
index 07c350111..c26d36d0b 100644
--- a/master/cleanlab/datalab/internal/data_issues.html
+++ b/master/cleanlab/datalab/internal/data_issues.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/data_issues.html" />
diff --git a/master/cleanlab/datalab/internal/factory.html b/master/cleanlab/datalab/internal/factory.html
index 7d3c25232..accbaf226 100644
--- a/master/cleanlab/datalab/internal/factory.html
+++ b/master/cleanlab/datalab/internal/factory.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/factory.html" />
diff --git a/master/cleanlab/datalab/internal/index.html b/master/cleanlab/datalab/internal/index.html
index 657e64b84..db490840a 100644
--- a/master/cleanlab/datalab/internal/index.html
+++ b/master/cleanlab/datalab/internal/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/index.html" />
diff --git a/master/cleanlab/datalab/internal/issue_finder.html b/master/cleanlab/datalab/internal/issue_finder.html
index 9b0f490a2..b1e221cb4 100644
--- a/master/cleanlab/datalab/internal/issue_finder.html
+++ b/master/cleanlab/datalab/internal/issue_finder.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_finder.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html b/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html
index 9da6e5804..b273ae7ec 100644
--- a/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html
+++ b/master/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/_notices/not_registered.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/data_valuation.html b/master/cleanlab/datalab/internal/issue_manager/data_valuation.html
index ce4aa7175..1eedaa1f6 100644
--- a/master/cleanlab/datalab/internal/issue_manager/data_valuation.html
+++ b/master/cleanlab/datalab/internal/issue_manager/data_valuation.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/data_valuation.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/duplicate.html b/master/cleanlab/datalab/internal/issue_manager/duplicate.html
index f7b5e2a9b..f32ae7c43 100644
--- a/master/cleanlab/datalab/internal/issue_manager/duplicate.html
+++ b/master/cleanlab/datalab/internal/issue_manager/duplicate.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/duplicate.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/imbalance.html b/master/cleanlab/datalab/internal/issue_manager/imbalance.html
index 6916b3c0d..0913150ae 100644
--- a/master/cleanlab/datalab/internal/issue_manager/imbalance.html
+++ b/master/cleanlab/datalab/internal/issue_manager/imbalance.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/imbalance.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/index.html b/master/cleanlab/datalab/internal/issue_manager/index.html
index 47b0c0792..7816e271a 100644
--- a/master/cleanlab/datalab/internal/issue_manager/index.html
+++ b/master/cleanlab/datalab/internal/issue_manager/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/index.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/issue_manager.html b/master/cleanlab/datalab/internal/issue_manager/issue_manager.html
index 5658b41ce..4541613fa 100644
--- a/master/cleanlab/datalab/internal/issue_manager/issue_manager.html
+++ b/master/cleanlab/datalab/internal/issue_manager/issue_manager.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/issue_manager.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/label.html b/master/cleanlab/datalab/internal/issue_manager/label.html
index 997e8dc4f..949ee3c84 100644
--- a/master/cleanlab/datalab/internal/issue_manager/label.html
+++ b/master/cleanlab/datalab/internal/issue_manager/label.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/label.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/noniid.html b/master/cleanlab/datalab/internal/issue_manager/noniid.html
index c6a05de86..d4e701d53 100644
--- a/master/cleanlab/datalab/internal/issue_manager/noniid.html
+++ b/master/cleanlab/datalab/internal/issue_manager/noniid.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/noniid.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/null.html b/master/cleanlab/datalab/internal/issue_manager/null.html
index 10abc1085..4e0bce359 100644
--- a/master/cleanlab/datalab/internal/issue_manager/null.html
+++ b/master/cleanlab/datalab/internal/issue_manager/null.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/null.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/outlier.html b/master/cleanlab/datalab/internal/issue_manager/outlier.html
index bface6d80..a4bba0631 100644
--- a/master/cleanlab/datalab/internal/issue_manager/outlier.html
+++ b/master/cleanlab/datalab/internal/issue_manager/outlier.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/outlier.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/regression/index.html b/master/cleanlab/datalab/internal/issue_manager/regression/index.html
index eafb7782f..4b36ba3ed 100644
--- a/master/cleanlab/datalab/internal/issue_manager/regression/index.html
+++ b/master/cleanlab/datalab/internal/issue_manager/regression/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/regression/index.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/regression/label.html b/master/cleanlab/datalab/internal/issue_manager/regression/label.html
index 169df8180..b063fb566 100644
--- a/master/cleanlab/datalab/internal/issue_manager/regression/label.html
+++ b/master/cleanlab/datalab/internal/issue_manager/regression/label.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/regression/label.html" />
diff --git a/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html b/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html
index c7103ce2e..8b35b03fc 100644
--- a/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html
+++ b/master/cleanlab/datalab/internal/issue_manager/underperforming_group.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/issue_manager/underperforming_group.html" />
diff --git a/master/cleanlab/datalab/internal/report.html b/master/cleanlab/datalab/internal/report.html
index 3dfb9a4ce..a1dae219c 100644
--- a/master/cleanlab/datalab/internal/report.html
+++ b/master/cleanlab/datalab/internal/report.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/internal/report.html" />
diff --git a/master/cleanlab/datalab/optional_dependencies.html b/master/cleanlab/datalab/optional_dependencies.html
index 39946bc2c..9f6b7c1ae 100644
--- a/master/cleanlab/datalab/optional_dependencies.html
+++ b/master/cleanlab/datalab/optional_dependencies.html
@@ -15,7 +15,7 @@
-
+/cleanlab/datalab/optional_dependencies.html" />
diff --git a/master/cleanlab/dataset.html b/master/cleanlab/dataset.html
index 5d5e63ab1..13518d36a 100644
--- a/master/cleanlab/dataset.html
+++ b/master/cleanlab/dataset.html
@@ -15,7 +15,7 @@
-
+/cleanlab/dataset.html" />
diff --git a/master/cleanlab/experimental/cifar_cnn.html b/master/cleanlab/experimental/cifar_cnn.html
index ba24a3c3e..20aab1fe3 100644
--- a/master/cleanlab/experimental/cifar_cnn.html
+++ b/master/cleanlab/experimental/cifar_cnn.html
@@ -15,7 +15,7 @@
-
+/cleanlab/experimental/cifar_cnn.html" />
diff --git a/master/cleanlab/experimental/coteaching.html b/master/cleanlab/experimental/coteaching.html
index 6a9906bbf..f5666dc49 100644
--- a/master/cleanlab/experimental/coteaching.html
+++ b/master/cleanlab/experimental/coteaching.html
@@ -15,7 +15,7 @@
-
+/cleanlab/experimental/coteaching.html" />
diff --git a/master/cleanlab/experimental/index.html b/master/cleanlab/experimental/index.html
index fe7f0c03d..266825580 100644
--- a/master/cleanlab/experimental/index.html
+++ b/master/cleanlab/experimental/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/experimental/index.html" />
diff --git a/master/cleanlab/experimental/label_issues_batched.html b/master/cleanlab/experimental/label_issues_batched.html
index 66421aca2..793cb4120 100644
--- a/master/cleanlab/experimental/label_issues_batched.html
+++ b/master/cleanlab/experimental/label_issues_batched.html
@@ -15,7 +15,7 @@
-
+/cleanlab/experimental/label_issues_batched.html" />
diff --git a/master/cleanlab/experimental/mnist_pytorch.html b/master/cleanlab/experimental/mnist_pytorch.html
index 54bc82b6b..7bd4ff507 100644
--- a/master/cleanlab/experimental/mnist_pytorch.html
+++ b/master/cleanlab/experimental/mnist_pytorch.html
@@ -15,7 +15,7 @@
-
+/cleanlab/experimental/mnist_pytorch.html" />
diff --git a/master/cleanlab/filter.html b/master/cleanlab/filter.html
index be428d31d..3674f3c34 100644
--- a/master/cleanlab/filter.html
+++ b/master/cleanlab/filter.html
@@ -15,7 +15,7 @@
-
+/cleanlab/filter.html" />
diff --git a/master/cleanlab/internal/index.html b/master/cleanlab/internal/index.html
index 6d7abbbb1..8432f98b0 100644
--- a/master/cleanlab/internal/index.html
+++ b/master/cleanlab/internal/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/internal/index.html" />
diff --git a/master/cleanlab/internal/label_quality_utils.html b/master/cleanlab/internal/label_quality_utils.html
index 5e3ca65b9..a22ecc22b 100644
--- a/master/cleanlab/internal/label_quality_utils.html
+++ b/master/cleanlab/internal/label_quality_utils.html
@@ -15,7 +15,7 @@
-
+/cleanlab/internal/label_quality_utils.html" />
diff --git a/master/cleanlab/internal/latent_algebra.html b/master/cleanlab/internal/latent_algebra.html
index 9a208286d..d1c97aa50 100644
--- a/master/cleanlab/internal/latent_algebra.html
+++ b/master/cleanlab/internal/latent_algebra.html
@@ -15,7 +15,7 @@
-
+/cleanlab/internal/latent_algebra.html" />
diff --git a/master/cleanlab/internal/multiannotator_utils.html b/master/cleanlab/internal/multiannotator_utils.html
index 6d8de9a9d..54204d5e0 100644
--- a/master/cleanlab/internal/multiannotator_utils.html
+++ b/master/cleanlab/internal/multiannotator_utils.html
@@ -15,7 +15,7 @@
-
+/cleanlab/internal/multiannotator_utils.html" />
diff --git a/master/cleanlab/internal/multilabel_scorer.html b/master/cleanlab/internal/multilabel_scorer.html
index 28110faf6..ab4559bbd 100644
--- a/master/cleanlab/internal/multilabel_scorer.html
+++ b/master/cleanlab/internal/multilabel_scorer.html
@@ -15,7 +15,7 @@
-
+/cleanlab/internal/multilabel_scorer.html" />
diff --git a/master/cleanlab/internal/multilabel_utils.html b/master/cleanlab/internal/multilabel_utils.html
index ae29bfdaa..6269caf57 100644
--- a/master/cleanlab/internal/multilabel_utils.html
+++ b/master/cleanlab/internal/multilabel_utils.html
@@ -15,7 +15,7 @@
-
+/cleanlab/internal/multilabel_utils.html" />
diff --git a/master/cleanlab/internal/outlier.html b/master/cleanlab/internal/outlier.html
index 23774c5b1..6168b3ffa 100644
--- a/master/cleanlab/internal/outlier.html
+++ b/master/cleanlab/internal/outlier.html
@@ -15,7 +15,7 @@
-
+/cleanlab/internal/outlier.html" />
diff --git a/master/cleanlab/internal/token_classification_utils.html b/master/cleanlab/internal/token_classification_utils.html
index cf6027ba2..5ae9d7320 100644
--- a/master/cleanlab/internal/token_classification_utils.html
+++ b/master/cleanlab/internal/token_classification_utils.html
@@ -15,7 +15,7 @@
-
+/cleanlab/internal/token_classification_utils.html" />
diff --git a/master/cleanlab/internal/util.html b/master/cleanlab/internal/util.html
index d7879643a..197ea4dfe 100644
--- a/master/cleanlab/internal/util.html
+++ b/master/cleanlab/internal/util.html
@@ -15,7 +15,7 @@
-
+/cleanlab/internal/util.html" />
diff --git a/master/cleanlab/internal/validation.html b/master/cleanlab/internal/validation.html
index 7b882d0bf..a0fc33be4 100644
--- a/master/cleanlab/internal/validation.html
+++ b/master/cleanlab/internal/validation.html
@@ -15,7 +15,7 @@
-
+/cleanlab/internal/validation.html" />
diff --git a/master/cleanlab/models/fasttext.html b/master/cleanlab/models/fasttext.html
index c0710a4d5..76bcf8772 100644
--- a/master/cleanlab/models/fasttext.html
+++ b/master/cleanlab/models/fasttext.html
@@ -15,7 +15,7 @@
-
+/cleanlab/models/fasttext.html" />
diff --git a/master/cleanlab/models/index.html b/master/cleanlab/models/index.html
index d9465949c..cd21eed72 100644
--- a/master/cleanlab/models/index.html
+++ b/master/cleanlab/models/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/models/index.html" />
diff --git a/master/cleanlab/models/keras.html b/master/cleanlab/models/keras.html
index 37a465b09..618127414 100644
--- a/master/cleanlab/models/keras.html
+++ b/master/cleanlab/models/keras.html
@@ -15,7 +15,7 @@
-
+/cleanlab/models/keras.html" />
diff --git a/master/cleanlab/multiannotator.html b/master/cleanlab/multiannotator.html
index f7007f2b9..266be3cc6 100644
--- a/master/cleanlab/multiannotator.html
+++ b/master/cleanlab/multiannotator.html
@@ -15,7 +15,7 @@
-
+/cleanlab/multiannotator.html" />
diff --git a/master/cleanlab/multilabel_classification/dataset.html b/master/cleanlab/multilabel_classification/dataset.html
index 115f112a6..80177f89a 100644
--- a/master/cleanlab/multilabel_classification/dataset.html
+++ b/master/cleanlab/multilabel_classification/dataset.html
@@ -15,7 +15,7 @@
-
+/cleanlab/multilabel_classification/dataset.html" />
diff --git a/master/cleanlab/multilabel_classification/filter.html b/master/cleanlab/multilabel_classification/filter.html
index 597a1e5ce..61a4fdea9 100644
--- a/master/cleanlab/multilabel_classification/filter.html
+++ b/master/cleanlab/multilabel_classification/filter.html
@@ -15,7 +15,7 @@
-
+/cleanlab/multilabel_classification/filter.html" />
diff --git a/master/cleanlab/multilabel_classification/index.html b/master/cleanlab/multilabel_classification/index.html
index 122632664..c1dcdbef3 100644
--- a/master/cleanlab/multilabel_classification/index.html
+++ b/master/cleanlab/multilabel_classification/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/multilabel_classification/index.html" />
diff --git a/master/cleanlab/multilabel_classification/rank.html b/master/cleanlab/multilabel_classification/rank.html
index 691a97059..9d2879993 100644
--- a/master/cleanlab/multilabel_classification/rank.html
+++ b/master/cleanlab/multilabel_classification/rank.html
@@ -15,7 +15,7 @@
-
+/cleanlab/multilabel_classification/rank.html" />
diff --git a/master/cleanlab/object_detection/filter.html b/master/cleanlab/object_detection/filter.html
index 3ecee1667..70f703375 100644
--- a/master/cleanlab/object_detection/filter.html
+++ b/master/cleanlab/object_detection/filter.html
@@ -15,7 +15,7 @@
-
+/cleanlab/object_detection/filter.html" />
diff --git a/master/cleanlab/object_detection/index.html b/master/cleanlab/object_detection/index.html
index 0391f9fe3..874915b76 100644
--- a/master/cleanlab/object_detection/index.html
+++ b/master/cleanlab/object_detection/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/object_detection/index.html" />
diff --git a/master/cleanlab/object_detection/rank.html b/master/cleanlab/object_detection/rank.html
index a23b6028f..cbb88e591 100644
--- a/master/cleanlab/object_detection/rank.html
+++ b/master/cleanlab/object_detection/rank.html
@@ -15,7 +15,7 @@
-
+/cleanlab/object_detection/rank.html" />
diff --git a/master/cleanlab/object_detection/summary.html b/master/cleanlab/object_detection/summary.html
index b1f1a7b74..20214d2da 100644
--- a/master/cleanlab/object_detection/summary.html
+++ b/master/cleanlab/object_detection/summary.html
@@ -15,7 +15,7 @@
-
+/cleanlab/object_detection/summary.html" />
diff --git a/master/cleanlab/outlier.html b/master/cleanlab/outlier.html
index 7828a2f16..fe335eee3 100644
--- a/master/cleanlab/outlier.html
+++ b/master/cleanlab/outlier.html
@@ -15,7 +15,7 @@
-
+/cleanlab/outlier.html" />
diff --git a/master/cleanlab/rank.html b/master/cleanlab/rank.html
index 4269688aa..c8b2cde39 100644
--- a/master/cleanlab/rank.html
+++ b/master/cleanlab/rank.html
@@ -15,7 +15,7 @@
-
+/cleanlab/rank.html" />
diff --git a/master/cleanlab/regression/index.html b/master/cleanlab/regression/index.html
index cde24e939..23e8612c6 100644
--- a/master/cleanlab/regression/index.html
+++ b/master/cleanlab/regression/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/regression/index.html" />
diff --git a/master/cleanlab/regression/learn.html b/master/cleanlab/regression/learn.html
index ca87f24ff..972671d31 100644
--- a/master/cleanlab/regression/learn.html
+++ b/master/cleanlab/regression/learn.html
@@ -15,7 +15,7 @@
-
+/cleanlab/regression/learn.html" />
diff --git a/master/cleanlab/regression/rank.html b/master/cleanlab/regression/rank.html
index b02b1f039..52d268fbf 100644
--- a/master/cleanlab/regression/rank.html
+++ b/master/cleanlab/regression/rank.html
@@ -15,7 +15,7 @@
-
+/cleanlab/regression/rank.html" />
diff --git a/master/cleanlab/segmentation/filter.html b/master/cleanlab/segmentation/filter.html
index ef044825e..acddca28d 100644
--- a/master/cleanlab/segmentation/filter.html
+++ b/master/cleanlab/segmentation/filter.html
@@ -15,7 +15,7 @@
-
+/cleanlab/segmentation/filter.html" />
diff --git a/master/cleanlab/segmentation/index.html b/master/cleanlab/segmentation/index.html
index 53bb80ebf..900272d1f 100644
--- a/master/cleanlab/segmentation/index.html
+++ b/master/cleanlab/segmentation/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/segmentation/index.html" />
diff --git a/master/cleanlab/segmentation/rank.html b/master/cleanlab/segmentation/rank.html
index 931d76589..269309e71 100644
--- a/master/cleanlab/segmentation/rank.html
+++ b/master/cleanlab/segmentation/rank.html
@@ -15,7 +15,7 @@
-
+/cleanlab/segmentation/rank.html" />
diff --git a/master/cleanlab/segmentation/summary.html b/master/cleanlab/segmentation/summary.html
index 9c785f8c6..b97e63aaf 100644
--- a/master/cleanlab/segmentation/summary.html
+++ b/master/cleanlab/segmentation/summary.html
@@ -15,7 +15,7 @@
-
+/cleanlab/segmentation/summary.html" />
diff --git a/master/cleanlab/token_classification/filter.html b/master/cleanlab/token_classification/filter.html
index 31fc72d00..b157433d2 100644
--- a/master/cleanlab/token_classification/filter.html
+++ b/master/cleanlab/token_classification/filter.html
@@ -15,7 +15,7 @@
-
+/cleanlab/token_classification/filter.html" />
diff --git a/master/cleanlab/token_classification/index.html b/master/cleanlab/token_classification/index.html
index 6e026707f..9186f7448 100644
--- a/master/cleanlab/token_classification/index.html
+++ b/master/cleanlab/token_classification/index.html
@@ -15,7 +15,7 @@
-
+/cleanlab/token_classification/index.html" />
diff --git a/master/cleanlab/token_classification/rank.html b/master/cleanlab/token_classification/rank.html
index c4e70d83a..4a7bb543a 100644
--- a/master/cleanlab/token_classification/rank.html
+++ b/master/cleanlab/token_classification/rank.html
@@ -15,7 +15,7 @@
-
+/cleanlab/token_classification/rank.html" />
diff --git a/master/cleanlab/token_classification/summary.html b/master/cleanlab/token_classification/summary.html
index ea816c65c..daf6eb621 100644
--- a/master/cleanlab/token_classification/summary.html
+++ b/master/cleanlab/token_classification/summary.html
@@ -15,7 +15,7 @@
-
+/cleanlab/token_classification/summary.html" />
diff --git a/master/index.html b/master/index.html
index f0b56a637..3a7e4041e 100644
--- a/master/index.html
+++ b/master/index.html
@@ -15,7 +15,7 @@
-
+/index.html" />
diff --git a/master/migrating/migrate_v2.html b/master/migrating/migrate_v2.html
index c2d2142a6..093548c51 100644
--- a/master/migrating/migrate_v2.html
+++ b/master/migrating/migrate_v2.html
@@ -15,7 +15,7 @@
-
+/migrating/migrate_v2.html" />
diff --git a/master/searchindex.js b/master/searchindex.js
index a81d52440..adf2d2a76 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/report", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/dataset_health", "tutorials/faq", "tutorials/image", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/tabular", "tutorials/text", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/fasttext.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/image.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/tabular.ipynb", "tutorials/text.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "datalab", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "report", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "outlier", "token_classification_utils", "util", "validation", "fasttext", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "Audio Classification with SpeechBrain and Cleanlab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Find Dataset-level Issues for Dataset Curation", "FAQ", "Image Classification with PyTorch and Cleanlab", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Classification with Tabular Data using Scikit-Learn and Cleanlab", "Text Classification with Noisy Labels", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 73, 75, 76, 83, 85, 86], "helper": [1, 14, 34, 38, 40, 41, 42, 43, 44, 45, 57, 80, 82, 94], "method": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "ar": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 25, 26, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "us": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 72, 73, 75, 80, 84, 89], "benchmark": [1, 31, 72, 73, 75, 76, 83, 85, 86], "cleanlab": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 75, 76, 80, 84, 89], "": [1, 2, 3, 8, 16, 30, 31, 35, 38, 41, 43, 45, 50, 51, 55, 57, 58, 59, 60, 62, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "core": [1, 4, 34, 36, 64, 66, 91], "algorithm": [1, 2, 6, 8, 27, 32, 45, 50, 59, 68, 70, 72, 81, 83, 85, 94], "These": [1, 2, 3, 6, 8, 19, 33, 36, 37, 48, 50, 51, 54, 58, 59, 63, 67, 68, 70, 71, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "introduc": [1, 74, 81, 83], "synthet": [1, 85, 86, 91], "nois": [1, 2, 3, 30, 36, 39, 45, 51, 75, 76, 80, 85], "label": [1, 2, 3, 4, 5, 6, 7, 10, 14, 18, 19, 20, 25, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 45, 46, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 75, 80, 84, 88, 89], "classif": [1, 3, 4, 5, 8, 12, 14, 28, 30, 34, 36, 39, 41, 42, 45, 50, 51, 52, 53, 54, 59, 60, 68, 69, 70, 71, 72, 73, 75, 76, 84, 85, 88, 89, 90, 91], "dataset": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 22, 23, 24, 26, 27, 34, 35, 36, 39, 41, 45, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 77, 78, 84, 85, 89, 92], "specif": [1, 3, 4, 7, 12, 13, 14, 23, 28, 33, 48, 52, 55, 58, 67, 71, 76, 78, 79, 82, 83, 94], "thi": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 38, 39, 41, 42, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "modul": [1, 3, 11, 12, 13, 14, 19, 25, 28, 30, 31, 32, 33, 34, 35, 36, 43, 45, 48, 50, 59, 60, 72, 81, 82, 86], "provid": [1, 2, 3, 4, 5, 6, 8, 12, 14, 16, 21, 26, 30, 31, 32, 34, 35, 36, 39, 45, 49, 50, 51, 52, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 88, 89, 90, 91, 92, 93, 94], "gener": [1, 2, 3, 5, 8, 16, 21, 28, 30, 41, 45, 46, 59, 60, 62, 67, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 89, 90, 91, 93, 94], "valid": [1, 2, 3, 4, 8, 10, 30, 36, 37, 39, 40, 41, 43, 45, 50, 52, 55, 58, 60, 62, 63, 71, 73, 74, 75, 76, 78, 79, 80, 81, 83, 84, 86, 87, 90, 91, 92, 93, 94], "matric": [1, 3, 39, 81], "which": [1, 2, 3, 4, 8, 10, 11, 12, 14, 16, 20, 22, 28, 30, 31, 35, 36, 39, 41, 44, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 66, 67, 68, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "learn": [1, 2, 3, 4, 8, 12, 14, 20, 26, 28, 32, 33, 34, 35, 36, 38, 40, 45, 48, 50, 52, 59, 61, 63, 66, 70, 72, 74, 75, 78, 79, 80, 82, 84, 85, 90, 93], "i": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "possibl": [1, 2, 3, 8, 30, 31, 35, 36, 38, 39, 41, 52, 53, 54, 55, 57, 58, 59, 60, 62, 68, 70, 71, 76, 81, 83, 85, 86, 87, 90, 91, 94], "noisi": [1, 2, 3, 8, 30, 32, 35, 36, 39, 45, 51, 52, 54, 60, 62, 63, 64, 66, 67, 73, 75, 76, 78, 79, 81, 84, 85], "given": [1, 2, 3, 8, 26, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 44, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "matrix": [1, 2, 3, 4, 8, 14, 16, 27, 30, 36, 38, 39, 42, 45, 46, 52, 57, 58, 59, 60, 78, 88], "trace": [1, 75, 76, 83, 85, 86], "valu": [1, 2, 3, 4, 8, 10, 11, 14, 16, 20, 22, 23, 30, 31, 32, 34, 35, 36, 38, 39, 41, 43, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 71, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "more": [1, 2, 3, 4, 5, 8, 11, 14, 16, 22, 30, 31, 34, 35, 38, 41, 43, 45, 50, 51, 52, 53, 54, 55, 57, 58, 60, 62, 63, 66, 67, 68, 70, 72, 74, 75, 78, 79, 80, 81, 82, 85, 86, 87, 88, 91, 94], "function": [1, 2, 3, 4, 5, 11, 12, 14, 21, 22, 26, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 76, 80, 81, 83, 85, 86, 87, 91, 92, 93, 94], "noise_matrix_is_valid": 1, "noise_matrix": [1, 2, 3, 8, 39, 45, 75, 76, 83, 85, 86], "py": [1, 3, 28, 31, 32, 36, 39, 41, 75, 76, 83, 85, 86], "verbos": [1, 2, 4, 5, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 34, 36, 50, 51, 52, 57, 59, 60, 62, 64, 66, 67, 71, 75, 83, 85], "fals": [1, 2, 3, 4, 5, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 34, 35, 36, 40, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 74, 75, 76, 78, 79, 81, 82, 83, 85, 87, 88, 90, 91, 93], "sourc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71], "prior": [1, 2, 3, 30, 36, 39, 41], "repres": [1, 2, 3, 5, 8, 10, 14, 16, 22, 30, 34, 36, 39, 42, 43, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "p": [1, 2, 3, 8, 30, 36, 38, 39, 45, 50, 58, 59, 60, 64, 76, 78, 79, 82, 83, 85, 94], "true_label": [1, 2, 3, 30, 39, 45, 83, 85], "k": [1, 2, 3, 4, 6, 8, 10, 14, 16, 17, 21, 22, 24, 27, 30, 34, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 53, 54, 55, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 74, 75, 76, 81, 83, 85, 86, 87, 88, 91, 92, 94], "check": [1, 2, 4, 7, 8, 10, 14, 23, 31, 34, 35, 40, 46, 49, 55, 58, 62, 72, 74, 75, 76, 81, 82, 83, 85, 86, 90, 92, 93], "learnabl": 1, "mean": [1, 2, 5, 6, 10, 11, 20, 22, 32, 35, 39, 41, 43, 57, 62, 76, 79, 81, 83, 85, 86, 88, 90, 93], "achiev": [1, 2, 31, 32, 35, 62, 81, 85, 94], "better": [1, 4, 36, 50, 52, 60, 62, 63, 72, 74, 76, 78, 79, 81, 83, 86, 87, 88, 93, 94], "than": [1, 2, 3, 5, 8, 22, 24, 27, 30, 36, 45, 49, 50, 55, 57, 59, 60, 62, 66, 70, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 94], "random": [1, 2, 3, 5, 8, 16, 27, 34, 41, 50, 60, 62, 74, 75, 76, 78, 81, 82, 83, 85, 86, 88, 92], "perform": [1, 2, 5, 8, 22, 24, 27, 31, 35, 41, 58, 62, 72, 75, 81, 83, 85, 86, 89, 90, 92, 93], "averag": [1, 3, 8, 20, 24, 30, 31, 35, 41, 43, 50, 51, 58, 59, 60, 81, 85, 88], "amount": [1, 3, 82], "paramet": [1, 2, 3, 4, 7, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 76, 79, 82, 92, 93], "np": [1, 2, 3, 4, 5, 14, 16, 27, 30, 32, 34, 36, 38, 39, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 70, 71, 74, 75, 76, 78, 80, 81, 82, 83, 85, 86, 88, 90, 91, 92, 93, 94], "ndarrai": [1, 2, 3, 4, 14, 21, 22, 26, 27, 30, 32, 34, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 94], "an": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 43, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "arrai": [1, 2, 3, 4, 5, 8, 10, 14, 16, 22, 30, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 75, 76, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "shape": [1, 2, 3, 4, 14, 16, 30, 32, 34, 36, 38, 39, 40, 41, 43, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 80, 81, 83, 86, 87, 88, 91, 94], "condit": [1, 2, 3, 39, 44, 45, 60, 82, 83, 94], "probabl": [1, 2, 3, 4, 6, 8, 14, 21, 24, 30, 34, 35, 36, 38, 39, 41, 42, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 73, 80, 81, 83, 84, 86, 87, 88, 91, 94], "k_": [1, 2, 3, 39, 45], "k_y": [1, 2, 3, 39, 45], "contain": [1, 2, 3, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93], "fraction": [1, 2, 3, 8, 18, 32, 39, 45, 50, 62, 78, 81], "exampl": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 44, 45, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 85, 86, 87, 89, 90, 91, 92, 93, 94], "everi": [1, 2, 3, 4, 14, 31, 35, 36, 39, 44, 45, 52, 60, 62, 63, 74, 75, 76, 78, 79, 81, 82, 85, 87, 89, 91, 92, 94], "class": [1, 2, 3, 4, 5, 7, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 44, 45, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 92, 93, 94], "other": [1, 2, 3, 4, 8, 14, 20, 23, 30, 31, 33, 34, 35, 36, 39, 42, 45, 46, 48, 50, 51, 54, 58, 59, 60, 62, 67, 74, 75, 76, 78, 79, 81, 82, 83, 86, 88, 91, 94], "assum": [1, 2, 3, 10, 36, 39, 44, 45, 60, 64, 67, 81, 88, 91, 94], "column": [1, 2, 3, 4, 8, 10, 11, 26, 30, 34, 36, 39, 41, 42, 44, 45, 50, 51, 52, 54, 55, 58, 59, 60, 62, 67, 68, 70, 71, 74, 75, 76, 79, 80, 81, 82, 83, 85, 87, 90, 91, 92, 93, 94], "sum": [1, 2, 3, 22, 27, 30, 39, 41, 45, 51, 52, 54, 57, 62, 75, 76, 81, 82, 83, 85, 86, 91, 94], "1": [1, 2, 3, 4, 5, 8, 10, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 80, 81, 89], "each": [1, 2, 3, 4, 5, 6, 7, 11, 12, 14, 18, 20, 21, 22, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 45, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "true": [1, 2, 3, 4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 39, 41, 44, 45, 46, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "return": [1, 2, 3, 4, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "type": [1, 2, 3, 4, 5, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 86, 87, 91, 92, 94], "bool": [1, 2, 3, 4, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 34, 35, 36, 41, 44, 45, 50, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71], "is_valid": 1, "whether": [1, 3, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 31, 34, 35, 36, 45, 50, 51, 52, 54, 55, 71, 74, 76, 78, 79, 80, 81, 82, 83, 90, 93, 94], "generate_noisy_label": [1, 75, 76, 83, 85, 86], "from": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 21, 23, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 39, 41, 42, 43, 44, 45, 50, 52, 54, 57, 58, 59, 60, 62, 63, 68, 70, 71, 72, 74, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 91, 94], "perfect": [1, 2, 30, 62, 83, 87], "exactli": [1, 3, 8, 30, 31, 35, 36, 53, 59, 75, 76, 78, 79, 82, 83], "yield": [1, 31, 35], "between": [1, 4, 8, 13, 14, 19, 20, 22, 25, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 43, 48, 50, 51, 54, 57, 59, 60, 62, 63, 66, 70, 71, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "below": [1, 3, 4, 8, 30, 31, 34, 35, 36, 38, 41, 50, 51, 52, 57, 58, 66, 70, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "we": [1, 2, 3, 4, 5, 8, 11, 20, 31, 34, 35, 36, 41, 45, 46, 50, 57, 58, 60, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "loop": [1, 3, 39, 45, 82], "implement": [1, 2, 3, 4, 7, 12, 20, 31, 32, 34, 35, 39, 45, 62, 72, 74, 75, 78, 88, 89, 92], "what": [1, 4, 7, 8, 14, 28, 30, 32, 34, 36, 50, 51, 55, 57, 74, 75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "doe": [1, 2, 3, 8, 34, 35, 36, 41, 46, 57, 58, 62, 64, 66, 70, 74, 75, 76, 78, 79, 82, 86, 90, 91, 93], "do": [1, 2, 4, 8, 30, 34, 35, 45, 46, 59, 60, 64, 74, 75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "fast": 1, "explain": [1, 8], "python": [1, 2, 35, 49, 62, 75, 76, 80, 88], "pseudocod": [1, 89], "happen": [1, 8, 36, 52, 79, 85, 91], "n": [1, 2, 3, 4, 5, 30, 31, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 74, 79, 80, 81, 82, 85, 86, 90, 91, 92, 93, 94], "without": [1, 2, 4, 8, 10, 12, 18, 31, 35, 54, 62, 72, 74, 79, 83, 87, 88, 93], "ani": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 38, 40, 43, 44, 45, 49, 50, 52, 54, 55, 57, 58, 60, 62, 64, 66, 67, 72, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93], "distinct": [1, 16, 45, 94], "natur": [1, 8, 85, 88], "number": [1, 2, 3, 4, 5, 6, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 91, 94], "0": [1, 2, 3, 4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "count_joint": 1, "len": [1, 2, 3, 5, 30, 34, 39, 44, 45, 46, 59, 60, 62, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93, 94], "y": [1, 2, 3, 4, 6, 16, 26, 27, 35, 39, 41, 45, 46, 49, 58, 62, 63, 74, 75, 76, 78, 81, 83, 85, 86, 88, 90, 93], "round": [1, 34, 36, 45, 62, 81, 90], "astyp": [1, 85], "int": [1, 2, 3, 4, 5, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 41, 42, 43, 44, 45, 51, 52, 54, 58, 59, 60, 62, 64, 66, 67, 68, 71, 74, 75, 82, 88], "rang": [1, 3, 4, 5, 10, 39, 41, 43, 45, 58, 62, 63, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 94], "idx_flip": 1, "where": [1, 2, 3, 4, 5, 8, 10, 11, 14, 20, 30, 34, 36, 39, 40, 41, 42, 43, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94], "pragma": 1, "cover": [1, 3, 73, 80], "choic": [1, 6, 36, 43, 81, 82, 86, 88], "replac": [1, 44, 49, 60, 75, 76, 79, 80, 81, 82, 85, 88, 92, 93], "generate_noise_matrix_from_trac": [1, 75, 76, 83, 85, 86], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 60, 74, 75, 76], "05": [1, 8, 22, 26, 44, 58, 62, 68, 70, 78, 80, 81, 83, 87, 91], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 75, 76, 83, 85, 86], "none": [1, 2, 3, 4, 5, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 75, 76, 81, 82, 83, 85, 86, 91], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 8, 22, 35, 41, 62, 74, 75, 76, 78, 80, 83, 85, 86, 92], "max_it": [1, 74, 79, 88, 93], "10000": [1, 34, 80, 81], "x": [1, 2, 3, 4, 8, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 31, 32, 35, 36, 38, 39, 41, 44, 45, 46, 49, 50, 52, 58, 59, 60, 62, 64, 74, 75, 76, 78, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93], "diagon": [1, 3, 4, 36, 39, 45], "equal": [1, 3, 8, 10, 52, 57, 67, 89], "creat": [1, 2, 7, 14, 16, 31, 34, 35, 36, 45, 62, 72, 74, 78, 79, 81, 82, 91, 93, 94], "impli": [1, 8, 30, 51, 58], "float": [1, 2, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 40, 41, 43, 44, 45, 50, 51, 52, 54, 57, 58, 62, 66, 70, 74, 75, 76, 83, 85, 86], "entri": [1, 3, 4, 30, 31, 35, 36, 38, 42, 43, 45, 50, 51, 52, 55, 78, 79, 83, 86, 87, 92, 93], "maximum": [1, 8, 59, 67, 71, 91], "minimum": [1, 6, 8, 18, 36, 38, 52, 57, 70], "noise_r": 1, "non": [1, 2, 3, 4, 7, 14, 22, 31, 35, 36, 57, 62, 75, 81, 83, 85, 87, 88], "default": [1, 2, 3, 4, 5, 8, 12, 14, 24, 26, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 75, 81, 82, 91], "If": [1, 2, 3, 4, 8, 10, 11, 14, 22, 24, 30, 31, 34, 35, 36, 38, 39, 41, 44, 45, 49, 50, 51, 52, 55, 57, 58, 59, 62, 63, 64, 66, 67, 70, 71, 72, 73, 74, 75, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "have": [1, 2, 3, 4, 8, 14, 19, 22, 25, 30, 31, 33, 34, 35, 36, 39, 41, 45, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 67, 71, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "all": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 20, 28, 30, 31, 34, 35, 36, 39, 41, 42, 44, 45, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "necessari": [1, 2, 3, 5, 8, 10, 44, 75], "In": [1, 2, 3, 8, 30, 31, 34, 35, 50, 51, 53, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 88, 89, 90, 91, 92, 93, 94], "particular": [1, 4, 8, 11, 12, 14, 17, 18, 20, 22, 23, 24, 27, 31, 35, 45, 50, 54, 58, 62, 67, 71, 72, 74, 76, 79, 81, 85, 86, 88, 90, 92, 93], "satisfi": [1, 3, 30], "requir": [1, 2, 4, 5, 6, 7, 8, 9, 10, 26, 29, 31, 32, 33, 34, 35, 36, 39, 45, 48, 49, 52, 59, 60, 62, 64, 72, 73, 74, 80, 81, 83, 89], "argument": [1, 2, 3, 4, 8, 14, 21, 23, 26, 27, 31, 34, 35, 36, 41, 46, 49, 50, 51, 52, 54, 57, 58, 59, 60, 62, 66, 67, 68, 70, 76, 79, 80, 81, 82, 87, 90, 93, 94], "when": [1, 2, 3, 4, 8, 10, 12, 21, 22, 31, 35, 36, 39, 41, 45, 49, 52, 54, 55, 57, 59, 60, 62, 63, 75, 76, 78, 79, 82, 85, 89, 90, 91, 92, 93, 94], "The": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 38, 39, 40, 41, 42, 43, 45, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 67, 68, 70, 72, 74, 75, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "rate": [1, 2, 3, 8, 32, 45, 74, 94], "set": [1, 2, 3, 4, 7, 8, 10, 11, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 40, 41, 43, 45, 49, 50, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 75, 76, 78, 79, 81, 85, 86, 88, 89, 90, 91, 92, 93, 94], "note": [1, 2, 3, 5, 6, 8, 23, 27, 31, 34, 35, 36, 41, 45, 50, 55, 57, 58, 59, 60, 62, 63, 67, 73, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "you": [1, 2, 3, 4, 5, 8, 12, 14, 30, 31, 33, 34, 35, 36, 41, 48, 49, 50, 52, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "high": [1, 2, 14, 34, 36, 45, 57, 60, 62, 75, 76, 80, 82, 83, 87, 90, 91, 92, 93, 94], "mai": [1, 2, 3, 4, 8, 11, 19, 20, 25, 30, 31, 33, 34, 35, 36, 39, 41, 45, 50, 51, 55, 57, 58, 59, 60, 62, 64, 67, 71, 73, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 89, 90, 91, 93, 94], "imposs": [1, 8, 83], "also": [1, 2, 3, 4, 5, 8, 20, 30, 31, 34, 35, 36, 44, 49, 50, 59, 62, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "low": [1, 8, 45, 50, 72, 75, 76, 79, 83, 87, 91], "zero": [1, 3, 4, 31, 35, 38, 45, 46, 75, 82, 86, 87, 88], "forc": [1, 2, 3, 4, 35, 75, 94], "instead": [1, 2, 3, 8, 11, 14, 28, 30, 31, 34, 35, 36, 39, 45, 49, 50, 52, 54, 58, 59, 60, 62, 63, 66, 68, 70, 73, 74, 78, 79, 81, 82, 83, 86, 87, 88, 90, 91, 92, 93, 94], "onli": [1, 2, 3, 4, 5, 8, 14, 21, 22, 26, 30, 31, 34, 35, 36, 38, 39, 44, 45, 49, 50, 59, 60, 62, 64, 66, 70, 71, 72, 74, 75, 76, 79, 82, 85, 86, 87, 88, 89, 90, 91, 93, 94], "guarante": [1, 3, 4, 13, 19, 25, 31, 33, 35, 37, 39, 48, 73], "produc": [1, 2, 4, 8, 14, 41, 50, 60, 62, 64, 66, 72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 93, 94], "higher": [1, 4, 8, 30, 36, 38, 39, 41, 43, 50, 51, 62, 76, 79, 81, 87], "opposit": [1, 94], "occur": [1, 3, 8, 30, 44, 57, 75, 76, 81, 82, 88], "small": [1, 3, 8, 30, 34, 41, 45, 51, 58, 79, 80, 82, 86, 88, 93], "numpi": [1, 3, 4, 5, 8, 10, 16, 27, 34, 35, 41, 43, 44, 46, 49, 54, 57, 62, 63, 68, 70, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "max": [1, 36, 59, 60, 76, 82, 88], "tri": [1, 31, 35, 89], "befor": [1, 2, 3, 31, 35, 43, 45, 59, 62, 67, 79, 81, 83, 85, 88, 90, 92, 93], "option": [1, 2, 3, 4, 5, 6, 7, 10, 11, 14, 21, 22, 26, 30, 31, 34, 35, 36, 39, 41, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 72, 74, 75, 76, 78, 81, 82, 83, 90, 91, 92], "left": [1, 2, 36, 38, 43, 45, 52, 55, 58, 75, 76, 86, 87, 88, 91], "stochast": 1, "exceed": 1, "generate_n_rand_probabilities_that_sum_to_m": 1, "m": [1, 4, 31, 35, 40, 41, 50, 55, 57, 58, 59, 75, 76, 80, 85, 86, 87, 94], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 31, 35, 49, 81, 83, 91], "length": [1, 4, 10, 22, 23, 30, 32, 36, 45, 52, 55, 59, 60, 62, 64, 67, 71, 74, 86, 88, 91, 92, 94], "must": [1, 2, 3, 4, 14, 30, 31, 32, 33, 35, 36, 39, 41, 42, 45, 48, 49, 50, 51, 52, 59, 60, 62, 64, 66, 67, 68, 70, 71, 74, 85, 89, 91, 94], "randomly_distribute_n_balls_into_k_bin": 1, "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 8, 10, 30, 34, 36, 42, 45, 46, 50, 52, 58, 64, 66, 67, 68, 70, 71, 74, 81, 85, 86, 87, 91, 92, 93, 94], "ball": [1, 80], "bin": [1, 3, 52, 75, 76, 88], "ensur": [1, 2, 8, 31, 35, 45, 46, 57, 60, 62, 74, 75, 76, 79, 81, 82, 83, 88, 89, 90, 92, 93], "most": [1, 3, 4, 5, 8, 14, 30, 34, 36, 41, 49, 50, 51, 52, 55, 57, 58, 59, 60, 63, 66, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93], "least": [1, 8, 16, 27, 30, 34, 50, 51, 57, 60, 70, 76, 81, 82, 85, 88, 91], "int_arrai": [1, 45], "can": [2, 3, 4, 5, 6, 7, 11, 12, 14, 28, 30, 31, 32, 33, 34, 35, 36, 40, 41, 42, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 71, 72, 73, 74, 75, 78, 79, 82, 86, 87, 88, 89, 90, 91, 92, 93, 94], "model": [2, 3, 4, 8, 14, 16, 26, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 44, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 75, 76, 80, 84, 89, 91, 94], "For": [2, 3, 4, 5, 7, 8, 9, 14, 20, 29, 30, 31, 34, 35, 36, 39, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 68, 70, 71, 72, 74, 76, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 93, 94], "regular": [2, 3, 34, 49], "multi": [2, 3, 8, 30, 31, 34, 35, 36, 40, 41, 42, 45, 46, 51, 52, 53, 54, 59, 60, 72, 81, 83, 84], "task": [2, 4, 5, 8, 10, 12, 13, 14, 26, 28, 30, 34, 39, 41, 42, 43, 45, 50, 52, 60, 62, 72, 74, 79, 80, 81, 83, 86, 88, 91, 93, 94], "cleanlearn": [2, 3, 8, 21, 26, 31, 45, 49, 62, 63, 72, 73, 90, 92, 93], "wrap": [2, 31, 35, 49, 59, 62, 72, 75, 76, 78, 79, 83, 90, 92, 93], "instanc": [2, 3, 4, 5, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 35, 41, 49, 58, 59, 62, 67, 74, 75, 76, 78, 79, 82, 83, 92], "sklearn": [2, 3, 4, 6, 8, 16, 27, 30, 35, 41, 45, 49, 59, 62, 63, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 89, 90, 92, 93], "classifi": [2, 3, 35, 41, 45, 50, 53, 59, 60, 72, 73, 74, 78, 79, 81, 85, 86, 88, 89, 91, 92, 93, 94], "adher": [2, 35, 62], "estim": [2, 3, 4, 7, 11, 20, 30, 34, 35, 36, 39, 45, 50, 51, 52, 57, 59, 62, 64, 66, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 84, 86, 87, 88, 89, 90, 91, 94], "api": [2, 3, 12, 49, 59, 62, 73, 81, 90], "defin": [2, 3, 4, 5, 8, 12, 20, 30, 31, 32, 34, 35, 36, 60, 62, 64, 75, 76, 78, 81, 85, 88, 94], "four": [2, 8, 80, 83, 94], "clf": [2, 3, 4, 41, 62, 72, 78, 81, 83, 86, 92], "fit": [2, 3, 4, 6, 8, 16, 35, 49, 59, 62, 72, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 89, 90, 92, 93, 94], "sample_weight": [2, 35, 62, 83], "predict_proba": [2, 4, 30, 35, 41, 49, 74, 75, 76, 78, 79, 81, 83, 85, 86, 88, 92], "predict": [2, 3, 4, 6, 8, 14, 20, 21, 24, 26, 30, 34, 35, 36, 38, 39, 41, 42, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 80, 81, 83, 84, 88, 90, 91, 93, 94], "score": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 34, 36, 38, 41, 43, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 88, 90, 92, 93], "data": [2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 32, 33, 34, 35, 36, 41, 42, 45, 48, 49, 50, 51, 52, 53, 57, 59, 60, 61, 62, 67, 68, 69, 70, 71, 73, 77, 82, 84, 89, 93], "e": [2, 3, 4, 8, 10, 20, 30, 31, 34, 35, 36, 39, 41, 42, 45, 46, 50, 51, 52, 53, 59, 60, 62, 64, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93], "featur": [2, 3, 4, 6, 8, 14, 17, 21, 22, 23, 24, 26, 27, 41, 45, 59, 62, 72, 75, 76, 78, 79, 81, 83, 85, 90, 92], "element": [2, 3, 4, 30, 36, 38, 45, 50, 52, 60, 67, 68, 70, 74, 79, 81, 93, 94], "first": [2, 4, 8, 15, 22, 23, 30, 34, 41, 45, 50, 51, 55, 58, 60, 62, 74, 75, 78, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "index": [2, 8, 22, 30, 36, 44, 45, 46, 51, 60, 62, 67, 70, 71, 74, 75, 76, 78, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "should": [2, 3, 4, 5, 8, 12, 20, 22, 27, 30, 31, 34, 35, 36, 38, 39, 41, 43, 44, 45, 49, 50, 51, 54, 55, 57, 58, 59, 60, 62, 63, 67, 68, 70, 71, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "correspond": [2, 3, 4, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 38, 39, 41, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "differ": [2, 4, 5, 8, 11, 13, 19, 22, 23, 25, 30, 31, 33, 34, 35, 36, 37, 41, 45, 46, 48, 50, 55, 57, 59, 62, 74, 75, 76, 78, 79, 82, 83, 85, 88, 89, 92], "sampl": [2, 3, 4, 6, 8, 14, 18, 36, 38, 41, 52, 55, 58, 60, 62, 63, 72, 73, 80, 81, 83, 84, 86, 87, 90, 91, 93, 94], "size": [2, 8, 27, 31, 34, 35, 36, 41, 52, 57, 58, 62, 64, 66, 78, 81, 82, 83, 85, 86, 89, 91, 93], "here": [2, 4, 5, 8, 12, 34, 36, 39, 49, 50, 51, 52, 54, 55, 58, 59, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "re": [2, 4, 31, 35, 44, 50, 62, 72, 74, 75, 78, 79, 81, 90, 91, 92, 93, 94], "weight": [2, 8, 31, 32, 35, 41, 50, 57, 60, 62, 74, 75, 76, 79, 88, 93], "loss": [2, 32, 49, 60, 62, 82], "while": [2, 3, 8, 31, 34, 35, 40, 41, 45, 55, 58, 62, 72, 81, 82, 83, 85, 90], "train": [2, 3, 4, 8, 14, 16, 31, 32, 35, 41, 45, 49, 50, 55, 58, 59, 62, 63, 73, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 89, 91, 94], "support": [2, 3, 4, 10, 34, 41, 45, 46, 59, 60, 70, 72, 73, 74, 75, 76, 81, 82], "your": [2, 3, 4, 7, 8, 14, 30, 31, 33, 34, 35, 36, 41, 45, 48, 49, 50, 51, 52, 54, 59, 60, 62, 63, 64, 66, 67, 73, 74, 78, 80, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "recommend": [2, 4, 8, 11, 14, 34, 36, 50, 75, 76, 81, 82, 89, 90], "furthermor": 2, "correctli": [2, 3, 8, 30, 31, 35, 36, 39, 46, 51, 52, 57, 58, 62, 64, 79, 81, 86, 87, 90, 91, 93], "clonabl": [2, 62], "via": [2, 4, 8, 11, 14, 16, 20, 30, 32, 34, 35, 41, 45, 50, 55, 58, 59, 60, 62, 63, 66, 70, 74, 75, 76, 78, 79, 80, 81, 82, 86, 87, 88, 89, 90, 91, 92, 93, 94], "base": [2, 3, 4, 5, 8, 10, 11, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 57, 59, 60, 62, 63, 66, 68, 70, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 94], "clone": [2, 62, 86], "intern": [2, 3, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 34, 38, 39, 40, 41, 42, 43, 44, 45, 46, 54, 58, 62, 68, 73, 75, 81, 83, 85, 86, 88, 94], "multipl": [2, 3, 4, 10, 11, 30, 36, 44, 50, 51, 52, 54, 57, 58, 62, 72, 75, 76, 81, 82, 84, 86, 87, 90], "g": [2, 3, 4, 8, 10, 20, 30, 31, 35, 36, 42, 45, 52, 53, 59, 60, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93], "manual": [2, 62, 74, 81, 88, 89, 90, 92, 93, 94], "pytorch": [2, 31, 32, 35, 62, 72, 74, 81, 84, 86, 91], "call": [2, 3, 4, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 41, 45, 49, 59, 62, 74, 75, 76, 79, 81, 83, 88, 89, 91, 93, 94], "__init__": [2, 32, 62, 82], "independ": [2, 3, 8, 51, 62, 79, 89, 94], "compat": [2, 31, 34, 35, 49, 62, 63, 66, 70, 72, 81, 89, 90, 92, 93], "neural": [2, 32, 49, 59, 62, 74, 81, 82, 86, 88], "network": [2, 31, 32, 35, 49, 59, 62, 74, 79, 81, 82, 86, 88, 93], "typic": [2, 31, 35, 59, 62, 74, 76, 78, 79, 82, 88, 89, 92, 93], "initi": [2, 3, 11, 16, 31, 35, 50, 62, 79, 81, 92], "insid": [2, 35, 62, 81, 83], "There": [2, 3, 72, 83, 85, 86], "two": [2, 3, 8, 16, 22, 30, 31, 34, 35, 42, 45, 55, 57, 58, 73, 75, 76, 78, 79, 81, 82, 83, 86, 90, 91, 93, 94], "new": [2, 5, 12, 20, 31, 34, 35, 40, 44, 45, 50, 62, 74, 75, 79, 80, 81, 88, 89, 93, 94], "notion": 2, "confid": [2, 3, 8, 20, 30, 34, 36, 39, 41, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 66, 70, 72, 78, 79, 82, 83, 85, 86, 87, 89, 91, 92, 94], "packag": [2, 4, 5, 7, 8, 9, 13, 29, 33, 36, 37, 45, 48, 55, 58, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "prune": [2, 3, 36, 52, 62, 73, 87], "everyth": [2, 58, 83], "els": [2, 58, 75, 80, 81, 82, 85, 86], "mathemat": [2, 3, 8, 39], "keep": [2, 11, 12, 45, 72, 75, 80, 81, 91], "belong": [2, 3, 8, 30, 36, 38, 39, 51, 52, 53, 54, 59, 60, 64, 68, 70, 71, 76, 78, 79, 82, 83, 86, 88, 91, 94], "2": [2, 3, 4, 5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 49, 51, 52, 54, 59, 60, 62, 63, 67, 68, 70, 71, 80, 81, 89], "error": [2, 3, 4, 8, 31, 35, 36, 38, 39, 45, 51, 52, 54, 55, 57, 58, 60, 62, 64, 66, 67, 70, 73, 74, 75, 76, 78, 79, 80, 84, 92], "erron": [2, 3, 30, 36, 39, 45, 51, 52, 60, 62, 63, 64, 88, 90], "import": [2, 3, 4, 5, 6, 8, 10, 11, 12, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 34, 41, 43, 44, 50, 54, 57, 62, 63, 68, 70, 71, 72, 78, 79, 81, 86, 87, 88, 90, 91, 92, 93, 94], "linear_model": [2, 4, 30, 45, 62, 72, 74, 75, 76, 79, 81, 83, 85, 88, 93], "logisticregress": [2, 3, 4, 30, 45, 72, 74, 75, 76, 79, 81, 83, 85, 88, 93], "logreg": 2, "cl": [2, 12, 26, 62, 72, 81, 83, 90, 92, 93], "pass": [2, 3, 4, 6, 8, 10, 11, 12, 14, 21, 26, 28, 31, 34, 35, 36, 40, 41, 45, 49, 50, 52, 59, 60, 62, 68, 72, 74, 75, 76, 79, 80, 81, 83, 85, 87, 88, 90, 93], "x_train": [2, 75, 76, 83, 85, 86, 90, 92], "labels_maybe_with_error": 2, "had": [2, 3, 62, 87], "issu": [2, 3, 4, 6, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 27, 28, 30, 31, 33, 34, 35, 36, 48, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 77, 84, 85, 89, 90, 93], "pred": [2, 36, 45, 89, 90, 92, 93], "x_test": [2, 75, 76, 83, 86, 90, 92], "might": [2, 50, 62, 67, 75, 76, 81, 82, 92, 93], "case": [2, 3, 11, 30, 41, 50, 62, 74, 75, 76, 78, 80, 81, 82, 83, 88, 90, 92, 93, 94], "standard": [2, 3, 4, 26, 30, 36, 49, 51, 52, 54, 60, 62, 72, 75, 76, 78, 80, 83, 92], "adapt": [2, 31, 33, 45, 48, 62, 88], "skorch": [2, 62, 72, 81], "kera": [2, 48, 62, 72, 81], "scikera": [2, 49, 62, 81], "open": [2, 34, 80, 87, 94], "doesn": [2, 62, 72], "t": [2, 3, 8, 15, 23, 31, 32, 34, 35, 36, 41, 43, 44, 54, 59, 60, 62, 68, 70, 71, 72, 75, 76, 78, 79, 80, 82, 83, 86, 87, 94], "alreadi": [2, 4, 8, 14, 31, 34, 35, 39, 49, 50, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 92, 93], "exist": [2, 4, 8, 10, 16, 31, 34, 35, 44, 49, 55, 57, 59, 62, 72, 73, 75, 76, 79, 85, 86, 93, 94], "made": [2, 4, 14, 62, 79, 81, 82, 85, 87, 89, 90, 92, 93], "easi": [2, 39, 62, 75, 76, 80, 81, 83, 86], "inherit": [2, 5, 32, 62], "baseestim": [2, 35, 62], "yourmodel": [2, 62], "def": [2, 5, 12, 31, 35, 49, 62, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 93, 94], "self": [2, 3, 4, 5, 8, 10, 11, 12, 14, 27, 31, 32, 34, 35, 36, 41, 59, 60, 62, 75, 80, 82, 86, 91, 92, 94], "refer": [2, 8, 14, 31, 35, 51, 52, 54, 55, 57, 58, 62, 66, 67, 75, 76, 78, 79, 81, 82, 83, 89, 90], "origin": [2, 4, 8, 35, 36, 44, 45, 49, 51, 52, 55, 58, 59, 62, 63, 66, 68, 70, 75, 78, 79, 81, 82, 83, 87, 88, 90, 92, 93, 94], "total": [2, 3, 30, 34, 45, 51, 71, 81, 82, 91], "state": [2, 3, 4, 31, 32, 35, 40, 62, 83, 86, 87, 94], "art": [2, 32, 83, 86], "northcutt": [2, 3, 30, 59, 60], "et": [2, 3, 30, 32, 59, 60], "al": [2, 3, 30, 32, 59, 60], "2021": [2, 3, 30, 59, 60], "weak": [2, 58], "supervis": [2, 8, 75, 76, 81, 85], "find": [2, 4, 8, 11, 12, 14, 17, 18, 20, 21, 22, 23, 24, 27, 30, 31, 33, 34, 35, 36, 40, 44, 45, 48, 55, 58, 59, 60, 62, 64, 68, 70, 73, 75, 84, 89], "uncertainti": [2, 8, 38, 59, 62, 81, 88, 90], "It": [2, 3, 4, 5, 8, 10, 11, 14, 20, 23, 26, 28, 31, 35, 36, 39, 41, 50, 57, 58, 62, 72, 75, 76, 81, 82, 83, 86, 89], "work": [2, 3, 4, 5, 8, 10, 26, 30, 31, 34, 35, 36, 39, 44, 45, 46, 49, 50, 60, 62, 72, 73, 75, 76, 80, 88, 90, 93], "includ": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 33, 34, 35, 44, 45, 48, 50, 51, 54, 55, 59, 60, 62, 66, 67, 68, 70, 72, 73, 75, 76, 78, 79, 81, 82, 83, 86, 87, 88, 94], "deep": [2, 33, 35, 48, 49, 62, 79], "see": [2, 3, 4, 11, 30, 31, 34, 35, 36, 41, 45, 49, 51, 52, 54, 55, 58, 59, 60, 62, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "subfield": 2, "theori": [2, 83], "machin": [2, 4, 12, 14, 28, 33, 48, 62, 75, 76, 80, 85], "across": [2, 3, 4, 5, 8, 11, 20, 30, 34, 41, 51, 58, 59, 75, 76, 78, 79, 80, 81, 82, 83, 87, 89], "varieti": [2, 81, 92, 93], "like": [2, 3, 4, 5, 8, 12, 28, 30, 31, 34, 35, 36, 39, 45, 49, 50, 51, 54, 55, 57, 60, 62, 63, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "pu": [2, 45], "input": [2, 3, 4, 8, 14, 22, 30, 31, 34, 35, 39, 41, 44, 45, 46, 49, 58, 62, 72, 73, 76, 79, 80, 81, 82, 83, 85, 86, 87, 90, 91, 93, 94], "discret": [2, 36, 39, 45, 59, 60, 64, 66, 67], "vector": [2, 3, 4, 8, 14, 36, 39, 41, 42, 45, 59, 60, 72, 74, 75, 76, 78, 79, 82, 83, 86, 87, 88, 91, 93, 94], "would": [2, 3, 4, 31, 34, 35, 36, 45, 52, 62, 72, 75, 81, 82, 83, 88, 90, 93, 94], "obtain": [2, 4, 6, 8, 14, 36, 50, 52, 55, 58, 60, 63, 74, 76, 79, 81, 85, 87, 89, 91, 94], "been": [2, 30, 36, 39, 44, 45, 50, 51, 55, 57, 59, 60, 62, 74, 75, 78, 81, 83, 85, 86, 87, 88, 91, 94], "dure": [2, 8, 14, 59, 62, 74, 78, 79, 81, 83, 86, 89, 90, 92, 93, 94], "denot": [2, 3, 39, 41, 45, 52, 59, 60, 70], "tild": 2, "paper": [2, 8, 50, 59, 68, 70, 80, 83, 85, 88, 90, 94], "cv_n_fold": [2, 3, 62, 93], "5": [2, 3, 4, 6, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 35, 36, 38, 40, 41, 45, 50, 51, 54, 55, 58, 62, 63, 70, 75, 79, 80, 81, 86, 87, 88, 89, 91, 93, 94], "converge_latent_estim": [2, 3], "pulearn": [2, 45], "find_label_issues_kwarg": [2, 8, 62, 73, 81, 83], "label_quality_scores_kwarg": [2, 8], "low_memori": [2, 52, 68, 81], "clean": [2, 57, 60, 62, 63, 72, 75, 76, 80, 90, 92, 93], "even": [2, 3, 30, 34, 38, 39, 45, 62, 74, 81, 83, 85, 86, 87], "messi": [2, 62, 83], "ridden": [2, 62], "autom": [2, 62, 72, 76, 80, 81], "robust": [2, 39, 62, 76, 81], "prone": [2, 62], "out": [2, 3, 4, 8, 14, 24, 31, 35, 36, 41, 49, 52, 53, 55, 58, 59, 60, 62, 63, 71, 72, 73, 80, 81, 83, 84, 86, 87, 88, 90, 91, 93, 94], "current": [2, 3, 5, 8, 11, 12, 20, 31, 35, 36, 41, 50, 57, 62, 75, 76, 81, 85], "intend": [2, 11, 12, 13, 14, 28, 37, 50, 66, 70, 74, 75, 76, 79, 83], "A": [2, 3, 4, 5, 8, 10, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 35, 36, 39, 40, 41, 42, 43, 44, 45, 49, 50, 51, 54, 57, 58, 59, 60, 62, 64, 66, 67, 71, 73, 74, 75, 78, 79, 80, 81, 82, 83, 85, 87, 89, 92, 93, 94], "follow": [2, 3, 8, 12, 26, 30, 31, 34, 35, 41, 43, 50, 51, 55, 57, 58, 59, 62, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "experiment": [2, 31, 32, 34, 35, 52, 73, 81], "wrapper": [2, 4, 49, 74, 90, 92, 93], "around": [2, 4, 57, 75, 76, 87, 88, 94], "fasttext": [2, 48], "store": [2, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 59, 62, 78, 79, 80, 81, 91, 92, 93, 94], "along": [2, 41, 52, 70, 75, 76, 81, 82, 88], "dimens": [2, 45, 64, 67, 81, 82, 88, 91], "select": [2, 7, 8, 22, 50, 60, 82, 85, 88], "split": [2, 3, 4, 8, 10, 34, 41, 44, 45, 62, 74, 75, 76, 78, 79, 80, 82, 83, 86, 89, 92, 94], "cross": [2, 3, 8, 30, 36, 39, 40, 41, 52, 55, 58, 60, 62, 63, 73, 74, 75, 76, 78, 79, 80, 81, 83, 84, 86, 87, 90, 91, 92, 93, 94], "fold": [2, 3, 30, 36, 39, 62, 74, 78, 80, 81, 87, 91, 92], "By": [2, 4, 30, 51, 52, 62, 75, 81, 91], "need": [2, 3, 8, 30, 31, 34, 35, 36, 51, 52, 54, 59, 62, 72, 74, 75, 76, 79, 81, 83, 85, 86, 87, 91, 93], "holdout": [2, 3, 62], "comput": [2, 3, 4, 5, 6, 8, 17, 18, 20, 21, 22, 23, 24, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 45, 50, 51, 52, 54, 57, 58, 59, 60, 62, 63, 64, 66, 72, 73, 75, 76, 80, 83, 84, 86, 87, 88, 90, 91, 93], "them": [2, 3, 4, 5, 7, 8, 9, 10, 23, 29, 31, 33, 34, 35, 36, 48, 50, 59, 62, 73, 75, 76, 78, 79, 81, 82, 85, 86, 88, 90, 91, 92, 93, 94], "numer": [2, 3, 4, 8, 11, 20, 26, 41, 57, 59, 62, 67, 72, 73, 74, 75, 76, 77, 79, 82, 83, 85, 88, 90, 92, 93], "consist": [2, 3, 31, 35, 45, 50, 91, 94], "latent": [2, 3, 39], "thei": [2, 3, 4, 13, 19, 22, 25, 31, 32, 33, 35, 36, 37, 43, 45, 49, 52, 57, 60, 62, 63, 66, 70, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 88, 90, 93, 94], "relat": [2, 3, 11, 17, 18, 22, 23, 24, 27, 39, 45, 51, 62, 76, 79], "close": [2, 3, 8, 34, 39, 59, 74, 75, 76, 78, 79, 81, 82, 83, 87], "form": [2, 3, 8, 31, 32, 35, 39, 44, 45, 60, 62, 81], "equival": [2, 3, 31, 35, 39, 59, 88], "iter": [2, 3, 30, 31, 35, 36, 45, 51, 52, 62, 81, 85, 91], "enforc": [2, 31, 35, 45], "perfectli": [2, 30, 51, 83], "certain": [2, 3, 4, 31, 35, 49, 58, 62, 75, 76, 80, 88], "dict": [2, 3, 4, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 40, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 70, 75, 76, 81, 82, 94], "keyword": [2, 3, 4, 8, 14, 21, 23, 26, 31, 34, 35, 36, 38, 41, 44, 49, 50, 52, 59, 60, 62, 68, 70, 75], "filter": [2, 3, 8, 34, 44, 51, 53, 54, 56, 58, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 79, 80, 81, 82, 86, 87, 90, 91, 92, 93, 94], "find_label_issu": [2, 3, 8, 26, 34, 36, 51, 52, 54, 55, 57, 58, 62, 64, 66, 67, 68, 70, 71, 72, 73, 81, 86, 87, 90, 91, 92, 93, 94], "particularli": [2, 72, 85, 88], "filter_bi": [2, 3, 34, 36, 52, 73, 81], "frac_nois": [2, 36, 52, 68, 81], "min_examples_per_class": [2, 36, 52, 76, 81, 83], "impact": [2, 8, 75, 76, 82], "ml": [2, 4, 8, 13, 62, 72, 75, 76, 78, 79, 82, 85, 92, 93], "accuraci": [2, 32, 60, 74, 81, 82, 83, 85, 88, 90, 91, 92, 93], "n_job": [2, 34, 36, 52, 64, 66, 68, 81, 88, 91], "disabl": [2, 31, 35, 36, 88], "process": [2, 3, 5, 11, 14, 34, 36, 44, 50, 52, 58, 64, 66, 68, 74, 75, 81, 85, 89, 93], "caus": [2, 36, 41, 75, 76, 81], "rank": [2, 3, 8, 30, 34, 36, 41, 51, 52, 53, 55, 56, 58, 59, 61, 65, 67, 68, 69, 71, 72, 73, 75, 76, 80, 81, 86, 87, 88, 90, 91, 92, 93, 94], "get_label_quality_scor": [2, 34, 36, 41, 50, 52, 54, 55, 57, 60, 63, 66, 68, 70, 73, 83, 86, 87, 90, 91, 94], "adjust_pred_prob": [2, 8, 54, 59, 60, 83], "control": [2, 4, 7, 8, 14, 34, 36, 50, 58, 59, 62, 68, 70, 75, 76, 80, 81], "how": [2, 3, 4, 8, 11, 12, 14, 20, 30, 31, 32, 34, 35, 39, 45, 50, 51, 54, 55, 57, 59, 60, 62, 66, 70, 72, 75, 76, 78, 79, 80, 82, 87, 88, 89, 90, 91, 92, 93], "much": [2, 8, 30, 34, 36, 62, 81, 83, 85, 88], "output": [2, 3, 4, 8, 14, 31, 32, 35, 39, 45, 49, 50, 51, 55, 57, 58, 59, 62, 66, 67, 70, 71, 72, 73, 74, 75, 79, 80, 81, 82, 87, 88, 89, 90, 93], "print": [2, 4, 5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 45, 50, 51, 52, 57, 59, 60, 62, 64, 66, 67, 71, 73, 74, 76, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "suppress": [2, 34, 50, 57, 59, 60, 62, 64, 66, 67, 91, 94], "statement": [2, 34, 50, 57, 59, 60, 62, 64, 66, 67], "big": [2, 34, 52, 58, 62, 83], "limit": [2, 4, 14, 34, 52, 87, 91, 94], "memori": [2, 31, 34, 35, 52, 58, 64, 66, 75, 91], "label_issues_batch": [2, 33, 52, 81], "find_label_issues_batch": [2, 34, 52, 81], "pred_prob": [2, 3, 4, 6, 8, 14, 21, 22, 24, 27, 30, 34, 36, 38, 39, 40, 41, 42, 45, 46, 50, 51, 52, 54, 55, 58, 59, 60, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 92, 93], "threshold": [2, 3, 5, 8, 16, 17, 18, 20, 24, 26, 27, 34, 57, 58, 59, 60, 66, 70, 75, 87, 88, 91, 94], "inverse_noise_matrix": [2, 3, 8, 39, 45, 73, 83], "label_issu": [2, 34, 36, 52, 55, 62, 64, 73, 74, 79, 81, 82, 83, 90, 92, 93], "clf_kwarg": [2, 3, 8, 62], "clf_final_kwarg": [2, 62], "validation_func": [2, 3, 8], "correct": [2, 4, 8, 30, 34, 36, 38, 50, 51, 52, 54, 55, 57, 58, 60, 62, 63, 66, 70, 72, 74, 78, 79, 82, 83, 85, 87, 89, 90], "result": [2, 3, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 34, 35, 36, 38, 43, 45, 52, 54, 55, 58, 60, 62, 63, 64, 66, 70, 74, 75, 76, 78, 79, 81, 82, 83, 85, 90, 91, 92, 93, 94], "identifi": [2, 3, 4, 5, 8, 10, 14, 23, 28, 30, 34, 36, 52, 55, 58, 60, 62, 63, 64, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 86, 88, 90, 91, 92, 93, 94], "final": [2, 8, 62, 78, 87, 89, 90, 92], "remain": [2, 62, 73, 82, 90, 92, 93, 94], "datasetlik": [2, 45, 62], "beyond": [2, 4, 5, 7, 9, 29, 72, 91], "pd": [2, 3, 4, 5, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 40, 49, 50, 51, 62, 70, 74, 75, 76, 78, 79, 81, 83, 85, 90, 92, 93, 94], "datafram": [2, 3, 4, 5, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 34, 40, 45, 46, 49, 50, 51, 62, 67, 71, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 90, 91, 93, 94], "scipi": [2, 4, 11, 45], "spars": [2, 4, 8, 11, 14, 16, 27, 45, 46, 78], "csr_matrix": [2, 4, 11, 14, 16, 27], "torch": [2, 31, 32, 35, 74, 79, 80, 82, 88, 93], "util": [2, 4, 8, 14, 28, 31, 32, 35, 37, 50, 62, 72, 73, 74, 75, 76, 81, 82, 83, 88], "tensorflow": [2, 45, 49, 72, 74, 81], "object": [2, 4, 8, 10, 11, 14, 28, 31, 32, 34, 35, 41, 45, 46, 49, 52, 55, 56, 57, 58, 59, 62, 70, 72, 74, 76, 78, 82, 83, 84, 90, 93], "list": [2, 3, 4, 10, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 42, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 66, 67, 68, 70, 71, 73, 74, 75, 76, 80, 81, 82, 83, 86, 87, 90, 93, 94], "index_list": 2, "subset": [2, 3, 4, 14, 30, 34, 36, 45, 60, 67, 71, 74, 78, 79, 81, 82, 86, 87, 88, 89, 90, 92, 93, 94], "wa": [2, 3, 10, 12, 34, 45, 50, 51, 57, 59, 71, 74, 75, 76, 78, 79, 81, 83, 86, 87, 89, 91, 92, 93, 94], "abl": [2, 3, 8, 62, 74, 81, 83, 85, 86], "format": [2, 3, 4, 8, 10, 31, 34, 35, 36, 39, 40, 41, 42, 45, 46, 49, 50, 51, 52, 55, 58, 59, 60, 62, 64, 66, 67, 70, 71, 75, 76, 78, 80, 82, 85, 90, 91, 92, 94], "make": [2, 3, 16, 31, 34, 35, 41, 49, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "sure": [2, 34, 36, 41, 74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 92, 93], "shuffl": [2, 8, 45, 74, 79, 82, 86, 88], "ha": [2, 3, 4, 8, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 31, 35, 39, 41, 44, 45, 50, 55, 57, 62, 68, 70, 71, 72, 74, 75, 76, 78, 79, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "batch": [2, 34, 45, 49, 50, 64, 66, 81, 82, 88], "order": [2, 4, 8, 30, 31, 35, 36, 39, 40, 41, 45, 50, 51, 52, 55, 58, 59, 60, 64, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 87, 90, 91, 93, 94], "destroi": [2, 45], "oper": [2, 31, 34, 35, 45, 49, 60, 72, 79, 88, 92, 93], "eg": [2, 8, 45, 55, 58, 75, 76, 81], "repeat": [2, 45, 50, 85, 88], "appli": [2, 31, 35, 36, 41, 42, 44, 45, 54, 59, 68, 74, 75, 76, 78, 81, 82, 85, 86, 88, 89, 90, 91, 92, 93], "array_lik": [2, 3, 30, 36, 45, 52, 59, 63], "some": [2, 3, 4, 8, 12, 20, 30, 31, 33, 35, 36, 39, 44, 45, 48, 50, 51, 52, 54, 55, 58, 59, 60, 62, 64, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "seri": [2, 3, 34, 45, 46, 62, 70, 81], "row": [2, 3, 4, 8, 11, 23, 30, 34, 36, 38, 39, 45, 50, 51, 52, 54, 59, 60, 62, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 88, 92, 94], "rather": [2, 3, 22, 30, 45, 49, 50, 57, 66, 70, 85, 89, 91, 93, 94], "leav": [2, 36], "per": [2, 3, 11, 30, 34, 36, 41, 44, 50, 51, 52, 54, 57, 58, 60, 63, 64, 66, 70, 76, 81, 87, 94], "determin": [2, 3, 8, 14, 20, 22, 26, 30, 34, 36, 41, 45, 50, 52, 55, 57, 60, 66, 70, 75, 81, 85, 88, 90], "cutoff": [2, 3, 88], "consid": [2, 3, 4, 8, 11, 14, 21, 22, 24, 27, 30, 31, 35, 36, 45, 50, 57, 59, 60, 63, 66, 70, 74, 76, 78, 79, 81, 82, 83, 87, 88, 89, 90, 91, 92, 93], "section": [2, 3, 5, 8, 73, 78, 82], "3": [2, 3, 4, 5, 8, 30, 31, 35, 36, 39, 40, 41, 42, 43, 44, 45, 49, 52, 59, 60, 62, 63, 68, 70, 80, 81, 89], "equat": [2, 3, 39], "advanc": [2, 3, 4, 7, 8, 14, 57, 59, 70, 73, 76, 77, 83], "user": [2, 3, 4, 8, 12, 14, 23, 28, 31, 35, 36, 57, 59, 60, 62, 66, 70, 83], "specifi": [2, 3, 4, 6, 8, 11, 12, 14, 16, 27, 28, 31, 34, 35, 36, 41, 44, 50, 51, 52, 55, 57, 59, 60, 62, 63, 71, 73, 74, 76, 79, 82, 85, 87, 90, 93], "automat": [2, 3, 4, 22, 30, 72, 78, 79, 80, 81, 82, 85, 87, 90, 91, 92, 93, 94], "greater": [2, 3, 4, 7, 8, 24, 34, 45, 57, 76, 80, 81, 94], "count": [2, 20, 22, 30, 34, 36, 39, 45, 51, 52, 58, 73, 81, 82], "observ": [2, 3, 39, 74, 75, 76, 85, 88, 90], "mislabel": [2, 8, 30, 34, 36, 39, 50, 51, 52, 55, 57, 60, 66, 68, 70, 72, 74, 78, 79, 81, 82, 83, 86, 87, 90, 92, 93], "one": [2, 3, 4, 8, 22, 30, 31, 34, 35, 36, 41, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 82, 85, 88, 89, 90, 92, 93, 94], "get_label_issu": [2, 34, 62, 83, 90, 92, 93], "either": [2, 3, 5, 8, 31, 34, 35, 36, 50, 52, 57, 59, 60, 64, 66, 76, 86, 87], "boolean": [2, 5, 8, 20, 34, 36, 44, 50, 52, 55, 60, 62, 64, 66, 67, 72, 74, 76, 79, 81, 82, 87, 90, 91, 93], "label_issues_mask": [2, 36, 60, 62, 73], "indic": [2, 3, 4, 5, 8, 11, 20, 30, 34, 35, 36, 38, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 68, 70, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "its": [2, 4, 7, 8, 14, 31, 34, 35, 36, 43, 44, 52, 55, 58, 59, 60, 62, 64, 68, 70, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 93, 94], "return_indices_ranked_bi": [2, 34, 36, 52, 68, 73, 81, 83, 86, 92, 93], "significantli": [2, 82, 83, 85, 89], "reduc": [2, 34, 36, 45, 74, 81], "time": [2, 8, 31, 34, 35, 45, 50, 73, 75, 80, 81, 82, 83, 87, 88, 90, 91, 92, 93, 94], "take": [2, 4, 8, 30, 31, 35, 40, 41, 45, 49, 60, 78, 82, 85, 92, 94], "run": [2, 4, 5, 7, 9, 12, 14, 22, 23, 29, 31, 34, 35, 62, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "skip": [2, 8, 31, 35, 62, 74, 81, 86, 94], "slow": [2, 3], "step": [2, 5, 22, 41, 58, 81, 82, 83, 85, 89], "caution": [2, 4, 81], "previous": [2, 4, 11, 45, 59, 62, 73, 74, 75, 78, 79, 85, 89, 92], "assign": [2, 5, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 40, 41, 45, 62, 75, 78, 81, 82, 90, 91, 92, 94], "individu": [2, 8, 11, 22, 31, 35, 50, 54, 57, 60, 62, 68, 70, 73, 76, 78, 81, 85, 86, 87, 92, 94], "still": [2, 34, 35, 45, 59, 81, 82, 88, 92], "extra": [2, 31, 35, 45, 49, 50, 51, 62, 79, 81, 82, 85, 88], "receiv": [2, 8, 31, 35, 51, 54, 55, 62, 64, 68, 76, 87], "overwritten": [2, 62], "callabl": [2, 3, 41, 44, 49, 54, 81], "x_val": 2, "y_val": 2, "map": [2, 3, 10, 34, 35, 40, 44, 45, 58, 60, 62, 67, 74, 75, 76, 81, 82, 83, 86, 94], "appropri": [2, 8, 14, 52, 60, 75, 78, 86, 87], "earli": [2, 82], "stop": [2, 82], "x_valid": 2, "y_valid": 2, "could": [2, 8, 20, 30, 45, 59, 75, 78, 82, 86, 90, 92, 94], "f": [2, 5, 74, 75, 78, 79, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93], "ignor": [2, 31, 35, 44, 49, 62, 67, 71, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "allow": [2, 30, 31, 34, 35, 38, 45, 50, 58, 59, 62, 64, 66, 74, 81, 82, 89, 91, 93], "access": [2, 8, 11, 31, 35, 62, 76, 82, 86], "hyperparamet": [2, 54, 59, 82], "purpos": [2, 75, 76, 81, 86, 90], "want": [2, 4, 8, 30, 34, 46, 50, 52, 62, 75, 79, 80, 82, 85, 87, 88, 89, 91, 93, 94], "explicitli": [2, 6, 8, 35, 62], "yourself": [2, 4, 34, 76], "altern": [2, 5, 8, 41, 45, 49, 50, 60, 73, 74, 78, 79, 81, 82, 83, 85, 86, 88, 90, 93], "same": [2, 3, 4, 5, 8, 10, 12, 14, 22, 26, 31, 34, 35, 36, 45, 49, 50, 52, 59, 60, 62, 66, 67, 70, 71, 72, 75, 76, 78, 79, 81, 82, 87, 88, 89, 90, 91, 92, 93], "effect": [2, 8, 23, 31, 35, 50, 59, 62, 78, 79, 81, 82, 88], "offer": [2, 4, 74, 75, 76, 79, 81, 83, 86, 93], "after": [2, 3, 4, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 45, 50, 62, 75, 79, 81, 82, 83, 85, 87, 88, 89, 90, 91, 93], "attribut": [2, 4, 5, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 41, 59, 62, 75, 92], "label_issues_df": [2, 62, 82], "similar": [2, 8, 30, 31, 35, 45, 50, 54, 55, 57, 59, 62, 66, 70, 75, 76, 78, 79, 81, 82, 83, 87, 88, 91], "document": [2, 3, 4, 8, 12, 14, 30, 31, 34, 35, 36, 41, 44, 49, 51, 52, 54, 57, 58, 59, 62, 66, 67, 68, 70, 73, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "descript": [2, 4, 5, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 45, 55, 62, 75, 76], "were": [2, 3, 4, 30, 35, 51, 57, 70, 74, 78, 81, 83, 85, 87, 89, 91, 92], "present": [2, 3, 4, 8, 10, 11, 18, 30, 45, 59, 67, 72, 78, 81, 82, 88], "actual": [2, 3, 4, 30, 50, 51, 60, 76, 81, 83, 94], "num_class": [2, 30, 34, 45, 49, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 92, 93], "uniqu": [2, 27, 45, 67, 75, 81, 86, 88], "given_label": [2, 4, 26, 30, 39, 62, 67, 71, 74, 75, 76, 78, 79, 82, 83, 90, 91, 93, 94], "normal": [2, 3, 16, 22, 27, 36, 38, 41, 43, 44, 45, 60, 81, 83, 88], "trick": [2, 81], "distribut": [2, 3, 4, 8, 22, 24, 30, 35, 36, 40, 43, 50, 58, 59, 60, 72, 75, 76, 78, 79, 82, 88], "account": [2, 30, 50, 54, 59, 60, 79, 81, 83, 85, 86, 88, 90, 93], "word": [2, 3, 44, 70, 71, 81], "remov": [2, 8, 27, 30, 31, 35, 36, 62, 72, 79, 80, 81, 82, 88, 90, 92, 93], "so": [2, 3, 4, 5, 8, 12, 22, 30, 31, 34, 35, 36, 45, 50, 51, 57, 60, 62, 66, 70, 74, 75, 76, 79, 82, 83, 88, 91], "proportion": [2, 8, 36], "just": [2, 3, 4, 8, 11, 30, 32, 34, 45, 49, 60, 62, 64, 72, 73, 74, 76, 78, 79, 81, 82, 83, 86, 87, 88, 89, 91, 92, 93], "procedur": 2, "get": [2, 3, 4, 6, 11, 27, 31, 32, 35, 36, 41, 44, 45, 50, 52, 54, 59, 60, 62, 63, 64, 72, 74, 79, 80, 81, 82, 83, 88, 89, 90, 92, 93], "detect": [2, 4, 5, 7, 11, 12, 14, 16, 20, 24, 43, 53, 55, 56, 57, 58, 59, 60, 61, 62, 65, 69, 72, 75, 77, 82, 84, 86, 90, 91, 92, 93, 94], "arg": [2, 10, 20, 23, 27, 31, 32, 35, 41, 45, 60, 62], "kwarg": [2, 5, 8, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 41, 49, 62, 64, 66, 68, 81], "test": [2, 8, 22, 35, 41, 49, 62, 72, 75, 76, 78, 79, 82, 89, 90, 92, 93, 94], "expect": [2, 3, 31, 35, 36, 41, 50, 59, 60, 62, 81, 83, 85, 86, 87, 90, 92, 93, 94], "class_predict": 2, "evalu": [2, 8, 31, 32, 34, 35, 58, 62, 74, 75, 76, 81, 82, 83, 85, 89, 90, 91, 92, 93], "simpli": [2, 30, 60, 75, 76, 78, 79, 81, 83, 90, 91, 93, 94], "quantifi": [2, 4, 5, 8, 11, 36, 54, 59, 62, 72, 76, 78, 79, 82, 83, 87], "save_spac": [2, 8, 62], "potenti": [2, 8, 30, 36, 44, 52, 55, 58, 60, 62, 64, 66, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 91, 92, 94], "cach": [2, 79, 88, 93], "panda": [2, 4, 5, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 45, 46, 49, 50, 51, 73, 74, 75, 76, 78, 79, 80, 81, 83, 85, 90, 91, 92, 93], "unlik": [2, 8, 36, 38, 41, 49, 51, 52, 54, 70, 75, 85, 86, 88, 90], "both": [2, 4, 8, 14, 22, 30, 31, 35, 36, 45, 50, 52, 60, 64, 66, 71, 72, 75, 81, 82, 83, 85, 94], "mask": [2, 34, 36, 44, 45, 52, 55, 60, 62, 64, 66, 67, 72, 80, 81, 85, 87, 91, 94], "prefer": [2, 60, 68], "plan": 2, "subsequ": [2, 3, 31, 35, 79, 81, 83, 87, 93], "invok": [2, 31, 35, 83, 89], "scratch": [2, 62], "To": [2, 4, 5, 7, 8, 9, 11, 14, 22, 29, 31, 34, 35, 36, 49, 50, 52, 54, 58, 59, 60, 62, 63, 64, 66, 72, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "share": [2, 60, 62], "mostli": [2, 45, 57, 62], "longer": [2, 40, 44, 62, 73, 79, 81, 87, 93], "info": [2, 4, 5, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 62, 70, 75, 76, 80, 81, 94], "about": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 32, 34, 38, 50, 51, 54, 58, 62, 67, 70, 74, 75, 78, 79, 80, 81, 82, 83, 85, 88], "docstr": [2, 30, 31, 35, 45, 62, 80, 83], "unless": [2, 31, 35, 62, 81], "our": [2, 3, 8, 49, 50, 60, 62, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "is_label_issu": [2, 26, 62, 74, 75, 76, 78, 79, 82, 83, 90, 93], "entir": [2, 8, 22, 34, 36, 39, 51, 52, 57, 60, 62, 64, 66, 67, 72, 75, 76, 79, 81, 82, 83, 87, 88, 89, 91, 94], "accur": [2, 3, 4, 8, 14, 30, 34, 36, 50, 51, 52, 55, 58, 60, 62, 63, 64, 66, 67, 73, 76, 78, 79, 81, 82, 85, 90], "label_qu": [2, 50, 62, 83, 85, 90, 93], "measur": [2, 30, 50, 51, 62, 72, 80, 81, 83, 85, 86, 91, 92, 94], "qualiti": [2, 3, 4, 5, 8, 11, 26, 27, 30, 34, 36, 38, 41, 50, 51, 52, 54, 55, 57, 60, 62, 63, 66, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 84, 90, 92, 93], "lower": [2, 4, 5, 8, 11, 24, 34, 41, 43, 50, 51, 54, 57, 58, 60, 62, 63, 66, 70, 74, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 93, 94], "eas": 2, "comparison": [2, 31, 35, 58, 83, 85, 90], "against": [2, 31, 35, 75, 78, 81, 85, 86], "predicted_label": [2, 4, 26, 62, 67, 71, 74, 75, 76, 78, 79, 82, 83, 90, 91, 93], "ad": [2, 31, 35, 76, 85, 90], "precis": [2, 52, 55, 58, 81, 83, 91, 94], "definit": [2, 5, 62, 78, 92], "accessor": [2, 62], "describ": [2, 8, 16, 50, 59, 60, 62, 68, 70, 83, 85, 86, 87, 89, 94], "precomput": [2, 4, 39, 62, 80], "clear": [2, 62, 79, 90, 93], "save": [2, 4, 14, 31, 34, 35, 58, 62, 81, 87, 91, 94], "space": [2, 8, 59, 62, 78, 80, 82], "place": [2, 31, 35, 45, 62, 85, 92], "larg": [2, 34, 62, 78, 79, 81, 82, 88, 91, 94], "deploi": [2, 62, 78, 79, 81, 82], "care": [2, 8, 31, 35, 62, 79, 81, 83], "avail": [2, 4, 5, 10, 12, 28, 35, 62, 81, 83, 85, 87, 90], "cannot": [2, 4, 10, 12, 45, 89, 94], "anymor": 2, "classmethod": [2, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 35, 41, 62], "__init_subclass__": [2, 35, 62], "set_": [2, 35, 62], "_request": [2, 35, 62], "pep": [2, 35, 62], "487": [2, 35, 62], "look": [2, 4, 5, 14, 31, 35, 45, 62, 67, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 91, 92, 94], "inform": [2, 4, 5, 8, 11, 14, 28, 31, 35, 45, 50, 51, 55, 58, 62, 67, 70, 71, 72, 74, 75, 78, 79, 83, 85, 88, 91, 94], "__metadata_request__": [2, 35, 62], "infer": [2, 35, 45, 62, 67, 71, 82, 85, 86, 90, 92, 93], "signatur": [2, 31, 35, 62], "accept": [2, 31, 35, 60, 62, 75, 76], "metadata": [2, 35, 62, 78, 79, 82, 94], "through": [2, 4, 5, 35, 62, 74, 76, 79, 80, 81, 85, 88, 90, 93], "develop": [2, 7, 35, 62, 81, 83, 94], "request": [2, 35, 62, 76, 79, 80, 86, 92, 93, 94], "those": [2, 3, 8, 34, 35, 36, 49, 50, 52, 58, 62, 66, 70, 71, 72, 74, 81, 82, 87, 91], "http": [2, 4, 5, 7, 8, 9, 16, 29, 31, 32, 34, 35, 38, 45, 59, 62, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "www": [2, 35, 62, 88], "org": [2, 16, 31, 32, 35, 45, 59, 62, 81, 83, 94], "dev": [2, 35, 62], "0487": [2, 35, 62], "get_metadata_rout": [2, 35, 62], "rout": [2, 35, 62], "pleas": [2, 31, 35, 49, 62, 72, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 90, 93, 94], "guid": [2, 5, 35, 62, 73, 82], "mechan": [2, 31, 35, 62], "metadatarequest": [2, 35, 62], "encapsul": [2, 14, 35, 57, 62], "get_param": [2, 35, 49, 62], "subobject": [2, 35, 62], "param": [2, 8, 31, 35, 49, 59, 62, 81], "name": [2, 4, 5, 8, 10, 11, 30, 31, 35, 40, 41, 45, 49, 50, 51, 58, 62, 67, 71, 74, 76, 79, 80, 81, 82, 83, 86, 91, 93, 94], "set_fit_request": [2, 35, 62], "union": [2, 3, 4, 10, 34, 35, 41, 45, 46, 52, 58, 62, 66, 70, 81], "str": [2, 3, 4, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 39, 41, 44, 45, 49, 50, 51, 55, 57, 58, 60, 62, 67, 71, 74, 75, 81, 85, 86, 94], "unchang": [2, 31, 35, 62, 94], "relev": [2, 14, 22, 35, 62, 82], "enable_metadata_rout": [2, 35, 62], "set_config": [2, 35, 62], "meta": [2, 35, 62], "rais": [2, 4, 10, 11, 31, 35, 38, 41, 62, 81], "alia": [2, 31, 35, 62], "metadata_rout": [2, 35, 62], "retain": [2, 35, 45, 62], "chang": [2, 31, 34, 35, 38, 62, 70, 74, 75, 79, 81, 87, 88, 93, 94], "version": [2, 4, 5, 7, 8, 9, 13, 19, 25, 29, 31, 33, 35, 37, 38, 45, 48, 49, 60, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "sub": [2, 35, 57, 62], "pipelin": [2, 35, 62], "otherwis": [2, 8, 30, 31, 34, 35, 36, 42, 44, 45, 52, 59, 62, 64, 66, 67, 71, 79, 81, 93], "updat": [2, 11, 31, 34, 35, 62, 73, 75, 82], "set_param": [2, 35, 49, 62], "simpl": [2, 31, 35, 36, 50, 60, 62, 75, 76, 78, 79, 82, 85, 88, 90, 92, 93], "well": [2, 3, 8, 31, 35, 38, 39, 50, 52, 58, 60, 62, 67, 70, 71, 73, 75, 76, 78, 79, 81, 82, 83, 85, 87, 88], "nest": [2, 31, 35, 62, 68, 70, 71, 94], "latter": [2, 31, 35, 62, 88], "compon": [2, 35, 62], "__": [2, 35, 62], "set_score_request": [2, 62], "structur": [3, 59, 78, 92], "unobserv": 3, "less": [3, 4, 8, 27, 34, 41, 50, 59, 60, 64, 66, 70, 76, 78, 80, 81, 82, 83, 87, 94], "channel": [3, 74, 83], "character": 3, "flip": 3, "nm": 3, "invers": [3, 8, 30, 39, 45, 51, 76, 80, 93], "inv": 3, "confident_joint": [3, 20, 30, 36, 45, 51, 52, 73, 81, 83], "un": 3, "under": [3, 8, 31, 35, 51, 58, 59, 76, 78, 79, 82, 83, 88], "joint": [3, 30, 36, 39, 45, 51, 52, 80], "num_label_issu": [3, 34, 36, 52, 67, 71, 73], "estimation_method": [3, 34], "off_diagon": 3, "multi_label": [3, 30, 36, 45, 46, 52, 86], "don": [3, 72, 76, 78, 79, 82, 83, 87], "statis": 3, "compute_confident_joint": [3, 30, 36, 45, 52, 83], "off": [3, 36, 45, 57, 82, 83, 87, 88], "j": [3, 4, 30, 31, 35, 36, 52, 55, 58, 59, 68, 70, 71, 75, 76, 83, 91, 94], "confident_learn": [3, 36, 52, 83], "off_diagonal_calibr": 3, "calibr": [3, 36, 45, 50, 85], "cj": [3, 39, 45], "axi": [3, 27, 39, 41, 43, 64, 67, 74, 75, 76, 81, 82, 83, 85, 86, 88, 90, 91], "bincount": [3, 75, 76, 83, 85, 86], "alwai": [3, 8, 31, 35, 45, 74, 83, 90, 92, 93], "estimate_issu": 3, "over": [3, 8, 31, 34, 35, 57, 58, 64, 66, 76, 78, 80, 81, 82, 83, 88, 90, 92], "As": [3, 5, 72, 75, 76, 79, 83, 90, 94], "add": [3, 4, 5, 11, 31, 35, 49, 58, 74, 75, 76, 79, 81, 82, 83, 86, 93], "approach": [3, 30, 34, 36, 78, 83, 86, 88, 90, 92], "custom": [3, 5, 8, 9, 26, 31, 34, 35, 41, 44, 60, 76, 79, 83, 93], "know": [3, 75, 76, 78, 79, 81, 82, 83, 85], "cut": [3, 57, 72, 83], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 88, 94], "underestim": 3, "few": [3, 58, 72, 76, 81, 85, 86, 87, 88, 94], "4": [3, 4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 40, 41, 44, 54, 55, 57, 58, 60, 63, 70, 80, 81, 86, 91, 94], "detail": [3, 4, 8, 12, 14, 30, 31, 35, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 66, 67, 68, 72, 73, 74, 86, 88, 94], "num_issu": [3, 5, 34, 74, 75, 76, 78, 79, 82, 83], "calibrate_confident_joint": 3, "up": [3, 8, 15, 22, 23, 26, 36, 41, 50, 80, 81, 87, 90, 93, 94], "p_": [3, 30, 36], "pair": [3, 4, 8, 30, 36, 83], "v": [3, 8, 34, 51, 52, 54, 60, 75, 76, 86, 88, 89], "rest": [3, 4, 5, 7, 8, 9, 29, 51, 52, 54, 62, 75, 76, 78, 79, 81, 82, 83, 85, 90, 92, 93], "fashion": [3, 4, 64, 92], "2x2": 3, "incorrectli": [3, 30, 51, 52, 55, 78, 94], "calibrated_cj": 3, "c": [3, 8, 44, 52, 60, 72, 74, 75, 76, 78, 79, 81, 83, 86, 88, 89, 90, 92], "whose": [3, 4, 8, 24, 31, 35, 39, 44, 50, 54, 57, 63, 66, 70, 71, 74, 75, 76, 78, 79, 81, 82, 83, 86, 87, 88, 91, 94], "truli": [3, 88, 91], "estimate_joint": [3, 30, 83], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 52, 58, 83, 87, 89, 91, 94], "return_indices_of_off_diagon": 3, "frequenc": [3, 22, 50, 51, 58, 67, 88], "done": [3, 8, 62, 75, 81, 83, 86, 88, 89], "overfit": [3, 8, 55, 58, 74, 75, 76, 78, 79, 82, 89, 92], "classifict": 3, "singl": [3, 4, 22, 30, 31, 35, 41, 42, 45, 50, 51, 57, 58, 59, 60, 70, 74, 75, 81, 83, 86, 87, 92], "baselin": [3, 31, 36, 88, 90, 93], "proxi": 3, "tupl": [3, 27, 31, 35, 39, 40, 42, 44, 45, 50, 52, 58, 66, 68, 70, 71, 74, 94], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 8, 34, 39, 50, 64, 66, 72, 81, 82, 91, 93], "practic": [3, 76, 82, 83, 88, 90, 92, 93], "complet": [3, 74, 75, 76, 78, 79, 81, 82, 83, 87], "gist": 3, "cj_ish": 3, "guess": [3, 39, 83, 85], "8": [3, 4, 5, 6, 40, 41, 42, 44, 54, 68, 70, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "parallel": [3, 36, 58, 68, 80], "again": [3, 49, 81, 88, 92], "simplifi": [3, 12], "understand": [3, 7, 30, 51, 58, 76, 83, 90, 91, 94], "100": [3, 31, 35, 60, 75, 76, 78, 80, 81, 82, 83, 86, 91, 92, 93, 94], "optim": [3, 31, 32, 35, 49, 82, 85], "speed": [3, 36, 80, 81, 90, 93], "dtype": [3, 21, 22, 27, 31, 35, 44, 45, 54, 70, 74, 87], "enumer": [3, 31, 35, 74, 75, 76, 82, 94], "s_label": 3, "confident_bin": 3, "6": [3, 4, 35, 41, 45, 70, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "num_confident_bin": 3, "argmax": [3, 36, 60, 64, 67, 74, 81, 83, 88, 91], "elif": 3, "estimate_lat": 3, "py_method": [3, 39], "cnt": [3, 39], "1d": [3, 4, 14, 34, 36, 41, 42, 45, 46, 54, 63, 74, 92], "eqn": [3, 39], "margin": [3, 36, 39, 41, 60], "marginal_p": [3, 39], "shorthand": [3, 11], "proport": [3, 8, 30, 51, 83, 89], "poorli": [3, 39, 92], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 83], "variabl": [3, 5, 12, 23, 45, 62, 63, 74, 75, 78, 83, 86, 90], "exact": [3, 39, 75, 76, 78, 82, 92], "within": [3, 4, 8, 13, 31, 32, 35, 37, 52, 57, 66, 68, 70, 75, 76, 81, 82, 87, 91], "percent": 3, "often": [3, 30, 39, 51, 81, 83, 89, 91], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 8, 45, 46, 58, 74, 75, 78, 79, 81, 82, 87, 88, 93], "wai": [3, 4, 49, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 89, 92, 93], "pro": 3, "con": 3, "pred_proba": [3, 89], "combin": [3, 30, 75, 80, 81, 82, 83, 89, 90], "becaus": [3, 39, 45, 57, 79, 81, 83, 85, 87], "littl": [3, 34, 80, 87, 94], "uniform": [3, 60, 80, 81, 83], "20": [3, 5, 71, 74, 76, 79, 80, 81, 82, 83, 91, 94], "Such": [3, 82, 88], "bound": [3, 21, 31, 35, 55, 57, 58, 87], "reason": [3, 20, 31, 35], "comment": [3, 44, 94], "end": [3, 4, 31, 35, 58, 82, 91, 94], "file": [3, 4, 10, 33, 34, 48, 58, 74, 75, 78, 79, 80, 81, 87, 88, 91, 92, 94], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 83], "handl": [3, 4, 5, 8, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 73, 75, 76, 78, 79, 82, 83, 91, 92, 94], "five": [3, 55, 58, 83, 87], "estimate_cv_predicted_prob": [3, 83], "estimate_noise_matric": 3, "get_confident_threshold": [3, 34], "amongst": [3, 8], "confident_threshold": [3, 8, 20, 34, 59], "unifi": 4, "audit": [4, 7, 10, 11, 14, 74, 77, 78, 79, 81, 82, 83, 87], "kind": [4, 5, 74, 75, 78, 79, 80, 82, 83], "addit": [4, 5, 7, 8, 9, 11, 28, 29, 31, 35, 41, 46, 50, 58, 68, 74, 75, 78, 79, 82, 83, 85, 88, 89, 92, 93], "depend": [4, 5, 7, 8, 9, 10, 11, 29, 33, 36, 38, 45, 48, 52, 59, 62, 63, 72], "instal": [4, 5, 7, 8, 9, 29, 31, 33, 34, 35, 36, 48, 49, 64, 66], "pip": [4, 5, 7, 9, 29, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "development": [4, 5, 7, 9, 29], "git": [4, 5, 7, 9, 29, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "github": [4, 5, 7, 9, 29, 31, 32, 45, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "com": [4, 5, 7, 9, 29, 31, 32, 34, 38, 45, 59, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "egg": [4, 5, 7, 9, 29, 72, 80], "label_nam": [4, 5, 6, 8, 10, 16, 27, 72, 74, 75, 76, 78, 79, 81, 82, 83], "image_kei": [4, 82], "interfac": [4, 72, 81, 83], "librari": [4, 8, 35, 55, 58, 59, 72, 75, 79, 80, 81, 93], "goal": 4, "track": [4, 11, 12, 72, 75, 80, 81, 83], "intermedi": [4, 7, 76], "statist": [4, 8, 11, 20, 22, 30, 50, 51, 58, 76, 78, 79, 82, 83], "convert": [4, 10, 31, 35, 42, 43, 46, 50, 57, 66, 70, 73, 74, 79, 80, 81, 82, 85, 86, 87, 93], "hug": [4, 10, 82], "face": [4, 10, 14, 80, 82, 86], "kei": [4, 5, 8, 10, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 35, 41, 50, 51, 57, 59, 75, 76, 79, 81, 82, 83, 85, 87], "string": [4, 8, 10, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 35, 45, 50, 51, 63, 67, 70, 71, 78, 79, 81, 85, 86, 93, 94], "dictionari": [4, 5, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 35, 40, 45, 50, 51, 54, 55, 57, 58, 75, 76, 78, 79, 83, 85, 86, 87], "path": [4, 10, 31, 34, 35, 58, 74, 75, 81, 87], "local": [4, 10, 31, 32, 35, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "text": [4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 41, 59, 68, 70, 71, 72, 75, 76, 77, 80, 81, 83, 84, 85, 88], "txt": [4, 10, 94], "csv": [4, 10, 78, 79, 90, 92, 93], "json": [4, 10], "hub": [4, 10, 88], "regress": [4, 5, 10, 12, 14, 19, 26, 28, 75, 76, 79, 84, 85, 88, 93], "imag": [4, 7, 30, 35, 55, 57, 58, 59, 64, 66, 67, 72, 75, 76, 80, 81, 84, 85, 86, 87, 89, 91], "point": [4, 5, 8, 16, 22, 31, 35, 75, 76, 78, 79, 81, 82, 83, 85], "field": [4, 8, 31, 35], "themselv": [4, 90, 92, 93], "cleanvis": [4, 8], "level": [4, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 44, 68, 70, 76, 82, 84, 91], "load_dataset": [4, 10, 82], "glue": 4, "sst2": 4, "properti": [4, 10, 11], "has_label": [4, 10], "class_nam": [4, 10, 18, 30, 51, 58, 67, 71, 72, 80, 83, 87, 91, 94], "empti": [4, 10, 39, 50, 76, 81, 86], "find_issu": [4, 5, 6, 8, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 72, 74, 75, 76, 78, 79, 81, 82, 83], "knn_graph": [4, 8, 14, 16, 17, 22, 24, 27, 78], "issue_typ": [4, 5, 6, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 74, 75, 76, 78, 79, 81, 82, 83], "sort": [4, 14, 34, 36, 41, 50, 52, 55, 57, 58, 60, 66, 68, 70, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 90, 91, 92, 93, 94], "common": [4, 11, 14, 76, 77, 80, 81, 83, 86, 87, 91], "real": [4, 14, 72, 75, 76, 81, 83, 85, 86, 90, 91], "world": [4, 14, 72, 75, 76, 81, 83, 85, 90, 91], "interact": [4, 14, 79, 81], "embed": [4, 8, 14, 59, 72, 74, 75, 76, 78, 79, 83, 93], "thereof": [4, 14], "insight": [4, 14, 58, 85], "act": [4, 8, 57, 75], "issuefind": [4, 14, 28], "logic": [4, 12, 34, 36, 64, 66, 91], "best": [4, 14, 40, 50, 60, 75, 76, 78, 79, 81, 82, 85, 86, 88, 90, 92, 93, 94], "2d": [4, 14, 34, 41, 42, 44, 45, 50, 74, 86, 92], "num_exampl": [4, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 51, 74, 75, 76, 78, 79, 82, 83], "represent": [4, 8, 14, 31, 35, 42, 52, 72, 74, 75, 76, 79, 81, 82, 83, 88, 93], "num_featur": [4, 14, 31, 35, 49], "distanc": [4, 8, 14, 16, 22, 24, 27, 43, 57, 59, 78, 88], "nearest": [4, 8, 14, 21, 22, 24, 43, 59, 76, 79, 88], "neighbor": [4, 8, 14, 16, 21, 22, 24, 43, 59, 75, 76, 78, 79, 81, 82, 88], "graph": [4, 8, 11, 14, 16, 22, 27], "squar": [4, 45, 62, 80, 90], "csr": 4, "evenli": 4, "omit": [4, 57, 58, 82, 87], "itself": [4, 31, 35, 87], "three": [4, 8, 30, 50, 51, 62, 67, 74, 75, 76, 78, 80, 83, 85, 89, 90, 91, 92, 94], "indptr": 4, "wise": 4, "start": [4, 5, 8, 31, 32, 35, 72, 78, 86, 94], "th": [4, 40, 44, 45, 50, 52, 55, 57, 58, 59, 68, 70, 71, 79, 86, 87, 94], "ascend": [4, 30, 51, 82, 83], "segment": [4, 64, 66, 67, 84], "reflect": [4, 78, 79, 85, 87, 88, 90, 92, 93], "maintain": 4, "posit": [4, 31, 35, 43, 45, 58, 80, 88], "nearestneighbor": [4, 8, 16, 59, 78, 88], "kneighbors_graph": [4, 16, 78], "illustr": 4, "todens": 4, "second": [4, 41, 45, 58, 60, 75, 81, 83, 94], "duplic": [4, 7, 19, 20, 31, 35, 72, 75, 83], "explicit": 4, "precend": 4, "construct": [4, 5, 8, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 35, 41, 49], "neither": [4, 8, 12, 87], "nor": [4, 8, 12], "collect": [4, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 50, 81, 85, 94], "unspecifi": [4, 14, 36, 52], "interest": [4, 14, 20, 67, 71, 79, 83, 91, 92, 93, 94], "constructor": [4, 8, 14, 21, 26], "issuemanag": [4, 7, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28], "respons": [4, 14, 20, 62, 63, 80, 90, 94], "random_st": [4, 74, 75, 76, 82, 83, 86, 88, 92], "lab": [4, 6, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 34, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86], "comprehens": [4, 72, 82], "nbr": 4, "n_neighbor": [4, 8, 16, 59], "metric": [4, 8, 17, 22, 27, 45, 49, 58, 59, 74, 78, 79, 82, 83, 90, 92, 93], "euclidean": [4, 8, 57, 59, 78], "mode": [4, 16, 31, 34, 35, 88], "4x4": 4, "float64": [4, 22, 31, 35, 70], "compress": [4, 8, 45, 64, 66], "toarrai": 4, "NOT": [4, 34, 79], "23606798": 4, "41421356": 4, "configur": [4, 14, 41, 76], "suppos": [4, 8, 55, 88, 90, 92, 93], "who": [4, 57, 78, 83, 92, 94], "manag": [4, 6, 7, 8, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "clean_learning_kwarg": [4, 8, 21, 26], "labelissuemanag": [4, 8, 21], "prune_method": [4, 73], "prune_by_noise_r": [4, 36, 52, 83], "report": [4, 5, 9, 13, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 71, 72, 74, 75, 76, 78, 79, 83, 94], "include_descript": [4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28], "show_summary_scor": [4, 28], "summari": [4, 5, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 49, 51, 56, 65, 66, 68, 69, 70, 73, 74, 75, 76, 78, 79, 80, 82, 83, 87, 91, 94], "show": [4, 22, 31, 35, 40, 45, 58, 67, 71, 76, 78, 79, 80, 81, 82, 83, 85, 88, 90, 91, 92, 94], "top": [4, 8, 30, 34, 36, 45, 52, 55, 58, 60, 67, 71, 72, 74, 75, 76, 78, 79, 80, 81, 83, 87, 88, 90, 93, 94], "suffer": [4, 8, 11, 20, 52, 60, 71, 94], "onc": [4, 20, 30, 31, 35, 75, 81, 83, 86, 87, 92], "familiar": 4, "usag": [4, 34, 49], "found": [4, 5, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 45, 72, 74, 75, 76, 78, 79, 81, 82, 88, 90, 92, 93, 94], "issue_summari": [4, 8, 11, 75], "overal": [4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 41, 50, 51, 54, 57, 58, 62, 66, 67, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 87, 94], "sever": [4, 5, 8, 10, 11, 20, 31, 34, 35, 36, 54, 57, 59, 60, 66, 70, 72, 74, 75, 76, 78, 79, 80, 81, 83, 87, 88, 92, 93, 94], "dataissu": [4, 11, 14, 28], "outlier": [4, 7, 12, 19, 20, 27, 37, 60, 72, 75, 76, 83, 84], "someth": [4, 5, 31, 35, 60], "123": [4, 75, 76], "456": [4, 74, 79, 92, 93], "nearest_neighbor": 4, "7": [4, 41, 42, 49, 68, 70, 74, 75, 76, 78, 79, 80, 81, 85, 86, 87, 88, 90, 91, 92, 93, 94], "9": [4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 41, 42, 54, 68, 70, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "distance_to_nearest_neighbor": [4, 75, 76, 78, 79, 82, 83], "789": 4, "get_issu": [4, 8, 11, 74, 76, 78, 79, 81, 82], "issue_nam": [4, 5, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75, 76], "focu": [4, 11, 79, 91, 94], "full": [4, 8, 11, 34, 58, 82, 94], "summar": [4, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 67, 71, 72, 91], "valueerror": [4, 10, 11, 38, 41, 81], "specific_issu": [4, 11], "exhibit": [4, 8, 11, 67, 76, 78, 79, 82, 83, 87], "lie": [4, 8, 59, 60, 74, 75, 76, 78, 79, 82, 83, 93], "directli": [4, 12, 14, 28, 34, 49, 50, 76, 79, 86, 87, 90, 93], "compar": [4, 50, 59, 70, 75, 76, 78, 83], "get_issue_summari": [4, 11, 76], "get_info": [4, 11, 76, 79], "yet": [4, 15, 19, 23, 80, 85], "list_possible_issue_typ": [4, 12], "regist": [4, 5, 12, 13, 15, 23, 31, 35, 75], "registri": [4, 12], "list_default_issue_typ": [4, 12], "folder": [4, 74, 75, 82], "load": [4, 10, 34, 58, 80, 81, 82, 83, 87, 88, 91, 94], "futur": [4, 8, 20, 31, 35, 50, 72, 75, 79], "overwrit": [4, 75], "separ": [4, 30, 41, 54, 75, 76, 81, 82, 87, 89], "static": 4, "rememb": [4, 79, 81, 83], "part": [4, 8, 31, 35, 36, 55, 57, 58, 74, 75, 80, 91, 94], "ident": [4, 8, 20, 45, 79], "walk": 5, "alongsid": [5, 31, 35, 75, 81], "pre": [5, 6, 8, 31, 35, 75, 76, 82, 91, 94], "runtim": [5, 31, 34, 35, 62, 64, 66, 74, 81, 82], "issue_manager_factori": [5, 12, 75], "myissuemanag": [5, 12], "myissuemanagerforregress": 5, "decor": [5, 12], "ll": [5, 41, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "thing": [5, 35, 83, 90, 93], "next": [5, 50, 72, 74, 78, 79, 81, 85, 87, 90, 92, 93, 94], "dummi": 5, "randint": [5, 27, 41, 75, 76, 81], "mark": [5, 8, 73, 87, 88, 90], "regard": [5, 76, 83], "rand": [5, 41, 75, 76], "is_": [5, 8, 75], "_issu": [5, 8, 75], "issue_score_kei": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "whole": [5, 22, 31, 35, 76], "make_summari": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "popul": [5, 76, 79], "verbosity_level": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "std": 5, "raw_scor": 5, "bit": 5, "involv": [5, 34, 67, 71, 81, 86], "intermediate_arg": 5, "min": [5, 41, 57, 70, 75, 81, 88], "sin_filt": 5, "sin": 5, "arang": 5, "kernel": 5, "wip": 5, "progress": 5, "issue_manag": [5, 8, 9, 11, 13, 16, 17, 18, 21, 22, 23, 24, 26, 27, 75], "instanti": [5, 14, 34, 49, 59, 74, 76, 78, 93], "477762": 5, "286455": 5, "term": [5, 8, 39, 45, 58, 74, 75, 76, 78, 79, 82, 83], "4778": 5, "is_basic_issu": 5, "basic_scor": 5, "13": [5, 17, 24, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "003042": 5, "058117": 5, "11": [5, 49, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "121908": 5, "15": [5, 43, 62, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "169312": 5, "17": [5, 74, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "229044": 5, "2865": 5, "is_intermediate_issu": 5, "intermediate_scor": 5, "000000": [5, 75, 76, 80, 83], "007059": 5, "009967": 5, "010995": 5, "087332": 5, "016296": 5, "03947": 5, "019459": 5, "794251": 5, "underperform": [6, 7, 27], "group": [6, 7, 22, 27, 80, 87, 94], "dbscan": [6, 8, 27, 81], "hdbscan": [6, 81], "etc": [6, 8, 20, 31, 35, 39, 49, 50, 68, 72, 75, 76, 78, 79, 81, 82, 83], "sensit": [6, 8, 43], "ep": [6, 27, 58], "radiu": 6, "min_sampl": [6, 27], "datalab": [6, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 72, 74, 81, 82, 85, 92, 93], "kmean": [6, 81], "your_data": 6, "get_pred_prob": 6, "n_cluster": [6, 27, 81], "cluster_id": [6, 8, 27, 81], "labels_": 6, "underperforming_group": [6, 8, 19, 81], "search": [7, 8, 18, 22, 23, 44, 62, 81, 89], "nondefault": 7, "Near": [7, 81], "iid": [7, 22, 76, 78, 82, 83], "imbal": [7, 19, 54, 59, 60, 76], "null": [7, 19, 76, 79, 82, 83], "valuat": [7, 16], "togeth": [7, 8, 39, 75, 76, 78, 79, 82, 83, 90, 93, 94], "built": [7, 41], "own": [7, 31, 33, 35, 48, 54, 55, 58, 64, 68, 74, 76, 78, 79, 81, 82, 85, 86, 90, 91, 92, 93, 94], "prerequisit": 7, "basic": [7, 35, 49, 78, 79, 88], "page": [8, 76, 81, 83], "variou": [8, 11, 26, 33, 46, 48, 72, 75, 76, 78, 79, 80, 83, 85, 87, 92], "sai": [8, 31, 35, 86, 91], "why": [8, 79], "matter": [8, 30, 51], "_score": 8, "flag": [8, 20, 22, 36, 41, 51, 52, 55, 62, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 90, 91, 93], "badli": [8, 57, 94], "code": [8, 31, 35, 39, 45, 49, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "issue_scor": 8, "outlier_scor": [8, 24, 75, 76, 78, 79, 82, 83, 88], "atyp": [8, 59, 75, 76, 78, 79, 82, 83, 88], "datapoint": [8, 27, 36, 41, 45, 60, 63, 72, 74, 75, 76, 78, 79, 81, 89, 90, 92, 93], "is_issu": [8, 20], "is_outlier_issu": [8, 75, 76, 78, 79, 82, 83], "annot": [8, 30, 40, 50, 51, 52, 54, 55, 57, 58, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 84, 87, 91], "transform": [8, 41, 43, 45, 59, 60, 76, 79, 82, 88, 92, 93, 94], "dissimilar": [8, 78, 79], "preced": 8, "cosin": [8, 59, 88], "incorrect": [8, 57, 60, 63, 74, 75, 76, 78, 79, 82, 83, 87, 90, 92], "due": [8, 34, 36, 60, 64, 66, 74, 75, 76, 78, 79, 82, 83], "appear": [8, 30, 40, 51, 52, 55, 63, 76, 78, 79, 82, 90, 91], "likelihood": [8, 34, 36, 52, 57, 59, 60, 64, 68], "now": [8, 34, 73, 74, 76, 85, 87, 88, 90, 92, 93, 94], "u": [8, 74, 75, 78, 81, 82, 83, 85, 86, 89, 90, 91, 92, 93, 94], "token": [8, 44, 66, 67, 68, 69, 70, 71, 81, 83, 84], "calcul": [8, 16, 22, 34, 41, 50, 54, 55, 57, 58, 59, 62, 66, 80, 82], "hamper": [8, 80, 82], "analyt": [8, 72, 81, 85], "lead": [8, 57, 60, 82, 87], "draw": [8, 75, 76], "conclus": [8, 79], "try": [8, 34, 36, 49, 50, 64, 66, 72, 76, 78, 79, 81, 82, 83, 91], "veri": [8, 30, 51, 55, 57, 75, 76, 78, 79, 81, 82, 83, 85, 88, 90, 93], "rare": [8, 36, 58, 75, 76, 78, 79, 81, 82, 83], "anomal": [8, 60, 75, 76, 78, 79, 82, 83], "articl": [8, 34, 81], "ai": [8, 72, 74, 75, 76, 78, 79, 80, 81, 82, 84, 85, 86, 88, 90, 92, 93, 94], "blog": 8, "unexpect": [8, 31, 35, 79], "consequ": 8, "inspect": [8, 74, 76, 82, 83, 87, 90, 93], "neg": [8, 57, 58, 75, 76, 80], "affect": [8, 31, 35, 64, 70, 79, 81], "extrem": [8, 75, 76, 78, 79, 81, 82, 83], "rel": [8, 30, 50, 51, 59, 75, 76, 78, 79, 82, 83, 88], "record": [8, 31, 35, 74, 78, 90], "abbrevi": 8, "misspel": 8, "typo": [8, 71], "resolut": 8, "video": [8, 80], "audio": [8, 75, 76, 81, 84], "minor": [8, 44], "variat": 8, "translat": 8, "d": [8, 43, 78, 79, 83, 86, 92, 94], "constant": [8, 27, 62], "median": [8, 26, 43], "question": [8, 20, 72, 83], "nearli": [8, 20, 76, 78, 79, 82], "awar": [8, 73, 83], "presenc": [8, 83], "signific": [8, 76, 78, 79, 82, 83], "violat": [8, 76, 78, 79, 82, 83], "assumpt": [8, 76, 78, 79, 82, 83], "changepoint": [8, 76, 78, 79, 82, 83], "shift": [8, 76, 78, 79, 82, 83], "drift": [8, 76, 78, 82, 83], "autocorrel": [8, 76, 78, 79, 82, 83], "almost": [8, 76, 78, 79, 82, 83], "adjac": [8, 76, 78, 79, 82, 83], "tend": [8, 30, 39, 76, 78, 79, 82, 83, 91, 94], "sequenti": [8, 31, 35, 49, 82], "gap": 8, "b": [8, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 44, 45, 70, 78, 79, 80, 83, 89, 92, 94], "x1": [8, 55, 58, 87], "x2": [8, 55, 58, 87], "10th": 8, "100th": 8, "90": [8, 70, 78, 83, 89, 91, 92], "similarli": [8, 31, 35, 75, 78, 81, 82, 87], "math": [8, 82], "behind": [8, 59, 83], "fundament": 8, "proper": [8, 45, 50, 55, 58, 79, 82, 85, 87, 92], "closer": [8, 57, 87], "scenario": [8, 60, 75, 76], "underli": [8, 59, 68, 70, 94], "stem": [8, 59, 88], "evolv": 8, "influenc": 8, "accordingli": 8, "emploi": [8, 86, 88], "partit": [8, 89], "ahead": 8, "good": [8, 31, 35, 43, 49, 51, 57, 60, 64, 66, 67, 72, 78, 79, 82], "fix": [8, 50, 79, 83, 90, 93], "problem": [8, 34, 41, 67, 72, 75, 76, 79, 81, 82], "deploy": [8, 83, 90, 92, 93], "overlook": [8, 57, 87], "fact": 8, "thu": [8, 30, 35, 51, 74, 78, 79, 83, 89, 92, 94], "diagnos": [8, 76, 81], "rarest": [8, 76, 78, 79, 82, 83], "q": [8, 87], "fall": [8, 57, 66, 70, 83, 88], "subpar": 8, "special": [8, 44], "techniqu": 8, "smote": 8, "asymmetr": [8, 30], "properli": [8, 34, 40, 45, 46, 64, 81, 86, 88, 90, 91], "too": [8, 36, 41, 59, 76, 81, 82, 87], "dark": [8, 91], "bright": [8, 94], "blurri": [8, 82], "abnorm": [8, 58, 82], "cluster": [8, 16, 27], "slice": 8, "poor": 8, "subpopul": 8, "lowest": [8, 50, 58, 76, 81, 82, 85, 86, 87, 91], "get_self_confidence_for_each_label": [8, 41, 60], "power": [8, 78, 79, 80, 82, 83, 94], "r": [8, 34, 62, 75, 76, 90, 91], "tabular": [8, 72, 75, 76, 77, 81, 84, 85], "categor": [8, 59, 75, 76, 77, 81, 90, 92], "encod": [8, 42, 58, 64, 67, 78, 79, 81, 90, 91, 92, 93], "miss": [8, 23, 31, 35, 45, 55, 57, 76, 78, 79, 81, 82, 83, 87, 90], "pattern": 8, "contribut": [8, 16, 87], "isn": [8, 15, 23], "approxim": [8, 16, 34, 59, 85], "shaplei": [8, 16], "knn": [8, 11, 16, 22, 27, 59, 78, 88], "scalabl": 8, "sacrific": 8, "One": [8, 45, 59, 81], "quantif": 8, "exert": [8, 76], "possible_issue_typ": 8, "label_kwarg": 8, "outlier_kwarg": 8, "near_dupl": [8, 12, 17, 75, 76, 78, 79, 81, 82, 83], "near_duplicate_kwarg": 8, "non_iid": [8, 12, 22, 76, 78, 79, 82, 83], "non_iid_kwarg": 8, "class_imbal": [8, 18, 76, 78, 79, 82, 83], "class_imbalance_kwarg": 8, "underperforming_group_kwarg": 8, "null_kwarg": 8, "health_summary_paramet": [8, 21, 26], "health_summari": [8, 21, 30, 72, 80], "health_summary_kwarg": 8, "tandem": [8, 80], "view": [8, 31, 35, 36, 66, 68, 70, 72, 74, 75, 76, 78, 79, 80, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "ood_kwarg": 8, "outofdistribut": [8, 24, 59, 88], "outsid": 8, "outlierissuemanag": [8, 12, 24, 75], "nearduplicateissuemanag": [8, 12, 17], "noniidissuemanag": [8, 12, 22], "num_permut": [8, 22], "permut": [8, 22], "significance_threshold": [8, 22], "signic": 8, "noniid": [8, 19], "classimbalanceissuemanag": [8, 18], "underperforminggroupissuemanag": [8, 27], "determinin": 8, "neighbour": 8, "min_cluster_sampl": [8, 27], "filter_cluster_id": [8, 27], "clustering_kwarg": [8, 27], "faq": [8, 72, 76, 78, 79, 82, 84], "nullissuemanag": [8, 23], "data_valuation_kwarg": 8, "data_valu": [8, 19], "datavaluationissuemanag": [8, 16], "codeblock": 8, "demonstr": [8, 34, 75, 76, 79, 81, 82, 83, 85, 86, 87, 90, 91], "howev": [8, 31, 35, 45, 74, 78, 79, 82, 85, 89, 91, 92, 93], "mandatori": 8, "image_issue_types_kwarg": 8, "32": [8, 75, 80, 82, 85, 87, 91, 94], "fewer": [8, 36, 45, 87], "vice": [8, 51], "versa": [8, 51], "light": [8, 80, 82, 87, 91], "29": [8, 80, 82, 85, 86, 87, 91, 94], "low_inform": [8, 82], "odd_aspect_ratio": [8, 82], "35": [8, 75, 80, 82, 85, 86, 87, 91], "odd_siz": [8, 82], "10": [8, 16, 17, 21, 22, 27, 31, 32, 58, 59, 60, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "doc": [8, 31, 35, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "data_issu": [9, 13, 14, 28, 75], "issue_find": [9, 13], "factori": [9, 13, 14], "except": [10, 49, 60, 75, 76, 82, 85], "dataformaterror": 10, "with_traceback": 10, "tb": 10, "__traceback__": 10, "datasetdicterror": 10, "datasetdict": 10, "usual": [10, 28, 82, 85, 90], "datasetloaderror": 10, "dataset_typ": 10, "fail": 10, "map_to_int": 10, "hold": 10, "is_avail": [10, 82], "serv": [11, 14, 85], "central": [11, 94], "repositori": 11, "strategi": [11, 41, 81], "being": [11, 30, 31, 35, 36, 41, 44, 45, 60, 78, 81, 83, 90, 91, 92], "_infostrategi": 11, "basi": 11, "collect_statist": 11, "reus": [11, 20], "avoid": [11, 31, 34, 35, 36, 45, 52, 55, 58, 62, 64, 66, 75, 76, 81], "recomput": [11, 93], "weighted_knn_graph": 11, "issue_manager_that_computes_knn_graph": 11, "collect_issues_from_issue_manag": 11, "collect_issues_from_imagelab": 11, "imagelab": 11, "set_health_scor": 11, "health": [11, 21, 30, 51, 72], "get_data_statist": 11, "concret": 12, "subclass": [12, 31, 35, 59, 75], "my_issu": 12, "stabl": [13, 19, 25, 33, 37, 45, 48, 59, 73], "unregist": 13, "instati": 14, "public": [14, 83, 87, 91, 94], "creation": [14, 35], "execut": [14, 31, 35, 75, 81, 87], "coordin": [14, 55, 57, 58, 87, 94], "behavior": [14, 30, 31, 35, 58], "At": [14, 58, 81], "associ": [14, 31, 35, 58, 85], "get_available_issue_typ": 14, "direct": [15, 23, 31, 35], "valuabl": 16, "vstack": [16, 45, 80, 81, 82, 83, 85, 86], "25": [16, 22, 31, 41, 43, 76, 80, 82, 83, 85, 86, 87, 91, 94], "classvar": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "short": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 44, 45], "data_valuation_scor": 16, "item": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 45, 75, 76, 81, 82, 83, 85, 86], "some_info_kei": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "additional_info_kei": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "default_threshold": [16, 24], "arxiv": [16, 83], "ab": [16, 83], "1911": 16, "07128": 16, "larger": [16, 62, 64, 66, 79, 80, 81, 82], "collect_info": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "info_to_omit": [16, 17, 18, 20, 21, 22, 24, 26, 27], "compos": [16, 17, 18, 20, 21, 22, 24, 26, 27, 31, 35, 79, 88, 93], "is_x_issu": [16, 17, 18, 20, 21, 22, 24, 26, 27], "x_score": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_a": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_b1": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_b2": [16, 17, 18, 20, 21, 22, 24, 26, 27], "report_str": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28], "_": [17, 20, 21, 22, 23, 26, 27, 41, 44, 45, 74, 75, 80, 82, 83, 86, 92], "near_duplicate_set": [17, 75, 76, 78, 79, 81, 82, 83], "occurr": [17, 18, 20, 22, 23, 24, 27, 44], "median_nn_dist": 17, "near_duplicate_scor": [17, 75, 76, 78, 79, 81, 82, 83], "class_imbalance_scor": [18, 76, 78, 79, 82, 83], "bleed": [19, 25, 33], "edg": [19, 25, 33, 57, 72, 83, 94], "sharp": [19, 25, 33], "abc": 20, "believ": [20, 91], "priori": [20, 83], "global": 20, "anoth": [20, 30, 34, 44, 57, 60, 78, 79, 81, 83, 85, 88, 93], "abstract": 20, "applic": [21, 50, 81, 83, 85, 86, 94], "typevar": [21, 31, 35, 57, 58], "_scalartype_co": 21, "covari": [21, 62, 90], "get_health_summari": 21, "summary_dict": 21, "label_scor": [21, 26, 74, 75, 76, 78, 79, 82, 83], "simplified_kolmogorov_smirnov_test": 22, "neighbor_histogram": 22, "non_neighbor_histogram": 22, "kolmogorov": 22, "smirnov": 22, "largest": [22, 34, 41, 60, 64, 66, 91], "empir": [22, 40, 50], "cumul": 22, "ecdf": 22, "histogram": [22, 78, 90], "absolut": [22, 26], "dimension": [22, 45, 74, 83, 88], "trial": 22, "non_iid_scor": [22, 76, 78, 79, 82, 83], "null_track": 23, "extend": [23, 42, 82, 88, 94], "superclass": 23, "arbitrari": [23, 30, 66, 70, 75, 88, 90], "prompt": 23, "address": [23, 75, 76, 79, 81, 93], "enabl": [23, 35], "null_scor": [23, 76, 79, 82, 83], "37037": 24, "q3_avg_dist": 24, "iqr_avg_dist": 24, "median_outlier_scor": 24, "ood": [24, 59, 60, 75, 76, 79, 82, 83, 88], "regressionlabelissuemanag": 26, "multipli": 26, "find_issues_with_predict": 26, "find_issues_with_featur": 26, "deleg": 26, "confus": [27, 30, 31, 35, 36, 45, 58, 93, 94], "50": [27, 35, 81, 82, 83, 85, 87, 88, 91], "keepdim": [27, 81], "outlier_cluster_label": 27, "no_underperforming_cluster_id": 27, "signifi": 27, "absenc": 27, "set_knn_graph": 27, "find_issues_kwarg": 27, "perform_clust": 27, "npt": 27, "int_": 27, "id": [27, 50, 75, 81, 82, 85], "int64": [27, 74, 85], "unique_cluster_id": 27, "get_worst_clust": 27, "_description_": 27, "performed_clust": 27, "worst_cluster_id": 27, "underperforming_group_scor": 27, "exclud": [28, 67, 71, 75, 94], "get_report": 28, "overview": [30, 74, 76, 78, 79, 82, 85, 87, 88, 90, 92, 93, 94], "modifi": [30, 31, 34, 35, 45, 81, 83], "help": [30, 31, 35, 58, 72, 73, 74, 75, 78, 79, 80, 81, 82, 85, 86, 90, 91, 92, 93, 94], "rank_classes_by_label_qu": [30, 76], "merg": [30, 44, 72, 80, 81, 94], "find_overlapping_class": [30, 81, 83], "problemat": [30, 51, 67, 71, 74, 87, 94], "unnorm": [30, 51, 83], "abov": [30, 31, 34, 35, 45, 50, 57, 58, 60, 66, 70, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "model_select": [30, 41, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 90, 92, 93], "cross_val_predict": [30, 35, 74, 75, 76, 78, 79, 81, 83, 85, 89, 90, 92, 93], "get_data_labels_from_dataset": 30, "yourfavoritemodel": [30, 83], "cv": [30, 41, 74, 75, 76, 78, 83, 85, 92], "df": [30, 45, 71, 74, 81], "overall_label_qu": [30, 51], "col": 30, "prob": [30, 44, 83, 89], "divid": [30, 51, 60], "label_nois": [30, 51], "human": [30, 80, 91, 94], "clearli": [30, 60, 82, 87, 91], "num": [30, 51, 80, 83], "overlap": [30, 72, 80, 81, 83], "ontolog": 30, "publish": [30, 94], "therefor": [30, 60], "vehicl": [30, 80], "truck": [30, 80, 88, 91], "intuit": [30, 51], "car": [30, 80, 87, 91], "frequent": [30, 50, 78, 81, 90], "characterist": 30, "l": [30, 31, 35, 55, 57, 58], "class1": 30, "class2": 30, "relationship": 30, "match": [30, 31, 35, 36, 50, 51, 60, 75, 76, 80, 82, 87, 89, 91], "dog": [30, 45, 51, 53, 67, 80, 81, 88, 89, 94], "cat": [30, 45, 51, 53, 80, 81, 88, 89], "captur": [30, 74, 87, 88, 91], "co": [30, 31, 32], "noisy_label": [30, 75, 76, 86], "overlapping_class": 30, "descend": [30, 31, 35, 41, 51, 58], "overall_label_health_scor": [30, 51, 83], "suggest": [30, 50, 51, 57, 79, 81, 82, 90, 93], "half": [30, 31, 35, 51, 80, 94], "health_scor": [30, 51], "classes_by_label_qu": [30, 76], "cnn": [31, 35, 82], "cifar": [31, 32, 80, 88], "teach": [31, 32], "bhanml": 31, "blob": 31, "master": [31, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "call_bn": 31, "bn": 31, "input_channel": 31, "n_output": 31, "dropout_r": 31, "top_bn": 31, "architectur": [31, 35], "shown": [31, 58, 75, 81, 85, 88, 89, 91, 94], "forward": [31, 32, 35, 82, 85], "overridden": [31, 35], "although": [31, 35, 59, 78, 92], "recip": [31, 35], "afterward": [31, 35], "sinc": [31, 35, 38, 46, 51, 58, 66, 70, 81, 85, 86, 87, 89, 94], "former": [31, 35], "hook": [31, 35, 80], "silent": [31, 34, 35], "t_destin": [31, 35], "__call__": [31, 35, 41], "add_modul": [31, 35], "child": [31, 35], "fn": [31, 35, 58], "recurs": [31, 35, 41], "submodul": [31, 35], "children": [31, 35, 94], "nn": [31, 32, 35, 82], "init": [31, 35, 83], "no_grad": [31, 35, 82, 88], "init_weight": [31, 35], "linear": [31, 35, 79, 82, 93], "fill_": [31, 35], "net": [31, 35, 74, 80, 82], "in_featur": [31, 35], "out_featur": [31, 35], "bia": [31, 35, 82], "tensor": [31, 32, 35, 74, 82, 88], "requires_grad": [31, 35], "bfloat16": [31, 35], "cast": [31, 35, 74], "buffer": [31, 35], "datatyp": [31, 35], "member": [31, 35, 75, 76], "xdoctest": [31, 35], "undefin": [31, 35], "var": [31, 35], "buf": [31, 35], "20l": [31, 35], "1l": [31, 35], "5l": [31, 35], "immedi": [31, 35, 88], "cpu": [31, 35, 36, 74, 82], "move": [31, 35, 41, 73, 80], "cuda": [31, 35, 74, 82], "devic": [31, 35, 74, 82], "gpu": [31, 35, 74, 79, 93], "live": [31, 35], "copi": [31, 35, 62, 74, 75, 76, 78, 81, 86, 89, 90, 92], "doubl": [31, 35], "dump_patch": [31, 35], "eval": [31, 35, 82, 86, 88], "dropout": [31, 35], "batchnorm": [31, 35], "grad": [31, 35], "extra_repr": [31, 35], "line": [31, 35, 72, 75, 80, 85, 88, 94], "get_buff": [31, 35], "target": [31, 32, 35, 62, 63, 88, 90], "throw": [31, 35], "get_submodul": [31, 35], "explan": [31, 35], "fulli": [31, 35, 49, 81], "qualifi": [31, 35], "referenc": [31, 35], "attributeerror": [31, 35], "invalid": [31, 35, 79], "resolv": [31, 35, 94], "get_extra_st": [31, 35], "state_dict": [31, 35], "set_extra_st": [31, 35], "build": [31, 35, 82, 91], "pickleabl": [31, 35], "serial": [31, 35], "backward": [31, 35, 82], "break": [31, 35, 82], "pickl": [31, 35, 87], "get_paramet": [31, 35], "let": [31, 35, 59, 60, 74, 76, 78, 79, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "net_b": [31, 35], "net_c": [31, 35], "conv": [31, 35], "conv2d": [31, 35, 82], "16": [31, 35, 41, 66, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "33": [31, 35, 80, 82, 87, 91, 94], "kernel_s": [31, 35], "stride": [31, 35], "200": [31, 35, 60, 80, 87, 94], "diagram": [31, 35, 89], "degre": [31, 35, 90], "queri": [31, 35, 76, 81, 82], "named_modul": [31, 35], "o": [31, 35, 43, 44, 74, 75, 76, 80, 81, 83, 86, 87, 94], "transit": [31, 35], "ipu": [31, 35], "load_state_dict": [31, 35], "strict": [31, 35, 41], "persist": [31, 35], "strictli": [31, 35], "namedtupl": [31, 35], "missing_kei": [31, 35], "unexpected_kei": [31, 35], "runtimeerror": [31, 35], "idx": [31, 35, 45, 46, 58, 75, 81, 82, 83, 85, 87, 88], "named_buff": [31, 35], "prefix": [31, 35, 74, 94], "prepend": [31, 35], "running_var": [31, 35], "named_children": [31, 35], "conv4": [31, 35], "conv5": [31, 35], "memo": [31, 35], "remove_dupl": [31, 35], "named_paramet": [31, 35], "register_backward_hook": [31, 35], "deprec": [31, 35, 38], "favor": [31, 35], "register_full_backward_hook": [31, 35], "removablehandl": [31, 35], "register_buff": [31, 35], "running_mean": [31, 35], "register_forward_hook": [31, 35], "won": [31, 35, 75, 76, 81, 86], "inplac": [31, 35, 85], "register_forward_pre_hook": [31, 35], "gradient": [31, 35, 78, 82, 90], "respect": [31, 35, 58, 83], "grad_input": [31, 35], "grad_output": [31, 35], "technic": [31, 35], "caller": [31, 35], "register_load_state_dict_post_hook": [31, 35], "post": [31, 35], "incompatible_kei": [31, 35], "modif": [31, 35], "thrown": [31, 35], "clearn": [31, 35], "register_modul": [31, 35], "register_paramet": [31, 35], "requires_grad_": [31, 35], "autograd": [31, 35], "freez": [31, 35, 74, 79, 93], "finetun": [31, 35], "gan": [31, 35], "share_memori": [31, 35], "share_memory_": [31, 35], "destin": [31, 35], "keep_var": [31, 35], "shallow": [31, 35], "releas": [31, 35, 73, 81, 88], "design": [31, 35], "ordereddict": [31, 35], "detach": [31, 35, 82], "non_block": [31, 35], "memory_format": [31, 35], "channels_last": [31, 35], "Its": [31, 35, 41, 51, 57], "complex": [31, 35], "integr": [31, 35, 72], "asynchron": [31, 35], "host": [31, 35], "pin": [31, 35, 79, 80, 93], "desir": [31, 35, 44, 58], "4d": [31, 35], "ignore_w": [31, 35], "determinist": [31, 35, 74], "1913": [31, 35], "3420": [31, 35], "5113": [31, 35], "2325": [31, 35], "env": [31, 35], "torch_doctest_cuda1": [31, 35], "gpu1": [31, 35], "1914": [31, 35], "5112": [31, 35], "2324": [31, 35], "float16": [31, 35], "cdoubl": [31, 35], "3741": [31, 35], "2382": [31, 35], "5593": [31, 35], "4443": [31, 35], "complex128": [31, 35], "6122": [31, 35], "1150": [31, 35], "to_empti": [31, 35], "storag": [31, 35], "dst_type": [31, 35], "xpu": [31, 35], "zero_grad": [31, 35, 82], "set_to_non": [31, 35], "context": [31, 35, 87], "noisili": [32, 83], "han": 32, "2018": 32, "cifar_cnn": [32, 33], "loss_coteach": 32, "y_1": 32, "y_2": 32, "forget_r": 32, "class_weight": 32, "logit": [32, 49, 82], "decim": [32, 45], "quickli": [32, 74, 78, 79, 81, 82, 86, 88, 91, 92, 94], "forget": [32, 41, 94], "rate_schedul": 32, "epoch": [32, 35, 81, 82], "initialize_lr_schedul": 32, "lr": [32, 35], "001": [32, 60, 81], "250": [32, 75, 76, 83, 87], "epoch_decay_start": 32, "80": [32, 78, 82, 86, 90, 91, 92], "schedul": 32, "adjust": [32, 36, 54, 59, 60, 72, 83], "beta": 32, "adam": 32, "adjust_learning_r": 32, "alpha_plan": 32, "beta1_plan": 32, "forget_rate_schedul": 32, "num_gradu": 32, "expon": 32, "tell": [32, 79, 82, 83, 93], "train_load": [32, 35], "model1": [32, 83], "optimizer1": 32, "model2": [32, 83], "optimizer2": 32, "dataload": [32, 82, 88], "parser": 32, "parse_arg": 32, "num_iter_per_epoch": 32, "print_freq": 32, "topk": 32, "top1": 32, "top5": 32, "test_load": 32, "offici": [33, 48, 94], "wish": [33, 48, 88, 91, 94], "mnist_pytorch": 33, "coteach": [33, 73], "mini": [34, 64, 66, 81], "With": [34, 79, 83, 85, 90, 91, 93, 94], "low_self_confid": [34, 36, 52], "self_confid": [34, 36, 41, 52, 54, 60, 68, 70, 81, 83, 86, 92, 93], "conveni": [34, 74, 79, 93], "script": 34, "labelinspector": [34, 81], "adj_confident_thresholds_shar": 34, "labels_shar": 34, "pred_probs_shar": 34, "labels_fil": [34, 81], "pred_probs_fil": [34, 81], "batch_siz": [34, 35, 64, 66, 81, 82, 88, 91], "quality_score_kwarg": 34, "num_issue_kwarg": 34, "return_mask": 34, "variant": [34, 50, 91], "read": [34, 38, 76, 81, 83, 88, 94], "zarr": [34, 81], "memmap": [34, 91], "pythonspe": 34, "mmap": [34, 81], "hdf5": 34, "further": [34, 51, 52, 54, 57, 58, 66, 67, 74, 81], "yourfil": 34, "npy": [34, 80, 81, 91], "mmap_mod": [34, 91], "tip": [34, 36, 49, 81], "save_arrai": 34, "your_arrai": 34, "disk": [34, 80, 81], "npz": [34, 94], "maxim": [34, 50, 64, 66, 91], "multiprocess": [34, 36, 52, 64, 66, 81, 82, 91], "linux": [34, 64, 66], "physic": [34, 36, 64, 66, 87, 91], "psutil": [34, 36, 64, 66, 91], "labels_arrai": [34, 46], "predprob": 34, "pred_probs_arrai": 34, "back": [34, 58, 75, 81, 87, 88], "store_result": 34, "becom": [34, 88], "verifi": [34, 81, 85, 88], "long": [34, 50, 59, 85], "enough": [34, 45, 81], "chunk": [34, 89], "ram": [34, 80], "faster": [34, 59, 62, 64, 66, 81, 83], "end_index": 34, "labels_batch": 34, "pred_probs_batch": 34, "update_confident_threshold": 34, "batch_result": 34, "score_label_qu": 34, "indices_of_examples_with_issu": [34, 81], "shortcut": 34, "encount": [34, 36, 64], "1000": [34, 74, 79, 81, 82, 88], "aggreg": [34, 41, 50, 54, 57, 60, 70, 81, 83, 85], "get_num_issu": 34, "fetch": [34, 74, 76], "seen": [34, 81, 88, 94], "far": [34, 50], "get_quality_scor": 34, "label_quality_scor": [34, 54, 57, 60, 63, 83, 87, 90], "method1": 34, "method2": 34, "normalized_margin": [34, 36, 41, 52, 54, 60, 68, 70], "low_normalized_margin": [34, 36, 52], "issue_indic": [34, 57, 82], "update_num_issu": 34, "split_arr": 34, "arr": [34, 81], "chunksiz": 34, "convnet": 35, "bespok": [35, 49], "get_mnist_dataset": 35, "loader": [35, 82], "download": [35, 74, 81, 88], "mnist": [35, 72, 74, 80], "get_sklearn_digits_dataset": 35, "handwritten": 35, "digit": [35, 74, 80], "last": [35, 41, 55, 58, 75, 76, 81, 85, 94], "sklearn_digits_test_s": 35, "hard": [35, 80, 88], "simplenet": 35, "64": [35, 78, 82, 83, 87, 91, 92], "log_interv": 35, "01": [35, 60, 62, 74, 82, 83, 86, 87, 91, 94], "momentum": 35, "no_cuda": 35, "test_batch_s": [35, 82], "templat": 35, "flexibli": 35, "among": [35, 50, 83], "test_set": 35, "Be": 35, "overrid": 35, "train_idx": [35, 45, 88], "train_label": [35, 88, 93], "scikit": [35, 45, 59, 72, 74, 75, 76, 78, 79, 81, 84, 90, 93], "set_predict_proba_request": 35, "set_predict_request": 35, "encourag": [36, 52, 60, 63], "multilabel_classif": [36, 51, 52, 54, 60, 81, 86], "pred_probs_by_class": 36, "prune_count_matrix_col": 36, "rank_by_kwarg": [36, 52, 60, 83], "num_to_remove_per_class": [36, 52], "bad": [36, 52, 57, 60, 79, 81, 93], "seem": [36, 83, 86], "aren": 36, "confidence_weighted_entropi": [36, 41, 52, 54, 60, 68, 70], "label_issues_idx": [36, 60], "entropi": [36, 38, 40, 41, 59, 60], "prune_by_class": [36, 52, 83], "predicted_neq_given": [36, 52, 83], "prune_counts_matrix": 36, "smallest": [36, 60], "unus": 36, "number_of_mislabeled_examples_in_class_k": 36, "delet": [36, 72, 81, 93], "thread": [36, 52], "window": [36, 80], "shorter": [36, 55], "find_predicted_neq_given": 36, "find_label_issues_using_argmax_confusion_matrix": 36, "latent_algebra": [37, 73], "label_quality_util": 37, "multilabel_util": [37, 86], "multilabel_scor": [37, 54], "token_classification_util": [37, 94], "get_normalized_entropi": 38, "min_allowed_prob": 38, "wikipedia": 38, "activ": [38, 40, 50, 72, 85], "towardsdatasci": 38, "cheatsheet": 38, "ec57bc067c0b": 38, "clip": [38, 45, 74], "behav": 38, "unnecessari": [38, 81], "slightli": [38, 92, 93], "interv": [38, 41, 88], "herein": 39, "inexact": 39, "cours": 39, "propag": 39, "throughout": [39, 45, 62, 74, 85, 91, 94], "compute_ps_py_inv_noise_matrix": 39, "compute_py_inv_noise_matrix": 39, "compute_inv_noise_matrix": 39, "easili": [39, 73, 74, 76, 78, 79, 83, 85, 86, 88, 89, 90, 91, 92, 93], "increas": [39, 57, 59, 60, 74, 75, 81, 85, 86, 94], "dot": [39, 70, 81], "compute_noise_matrix_from_invers": 39, "compute_pi": 39, "true_labels_class_count": 39, "compute_pyx": 39, "pyx": 39, "multiannot": 40, "assert_valid_inputs_multiannot": 40, "labels_multiannot": [40, 50], "ensembl": [40, 41, 50, 60, 78, 81, 86, 88, 90, 92], "allow_single_label": 40, "annotator_id": 40, "assert_valid_pred_prob": 40, "pred_probs_unlabel": [40, 50], "format_multiannotator_label": [40, 50, 85], "lexicograph": [40, 45], "formatted_label": [40, 45], "old": [40, 45, 73, 80], "check_consensus_label_class": 40, "consensus_label": [40, 50, 85], "consensus_method": [40, 50], "consensu": [40, 50, 72, 84, 94], "establish": [40, 90, 93], "compute_soft_cross_entropi": 40, "soft": [40, 80], "find_best_temp_scal": 40, "coarse_search_rang": [40, 62, 81], "fine_search_s": [40, 62, 81], "temperatur": [40, 41, 57, 66, 70], "scale": [40, 43, 80, 81, 88, 91, 92], "factor": [40, 41, 43, 64, 66], "minim": [40, 57, 88], "temp_scale_pred_prob": 40, "temp": 40, "sharpen": [40, 80], "smoothen": 40, "classlabelscor": 41, "enum": 41, "get_normalized_margin_for_each_label": [41, 60], "get_confidence_weighted_entropy_for_each_label": [41, 60], "75": [41, 75, 76, 80, 85, 86, 87, 90, 91, 94], "from_str": 41, "scorer": 41, "exponential_moving_averag": [41, 54], "alpha": [41, 54, 57, 75, 76, 83, 86, 90], "exponenti": 41, "ema": 41, "s_1": 41, "s_k": 41, "ema_k": 41, "accord": [41, 52, 78, 79, 83, 94], "formula": [41, 43], "_t": 41, "cdot": 41, "s_t": 41, "qquad": 41, "leq": 41, "_1": 41, "give": [41, 60, 83, 85, 91], "recent": [41, 94], "success": 41, "previou": [41, 81, 82, 87], "discount": 41, "s_ema": 41, "175": [41, 83, 87], "softmin": [41, 54, 57, 66, 70], "underflow": 41, "nan": [41, 50, 78, 85, 90, 92], "possible_method": 41, "aggregated_scor": 41, "multilabelscor": 41, "base_scor": 41, "base_scorer_kwarg": 41, "aggregator_kwarg": [41, 54], "n_sampl": 41, "n_label": 41, "binari": [41, 45, 52, 54, 83, 94], "worst": [41, 85], "class_label_quality_scor": 41, "get_class_label_quality_scor": 41, "42": [41, 80, 82, 87, 91, 94], "452": [41, 79], "new_scor": 41, "575": 41, "get_label_quality_scores_per_class": [41, 54], "ml_scorer": 41, "multilabel_pi": 41, "binar": [41, 42], "get_cross_validated_multilabel_pred_prob": 41, "reformat": [41, 74], "wider": 41, "splitter": 41, "kfold": [41, 82], "multiclass": [41, 45, 50, 86], "onevsrestclassifi": [41, 86], "randomforestclassifi": [41, 83, 86], "n_split": [41, 76, 82, 86], "stack_compl": 42, "pred_prob_slic": 42, "get_onehot_num_class": 42, "onehot": 42, "multilabel": [42, 86], "int2onehot": [42, 86], "hot": [42, 52, 58, 64, 67, 78, 80, 81, 90, 91, 92], "onehot2int": [42, 86], "onehot_matrix": 42, "transform_distances_to_scor": 43, "avg_dist": 43, "scaling_factor": 43, "exp": [43, 59, 60, 75], "dt": 43, "right": [43, 55, 58, 79, 86, 87, 88, 93], "strength": [43, 58], "pronounc": 43, "differenti": 43, "ly": 43, "rule": [43, 44, 80], "thumb": 43, "ood_features_scor": [43, 59, 88], "88988177": 43, "80519832": 43, "token_classif": [44, 68, 70, 71, 81], "get_sent": [44, 94], "sentenc": [44, 68, 70, 71, 79, 93], "readabl": 44, "filter_sent": [44, 94], "lambda": [44, 74, 75, 81, 85], "long_sent": 44, "headlin": 44, "process_token": 44, "charact": [44, 45], "s1": 44, "s2": 44, "processed_token": 44, "alecnlcb": 44, "entiti": [44, 72, 81, 94], "mapped_ent": 44, "unique_ident": 44, "loc": [44, 75, 76, 82, 94], "merge_prob": 44, "probs_merg": 44, "55": [44, 80, 87, 90, 91], "0125": [44, 70], "0375": 44, "075": 44, "025": 44, "color_sent": 44, "color": [44, 67, 75, 76, 78, 83, 86, 88, 90, 91], "red": [44, 58, 75, 76, 80, 83, 86, 87, 88, 91], "colored_sent": 44, "termcolor": 44, "31msentenc": 44, "0m": 44, "ancillari": 45, "remove_noise_from_class": 45, "class_without_nois": 45, "any_other_class": 45, "choos": [45, 60, 78, 81, 83, 90, 92], "tradition": 45, "clip_noise_r": 45, "clip_valu": 45, "new_sum": 45, "preserv": 45, "value_count": [45, 81], "fill": 45, "wherea": [45, 52, 89], "come": [45, 75, 76, 81, 82, 91], "major": [45, 50, 73, 82, 88], "versu": [45, 83], "value_counts_fill_missing_class": 45, "get_missing_class": 45, "round_preserving_sum": 45, "obviou": 45, "cgdeboer": 45, "iteround": 45, "round_preserving_row_tot": 45, "reach": 45, "estimate_pu_f1": 45, "prob_s_eq_1": 45, "claesen": 45, "f1": [45, 58, 79, 83], "confusion_matrix": 45, "BE": 45, "print_square_matrix": 45, "left_nam": 45, "top_nam": 45, "titl": [45, 75, 76, 83, 86, 88], "short_titl": 45, "round_plac": 45, "pretti": [45, 83], "print_noise_matrix": [45, 83], "print_inverse_noise_matrix": 45, "print_joint_matrix": [45, 83], "joint_matrix": 45, "compress_int_arrai": 45, "num_possible_valu": 45, "train_val_split": 45, "holdout_idx": 45, "subset_x_i": 45, "extract": [45, 59, 74, 79, 85, 88, 91, 93], "subset_label": 45, "subset_data": 45, "extract_indices_tf": 45, "allow_shuffl": 45, "turn": [45, 72, 87], "unshuffle_tensorflow_dataset": 45, "shuffledataset": 45, "histori": 45, "pre_x": 45, "buffer_s": 45, "is_torch_dataset": 45, "is_tensorflow_dataset": 45, "csr_vstack": 45, "csr_matric": 45, "append": [45, 74, 80, 81, 82, 83, 85, 86, 88, 94], "bottom": [45, 55, 58, 87], "append_extra_datapoint": 45, "to_data": 45, "from_data": 45, "taken": 45, "get_num_class": 45, "label_matrix": 45, "canon": 45, "num_unique_class": 45, "get_unique_class": 45, "format_label": 45, "smart_display_datafram": 45, "displai": [45, 58, 67, 71, 74, 79, 83, 93, 94], "jupyt": [45, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "notebook": [45, 50, 74, 76, 80, 81, 83, 85, 86, 87, 91, 94], "consol": 45, "force_two_dimens": 45, "html": [45, 59, 78, 81, 83], "assert_valid_input": 46, "allow_missing_class": 46, "allow_one_class": 46, "assert_valid_class_label": 46, "assert_nonempty_input": 46, "assert_indexing_work": 46, "length_x": 46, "labels_to_arrai": 46, "labellik": 46, "keraswrappermodel": [49, 72], "keraswrappersequenti": 49, "tf": [49, 74], "legaci": 49, "lack": 49, "keraswrapp": 49, "huggingface_keras_imdb": 49, "unit": [49, 94], "model_kwarg": [49, 62], "compile_kwarg": 49, "sparsecategoricalcrossentropi": 49, "layer": [49, 74, 79, 88, 93], "dens": 49, "my_keras_model": 49, "from_logit": 49, "compil": 49, "declar": 49, "apply_softmax": 49, "analysi": 50, "analyz": [50, 72, 83, 85, 86], "get_label_quality_multiannot": [50, 85], "vote": 50, "crowdsourc": [50, 72, 85], "dawid": [50, 85], "skene": [50, 85], "analog": [50, 80, 85], "chosen": [50, 60, 81, 85], "crowdlab": [50, 85], "unlabel": [50, 78, 79, 82, 85, 88, 91], "decid": [50, 79, 80, 85, 90, 93, 94], "get_active_learning_scor": [50, 85], "activelab": [50, 85], "priorit": [50, 57, 87, 91, 94], "showcas": 50, "main": 50, "best_qual": 50, "quality_method": 50, "calibrate_prob": 50, "return_detailed_qu": 50, "return_annotator_stat": 50, "return_weight": 50, "label_quality_score_kwarg": 50, "necessarili": [50, 58, 79, 83], "did": [50, 51, 74, 78, 83, 85, 90, 92, 93], "majority_vot": 50, "ti": 50, "broken": [50, 58, 80], "highest": [50, 58, 75, 82, 89], "0th": 50, "consensus_quality_scor": [50, 85], "annotator_agr": [50, 85], "reman": 50, "1st": 50, "2nd": [50, 64], "3rd": 50, "consensus_label_suffix": 50, "consensus_quality_score_suffix": 50, "suffix": 50, "emsembl": 50, "weigh": [50, 80], "agreement": [50, 85], "agre": 50, "prevent": [50, 81], "overconfid": [50, 89], "wrong": [50, 55, 57, 73, 75, 76, 79, 81, 83, 87, 93], "detailed_label_qu": [50, 85], "annotator_stat": [50, 85], "model_weight": 50, "annotator_weight": 50, "warn": [50, 75, 76], "labels_info": 50, "num_annot": [50, 85], "deriv": [50, 85], "quality_annotator_1": 50, "quality_annotator_2": 50, "quality_annotator_m": 50, "annotator_qu": [50, 85], "num_examples_label": [50, 85], "agreement_with_consensu": [50, 85], "worst_class": [50, 85], "trustworthi": [50, 85, 90], "get_label_quality_multiannotator_ensembl": 50, "weigtht": 50, "budget": 50, "retrain": [50, 90, 93], "active_learning_scor": 50, "improv": [50, 76, 80, 81, 82, 83, 90, 91, 92, 93], "active_learning_scores_unlabel": 50, "get_active_learning_scores_ensembl": 50, "henc": [50, 74, 75, 85], "get_majority_vote_label": [50, 85], "event": 50, "lastli": [50, 78], "convert_long_to_wide_dataset": 50, "labels_multiannotator_long": 50, "wide": [50, 74, 92, 93], "suitabl": [50, 78, 92], "labels_multiannotator_wid": 50, "common_multilabel_issu": 51, "mutual": [51, 86], "exclus": [51, 86], "rank_classes_by_multilabel_qu": 51, "overall_multilabel_health_scor": 51, "multilabel_health_summari": 51, "classes_by_multilabel_qu": 51, "inner": [52, 66], "find_multilabel_issues_per_class": 52, "per_class_label_issu": 52, "label_issues_list": 52, "labels_list": 52, "pred_probs_list": [52, 60, 82, 83], "anim": [53, 88], "rat": 53, "predat": 53, "pet": 53, "reptil": 53, "manner": [54, 85, 90, 92, 93], "box": [55, 57, 58, 80, 87], "object_detect": [55, 57, 58, 87], "return_indices_ranked_by_scor": [55, 87], "overlapping_label_check": [55, 57], "suboptim": [55, 57], "locat": [55, 57, 87, 91, 94], "bbox": [55, 58, 87], "image_nam": [55, 58], "y1": [55, 58, 87], "y2": [55, 58, 87], "later": [55, 58, 59, 93, 94], "mmdetect": [55, 58, 87], "corner": [55, 58, 87], "swap": [55, 57, 67, 71], "penal": [55, 57], "concern": [55, 57, 72, 76], "aggregation_weight": 57, "imperfect": [57, 81], "chose": [57, 85, 87], "imperfectli": [57, 87], "dirti": [57, 60, 63, 90], "subtyp": 57, "badloc": 57, "nonneg": 57, "issues_from_scor": [57, 66, 67, 70, 71, 87, 91, 94], "compute_overlooked_box_scor": 57, "high_probability_threshold": 57, "auxiliary_input": [57, 58], "vari": [57, 76], "iou": [57, 58], "heavili": 57, "auxiliarytypesdict": 57, "pred_label": [57, 93], "pred_label_prob": 57, "pred_bbox": 57, "lab_label": 57, "lab_bbox": 57, "similarity_matrix": 57, "min_possible_similar": 57, "scores_overlook": 57, "compute_badloc_box_scor": 57, "low_probability_threshold": 57, "scores_badloc": 57, "compute_swap_box_scor": 57, "accident": [57, 78, 79, 81, 93], "scores_swap": 57, "pool_box_scores_per_imag": 57, "box_scor": 57, "image_scor": [57, 66, 91], "object_counts_per_imag": 58, "discov": [58, 76, 94], "auxiliari": [58, 88, 91], "_get_valid_inputs_for_compute_scor": 58, "object_count": 58, "bounding_box_size_distribut": 58, "down": 58, "bbox_siz": 58, "class_label_distribut": 58, "class_distribut": 58, "get_sorted_bbox_count_idx": 58, "plot": [58, 75, 76, 83, 86, 88, 90, 91], "sorted_idx": [58, 88], "plot_class_size_distribut": 58, "class_to_show": 58, "hidden": [58, 88], "max_class_to_show": 58, "plot_class_distribut": 58, "visual": [58, 75, 76, 82, 90, 92, 94], "prediction_threshold": 58, "overlai": [58, 87], "figsiz": [58, 75, 76, 82, 83, 86, 88], "save_path": [58, 87], "blue": [58, 80, 83, 87], "overlaid": 58, "side": [58, 80, 87], "figur": [58, 83, 86, 88, 90], "extens": [58, 83, 85], "png": [58, 87], "pdf": [58, 59], "svg": 58, "matplotlib": [58, 75, 76, 82, 83, 86, 87, 88, 90], "get_average_per_class_confusion_matrix": 58, "num_proc": [58, 82], "intersect": [58, 81], "tp": 58, "fp": 58, "ground": [58, 80, 83, 85, 90], "truth": [58, 83, 85, 90], "bias": 58, "avg_metr": 58, "distionari": 58, "95": [58, 68, 70, 76, 78, 80, 83, 90, 91], "calculate_per_class_metr": 58, "per_class_metr": 58, "Of": 59, "li": 59, "smaller": [59, 86, 87], "find_top_issu": [59, 60, 88], "reli": [59, 74, 75, 76, 79, 87, 88, 93], "dist_metr": 59, "dim": [59, 82, 91], "subtract": [59, 60], "renorm": [59, 60, 81], "least_confid": 59, "sum_": 59, "log": [59, 60, 73], "softmax": [59, 66, 70, 82], "literatur": 59, "gen": 59, "liu": 59, "lochman": 59, "zach": 59, "openaccess": 59, "thecvf": 59, "content": [59, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "cvpr2023": 59, "liu_gen_pushing_the_limits_of_softmax": 59, "based_out": 59, "distribution_detection_cvpr_2023_pap": 59, "fit_scor": [59, 88], "ood_predictions_scor": 59, "pretrain": [59, 74, 79, 88, 93], "adjust_confident_threshold": 59, "probabilist": [59, 74, 75, 76, 78, 79, 88, 89, 92], "order_label_issu": [60, 73], "whichev": [60, 89], "argsort": [60, 79, 82, 83, 88, 90, 93], "max_": 60, "get_label_quality_ensemble_scor": [60, 81, 83], "weight_ensemble_members_bi": 60, "custom_weight": 60, "log_loss_search_t_valu": 60, "0001": [60, 80], "scheme": 60, "log_loss_search": 60, "log_loss": [60, 79], "1e0": 60, "1e1": 60, "1e2": 60, "2e2": 60, "quality_scor": [60, 88], "forth": 60, "top_issue_indic": 60, "rank_bi": [60, 73], "weird": [60, 71], "minu": 60, "prob_label": 60, "max_prob_not_label": 60, "idea": 60, "AND": [60, 79], "corrupt": [62, 90], "linearregress": [62, 81, 90], "y_with_nois": 62, "n_boot": [62, 81], "include_aleatoric_uncertainti": [62, 81], "sole": [62, 75, 85, 88, 92], "bootstrap": [62, 81, 90], "resampl": [62, 74, 81], "epistem": [62, 81, 88, 90], "aleator": [62, 81, 90], "model_final_kwarg": 62, "coars": 62, "thorough": [62, 81], "fine": [62, 74, 79, 88, 93], "grain": 62, "grid": 62, "get_epistemic_uncertainti": 62, "varianc": [62, 83], "epistemic_uncertainti": 62, "get_aleatoric_uncertainti": 62, "residu": [62, 63, 81], "deviat": [62, 90], "ie": 62, "aleatoric_uncertainti": 62, "outr": 63, "contin": 63, "raw": [63, 72, 73, 76, 80, 82, 85, 87, 88], "aka": [63, 74, 83, 94], "00323821": 63, "33692597": 63, "00191686": 63, "semant": [64, 66, 67, 84], "pixel": [64, 66, 67, 88, 91], "h": [64, 66, 67, 91], "height": [64, 66, 67, 91], "w": [64, 66, 67, 91], "width": [64, 66, 67, 91], "labels_one_hot": [64, 67, 91], "stream": [64, 88, 94], "downsampl": [64, 66, 91], "shrink": [64, 66], "divis": [64, 66, 75], "segmant": [66, 67], "num_pixel_issu": [66, 91], "product": [66, 81, 82], "pixel_scor": [66, 91], "display_issu": [66, 67, 68, 70, 71, 91, 94], "highlight": [67, 71, 75, 76, 78, 91], "enter": 67, "legend": [67, 75, 76, 86, 87, 90, 91], "colormap": 67, "background": 67, "person": [67, 81, 87, 91, 94], "common_label_issu": [67, 71, 91, 94], "ambigu": [67, 71, 74, 79, 80, 83, 93, 94], "systemat": [67, 71, 85], "misunderstood": [67, 71], "issues_df": [67, 82], "filter_by_class": [67, 91], "class_index": 67, "issues_subset": [67, 71], "token_score_method": 70, "sentence_score_method": 70, "sentence_score_kwarg": 70, "compris": [70, 71], "token_scor": [70, 94], "converg": 70, "toward": 70, "_softmin_sentence_scor": 70, "sentence_scor": [70, 94], "token_info": 70, "70": [70, 78, 82, 90, 91], "02": [70, 75, 76, 82, 83, 87, 91, 94], "03": [70, 78, 80, 83, 87, 91, 94], "04": [70, 78, 82, 87, 91], "08": [70, 79, 83, 87, 91, 94], "commonli": [71, 73, 75, 76, 86, 94], "filter_by_token": [71, 94], "But": [71, 79, 83, 94], "restrict": [71, 81], "reliabl": [72, 74, 81, 85, 91, 92], "thousand": 72, "imagenet": [72, 80], "popular": [72, 85, 87], "centric": [72, 78, 79, 82, 84], "capabl": 72, "minut": [72, 74, 78, 79, 80, 85, 86, 87, 90, 91, 92, 93, 94], "conda": 72, "feature_embed": [72, 88], "Then": [72, 81, 82, 90, 92, 93], "your_dataset": [72, 74, 75, 76, 78, 79, 81, 82], "column_name_of_label": [72, 74, 75, 76, 78, 79, 82], "plagu": [72, 76], "untrain": 72, "\u30c4": 72, "label_issues_info": [72, 76], "sklearn_compatible_model": 72, "framework": [72, 86, 87], "complianc": 72, "tag": [72, 86, 94], "sequenc": 72, "recognit": [72, 74, 81, 94], "train_data": [72, 88, 90, 92, 93], "gotten": 72, "test_data": [72, 83, 86, 88, 90, 92, 93], "deal": [72, 76], "tutori": [72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "feel": [72, 74, 76, 81], "free": [72, 74, 76, 78, 79, 81, 82, 83], "ask": [72, 81], "slack": [72, 81], "project": [72, 90], "welcom": 72, "commun": [72, 81], "guidelin": [72, 87], "piec": 72, "studio": [72, 76, 78, 79, 81, 82], "platform": [72, 78, 79, 81, 82], "automl": [72, 81], "foundat": 72, "smart": [72, 78, 79, 81, 82], "edit": [72, 81], "easier": [72, 83], "unreli": [72, 74, 78, 79, 92], "older": 73, "outlin": 73, "substitut": 73, "v2": [73, 78, 92], "get_noise_indic": 73, "psx": 73, "sorted_index_method": 73, "order_label_error": 73, "label_errors_bool": 73, "latent_estim": 73, "num_label_error": 73, "learningwithnoisylabel": 73, "neatli": 73, "organ": [73, 78, 80, 92, 94], "reorgan": 73, "baseline_method": 73, "incorpor": [73, 83], "research": [73, 83], "polyplex": 73, "terminologi": 73, "label_error": 73, "quickstart": [74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "spoken": 74, "500": [74, 88, 94], "english": [74, 80], "pronunci": 74, "wav": 74, "huggingfac": [74, 75, 76, 82], "voxceleb": 74, "speech": [74, 94], "your_pred_prob": [74, 75, 76, 78, 79], "tensorflow_io": 74, "26": [74, 75, 80, 82, 83, 85, 87, 91], "huggingface_hub": 74, "12": [74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "branch": [74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "reproduc": [74, 78, 83, 85], "command": 74, "wget": [74, 87, 91, 94], "navig": 74, "link": [74, 80, 87], "browser": 74, "jakobovski": 74, "archiv": [74, 94], "v1": 74, "tar": [74, 88], "gz": [74, 88], "mkdir": [74, 94], "spoken_digit": 74, "xf": 74, "6_nicolas_32": 74, "data_path": 74, "listdir": 74, "nondeterminist": 74, "file_nam": 74, "endswith": 74, "file_path": 74, "join": [74, 81, 82], "39": [74, 75, 79, 80, 81, 82, 87, 90, 91, 93, 94], "7_george_26": 74, "0_nicolas_24": 74, "0_nicolas_6": 74, "listen": 74, "display_exampl": 74, "click": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "expand": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "pulldown": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "colab": [74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "tfio": 74, "pathlib": 74, "ipython": 74, "load_wav_16k_mono": 74, "filenam": 74, "khz": 74, "file_cont": 74, "io": [74, 80], "read_fil": 74, "sample_r": 74, "decode_wav": 74, "desired_channel": 74, "squeez": 74, "rate_in": 74, "rate_out": 74, "16000": 74, "wav_file_nam": 74, "audio_r": 74, "wav_file_exampl": 74, "plai": [74, 80, 81], "button": 74, "wav_file_name_exampl": 74, "7_jackson_43": 74, "hear": 74, "extractor": 74, "encoderclassifi": 74, "spkrec": 74, "xvect": 74, "feature_extractor": 74, "from_hparam": 74, "run_opt": 74, "uncom": 74, "wav_audio_file_path": 74, "head": [74, 76, 78, 79, 80, 82, 83, 85, 90, 92, 93], "torchaudio": 74, "extract_audio_embed": 74, "emb": [74, 82], "signal": 74, "encode_batch": 74, "embeddings_list": [74, 82], "embeddings_arrai": 74, "512": [74, 82], "14": [74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "196315": 74, "3194594": 74, "478977": 74, "2890828": 74, "8170278": 74, "892647": 74, "24": [74, 80, 82, 83, 85, 87, 91], "898054": 74, "256194": 74, "559642": 74, "559715": 74, "620667": 74, "285246": 74, "21": [74, 75, 80, 81, 83, 87, 91, 94], "709623": 74, "5033712": 74, "913803": 74, "8198366": 74, "1831512": 74, "208761": 74, "08426": 74, "3210406": 74, "005453": 74, "2161605": 74, "478239": 74, "682179": 74, "0538025": 74, "242471": 74, "0914207": 74, "7833488": 74, "039538": 74, "23": [74, 80, 82, 83, 87, 91], "56918": 74, "19": [74, 79, 80, 81, 82, 83, 88, 90, 91, 93, 94], "761095": 74, "1258287": 74, "753235": 74, "3508894": 74, "598273": 74, "237122": 74, "2500": 74, "leverag": [74, 79, 81, 83, 85, 93], "tune": [74, 79, 80, 88, 93], "computation": [74, 79, 93], "intens": [74, 79, 93], "held": [74, 78, 79, 80, 87, 88, 89, 92], "straightforward": [74, 78, 92], "benefit": [74, 89, 91, 92], "tol": 74, "num_crossval_fold": [74, 78, 85, 92], "decreas": [74, 81], "never": [74, 83, 86, 88, 89], "accuracy_scor": [74, 79, 83, 92, 93], "cv_accuraci": 74, "9772": 74, "probabilit": [74, 93], "9980": 74, "176": [74, 80, 83, 86], "006488": 74, "2318": 74, "008269": 74, "986": 74, "010354": 74, "469": 74, "013459": 74, "516": 74, "013478": 74, "investig": 74, "100541": 74, "998729": 74, "998768": 74, "980980": 74, "998217": 74, "18": [74, 79, 80, 81, 82, 83, 87, 88, 90, 91, 93], "identified_label_issu": [74, 79], "lowest_quality_label": [74, 79, 83, 90, 93], "sort_valu": [74, 76, 78, 79, 81, 82, 83, 85], "1946": 74, "1871": 74, "1955": 74, "2132": 74, "worth": [74, 83], "iloc": [74, 78, 79, 90, 92, 93], "6_yweweler_35": 74, "6_yweweler_36": 74, "6_yweweler_14": 74, "6_theo_27": 74, "4_george_31": 74, "6_nicolas_8": 74, "sound": 74, "quit": [74, 88], "22": [74, 75, 80, 82, 83, 86, 87, 91, 94], "blindli": [74, 81, 90, 92, 93], "trust": [74, 81, 83, 85, 89, 90, 92, 93], "underneath": 75, "hood": 75, "alert": 75, "introduct": 75, "mayb": [75, 76, 79], "examin": [75, 76, 78, 92], "your_feature_matrix": [75, 76], "toi": [75, 76, 80, 82, 83, 85], "train_test_split": [75, 76, 88, 92, 93], "inf": [75, 76], "mid": [75, 76], "bins_map": [75, 76], "create_data": [75, 76], "y_bin": [75, 76], "y_i": [75, 76], "y_bin_idx": [75, 76], "y_train": [75, 76, 83, 90], "y_test": [75, 76, 83, 90], "y_train_idx": [75, 76], "y_test_idx": [75, 76], "test_siz": [75, 76, 92, 93], "slide": [75, 76, 80], "decis": [75, 76, 92], "boundari": [75, 76], "frame": [75, 76], "x_out": [75, 76], "tini": [75, 76], "concaten": [75, 76, 81, 89], "y_out": [75, 76], "y_out_bin": [75, 76], "y_out_bin_idx": [75, 76], "exact_duplicate_idx": [75, 76], "x_duplic": [75, 76], "y_duplic": [75, 76], "y_duplicate_idx": [75, 76], "noisy_labels_idx": [75, 76, 86], "scatter": [75, 76, 83, 86, 90], "black": [75, 76, 80, 90], "cyan": [75, 76], "pyplot": [75, 76, 82, 83, 86, 88, 90], "plt": [75, 76, 82, 83, 86, 88, 90], "plot_data": [75, 76, 83, 86, 90], "fig": [75, 76, 80, 82, 88, 90], "ax": [75, 76, 82, 88, 90], "subplot": [75, 76, 82, 88], "set_titl": [75, 76, 82, 88], "set_xlabel": [75, 76], "x_1": [75, 76], "fontsiz": [75, 76, 82, 83, 86], "set_ylabel": [75, 76], "x_2": [75, 76], "set_xlim": [75, 76], "set_ylim": [75, 76], "linestyl": [75, 76], "circl": [75, 76, 83, 86], "misclassifi": [75, 76], "zip": [75, 76, 82, 87, 94], "label_err": [75, 76], "180": [75, 76, 87], "marker": [75, 76], "facecolor": [75, 76], "edgecolor": [75, 76], "linewidth": [75, 76, 88], "dup": [75, 76], "first_legend": [75, 76], "align": [75, 76], "title_fontproperti": [75, 76], "semibold": [75, 76], "second_legend": [75, 76], "45": [75, 76, 80, 82, 83, 87, 91, 94], "gca": [75, 76], "add_artist": [75, 76], "tight_layout": [75, 76], "ideal": [75, 76], "logist": [75, 76, 79, 85, 88, 93], "remaind": 75, "modal": [75, 76, 81, 85], "regardless": [75, 76], "132": [75, 76, 83, 87], "9318": 75, "77": [75, 76, 78, 87, 91, 92], "006939": 75, "007830": 75, "40": [75, 76, 79, 80, 82, 91], "014826": 75, "107": [75, 76, 83, 86], "021220": 75, "120": [75, 76, 92], "026403": 75, "notic": [75, 83, 85, 87], "3558": [75, 76], "126": [75, 76, 83, 87, 94], "006636": [75, 76], "130": [75, 76], "012571": [75, 76], "129": [75, 76, 94], "127": [75, 76], "014909": [75, 76], "128": [75, 76, 82], "017443": [75, 76], "6160": [75, 76], "is_near_duplicate_issu": [75, 76, 78, 79, 81, 82, 83], "131": [75, 76, 91], "000000e": [75, 76], "00": [75, 76, 78, 80, 82, 91, 92], "000002": [75, 76], "463180e": [75, 76], "07": [75, 76, 78, 82, 83, 87, 91], "51": [75, 76, 78, 80, 82, 83, 87, 91], "161148": [75, 76], "859087e": [75, 76], "30": [75, 76, 80, 81, 82, 86, 91, 94], "3453": 75, "029542": 75, "031182": 75, "057961": 75, "058244": 75, "home": [75, 76, 79, 80, 88, 93], "runner": [75, 76, 79, 88, 93], "300": [75, 85, 94], "userwarn": [75, 76], "330": [75, 82, 87], "309": 75, "34": [75, 80, 82, 83, 85, 87, 88, 91, 94], "54": [75, 80, 83, 87, 91, 94], "039117": 75, "53": [75, 76, 78, 80, 86, 87, 91, 92], "044594": 75, "105": 75, "105121": 75, "133588": 75, "43": [75, 80, 83, 87, 91, 93], "168035": 75, "125": 75, "101107": 75, "37": [75, 80, 91], "183382": 75, "109": [75, 80, 87], "209259": 75, "211042": 75, "221316": 75, "average_ood_scor": 75, "34530442089193386": 75, "52": [75, 80, 87, 91, 94], "169820": [75, 91], "087324e": 75, "89": [75, 78, 87, 90, 91], "92": [75, 83, 87, 91, 92], "259024": 75, "583757e": 75, "91": [75, 87, 91, 93], "346458": 75, "341292e": 75, "specfi": 75, "new_lab": 75, "scoring_funct": 75, "div": 75, "rem": 75, "inv_scal": 75, "49": [75, 80, 82, 83, 87, 91, 94], "superstitionissuemanag": 75, "unlucki": 75, "superstit": 75, "to_seri": 75, "issues_mask": 75, "summary_scor": 75, "9242": 75, "is_superstition_issu": 75, "superstition_scor": 75, "047581": 75, "090635": 75, "129591": 75, "65": [75, 82, 87, 91, 92], "164840": 75, "demo": [76, 78, 86, 92], "lurk": [76, 82, 83], "opt": 76, "hostedtoolcach": 76, "x64": 76, "lib": 76, "python3": 76, "site": 76, "_split": 76, "737": 76, "thoroughli": 76, "preprocess": [76, 78, 88, 90, 92, 93], "904": 76, "review": [76, 78, 79, 80, 81, 83, 87, 90, 91, 92, 93, 94], "8561": 76, "001894": 76, "58": [76, 78, 80, 83, 87, 91, 92], "003565": 76, "007326": 76, "008974": 76, "009699": 76, "0227": 76, "is_class_imbalance_issu": [76, 78, 79, 82, 83], "022727": 76, "86": [76, 78, 82, 83, 87, 90, 91, 92], "87": [76, 82, 87, 90, 91, 93], "0000": [76, 79, 80, 82, 83], "is_null_issu": [76, 79, 82, 83], "96": [76, 78, 80, 83, 86, 87, 90, 91], "94": [76, 78, 80, 83, 87, 90, 91, 92], "93": [76, 80, 87, 90, 91, 92], "8218": 76, "is_non_iid_issu": [76, 78, 79, 82, 83], "810274": 76, "826147": 76, "849587": 76, "855359": 76, "855485": 76, "821750488732925": 76, "auto": [76, 80, 81, 90, 92, 93], "conceptu": 76, "856061": 76, "355772": 76, "616034": 76, "821750": 76, "betweeen": 76, "859109": 76, "417707": 76, "664083": 76, "970324": 76, "816965": 76, "375317": 76, "641516": 76, "890575": 76, "530924": 76, "460593": 76, "601188": 76, "752776": 76, "321635": 76, "562539": 76, "948362": 76, "090224": 76, "472909": 76, "746763": 76, "878267": 76, "examples_w_issu": [76, 81], "013444": 76, "025173": 76, "026416": 76, "inde": [76, 79], "miscellan": [76, 94], "428571": 76, "111111": 76, "571429": 76, "407407": 76, "592593": 76, "337838": 76, "092593": 76, "662162": 76, "333333": [76, 80], "952381": 76, "666667": 76, "portion": 76, "huge": [76, 83], "worri": [76, 79], "critic": 76, "highli": [76, 82], "sql": [78, 92], "databas": [78, 92], "excel": [78, 92], "parquet": [78, 92], "student": [78, 90, 92, 94], "grade": [78, 90, 92], "900": [78, 90, 92], "exam": [78, 90, 92], "letter": [78, 92, 94], "hundr": [78, 92], "histgradientboostingclassifi": 78, "standardscal": [78, 88, 92], "possibli": [78, 92], "grades_data": [78, 92], "read_csv": [78, 79, 90, 92, 93], "stud_id": [78, 92], "exam_1": [78, 90, 92], "exam_2": [78, 90, 92], "exam_3": [78, 90, 92], "letter_grad": [78, 92], "f48f73": [78, 92], "0bd4e7": [78, 92], "81": [78, 79, 87, 90, 91, 92, 94], "great": [78, 80, 92], "particip": [78, 92], "cb9d7a": [78, 92], "61": [78, 82, 83, 87, 91, 92], "78": [78, 80, 83, 87, 90, 91, 92], "9acca4": [78, 92], "48": [78, 80, 83, 87, 91, 92], "x_raw": [78, 92], "cat_featur": 78, "x_encod": [78, 92], "get_dummi": [78, 90, 92], "drop_first": [78, 92], "numeric_featur": [78, 92], "scaler": [78, 88, 92], "x_process": [78, 92], "fit_transform": [78, 92], "bring": [78, 79, 82, 85, 90, 92, 93], "byod": [78, 79, 82, 85, 90, 92, 93], "boost": [78, 81, 85, 90], "xgboost": [78, 81, 90], "think": [78, 79, 81, 86, 91, 94], "carefulli": [78, 79, 82, 92], "nonzero": 78, "suspici": [78, 92], "tabl": [78, 80, 85, 92], "358": 78, "294": [78, 87], "46": [78, 80, 83, 87, 91], "941": 78, "7109": 78, "000005": [78, 79, 82], "886": 78, "000059": 78, "709": 78, "000104": 78, "723": 78, "000169": 78, "689": 78, "000181": 78, "3590": 78, "051882e": 78, "683133e": 78, "536582e": 78, "406589e": 78, "324246e": 78, "6165": 78, "582": 78, "185": [78, 80, 87], "187": [78, 80], "27": [78, 80, 83, 87, 91, 94], "898": 78, "637": [78, 92], "0014": [78, 80], "595": 78, "702427": 78, "147": [78, 83, 87], "711186": 78, "157": [78, 83], "721394": 78, "771": 78, "731979": 78, "740335": 78, "0014153602099278074": 78, "1562": 78, "393": 78, "156217": 78, "391": 78, "806": 78, "805": 78, "156": [78, 83], "na": [78, 79, 82, 83, 85], "issue_result": 78, "000842": 78, "555944": 78, "004374": 78, "sorted_issu": 78, "73": [78, 80, 82, 86, 87, 90, 91], "deserv": 78, "outlier_result": 78, "sorted_outli": 78, "56": [78, 80, 90, 91], "lt": [78, 79, 80, 82, 85, 91], "style": [78, 91], "font": 78, "18px": 78, "ff00ff": 78, "bac": 78, "unintend": [78, 79], "mistak": [78, 79, 82, 92, 93], "duplicate_result": 78, "690": 78, "246": [78, 87], "perhap": [78, 83, 85], "twice": 78, "67": [78, 80, 82, 87, 90, 91], "wari": [78, 79, 81], "super": [78, 79, 82], "system": [78, 79, 82, 91], "intent": [79, 93], "servic": [79, 81, 93], "onlin": [79, 93], "bank": [79, 80, 93], "banking77": [79, 93], "oo": [79, 93], "000": [79, 80, 82, 93, 94], "categori": [79, 82, 93], "scope": [79, 93], "dive": 79, "your_featur": 79, "sentence_transform": [79, 93], "sentencetransform": [79, 93], "payment": [79, 93], "cancel_transf": [79, 93], "transfer": [79, 93], "fund": [79, 93], "cancel": [79, 93], "transact": [79, 93], "my": [79, 93], "revert": [79, 93], "morn": [79, 93], "realis": [79, 93], "yesterdai": [79, 93], "rent": [79, 93], "realli": [79, 85, 91, 93], "tomorrow": [79, 93], "raw_text": [79, 93], "card_about_to_expir": [79, 93], "getting_spare_card": [79, 93], "lost_or_stolen_phon": [79, 93], "apple_pay_or_google_pai": [79, 93], "change_pin": [79, 93], "supported_cards_and_curr": [79, 93], "beneficiary_not_allow": [79, 93], "visa_or_mastercard": [79, 93], "card_payment_fee_charg": [79, 93], "utter": [79, 93], "continu": [79, 81, 82, 85, 90, 92, 93, 94], "suit": [79, 80, 81, 93], "electra": [79, 93], "discrimin": [79, 93], "googl": [79, 93], "text_embed": 79, "No": [79, 81, 93], "google_electra": [79, 93], "pool": [79, 81, 88, 93], "400": [79, 93], "data_dict": [79, 83, 85], "84": [79, 87, 91], "41": [79, 80, 87, 90, 91], "38": [79, 80, 87, 91], "9720": 79, "981": 79, "974": 79, "000150": 79, "982": [79, 80], "000218": 79, "971": 79, "000512": 79, "980": [79, 80], "000947": 79, "3584": 79, "994": 79, "009642": 79, "999": 79, "013067": 79, "013841": 79, "433": 79, "014722": 79, "989": 79, "018224": 79, "6070": 79, "160": [79, 83], "095724": 79, "148": 79, "006237": 79, "546": 79, "099340": 79, "514": 79, "006485": 79, "481": 79, "123416": 79, "008165": 79, "313": [79, 87], "564102": 79, "572258": 79, "28": [79, 80, 82, 83, 85, 91, 94], "574915": 79, "31": [79, 80, 83, 85, 87, 91], "575507": 79, "575874": 79, "658": 79, "659": [79, 90], "660": 79, "661": 79, "0800": 79, "454": 79, "453": 79, "455": 79, "791961": 79, "258508": 79, "699010": 79, "183136": 79, "771112": 79, "to_numpi": [79, 81, 90, 93], "data_with_suggested_label": 79, "suggested_label": 79, "charg": [79, 93], "cash": [79, 93], "holidai": [79, 93], "sent": [79, 93, 94], "card": [79, 80, 93], "mine": [79, 93], "expir": [79, 93], "me": [79, 93], "withdraw": 79, "monei": 79, "whoever": [79, 93], "outlier_issu": [79, 82], "lowest_quality_outli": 79, "OR": 79, "636c65616e6c616220697320617765736f6d6521": 79, "phone": [79, 80], "gone": 79, "gt": [79, 85, 94], "samp": 79, "br": 79, "press": [79, 94], "nonsens": 79, "sens": 79, "detriment": 79, "duplicate_issu": 79, "fee": 79, "pai": 79, "go": [79, 80, 83], "strongli": 79, "p_valu": 79, "benign": 79, "shortlist": [79, 90, 93], "curat": [79, 84], "mnist_test_set": 80, "imagenet_val_set": 80, "tench": 80, "goldfish": 80, "white": [80, 94], "shark": 80, "tiger": 80, "hammerhead": 80, "electr": 80, "rai": 80, "stingrai": 80, "cock": 80, "hen": 80, "ostrich": 80, "brambl": 80, "goldfinch": 80, "hous": 80, "finch": 80, "junco": 80, "indigo": 80, "bunt": 80, "american": [80, 94], "robin": 80, "bulbul": 80, "jai": 80, "magpi": 80, "chickade": 80, "dipper": 80, "kite": 80, "bald": 80, "eagl": 80, "vultur": 80, "grei": 80, "owl": 80, "fire": 80, "salamand": 80, "smooth": 80, "newt": 80, "spot": [80, 87], "axolotl": 80, "bullfrog": 80, "tree": 80, "frog": [80, 88], "tail": 80, "loggerhead": 80, "sea": 80, "turtl": 80, "leatherback": 80, "mud": 80, "terrapin": 80, "band": 80, "gecko": 80, "green": [80, 94], "iguana": 80, "carolina": 80, "anol": 80, "desert": 80, "grassland": 80, "whiptail": 80, "lizard": 80, "agama": 80, "frill": 80, "neck": 80, "allig": 80, "gila": 80, "monster": 80, "european": 80, "chameleon": 80, "komodo": 80, "dragon": 80, "nile": 80, "crocodil": 80, "triceratop": 80, "worm": 80, "snake": 80, "ring": 80, "eastern": 80, "hog": 80, "nose": 80, "kingsnak": 80, "garter": 80, "water": 80, "vine": 80, "night": 80, "boa": 80, "constrictor": 80, "african": 80, "rock": 80, "indian": 80, "cobra": 80, "mamba": 80, "saharan": 80, "horn": 80, "viper": 80, "diamondback": 80, "rattlesnak": 80, "sidewind": 80, "trilobit": 80, "harvestman": 80, "scorpion": 80, "yellow": 80, "garden": 80, "spider": 80, "barn": 80, "southern": 80, "widow": 80, "tarantula": 80, "wolf": 80, "tick": 80, "centiped": 80, "grous": 80, "ptarmigan": 80, "ruf": 80, "prairi": 80, "peacock": 80, "quail": 80, "partridg": 80, "parrot": 80, "macaw": 80, "sulphur": 80, "crest": 80, "cockatoo": 80, "lorikeet": 80, "coucal": 80, "bee": 80, "eater": 80, "hornbil": 80, "hummingbird": 80, "jacamar": 80, "toucan": 80, "duck": [80, 93], "breast": 80, "mergans": 80, "goos": 80, "swan": 80, "tusker": 80, "echidna": 80, "platypu": 80, "wallabi": 80, "koala": 80, "wombat": 80, "jellyfish": 80, "anemon": 80, "brain": 80, "coral": 80, "flatworm": 80, "nematod": 80, "conch": 80, "snail": 80, "slug": 80, "chiton": 80, "chamber": 80, "nautilu": 80, "dung": 80, "crab": 80, "fiddler": 80, "king": 80, "lobster": 80, "spini": 80, "crayfish": 80, "hermit": 80, "isopod": 80, "stork": 80, "spoonbil": 80, "flamingo": 80, "heron": 80, "egret": 80, "bittern": 80, "crane": 80, "bird": [80, 88], "limpkin": 80, "gallinul": 80, "coot": 80, "bustard": 80, "ruddi": 80, "turnston": 80, "dunlin": 80, "redshank": 80, "dowitch": 80, "oystercatch": 80, "pelican": 80, "penguin": 80, "albatross": 80, "whale": 80, "killer": 80, "dugong": 80, "lion": 80, "chihuahua": 80, "japanes": 80, "chin": 80, "maltes": 80, "pekinges": 80, "shih": 80, "tzu": 80, "charl": 80, "spaniel": 80, "papillon": 80, "terrier": 80, "rhodesian": 80, "ridgeback": 80, "afghan": [80, 94], "hound": 80, "basset": 80, "beagl": 80, "bloodhound": 80, "bluetick": 80, "coonhound": 80, "tan": 80, "walker": 80, "foxhound": 80, "redbon": 80, "borzoi": 80, "irish": 80, "wolfhound": 80, "italian": 80, "greyhound": 80, "whippet": 80, "ibizan": 80, "norwegian": 80, "elkhound": 80, "otterhound": 80, "saluki": 80, "scottish": 80, "deerhound": 80, "weimaran": 80, "staffordshir": 80, "bull": 80, "bedlington": 80, "border": 80, "kerri": 80, "norfolk": 80, "norwich": 80, "yorkshir": 80, "wire": 80, "fox": 80, "lakeland": 80, "sealyham": 80, "airedal": 80, "cairn": 80, "australian": 80, "dandi": 80, "dinmont": 80, "boston": 80, "miniatur": 80, "schnauzer": 80, "giant": 80, "tibetan": 80, "silki": 80, "coat": [80, 82], "wheaten": 80, "west": 80, "highland": 80, "lhasa": 80, "apso": 80, "flat": 80, "retriev": 80, "curli": 80, "golden": 80, "labrador": 80, "chesapeak": 80, "bai": 80, "german": [80, 94], "shorthair": 80, "pointer": 80, "vizsla": 80, "setter": 80, "gordon": 80, "brittani": 80, "clumber": 80, "springer": 80, "welsh": 80, "cocker": 80, "sussex": 80, "kuvasz": 80, "schipperk": 80, "groenendael": 80, "malinoi": 80, "briard": 80, "kelpi": 80, "komondor": 80, "sheepdog": 80, "shetland": 80, "colli": 80, "bouvier": 80, "de": 80, "flandr": 80, "rottweil": 80, "shepherd": 80, "dobermann": 80, "pinscher": 80, "swiss": [80, 94], "mountain": 80, "bernes": 80, "appenzel": 80, "sennenhund": 80, "entlebuch": 80, "boxer": 80, "bullmastiff": 80, "mastiff": 80, "french": 80, "bulldog": 80, "dane": 80, "st": 80, "bernard": 80, "huski": 80, "alaskan": 80, "malamut": 80, "siberian": 80, "dalmatian": 80, "affenpinsch": 80, "basenji": 80, "pug": 80, "leonberg": 80, "newfoundland": 80, "pyrenean": 80, "samoi": 80, "pomeranian": 80, "chow": 80, "keeshond": 80, "griffon": 80, "bruxelloi": 80, "pembrok": 80, "corgi": 80, "cardigan": 80, "poodl": 80, "mexican": 80, "hairless": 80, "tundra": 80, "coyot": 80, "dingo": 80, "dhole": 80, "wild": 80, "hyena": 80, "kit": 80, "arctic": 80, "tabbi": 80, "persian": 80, "siames": 80, "egyptian": 80, "mau": 80, "cougar": 80, "lynx": 80, "leopard": 80, "snow": 80, "jaguar": 80, "cheetah": 80, "brown": [80, 91], "bear": 80, "polar": 80, "sloth": 80, "mongoos": 80, "meerkat": 80, "beetl": 80, "ladybug": 80, "longhorn": 80, "leaf": 80, "rhinocero": 80, "weevil": 80, "fly": 80, "ant": 80, "grasshopp": 80, "cricket": 80, "stick": 80, "insect": 80, "cockroach": 80, "manti": 80, "cicada": 80, "leafhopp": 80, "lacew": 80, "dragonfli": 80, "damselfli": 80, "admir": 80, "ringlet": 80, "monarch": 80, "butterfli": 80, "gossam": 80, "wing": 80, "starfish": 80, "urchin": 80, "cucumb": 80, "cottontail": 80, "rabbit": 80, "hare": 80, "angora": 80, "hamster": 80, "porcupin": 80, "squirrel": 80, "marmot": 80, "beaver": 80, "guinea": 80, "pig": 80, "sorrel": 80, "zebra": 80, "boar": 80, "warthog": 80, "hippopotamu": 80, "ox": 80, "buffalo": 80, "bison": 80, "bighorn": 80, "sheep": 80, "alpin": 80, "ibex": 80, "hartebeest": 80, "impala": 80, "gazel": 80, "dromedari": 80, "llama": 80, "weasel": 80, "mink": 80, "polecat": 80, "foot": 80, "ferret": 80, "otter": 80, "skunk": 80, "badger": 80, "armadillo": 80, "toed": 80, "orangutan": 80, "gorilla": 80, "chimpanze": 80, "gibbon": 80, "siamang": 80, "guenon": 80, "pata": 80, "monkei": 80, "baboon": 80, "macaqu": 80, "langur": 80, "colobu": 80, "probosci": 80, "marmoset": 80, "capuchin": 80, "howler": 80, "titi": 80, "geoffroi": 80, "lemur": 80, "indri": 80, "asian": 80, "eleph": 80, "bush": 80, "snoek": 80, "eel": 80, "coho": 80, "salmon": 80, "beauti": 80, "clownfish": 80, "sturgeon": 80, "garfish": 80, "lionfish": 80, "pufferfish": 80, "abacu": 80, "abaya": 80, "academ": 80, "gown": 80, "accordion": 80, "acoust": 80, "guitar": 80, "aircraft": 80, "carrier": 80, "airlin": 80, "airship": 80, "altar": 80, "ambul": 80, "amphibi": 80, "clock": [80, 94], "apiari": 80, "apron": 80, "wast": 80, "assault": 80, "rifl": 80, "backpack": 80, "bakeri": 80, "balanc": 80, "beam": 80, "balloon": 80, "ballpoint": 80, "pen": 80, "aid": 80, "banjo": 80, "balust": 80, "barbel": 80, "barber": 80, "chair": [80, 87], "barbershop": 80, "baromet": 80, "barrel": 80, "wheelbarrow": 80, "basebal": 80, "basketbal": 80, "bassinet": 80, "bassoon": 80, "swim": 80, "cap": 80, "bath": 80, "towel": 80, "bathtub": 80, "station": 80, "wagon": 80, "lighthous": 80, "beaker": 80, "militari": 80, "beer": 80, "bottl": 80, "glass": 80, "bell": 80, "cot": 80, "bib": 80, "bicycl": [80, 91], "bikini": 80, "binder": 80, "binocular": 80, "birdhous": 80, "boathous": 80, "bobsleigh": 80, "bolo": 80, "tie": 80, "poke": 80, "bonnet": 80, "bookcas": 80, "bookstor": 80, "bow": 80, "brass": 80, "bra": 80, "breakwat": 80, "breastplat": 80, "broom": 80, "bucket": 80, "buckl": 80, "bulletproof": 80, "vest": 80, "butcher": 80, "shop": 80, "taxicab": 80, "cauldron": 80, "candl": 80, "cannon": 80, "cano": 80, "mirror": [80, 87], "carousel": 80, "tool": [80, 83, 85], "carton": 80, "wheel": 80, "teller": 80, "cassett": 80, "player": 80, "castl": 80, "catamaran": 80, "cd": 80, "cello": 80, "mobil": [80, 94], "chain": 80, "fenc": [80, 91], "mail": 80, "chainsaw": 80, "chest": 80, "chiffoni": 80, "chime": 80, "china": 80, "cabinet": 80, "christma": 80, "stock": 80, "church": 80, "movi": 80, "theater": 80, "cleaver": 80, "cliff": 80, "dwell": 80, "cloak": 80, "clog": 80, "cocktail": 80, "shaker": 80, "coffe": 80, "mug": 80, "coffeemak": 80, "coil": 80, "lock": 80, "keyboard": 80, "confectioneri": 80, "ship": [80, 88], "corkscrew": 80, "cornet": 80, "cowboi": 80, "boot": 80, "hat": 80, "cradl": 80, "crash": 80, "helmet": 80, "crate": 80, "infant": 80, "bed": 80, "crock": 80, "pot": 80, "croquet": 80, "crutch": 80, "cuirass": 80, "dam": 80, "desk": 80, "desktop": 80, "rotari": 80, "dial": 80, "telephon": 80, "diaper": 80, "watch": 80, "dine": 80, "dishcloth": 80, "dishwash": 80, "disc": 80, "brake": 80, "dock": 80, "sled": 80, "dome": 80, "doormat": 80, "drill": 80, "rig": 80, "drum": 80, "drumstick": 80, "dumbbel": 80, "dutch": 80, "oven": 80, "fan": 80, "locomot": 80, "entertain": 80, "center": 80, "envelop": 80, "espresso": 80, "powder": 80, "feather": 80, "fireboat": 80, "engin": [80, 91], "screen": 80, "sheet": 80, "flagpol": 80, "flute": 80, "footbal": 80, "forklift": 80, "fountain": 80, "poster": 80, "freight": 80, "fry": 80, "pan": 80, "fur": 80, "garbag": 80, "ga": 80, "pump": 80, "goblet": 80, "kart": 80, "golf": 80, "cart": 80, "gondola": 80, "gong": 80, "grand": 80, "piano": 80, "greenhous": 80, "grill": 80, "groceri": 80, "guillotin": 80, "barrett": 80, "hair": 80, "sprai": 80, "hammer": 80, "dryer": 80, "hand": [80, 83], "handkerchief": 80, "drive": 80, "harmonica": 80, "harp": 80, "harvest": 80, "hatchet": 80, "holster": 80, "honeycomb": 80, "hoop": 80, "skirt": 80, "horizont": 80, "bar": 80, "hors": [80, 88, 93], "drawn": 80, "hourglass": 80, "ipod": 80, "cloth": 80, "iron": 80, "jack": 80, "lantern": 80, "jean": 80, "jeep": 80, "shirt": [80, 82], "jigsaw": 80, "puzzl": 80, "pull": 80, "rickshaw": 80, "joystick": 80, "kimono": 80, "knee": 80, "pad": 80, "knot": 80, "ladl": 80, "lampshad": 80, "laptop": 80, "lawn": 80, "mower": 80, "knife": 80, "lifeboat": 80, "lighter": 80, "limousin": 80, "ocean": 80, "liner": 80, "lipstick": 80, "slip": 80, "shoe": 80, "lotion": 80, "speaker": 80, "loup": 80, "sawmil": 80, "magnet": 80, "compass": 80, "bag": [80, 82, 88, 89], "mailbox": 80, "tight": 80, "tank": 80, "manhol": 80, "maraca": 80, "marimba": 80, "maypol": 80, "maze": 80, "cup": [80, 87], "medicin": 80, "megalith": 80, "microphon": 80, "microwav": 80, "milk": 80, "minibu": 80, "miniskirt": 80, "minivan": 80, "missil": 80, "mitten": 80, "mix": 80, "bowl": 80, "modem": 80, "monasteri": 80, "monitor": 80, "mope": 80, "mortar": 80, "mosqu": 80, "mosquito": 80, "scooter": 80, "bike": 80, "tent": 80, "mous": [80, 81], "mousetrap": 80, "van": 80, "muzzl": 80, "nail": 80, "brace": 80, "necklac": 80, "nippl": 80, "obelisk": 80, "obo": 80, "ocarina": 80, "odomet": 80, "oil": 80, "oscilloscop": 80, "overskirt": 80, "bullock": 80, "oxygen": 80, "packet": 80, "paddl": 80, "padlock": 80, "paintbrush": 80, "pajama": 80, "palac": [80, 94], "parachut": 80, "park": 80, "bench": 80, "meter": 80, "passeng": 80, "patio": 80, "payphon": 80, "pedest": 80, "pencil": 80, "perfum": 80, "petri": 80, "dish": 80, "photocopi": 80, "plectrum": 80, "pickelhaub": 80, "picket": 80, "pickup": 80, "pier": 80, "piggi": 80, "pill": 80, "pillow": 80, "ping": 80, "pong": 80, "pinwheel": 80, "pirat": 80, "pitcher": 80, "plane": 80, "planetarium": 80, "plastic": 80, "plate": 80, "rack": 80, "plow": 80, "plunger": 80, "polaroid": 80, "camera": 80, "pole": [80, 91], "polic": 80, "poncho": 80, "billiard": 80, "soda": 80, "potter": 80, "prayer": 80, "rug": 80, "printer": 80, "prison": 80, "projectil": 80, "projector": 80, "hockei": 80, "puck": 80, "punch": 80, "purs": 80, "quill": 80, "quilt": 80, "race": 80, "racket": 80, "radiat": 80, "radio": 80, "telescop": 80, "rain": 80, "recreat": 80, "reel": 80, "reflex": 80, "refriger": 80, "remot": 80, "restaur": 80, "revolv": 80, "rotisseri": 80, "eras": 80, "rugbi": 80, "ruler": 80, "safe": 80, "safeti": 80, "salt": 80, "sandal": [80, 82], "sarong": 80, "saxophon": 80, "scabbard": 80, "school": 80, "bu": [80, 91], "schooner": 80, "scoreboard": 80, "crt": 80, "screw": 80, "screwdriv": 80, "seat": 80, "belt": 80, "sew": 80, "shield": 80, "shoji": 80, "basket": 80, "shovel": 80, "shower": 80, "curtain": 80, "ski": 80, "sleep": 80, "door": 80, "slot": 80, "snorkel": 80, "snowmobil": 80, "snowplow": 80, "soap": 80, "dispens": 80, "soccer": [80, 94], "sock": 80, "solar": 80, "thermal": 80, "collector": 80, "sombrero": 80, "soup": 80, "heater": 80, "shuttl": 80, "spatula": 80, "motorboat": 80, "web": 80, "spindl": 80, "sport": [80, 94], "spotlight": 80, "stage": 80, "steam": 80, "arch": 80, "bridg": 80, "steel": 80, "stethoscop": 80, "scarf": 80, "stone": 80, "wall": [80, 91], "stopwatch": 80, "stove": 80, "strainer": 80, "tram": 80, "stretcher": 80, "couch": 80, "stupa": 80, "submarin": 80, "sundial": 80, "sunglass": 80, "sunscreen": 80, "suspens": 80, "mop": 80, "sweatshirt": 80, "swimsuit": 80, "swing": 80, "switch": 80, "syring": 80, "lamp": 80, "tape": 80, "teapot": 80, "teddi": 80, "televis": [80, 94], "tenni": 80, "thatch": 80, "roof": 80, "front": 80, "thimbl": 80, "thresh": 80, "throne": 80, "tile": 80, "toaster": 80, "tobacco": 80, "toilet": 80, "totem": 80, "tow": 80, "tractor": 80, "semi": 80, "trailer": 80, "trai": 80, "trench": 80, "tricycl": 80, "trimaran": 80, "tripod": 80, "triumphal": 80, "trolleybu": 80, "trombon": 80, "tub": 80, "turnstil": 80, "typewrit": 80, "umbrella": 80, "unicycl": 80, "upright": 80, "vacuum": 80, "cleaner": 80, "vase": 80, "vault": 80, "velvet": 80, "vend": 80, "vestment": 80, "viaduct": 80, "violin": 80, "volleybal": 80, "waffl": 80, "wallet": 80, "wardrob": 80, "sink": 80, "wash": 80, "jug": 80, "tower": 80, "whiskei": 80, "whistl": 80, "wig": 80, "shade": [80, 91], "windsor": 80, "wine": 80, "wok": 80, "wooden": 80, "spoon": 80, "wool": 80, "rail": 80, "shipwreck": 80, "yawl": 80, "yurt": 80, "websit": 80, "comic": 80, "book": 80, "crossword": 80, "traffic": [80, 87, 91], "sign": [80, 91, 94], "dust": 80, "jacket": [80, 87], "menu": 80, "guacamol": 80, "consomm": 80, "trifl": 80, "ic": 80, "cream": 80, "pop": 80, "baguett": 80, "bagel": 80, "pretzel": 80, "cheeseburg": 80, "mash": 80, "potato": 80, "cabbag": 80, "broccoli": 80, "cauliflow": 80, "zucchini": 80, "spaghetti": 80, "squash": 80, "acorn": 80, "butternut": 80, "artichok": 80, "pepper": 80, "cardoon": 80, "mushroom": 80, "granni": 80, "smith": 80, "strawberri": 80, "orang": 80, "lemon": 80, "pineappl": 80, "banana": 80, "jackfruit": 80, "custard": 80, "appl": 80, "pomegran": 80, "hai": 80, "carbonara": 80, "chocol": 80, "syrup": 80, "dough": 80, "meatloaf": 80, "pizza": 80, "pie": 80, "burrito": 80, "eggnog": 80, "alp": 80, "bubbl": 80, "reef": 80, "geyser": 80, "lakeshor": 80, "promontori": 80, "shoal": 80, "seashor": 80, "vallei": 80, "volcano": 80, "bridegroom": 80, "scuba": 80, "diver": 80, "rapese": 80, "daisi": 80, "ladi": 80, "slipper": 80, "corn": 80, "rose": 80, "hip": 80, "chestnut": 80, "fungu": 80, "agar": 80, "gyromitra": 80, "stinkhorn": 80, "earth": 80, "star": 80, "wood": 80, "bolet": 80, "ear": 80, "cifar10_test_set": 80, "airplan": [80, 88], "automobil": [80, 88], "deer": [80, 88], "cifar100_test_set": 80, "aquarium_fish": 80, "babi": 80, "boi": 80, "camel": 80, "caterpillar": 80, "cattl": [80, 94], "cloud": 80, "dinosaur": 80, "dolphin": 80, "flatfish": 80, "forest": 80, "girl": 80, "kangaroo": 80, "lawn_mow": 80, "man": 80, "maple_tre": 80, "motorcycl": [80, 91], "oak_tre": 80, "orchid": 80, "palm_tre": 80, "pear": 80, "pickup_truck": 80, "pine_tre": 80, "plain": 80, "poppi": 80, "possum": 80, "raccoon": 80, "road": [80, 91], "rocket": 80, "seal": 80, "shrew": 80, "skyscrap": 80, "streetcar": 80, "sunflow": 80, "sweet_pepp": 80, "trout": 80, "tulip": 80, "willow_tre": 80, "woman": [80, 87], "caltech256": 80, "ak47": 80, "bat": 80, "glove": 80, "birdbath": 80, "blimp": 80, "bonsai": 80, "boom": 80, "breadmak": 80, "buddha": 80, "bulldoz": 80, "cactu": 80, "cake": 80, "tire": 80, "cartman": 80, "cereal": 80, "chandeli": 80, "chess": 80, "board": 80, "chimp": 80, "chopstick": 80, "coffin": 80, "coin": 80, "comet": 80, "cormor": 80, "globe": 80, "diamond": 80, "dice": 80, "doorknob": 80, "drink": 80, "straw": 80, "dumb": 80, "eiffel": 80, "elk": 80, "ewer": 80, "eyeglass": 80, "fern": 80, "fighter": 80, "jet": [80, 90], "extinguish": 80, "hydrant": 80, "firework": 80, "flashlight": 80, "floppi": 80, "fri": 80, "frisbe": 80, "galaxi": 80, "giraff": 80, "goat": 80, "gate": 80, "grape": 80, "pick": [80, 81], "hamburg": 80, "hammock": 80, "harpsichord": 80, "hawksbil": 80, "helicopt": 80, "hibiscu": 80, "homer": 80, "simpson": 80, "horsesho": 80, "air": 80, "skeleton": 80, "ibi": 80, "cone": 80, "iri": 80, "jesu": 80, "christ": 80, "joi": 80, "kayak": 80, "ketch": 80, "ladder": 80, "lath": 80, "licens": 80, "lightbulb": 80, "lightn": 80, "mandolin": 80, "mar": 80, "mattress": 80, "megaphon": 80, "menorah": 80, "microscop": 80, "minaret": 80, "minotaur": 80, "motorbik": 80, "mussel": 80, "neckti": 80, "octopu": 80, "palm": 80, "pilot": 80, "paperclip": 80, "shredder": 80, "pci": 80, "peopl": [80, 87], "pez": 80, "picnic": 80, "pram": 80, "prai": 80, "pyramid": 80, "rainbow": 80, "roulett": 80, "saddl": 80, "saturn": 80, "segwai": 80, "propel": 80, "sextant": 80, "music": 80, "skateboard": 80, "smokestack": 80, "sneaker": 80, "boat": 80, "stain": 80, "steer": 80, "stirrup": 80, "superman": 80, "sushi": 80, "armi": [80, 94], "sword": 80, "tambourin": 80, "teepe": 80, "court": 80, "theodolit": 80, "tomato": 80, "tombston": 80, "tour": 80, "pisa": 80, "treadmil": 80, "fork": 80, "tweezer": 80, "unicorn": 80, "vcr": 80, "waterfal": 80, "watermelon": 80, "weld": 80, "windmil": 80, "xylophon": 80, "yarmulk": 80, "yo": 80, "toad": 80, "twenty_news_test_set": 80, "alt": 80, "atheism": 80, "comp": 80, "graphic": [80, 91], "misc": [80, 94], "sy": 80, "ibm": 80, "pc": 80, "hardwar": 80, "mac": 80, "forsal": 80, "rec": 80, "sci": 80, "crypt": 80, "electron": 80, "med": 80, "soc": 80, "religion": 80, "christian": [80, 94], "talk": [80, 94], "polit": 80, "gun": 80, "mideast": 80, "amazon": 80, "neutral": 80, "imdb_test_set": 80, "all_class": 80, "20news_test_set": 80, "_load_classes_predprobs_label": 80, "dataset_nam": 80, "labelerror": 80, "url_bas": 80, "5392f6c71473055060be3044becdde1cbc18284d": 80, "url_label": 80, "original_test_label": 80, "_original_label": 80, "url_prob": 80, "cross_validated_predicted_prob": 80, "_pyx": 80, "num_part": 80, "datatset": 80, "bytesio": 80, "allow_pickl": 80, "pred_probs_part": 80, "url": 80, "_of_": 80, "nload": 80, "imdb": 80, "ve": [80, 81, 83, 85, 87], "interpret": [80, 81, 83], "capit": 80, "29780": 80, "256": [80, 81, 87], "780": 80, "medic": [80, 94], "doctor": 80, "254": [80, 87], "359223": 80, "640777": 80, "184": [80, 83], "258427": 80, "341176": 80, "263158": 80, "658824": 80, "337349": 80, "246575": 80, "662651": 80, "248": 80, "330000": 80, "355769": 80, "670000": 80, "251": [80, 87], "167": [80, 83, 87], "252": 80, "112": 80, "253": [80, 87], "022989": 80, "255": [80, 82], "049505": 80, "190": [80, 83, 87], "66": [80, 82, 91], "002216": 80, "000974": 80, "59": [80, 87, 91], "88": [80, 82, 83, 86, 87, 90, 91], "000873": 80, "000739": 80, "79": [80, 87, 91, 92], "32635": 80, "32636": 80, "47": [80, 82, 87, 91], "32637": 80, "32638": 80, "32639": 80, "32640": 80, "051": 80, "002242": 80, "997758": 80, "002088": 80, "001045": 80, "997912": 80, "002053": 80, "997947": 80, "001980": 80, "000991": 80, "998020": 80, "001946": 80, "002915": 80, "998054": 80, "001938": 80, "002904": 80, "998062": 80, "001020": 80, "998980": 80, "001018": 80, "002035": 80, "998982": 80, "999009": 80, "0003": 80, "0002": 80, "36": [80, 91, 94], "44": [80, 86, 87, 91], "71": [80, 83, 87, 91], "071": 80, "067269": 80, "929": 80, "046": 80, "058243": 80, "954": 80, "035": 80, "032096": 80, "965": 80, "031": 80, "012232": 80, "969": 80, "022": 80, "025896": 80, "978": 80, "020": [80, 83], "013092": 80, "018": 80, "013065": 80, "016": 80, "030542": 80, "984": 80, "013": 80, "020833": 80, "987": 80, "012": 80, "010020": 80, "988": 80, "0073": 80, "0020": 80, "0016": 80, "0015": 80, "0013": 80, "0012": 80, "0010": 80, "0008": 80, "0007": 80, "0006": 80, "0005": 80, "0004": 80, "244": [80, 87, 94], "98": [80, 81, 90, 91], "452381": 80, "459770": 80, "72": [80, 82, 83, 86, 90, 91], "523364": 80, "460784": 80, "446602": 80, "57": [80, 82, 83, 91, 94], "68": [80, 82, 83, 87, 91, 92], "103774": 80, "030612": 80, "97": [80, 81, 83, 87, 90, 91, 92, 94], "110092": 80, "049020": 80, "99": [80, 83, 91, 92, 94], "0034": 80, "0032": 80, "0026": 80, "0025": 80, "4945": 80, "4946": 80, "4947": 80, "4948": 80, "4949": 80, "4950": 80, "846": 80, "82": [80, 82, 83, 87, 91], "7532": 80, "532": 80, "034483": 80, "009646": 80, "965517": 80, "030457": 80, "020513": 80, "969543": 80, "028061": 80, "035443": 80, "971939": 80, "025316": 80, "005168": 80, "974684": 80, "049751": 80, "979487": 80, "019920": 80, "042802": 80, "980080": 80, "017677": 80, "005115": 80, "982323": 80, "012987": 80, "005236": 80, "987013": 80, "012723": 80, "025126": 80, "987277": 80, "010989": 80, "008264": 80, "989011": 80, "010283": 80, "027778": 80, "989717": 80, "009677": 80, "990323": 80, "007614": 80, "010127": 80, "992386": 80, "005051": 80, "994949": 80, "005025": 80, "994975": 80, "005013": 80, "994987": 80, "001859": 80, "001328": 80, "000929": 80, "000664": 80, "186": [80, 83], "188": [80, 83, 86], "189": [80, 83], "snippet": 81, "nlp": [81, 94], "mind": [81, 83], "number_of_class": 81, "total_number_of_data_point": 81, "drop": [81, 85, 90, 93], "feed": 81, "alphabet": 81, "labels_proper_format": 81, "your_classifi": 81, "issues_datafram": 81, "class_predicted_for_flagged_exampl": 81, "class_predicted_for_all_exampl": 81, "grant": 81, "datataset": 81, "fair": [81, 83], "game": 81, "speedup": [81, 88], "flexibl": 81, "tempfil": 81, "mkdtemp": 81, "sped": 81, "anywai": 81, "pred_probs_merg": 81, "merge_rare_class": 81, "count_threshold": 81, "class_mapping_orig2new": 81, "heath_summari": 81, "num_examples_per_class": 81, "rare_class": 81, "num_classes_merg": 81, "other_class": 81, "labels_merg": 81, "new_c": 81, "merged_prob": 81, "hstack": [81, 82, 83, 85], "new_class": 81, "original_class": 81, "num_check": 81, "ones_array_ref": 81, "isclos": 81, "though": [81, 83, 94], "successfulli": 81, "meaning": [81, 88], "virtuou": [81, 85], "cycl": [81, 85], "jointli": 81, "junk": 81, "clutter": 81, "unknown": 81, "caltech": 81, "combined_boolean_mask": 81, "mask1": 81, "mask2": 81, "gradientboostingclassifi": [81, 83], "true_error": [81, 83, 86], "101": [81, 87], "102": [81, 86, 87], "104": [81, 83, 87], "model_to_find_error": 81, "model_to_return": 81, "cl0": 81, "randomizedsearchcv": 81, "expens": 81, "param_distribut": 81, "learning_r": [81, 83], "max_depth": [81, 83], "magnitud": 81, "coeffici": [81, 90], "optin": 81, "environ": [81, 83], "rerun": [81, 83], "cell": [81, 83], "On": [81, 83, 87], "unabl": [81, 83], "render": [81, 83], "nbviewer": [81, 83], "cleanlearningcleanlearn": [81, 83], "linearregressionlinearregress": 81, "n_init": 81, "fit_predict": 81, "continuous_column": 81, "categorical_column": 81, "data_df": 81, "feature_a": 81, "feature_b": 81, "unexpectedli": 81, "emphas": 81, "especi": [81, 82, 90, 92, 93], "crucial": 81, "merge_duplicate_set": 81, "merge_kei": 81, "construct_group_kei": 81, "merged_set": 81, "consolidate_set": 81, "tolist": [81, 86], "issubset": 81, "frozenset": 81, "sets_list": 81, "mutabl": 81, "new_set": 81, "current_set": 81, "intersecting_set": 81, "lowest_score_strategi": 81, "sub_df": 81, "idxmin": 81, "filter_near_dupl": 81, "strategy_fn": 81, "strategy_kwarg": 81, "duplicate_row": 81, "group_kei": 81, "to_keep_indic": 81, "groupbi": 81, "explod": 81, "to_remov": 81, "isin": [81, 88], "kept": 81, "near_duplicate_issu": [81, 82], "ids_to_remove_seri": 81, "assist": 81, "streamlin": 81, "ux": 81, "agpl": 81, "compani": 81, "commerci": 81, "alter": 81, "email": 81, "discuss": 81, "anywher": 81, "profession": 81, "expert": 81, "60": [82, 83, 91], "excess": 82, "torchvis": [82, 88], "tensordataset": 82, "stratifiedkfold": [82, 86], "tqdm": 82, "fashion_mnist": 82, "num_row": 82, "60000": 82, "pil": 82, "transformed_dataset": 82, "with_format": 82, "unsqueez": 82, "cpu_count": 82, "torch_dataset": 82, "quick": [82, 86], "relu": 82, "batchnorm2d": 82, "maxpool2d": 82, "lazylinear": 82, "flatten": 82, "get_test_accuraci": 82, "testload": [82, 88], "energi": 82, "trainload": [82, 88], "n_epoch": 82, "patienc": 82, "criterion": 82, "crossentropyloss": 82, "adamw": 82, "best_test_accuraci": 82, "start_epoch": 82, "running_loss": 82, "best_epoch": 82, "end_epoch": 82, "3f": [82, 90], "acc": [82, 83], "time_taken": 82, "compute_embed": 82, "compute_pred_prob": 82, "train_batch_s": 82, "num_work": 82, "worker": [82, 94], "train_id_list": 82, "test_id_list": 82, "train_id": 82, "test_id": 82, "embeddings_model": 82, "ntrain": 82, "trainset": 82, "testset": 82, "pin_memori": 82, "fold_embed": 82, "fold_pred_prob": 82, "finish": 82, "483": 82, "835": 82, "643": 82, "331": 82, "310": 82, "363": 82, "stderr": [82, 91], "sphinxverbatim": [82, 91, 94], "30it": [82, 91], "55it": [82, 91], "40it": [82, 91], "39it": [82, 91], "90it": [82, 91], "63": [82, 83, 87, 91], "24it": [82, 91], "69it": [82, 91], "91it": [82, 91], "62": [82, 83, 87, 90, 91], "34it": [82, 91], "86it": [82, 91], "32it": [82, 91], "492": 82, "085": 82, "550": 82, "290": [82, 87], "573": 82, "49it": [82, 91], "97it": [82, 91], "64it": [82, 91], "05it": [82, 91], "01it": [82, 91], "60it": [82, 91], "38it": [82, 91], "98it": [82, 91], "15it": [82, 91], "37it": [82, 91], "476": 82, "305": [82, 90], "625": 82, "328": [82, 87], "335": 82, "235": [82, 87], "42it": [82, 91], "62it": [82, 91], "79it": [82, 91], "65it": 82, "85": [82, 87, 90, 91], "57it": [82, 91], "00it": [82, 91], "54it": [82, 91], "21it": [82, 91], "16it": [82, 91], "36it": [82, 91], "02it": [82, 91], "reorder": 82, "vision": 82, "grayscal": 82, "exce": 82, "max_preval": 82, "7620": 82, "3692": 82, "3521": 82, "225": [82, 86], "166": 82, "3691": 82, "40378": 82, "943831e": 82, "54473": 82, "066211e": 82, "06": [82, 83, 87, 91, 94], "29412": 82, "899069e": 82, "25316": 82, "984817e": 82, "52247": 82, "245879e": 82, "9581": 82, "19228": 82, "dress": 82, "54078": 82, "000010": 82, "pullov": 82, "32657": 82, "21282": 82, "000011": 82, "11262": 82, "000014": 82, "6294": 82, "30659": 82, "000798": 82, "30968": 82, "000015": 82, "258": 82, "000907": 82, "9762": 82, "54565": 82, "47139": 82, "000017": 82, "001423": 82, "000026": 82, "39992": 82, "39993": 82, "39994": 82, "39995": 82, "7834": 82, "42819": 82, "629362": 82, "51431": 82, "654330": 82, "55548": 82, "658364": 82, "51191": 82, "668572": 82, "50081": 82, "669703": 82, "7834321613629787": 82, "13732": 82, "13733": 82, "13734": 82, "47635": 82, "110901": 82, "974390": 82, "998733": 82, "937117": 82, "998755": 82, "53564": 82, "5473": 82, "trouser": 82, "plot_label_issue_exampl": 82, "ncol": [82, 88], "nrow": [82, 88], "ceil": 82, "axes_list": 82, "label_issue_indic": 82, "gl": 82, "sl": 82, "fontdict": 82, "imshow": [82, 88], "cmap": [82, 90], "grai": 82, "subplots_adjust": 82, "hspace": 82, "outsiz": 82, "outlier_issues_df": 82, "depict": [82, 86, 87, 88, 89, 91], "plot_outlier_issues_exampl": 82, "n_comparison_imag": 82, "sample_from_class": 82, "number_of_sampl": 82, "non_outlier_indic": 82, "isnul": 82, "non_outlier_indices_excluding_curr": 82, "sampled_indic": 82, "label_scores_of_sampl": 82, "top_score_indic": 82, "top_label_indic": 82, "sampled_imag": 82, "get_image_given_label_and_sampl": 82, "image_from_dataset": 82, "corresponding_label": 82, "comparison_imag": 82, "images_to_plot": 82, "idlist": 82, "iterrow": 82, "closest": 82, "counterpart": 82, "near_duplicate_issues_df": 82, "plot_near_duplicate_issue_exampl": 82, "seen_id_pair": 82, "get_image_and_given_label_and_predicted_label": 82, "duplicate_imag": 82, "nd_set": 82, "challeng": 82, "dark_issu": 82, "reveal": [82, 91], "dark_scor": 82, "dark_issues_df": 82, "is_dark_issu": 82, "34848": 82, "203922": 82, "50270": 82, "204588": 82, "3936": 82, "213098": 82, "733": 82, "217686": 82, "8094": 82, "230118": 82, "plot_image_issue_exampl": 82, "difficult": 82, "disproportion": 82, "lowinfo_issu": 82, "low_information_scor": 82, "lowinfo_issues_df": 82, "is_low_information_issu": 82, "53050": 82, "067975": 82, "40875": 82, "089929": 82, "9594": 82, "092601": 82, "34825": 82, "107744": 82, "37530": 82, "108516": 82, "lot": 82, "depth": 83, "survei": [83, 94], "focus": [83, 85], "scienc": 83, "multivariate_norm": [83, 85, 86], "make_data": [83, 85], "cov": [83, 85, 86], "avg_trac": [83, 86], "test_label": [83, 86, 88, 93], "py_tru": 83, "noise_matrix_tru": 83, "noise_marix": 83, "s_test": 83, "noisy_test_label": 83, "purpl": 83, "val": 83, "namespac": 83, "exec": 83, "markerfacecolor": [83, 86], "markeredgecolor": [83, 86, 90], "markers": [83, 86, 90], "markeredgewidth": [83, 86, 90], "realist": 83, "7560": 83, "638483e": 83, "897052e": 83, "548986e": 83, "924634e": 83, "374580e": 83, "3454": 83, "014051": 83, "020451": 83, "249": [83, 87], "042594": 83, "043859": 83, "045954": 83, "6120": 83, "023714": 83, "007136": 83, "119": [83, 87], "107266": 83, "103": [83, 87], "033738": 83, "238": [83, 87], "119505": 83, "236": [83, 87], "037843": 83, "222": 83, "614915": 83, "122": [83, 87], "624422": 83, "625965": 83, "626079": 83, "118": 83, "627675": 83, "158": 83, "159": [83, 86, 87], "161": 83, "1960": 83, "196": [83, 87], "223": [83, 87], "221": 83, "219": [83, 87], "695174": 83, "323529": 83, "522929": 83, "013722": 83, "675606": 83, "646438": 83, "anyth": 83, "enhanc": [83, 85, 87], "magic": 83, "83": [83, 87, 90, 91, 92, 94], "liter": 83, "identif": 83, "x27": 83, "logisticregressionlogisticregress": 83, "ever": 83, "092": 83, "040": 83, "024": 83, "004": 83, "surpris": 83, "1705": 83, "01936": 83, "ton": 83, "yourfavoritemodel1": 83, "merged_label": 83, "merged_test_label": 83, "newli": [83, 85], "yourfavoritemodel2": 83, "yourfavoritemodel3": 83, "cl3": 83, "takeawai": 83, "That": [83, 86], "randomli": 83, "my_test_pred_prob": 83, "my_test_pr": 83, "issues_test": 83, "corrected_test_label": 83, "pretend": 83, "cl_test_pr": 83, "69": [83, 90, 91], "fairli": 83, "label_acc": 83, "percentag": 83, "offset": 83, "nquestion": 83, "overestim": 83, "answer": 83, "experienc": 83, "76": [83, 86, 87, 90, 91, 92], "knowledg": 83, "quantiti": [83, 90], "prioiri": 83, "known": 83, "versatil": 83, "label_issues_indic": 83, "213": [83, 87], "212": [83, 92], "218": [83, 87], "152": 83, "197": [83, 87], "170": 83, "214": 83, "164": [83, 86], "198": [83, 87, 94], "191": [83, 87], "121": [83, 93, 94], "117": [83, 90], "206": [83, 87], "115": [83, 87], "193": 83, "194": 83, "201": [83, 87], "174": 83, "163": 83, "150": [83, 85, 87], "169": 83, "151": [83, 87], "168": 83, "precision_scor": 83, "recall_scor": 83, "f1_score": 83, "true_label_issu": 83, "filter_by_list": 83, "718750": [83, 85], "807018": 83, "912": 83, "733333": 83, "800000": 83, "721311": 83, "792793": 83, "908": 83, "676923": 83, "765217": 83, "892": 83, "567901": 83, "702290": 83, "844": 83, "gaug": 83, "label_issues_count": 83, "155": [83, 87], "172": [83, 86], "easiest": 83, "modular": 83, "penalti": 83, "l2": 83, "model3": 83, "n_estim": 83, "cv_pred_probs_1": 83, "cv_pred_probs_2": 83, "cv_pred_probs_3": 83, "label_quality_scores_best": 83, "cv_pred_probs_ensembl": 83, "label_quality_scores_bett": 83, "superior": [83, 89], "workflow": [84, 90], "speechbrain": 84, "timm": 84, "glad": 85, "multiannotator_label": 85, "noisier": 85, "111": [85, 90], "local_data": [85, 86], "true_labels_train": [85, 86], "noise_matrix_bett": 85, "noise_matrix_wors": 85, "transpos": [85, 88], "dropna": 85, "zfill": 85, "row_na_check": 85, "notna": 85, "reset_index": 85, "a0001": 85, "a0002": 85, "a0003": 85, "a0004": 85, "a0005": 85, "a0006": 85, "a0007": 85, "a0008": 85, "a0009": 85, "a0010": 85, "a0041": 85, "a0042": 85, "a0043": 85, "a0044": 85, "a0045": 85, "a0046": 85, "a0047": 85, "a0048": 85, "a0049": 85, "a0050": 85, "60856743": 85, "41693214": 85, "40908785": 85, "87147629": 85, "64941785": 85, "10774851": 85, "0524466": 85, "71853246": 85, "37169848": 85, "66031048": 85, "multiannotator_util": 85, "crude": 85, "straight": 85, "majority_vote_label": 85, "736157": 85, "757738": 85, "782255": 85, "715585": 85, "824273": 85, "quality_annotator_a0001": 85, "quality_annotator_a0002": 85, "quality_annotator_a0003": 85, "quality_annotator_a0004": 85, "quality_annotator_a0005": 85, "quality_annotator_a0006": 85, "quality_annotator_a0007": 85, "quality_annotator_a0008": 85, "quality_annotator_a0009": 85, "quality_annotator_a0010": 85, "quality_annotator_a0041": 85, "quality_annotator_a0042": 85, "quality_annotator_a0043": 85, "quality_annotator_a0044": 85, "quality_annotator_a0045": 85, "quality_annotator_a0046": 85, "quality_annotator_a0047": 85, "quality_annotator_a0048": 85, "quality_annotator_a0049": 85, "quality_annotator_a0050": 85, "070551": 85, "216064": 85, "119178": 85, "alongisd": 85, "244982": 85, "208333": 85, "295978": 85, "294118": 85, "324194": 85, "310345": 85, "355315": 85, "346154": 85, "439728": 85, "480000": 85, "a0031": 85, "523205": 85, "580645": 85, "a0034": 85, "535313": 85, "607143": 85, "a0021": 85, "607002": 85, "a0015": 85, "609527": 85, "678571": 85, "a0011": 85, "621101": 85, "692308": 85, "wors": 85, "improved_consensus_label": 85, "majority_vote_accuraci": 85, "cleanlab_label_accuraci": 85, "8581081081081081": 85, "9797297297297297": 85, "besid": 85, "sorted_consensus_quality_scor": 85, "worst_qual": 85, "better_qu": 85, "worst_quality_accuraci": 85, "better_quality_accuraci": 85, "9893238434163701": 85, "improved_pred_prob": 85, "treat": [85, 86, 90, 94], "analzi": 85, "copyright": 86, "advertis": 86, "violenc": 86, "nsfw": 86, "ranked_label_issu": [86, 92, 93], "multioutput": 86, "multioutputclassifi": 86, "celeba": 86, "make_multilabel_data": 86, "boxes_coordin": 86, "box_multilabel": 86, "make_multi": 86, "bx1": 86, "by1": 86, "bx2": 86, "by2": 86, "label_list": 86, "ur": 86, "upper": 86, "inidx": 86, "logical_and": 86, "inv_d": 86, "labels_idx": 86, "true_labels_test": 86, "dict_unique_label": 86, "get_color_arrai": 86, "dcolor": 86, "aa4400": 86, "55227f": 86, "55a100": 86, "00ff00": 86, "007f7f": 86, "386b55": 86, "0000ff": 86, "simplic": 86, "advis": 86, "y_onehot": 86, "single_class_label": 86, "stratifi": [86, 89], "kf": 86, "train_index": 86, "test_index": 86, "clf_cv": 86, "x_train_cv": 86, "x_test_cv": 86, "y_train_cv": 86, "y_test_cv": 86, "y_pred_cv": 86, "saw": 86, "num_to_displai": 86, "09": [86, 87, 91], "275": 86, "267": 86, "171": 86, "234": 86, "165": 86, "227": [86, 87], "262": [86, 87], "263": [86, 87], "266": [86, 87], "139": 86, "143": [86, 87, 94], "216": [86, 87, 94], "265": 86, "despit": [86, 94], "suspect": 86, "888": 86, "8224": 86, "9632": 86, "968": 86, "6512": 86, "0444": 86, "774": 86, "labels_binary_format": 86, "labels_list_format": 86, "surround": 87, "scene": 87, "coco": 87, "everydai": 87, "has_label_issu": 87, "insal": 87, "nc": [87, 91, 94], "s3": [87, 91, 94], "amazonaw": [87, 91, 94], "objectdetectionbenchmark": 87, "tutorial_obj": 87, "pkl": 87, "example_imag": 87, "unzip": [87, 94], "begin": 87, "detectron2": 87, "image_path": 87, "rb": 87, "image_to_visu": 87, "seg_map": 87, "334": 87, "float32": 87, "bboxes_ignor": 87, "286": 87, "285": 87, "224": 87, "231": [87, 94], "293": 87, "289": [87, 90], "282": 87, "74": [87, 90, 91, 92], "281": 87, "271": 87, "280": 87, "277": 87, "279": 87, "287": 87, "299": 87, "276": 87, "307": 87, "321": 87, "326": 87, "333": 87, "261": 87, "319": 87, "257": 87, "295": 87, "283": 87, "243": 87, "303": 87, "316": 87, "247": 87, "323": 87, "327": 87, "226": 87, "228": 87, "232": 87, "239": 87, "240": 87, "209": 87, "242": 87, "202": 87, "230": 87, "215": 87, "220": 87, "229": 87, "217": [87, 94], "237": 87, "207": 87, "204": 87, "205": 87, "153": 87, "149": 87, "140": 87, "124": 87, "268": 87, "273": 87, "108": 87, "284": 87, "110": 87, "136": 87, "145": 87, "173": 87, "297": 87, "317": 87, "192": 87, "329": 87, "332": 87, "324": 87, "203": 87, "320": 87, "314": 87, "199": 87, "291": 87, "000000481413": 87, "jpg": 87, "42398": 87, "44503": 87, "337": [87, 93], "29968": 87, "336": 87, "21005": 87, "9978472": 87, "forgot": 87, "drew": 87, "label_issue_idx": 87, "num_examples_to_show": 87, "113": [87, 90], "candid": 87, "97489622": 87, "70610878": 87, "98764951": 87, "88899237": 87, "99085805": 87, "issue_idx": 87, "95569726e": 87, "03354841e": 87, "57510169e": 87, "58447666e": 87, "39755858e": 87, "suppli": 87, "issue_to_visu": 87, "000000009483": 87, "95569726168054e": 87, "addition": [87, 91], "visibl": 87, "missmatch": 87, "likelei": 87, "agnost": 87, "vaidat": 87, "inconsist": 87, "000000395701": 87, "033548411774308e": 87, "armchair": 87, "tv": 87, "000000154004": 87, "38300759625496356": 87, "foreground": 87, "000000448410": 87, "0008575101690203273": 87, "crowd": 87, "alon": 87, "explor": [87, 88], "resembl": [87, 88], "000000499768": 87, "9748962231208227": 87, "000000521141": 87, "8889923658893665": 87, "000000143931": 87, "9876495074395956": 87, "train_feature_embed": 88, "ood_train_feature_scor": 88, "test_feature_embed": 88, "ood_test_feature_scor": 88, "ood_train_predictions_scor": 88, "train_pred_prob": 88, "ood_test_predictions_scor": 88, "test_pred_prob": 88, "pylab": 88, "rcparam": 88, "baggingclassifi": 88, "therebi": 88, "rescal": 88, "transform_norm": 88, "totensor": 88, "root": 88, "animal_class": 88, "non_animal_class": 88, "animal_idx": 88, "test_idx": 88, "toronto": 88, "edu": 88, "kriz": 88, "5000": 88, "plot_imag": 88, "visualize_outli": 88, "txt_class": 88, "img": [88, 90], "npimg": 88, "show_label": 88, "data_subset": 88, "resnet50": 88, "corpu": 88, "2048": 88, "embed_imag": 88, "create_model": 88, "rwightman": 88, "v0": 88, "rsb": 88, "resnet50_a1_0": 88, "14fe96d1": 88, "pth": 88, "checkpoint": 88, "strang": 88, "odd": 88, "train_ood_features_scor": 88, "top_train_ood_features_idx": 88, "fun": 88, "negat": 88, "homogen": 88, "bottom_train_ood_features_idx": 88, "test_ood_features_scor": 88, "top_ood_features_idx": 88, "inevit": 88, "trade": 88, "5th": 88, "percentil": 88, "fifth_percentil": 88, "plt_rang": 88, "hist": 88, "train_outlier_scor": 88, "ylabel": 88, "axvlin": 88, "test_outlier_scor": 88, "ood_features_indic": 88, "revisit": 88, "unusu": 88, "return_invers": 88, "train_feature_embeddings_sc": 88, "test_feature_embeddings_sc": 88, "train_pred_label": 88, "9702": 88, "train_ood_predictions_scor": 88, "test_ood_predictions_scor": 88, "mainli": [88, 94], "lost": 88, "unsuit": 89, "ok": [89, 94], "convention": 89, "aforement": 89, "hypothet": 89, "contrast": 89, "tradit": 89, "disjoint": 89, "out_of_sample_pred_probs_for_a": 89, "out_of_sample_pred_probs_for_b": 89, "out_of_sample_pred_probs_for_c": 89, "out_of_sample_pred_prob": 89, "price": 90, "incom": 90, "ag": 90, "histgradientboostingregressor": 90, "r2_score": 90, "student_grades_r": 90, "final_scor": 90, "true_final_scor": 90, "homework": 90, "3d": 90, "hue": 90, "mpl_toolkit": 90, "mplot3d": 90, "axes3d": 90, "errors_idx": 90, "add_subplot": 90, "z": 90, "colorbar": 90, "errors_mask": 90, "feature_column": 90, "predicted_column": 90, "x_train_raw": 90, "x_test_raw": 90, "categorical_featur": [90, 92], "randomforestregressor": 90, "629763": 90, "521450": 90, "954607": 90, "547234": 90, "338296": 90, "754531": 90, "619090": 90, "312295": 90, "806626": 90, "784048": 90, "identified_issu": [90, 93], "367": 90, "560": 90, "318": 90, "688": 90, "657": 90, "view_datapoint": 90, "concat": 90, "consum": [90, 93], "baseline_model": [90, 93], "preds_og": 90, "r2_og": 90, "838": 90, "robustli": [90, 92, 93], "acceler": [90, 93], "found_label_issu": 90, "preds_cl": 90, "r2_cl": 90, "925": 90, "effort": [90, 92, 93], "favorit": 90, "13091885": 90, "48412548": 90, "00695165": 90, "44421119": 90, "43029854": 90, "synthia": 91, "imagesegment": 91, "given_mask": 91, "predicted_mask": 91, "set_printopt": [91, 94], "sky": 91, "sidewalk": 91, "veget": 91, "terrain": 91, "rider": 91, "pred_probs_filepath": 91, "1088": 91, "1920": 91, "label_filepath": 91, "synthia_class": 91, "maunal": 91, "100000": 91, "244800": 91, "leftmost": 91, "area": 91, "middl": [91, 94], "infact": 91, "rightmost": 91, "discrep": 91, "4997817": 91, "16894": 91, "168930": 91, "09it": 91, "33840": 91, "169232": 91, "87it": 91, "50764": 91, "168912": 91, "67681": 91, "169008": 91, "95it": 91, "84582": 91, "168597": 91, "17it": 91, "101562": 91, "169001": 91, "77it": 91, "118463": 91, "168946": 91, "135439": 91, "169203": 91, "06it": 91, "152375": 91, "169249": 91, "23it": 91, "169316": 91, "169296": 91, "89it": 91, "186292": 91, "169436": 91, "203347": 91, "169773": 91, "68it": 91, "220405": 91, "170016": 91, "96it": 91, "237407": 91, "169928": 91, "254477": 91, "170158": 91, "45it": 91, "271493": 91, "163317": 91, "46it": 91, "288574": 91, "165502": 91, "305691": 91, "167167": 91, "31it": 91, "322684": 91, "167981": 91, "339600": 91, "168329": 91, "84it": 91, "356742": 91, "169247": 91, "373883": 91, "169890": 91, "33it": 91, "390996": 91, "170256": 91, "83it": 91, "408029": 91, "170233": 91, "59it": 91, "425064": 91, "170265": 91, "58it": 91, "442095": 91, "170175": 91, "459116": 91, "169906": 91, "476109": 91, "169776": 91, "11it": 91, "493088": 91, "169632": 91, "510053": 91, "169530": 91, "04it": 91, "527116": 91, "169855": 91, "10it": 91, "544102": 91, "169441": 91, "47it": 91, "561135": 91, "169705": 91, "92it": 91, "578144": 91, "169819": 91, "595127": 91, "169307": 91, "78it": 91, "612059": 91, "168901": 91, "50it": 91, "628950": 91, "168898": 91, "645884": 91, "169026": 91, "35it": 91, "662787": 91, "168976": 91, "71it": 91, "679685": 91, "168905": 91, "61it": 91, "696576": 91, "168804": 91, "713464": 91, "168825": 91, "730347": 91, "168773": 91, "94it": 91, "747247": 91, "168838": 91, "764233": 91, "169140": 91, "44it": 91, "781186": 91, "169252": 91, "22it": 91, "798112": 91, "167898": 91, "815132": 91, "168581": 91, "63it": 91, "832361": 91, "169686": 91, "849444": 91, "170023": 91, "43it": 91, "866543": 91, "170309": 91, "883576": 91, "170166": 91, "900734": 91, "170585": 91, "917906": 91, "170923": 91, "19it": 91, "935029": 91, "171010": 91, "93it": 91, "952131": 91, "170707": 91, "969203": 91, "163872": 91, "986256": 91, "165809": 91, "1003150": 91, "166727": 91, "1020118": 91, "167597": 91, "66it": 91, "1037173": 91, "168470": 91, "1054270": 91, "169210": 91, "25it": 91, "1071205": 91, "168701": 91, "48it": 91, "1088132": 91, "168870": 91, "1105026": 91, "167979": 91, "1122032": 91, "03it": 91, "1139097": 91, "169207": 91, "1156205": 91, "169765": 91, "1173249": 91, "169965": 91, "1190325": 91, "170201": 91, "1207355": 91, "170227": 91, "1224496": 91, "170578": 91, "51it": 91, "1241555": 91, "170573": 91, "1258641": 91, "170656": 91, "1275707": 91, "169747": 91, "1292837": 91, "170209": 91, "1309860": 91, "166504": 91, "1327006": 91, "167961": 91, "20it": 91, "1344103": 91, "168850": 91, "1361223": 91, "169546": 91, "1378268": 91, "169813": 91, "1395358": 91, "170134": 91, "1412443": 91, "170344": 91, "1429592": 91, "170683": 91, "1446663": 91, "170624": 91, "1463728": 91, "170574": 91, "1480787": 91, "170117": 91, "1497806": 91, "170137": 91, "1514821": 91, "170012": 91, "1531832": 91, "170038": 91, "1548896": 91, "170215": 91, "1566000": 91, "170459": 91, "41it": 91, "1583047": 91, "170272": 91, "52it": 91, "1600075": 91, "170017": 91, "1617077": 91, "169842": 91, "1634062": 91, "169498": 91, "1651013": 91, "168545": 91, "1668143": 91, "169363": 91, "67it": 91, "1685225": 91, "169793": 91, "1702217": 91, "169829": 91, "27it": 91, "1719201": 91, "169735": 91, "1736176": 91, "169526": 91, "1753215": 91, "169781": 91, "56it": 91, "1770194": 91, "169779": 91, "29it": 91, "1787173": 91, "169599": 91, "1804134": 91, "169303": 91, "75it": 91, "1821065": 91, "162204": 91, "1838101": 91, "164570": 91, "88it": 91, "1855142": 91, "166280": 91, "1872038": 91, "167067": 91, "1889187": 91, "168375": 91, "1906283": 91, "169143": 91, "1923326": 91, "169524": 91, "1940290": 91, "169381": 91, "1957260": 91, "169473": 91, "1974242": 91, "169572": 91, "1991316": 91, "169920": 91, "2008311": 91, "169715": 91, "14it": 91, "2025285": 91, "169583": 91, "2042256": 91, "169618": 91, "2059371": 91, "170075": 91, "2076380": 91, "169403": 91, "2093428": 91, "169721": 91, "2110449": 91, "169865": 91, "2127507": 91, "170076": 91, "2144516": 91, "169981": 91, "2161515": 91, "169803": 91, "2178496": 91, "169117": 91, "72it": 91, "2195409": 91, "168534": 91, "2212388": 91, "168906": 91, "2229280": 91, "168637": 91, "2246219": 91, "168857": 91, "2263106": 91, "168820": 91, "2279989": 91, "168568": 91, "2296887": 91, "168651": 91, "70it": 91, "2313821": 91, "168856": 91, "2330819": 91, "169189": 91, "2347801": 91, "169374": 91, "2364749": 91, "2381869": 91, "169939": 91, "2398864": 91, "169919": 91, "2415940": 91, "170168": 91, "2432957": 91, "170055": 91, "2449963": 91, "170008": 91, "2466964": 91, "169952": 91, "2483960": 91, "169636": 91, "2500924": 91, "169525": 91, "2517877": 91, "169246": 91, "2534802": 91, "168648": 91, "2551859": 91, "169220": 91, "2568790": 91, "169243": 91, "74it": 91, "2585772": 91, "169413": 91, "2602736": 91, "169477": 91, "2619684": 91, "169184": 91, "2636634": 91, "169276": 91, "2653622": 91, "169453": 91, "2670568": 91, "169322": 91, "80it": 91, "2687501": 91, "169134": 91, "2704449": 91, "169234": 91, "73it": 91, "2721441": 91, "169438": 91, "2738385": 91, "2755322": 91, "168970": 91, "2772343": 91, "169339": 91, "2789278": 91, "169325": 91, "2806348": 91, "169733": 91, "2823429": 91, "170053": 91, "2840435": 91, "169836": 91, "2857419": 91, "169579": 91, "2874445": 91, "2891517": 91, "170059": 91, "2908868": 91, "171088": 91, "81it": 91, "2925996": 91, "171143": 91, "2943183": 91, "171359": 91, "2960320": 91, "171311": 91, "2977540": 91, "171575": 91, "2994733": 91, "171677": 91, "3011937": 91, "171783": 91, "3029257": 91, "172203": 91, "3046478": 91, "169427": 91, "3063586": 91, "169913": 91, "3080733": 91, "170374": 91, "3097777": 91, "170379": 91, "3114863": 91, "170521": 91, "3131918": 91, "170371": 91, "3148958": 91, "3166085": 91, "170095": 91, "3183249": 91, "170553": 91, "3200429": 91, "170922": 91, "3217523": 91, "3234631": 91, "170758": 91, "3251708": 91, "170664": 91, "3268775": 91, "170027": 91, "3285907": 91, "170384": 91, "3302958": 91, "170419": 91, "3320005": 91, "170431": 91, "3337049": 91, "170277": 91, "3354101": 91, "170345": 91, "3371136": 91, "170228": 91, "3388160": 91, "169938": 91, "3405154": 91, "169827": 91, "3422211": 91, "170045": 91, "3439365": 91, "170489": 91, "3456541": 91, "170867": 91, "26it": 91, "3473744": 91, "171210": 91, "3491131": 91, "172003": 91, "3508380": 91, "172145": 91, "3525653": 91, "172317": 91, "82it": 91, "3543024": 91, "172730": 91, "3560298": 91, "172529": 91, "3577646": 91, "172812": 91, "12it": 91, "3594928": 91, "172575": 91, "3612186": 91, "172357": 91, "3629560": 91, "172769": 91, "3647045": 91, "173390": 91, "18it": 91, "3664444": 91, "173567": 91, "3681801": 91, "173545": 91, "3699161": 91, "173557": 91, "3716548": 91, "173647": 91, "3733913": 91, "173069": 91, "3751244": 91, "173136": 91, "3768646": 91, "173398": 91, "3786035": 91, "173543": 91, "3803496": 91, "173859": 91, "3820883": 91, "173726": 91, "3838320": 91, "173916": 91, "3855712": 91, "173459": 91, "3873059": 91, "172994": 91, "3890432": 91, "173205": 91, "3907753": 91, "173026": 91, "3925056": 91, "172923": 91, "3942349": 91, "172426": 91, "3959737": 91, "172857": 91, "3977024": 91, "172594": 91, "3994284": 91, "172302": 91, "4011554": 91, "172417": 91, "4028936": 91, "172835": 91, "07it": 91, "4046220": 91, "172545": 91, "4063475": 91, "172273": 91, "4080783": 91, "172511": 91, "4098035": 91, "172243": 91, "4115260": 91, "171411": 91, "4132402": 91, "170566": 91, "4149460": 91, "169858": 91, "4166447": 91, "4183346": 91, "168708": 91, "4200264": 91, "168846": 91, "4217215": 91, "169042": 91, "4234122": 91, "169046": 91, "4251190": 91, "169532": 91, "4268278": 91, "169933": 91, "4285272": 91, "169817": 91, "4302254": 91, "169255": 91, "4319210": 91, "169345": 91, "4336149": 91, "169354": 91, "4353220": 91, "169758": 91, "4370197": 91, "169598": 91, "4387158": 91, "169274": 91, "4404086": 91, "168827": 91, "4421002": 91, "168923": 91, "4437895": 91, "168606": 91, "4454904": 91, "169048": 91, "4471810": 91, "168624": 91, "4488768": 91, "168907": 91, "4505693": 91, "169006": 91, "4522681": 91, "169265": 91, "4539608": 91, "169250": 91, "4556534": 91, "169120": 91, "4573478": 91, "169212": 91, "4590405": 91, "169225": 91, "99it": 91, "4607328": 91, "168951": 91, "4624224": 91, "168422": 91, "4641067": 91, "168260": 91, "4657969": 91, "168483": 91, "4674900": 91, "168729": 91, "4691857": 91, "168977": 91, "4708755": 91, "168949": 91, "4725694": 91, "169078": 91, "4742602": 91, "168968": 91, "4759560": 91, "169149": 91, "53it": 91, "4776476": 91, "163091": 91, "4792993": 91, "163697": 91, "4809801": 91, "164984": 91, "4826610": 91, "165895": 91, "4843220": 91, "165180": 91, "4860457": 91, "167314": 91, "4877808": 91, "169156": 91, "4895138": 91, "170391": 91, "4912284": 91, "170708": 91, "4929461": 91, "171024": 91, "4946762": 91, "171616": 91, "4963929": 91, "171628": 91, "4981169": 91, "171855": 91, "3263230": 91, "783379": 91, "275110": 91, "255792": 91, "78225": 91, "55990": 91, "54427": 91, "33591": 91, "24645": 91, "21308": 91, "15045": 91, "14171": 91, "13832": 91, "13498": 91, "11490": 91, "9164": 91, "8769": 91, "6999": 91, "6031": 91, "5011": 91, "mistakenli": 91, "class_issu": 91, "aim": [91, 94], "domin": 91, "extratreesclassifi": 92, "extratre": 92, "labelencod": [92, 93], "labels_raw": 92, "interg": [92, 93], "tress": 92, "827": 92, "cheat": 92, "0pt": 92, "233": 92, "labels_train": 92, "labels_test": 92, "acc_og": [92, 93], "783068783068783": 92, "acc_cl": [92, 93], "8095238095238095": 92, "earlier": [93, 94], "raw_label": 93, "raw_train_text": 93, "raw_test_text": 93, "raw_train_label": 93, "raw_test_label": 93, "encond": 93, "train_text": 93, "test_text": 93, "858050": 93, "545854": 93, "826194": 93, "965814": 93, "791923": 93, "646": 93, "390": 93, "628": 93, "702": 93, "863": 93, "135": 93, "735": 93, "print_as_df": 93, "inverse_transform": 93, "fight": 93, "bunch": 94, "conll": 94, "2003": 94, "love": 94, "n_i": 94, "optional_list_of_ordered_class_nam": 94, "deepai": 94, "conll2003": 94, "rm": 94, "tokenclassif": 94, "2024": 94, "179": 94, "2400": 94, "52e0": 94, "1a01": 94, "996": 94, "connect": 94, "443": 94, "await": 94, "982975": 94, "960k": 94, "kb": 94, "959": 94, "94k": 94, "15mb": 94, "mb": 94, "directori": 94, "inflat": 94, "17045998": 94, "16m": 94, "octet": 94, "64k": 94, "597kb": 94, "10m": 94, "60mb": 94, "40m": 94, "6mb": 94, "12m": 94, "0mb": 94, "26m": 94, "1mb": 94, "bert": 94, "read_npz": 94, "filepath": 94, "corrsespond": 94, "iob2": 94, "given_ent": 94, "entity_map": 94, "readfil": 94, "sep": 94, "startswith": 94, "docstart": 94, "isalpha": 94, "isupp": 94, "indices_to_preview": 94, "nsentenc": 94, "eu": 94, "reject": 94, "boycott": 94, "british": 94, "lamb": 94, "00030412": 94, "00023826": 94, "99936208": 94, "00007009": 94, "00002545": 94, "99998795": 94, "00000401": 94, "00000218": 94, "00000455": 94, "00000131": 94, "00000749": 94, "99996115": 94, "00001371": 94, "0000087": 94, "00000895": 94, "99998936": 94, "00000382": 94, "00000178": 94, "00000366": 94, "00000137": 94, "99999101": 94, "00000266": 94, "00000174": 94, "0000035": 94, "00000109": 94, "99998768": 94, "00000482": 94, "00000202": 94, "00000438": 94, "0000011": 94, "00000465": 94, "99996392": 94, "00001105": 94, "0000116": 94, "00000878": 94, "99998671": 94, "00000364": 94, "00000213": 94, "00000472": 94, "00000281": 94, "99999073": 94, "00000211": 94, "00000159": 94, "00000442": 94, "00000115": 94, "peter": 94, "blackburn": 94, "00000358": 94, "00000529": 94, "99995623": 94, "000022": 94, "0000129": 94, "0000024": 94, "00001812": 94, "99994141": 94, "00001645": 94, "00002162": 94, "brussel": 94, "1996": 94, "00001172": 94, "00000821": 94, "00004661": 94, "0000618": 94, "99987167": 94, "99999061": 94, "00000201": 94, "00000195": 94, "00000408": 94, "00000135": 94, "2254": 94, "2907": 94, "19392": 94, "9962": 94, "8904": 94, "19303": 94, "12918": 94, "9256": 94, "11855": 94, "18392": 94, "20426": 94, "19402": 94, "14744": 94, "19371": 94, "4645": 94, "10331": 94, "9430": 94, "6143": 94, "18367": 94, "12914": 94, "todai": 94, "weather": 94, "march": 94, "scalfaro": 94, "northern": 94, "himself": 94, "said": 94, "germani": 94, "nastja": 94, "rysich": 94, "north": 94, "spla": 94, "fought": 94, "khartoum": 94, "govern": 94, "south": 94, "1983": 94, "autonomi": 94, "animist": 94, "region": 94, "moslem": 94, "arabis": 94, "mayor": 94, "antonio": 94, "gonzalez": 94, "garcia": 94, "revolutionari": 94, "parti": 94, "wednesdai": 94, "troop": 94, "raid": 94, "farm": 94, "stole": 94, "rape": 94, "women": 94, "spring": 94, "chg": 94, "hrw": 94, "12pct": 94, "princ": 94, "photo": 94, "moment": 94, "spokeswoman": 94, "rainier": 94, "told": 94, "reuter": 94, "danila": 94, "carib": 94, "w224": 94, "equip": 94, "radiomet": 94, "earn": 94, "19996": 94, "london": 94, "denom": 94, "sale": 94, "uk": 94, "jp": 94, "fr": 94, "maccabi": 94, "hapoel": 94, "haifa": 94, "tel": 94, "aviv": 94, "hospit": 94, "rever": 94, "roman": 94, "cathol": 94, "nun": 94, "admit": 94, "calcutta": 94, "week": 94, "ago": 94, "fever": 94, "vomit": 94, "allianc": 94, "embattl": 94, "kabul": 94, "salang": 94, "highwai": 94, "mondai": 94, "tuesdai": 94, "suprem": 94, "council": 94, "led": 94, "jumbish": 94, "milli": 94, "movement": 94, "warlord": 94, "abdul": 94, "rashid": 94, "dostum": 94, "dollar": 94, "exchang": 94, "3570": 94, "12049": 94, "born": 94, "1937": 94, "provinc": 94, "anhui": 94, "dai": 94, "came": 94, "shanghai": 94, "citi": 94, "prolif": 94, "author": 94, "teacher": 94, "chines": 94, "16764": 94, "1990": 94, "historian": 94, "alan": 94, "john": 94, "percival": 94, "taylor": 94, "di": 94, "20446": 94, "pace": 94, "bowler": 94, "ian": 94, "harvei": 94, "claim": 94, "victoria": 94, "15514": 94, "cotti": 94, "osc": 94, "foreign": 94, "minist": 94, "7525": 94, "sultan": 94, "specter": 94, "met": 94, "crown": 94, "abdullah": 94, "defenc": 94, "aviat": 94, "jeddah": 94, "saudi": 94, "agenc": 94, "2288": 94, "hi": 94, "customari": 94, "outfit": 94, "champion": 94, "damp": 94, "scalp": 94, "canada": 94, "reign": 94, "olymp": 94, "donovan": 94, "bailei": 94, "1992": 94, "linford": 94, "christi": 94, "britain": 94, "1984": 94, "1988": 94, "carl": 94, "lewi": 94, "ambigi": 94, "punctuat": 94, "chicago": 94, "digest": 94, "philadelphia": 94, "usda": 94, "york": 94, "token_issu": 94, "471": 94, "kean": 94, "year": 94, "contract": 94, "manchest": 94, "19072": 94, "societi": 94, "million": 94, "bite": 94, "deliv": 94, "19910": 94, "father": 94, "clarenc": 94, "woolmer": 94, "renam": 94, "uttar": 94, "pradesh": 94, "india": 94, "ranji": 94, "trophi": 94, "nation": 94, "championship": 94, "captain": 94, "1949": 94, "15658": 94, "19879": 94, "iii": 94, "brian": 94, "shimer": 94, "randi": 94, "jone": 94, "19104": 94}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [9, 0, 0, "-", "datalab"], [30, 0, 0, "-", "dataset"], [33, 0, 0, "-", "experimental"], [36, 0, 0, "-", "filter"], [37, 0, 0, "-", "internal"], [48, 0, 0, "-", "models"], [50, 0, 0, "-", "multiannotator"], [53, 0, 0, "-", "multilabel_classification"], [56, 0, 0, "-", "object_detection"], [59, 0, 0, "-", "outlier"], [60, 0, 0, "-", "rank"], [61, 0, 0, "-", "regression"], [65, 0, 0, "-", "segmentation"], [69, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.datalab": [[4, 0, 0, "-", "datalab"], [13, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[4, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[4, 4, 1, "", "class_names"], [4, 3, 1, "", "find_issues"], [4, 3, 1, "", "get_info"], [4, 3, 1, "", "get_issue_summary"], [4, 3, 1, "", "get_issues"], [4, 4, 1, "", "has_labels"], [4, 4, 1, "", "info"], [4, 4, 1, "", "issue_summary"], [4, 4, 1, "", "issues"], [4, 4, 1, "", "labels"], [4, 3, 1, "", "list_default_issue_types"], [4, 3, 1, "", "list_possible_issue_types"], [4, 3, 1, "", "load"], [4, 3, 1, "", "report"], [4, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[10, 0, 0, "-", "data"], [11, 0, 0, "-", "data_issues"], [14, 0, 0, "-", "issue_finder"], [12, 0, 0, "-", "issue_manager_factory"], [28, 0, 0, "-", "report"]], "cleanlab.datalab.internal.data": [[10, 2, 1, "", "Data"], [10, 5, 1, "", "DataFormatError"], [10, 5, 1, "", "DatasetDictError"], [10, 5, 1, "", "DatasetLoadError"], [10, 2, 1, "", "Label"]], "cleanlab.datalab.internal.data.Data": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[11, 2, 1, "", "DataIssues"], [11, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[11, 3, 1, "", "collect_issues_from_imagelab"], [11, 3, 1, "", "collect_issues_from_issue_manager"], [11, 3, 1, "", "collect_statistics"], [11, 3, 1, "", "get_info"], [11, 3, 1, "", "get_issue_summary"], [11, 3, 1, "", "get_issues"], [11, 6, 1, "", "info"], [11, 6, 1, "", "issue_summary"], [11, 6, 1, "", "issues"], [11, 3, 1, "", "set_health_score"], [11, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[14, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[14, 3, 1, "", "find_issues"], [14, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[16, 0, 0, "-", "data_valuation"], [17, 0, 0, "-", "duplicate"], [18, 0, 0, "-", "imbalance"], [20, 0, 0, "-", "issue_manager"], [21, 0, 0, "-", "label"], [22, 0, 0, "-", "noniid"], [23, 0, 0, "-", "null"], [24, 0, 0, "-", "outlier"], [27, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[16, 6, 1, "", "DEFAULT_THRESHOLD"], [16, 3, 1, "", "collect_info"], [16, 6, 1, "", "description"], [16, 3, 1, "", "find_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_name"], [16, 6, 1, "", "issue_score_key"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "make_summary"], [16, 3, 1, "", "report"], [16, 6, 1, "", "summary"], [16, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[17, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[17, 3, 1, "", "collect_info"], [17, 6, 1, "", "description"], [17, 3, 1, "", "find_issues"], [17, 6, 1, "", "info"], [17, 6, 1, "", "issue_name"], [17, 6, 1, "", "issue_score_key"], [17, 6, 1, "", "issues"], [17, 3, 1, "", "make_summary"], [17, 6, 1, "", "near_duplicate_sets"], [17, 3, 1, "", "report"], [17, 6, 1, "", "summary"], [17, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[18, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[18, 3, 1, "", "collect_info"], [18, 6, 1, "", "description"], [18, 3, 1, "", "find_issues"], [18, 6, 1, "", "info"], [18, 6, 1, "", "issue_name"], [18, 6, 1, "", "issue_score_key"], [18, 6, 1, "", "issues"], [18, 3, 1, "", "make_summary"], [18, 3, 1, "", "report"], [18, 6, 1, "", "summary"], [18, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[20, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[21, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 3, 1, "", "get_health_summary"], [21, 6, 1, "", "health_summary_parameters"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[22, 2, 1, "", "NonIIDIssueManager"], [22, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[23, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[24, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[24, 6, 1, "", "DEFAULT_THRESHOLDS"], [24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 6, 1, "", "ood"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[26, 2, 1, "", "RegressionLabelIssueManager"], [26, 1, 1, "", "find_issues_with_features"], [26, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[27, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[27, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [27, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "filter_cluster_ids"], [27, 3, 1, "", "find_issues"], [27, 3, 1, "", "get_worst_cluster"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "perform_clustering"], [27, 3, 1, "", "report"], [27, 3, 1, "", "set_knn_graph"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, 7, 1, "", "REGISTRY"], [12, 1, 1, "", "list_default_issue_types"], [12, 1, 1, "", "list_possible_issue_types"], [12, 1, 1, "", "register"]], "cleanlab.datalab.internal.report": [[28, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[28, 3, 1, "", "get_report"], [28, 3, 1, "", "report"]], "cleanlab.dataset": [[30, 1, 1, "", "find_overlapping_classes"], [30, 1, 1, "", "health_summary"], [30, 1, 1, "", "overall_label_health_score"], [30, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[31, 0, 0, "-", "cifar_cnn"], [32, 0, 0, "-", "coteaching"], [34, 0, 0, "-", "label_issues_batched"], [35, 0, 0, "-", "mnist_pytorch"]], "cleanlab.experimental.cifar_cnn": [[31, 2, 1, "", "CNN"], [31, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[31, 6, 1, "", "T_destination"], [31, 3, 1, "", "__call__"], [31, 3, 1, "", "add_module"], [31, 3, 1, "", "apply"], [31, 3, 1, "", "bfloat16"], [31, 3, 1, "", "buffers"], [31, 3, 1, "", "children"], [31, 3, 1, "", "cpu"], [31, 3, 1, "", "cuda"], [31, 3, 1, "", "double"], [31, 6, 1, "", "dump_patches"], [31, 3, 1, "", "eval"], [31, 3, 1, "", "extra_repr"], [31, 3, 1, "", "float"], [31, 3, 1, "id0", "forward"], [31, 3, 1, "", "get_buffer"], [31, 3, 1, "", "get_extra_state"], [31, 3, 1, "", "get_parameter"], [31, 3, 1, "", "get_submodule"], [31, 3, 1, "", "half"], [31, 3, 1, "", "ipu"], [31, 3, 1, "", "load_state_dict"], [31, 3, 1, "", "modules"], [31, 3, 1, "", "named_buffers"], [31, 3, 1, "", "named_children"], [31, 3, 1, "", "named_modules"], [31, 3, 1, "", "named_parameters"], [31, 3, 1, "", "parameters"], [31, 3, 1, "", "register_backward_hook"], [31, 3, 1, "", "register_buffer"], [31, 3, 1, "", "register_forward_hook"], [31, 3, 1, "", "register_forward_pre_hook"], [31, 3, 1, "", "register_full_backward_hook"], [31, 3, 1, "", "register_load_state_dict_post_hook"], [31, 3, 1, "", "register_module"], [31, 3, 1, "", "register_parameter"], [31, 3, 1, "", "requires_grad_"], [31, 3, 1, "", "set_extra_state"], [31, 3, 1, "", "share_memory"], [31, 3, 1, "", "state_dict"], [31, 3, 1, "", "to"], [31, 3, 1, "", "to_empty"], [31, 3, 1, "", "train"], [31, 6, 1, "", "training"], [31, 3, 1, "", "type"], [31, 3, 1, "", "xpu"], [31, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[32, 1, 1, "", "adjust_learning_rate"], [32, 1, 1, "", "evaluate"], [32, 1, 1, "", "forget_rate_scheduler"], [32, 1, 1, "", "initialize_lr_scheduler"], [32, 1, 1, "", "loss_coteaching"], [32, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[34, 2, 1, "", "LabelInspector"], [34, 7, 1, "", "adj_confident_thresholds_shared"], [34, 1, 1, "", "find_label_issues_batched"], [34, 7, 1, "", "labels_shared"], [34, 7, 1, "", "pred_probs_shared"], [34, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[34, 3, 1, "", "get_confident_thresholds"], [34, 3, 1, "", "get_label_issues"], [34, 3, 1, "", "get_num_issues"], [34, 3, 1, "", "get_quality_scores"], [34, 3, 1, "", "score_label_quality"], [34, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[35, 2, 1, "", "CNN"], [35, 2, 1, "", "SimpleNet"], [35, 1, 1, "", "get_mnist_dataset"], [35, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[35, 3, 1, "", "__init_subclass__"], [35, 6, 1, "", "batch_size"], [35, 6, 1, "", "dataset"], [35, 6, 1, "", "epochs"], [35, 3, 1, "id0", "fit"], [35, 3, 1, "", "get_metadata_routing"], [35, 3, 1, "", "get_params"], [35, 6, 1, "", "loader"], [35, 6, 1, "", "log_interval"], [35, 6, 1, "", "lr"], [35, 6, 1, "", "momentum"], [35, 6, 1, "", "no_cuda"], [35, 3, 1, "id1", "predict"], [35, 3, 1, "id4", "predict_proba"], [35, 6, 1, "", "seed"], [35, 3, 1, "", "set_fit_request"], [35, 3, 1, "", "set_params"], [35, 3, 1, "", "set_predict_proba_request"], [35, 3, 1, "", "set_predict_request"], [35, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[35, 6, 1, "", "T_destination"], [35, 3, 1, "", "__call__"], [35, 3, 1, "", "add_module"], [35, 3, 1, "", "apply"], [35, 3, 1, "", "bfloat16"], [35, 3, 1, "", "buffers"], [35, 3, 1, "", "children"], [35, 3, 1, "", "cpu"], [35, 3, 1, "", "cuda"], [35, 3, 1, "", "double"], [35, 6, 1, "", "dump_patches"], [35, 3, 1, "", "eval"], [35, 3, 1, "", "extra_repr"], [35, 3, 1, "", "float"], [35, 3, 1, "", "forward"], [35, 3, 1, "", "get_buffer"], [35, 3, 1, "", "get_extra_state"], [35, 3, 1, "", "get_parameter"], [35, 3, 1, "", "get_submodule"], [35, 3, 1, "", "half"], [35, 3, 1, "", "ipu"], [35, 3, 1, "", "load_state_dict"], [35, 3, 1, "", "modules"], [35, 3, 1, "", "named_buffers"], [35, 3, 1, "", "named_children"], [35, 3, 1, "", "named_modules"], [35, 3, 1, "", "named_parameters"], [35, 3, 1, "", "parameters"], [35, 3, 1, "", "register_backward_hook"], [35, 3, 1, "", "register_buffer"], [35, 3, 1, "", "register_forward_hook"], [35, 3, 1, "", "register_forward_pre_hook"], [35, 3, 1, "", "register_full_backward_hook"], [35, 3, 1, "", "register_load_state_dict_post_hook"], [35, 3, 1, "", "register_module"], [35, 3, 1, "", "register_parameter"], [35, 3, 1, "", "requires_grad_"], [35, 3, 1, "", "set_extra_state"], [35, 3, 1, "", "share_memory"], [35, 3, 1, "", "state_dict"], [35, 3, 1, "", "to"], [35, 3, 1, "", "to_empty"], [35, 3, 1, "", "train"], [35, 6, 1, "", "training"], [35, 3, 1, "", "type"], [35, 3, 1, "", "xpu"], [35, 3, 1, "", "zero_grad"]], "cleanlab.filter": [[36, 1, 1, "", "find_label_issues"], [36, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [36, 1, 1, "", "find_predicted_neq_given"], [36, 7, 1, "", "pred_probs_by_class"], [36, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[38, 0, 0, "-", "label_quality_utils"], [39, 0, 0, "-", "latent_algebra"], [40, 0, 0, "-", "multiannotator_utils"], [41, 0, 0, "-", "multilabel_scorer"], [42, 0, 0, "-", "multilabel_utils"], [43, 0, 0, "-", "outlier"], [44, 0, 0, "-", "token_classification_utils"], [45, 0, 0, "-", "util"], [46, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[38, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[39, 1, 1, "", "compute_inv_noise_matrix"], [39, 1, 1, "", "compute_noise_matrix_from_inverse"], [39, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [39, 1, 1, "", "compute_py"], [39, 1, 1, "", "compute_py_inv_noise_matrix"], [39, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[40, 1, 1, "", "assert_valid_inputs_multiannotator"], [40, 1, 1, "", "assert_valid_pred_probs"], [40, 1, 1, "", "check_consensus_label_classes"], [40, 1, 1, "", "compute_soft_cross_entropy"], [40, 1, 1, "", "find_best_temp_scaler"], [40, 1, 1, "", "format_multiannotator_labels"], [40, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[41, 2, 1, "", "Aggregator"], [41, 2, 1, "", "ClassLabelScorer"], [41, 2, 1, "", "MultilabelScorer"], [41, 1, 1, "", "exponential_moving_average"], [41, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [41, 1, 1, "", "get_label_quality_scores"], [41, 1, 1, "", "multilabel_py"], [41, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[41, 3, 1, "", "__call__"], [41, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[41, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [41, 6, 1, "", "NORMALIZED_MARGIN"], [41, 6, 1, "", "SELF_CONFIDENCE"], [41, 3, 1, "", "__call__"], [41, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[41, 3, 1, "", "__call__"], [41, 3, 1, "", "aggregate"], [41, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[42, 1, 1, "", "get_onehot_num_classes"], [42, 1, 1, "", "int2onehot"], [42, 1, 1, "", "onehot2int"], [42, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[43, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[44, 1, 1, "", "color_sentence"], [44, 1, 1, "", "filter_sentence"], [44, 1, 1, "", "get_sentence"], [44, 1, 1, "", "mapping"], [44, 1, 1, "", "merge_probs"], [44, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[45, 1, 1, "", "append_extra_datapoint"], [45, 1, 1, "", "clip_noise_rates"], [45, 1, 1, "", "clip_values"], [45, 1, 1, "", "compress_int_array"], [45, 1, 1, "", "confusion_matrix"], [45, 1, 1, "", "csr_vstack"], [45, 1, 1, "", "estimate_pu_f1"], [45, 1, 1, "", "extract_indices_tf"], [45, 1, 1, "", "force_two_dimensions"], [45, 1, 1, "", "format_labels"], [45, 1, 1, "", "get_missing_classes"], [45, 1, 1, "", "get_num_classes"], [45, 1, 1, "", "get_unique_classes"], [45, 1, 1, "", "is_tensorflow_dataset"], [45, 1, 1, "", "is_torch_dataset"], [45, 1, 1, "", "num_unique_classes"], [45, 1, 1, "", "print_inverse_noise_matrix"], [45, 1, 1, "", "print_joint_matrix"], [45, 1, 1, "", "print_noise_matrix"], [45, 1, 1, "", "print_square_matrix"], [45, 1, 1, "", "remove_noise_from_class"], [45, 1, 1, "", "round_preserving_row_totals"], [45, 1, 1, "", "round_preserving_sum"], [45, 1, 1, "", "smart_display_dataframe"], [45, 1, 1, "", "subset_X_y"], [45, 1, 1, "", "subset_data"], [45, 1, 1, "", "subset_labels"], [45, 1, 1, "", "train_val_split"], [45, 1, 1, "", "unshuffle_tensorflow_dataset"], [45, 1, 1, "", "value_counts"], [45, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[46, 1, 1, "", "assert_indexing_works"], [46, 1, 1, "", "assert_nonempty_input"], [46, 1, 1, "", "assert_valid_class_labels"], [46, 1, 1, "", "assert_valid_inputs"], [46, 1, 1, "", "labels_to_array"]], "cleanlab.models": [[49, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[49, 2, 1, "", "KerasWrapperModel"], [49, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[49, 3, 1, "", "fit"], [49, 3, 1, "", "get_params"], [49, 3, 1, "", "predict"], [49, 3, 1, "", "predict_proba"], [49, 3, 1, "", "set_params"], [49, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[49, 3, 1, "", "fit"], [49, 3, 1, "", "get_params"], [49, 3, 1, "", "predict"], [49, 3, 1, "", "predict_proba"], [49, 3, 1, "", "set_params"], [49, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[50, 1, 1, "", "convert_long_to_wide_dataset"], [50, 1, 1, "", "get_active_learning_scores"], [50, 1, 1, "", "get_active_learning_scores_ensemble"], [50, 1, 1, "", "get_label_quality_multiannotator"], [50, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [50, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[51, 0, 0, "-", "dataset"], [52, 0, 0, "-", "filter"], [54, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[51, 1, 1, "", "common_multilabel_issues"], [51, 1, 1, "", "multilabel_health_summary"], [51, 1, 1, "", "overall_multilabel_health_score"], [51, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[52, 1, 1, "", "find_label_issues"], [52, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[54, 1, 1, "", "get_label_quality_scores"], [54, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[55, 0, 0, "-", "filter"], [57, 0, 0, "-", "rank"], [58, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[55, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[57, 1, 1, "", "compute_badloc_box_scores"], [57, 1, 1, "", "compute_overlooked_box_scores"], [57, 1, 1, "", "compute_swap_box_scores"], [57, 1, 1, "", "get_label_quality_scores"], [57, 1, 1, "", "issues_from_scores"], [57, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[58, 1, 1, "", "bounding_box_size_distribution"], [58, 1, 1, "", "calculate_per_class_metrics"], [58, 1, 1, "", "class_label_distribution"], [58, 1, 1, "", "get_average_per_class_confusion_matrix"], [58, 1, 1, "", "get_sorted_bbox_count_idxs"], [58, 1, 1, "", "object_counts_per_image"], [58, 1, 1, "", "plot_class_distribution"], [58, 1, 1, "", "plot_class_size_distributions"], [58, 1, 1, "", "visualize"]], "cleanlab.outlier": [[59, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[59, 3, 1, "", "fit"], [59, 3, 1, "", "fit_score"], [59, 3, 1, "", "score"]], "cleanlab.rank": [[60, 1, 1, "", "find_top_issues"], [60, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [60, 1, 1, "", "get_label_quality_ensemble_scores"], [60, 1, 1, "", "get_label_quality_scores"], [60, 1, 1, "", "get_normalized_margin_for_each_label"], [60, 1, 1, "", "get_self_confidence_for_each_label"], [60, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[62, 0, 0, "-", "learn"], [63, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[62, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[62, 3, 1, "", "__init_subclass__"], [62, 3, 1, "", "find_label_issues"], [62, 3, 1, "", "fit"], [62, 3, 1, "", "get_aleatoric_uncertainty"], [62, 3, 1, "", "get_epistemic_uncertainty"], [62, 3, 1, "", "get_label_issues"], [62, 3, 1, "", "get_metadata_routing"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "save_space"], [62, 3, 1, "", "score"], [62, 3, 1, "", "set_fit_request"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[63, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"], [67, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[64, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[67, 1, 1, "", "common_label_issues"], [67, 1, 1, "", "display_issues"], [67, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[68, 0, 0, "-", "filter"], [70, 0, 0, "-", "rank"], [71, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[68, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[70, 1, 1, "", "get_label_quality_scores"], [70, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[71, 1, 1, "", "common_label_issues"], [71, 1, 1, "", "display_issues"], [71, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 74, 78, 79, 81, 82, 83, 86, 92, 93, 94], "count": [3, 83], "datalab": [4, 5, 7, 8, 9, 75, 76, 77, 78, 79, 83], "creat": [5, 75, 76, 83, 85], "your": [5, 72, 75, 76, 79, 81, 83], "own": 5, "issu": [5, 7, 8, 19, 26, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 91, 92, 94], "manag": [5, 19], "prerequisit": 5, "implement": 5, "issuemanag": [5, 75], "basic": 5, "check": 5, "intermedi": 5, "advanc": [5, 75], "us": [5, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "gener": 6, "cluster": [6, 81], "id": 6, "guid": [7, 9], "type": [7, 8, 83], "custom": [7, 75], "can": [8, 76, 80, 81, 83, 85], "detect": [8, 76, 78, 79, 81, 83, 87, 88], "estim": [8, 83, 85], "each": 8, "label": [8, 21, 26, 72, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 90, 91, 92, 93, 94], "outlier": [8, 24, 43, 59, 78, 79, 82, 88], "Near": [8, 76, 78, 79, 82], "duplic": [8, 17, 76, 78, 79, 81, 82], "non": [8, 79], "iid": [8, 79], "class": [8, 73, 83, 91], "imbal": [8, 18], "imag": [8, 82, 88], "specif": [8, 19, 91], "underperform": [8, 81], "group": [8, 81], "null": [8, 23], "data": [8, 10, 72, 74, 75, 76, 78, 79, 80, 81, 83, 85, 86, 87, 88, 90, 91, 92, 94], "valuat": 8, "option": 8, "paramet": [8, 83], "get": [9, 75, 76, 85, 86, 87, 91, 94], "start": [9, 80], "api": 9, "refer": 9, "data_issu": 11, "factori": 12, "intern": [13, 37], "issue_find": 14, "data_valu": 16, "issue_manag": [19, 20], "regist": 19, "unregist": 19, "ml": [19, 81, 83], "task": 19, "noniid": 22, "regress": [25, 61, 62, 63, 81, 90], "prioriti": 26, "order": 26, "find": [26, 72, 74, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "underperforming_group": 27, "report": [28, 82], "dataset": [30, 51, 72, 76, 79, 80, 81, 82, 83, 86, 87, 88, 90, 91, 93, 94], "cifar_cnn": 31, "coteach": 32, "experiment": 33, "label_issues_batch": 34, "mnist_pytorch": 35, "filter": [36, 52, 55, 64, 68, 83], "label_quality_util": 38, "latent_algebra": 39, "multiannotator_util": 40, "multilabel_scor": 41, "multilabel_util": 42, "token_classification_util": 44, "util": 45, "valid": [46, 82, 89], "fasttext": 47, "model": [48, 72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "kera": 49, "multiannot": [50, 85], "multilabel_classif": 53, "rank": [54, 57, 60, 63, 66, 70, 83], "object_detect": 56, "summari": [58, 67, 71], "learn": [62, 76, 81, 83, 92], "segment": [65, 91], "token_classif": [69, 94], "cleanlab": [72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "open": [72, 81], "sourc": [72, 81], "document": 72, "quickstart": 72, "1": [72, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "instal": [72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "2": [72, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "common": [72, 73, 94], "3": [72, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "handl": [72, 81], "error": [72, 81, 82, 83, 85, 86, 87, 90, 91, 93, 94], "train": [72, 74, 81, 88, 90, 92, 93], "robust": [72, 83, 90, 92, 93], "noisi": [72, 83, 90, 92, 93], "4": [72, 74, 75, 76, 78, 79, 82, 83, 85, 87, 88, 90, 92, 93], "curat": [72, 80], "fix": [72, 81], "level": [72, 80, 83, 94], "5": [72, 74, 76, 78, 82, 83, 85, 90, 92], "improv": [72, 85], "via": [72, 83, 85], "mani": [72, 83], "other": [72, 85, 87, 90], "techniqu": 72, "contribut": 72, "easi": [72, 78, 79, 82], "mode": [72, 78, 79, 82], "how": [73, 81, 83, 85, 86, 94], "migrat": 73, "version": 73, "0": 73, "from": [73, 75, 76, 83, 90, 92, 93], "pre": [73, 74, 81, 88], "function": [73, 75], "name": 73, "chang": 73, "modul": [73, 83], "new": 73, "remov": 73, "argument": [73, 75], "variabl": 73, "audio": 74, "speechbrain": 74, "depend": [74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "import": [74, 75, 76, 80, 82, 83, 85], "them": [74, 80, 83], "load": [74, 75, 76, 78, 79, 90, 92, 93], "featur": [74, 82, 88], "fit": 74, "linear": 74, "comput": [74, 78, 79, 81, 82, 85, 89, 92], "out": [74, 75, 76, 78, 79, 82, 85, 89, 92], "sampl": [74, 75, 76, 78, 79, 82, 85, 89, 92], "predict": [74, 75, 76, 78, 79, 82, 85, 86, 87, 89, 92], "probabl": [74, 75, 76, 78, 79, 82, 85, 89, 92], "workflow": [75, 83], "audit": [75, 76], "requir": [75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "classifi": [75, 76], "instanti": 75, "object": [75, 87], "increment": 75, "search": 75, "specifi": [75, 81], "nondefault": 75, "save": 75, "ad": 75, "A": 76, "unifi": 76, "all": [76, 83], "kind": [76, 87], "skip": [76, 80, 83, 85], "detail": [76, 80, 83, 85], "more": [76, 83, 90, 92, 93], "about": 76, "addit": 76, "inform": [76, 82], "tutori": [77, 80, 84], "tabular": [78, 92], "numer": 78, "categor": 78, "column": 78, "process": [78, 88, 90, 92], "select": [78, 92], "construct": 78, "k": [78, 82, 89], "nearest": 78, "neighbour": 78, "graph": 78, "text": [79, 93, 94], "format": [79, 81, 86, 87, 93], "defin": [79, 82, 90, 93], "drift": 79, "fetch": [80, 82], "evalu": 80, "health": [80, 83], "8": [80, 83], "popular": 80, "faq": 81, "what": [81, 83, 89], "do": [81, 83], "i": [81, 83, 89], "infer": 81, "correct": 81, "exampl": [81, 82, 83, 88], "ha": 81, "flag": 81, "should": 81, "v": 81, "test": [81, 83, 88], "big": 81, "limit": 81, "memori": 81, "why": 81, "isn": 81, "t": 81, "cleanlearn": [81, 83], "work": [81, 83, 85, 94], "me": 81, "differ": [81, 87], "clean": [81, 83], "final": 81, "hyperparamet": 81, "tune": 81, "onli": 81, "one": [81, 83, 86, 91], "doe": [81, 85, 94], "take": 81, "so": 81, "long": 81, "slice": 81, "when": [81, 83], "identifi": [81, 87], "run": 81, "licens": 81, "under": 81, "an": 81, "answer": 81, "question": 81, "pytorch": [82, 88], "normal": 82, "fashion": 82, "mnist": 82, "prepar": 82, "fold": [82, 89], "cross": [82, 89], "embed": [82, 88], "7": [82, 83], "view": 82, "most": [82, 94], "like": 82, "sever": 82, "set": [82, 83], "dark": 82, "top": [82, 91], "low": 82, "The": 83, "centric": 83, "ai": 83, "machin": 83, "find_label_issu": 83, "line": 83, "code": 83, "visual": [83, 87, 88, 91], "twenti": 83, "lowest": 83, "qualiti": [83, 85, 86, 87, 91, 94], "see": 83, "now": 83, "let": 83, "": 83, "happen": 83, "we": 83, "merg": 83, "seafoam": 83, "green": 83, "yellow": 83, "too": 83, "you": 83, "re": 83, "6": 83, "One": 83, "score": [83, 85, 86, 87, 91, 94], "rule": 83, "overal": [83, 91], "accur": 83, "thi": 83, "directli": 83, "fulli": 83, "character": 83, "nois": 83, "matrix": [83, 86], "joint": 83, "prior": 83, "true": 83, "distribut": 83, "flip": 83, "rate": 83, "ani": 83, "again": 83, "support": 83, "lot": 83, "method": 83, "filter_bi": 83, "automat": 83, "everi": 83, "uniqu": 83, "num_label_issu": 83, "threshold": 83, "found": 83, "Not": 83, "sure": 83, "ensembl": 83, "multipl": [83, 85], "predictor": 83, "consensu": 85, "annot": 85, "initi": 85, "major": 85, "vote": 85, "better": 85, "statist": 85, "compar": 85, "inspect": 85, "potenti": [85, 90, 93], "retrain": 85, "further": 85, "multi": 86, "given": 86, "hot": 86, "binari": 86, "download": [87, 91, 94], "objectlab": 87, "timm": 88, "cifar10": 88, "some": 88, "pred_prob": [88, 91, 94], "wai": 90, "semant": 91, "which": 91, "ar": 91, "commonli": 91, "mislabel": [91, 94], "focus": 91, "scikit": 92, "token": 94, "word": 94, "sentenc": 94, "contain": 94, "particular": 94}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 56}})
\ No newline at end of file
+Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/report", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", "cleanlab/models/fasttext", "cleanlab/models/index", "cleanlab/models/keras", "cleanlab/multiannotator", "cleanlab/multilabel_classification/dataset", "cleanlab/multilabel_classification/filter", "cleanlab/multilabel_classification/index", "cleanlab/multilabel_classification/rank", "cleanlab/object_detection/filter", "cleanlab/object_detection/index", "cleanlab/object_detection/rank", "cleanlab/object_detection/summary", "cleanlab/outlier", "cleanlab/rank", "cleanlab/regression/index", "cleanlab/regression/learn", "cleanlab/regression/rank", "cleanlab/segmentation/filter", "cleanlab/segmentation/index", "cleanlab/segmentation/rank", "cleanlab/segmentation/summary", "cleanlab/token_classification/filter", "cleanlab/token_classification/index", "cleanlab/token_classification/rank", "cleanlab/token_classification/summary", "index", "migrating/migrate_v2", "tutorials/audio", "tutorials/datalab/datalab_advanced", "tutorials/datalab/datalab_quickstart", "tutorials/datalab/index", "tutorials/datalab/tabular", "tutorials/datalab/text", "tutorials/dataset_health", "tutorials/faq", "tutorials/image", "tutorials/indepth_overview", "tutorials/index", "tutorials/multiannotator", "tutorials/multilabel_classification", "tutorials/object_detection", "tutorials/outliers", "tutorials/pred_probs_cross_val", "tutorials/regression", "tutorials/segmentation", "tutorials/tabular", "tutorials/text", "tutorials/token_classification"], "filenames": ["cleanlab/benchmarking/index.rst", "cleanlab/benchmarking/noise_generation.rst", "cleanlab/classification.rst", "cleanlab/count.rst", "cleanlab/datalab/datalab.rst", "cleanlab/datalab/guide/custom_issue_manager.rst", "cleanlab/datalab/guide/generating_cluster_ids.rst", "cleanlab/datalab/guide/index.rst", "cleanlab/datalab/guide/issue_type_description.rst", "cleanlab/datalab/index.rst", "cleanlab/datalab/internal/data.rst", "cleanlab/datalab/internal/data_issues.rst", "cleanlab/datalab/internal/factory.rst", "cleanlab/datalab/internal/index.rst", "cleanlab/datalab/internal/issue_finder.rst", "cleanlab/datalab/internal/issue_manager/_notices/not_registered.rst", "cleanlab/datalab/internal/issue_manager/data_valuation.rst", "cleanlab/datalab/internal/issue_manager/duplicate.rst", "cleanlab/datalab/internal/issue_manager/imbalance.rst", "cleanlab/datalab/internal/issue_manager/index.rst", "cleanlab/datalab/internal/issue_manager/issue_manager.rst", "cleanlab/datalab/internal/issue_manager/label.rst", "cleanlab/datalab/internal/issue_manager/noniid.rst", "cleanlab/datalab/internal/issue_manager/null.rst", "cleanlab/datalab/internal/issue_manager/outlier.rst", "cleanlab/datalab/internal/issue_manager/regression/index.rst", "cleanlab/datalab/internal/issue_manager/regression/label.rst", "cleanlab/datalab/internal/issue_manager/underperforming_group.rst", "cleanlab/datalab/internal/report.rst", "cleanlab/datalab/optional_dependencies.rst", "cleanlab/dataset.rst", "cleanlab/experimental/cifar_cnn.rst", "cleanlab/experimental/coteaching.rst", "cleanlab/experimental/index.rst", "cleanlab/experimental/label_issues_batched.rst", "cleanlab/experimental/mnist_pytorch.rst", "cleanlab/filter.rst", "cleanlab/internal/index.rst", "cleanlab/internal/label_quality_utils.rst", "cleanlab/internal/latent_algebra.rst", "cleanlab/internal/multiannotator_utils.rst", "cleanlab/internal/multilabel_scorer.rst", "cleanlab/internal/multilabel_utils.rst", "cleanlab/internal/outlier.rst", "cleanlab/internal/token_classification_utils.rst", "cleanlab/internal/util.rst", "cleanlab/internal/validation.rst", "cleanlab/models/fasttext.rst", "cleanlab/models/index.rst", "cleanlab/models/keras.rst", "cleanlab/multiannotator.rst", "cleanlab/multilabel_classification/dataset.rst", "cleanlab/multilabel_classification/filter.rst", "cleanlab/multilabel_classification/index.rst", "cleanlab/multilabel_classification/rank.rst", "cleanlab/object_detection/filter.rst", "cleanlab/object_detection/index.rst", "cleanlab/object_detection/rank.rst", "cleanlab/object_detection/summary.rst", "cleanlab/outlier.rst", "cleanlab/rank.rst", "cleanlab/regression/index.rst", "cleanlab/regression/learn.rst", "cleanlab/regression/rank.rst", "cleanlab/segmentation/filter.rst", "cleanlab/segmentation/index.rst", "cleanlab/segmentation/rank.rst", "cleanlab/segmentation/summary.rst", "cleanlab/token_classification/filter.rst", "cleanlab/token_classification/index.rst", "cleanlab/token_classification/rank.rst", "cleanlab/token_classification/summary.rst", "index.rst", "migrating/migrate_v2.rst", "tutorials/audio.ipynb", "tutorials/datalab/datalab_advanced.ipynb", "tutorials/datalab/datalab_quickstart.ipynb", "tutorials/datalab/index.rst", "tutorials/datalab/tabular.ipynb", "tutorials/datalab/text.ipynb", "tutorials/dataset_health.ipynb", "tutorials/faq.ipynb", "tutorials/image.ipynb", "tutorials/indepth_overview.ipynb", "tutorials/index.rst", "tutorials/multiannotator.ipynb", "tutorials/multilabel_classification.ipynb", "tutorials/object_detection.ipynb", "tutorials/outliers.ipynb", "tutorials/pred_probs_cross_val.rst", "tutorials/regression.ipynb", "tutorials/segmentation.ipynb", "tutorials/tabular.ipynb", "tutorials/text.ipynb", "tutorials/token_classification.ipynb"], "titles": ["benchmarking", "noise_generation", "classification", "count", "datalab", "Creating Your Own Issues Manager", "Generating Cluster IDs", "Datalab guides", "Datalab Issue Types", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "data_valuation", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "noniid", "null", "outlier", "regression", "label", "underperforming_group", "report", "<no title>", "dataset", "cifar_cnn", "coteaching", "experimental", "label_issues_batched", "mnist_pytorch", "filter", "internal", "label_quality_utils", "latent_algebra", "multiannotator_utils", "multilabel_scorer", "multilabel_utils", "outlier", "token_classification_utils", "util", "validation", "fasttext", "models", "keras", "multiannotator", "dataset", "filter", "multilabel_classification", "rank", "filter", "object_detection", "rank", "summary", "outlier", "rank", "regression", "regression.learn", "regression.rank", "filter", "segmentation", "rank", "summary", "filter", "token_classification", "rank", "summary", "cleanlab open-source documentation", "How to migrate to versions >= 2.0.0 from pre 1.0.1", "Audio Classification with SpeechBrain and Cleanlab", "Datalab: Advanced workflows to audit your data", "Datalab: A unified audit to detect all kinds of issues in data and labels", "Datalab Tutorials", "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab", "Detecting Issues in a Text Dataset with Datalab", "Find Dataset-level Issues for Dataset Curation", "FAQ", "Image Classification with PyTorch and Cleanlab", "The Workflows of Data-centric AI for Classification with Noisy Labels", "Tutorials", "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators", "Find Label Errors in Multi-Label Classification Datasets", "Finding Label Errors in Object Detection Datasets", "Detect Outliers with Cleanlab and PyTorch Image Models (timm)", "Computing Out-of-Sample Predicted Probabilities with Cross-Validation", "Find Noisy Labels in Regression Datasets", "Find Label Errors in Semantic Segmentation Datasets", "Classification with Tabular Data using Scikit-Learn and Cleanlab", "Text Classification with Noisy Labels", "Find Label Errors in Token Classification (Text) Datasets"], "terms": {"noise_gener": [0, 73, 75, 76, 83, 85, 86], "helper": [1, 14, 34, 38, 40, 41, 42, 43, 44, 45, 57, 80, 82, 94], "method": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "ar": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 16, 18, 19, 20, 21, 22, 25, 26, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "us": [1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 72, 73, 75, 80, 84, 89], "benchmark": [1, 31, 72, 73, 75, 76, 83, 85, 86], "cleanlab": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 75, 76, 80, 84, 89], "": [1, 2, 3, 8, 16, 30, 31, 35, 38, 41, 43, 45, 50, 51, 55, 57, 58, 59, 60, 62, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "core": [1, 4, 34, 36, 64, 66, 91], "algorithm": [1, 2, 6, 8, 27, 32, 45, 50, 59, 68, 70, 72, 81, 83, 85, 94], "These": [1, 2, 3, 6, 8, 19, 33, 36, 37, 48, 50, 51, 54, 58, 59, 63, 67, 68, 70, 71, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "introduc": [1, 74, 81, 83], "synthet": [1, 85, 86, 91], "nois": [1, 2, 3, 30, 36, 39, 45, 51, 75, 76, 80, 85], "label": [1, 2, 3, 4, 5, 6, 7, 10, 14, 18, 19, 20, 25, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 45, 46, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 75, 80, 84, 88, 89], "classif": [1, 3, 4, 5, 8, 12, 14, 28, 30, 34, 36, 39, 41, 42, 45, 50, 51, 52, 53, 54, 59, 60, 68, 69, 70, 71, 72, 73, 75, 76, 84, 85, 88, 89, 90, 91], "dataset": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 22, 23, 24, 26, 27, 34, 35, 36, 39, 41, 45, 49, 50, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 77, 78, 84, 85, 89, 92], "specif": [1, 3, 4, 7, 12, 13, 14, 23, 28, 33, 48, 52, 55, 58, 67, 71, 76, 78, 79, 82, 83, 94], "thi": [1, 2, 3, 4, 5, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 38, 39, 41, 42, 44, 45, 46, 48, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "modul": [1, 3, 11, 12, 13, 14, 19, 25, 28, 30, 31, 32, 33, 34, 35, 36, 43, 45, 48, 50, 59, 60, 72, 81, 82, 86], "provid": [1, 2, 3, 4, 5, 6, 8, 12, 14, 16, 21, 26, 30, 31, 32, 34, 35, 36, 39, 45, 49, 50, 51, 52, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 88, 89, 90, 91, 92, 93, 94], "gener": [1, 2, 3, 5, 8, 16, 21, 28, 30, 41, 45, 46, 59, 60, 62, 67, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 89, 90, 91, 93, 94], "valid": [1, 2, 3, 4, 8, 10, 30, 36, 37, 39, 40, 41, 43, 45, 50, 52, 55, 58, 60, 62, 63, 71, 73, 74, 75, 76, 78, 79, 80, 81, 83, 84, 86, 87, 90, 91, 92, 93, 94], "matric": [1, 3, 39, 81], "which": [1, 2, 3, 4, 8, 10, 11, 12, 14, 16, 20, 22, 28, 30, 31, 35, 36, 39, 41, 44, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 66, 67, 68, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "learn": [1, 2, 3, 4, 8, 12, 14, 20, 26, 28, 32, 33, 34, 35, 36, 38, 40, 45, 48, 50, 52, 59, 61, 63, 66, 70, 72, 74, 75, 78, 79, 80, 82, 84, 85, 90, 93], "i": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "possibl": [1, 2, 3, 8, 30, 31, 35, 36, 38, 39, 41, 52, 53, 54, 55, 57, 58, 59, 60, 62, 68, 70, 71, 76, 81, 83, 85, 86, 87, 90, 91, 94], "noisi": [1, 2, 3, 8, 30, 32, 35, 36, 39, 45, 51, 52, 54, 60, 62, 63, 64, 66, 67, 73, 75, 76, 78, 79, 81, 84, 85], "given": [1, 2, 3, 8, 26, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 44, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "matrix": [1, 2, 3, 4, 8, 14, 16, 27, 30, 36, 38, 39, 42, 45, 46, 52, 57, 58, 59, 60, 78, 88], "trace": [1, 75, 76, 83, 85, 86], "valu": [1, 2, 3, 4, 8, 10, 11, 14, 16, 20, 22, 23, 30, 31, 32, 34, 35, 36, 38, 39, 41, 43, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 71, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "more": [1, 2, 3, 4, 5, 8, 11, 14, 16, 22, 30, 31, 34, 35, 38, 41, 43, 45, 50, 51, 52, 53, 54, 55, 57, 58, 60, 62, 63, 66, 67, 68, 70, 72, 74, 75, 78, 79, 80, 81, 82, 85, 86, 87, 88, 91, 94], "function": [1, 2, 3, 4, 5, 11, 12, 14, 21, 22, 26, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 76, 80, 81, 83, 85, 86, 87, 91, 92, 93, 94], "noise_matrix_is_valid": 1, "noise_matrix": [1, 2, 3, 8, 39, 45, 75, 76, 83, 85, 86], "py": [1, 3, 28, 31, 32, 36, 39, 41, 75, 76, 83, 85, 86], "verbos": [1, 2, 4, 5, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 34, 36, 50, 51, 52, 57, 59, 60, 62, 64, 66, 67, 71, 75, 83, 85], "fals": [1, 2, 3, 4, 5, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 34, 35, 36, 40, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 74, 75, 76, 78, 79, 81, 82, 83, 85, 87, 88, 90, 91, 93], "sourc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71], "prior": [1, 2, 3, 30, 36, 39, 41], "repres": [1, 2, 3, 5, 8, 10, 14, 16, 22, 30, 34, 36, 39, 42, 43, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "p": [1, 2, 3, 8, 30, 36, 38, 39, 45, 50, 58, 59, 60, 64, 76, 78, 79, 82, 83, 85, 94], "true_label": [1, 2, 3, 30, 39, 45, 83, 85], "k": [1, 2, 3, 4, 6, 8, 10, 14, 16, 17, 21, 22, 24, 27, 30, 34, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 53, 54, 55, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 74, 75, 76, 81, 83, 85, 86, 87, 88, 91, 92, 94], "check": [1, 2, 4, 7, 8, 10, 14, 23, 31, 34, 35, 40, 46, 49, 55, 58, 62, 72, 74, 75, 76, 81, 82, 83, 85, 86, 90, 92, 93], "learnabl": 1, "mean": [1, 2, 5, 6, 10, 11, 20, 22, 32, 35, 39, 41, 43, 57, 62, 76, 79, 81, 83, 85, 86, 88, 90, 93], "achiev": [1, 2, 31, 32, 35, 62, 81, 85, 94], "better": [1, 4, 36, 50, 52, 60, 62, 63, 72, 74, 76, 78, 79, 81, 83, 86, 87, 88, 93, 94], "than": [1, 2, 3, 5, 8, 22, 24, 27, 30, 36, 45, 49, 50, 55, 57, 59, 60, 62, 66, 70, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 94], "random": [1, 2, 3, 5, 8, 16, 27, 34, 41, 50, 60, 62, 74, 75, 76, 78, 81, 82, 83, 85, 86, 88, 92], "perform": [1, 2, 5, 8, 22, 24, 27, 31, 35, 41, 58, 62, 72, 75, 81, 83, 85, 86, 89, 90, 92, 93], "averag": [1, 3, 8, 20, 24, 30, 31, 35, 41, 43, 50, 51, 58, 59, 60, 81, 85, 88], "amount": [1, 3, 82], "paramet": [1, 2, 3, 4, 7, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 76, 79, 82, 92, 93], "np": [1, 2, 3, 4, 5, 14, 16, 27, 30, 32, 34, 36, 38, 39, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 70, 71, 74, 75, 76, 78, 80, 81, 82, 83, 85, 86, 88, 90, 91, 92, 93, 94], "ndarrai": [1, 2, 3, 4, 14, 21, 22, 26, 27, 30, 32, 34, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 94], "an": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 43, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "arrai": [1, 2, 3, 4, 5, 8, 10, 14, 16, 22, 30, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 75, 76, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "shape": [1, 2, 3, 4, 14, 16, 30, 32, 34, 36, 38, 39, 40, 41, 43, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 80, 81, 83, 86, 87, 88, 91, 94], "condit": [1, 2, 3, 39, 44, 45, 60, 82, 83, 94], "probabl": [1, 2, 3, 4, 6, 8, 14, 21, 24, 30, 34, 35, 36, 38, 39, 41, 42, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 73, 80, 81, 83, 84, 86, 87, 88, 91, 94], "k_": [1, 2, 3, 39, 45], "k_y": [1, 2, 3, 39, 45], "contain": [1, 2, 3, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93], "fraction": [1, 2, 3, 8, 18, 32, 39, 45, 50, 62, 78, 81], "exampl": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 44, 45, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 85, 86, 87, 89, 90, 91, 92, 93, 94], "everi": [1, 2, 3, 4, 14, 31, 35, 36, 39, 44, 45, 52, 60, 62, 63, 74, 75, 76, 78, 79, 81, 82, 85, 87, 89, 91, 92, 94], "class": [1, 2, 3, 4, 5, 7, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 44, 45, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 92, 93, 94], "other": [1, 2, 3, 4, 8, 14, 20, 23, 30, 31, 33, 34, 35, 36, 39, 42, 45, 46, 48, 50, 51, 54, 58, 59, 60, 62, 67, 74, 75, 76, 78, 79, 81, 82, 83, 86, 88, 91, 94], "assum": [1, 2, 3, 10, 36, 39, 44, 45, 60, 64, 67, 81, 88, 91, 94], "column": [1, 2, 3, 4, 8, 10, 11, 26, 30, 34, 36, 39, 41, 42, 44, 45, 50, 51, 52, 54, 55, 58, 59, 60, 62, 67, 68, 70, 71, 74, 75, 76, 79, 80, 81, 82, 83, 85, 87, 90, 91, 92, 93, 94], "sum": [1, 2, 3, 22, 27, 30, 39, 41, 45, 51, 52, 54, 57, 62, 75, 76, 81, 82, 83, 85, 86, 91, 94], "1": [1, 2, 3, 4, 5, 8, 10, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 80, 81, 89], "each": [1, 2, 3, 4, 5, 6, 7, 11, 12, 14, 18, 20, 21, 22, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 42, 43, 45, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "true": [1, 2, 3, 4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 39, 41, 44, 45, 46, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "return": [1, 2, 3, 4, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "type": [1, 2, 3, 4, 5, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 46, 48, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 86, 87, 91, 92, 94], "bool": [1, 2, 3, 4, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 34, 35, 36, 41, 44, 45, 50, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 71], "is_valid": 1, "whether": [1, 3, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 31, 34, 35, 36, 45, 50, 51, 52, 54, 55, 71, 74, 76, 78, 79, 80, 81, 82, 83, 90, 93, 94], "generate_noisy_label": [1, 75, 76, 83, 85, 86], "from": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 21, 23, 26, 27, 28, 29, 30, 31, 32, 34, 35, 36, 39, 41, 42, 43, 44, 45, 50, 52, 54, 57, 58, 59, 60, 62, 63, 68, 70, 71, 72, 74, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 91, 94], "perfect": [1, 2, 30, 62, 83, 87], "exactli": [1, 3, 8, 30, 31, 35, 36, 53, 59, 75, 76, 78, 79, 82, 83], "yield": [1, 31, 35], "between": [1, 4, 8, 13, 14, 19, 20, 22, 25, 30, 31, 32, 33, 34, 35, 36, 37, 38, 40, 43, 48, 50, 51, 54, 57, 59, 60, 62, 63, 66, 70, 71, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 93, 94], "below": [1, 3, 4, 8, 30, 31, 34, 35, 36, 38, 41, 50, 51, 52, 57, 58, 66, 70, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "we": [1, 2, 3, 4, 5, 8, 11, 20, 31, 34, 35, 36, 41, 45, 46, 50, 57, 58, 60, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "loop": [1, 3, 39, 45, 82], "implement": [1, 2, 3, 4, 7, 12, 20, 31, 32, 34, 35, 39, 45, 62, 72, 74, 75, 78, 88, 89, 92], "what": [1, 4, 7, 8, 14, 28, 30, 32, 34, 36, 50, 51, 55, 57, 74, 75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "doe": [1, 2, 3, 8, 34, 35, 36, 41, 46, 57, 58, 62, 64, 66, 70, 74, 75, 76, 78, 79, 82, 86, 90, 91, 93], "do": [1, 2, 4, 8, 30, 34, 35, 45, 46, 59, 60, 64, 74, 75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "fast": 1, "explain": [1, 8], "python": [1, 2, 35, 49, 62, 75, 76, 80, 88], "pseudocod": [1, 89], "happen": [1, 8, 36, 52, 79, 85, 91], "n": [1, 2, 3, 4, 5, 30, 31, 34, 35, 36, 38, 39, 40, 41, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 74, 79, 80, 81, 82, 85, 86, 90, 91, 92, 93, 94], "without": [1, 2, 4, 8, 10, 12, 18, 31, 35, 54, 62, 72, 74, 79, 83, 87, 88, 93], "ani": [1, 2, 3, 4, 5, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 38, 40, 43, 44, 45, 49, 50, 52, 54, 55, 57, 58, 60, 62, 64, 66, 67, 72, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93], "distinct": [1, 16, 45, 94], "natur": [1, 8, 85, 88], "number": [1, 2, 3, 4, 5, 6, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 91, 94], "0": [1, 2, 3, 4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "count_joint": 1, "len": [1, 2, 3, 5, 30, 34, 39, 44, 45, 46, 59, 60, 62, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93, 94], "y": [1, 2, 3, 4, 6, 16, 26, 27, 35, 39, 41, 45, 46, 49, 58, 62, 63, 74, 75, 76, 78, 81, 83, 85, 86, 88, 90, 93], "round": [1, 34, 36, 45, 62, 81, 90], "astyp": [1, 85], "int": [1, 2, 3, 4, 5, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 41, 42, 43, 44, 45, 51, 52, 54, 58, 59, 60, 62, 64, 66, 67, 68, 71, 74, 75, 82, 88], "rang": [1, 3, 4, 5, 10, 39, 41, 43, 45, 58, 62, 63, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 94], "idx_flip": 1, "where": [1, 2, 3, 4, 5, 8, 10, 11, 14, 20, 30, 34, 36, 39, 40, 41, 42, 43, 44, 45, 46, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 93, 94], "pragma": 1, "cover": [1, 3, 73, 80], "choic": [1, 6, 36, 43, 81, 82, 86, 88], "replac": [1, 44, 49, 60, 75, 76, 79, 80, 81, 82, 85, 88, 92, 93], "generate_noise_matrix_from_trac": [1, 75, 76, 83, 85, 86], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 60, 74, 75, 76], "05": [1, 8, 22, 26, 44, 58, 62, 68, 70, 78, 80, 81, 83, 87, 91], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 75, 76, 83, 85, 86], "none": [1, 2, 3, 4, 5, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 44, 45, 46, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 75, 76, 81, 82, 83, 85, 86, 91], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 8, 22, 35, 41, 62, 74, 75, 76, 78, 80, 83, 85, 86, 92], "max_it": [1, 74, 79, 88, 93], "10000": [1, 34, 80, 81], "x": [1, 2, 3, 4, 8, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 31, 32, 35, 36, 38, 39, 41, 44, 45, 46, 49, 50, 52, 58, 59, 60, 62, 64, 74, 75, 76, 78, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93], "diagon": [1, 3, 4, 36, 39, 45], "equal": [1, 3, 8, 10, 52, 57, 67, 89], "creat": [1, 2, 7, 14, 16, 31, 34, 35, 36, 45, 62, 72, 74, 78, 79, 81, 82, 91, 93, 94], "impli": [1, 8, 30, 51, 58], "float": [1, 2, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 40, 41, 43, 44, 45, 50, 51, 52, 54, 57, 58, 62, 66, 70, 74, 75, 76, 83, 85, 86], "entri": [1, 3, 4, 30, 31, 35, 36, 38, 42, 43, 45, 50, 51, 52, 55, 78, 79, 83, 86, 87, 92, 93], "maximum": [1, 8, 59, 67, 71, 91], "minimum": [1, 6, 8, 18, 36, 38, 52, 57, 70], "noise_r": 1, "non": [1, 2, 3, 4, 7, 14, 22, 31, 35, 36, 57, 62, 75, 81, 83, 85, 87, 88], "default": [1, 2, 3, 4, 5, 8, 12, 14, 24, 26, 28, 30, 31, 32, 34, 35, 36, 38, 39, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 75, 81, 82, 91], "If": [1, 2, 3, 4, 8, 10, 11, 14, 22, 24, 30, 31, 34, 35, 36, 38, 39, 41, 44, 45, 49, 50, 51, 52, 55, 57, 58, 59, 62, 63, 64, 66, 67, 70, 71, 72, 73, 74, 75, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "have": [1, 2, 3, 4, 8, 14, 19, 22, 25, 30, 31, 33, 34, 35, 36, 39, 41, 45, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 63, 67, 71, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "all": [1, 2, 3, 4, 5, 6, 8, 11, 12, 14, 20, 28, 30, 31, 34, 35, 36, 39, 41, 42, 44, 45, 49, 50, 51, 52, 53, 54, 57, 58, 59, 60, 62, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "necessari": [1, 2, 3, 5, 8, 10, 44, 75], "In": [1, 2, 3, 8, 30, 31, 34, 35, 50, 51, 53, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 88, 89, 90, 91, 92, 93, 94], "particular": [1, 4, 8, 11, 12, 14, 17, 18, 20, 22, 23, 24, 27, 31, 35, 45, 50, 54, 58, 62, 67, 71, 72, 74, 76, 79, 81, 85, 86, 88, 90, 92, 93], "satisfi": [1, 3, 30], "requir": [1, 2, 4, 5, 6, 7, 8, 9, 10, 26, 29, 31, 32, 33, 34, 35, 36, 39, 45, 48, 49, 52, 59, 60, 62, 64, 72, 73, 74, 80, 81, 83, 89], "argument": [1, 2, 3, 4, 8, 14, 21, 23, 26, 27, 31, 34, 35, 36, 41, 46, 49, 50, 51, 52, 54, 57, 58, 59, 60, 62, 66, 67, 68, 70, 76, 79, 80, 81, 82, 87, 90, 93, 94], "when": [1, 2, 3, 4, 8, 10, 12, 21, 22, 31, 35, 36, 39, 41, 45, 49, 52, 54, 55, 57, 59, 60, 62, 63, 75, 76, 78, 79, 82, 85, 89, 90, 91, 92, 93, 94], "The": [1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 38, 39, 40, 41, 42, 43, 45, 49, 50, 51, 52, 55, 57, 58, 59, 60, 62, 64, 67, 68, 70, 72, 74, 75, 76, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "rate": [1, 2, 3, 8, 32, 45, 74, 94], "set": [1, 2, 3, 4, 7, 8, 10, 11, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 40, 41, 43, 45, 49, 50, 52, 55, 57, 58, 59, 60, 62, 64, 66, 67, 75, 76, 78, 79, 81, 85, 86, 88, 89, 90, 91, 92, 93, 94], "note": [1, 2, 3, 5, 6, 8, 23, 27, 31, 34, 35, 36, 41, 45, 50, 55, 57, 58, 59, 60, 62, 63, 67, 73, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "you": [1, 2, 3, 4, 5, 8, 12, 14, 30, 31, 33, 34, 35, 36, 41, 48, 49, 50, 52, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "high": [1, 2, 14, 34, 36, 45, 57, 60, 62, 75, 76, 80, 82, 83, 87, 90, 91, 92, 93, 94], "mai": [1, 2, 3, 4, 8, 11, 19, 20, 25, 30, 31, 33, 34, 35, 36, 39, 41, 45, 50, 51, 55, 57, 58, 59, 60, 62, 64, 67, 71, 73, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 89, 90, 91, 93, 94], "imposs": [1, 8, 83], "also": [1, 2, 3, 4, 5, 8, 20, 30, 31, 34, 35, 36, 44, 49, 50, 59, 62, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "low": [1, 8, 45, 50, 72, 75, 76, 79, 83, 87, 91], "zero": [1, 3, 4, 31, 35, 38, 45, 46, 75, 82, 86, 87, 88], "forc": [1, 2, 3, 4, 35, 75, 94], "instead": [1, 2, 3, 8, 11, 14, 28, 30, 31, 34, 35, 36, 39, 45, 49, 50, 52, 54, 58, 59, 60, 62, 63, 66, 68, 70, 73, 74, 78, 79, 81, 82, 83, 86, 87, 88, 90, 91, 92, 93, 94], "onli": [1, 2, 3, 4, 5, 8, 14, 21, 22, 26, 30, 31, 34, 35, 36, 38, 39, 44, 45, 49, 50, 59, 60, 62, 64, 66, 70, 71, 72, 74, 75, 76, 79, 82, 85, 86, 87, 88, 89, 90, 91, 93, 94], "guarante": [1, 3, 4, 13, 19, 25, 31, 33, 35, 37, 39, 48, 73], "produc": [1, 2, 4, 8, 14, 41, 50, 60, 62, 64, 66, 72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 93, 94], "higher": [1, 4, 8, 30, 36, 38, 39, 41, 43, 50, 51, 62, 76, 79, 81, 87], "opposit": [1, 94], "occur": [1, 3, 8, 30, 44, 57, 75, 76, 81, 82, 88], "small": [1, 3, 8, 30, 34, 41, 45, 51, 58, 79, 80, 82, 86, 88, 93], "numpi": [1, 3, 4, 5, 8, 10, 16, 27, 34, 35, 41, 43, 44, 46, 49, 54, 57, 62, 63, 68, 70, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "max": [1, 36, 59, 60, 76, 82, 88], "tri": [1, 31, 35, 89], "befor": [1, 2, 3, 31, 35, 43, 45, 59, 62, 67, 79, 81, 83, 85, 88, 90, 92, 93], "option": [1, 2, 3, 4, 5, 6, 7, 10, 11, 14, 21, 22, 26, 30, 31, 34, 35, 36, 39, 41, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 67, 70, 71, 72, 74, 75, 76, 78, 81, 82, 83, 90, 91, 92], "left": [1, 2, 36, 38, 43, 45, 52, 55, 58, 75, 76, 86, 87, 88, 91], "stochast": 1, "exceed": 1, "generate_n_rand_probabilities_that_sum_to_m": 1, "m": [1, 4, 31, 35, 40, 41, 50, 55, 57, 58, 59, 75, 76, 80, 85, 86, 87, 94], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 31, 35, 49, 81, 83, 91], "length": [1, 4, 10, 22, 23, 30, 32, 36, 45, 52, 55, 59, 60, 62, 64, 67, 71, 74, 86, 88, 91, 92, 94], "must": [1, 2, 3, 4, 14, 30, 31, 32, 33, 35, 36, 39, 41, 42, 45, 48, 49, 50, 51, 52, 59, 60, 62, 64, 66, 67, 68, 70, 71, 74, 85, 89, 91, 94], "randomly_distribute_n_balls_into_k_bin": 1, "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 8, 10, 30, 34, 36, 42, 45, 46, 50, 52, 58, 64, 66, 67, 68, 70, 71, 74, 81, 85, 86, 87, 91, 92, 93, 94], "ball": [1, 80], "bin": [1, 3, 52, 75, 76, 88], "ensur": [1, 2, 8, 31, 35, 45, 46, 57, 60, 62, 74, 75, 76, 79, 81, 82, 83, 88, 89, 90, 92, 93], "most": [1, 3, 4, 5, 8, 14, 30, 34, 36, 41, 49, 50, 51, 52, 55, 57, 58, 59, 60, 63, 66, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93], "least": [1, 8, 16, 27, 30, 34, 50, 51, 57, 60, 70, 76, 81, 82, 85, 88, 91], "int_arrai": [1, 45], "can": [2, 3, 4, 5, 6, 7, 11, 12, 14, 28, 30, 31, 32, 33, 34, 35, 36, 40, 41, 42, 45, 46, 48, 49, 50, 51, 52, 53, 54, 55, 57, 58, 59, 60, 62, 63, 64, 67, 68, 71, 72, 73, 74, 75, 78, 79, 82, 86, 87, 88, 89, 90, 91, 92, 93, 94], "model": [2, 3, 4, 8, 14, 16, 26, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 44, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 73, 75, 76, 80, 84, 89, 91, 94], "For": [2, 3, 4, 5, 7, 8, 9, 14, 20, 29, 30, 31, 34, 35, 36, 39, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 66, 68, 70, 71, 72, 74, 76, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89, 91, 92, 93, 94], "regular": [2, 3, 34, 49], "multi": [2, 3, 8, 30, 31, 34, 35, 36, 40, 41, 42, 45, 46, 51, 52, 53, 54, 59, 60, 72, 81, 83, 84], "task": [2, 4, 5, 8, 10, 12, 13, 14, 26, 28, 30, 34, 39, 41, 42, 43, 45, 50, 52, 60, 62, 72, 74, 79, 80, 81, 83, 86, 88, 91, 93, 94], "cleanlearn": [2, 3, 8, 21, 26, 31, 45, 49, 62, 63, 72, 73, 90, 92, 93], "wrap": [2, 31, 35, 49, 59, 62, 72, 75, 76, 78, 79, 83, 90, 92, 93], "instanc": [2, 3, 4, 5, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 35, 41, 49, 58, 59, 62, 67, 74, 75, 76, 78, 79, 82, 83, 92], "sklearn": [2, 3, 4, 6, 8, 16, 27, 30, 35, 41, 45, 49, 59, 62, 63, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 89, 90, 92, 93], "classifi": [2, 3, 35, 41, 45, 50, 53, 59, 60, 72, 73, 74, 78, 79, 81, 85, 86, 88, 89, 91, 92, 93, 94], "adher": [2, 35, 62], "estim": [2, 3, 4, 7, 11, 20, 30, 34, 35, 36, 39, 45, 50, 51, 52, 57, 59, 62, 64, 66, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 84, 86, 87, 88, 89, 90, 91, 94], "api": [2, 3, 12, 49, 59, 62, 73, 81, 90], "defin": [2, 3, 4, 5, 8, 12, 20, 30, 31, 32, 34, 35, 36, 60, 62, 64, 75, 76, 78, 81, 85, 88, 94], "four": [2, 8, 80, 83, 94], "clf": [2, 3, 4, 41, 62, 72, 78, 81, 83, 86, 92], "fit": [2, 3, 4, 6, 8, 16, 35, 49, 59, 62, 72, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 89, 90, 92, 93, 94], "sample_weight": [2, 35, 62, 83], "predict_proba": [2, 4, 30, 35, 41, 49, 74, 75, 76, 78, 79, 81, 83, 85, 86, 88, 92], "predict": [2, 3, 4, 6, 8, 14, 20, 21, 24, 26, 30, 34, 35, 36, 38, 39, 41, 42, 44, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 73, 80, 81, 83, 84, 88, 90, 91, 93, 94], "score": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 34, 36, 38, 41, 43, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 88, 90, 92, 93], "data": [2, 3, 4, 5, 6, 7, 9, 11, 12, 13, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 32, 33, 34, 35, 36, 41, 42, 45, 48, 49, 50, 51, 52, 53, 57, 59, 60, 61, 62, 67, 68, 69, 70, 71, 73, 77, 82, 84, 89, 93], "e": [2, 3, 4, 8, 10, 20, 30, 31, 34, 35, 36, 39, 41, 42, 45, 46, 50, 51, 52, 53, 59, 60, 62, 64, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93], "featur": [2, 3, 4, 6, 8, 14, 17, 21, 22, 23, 24, 26, 27, 41, 45, 59, 62, 72, 75, 76, 78, 79, 81, 83, 85, 90, 92], "element": [2, 3, 4, 30, 36, 38, 45, 50, 52, 60, 67, 68, 70, 74, 79, 81, 93, 94], "first": [2, 4, 8, 15, 22, 23, 30, 34, 41, 45, 50, 51, 55, 58, 60, 62, 74, 75, 78, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "index": [2, 8, 22, 30, 36, 44, 45, 46, 51, 60, 62, 67, 70, 71, 74, 75, 76, 78, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "should": [2, 3, 4, 5, 8, 12, 20, 22, 27, 30, 31, 34, 35, 36, 38, 39, 41, 43, 44, 45, 49, 50, 51, 54, 55, 57, 58, 59, 60, 62, 63, 67, 68, 70, 71, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "correspond": [2, 3, 4, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 38, 39, 41, 44, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 64, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "differ": [2, 4, 5, 8, 11, 13, 19, 22, 23, 25, 30, 31, 33, 34, 35, 36, 37, 41, 45, 46, 48, 50, 55, 57, 59, 62, 74, 75, 76, 78, 79, 82, 83, 85, 88, 89, 92], "sampl": [2, 3, 4, 6, 8, 14, 18, 36, 38, 41, 52, 55, 58, 60, 62, 63, 72, 73, 80, 81, 83, 84, 86, 87, 90, 91, 93, 94], "size": [2, 8, 27, 31, 34, 35, 36, 41, 52, 57, 58, 62, 64, 66, 78, 81, 82, 83, 85, 86, 89, 91, 93], "here": [2, 4, 5, 8, 12, 34, 36, 39, 49, 50, 51, 52, 54, 55, 58, 59, 70, 72, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "re": [2, 4, 31, 35, 44, 50, 62, 72, 74, 75, 78, 79, 81, 90, 91, 92, 93, 94], "weight": [2, 8, 31, 32, 35, 41, 50, 57, 60, 62, 74, 75, 76, 79, 88, 93], "loss": [2, 32, 49, 60, 62, 82], "while": [2, 3, 8, 31, 34, 35, 40, 41, 45, 55, 58, 62, 72, 81, 82, 83, 85, 90], "train": [2, 3, 4, 8, 14, 16, 31, 32, 35, 41, 45, 49, 50, 55, 58, 59, 62, 63, 73, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 89, 91, 94], "support": [2, 3, 4, 10, 34, 41, 45, 46, 59, 60, 70, 72, 73, 74, 75, 76, 81, 82], "your": [2, 3, 4, 7, 8, 14, 30, 31, 33, 34, 35, 36, 41, 45, 48, 49, 50, 51, 52, 54, 59, 60, 62, 63, 64, 66, 67, 73, 74, 78, 80, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "recommend": [2, 4, 8, 11, 14, 34, 36, 50, 75, 76, 81, 82, 89, 90], "furthermor": 2, "correctli": [2, 3, 8, 30, 31, 35, 36, 39, 46, 51, 52, 57, 58, 62, 64, 79, 81, 86, 87, 90, 91, 93], "clonabl": [2, 62], "via": [2, 4, 8, 11, 14, 16, 20, 30, 32, 34, 35, 41, 45, 50, 55, 58, 59, 60, 62, 63, 66, 70, 74, 75, 76, 78, 79, 80, 81, 82, 86, 87, 88, 89, 90, 91, 92, 93, 94], "base": [2, 3, 4, 5, 8, 10, 11, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 49, 50, 51, 52, 54, 57, 59, 60, 62, 63, 66, 68, 70, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 94], "clone": [2, 62, 86], "intern": [2, 3, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 34, 38, 39, 40, 41, 42, 43, 44, 45, 46, 54, 58, 62, 68, 73, 75, 81, 83, 85, 86, 88, 94], "multipl": [2, 3, 4, 10, 11, 30, 36, 44, 50, 51, 52, 54, 57, 58, 62, 72, 75, 76, 81, 82, 84, 86, 87, 90], "g": [2, 3, 4, 8, 10, 20, 30, 31, 35, 36, 42, 45, 52, 53, 59, 60, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93], "manual": [2, 62, 74, 81, 88, 89, 90, 92, 93, 94], "pytorch": [2, 31, 32, 35, 62, 72, 74, 81, 84, 86, 91], "call": [2, 3, 4, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 41, 45, 49, 59, 62, 74, 75, 76, 79, 81, 83, 88, 89, 91, 93, 94], "__init__": [2, 32, 62, 82], "independ": [2, 3, 8, 51, 62, 79, 89, 94], "compat": [2, 31, 34, 35, 49, 62, 63, 66, 70, 72, 81, 89, 90, 92, 93], "neural": [2, 32, 49, 59, 62, 74, 81, 82, 86, 88], "network": [2, 31, 32, 35, 49, 59, 62, 74, 79, 81, 82, 86, 88, 93], "typic": [2, 31, 35, 59, 62, 74, 76, 78, 79, 82, 88, 89, 92, 93], "initi": [2, 3, 11, 16, 31, 35, 50, 62, 79, 81, 92], "insid": [2, 35, 62, 81, 83], "There": [2, 3, 72, 83, 85, 86], "two": [2, 3, 8, 16, 22, 30, 31, 34, 35, 42, 45, 55, 57, 58, 73, 75, 76, 78, 79, 81, 82, 83, 86, 90, 91, 93, 94], "new": [2, 5, 12, 20, 31, 34, 35, 40, 44, 45, 50, 62, 74, 75, 79, 80, 81, 88, 89, 93, 94], "notion": 2, "confid": [2, 3, 8, 20, 30, 34, 36, 39, 41, 45, 50, 51, 52, 55, 57, 58, 59, 60, 62, 66, 70, 72, 78, 79, 82, 83, 85, 86, 87, 89, 91, 92, 94], "packag": [2, 4, 5, 7, 8, 9, 13, 29, 33, 36, 37, 45, 48, 55, 58, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "prune": [2, 3, 36, 52, 62, 73, 87], "everyth": [2, 58, 83], "els": [2, 58, 75, 80, 81, 82, 85, 86], "mathemat": [2, 3, 8, 39], "keep": [2, 11, 12, 45, 72, 75, 80, 81, 91], "belong": [2, 3, 8, 30, 36, 38, 39, 51, 52, 53, 54, 59, 60, 64, 68, 70, 71, 76, 78, 79, 82, 83, 86, 88, 91, 94], "2": [2, 3, 4, 5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 42, 43, 44, 45, 49, 51, 52, 54, 59, 60, 62, 63, 67, 68, 70, 71, 80, 81, 89], "error": [2, 3, 4, 8, 31, 35, 36, 38, 39, 45, 51, 52, 54, 55, 57, 58, 60, 62, 64, 66, 67, 70, 73, 74, 75, 76, 78, 79, 80, 84, 92], "erron": [2, 3, 30, 36, 39, 45, 51, 52, 60, 62, 63, 64, 88, 90], "import": [2, 3, 4, 5, 6, 8, 10, 11, 12, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 34, 41, 43, 44, 50, 54, 57, 62, 63, 68, 70, 71, 72, 78, 79, 81, 86, 87, 88, 90, 91, 92, 93, 94], "linear_model": [2, 4, 30, 45, 62, 72, 74, 75, 76, 79, 81, 83, 85, 88, 93], "logisticregress": [2, 3, 4, 30, 45, 72, 74, 75, 76, 79, 81, 83, 85, 88, 93], "logreg": 2, "cl": [2, 12, 26, 62, 72, 81, 83, 90, 92, 93], "pass": [2, 3, 4, 6, 8, 10, 11, 12, 14, 21, 26, 28, 31, 34, 35, 36, 40, 41, 45, 49, 50, 52, 59, 60, 62, 68, 72, 74, 75, 76, 79, 80, 81, 83, 85, 87, 88, 90, 93], "x_train": [2, 75, 76, 83, 85, 86, 90, 92], "labels_maybe_with_error": 2, "had": [2, 3, 62, 87], "issu": [2, 3, 4, 6, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 27, 28, 30, 31, 33, 34, 35, 36, 48, 51, 52, 53, 54, 55, 56, 57, 58, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 77, 84, 85, 89, 90, 93], "pred": [2, 36, 45, 89, 90, 92, 93], "x_test": [2, 75, 76, 83, 86, 90, 92], "might": [2, 50, 62, 67, 75, 76, 81, 82, 92, 93], "case": [2, 3, 11, 30, 41, 50, 62, 74, 75, 76, 78, 80, 81, 82, 83, 88, 90, 92, 93, 94], "standard": [2, 3, 4, 26, 30, 36, 49, 51, 52, 54, 60, 62, 72, 75, 76, 78, 80, 83, 92], "adapt": [2, 31, 33, 45, 48, 62, 88], "skorch": [2, 62, 72, 81], "kera": [2, 48, 62, 72, 81], "scikera": [2, 49, 62, 81], "open": [2, 34, 80, 87, 94], "doesn": [2, 62, 72], "t": [2, 3, 8, 15, 23, 31, 32, 34, 35, 36, 41, 43, 44, 54, 59, 60, 62, 68, 70, 71, 72, 75, 76, 78, 79, 80, 82, 83, 86, 87, 94], "alreadi": [2, 4, 8, 14, 31, 34, 35, 39, 49, 50, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 92, 93], "exist": [2, 4, 8, 10, 16, 31, 34, 35, 44, 49, 55, 57, 59, 62, 72, 73, 75, 76, 79, 85, 86, 93, 94], "made": [2, 4, 14, 62, 79, 81, 82, 85, 87, 89, 90, 92, 93], "easi": [2, 39, 62, 75, 76, 80, 81, 83, 86], "inherit": [2, 5, 32, 62], "baseestim": [2, 35, 62], "yourmodel": [2, 62], "def": [2, 5, 12, 31, 35, 49, 62, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 93, 94], "self": [2, 3, 4, 5, 8, 10, 11, 12, 14, 27, 31, 32, 34, 35, 36, 41, 59, 60, 62, 75, 80, 82, 86, 91, 92, 94], "refer": [2, 8, 14, 31, 35, 51, 52, 54, 55, 57, 58, 62, 66, 67, 75, 76, 78, 79, 81, 82, 83, 89, 90], "origin": [2, 4, 8, 35, 36, 44, 45, 49, 51, 52, 55, 58, 59, 62, 63, 66, 68, 70, 75, 78, 79, 81, 82, 83, 87, 88, 90, 92, 93, 94], "total": [2, 3, 30, 34, 45, 51, 71, 81, 82, 91], "state": [2, 3, 4, 31, 32, 35, 40, 62, 83, 86, 87, 94], "art": [2, 32, 83, 86], "northcutt": [2, 3, 30, 59, 60], "et": [2, 3, 30, 32, 59, 60], "al": [2, 3, 30, 32, 59, 60], "2021": [2, 3, 30, 59, 60], "weak": [2, 58], "supervis": [2, 8, 75, 76, 81, 85], "find": [2, 4, 8, 11, 12, 14, 17, 18, 20, 21, 22, 23, 24, 27, 30, 31, 33, 34, 35, 36, 40, 44, 45, 48, 55, 58, 59, 60, 62, 64, 68, 70, 73, 75, 84, 89], "uncertainti": [2, 8, 38, 59, 62, 81, 88, 90], "It": [2, 3, 4, 5, 8, 10, 11, 14, 20, 23, 26, 28, 31, 35, 36, 39, 41, 50, 57, 58, 62, 72, 75, 76, 81, 82, 83, 86, 89], "work": [2, 3, 4, 5, 8, 10, 26, 30, 31, 34, 35, 36, 39, 44, 45, 46, 49, 50, 60, 62, 72, 73, 75, 76, 80, 88, 90, 93], "includ": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 31, 33, 34, 35, 44, 45, 48, 50, 51, 54, 55, 59, 60, 62, 66, 67, 68, 70, 72, 73, 75, 76, 78, 79, 81, 82, 83, 86, 87, 88, 94], "deep": [2, 33, 35, 48, 49, 62, 79], "see": [2, 3, 4, 11, 30, 31, 34, 35, 36, 41, 45, 49, 51, 52, 54, 55, 58, 59, 60, 62, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "subfield": 2, "theori": [2, 83], "machin": [2, 4, 12, 14, 28, 33, 48, 62, 75, 76, 80, 85], "across": [2, 3, 4, 5, 8, 11, 20, 30, 34, 41, 51, 58, 59, 75, 76, 78, 79, 80, 81, 82, 83, 87, 89], "varieti": [2, 81, 92, 93], "like": [2, 3, 4, 5, 8, 12, 28, 30, 31, 34, 35, 36, 39, 45, 49, 50, 51, 54, 55, 57, 60, 62, 63, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "pu": [2, 45], "input": [2, 3, 4, 8, 14, 22, 30, 31, 34, 35, 39, 41, 44, 45, 46, 49, 58, 62, 72, 73, 76, 79, 80, 81, 82, 83, 85, 86, 87, 90, 91, 93, 94], "discret": [2, 36, 39, 45, 59, 60, 64, 66, 67], "vector": [2, 3, 4, 8, 14, 36, 39, 41, 42, 45, 59, 60, 72, 74, 75, 76, 78, 79, 82, 83, 86, 87, 88, 91, 93, 94], "would": [2, 3, 4, 31, 34, 35, 36, 45, 52, 62, 72, 75, 81, 82, 83, 88, 90, 93, 94], "obtain": [2, 4, 6, 8, 14, 36, 50, 52, 55, 58, 60, 63, 74, 76, 79, 81, 85, 87, 89, 91, 94], "been": [2, 30, 36, 39, 44, 45, 50, 51, 55, 57, 59, 60, 62, 74, 75, 78, 81, 83, 85, 86, 87, 88, 91, 94], "dure": [2, 8, 14, 59, 62, 74, 78, 79, 81, 83, 86, 89, 90, 92, 93, 94], "denot": [2, 3, 39, 41, 45, 52, 59, 60, 70], "tild": 2, "paper": [2, 8, 50, 59, 68, 70, 80, 83, 85, 88, 90, 94], "cv_n_fold": [2, 3, 62, 93], "5": [2, 3, 4, 6, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 35, 36, 38, 40, 41, 45, 50, 51, 54, 55, 58, 62, 63, 70, 75, 79, 80, 81, 86, 87, 88, 89, 91, 93, 94], "converge_latent_estim": [2, 3], "pulearn": [2, 45], "find_label_issues_kwarg": [2, 8, 62, 73, 81, 83], "label_quality_scores_kwarg": [2, 8], "low_memori": [2, 52, 68, 81], "clean": [2, 57, 60, 62, 63, 72, 75, 76, 80, 90, 92, 93], "even": [2, 3, 30, 34, 38, 39, 45, 62, 74, 81, 83, 85, 86, 87], "messi": [2, 62, 83], "ridden": [2, 62], "autom": [2, 62, 72, 76, 80, 81], "robust": [2, 39, 62, 76, 81], "prone": [2, 62], "out": [2, 3, 4, 8, 14, 24, 31, 35, 36, 41, 49, 52, 53, 55, 58, 59, 60, 62, 63, 71, 72, 73, 80, 81, 83, 84, 86, 87, 88, 90, 91, 93, 94], "current": [2, 3, 5, 8, 11, 12, 20, 31, 35, 36, 41, 50, 57, 62, 75, 76, 81, 85], "intend": [2, 11, 12, 13, 14, 28, 37, 50, 66, 70, 74, 75, 76, 79, 83], "A": [2, 3, 4, 5, 8, 10, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 32, 35, 36, 39, 40, 41, 42, 43, 44, 45, 49, 50, 51, 54, 57, 58, 59, 60, 62, 64, 66, 67, 71, 73, 74, 75, 78, 79, 80, 81, 82, 83, 85, 87, 89, 92, 93, 94], "follow": [2, 3, 8, 12, 26, 30, 31, 34, 35, 41, 43, 50, 51, 55, 57, 58, 59, 62, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "experiment": [2, 31, 32, 34, 35, 52, 73, 81], "wrapper": [2, 4, 49, 74, 90, 92, 93], "around": [2, 4, 57, 75, 76, 87, 88, 94], "fasttext": [2, 48], "store": [2, 4, 8, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 59, 62, 78, 79, 80, 81, 91, 92, 93, 94], "along": [2, 41, 52, 70, 75, 76, 81, 82, 88], "dimens": [2, 45, 64, 67, 81, 82, 88, 91], "select": [2, 7, 8, 22, 50, 60, 82, 85, 88], "split": [2, 3, 4, 8, 10, 34, 41, 44, 45, 62, 74, 75, 76, 78, 79, 80, 82, 83, 86, 89, 92, 94], "cross": [2, 3, 8, 30, 36, 39, 40, 41, 52, 55, 58, 60, 62, 63, 73, 74, 75, 76, 78, 79, 80, 81, 83, 84, 86, 87, 90, 91, 92, 93, 94], "fold": [2, 3, 30, 36, 39, 62, 74, 78, 80, 81, 87, 91, 92], "By": [2, 4, 30, 51, 52, 62, 75, 81, 91], "need": [2, 3, 8, 30, 31, 34, 35, 36, 51, 52, 54, 59, 62, 72, 74, 75, 76, 79, 81, 83, 85, 86, 87, 91, 93], "holdout": [2, 3, 62], "comput": [2, 3, 4, 5, 6, 8, 17, 18, 20, 21, 22, 23, 24, 27, 30, 31, 32, 34, 35, 36, 38, 39, 40, 41, 45, 50, 51, 52, 54, 57, 58, 59, 60, 62, 63, 64, 66, 72, 73, 75, 76, 80, 83, 84, 86, 87, 88, 90, 91, 93], "them": [2, 3, 4, 5, 7, 8, 9, 10, 23, 29, 31, 33, 34, 35, 36, 48, 50, 59, 62, 73, 75, 76, 78, 79, 81, 82, 85, 86, 88, 90, 91, 92, 93, 94], "numer": [2, 3, 4, 8, 11, 20, 26, 41, 57, 59, 62, 67, 72, 73, 74, 75, 76, 77, 79, 82, 83, 85, 88, 90, 92, 93], "consist": [2, 3, 31, 35, 45, 50, 91, 94], "latent": [2, 3, 39], "thei": [2, 3, 4, 13, 19, 22, 25, 31, 32, 33, 35, 36, 37, 43, 45, 49, 52, 57, 60, 62, 63, 66, 70, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 88, 90, 93, 94], "relat": [2, 3, 11, 17, 18, 22, 23, 24, 27, 39, 45, 51, 62, 76, 79], "close": [2, 3, 8, 34, 39, 59, 74, 75, 76, 78, 79, 81, 82, 83, 87], "form": [2, 3, 8, 31, 32, 35, 39, 44, 45, 60, 62, 81], "equival": [2, 3, 31, 35, 39, 59, 88], "iter": [2, 3, 30, 31, 35, 36, 45, 51, 52, 62, 81, 85, 91], "enforc": [2, 31, 35, 45], "perfectli": [2, 30, 51, 83], "certain": [2, 3, 4, 31, 35, 49, 58, 62, 75, 76, 80, 88], "dict": [2, 3, 4, 8, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 34, 35, 36, 40, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 70, 75, 76, 81, 82, 94], "keyword": [2, 3, 4, 8, 14, 21, 23, 26, 31, 34, 35, 36, 38, 41, 44, 49, 50, 52, 59, 60, 62, 68, 70, 75], "filter": [2, 3, 8, 34, 44, 51, 53, 54, 56, 58, 65, 66, 67, 69, 70, 71, 72, 73, 74, 76, 79, 80, 81, 82, 86, 87, 90, 91, 92, 93, 94], "find_label_issu": [2, 3, 8, 26, 34, 36, 51, 52, 54, 55, 57, 58, 62, 64, 66, 67, 68, 70, 71, 72, 73, 81, 86, 87, 90, 91, 92, 93, 94], "particularli": [2, 72, 85, 88], "filter_bi": [2, 3, 34, 36, 52, 73, 81], "frac_nois": [2, 36, 52, 68, 81], "min_examples_per_class": [2, 36, 52, 76, 81, 83], "impact": [2, 8, 75, 76, 82], "ml": [2, 4, 8, 13, 62, 72, 75, 76, 78, 79, 82, 85, 92, 93], "accuraci": [2, 32, 60, 74, 81, 82, 83, 85, 88, 90, 91, 92, 93], "n_job": [2, 34, 36, 52, 64, 66, 68, 81, 88, 91], "disabl": [2, 31, 35, 36, 88], "process": [2, 3, 5, 11, 14, 34, 36, 44, 50, 52, 58, 64, 66, 68, 74, 75, 81, 85, 89, 93], "caus": [2, 36, 41, 75, 76, 81], "rank": [2, 3, 8, 30, 34, 36, 41, 51, 52, 53, 55, 56, 58, 59, 61, 65, 67, 68, 69, 71, 72, 73, 75, 76, 80, 81, 86, 87, 88, 90, 91, 92, 93, 94], "get_label_quality_scor": [2, 34, 36, 41, 50, 52, 54, 55, 57, 60, 63, 66, 68, 70, 73, 83, 86, 87, 90, 91, 94], "adjust_pred_prob": [2, 8, 54, 59, 60, 83], "control": [2, 4, 7, 8, 14, 34, 36, 50, 58, 59, 62, 68, 70, 75, 76, 80, 81], "how": [2, 3, 4, 8, 11, 12, 14, 20, 30, 31, 32, 34, 35, 39, 45, 50, 51, 54, 55, 57, 59, 60, 62, 66, 70, 72, 75, 76, 78, 79, 80, 82, 87, 88, 89, 90, 91, 92, 93], "much": [2, 8, 30, 34, 36, 62, 81, 83, 85, 88], "output": [2, 3, 4, 8, 14, 31, 32, 35, 39, 45, 49, 50, 51, 55, 57, 58, 59, 62, 66, 67, 70, 71, 72, 73, 74, 75, 79, 80, 81, 82, 87, 88, 89, 90, 93], "print": [2, 4, 5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 45, 50, 51, 52, 57, 59, 60, 62, 64, 66, 67, 71, 73, 74, 76, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "suppress": [2, 34, 50, 57, 59, 60, 62, 64, 66, 67, 91, 94], "statement": [2, 34, 50, 57, 59, 60, 62, 64, 66, 67], "big": [2, 34, 52, 58, 62, 83], "limit": [2, 4, 14, 34, 52, 87, 91, 94], "memori": [2, 31, 34, 35, 52, 58, 64, 66, 75, 91], "label_issues_batch": [2, 33, 52, 81], "find_label_issues_batch": [2, 34, 52, 81], "pred_prob": [2, 3, 4, 6, 8, 14, 21, 22, 24, 27, 30, 34, 36, 38, 39, 40, 41, 42, 45, 46, 50, 51, 52, 54, 55, 58, 59, 60, 64, 66, 67, 68, 70, 71, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 92, 93], "threshold": [2, 3, 5, 8, 16, 17, 18, 20, 24, 26, 27, 34, 57, 58, 59, 60, 66, 70, 75, 87, 88, 91, 94], "inverse_noise_matrix": [2, 3, 8, 39, 45, 73, 83], "label_issu": [2, 34, 36, 52, 55, 62, 64, 73, 74, 79, 81, 82, 83, 90, 92, 93], "clf_kwarg": [2, 3, 8, 62], "clf_final_kwarg": [2, 62], "validation_func": [2, 3, 8], "correct": [2, 4, 8, 30, 34, 36, 38, 50, 51, 52, 54, 55, 57, 58, 60, 62, 63, 66, 70, 72, 74, 78, 79, 82, 83, 85, 87, 89, 90], "result": [2, 3, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 34, 35, 36, 38, 43, 45, 52, 54, 55, 58, 60, 62, 63, 64, 66, 70, 74, 75, 76, 78, 79, 81, 82, 83, 85, 90, 91, 92, 93, 94], "identifi": [2, 3, 4, 5, 8, 10, 14, 23, 28, 30, 34, 36, 52, 55, 58, 60, 62, 63, 64, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 80, 82, 83, 86, 88, 90, 91, 92, 93, 94], "final": [2, 8, 62, 78, 87, 89, 90, 92], "remain": [2, 62, 73, 82, 90, 92, 93, 94], "datasetlik": [2, 45, 62], "beyond": [2, 4, 5, 7, 9, 29, 72, 91], "pd": [2, 3, 4, 5, 11, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 40, 49, 50, 51, 62, 70, 74, 75, 76, 78, 79, 81, 83, 85, 90, 92, 93, 94], "datafram": [2, 3, 4, 5, 10, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 34, 40, 45, 46, 49, 50, 51, 62, 67, 71, 73, 74, 75, 76, 78, 79, 81, 82, 83, 85, 90, 91, 93, 94], "scipi": [2, 4, 11, 45], "spars": [2, 4, 8, 11, 14, 16, 27, 45, 46, 78], "csr_matrix": [2, 4, 11, 14, 16, 27], "torch": [2, 31, 32, 35, 74, 79, 80, 82, 88, 93], "util": [2, 4, 8, 14, 28, 31, 32, 35, 37, 50, 62, 72, 73, 74, 75, 76, 81, 82, 83, 88], "tensorflow": [2, 45, 49, 72, 74, 81], "object": [2, 4, 8, 10, 11, 14, 28, 31, 32, 34, 35, 41, 45, 46, 49, 52, 55, 56, 57, 58, 59, 62, 70, 72, 74, 76, 78, 82, 83, 84, 90, 93], "list": [2, 3, 4, 10, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 32, 34, 35, 36, 42, 44, 45, 46, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 66, 67, 68, 70, 71, 73, 74, 75, 76, 80, 81, 82, 83, 86, 87, 90, 93, 94], "index_list": 2, "subset": [2, 3, 4, 14, 30, 34, 36, 45, 60, 67, 71, 74, 78, 79, 81, 82, 86, 87, 88, 89, 90, 92, 93, 94], "wa": [2, 3, 10, 12, 34, 45, 50, 51, 57, 59, 71, 74, 75, 76, 78, 79, 81, 83, 86, 87, 89, 91, 92, 93, 94], "abl": [2, 3, 8, 62, 74, 81, 83, 85, 86], "format": [2, 3, 4, 8, 10, 31, 34, 35, 36, 39, 40, 41, 42, 45, 46, 49, 50, 51, 52, 55, 58, 59, 60, 62, 64, 66, 67, 70, 71, 75, 76, 78, 80, 82, 85, 90, 91, 92, 94], "make": [2, 3, 16, 31, 34, 35, 41, 49, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "sure": [2, 34, 36, 41, 74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 92, 93], "shuffl": [2, 8, 45, 74, 79, 82, 86, 88], "ha": [2, 3, 4, 8, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 31, 35, 39, 41, 44, 45, 50, 55, 57, 62, 68, 70, 71, 72, 74, 75, 76, 78, 79, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "batch": [2, 34, 45, 49, 50, 64, 66, 81, 82, 88], "order": [2, 4, 8, 30, 31, 35, 36, 39, 40, 41, 45, 50, 51, 52, 55, 58, 59, 60, 64, 67, 68, 70, 71, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 87, 90, 91, 93, 94], "destroi": [2, 45], "oper": [2, 31, 34, 35, 45, 49, 60, 72, 79, 88, 92, 93], "eg": [2, 8, 45, 55, 58, 75, 76, 81], "repeat": [2, 45, 50, 85, 88], "appli": [2, 31, 35, 36, 41, 42, 44, 45, 54, 59, 68, 74, 75, 76, 78, 81, 82, 85, 86, 88, 89, 90, 91, 92, 93], "array_lik": [2, 3, 30, 36, 45, 52, 59, 63], "some": [2, 3, 4, 8, 12, 20, 30, 31, 33, 35, 36, 39, 44, 45, 48, 50, 51, 52, 54, 55, 58, 59, 60, 62, 64, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "seri": [2, 3, 34, 45, 46, 62, 70, 81], "row": [2, 3, 4, 8, 11, 23, 30, 34, 36, 38, 39, 45, 50, 51, 52, 54, 59, 60, 62, 67, 68, 70, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 88, 92, 94], "rather": [2, 3, 22, 30, 45, 49, 50, 57, 66, 70, 85, 89, 91, 93, 94], "leav": [2, 36], "per": [2, 3, 11, 30, 34, 36, 41, 44, 50, 51, 52, 54, 57, 58, 60, 63, 64, 66, 70, 76, 81, 87, 94], "determin": [2, 3, 8, 14, 20, 22, 26, 30, 34, 36, 41, 45, 50, 52, 55, 57, 60, 66, 70, 75, 81, 85, 88, 90], "cutoff": [2, 3, 88], "consid": [2, 3, 4, 8, 11, 14, 21, 22, 24, 27, 30, 31, 35, 36, 45, 50, 57, 59, 60, 63, 66, 70, 74, 76, 78, 79, 81, 82, 83, 87, 88, 89, 90, 91, 92, 93], "section": [2, 3, 5, 8, 73, 78, 82], "3": [2, 3, 4, 5, 8, 30, 31, 35, 36, 39, 40, 41, 42, 43, 44, 45, 49, 52, 59, 60, 62, 63, 68, 70, 80, 81, 89], "equat": [2, 3, 39], "advanc": [2, 3, 4, 7, 8, 14, 57, 59, 70, 73, 76, 77, 83], "user": [2, 3, 4, 8, 12, 14, 23, 28, 31, 35, 36, 57, 59, 60, 62, 66, 70, 83], "specifi": [2, 3, 4, 6, 8, 11, 12, 14, 16, 27, 28, 31, 34, 35, 36, 41, 44, 50, 51, 52, 55, 57, 59, 60, 62, 63, 71, 73, 74, 76, 79, 82, 85, 87, 90, 93], "automat": [2, 3, 4, 22, 30, 72, 78, 79, 80, 81, 82, 85, 87, 90, 91, 92, 93, 94], "greater": [2, 3, 4, 7, 8, 24, 34, 45, 57, 76, 80, 81, 94], "count": [2, 20, 22, 30, 34, 36, 39, 45, 51, 52, 58, 73, 81, 82], "observ": [2, 3, 39, 74, 75, 76, 85, 88, 90], "mislabel": [2, 8, 30, 34, 36, 39, 50, 51, 52, 55, 57, 60, 66, 68, 70, 72, 74, 78, 79, 81, 82, 83, 86, 87, 90, 92, 93], "one": [2, 3, 4, 8, 22, 30, 31, 34, 35, 36, 41, 45, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 64, 66, 67, 68, 70, 71, 72, 74, 75, 76, 78, 79, 82, 85, 88, 89, 90, 92, 93, 94], "get_label_issu": [2, 34, 62, 83, 90, 92, 93], "either": [2, 3, 5, 8, 31, 34, 35, 36, 50, 52, 57, 59, 60, 64, 66, 76, 86, 87], "boolean": [2, 5, 8, 20, 34, 36, 44, 50, 52, 55, 60, 62, 64, 66, 67, 72, 74, 76, 79, 81, 82, 87, 90, 91, 93], "label_issues_mask": [2, 36, 60, 62, 73], "indic": [2, 3, 4, 5, 8, 11, 20, 30, 34, 35, 36, 38, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 60, 62, 63, 66, 68, 70, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "its": [2, 4, 7, 8, 14, 31, 34, 35, 36, 43, 44, 52, 55, 58, 59, 60, 62, 64, 68, 70, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 89, 90, 91, 93, 94], "return_indices_ranked_bi": [2, 34, 36, 52, 68, 73, 81, 83, 86, 92, 93], "significantli": [2, 82, 83, 85, 89], "reduc": [2, 34, 36, 45, 74, 81], "time": [2, 8, 31, 34, 35, 45, 50, 73, 75, 80, 81, 82, 83, 87, 88, 90, 91, 92, 93, 94], "take": [2, 4, 8, 30, 31, 35, 40, 41, 45, 49, 60, 78, 82, 85, 92, 94], "run": [2, 4, 5, 7, 9, 12, 14, 22, 23, 29, 31, 34, 35, 62, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "skip": [2, 8, 31, 35, 62, 74, 81, 86, 94], "slow": [2, 3], "step": [2, 5, 22, 41, 58, 81, 82, 83, 85, 89], "caution": [2, 4, 81], "previous": [2, 4, 11, 45, 59, 62, 73, 74, 75, 78, 79, 85, 89, 92], "assign": [2, 5, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 40, 41, 45, 62, 75, 78, 81, 82, 90, 91, 92, 94], "individu": [2, 8, 11, 22, 31, 35, 50, 54, 57, 60, 62, 68, 70, 73, 76, 78, 81, 85, 86, 87, 92, 94], "still": [2, 34, 35, 45, 59, 81, 82, 88, 92], "extra": [2, 31, 35, 45, 49, 50, 51, 62, 79, 81, 82, 85, 88], "receiv": [2, 8, 31, 35, 51, 54, 55, 62, 64, 68, 76, 87], "overwritten": [2, 62], "callabl": [2, 3, 41, 44, 49, 54, 81], "x_val": 2, "y_val": 2, "map": [2, 3, 10, 34, 35, 40, 44, 45, 58, 60, 62, 67, 74, 75, 76, 81, 82, 83, 86, 94], "appropri": [2, 8, 14, 52, 60, 75, 78, 86, 87], "earli": [2, 82], "stop": [2, 82], "x_valid": 2, "y_valid": 2, "could": [2, 8, 20, 30, 45, 59, 75, 78, 82, 86, 90, 92, 94], "f": [2, 5, 74, 75, 78, 79, 80, 81, 82, 83, 85, 86, 88, 90, 92, 93], "ignor": [2, 31, 35, 44, 49, 62, 67, 71, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "allow": [2, 30, 31, 34, 35, 38, 45, 50, 58, 59, 62, 64, 66, 74, 81, 82, 89, 91, 93], "access": [2, 8, 11, 31, 35, 62, 76, 82, 86], "hyperparamet": [2, 54, 59, 82], "purpos": [2, 75, 76, 81, 86, 90], "want": [2, 4, 8, 30, 34, 46, 50, 52, 62, 75, 79, 80, 82, 85, 87, 88, 89, 91, 93, 94], "explicitli": [2, 6, 8, 35, 62], "yourself": [2, 4, 34, 76], "altern": [2, 5, 8, 41, 45, 49, 50, 60, 73, 74, 78, 79, 81, 82, 83, 85, 86, 88, 90, 93], "same": [2, 3, 4, 5, 8, 10, 12, 14, 22, 26, 31, 34, 35, 36, 45, 49, 50, 52, 59, 60, 62, 66, 67, 70, 71, 72, 75, 76, 78, 79, 81, 82, 87, 88, 89, 90, 91, 92, 93], "effect": [2, 8, 23, 31, 35, 50, 59, 62, 78, 79, 81, 82, 88], "offer": [2, 4, 74, 75, 76, 79, 81, 83, 86, 93], "after": [2, 3, 4, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 45, 50, 62, 75, 79, 81, 82, 83, 85, 87, 88, 89, 90, 91, 93], "attribut": [2, 4, 5, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 41, 59, 62, 75, 92], "label_issues_df": [2, 62, 82], "similar": [2, 8, 30, 31, 35, 45, 50, 54, 55, 57, 59, 62, 66, 70, 75, 76, 78, 79, 81, 82, 83, 87, 88, 91], "document": [2, 3, 4, 8, 12, 14, 30, 31, 34, 35, 36, 41, 44, 49, 51, 52, 54, 57, 58, 59, 62, 66, 67, 68, 70, 73, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "descript": [2, 4, 5, 8, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 45, 55, 62, 75, 76], "were": [2, 3, 4, 30, 35, 51, 57, 70, 74, 78, 81, 83, 85, 87, 89, 91, 92], "present": [2, 3, 4, 8, 10, 11, 18, 30, 45, 59, 67, 72, 78, 81, 82, 88], "actual": [2, 3, 4, 30, 50, 51, 60, 76, 81, 83, 94], "num_class": [2, 30, 34, 45, 49, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 92, 93], "uniqu": [2, 27, 45, 67, 75, 81, 86, 88], "given_label": [2, 4, 26, 30, 39, 62, 67, 71, 74, 75, 76, 78, 79, 82, 83, 90, 91, 93, 94], "normal": [2, 3, 16, 22, 27, 36, 38, 41, 43, 44, 45, 60, 81, 83, 88], "trick": [2, 81], "distribut": [2, 3, 4, 8, 22, 24, 30, 35, 36, 40, 43, 50, 58, 59, 60, 72, 75, 76, 78, 79, 82, 88], "account": [2, 30, 50, 54, 59, 60, 79, 81, 83, 85, 86, 88, 90, 93], "word": [2, 3, 44, 70, 71, 81], "remov": [2, 8, 27, 30, 31, 35, 36, 62, 72, 79, 80, 81, 82, 88, 90, 92, 93], "so": [2, 3, 4, 5, 8, 12, 22, 30, 31, 34, 35, 36, 45, 50, 51, 57, 60, 62, 66, 70, 74, 75, 76, 79, 82, 83, 88, 91], "proportion": [2, 8, 36], "just": [2, 3, 4, 8, 11, 30, 32, 34, 45, 49, 60, 62, 64, 72, 73, 74, 76, 78, 79, 81, 82, 83, 86, 87, 88, 89, 91, 92, 93], "procedur": 2, "get": [2, 3, 4, 6, 11, 27, 31, 32, 35, 36, 41, 44, 45, 50, 52, 54, 59, 60, 62, 63, 64, 72, 74, 79, 80, 81, 82, 83, 88, 89, 90, 92, 93], "detect": [2, 4, 5, 7, 11, 12, 14, 16, 20, 24, 43, 53, 55, 56, 57, 58, 59, 60, 61, 62, 65, 69, 72, 75, 77, 82, 84, 86, 90, 91, 92, 93, 94], "arg": [2, 10, 20, 23, 27, 31, 32, 35, 41, 45, 60, 62], "kwarg": [2, 5, 8, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 41, 49, 62, 64, 66, 68, 81], "test": [2, 8, 22, 35, 41, 49, 62, 72, 75, 76, 78, 79, 82, 89, 90, 92, 93, 94], "expect": [2, 3, 31, 35, 36, 41, 50, 59, 60, 62, 81, 83, 85, 86, 87, 90, 92, 93, 94], "class_predict": 2, "evalu": [2, 8, 31, 32, 34, 35, 58, 62, 74, 75, 76, 81, 82, 83, 85, 89, 90, 91, 92, 93], "simpli": [2, 30, 60, 75, 76, 78, 79, 81, 83, 90, 91, 93, 94], "quantifi": [2, 4, 5, 8, 11, 36, 54, 59, 62, 72, 76, 78, 79, 82, 83, 87], "save_spac": [2, 8, 62], "potenti": [2, 8, 30, 36, 44, 52, 55, 58, 60, 62, 64, 66, 73, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 91, 92, 94], "cach": [2, 79, 88, 93], "panda": [2, 4, 5, 10, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 45, 46, 49, 50, 51, 73, 74, 75, 76, 78, 79, 80, 81, 83, 85, 90, 91, 92, 93], "unlik": [2, 8, 36, 38, 41, 49, 51, 52, 54, 70, 75, 85, 86, 88, 90], "both": [2, 4, 8, 14, 22, 30, 31, 35, 36, 45, 50, 52, 60, 64, 66, 71, 72, 75, 81, 82, 83, 85, 94], "mask": [2, 34, 36, 44, 45, 52, 55, 60, 62, 64, 66, 67, 72, 80, 81, 85, 87, 91, 94], "prefer": [2, 60, 68], "plan": 2, "subsequ": [2, 3, 31, 35, 79, 81, 83, 87, 93], "invok": [2, 31, 35, 83, 89], "scratch": [2, 62], "To": [2, 4, 5, 7, 8, 9, 11, 14, 22, 29, 31, 34, 35, 36, 49, 50, 52, 54, 58, 59, 60, 62, 63, 64, 66, 72, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "share": [2, 60, 62], "mostli": [2, 45, 57, 62], "longer": [2, 40, 44, 62, 73, 79, 81, 87, 93], "info": [2, 4, 5, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 62, 70, 75, 76, 80, 81, 94], "about": [2, 3, 4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 32, 34, 38, 50, 51, 54, 58, 62, 67, 70, 74, 75, 78, 79, 80, 81, 82, 83, 85, 88], "docstr": [2, 30, 31, 35, 45, 62, 80, 83], "unless": [2, 31, 35, 62, 81], "our": [2, 3, 8, 49, 50, 60, 62, 72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "is_label_issu": [2, 26, 62, 74, 75, 76, 78, 79, 82, 83, 90, 93], "entir": [2, 8, 22, 34, 36, 39, 51, 52, 57, 60, 62, 64, 66, 67, 72, 75, 76, 79, 81, 82, 83, 87, 88, 89, 91, 94], "accur": [2, 3, 4, 8, 14, 30, 34, 36, 50, 51, 52, 55, 58, 60, 62, 63, 64, 66, 67, 73, 76, 78, 79, 81, 82, 85, 90], "label_qu": [2, 50, 62, 83, 85, 90, 93], "measur": [2, 30, 50, 51, 62, 72, 80, 81, 83, 85, 86, 91, 92, 94], "qualiti": [2, 3, 4, 5, 8, 11, 26, 27, 30, 34, 36, 38, 41, 50, 51, 52, 54, 55, 57, 60, 62, 63, 66, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 84, 90, 92, 93], "lower": [2, 4, 5, 8, 11, 24, 34, 41, 43, 50, 51, 54, 57, 58, 60, 62, 63, 66, 70, 74, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 93, 94], "eas": 2, "comparison": [2, 31, 35, 58, 83, 85, 90], "against": [2, 31, 35, 75, 78, 81, 85, 86], "predicted_label": [2, 4, 26, 62, 67, 71, 74, 75, 76, 78, 79, 82, 83, 90, 91, 93], "ad": [2, 31, 35, 76, 85, 90], "precis": [2, 52, 55, 58, 81, 83, 91, 94], "definit": [2, 5, 62, 78, 92], "accessor": [2, 62], "describ": [2, 8, 16, 50, 59, 60, 62, 68, 70, 83, 85, 86, 87, 89, 94], "precomput": [2, 4, 39, 62, 80], "clear": [2, 62, 79, 90, 93], "save": [2, 4, 14, 31, 34, 35, 58, 62, 81, 87, 91, 94], "space": [2, 8, 59, 62, 78, 80, 82], "place": [2, 31, 35, 45, 62, 85, 92], "larg": [2, 34, 62, 78, 79, 81, 82, 88, 91, 94], "deploi": [2, 62, 78, 79, 81, 82], "care": [2, 8, 31, 35, 62, 79, 81, 83], "avail": [2, 4, 5, 10, 12, 28, 35, 62, 81, 83, 85, 87, 90], "cannot": [2, 4, 10, 12, 45, 89, 94], "anymor": 2, "classmethod": [2, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 35, 41, 62], "__init_subclass__": [2, 35, 62], "set_": [2, 35, 62], "_request": [2, 35, 62], "pep": [2, 35, 62], "487": [2, 35, 62], "look": [2, 4, 5, 14, 31, 35, 45, 62, 67, 75, 76, 78, 79, 81, 83, 85, 86, 87, 88, 91, 92, 94], "inform": [2, 4, 5, 8, 11, 14, 28, 31, 35, 45, 50, 51, 55, 58, 62, 67, 70, 71, 72, 74, 75, 78, 79, 83, 85, 88, 91, 94], "__metadata_request__": [2, 35, 62], "infer": [2, 35, 45, 62, 67, 71, 82, 85, 86, 90, 92, 93], "signatur": [2, 31, 35, 62], "accept": [2, 31, 35, 60, 62, 75, 76], "metadata": [2, 35, 62, 78, 79, 82, 94], "through": [2, 4, 5, 35, 62, 74, 76, 79, 80, 81, 85, 88, 90, 93], "develop": [2, 7, 35, 62, 81, 83, 94], "request": [2, 35, 62, 76, 79, 80, 86, 92, 93, 94], "those": [2, 3, 8, 34, 35, 36, 49, 50, 52, 58, 62, 66, 70, 71, 72, 74, 81, 82, 87, 91], "http": [2, 4, 5, 7, 8, 9, 16, 29, 31, 32, 34, 35, 38, 45, 59, 62, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "www": [2, 35, 62, 88], "org": [2, 16, 31, 32, 35, 45, 59, 62, 81, 83, 94], "dev": [2, 35, 62], "0487": [2, 35, 62], "get_metadata_rout": [2, 35, 62], "rout": [2, 35, 62], "pleas": [2, 31, 35, 49, 62, 72, 74, 75, 76, 79, 80, 81, 82, 83, 85, 86, 88, 90, 93, 94], "guid": [2, 5, 35, 62, 73, 82], "mechan": [2, 31, 35, 62], "metadatarequest": [2, 35, 62], "encapsul": [2, 14, 35, 57, 62], "get_param": [2, 35, 49, 62], "subobject": [2, 35, 62], "param": [2, 8, 31, 35, 49, 59, 62, 81], "name": [2, 4, 5, 8, 10, 11, 30, 31, 35, 40, 41, 45, 49, 50, 51, 58, 62, 67, 71, 74, 76, 79, 80, 81, 82, 83, 86, 91, 93, 94], "set_fit_request": [2, 35, 62], "union": [2, 3, 4, 10, 34, 35, 41, 45, 46, 52, 58, 62, 66, 70, 81], "str": [2, 3, 4, 10, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 34, 35, 36, 39, 41, 44, 45, 49, 50, 51, 55, 57, 58, 60, 62, 67, 71, 74, 75, 81, 85, 86, 94], "unchang": [2, 31, 35, 62, 94], "relev": [2, 14, 22, 35, 62, 82], "enable_metadata_rout": [2, 35, 62], "set_config": [2, 35, 62], "meta": [2, 35, 62], "rais": [2, 4, 10, 11, 31, 35, 38, 41, 62, 81], "alia": [2, 31, 35, 62], "metadata_rout": [2, 35, 62], "retain": [2, 35, 45, 62], "chang": [2, 31, 34, 35, 38, 62, 70, 74, 75, 79, 81, 87, 88, 93, 94], "version": [2, 4, 5, 7, 8, 9, 13, 19, 25, 29, 31, 33, 35, 37, 38, 45, 48, 49, 60, 62, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93, 94], "sub": [2, 35, 57, 62], "pipelin": [2, 35, 62], "otherwis": [2, 8, 30, 31, 34, 35, 36, 42, 44, 45, 52, 59, 62, 64, 66, 67, 71, 79, 81, 93], "updat": [2, 11, 31, 34, 35, 62, 73, 75, 82], "set_param": [2, 35, 49, 62], "simpl": [2, 31, 35, 36, 50, 60, 62, 75, 76, 78, 79, 82, 85, 88, 90, 92, 93], "well": [2, 3, 8, 31, 35, 38, 39, 50, 52, 58, 60, 62, 67, 70, 71, 73, 75, 76, 78, 79, 81, 82, 83, 85, 87, 88], "nest": [2, 31, 35, 62, 68, 70, 71, 94], "latter": [2, 31, 35, 62, 88], "compon": [2, 35, 62], "__": [2, 35, 62], "set_score_request": [2, 62], "structur": [3, 59, 78, 92], "unobserv": 3, "less": [3, 4, 8, 27, 34, 41, 50, 59, 60, 64, 66, 70, 76, 78, 80, 81, 82, 83, 87, 94], "channel": [3, 74, 83], "character": 3, "flip": 3, "nm": 3, "invers": [3, 8, 30, 39, 45, 51, 76, 80, 93], "inv": 3, "confident_joint": [3, 20, 30, 36, 45, 51, 52, 73, 81, 83], "un": 3, "under": [3, 8, 31, 35, 51, 58, 59, 76, 78, 79, 82, 83, 88], "joint": [3, 30, 36, 39, 45, 51, 52, 80], "num_label_issu": [3, 34, 36, 52, 67, 71, 73], "estimation_method": [3, 34], "off_diagon": 3, "multi_label": [3, 30, 36, 45, 46, 52, 86], "don": [3, 72, 76, 78, 79, 82, 83, 87], "statis": 3, "compute_confident_joint": [3, 30, 36, 45, 52, 83], "off": [3, 36, 45, 57, 82, 83, 87, 88], "j": [3, 4, 30, 31, 35, 36, 52, 55, 58, 59, 68, 70, 71, 75, 76, 83, 91, 94], "confident_learn": [3, 36, 52, 83], "off_diagonal_calibr": 3, "calibr": [3, 36, 45, 50, 85], "cj": [3, 39, 45], "axi": [3, 27, 39, 41, 43, 64, 67, 74, 75, 76, 81, 82, 83, 85, 86, 88, 90, 91], "bincount": [3, 75, 76, 83, 85, 86], "alwai": [3, 8, 31, 35, 45, 74, 83, 90, 92, 93], "estimate_issu": 3, "over": [3, 8, 31, 34, 35, 57, 58, 64, 66, 76, 78, 80, 81, 82, 83, 88, 90, 92], "As": [3, 5, 72, 75, 76, 79, 83, 90, 94], "add": [3, 4, 5, 11, 31, 35, 49, 58, 74, 75, 76, 79, 81, 82, 83, 86, 93], "approach": [3, 30, 34, 36, 78, 83, 86, 88, 90, 92], "custom": [3, 5, 8, 9, 26, 31, 34, 35, 41, 44, 60, 76, 79, 83, 93], "know": [3, 75, 76, 78, 79, 81, 82, 83, 85], "cut": [3, 57, 72, 83], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 88, 94], "underestim": 3, "few": [3, 58, 72, 76, 81, 85, 86, 87, 88, 94], "4": [3, 4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 40, 41, 44, 54, 55, 57, 58, 60, 63, 70, 80, 81, 86, 91, 94], "detail": [3, 4, 8, 12, 14, 30, 31, 35, 41, 45, 49, 50, 51, 52, 54, 55, 57, 58, 59, 66, 67, 68, 72, 73, 74, 86, 88, 94], "num_issu": [3, 5, 34, 74, 75, 76, 78, 79, 82, 83], "calibrate_confident_joint": 3, "up": [3, 8, 15, 22, 23, 26, 36, 41, 50, 80, 81, 87, 90, 93, 94], "p_": [3, 30, 36], "pair": [3, 4, 8, 30, 36, 83], "v": [3, 8, 34, 51, 52, 54, 60, 75, 76, 86, 88, 89], "rest": [3, 4, 5, 7, 8, 9, 29, 51, 52, 54, 62, 75, 76, 78, 79, 81, 82, 83, 85, 90, 92, 93], "fashion": [3, 4, 64, 92], "2x2": 3, "incorrectli": [3, 30, 51, 52, 55, 78, 94], "calibrated_cj": 3, "c": [3, 8, 44, 52, 60, 72, 74, 75, 76, 78, 79, 81, 83, 86, 88, 89, 90, 92], "whose": [3, 4, 8, 24, 31, 35, 39, 44, 50, 54, 57, 63, 66, 70, 71, 74, 75, 76, 78, 79, 81, 82, 83, 86, 87, 88, 91, 94], "truli": [3, 88, 91], "estimate_joint": [3, 30, 83], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 52, 58, 83, 87, 89, 91, 94], "return_indices_of_off_diagon": 3, "frequenc": [3, 22, 50, 51, 58, 67, 88], "done": [3, 8, 62, 75, 81, 83, 86, 88, 89], "overfit": [3, 8, 55, 58, 74, 75, 76, 78, 79, 82, 89, 92], "classifict": 3, "singl": [3, 4, 22, 30, 31, 35, 41, 42, 45, 50, 51, 57, 58, 59, 60, 70, 74, 75, 81, 83, 86, 87, 92], "baselin": [3, 31, 36, 88, 90, 93], "proxi": 3, "tupl": [3, 27, 31, 35, 39, 40, 42, 44, 45, 50, 52, 58, 66, 68, 70, 71, 74, 94], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 8, 34, 39, 50, 64, 66, 72, 81, 82, 91, 93], "practic": [3, 76, 82, 83, 88, 90, 92, 93], "complet": [3, 74, 75, 76, 78, 79, 81, 82, 83, 87], "gist": 3, "cj_ish": 3, "guess": [3, 39, 83, 85], "8": [3, 4, 5, 6, 40, 41, 42, 44, 54, 68, 70, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "parallel": [3, 36, 58, 68, 80], "again": [3, 49, 81, 88, 92], "simplifi": [3, 12], "understand": [3, 7, 30, 51, 58, 76, 83, 90, 91, 94], "100": [3, 31, 35, 60, 75, 76, 78, 80, 81, 82, 83, 86, 91, 92, 93, 94], "optim": [3, 31, 32, 35, 49, 82, 85], "speed": [3, 36, 80, 81, 90, 93], "dtype": [3, 21, 22, 27, 31, 35, 44, 45, 54, 70, 74, 87], "enumer": [3, 31, 35, 74, 75, 76, 82, 94], "s_label": 3, "confident_bin": 3, "6": [3, 4, 35, 41, 45, 70, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "num_confident_bin": 3, "argmax": [3, 36, 60, 64, 67, 74, 81, 83, 88, 91], "elif": 3, "estimate_lat": 3, "py_method": [3, 39], "cnt": [3, 39], "1d": [3, 4, 14, 34, 36, 41, 42, 45, 46, 54, 63, 74, 92], "eqn": [3, 39], "margin": [3, 36, 39, 41, 60], "marginal_p": [3, 39], "shorthand": [3, 11], "proport": [3, 8, 30, 51, 83, 89], "poorli": [3, 39, 92], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 83], "variabl": [3, 5, 12, 23, 45, 62, 63, 74, 75, 78, 83, 86, 90], "exact": [3, 39, 75, 76, 78, 82, 92], "within": [3, 4, 8, 13, 31, 32, 35, 37, 52, 57, 66, 68, 70, 75, 76, 81, 82, 87, 91], "percent": 3, "often": [3, 30, 39, 51, 81, 83, 89, 91], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 8, 45, 46, 58, 74, 75, 78, 79, 81, 82, 87, 88, 93], "wai": [3, 4, 49, 72, 73, 74, 75, 76, 78, 79, 81, 83, 85, 86, 87, 89, 92, 93], "pro": 3, "con": 3, "pred_proba": [3, 89], "combin": [3, 30, 75, 80, 81, 82, 83, 89, 90], "becaus": [3, 39, 45, 57, 79, 81, 83, 85, 87], "littl": [3, 34, 80, 87, 94], "uniform": [3, 60, 80, 81, 83], "20": [3, 5, 71, 74, 76, 79, 80, 81, 82, 83, 91, 94], "Such": [3, 82, 88], "bound": [3, 21, 31, 35, 55, 57, 58, 87], "reason": [3, 20, 31, 35], "comment": [3, 44, 94], "end": [3, 4, 31, 35, 58, 82, 91, 94], "file": [3, 4, 10, 33, 34, 48, 58, 74, 75, 78, 79, 80, 81, 87, 88, 91, 92, 94], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 83], "handl": [3, 4, 5, 8, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 34, 35, 73, 75, 76, 78, 79, 82, 83, 91, 92, 94], "five": [3, 55, 58, 83, 87], "estimate_cv_predicted_prob": [3, 83], "estimate_noise_matric": 3, "get_confident_threshold": [3, 34], "amongst": [3, 8], "confident_threshold": [3, 8, 20, 34, 59], "unifi": 4, "audit": [4, 7, 10, 11, 14, 74, 77, 78, 79, 81, 82, 83, 87], "kind": [4, 5, 74, 75, 78, 79, 80, 82, 83], "addit": [4, 5, 7, 8, 9, 11, 28, 29, 31, 35, 41, 46, 50, 58, 68, 74, 75, 78, 79, 82, 83, 85, 88, 89, 92, 93], "depend": [4, 5, 7, 8, 9, 10, 11, 29, 33, 36, 38, 45, 48, 52, 59, 62, 63, 72], "instal": [4, 5, 7, 8, 9, 29, 31, 33, 34, 35, 36, 48, 49, 64, 66], "pip": [4, 5, 7, 9, 29, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "development": [4, 5, 7, 9, 29], "git": [4, 5, 7, 9, 29, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "github": [4, 5, 7, 9, 29, 31, 32, 45, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "com": [4, 5, 7, 9, 29, 31, 32, 34, 38, 45, 59, 72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "egg": [4, 5, 7, 9, 29, 72, 80], "label_nam": [4, 5, 6, 8, 10, 16, 27, 72, 74, 75, 76, 78, 79, 81, 82, 83], "image_kei": [4, 82], "interfac": [4, 72, 81, 83], "librari": [4, 8, 35, 55, 58, 59, 72, 75, 79, 80, 81, 93], "goal": 4, "track": [4, 11, 12, 72, 75, 80, 81, 83], "intermedi": [4, 7, 76], "statist": [4, 8, 11, 20, 22, 30, 50, 51, 58, 76, 78, 79, 82, 83], "convert": [4, 10, 31, 35, 42, 43, 46, 50, 57, 66, 70, 73, 74, 79, 80, 81, 82, 85, 86, 87, 93], "hug": [4, 10, 82], "face": [4, 10, 14, 80, 82, 86], "kei": [4, 5, 8, 10, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 35, 41, 50, 51, 57, 59, 75, 76, 79, 81, 82, 83, 85, 87], "string": [4, 8, 10, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 31, 35, 45, 50, 51, 63, 67, 70, 71, 78, 79, 81, 85, 86, 93, 94], "dictionari": [4, 5, 8, 10, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 31, 35, 40, 45, 50, 51, 54, 55, 57, 58, 75, 76, 78, 79, 83, 85, 86, 87], "path": [4, 10, 31, 34, 35, 58, 74, 75, 81, 87], "local": [4, 10, 31, 32, 35, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "text": [4, 5, 8, 10, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 41, 59, 68, 70, 71, 72, 75, 76, 77, 80, 81, 83, 84, 85, 88], "txt": [4, 10, 94], "csv": [4, 10, 78, 79, 90, 92, 93], "json": [4, 10], "hub": [4, 10, 88], "regress": [4, 5, 10, 12, 14, 19, 26, 28, 75, 76, 79, 84, 85, 88, 93], "imag": [4, 7, 30, 35, 55, 57, 58, 59, 64, 66, 67, 72, 75, 76, 80, 81, 84, 85, 86, 87, 89, 91], "point": [4, 5, 8, 16, 22, 31, 35, 75, 76, 78, 79, 81, 82, 83, 85], "field": [4, 8, 31, 35], "themselv": [4, 90, 92, 93], "cleanvis": [4, 8], "level": [4, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 30, 44, 68, 70, 76, 82, 84, 91], "load_dataset": [4, 10, 82], "glue": 4, "sst2": 4, "properti": [4, 10, 11], "has_label": [4, 10], "class_nam": [4, 10, 18, 30, 51, 58, 67, 71, 72, 80, 83, 87, 91, 94], "empti": [4, 10, 39, 50, 76, 81, 86], "find_issu": [4, 5, 6, 8, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 72, 74, 75, 76, 78, 79, 81, 82, 83], "knn_graph": [4, 8, 14, 16, 17, 22, 24, 27, 78], "issue_typ": [4, 5, 6, 8, 11, 12, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 74, 75, 76, 78, 79, 81, 82, 83], "sort": [4, 14, 34, 36, 41, 50, 52, 55, 57, 58, 60, 66, 68, 70, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 90, 91, 92, 93, 94], "common": [4, 11, 14, 76, 77, 80, 81, 83, 86, 87, 91], "real": [4, 14, 72, 75, 76, 81, 83, 85, 86, 90, 91], "world": [4, 14, 72, 75, 76, 81, 83, 85, 90, 91], "interact": [4, 14, 79, 81], "embed": [4, 8, 14, 59, 72, 74, 75, 76, 78, 79, 83, 93], "thereof": [4, 14], "insight": [4, 14, 58, 85], "act": [4, 8, 57, 75], "issuefind": [4, 14, 28], "logic": [4, 12, 34, 36, 64, 66, 91], "best": [4, 14, 40, 50, 60, 75, 76, 78, 79, 81, 82, 85, 86, 88, 90, 92, 93, 94], "2d": [4, 14, 34, 41, 42, 44, 45, 50, 74, 86, 92], "num_exampl": [4, 14, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28, 30, 51, 74, 75, 76, 78, 79, 82, 83], "represent": [4, 8, 14, 31, 35, 42, 52, 72, 74, 75, 76, 79, 81, 82, 83, 88, 93], "num_featur": [4, 14, 31, 35, 49], "distanc": [4, 8, 14, 16, 22, 24, 27, 43, 57, 59, 78, 88], "nearest": [4, 8, 14, 21, 22, 24, 43, 59, 76, 79, 88], "neighbor": [4, 8, 14, 16, 21, 22, 24, 43, 59, 75, 76, 78, 79, 81, 82, 88], "graph": [4, 8, 11, 14, 16, 22, 27], "squar": [4, 45, 62, 80, 90], "csr": 4, "evenli": 4, "omit": [4, 57, 58, 82, 87], "itself": [4, 31, 35, 87], "three": [4, 8, 30, 50, 51, 62, 67, 74, 75, 76, 78, 80, 83, 85, 89, 90, 91, 92, 94], "indptr": 4, "wise": 4, "start": [4, 5, 8, 31, 32, 35, 72, 78, 86, 94], "th": [4, 40, 44, 45, 50, 52, 55, 57, 58, 59, 68, 70, 71, 79, 86, 87, 94], "ascend": [4, 30, 51, 82, 83], "segment": [4, 64, 66, 67, 84], "reflect": [4, 78, 79, 85, 87, 88, 90, 92, 93], "maintain": 4, "posit": [4, 31, 35, 43, 45, 58, 80, 88], "nearestneighbor": [4, 8, 16, 59, 78, 88], "kneighbors_graph": [4, 16, 78], "illustr": 4, "todens": 4, "second": [4, 41, 45, 58, 60, 75, 81, 83, 94], "duplic": [4, 7, 19, 20, 31, 35, 72, 75, 83], "explicit": 4, "precend": 4, "construct": [4, 5, 8, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28, 31, 35, 41, 49], "neither": [4, 8, 12, 87], "nor": [4, 8, 12], "collect": [4, 8, 11, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 50, 81, 85, 94], "unspecifi": [4, 14, 36, 52], "interest": [4, 14, 20, 67, 71, 79, 83, 91, 92, 93, 94], "constructor": [4, 8, 14, 21, 26], "issuemanag": [4, 7, 11, 12, 14, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28], "respons": [4, 14, 20, 62, 63, 80, 90, 94], "random_st": [4, 74, 75, 76, 82, 83, 86, 88, 92], "lab": [4, 6, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 34, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86], "comprehens": [4, 72, 82], "nbr": 4, "n_neighbor": [4, 8, 16, 59], "metric": [4, 8, 17, 22, 27, 45, 49, 58, 59, 74, 78, 79, 82, 83, 90, 92, 93], "euclidean": [4, 8, 57, 59, 78], "mode": [4, 16, 31, 34, 35, 88], "4x4": 4, "float64": [4, 22, 31, 35, 70], "compress": [4, 8, 45, 64, 66], "toarrai": 4, "NOT": [4, 34, 79], "23606798": 4, "41421356": 4, "configur": [4, 14, 41, 76], "suppos": [4, 8, 55, 88, 90, 92, 93], "who": [4, 57, 78, 83, 92, 94], "manag": [4, 6, 7, 8, 11, 12, 13, 14, 15, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "clean_learning_kwarg": [4, 8, 21, 26], "labelissuemanag": [4, 8, 21], "prune_method": [4, 73], "prune_by_noise_r": [4, 36, 52, 83], "report": [4, 5, 9, 13, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 71, 72, 74, 75, 76, 78, 79, 83, 94], "include_descript": [4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 28], "show_summary_scor": [4, 28], "summari": [4, 5, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 49, 51, 56, 65, 66, 68, 69, 70, 73, 74, 75, 76, 78, 79, 80, 82, 83, 87, 91, 94], "show": [4, 22, 31, 35, 40, 45, 58, 67, 71, 76, 78, 79, 80, 81, 82, 83, 85, 88, 90, 91, 92, 94], "top": [4, 8, 30, 34, 36, 45, 52, 55, 58, 60, 67, 71, 72, 74, 75, 76, 78, 79, 80, 81, 83, 87, 88, 90, 93, 94], "suffer": [4, 8, 11, 20, 52, 60, 71, 94], "onc": [4, 20, 30, 31, 35, 75, 81, 83, 86, 87, 92], "familiar": 4, "usag": [4, 34, 49], "found": [4, 5, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 31, 35, 45, 72, 74, 75, 76, 78, 79, 81, 82, 88, 90, 92, 93, 94], "issue_summari": [4, 8, 11, 75], "overal": [4, 5, 8, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 41, 50, 51, 54, 57, 58, 62, 66, 67, 68, 70, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 87, 94], "sever": [4, 5, 8, 10, 11, 20, 31, 34, 35, 36, 54, 57, 59, 60, 66, 70, 72, 74, 75, 76, 78, 79, 80, 81, 83, 87, 88, 92, 93, 94], "dataissu": [4, 11, 14, 28], "outlier": [4, 7, 12, 19, 20, 27, 37, 60, 72, 75, 76, 83, 84], "someth": [4, 5, 31, 35, 60], "123": [4, 75, 76], "456": [4, 74, 79, 92, 93], "nearest_neighbor": 4, "7": [4, 41, 42, 49, 68, 70, 74, 75, 76, 78, 79, 80, 81, 85, 86, 87, 88, 90, 91, 92, 93, 94], "9": [4, 16, 17, 18, 20, 21, 22, 24, 26, 27, 41, 42, 54, 68, 70, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "distance_to_nearest_neighbor": [4, 75, 76, 78, 79, 82, 83], "789": 4, "get_issu": [4, 8, 11, 74, 76, 78, 79, 81, 82], "issue_nam": [4, 5, 8, 11, 12, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75, 76], "focu": [4, 11, 79, 91, 94], "full": [4, 8, 11, 34, 58, 82, 94], "summar": [4, 11, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 30, 51, 67, 71, 72, 91], "valueerror": [4, 10, 11, 38, 41, 81], "specific_issu": [4, 11], "exhibit": [4, 8, 11, 67, 76, 78, 79, 82, 83, 87], "lie": [4, 8, 59, 60, 74, 75, 76, 78, 79, 82, 83, 93], "directli": [4, 12, 14, 28, 34, 49, 50, 76, 79, 86, 87, 90, 93], "compar": [4, 50, 59, 70, 75, 76, 78, 83], "get_issue_summari": [4, 11, 76], "get_info": [4, 11, 76, 79], "yet": [4, 15, 19, 23, 80, 85], "list_possible_issue_typ": [4, 12], "regist": [4, 5, 12, 13, 15, 23, 31, 35, 75], "registri": [4, 12], "list_default_issue_typ": [4, 12], "folder": [4, 74, 75, 82], "load": [4, 10, 34, 58, 80, 81, 82, 83, 87, 88, 91, 94], "futur": [4, 8, 20, 31, 35, 50, 72, 75, 79], "overwrit": [4, 75], "separ": [4, 30, 41, 54, 75, 76, 81, 82, 87, 89], "static": 4, "rememb": [4, 79, 81, 83], "part": [4, 8, 31, 35, 36, 55, 57, 58, 74, 75, 80, 91, 94], "ident": [4, 8, 20, 45, 79], "walk": 5, "alongsid": [5, 31, 35, 75, 81], "pre": [5, 6, 8, 31, 35, 75, 76, 82, 91, 94], "runtim": [5, 31, 34, 35, 62, 64, 66, 74, 81, 82], "issue_manager_factori": [5, 12, 75], "myissuemanag": [5, 12], "myissuemanagerforregress": 5, "decor": [5, 12], "ll": [5, 41, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "thing": [5, 35, 83, 90, 93], "next": [5, 50, 72, 74, 78, 79, 81, 85, 87, 90, 92, 93, 94], "dummi": 5, "randint": [5, 27, 41, 75, 76, 81], "mark": [5, 8, 73, 87, 88, 90], "regard": [5, 76, 83], "rand": [5, 41, 75, 76], "is_": [5, 8, 75], "_issu": [5, 8, 75], "issue_score_kei": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "whole": [5, 22, 31, 35, 76], "make_summari": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 75], "popul": [5, 76, 79], "verbosity_level": [5, 16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "std": 5, "raw_scor": 5, "bit": 5, "involv": [5, 34, 67, 71, 81, 86], "intermediate_arg": 5, "min": [5, 41, 57, 70, 75, 81, 88], "sin_filt": 5, "sin": 5, "arang": 5, "kernel": 5, "wip": 5, "progress": 5, "issue_manag": [5, 8, 9, 11, 13, 16, 17, 18, 21, 22, 23, 24, 26, 27, 75], "instanti": [5, 14, 34, 49, 59, 74, 76, 78, 93], "477762": 5, "286455": 5, "term": [5, 8, 39, 45, 58, 74, 75, 76, 78, 79, 82, 83], "4778": 5, "is_basic_issu": 5, "basic_scor": 5, "13": [5, 17, 24, 74, 75, 76, 78, 79, 80, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "003042": 5, "058117": 5, "11": [5, 49, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "121908": 5, "15": [5, 43, 62, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "169312": 5, "17": [5, 74, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "229044": 5, "2865": 5, "is_intermediate_issu": 5, "intermediate_scor": 5, "000000": [5, 75, 76, 80, 83], "007059": 5, "009967": 5, "010995": 5, "087332": 5, "016296": 5, "03947": 5, "019459": 5, "794251": 5, "underperform": [6, 7, 27], "group": [6, 7, 22, 27, 80, 87, 94], "dbscan": [6, 8, 27, 81], "hdbscan": [6, 81], "etc": [6, 8, 20, 31, 35, 39, 49, 50, 68, 72, 75, 76, 78, 79, 81, 82, 83], "sensit": [6, 8, 43], "ep": [6, 27, 58], "radiu": 6, "min_sampl": [6, 27], "datalab": [6, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 72, 74, 81, 82, 85, 92, 93], "kmean": [6, 81], "your_data": 6, "get_pred_prob": 6, "n_cluster": [6, 27, 81], "cluster_id": [6, 8, 27, 81], "labels_": 6, "underperforming_group": [6, 8, 19, 81], "search": [7, 8, 18, 22, 23, 44, 62, 81, 89], "nondefault": 7, "Near": [7, 81], "iid": [7, 22, 76, 78, 82, 83], "imbal": [7, 19, 54, 59, 60, 76], "null": [7, 19, 76, 79, 82, 83], "valuat": [7, 16], "togeth": [7, 8, 39, 75, 76, 78, 79, 82, 83, 90, 93, 94], "built": [7, 41], "own": [7, 31, 33, 35, 48, 54, 55, 58, 64, 68, 74, 76, 78, 79, 81, 82, 85, 86, 90, 91, 92, 93, 94], "prerequisit": 7, "basic": [7, 35, 49, 78, 79, 88], "page": [8, 76, 81, 83], "variou": [8, 11, 26, 33, 46, 48, 72, 75, 76, 78, 79, 80, 83, 85, 87, 92], "sai": [8, 31, 35, 86, 91], "why": [8, 79], "matter": [8, 30, 51], "_score": 8, "flag": [8, 20, 22, 36, 41, 51, 52, 55, 62, 72, 74, 75, 76, 78, 79, 80, 82, 83, 87, 88, 90, 91, 93], "badli": [8, 57, 94], "code": [8, 31, 35, 39, 45, 49, 72, 73, 74, 75, 76, 78, 79, 80, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "issue_scor": 8, "outlier_scor": [8, 24, 75, 76, 78, 79, 82, 83, 88], "atyp": [8, 59, 75, 76, 78, 79, 82, 83, 88], "datapoint": [8, 27, 36, 41, 45, 60, 63, 72, 74, 75, 76, 78, 79, 81, 89, 90, 92, 93], "is_issu": [8, 20], "is_outlier_issu": [8, 75, 76, 78, 79, 82, 83], "annot": [8, 30, 40, 50, 51, 52, 54, 55, 57, 58, 67, 70, 71, 72, 74, 75, 76, 78, 79, 81, 82, 83, 84, 87, 91], "transform": [8, 41, 43, 45, 59, 60, 76, 79, 82, 88, 92, 93, 94], "dissimilar": [8, 78, 79], "preced": 8, "cosin": [8, 59, 88], "incorrect": [8, 57, 60, 63, 74, 75, 76, 78, 79, 82, 83, 87, 90, 92], "due": [8, 34, 36, 60, 64, 66, 74, 75, 76, 78, 79, 82, 83], "appear": [8, 30, 40, 51, 52, 55, 63, 76, 78, 79, 82, 90, 91], "likelihood": [8, 34, 36, 52, 57, 59, 60, 64, 68], "now": [8, 34, 73, 74, 76, 85, 87, 88, 90, 92, 93, 94], "u": [8, 74, 75, 78, 81, 82, 83, 85, 86, 89, 90, 91, 92, 93, 94], "token": [8, 44, 66, 67, 68, 69, 70, 71, 81, 83, 84], "calcul": [8, 16, 22, 34, 41, 50, 54, 55, 57, 58, 59, 62, 66, 80, 82], "hamper": [8, 80, 82], "analyt": [8, 72, 81, 85], "lead": [8, 57, 60, 82, 87], "draw": [8, 75, 76], "conclus": [8, 79], "try": [8, 34, 36, 49, 50, 64, 66, 72, 76, 78, 79, 81, 82, 83, 91], "veri": [8, 30, 51, 55, 57, 75, 76, 78, 79, 81, 82, 83, 85, 88, 90, 93], "rare": [8, 36, 58, 75, 76, 78, 79, 81, 82, 83], "anomal": [8, 60, 75, 76, 78, 79, 82, 83], "articl": [8, 34, 81], "ai": [8, 72, 74, 75, 76, 78, 79, 80, 81, 82, 84, 85, 86, 88, 90, 92, 93, 94], "blog": 8, "unexpect": [8, 31, 35, 79], "consequ": 8, "inspect": [8, 74, 76, 82, 83, 87, 90, 93], "neg": [8, 57, 58, 75, 76, 80], "affect": [8, 31, 35, 64, 70, 79, 81], "extrem": [8, 75, 76, 78, 79, 81, 82, 83], "rel": [8, 30, 50, 51, 59, 75, 76, 78, 79, 82, 83, 88], "record": [8, 31, 35, 74, 78, 90], "abbrevi": 8, "misspel": 8, "typo": [8, 71], "resolut": 8, "video": [8, 80], "audio": [8, 75, 76, 81, 84], "minor": [8, 44], "variat": 8, "translat": 8, "d": [8, 43, 78, 79, 83, 86, 92, 94], "constant": [8, 27, 62], "median": [8, 26, 43], "question": [8, 20, 72, 83], "nearli": [8, 20, 76, 78, 79, 82], "awar": [8, 73, 83], "presenc": [8, 83], "signific": [8, 76, 78, 79, 82, 83], "violat": [8, 76, 78, 79, 82, 83], "assumpt": [8, 76, 78, 79, 82, 83], "changepoint": [8, 76, 78, 79, 82, 83], "shift": [8, 76, 78, 79, 82, 83], "drift": [8, 76, 78, 82, 83], "autocorrel": [8, 76, 78, 79, 82, 83], "almost": [8, 76, 78, 79, 82, 83], "adjac": [8, 76, 78, 79, 82, 83], "tend": [8, 30, 39, 76, 78, 79, 82, 83, 91, 94], "sequenti": [8, 31, 35, 49, 82], "gap": 8, "b": [8, 16, 17, 18, 20, 21, 22, 24, 26, 27, 30, 44, 45, 70, 78, 79, 80, 83, 89, 92, 94], "x1": [8, 55, 58, 87], "x2": [8, 55, 58, 87], "10th": 8, "100th": 8, "90": [8, 70, 78, 83, 89, 91, 92, 94], "similarli": [8, 31, 35, 75, 78, 81, 82, 87], "math": [8, 82], "behind": [8, 59, 83], "fundament": 8, "proper": [8, 45, 50, 55, 58, 79, 82, 85, 87, 92], "closer": [8, 57, 87], "scenario": [8, 60, 75, 76], "underli": [8, 59, 68, 70, 94], "stem": [8, 59, 88], "evolv": 8, "influenc": 8, "accordingli": 8, "emploi": [8, 86, 88], "partit": [8, 89], "ahead": 8, "good": [8, 31, 35, 43, 49, 51, 57, 60, 64, 66, 67, 72, 78, 79, 82], "fix": [8, 50, 79, 83, 90, 93], "problem": [8, 34, 41, 67, 72, 75, 76, 79, 81, 82], "deploy": [8, 83, 90, 92, 93], "overlook": [8, 57, 87], "fact": 8, "thu": [8, 30, 35, 51, 74, 78, 79, 83, 89, 92, 94], "diagnos": [8, 76, 81], "rarest": [8, 76, 78, 79, 82, 83], "q": [8, 87], "fall": [8, 57, 66, 70, 83, 88], "subpar": 8, "special": [8, 44], "techniqu": 8, "smote": 8, "asymmetr": [8, 30], "properli": [8, 34, 40, 45, 46, 64, 81, 86, 88, 90, 91], "too": [8, 36, 41, 59, 76, 81, 82, 87], "dark": [8, 91], "bright": [8, 94], "blurri": [8, 82], "abnorm": [8, 58, 82], "cluster": [8, 16, 27], "slice": 8, "poor": 8, "subpopul": 8, "lowest": [8, 50, 58, 76, 81, 82, 85, 86, 87, 91], "get_self_confidence_for_each_label": [8, 41, 60], "power": [8, 78, 79, 80, 82, 83, 94], "r": [8, 34, 62, 75, 76, 90, 91], "tabular": [8, 72, 75, 76, 77, 81, 84, 85], "categor": [8, 59, 75, 76, 77, 81, 90, 92], "encod": [8, 42, 58, 64, 67, 78, 79, 81, 90, 91, 92, 93], "miss": [8, 23, 31, 35, 45, 55, 57, 76, 78, 79, 81, 82, 83, 87, 90], "pattern": 8, "contribut": [8, 16, 87], "isn": [8, 15, 23], "approxim": [8, 16, 34, 59, 85], "shaplei": [8, 16], "knn": [8, 11, 16, 22, 27, 59, 78, 88], "scalabl": 8, "sacrific": 8, "One": [8, 45, 59, 81], "quantif": 8, "exert": [8, 76], "possible_issue_typ": 8, "label_kwarg": 8, "outlier_kwarg": 8, "near_dupl": [8, 12, 17, 75, 76, 78, 79, 81, 82, 83], "near_duplicate_kwarg": 8, "non_iid": [8, 12, 22, 76, 78, 79, 82, 83], "non_iid_kwarg": 8, "class_imbal": [8, 18, 76, 78, 79, 82, 83], "class_imbalance_kwarg": 8, "underperforming_group_kwarg": 8, "null_kwarg": 8, "health_summary_paramet": [8, 21, 26], "health_summari": [8, 21, 30, 72, 80], "health_summary_kwarg": 8, "tandem": [8, 80], "view": [8, 31, 35, 36, 66, 68, 70, 72, 74, 75, 76, 78, 79, 80, 83, 85, 86, 87, 88, 89, 90, 92, 93, 94], "ood_kwarg": 8, "outofdistribut": [8, 24, 59, 88], "outsid": 8, "outlierissuemanag": [8, 12, 24, 75], "nearduplicateissuemanag": [8, 12, 17], "noniidissuemanag": [8, 12, 22], "num_permut": [8, 22], "permut": [8, 22], "significance_threshold": [8, 22], "signic": 8, "noniid": [8, 19], "classimbalanceissuemanag": [8, 18], "underperforminggroupissuemanag": [8, 27], "determinin": 8, "neighbour": 8, "min_cluster_sampl": [8, 27], "filter_cluster_id": [8, 27], "clustering_kwarg": [8, 27], "faq": [8, 72, 76, 78, 79, 82, 84], "nullissuemanag": [8, 23], "data_valuation_kwarg": 8, "data_valu": [8, 19], "datavaluationissuemanag": [8, 16], "codeblock": 8, "demonstr": [8, 34, 75, 76, 79, 81, 82, 83, 85, 86, 87, 90, 91], "howev": [8, 31, 35, 45, 74, 78, 79, 82, 85, 89, 91, 92, 93], "mandatori": 8, "image_issue_types_kwarg": 8, "32": [8, 75, 80, 85, 87, 91], "fewer": [8, 36, 45, 87], "vice": [8, 51], "versa": [8, 51], "light": [8, 80, 82, 87, 91], "29": [8, 80, 82, 85, 86, 87, 91, 94], "low_inform": [8, 82], "odd_aspect_ratio": [8, 82], "35": [8, 75, 80, 85, 86, 87, 91], "odd_siz": [8, 82], "10": [8, 16, 17, 21, 22, 27, 31, 32, 58, 59, 60, 71, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94], "doc": [8, 31, 35, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "data_issu": [9, 13, 14, 28, 75], "issue_find": [9, 13], "factori": [9, 13, 14], "except": [10, 49, 60, 75, 76, 82, 85], "dataformaterror": 10, "with_traceback": 10, "tb": 10, "__traceback__": 10, "datasetdicterror": 10, "datasetdict": 10, "usual": [10, 28, 82, 85, 90], "datasetloaderror": 10, "dataset_typ": 10, "fail": 10, "map_to_int": 10, "hold": 10, "is_avail": [10, 82], "serv": [11, 14, 85], "central": [11, 94], "repositori": 11, "strategi": [11, 41, 81], "being": [11, 30, 31, 35, 36, 41, 44, 45, 60, 78, 81, 83, 90, 91, 92], "_infostrategi": 11, "basi": 11, "collect_statist": 11, "reus": [11, 20], "avoid": [11, 31, 34, 35, 36, 45, 52, 55, 58, 62, 64, 66, 75, 76, 81], "recomput": [11, 93], "weighted_knn_graph": 11, "issue_manager_that_computes_knn_graph": 11, "collect_issues_from_issue_manag": 11, "collect_issues_from_imagelab": 11, "imagelab": 11, "set_health_scor": 11, "health": [11, 21, 30, 51, 72], "get_data_statist": 11, "concret": 12, "subclass": [12, 31, 35, 59, 75], "my_issu": 12, "stabl": [13, 19, 25, 33, 37, 45, 48, 59, 73], "unregist": 13, "instati": 14, "public": [14, 83, 87, 91, 94], "creation": [14, 35], "execut": [14, 31, 35, 75, 81, 87], "coordin": [14, 55, 57, 58, 87, 94], "behavior": [14, 30, 31, 35, 58], "At": [14, 58, 81], "associ": [14, 31, 35, 58, 85], "get_available_issue_typ": 14, "direct": [15, 23, 31, 35], "valuabl": 16, "vstack": [16, 45, 80, 81, 82, 83, 85, 86], "25": [16, 22, 31, 41, 43, 76, 80, 82, 83, 85, 86, 87, 91, 94], "classvar": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "short": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 44, 45], "data_valuation_scor": 16, "item": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 45, 75, 76, 81, 82, 83, 85, 86], "some_info_kei": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "additional_info_kei": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "default_threshold": [16, 24], "arxiv": [16, 83], "ab": [16, 83], "1911": 16, "07128": 16, "larger": [16, 62, 64, 66, 79, 80, 81, 82], "collect_info": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27], "info_to_omit": [16, 17, 18, 20, 21, 22, 24, 26, 27], "compos": [16, 17, 18, 20, 21, 22, 24, 26, 27, 31, 35, 79, 88, 93], "is_x_issu": [16, 17, 18, 20, 21, 22, 24, 26, 27], "x_score": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_a": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_b1": [16, 17, 18, 20, 21, 22, 24, 26, 27], "val_b2": [16, 17, 18, 20, 21, 22, 24, 26, 27], "report_str": [16, 17, 18, 20, 21, 22, 23, 24, 26, 27, 28], "_": [17, 20, 21, 22, 23, 26, 27, 41, 44, 45, 74, 75, 80, 82, 83, 86, 92], "near_duplicate_set": [17, 75, 76, 78, 79, 81, 82, 83], "occurr": [17, 18, 20, 22, 23, 24, 27, 44], "median_nn_dist": 17, "near_duplicate_scor": [17, 75, 76, 78, 79, 81, 82, 83], "class_imbalance_scor": [18, 76, 78, 79, 82, 83], "bleed": [19, 25, 33], "edg": [19, 25, 33, 57, 72, 83, 94], "sharp": [19, 25, 33], "abc": 20, "believ": [20, 91], "priori": [20, 83], "global": 20, "anoth": [20, 30, 34, 44, 57, 60, 78, 79, 81, 83, 85, 88, 93], "abstract": 20, "applic": [21, 50, 81, 83, 85, 86, 94], "typevar": [21, 31, 35, 57, 58], "_scalartype_co": 21, "covari": [21, 62, 90], "get_health_summari": 21, "summary_dict": 21, "label_scor": [21, 26, 74, 75, 76, 78, 79, 82, 83], "simplified_kolmogorov_smirnov_test": 22, "neighbor_histogram": 22, "non_neighbor_histogram": 22, "kolmogorov": 22, "smirnov": 22, "largest": [22, 34, 41, 60, 64, 66, 91], "empir": [22, 40, 50], "cumul": 22, "ecdf": 22, "histogram": [22, 78, 90], "absolut": [22, 26], "dimension": [22, 45, 74, 83, 88], "trial": 22, "non_iid_scor": [22, 76, 78, 79, 82, 83], "null_track": 23, "extend": [23, 42, 82, 88, 94], "superclass": 23, "arbitrari": [23, 30, 66, 70, 75, 88, 90], "prompt": 23, "address": [23, 75, 76, 79, 81, 93], "enabl": [23, 35], "null_scor": [23, 76, 79, 82, 83], "37037": 24, "q3_avg_dist": 24, "iqr_avg_dist": 24, "median_outlier_scor": 24, "ood": [24, 59, 60, 75, 76, 79, 82, 83, 88], "regressionlabelissuemanag": 26, "multipli": 26, "find_issues_with_predict": 26, "find_issues_with_featur": 26, "deleg": 26, "confus": [27, 30, 31, 35, 36, 45, 58, 93, 94], "50": [27, 35, 81, 83, 85, 87, 88, 91], "keepdim": [27, 81], "outlier_cluster_label": 27, "no_underperforming_cluster_id": 27, "signifi": 27, "absenc": 27, "set_knn_graph": 27, "find_issues_kwarg": 27, "perform_clust": 27, "npt": 27, "int_": 27, "id": [27, 50, 75, 81, 82, 85], "int64": [27, 74, 85], "unique_cluster_id": 27, "get_worst_clust": 27, "_description_": 27, "performed_clust": 27, "worst_cluster_id": 27, "underperforming_group_scor": 27, "exclud": [28, 67, 71, 75, 94], "get_report": 28, "overview": [30, 74, 76, 78, 79, 82, 85, 87, 88, 90, 92, 93, 94], "modifi": [30, 31, 34, 35, 45, 81, 83], "help": [30, 31, 35, 58, 72, 73, 74, 75, 78, 79, 80, 81, 82, 85, 86, 90, 91, 92, 93, 94], "rank_classes_by_label_qu": [30, 76], "merg": [30, 44, 72, 80, 81, 94], "find_overlapping_class": [30, 81, 83], "problemat": [30, 51, 67, 71, 74, 87, 94], "unnorm": [30, 51, 83], "abov": [30, 31, 34, 35, 45, 50, 57, 58, 60, 66, 70, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 89, 90, 91, 92, 93, 94], "model_select": [30, 41, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 90, 92, 93], "cross_val_predict": [30, 35, 74, 75, 76, 78, 79, 81, 83, 85, 89, 90, 92, 93], "get_data_labels_from_dataset": 30, "yourfavoritemodel": [30, 83], "cv": [30, 41, 74, 75, 76, 78, 83, 85, 92], "df": [30, 45, 71, 74, 81], "overall_label_qu": [30, 51], "col": 30, "prob": [30, 44, 83, 89], "divid": [30, 51, 60], "label_nois": [30, 51], "human": [30, 80, 91, 94], "clearli": [30, 60, 82, 87, 91], "num": [30, 51, 80, 83], "overlap": [30, 72, 80, 81, 83], "ontolog": 30, "publish": [30, 94], "therefor": [30, 60], "vehicl": [30, 80], "truck": [30, 80, 88, 91], "intuit": [30, 51], "car": [30, 80, 87, 91], "frequent": [30, 50, 78, 81, 90], "characterist": 30, "l": [30, 31, 35, 55, 57, 58], "class1": 30, "class2": 30, "relationship": 30, "match": [30, 31, 35, 36, 50, 51, 60, 75, 76, 80, 82, 87, 89, 91], "dog": [30, 45, 51, 53, 67, 80, 81, 88, 89, 94], "cat": [30, 45, 51, 53, 80, 81, 88, 89], "captur": [30, 74, 87, 88, 91], "co": [30, 31, 32], "noisy_label": [30, 75, 76, 86], "overlapping_class": 30, "descend": [30, 31, 35, 41, 51, 58], "overall_label_health_scor": [30, 51, 83], "suggest": [30, 50, 51, 57, 79, 81, 82, 90, 93], "half": [30, 31, 35, 51, 80, 94], "health_scor": [30, 51], "classes_by_label_qu": [30, 76], "cnn": [31, 35, 82], "cifar": [31, 32, 80, 88], "teach": [31, 32], "bhanml": 31, "blob": 31, "master": [31, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "call_bn": 31, "bn": 31, "input_channel": 31, "n_output": 31, "dropout_r": 31, "top_bn": 31, "architectur": [31, 35], "shown": [31, 58, 75, 81, 85, 88, 89, 91, 94], "forward": [31, 32, 35, 82, 85], "overridden": [31, 35], "although": [31, 35, 59, 78, 92], "recip": [31, 35], "afterward": [31, 35], "sinc": [31, 35, 38, 46, 51, 58, 66, 70, 81, 85, 86, 87, 89, 94], "former": [31, 35], "hook": [31, 35, 80], "silent": [31, 34, 35], "t_destin": [31, 35], "__call__": [31, 35, 41], "add_modul": [31, 35], "child": [31, 35], "fn": [31, 35, 58], "recurs": [31, 35, 41], "submodul": [31, 35], "children": [31, 35, 94], "nn": [31, 32, 35, 82], "init": [31, 35, 83], "no_grad": [31, 35, 82, 88], "init_weight": [31, 35], "linear": [31, 35, 79, 82, 93], "fill_": [31, 35], "net": [31, 35, 74, 80, 82], "in_featur": [31, 35], "out_featur": [31, 35], "bia": [31, 35, 82], "tensor": [31, 32, 35, 74, 82, 88], "requires_grad": [31, 35], "bfloat16": [31, 35], "cast": [31, 35, 74], "buffer": [31, 35], "datatyp": [31, 35], "member": [31, 35, 75, 76], "xdoctest": [31, 35], "undefin": [31, 35], "var": [31, 35], "buf": [31, 35], "20l": [31, 35], "1l": [31, 35], "5l": [31, 35], "immedi": [31, 35, 88], "cpu": [31, 35, 36, 74, 82], "move": [31, 35, 41, 73, 80], "cuda": [31, 35, 74, 82], "devic": [31, 35, 74, 82], "gpu": [31, 35, 74, 79, 93], "live": [31, 35], "copi": [31, 35, 62, 74, 75, 76, 78, 81, 86, 89, 90, 92], "doubl": [31, 35], "dump_patch": [31, 35], "eval": [31, 35, 82, 86, 88], "dropout": [31, 35], "batchnorm": [31, 35], "grad": [31, 35], "extra_repr": [31, 35], "line": [31, 35, 72, 75, 80, 85, 88, 94], "get_buff": [31, 35], "target": [31, 32, 35, 62, 63, 88, 90], "throw": [31, 35], "get_submodul": [31, 35], "explan": [31, 35], "fulli": [31, 35, 49, 81], "qualifi": [31, 35], "referenc": [31, 35], "attributeerror": [31, 35], "invalid": [31, 35, 79], "resolv": [31, 35, 94], "get_extra_st": [31, 35], "state_dict": [31, 35], "set_extra_st": [31, 35], "build": [31, 35, 82, 91], "pickleabl": [31, 35], "serial": [31, 35], "backward": [31, 35, 82], "break": [31, 35, 82], "pickl": [31, 35, 87], "get_paramet": [31, 35], "let": [31, 35, 59, 60, 74, 76, 78, 79, 81, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "net_b": [31, 35], "net_c": [31, 35], "conv": [31, 35], "conv2d": [31, 35, 82], "16": [31, 35, 41, 66, 74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 93, 94], "33": [31, 35, 80, 82, 87, 91], "kernel_s": [31, 35], "stride": [31, 35], "200": [31, 35, 60, 80, 87, 94], "diagram": [31, 35, 89], "degre": [31, 35, 90], "queri": [31, 35, 76, 81, 82], "named_modul": [31, 35], "o": [31, 35, 43, 44, 74, 75, 76, 80, 81, 83, 86, 87, 94], "transit": [31, 35], "ipu": [31, 35], "load_state_dict": [31, 35], "strict": [31, 35, 41], "persist": [31, 35], "strictli": [31, 35], "namedtupl": [31, 35], "missing_kei": [31, 35], "unexpected_kei": [31, 35], "runtimeerror": [31, 35], "idx": [31, 35, 45, 46, 58, 75, 81, 82, 83, 85, 87, 88], "named_buff": [31, 35], "prefix": [31, 35, 74, 94], "prepend": [31, 35], "running_var": [31, 35], "named_children": [31, 35], "conv4": [31, 35], "conv5": [31, 35], "memo": [31, 35], "remove_dupl": [31, 35], "named_paramet": [31, 35], "register_backward_hook": [31, 35], "deprec": [31, 35, 38], "favor": [31, 35], "register_full_backward_hook": [31, 35], "removablehandl": [31, 35], "register_buff": [31, 35], "running_mean": [31, 35], "register_forward_hook": [31, 35], "won": [31, 35, 75, 76, 81, 86], "inplac": [31, 35, 85], "register_forward_pre_hook": [31, 35], "gradient": [31, 35, 78, 82, 90], "respect": [31, 35, 58, 83], "grad_input": [31, 35], "grad_output": [31, 35], "technic": [31, 35], "caller": [31, 35], "register_load_state_dict_post_hook": [31, 35], "post": [31, 35], "incompatible_kei": [31, 35], "modif": [31, 35], "thrown": [31, 35], "clearn": [31, 35], "register_modul": [31, 35], "register_paramet": [31, 35], "requires_grad_": [31, 35], "autograd": [31, 35], "freez": [31, 35, 74, 79, 93], "finetun": [31, 35], "gan": [31, 35], "share_memori": [31, 35], "share_memory_": [31, 35], "destin": [31, 35], "keep_var": [31, 35], "shallow": [31, 35], "releas": [31, 35, 73, 81, 88], "design": [31, 35], "ordereddict": [31, 35], "detach": [31, 35, 82], "non_block": [31, 35], "memory_format": [31, 35], "channels_last": [31, 35], "Its": [31, 35, 41, 51, 57], "complex": [31, 35], "integr": [31, 35, 72], "asynchron": [31, 35], "host": [31, 35], "pin": [31, 35, 79, 80, 93], "desir": [31, 35, 44, 58], "4d": [31, 35], "ignore_w": [31, 35], "determinist": [31, 35, 74], "1913": [31, 35], "3420": [31, 35], "5113": [31, 35], "2325": [31, 35], "env": [31, 35], "torch_doctest_cuda1": [31, 35], "gpu1": [31, 35], "1914": [31, 35], "5112": [31, 35], "2324": [31, 35], "float16": [31, 35], "cdoubl": [31, 35], "3741": [31, 35], "2382": [31, 35], "5593": [31, 35], "4443": [31, 35], "complex128": [31, 35], "6122": [31, 35], "1150": [31, 35], "to_empti": [31, 35], "storag": [31, 35], "dst_type": [31, 35], "xpu": [31, 35], "zero_grad": [31, 35, 82], "set_to_non": [31, 35], "context": [31, 35, 87], "noisili": [32, 83], "han": 32, "2018": 32, "cifar_cnn": [32, 33], "loss_coteach": 32, "y_1": 32, "y_2": 32, "forget_r": 32, "class_weight": 32, "logit": [32, 49, 82], "decim": [32, 45], "quickli": [32, 74, 78, 79, 81, 82, 86, 88, 91, 92, 94], "forget": [32, 41, 94], "rate_schedul": 32, "epoch": [32, 35, 81, 82], "initialize_lr_schedul": 32, "lr": [32, 35], "001": [32, 60, 81], "250": [32, 75, 76, 83, 87], "epoch_decay_start": 32, "80": [32, 78, 86, 90, 91, 92], "schedul": 32, "adjust": [32, 36, 54, 59, 60, 72, 83], "beta": 32, "adam": 32, "adjust_learning_r": 32, "alpha_plan": 32, "beta1_plan": 32, "forget_rate_schedul": 32, "num_gradu": 32, "expon": 32, "tell": [32, 79, 82, 83, 93], "train_load": [32, 35], "model1": [32, 83], "optimizer1": 32, "model2": [32, 83], "optimizer2": 32, "dataload": [32, 82, 88], "parser": 32, "parse_arg": 32, "num_iter_per_epoch": 32, "print_freq": 32, "topk": 32, "top1": 32, "top5": 32, "test_load": 32, "offici": [33, 48, 94], "wish": [33, 48, 88, 91, 94], "mnist_pytorch": 33, "coteach": [33, 73], "mini": [34, 64, 66, 81], "With": [34, 79, 83, 85, 90, 91, 93, 94], "low_self_confid": [34, 36, 52], "self_confid": [34, 36, 41, 52, 54, 60, 68, 70, 81, 83, 86, 92, 93], "conveni": [34, 74, 79, 93], "script": 34, "labelinspector": [34, 81], "adj_confident_thresholds_shar": 34, "labels_shar": 34, "pred_probs_shar": 34, "labels_fil": [34, 81], "pred_probs_fil": [34, 81], "batch_siz": [34, 35, 64, 66, 81, 82, 88, 91], "quality_score_kwarg": 34, "num_issue_kwarg": 34, "return_mask": 34, "variant": [34, 50, 91], "read": [34, 38, 76, 81, 83, 88, 94], "zarr": [34, 81], "memmap": [34, 91], "pythonspe": 34, "mmap": [34, 81], "hdf5": 34, "further": [34, 51, 52, 54, 57, 58, 66, 67, 74, 81], "yourfil": 34, "npy": [34, 80, 81, 91], "mmap_mod": [34, 91], "tip": [34, 36, 49, 81], "save_arrai": 34, "your_arrai": 34, "disk": [34, 80, 81], "npz": [34, 94], "maxim": [34, 50, 64, 66, 91], "multiprocess": [34, 36, 52, 64, 66, 81, 82, 91], "linux": [34, 64, 66], "physic": [34, 36, 64, 66, 87, 91], "psutil": [34, 36, 64, 66, 91], "labels_arrai": [34, 46], "predprob": 34, "pred_probs_arrai": 34, "back": [34, 58, 75, 81, 87, 88], "store_result": 34, "becom": [34, 88], "verifi": [34, 81, 85, 88], "long": [34, 50, 59, 85], "enough": [34, 45, 81], "chunk": [34, 89], "ram": [34, 80], "faster": [34, 59, 62, 64, 66, 81, 83], "end_index": 34, "labels_batch": 34, "pred_probs_batch": 34, "update_confident_threshold": 34, "batch_result": 34, "score_label_qu": 34, "indices_of_examples_with_issu": [34, 81], "shortcut": 34, "encount": [34, 36, 64], "1000": [34, 74, 79, 81, 82, 88], "aggreg": [34, 41, 50, 54, 57, 60, 70, 81, 83, 85], "get_num_issu": 34, "fetch": [34, 74, 76], "seen": [34, 81, 88, 94], "far": [34, 50], "get_quality_scor": 34, "label_quality_scor": [34, 54, 57, 60, 63, 83, 87, 90], "method1": 34, "method2": 34, "normalized_margin": [34, 36, 41, 52, 54, 60, 68, 70], "low_normalized_margin": [34, 36, 52], "issue_indic": [34, 57, 82], "update_num_issu": 34, "split_arr": 34, "arr": [34, 81], "chunksiz": 34, "convnet": 35, "bespok": [35, 49], "get_mnist_dataset": 35, "loader": [35, 82], "download": [35, 74, 81, 88], "mnist": [35, 72, 74, 80], "get_sklearn_digits_dataset": 35, "handwritten": 35, "digit": [35, 74, 80], "last": [35, 41, 55, 58, 75, 76, 81, 85, 94], "sklearn_digits_test_s": 35, "hard": [35, 80, 88], "simplenet": 35, "64": [35, 78, 82, 83, 87, 91, 92], "log_interv": 35, "01": [35, 60, 62, 74, 82, 83, 86, 87, 91, 94], "momentum": 35, "no_cuda": 35, "test_batch_s": [35, 82], "templat": 35, "flexibli": 35, "among": [35, 50, 83], "test_set": 35, "Be": 35, "overrid": 35, "train_idx": [35, 45, 88], "train_label": [35, 88, 93], "scikit": [35, 45, 59, 72, 74, 75, 76, 78, 79, 81, 84, 90, 93], "set_predict_proba_request": 35, "set_predict_request": 35, "encourag": [36, 52, 60, 63], "multilabel_classif": [36, 51, 52, 54, 60, 81, 86], "pred_probs_by_class": 36, "prune_count_matrix_col": 36, "rank_by_kwarg": [36, 52, 60, 83], "num_to_remove_per_class": [36, 52], "bad": [36, 52, 57, 60, 79, 81, 93], "seem": [36, 83, 86], "aren": 36, "confidence_weighted_entropi": [36, 41, 52, 54, 60, 68, 70], "label_issues_idx": [36, 60], "entropi": [36, 38, 40, 41, 59, 60], "prune_by_class": [36, 52, 83], "predicted_neq_given": [36, 52, 83], "prune_counts_matrix": 36, "smallest": [36, 60], "unus": 36, "number_of_mislabeled_examples_in_class_k": 36, "delet": [36, 72, 81, 93], "thread": [36, 52], "window": [36, 80], "shorter": [36, 55], "find_predicted_neq_given": 36, "find_label_issues_using_argmax_confusion_matrix": 36, "latent_algebra": [37, 73], "label_quality_util": 37, "multilabel_util": [37, 86], "multilabel_scor": [37, 54], "token_classification_util": [37, 94], "get_normalized_entropi": 38, "min_allowed_prob": 38, "wikipedia": 38, "activ": [38, 40, 50, 72, 85], "towardsdatasci": 38, "cheatsheet": 38, "ec57bc067c0b": 38, "clip": [38, 45, 74], "behav": 38, "unnecessari": [38, 81], "slightli": [38, 92, 93], "interv": [38, 41, 88], "herein": 39, "inexact": 39, "cours": 39, "propag": 39, "throughout": [39, 45, 62, 74, 85, 91, 94], "compute_ps_py_inv_noise_matrix": 39, "compute_py_inv_noise_matrix": 39, "compute_inv_noise_matrix": 39, "easili": [39, 73, 74, 76, 78, 79, 83, 85, 86, 88, 89, 90, 91, 92, 93], "increas": [39, 57, 59, 60, 74, 75, 81, 85, 86, 94], "dot": [39, 70, 81], "compute_noise_matrix_from_invers": 39, "compute_pi": 39, "true_labels_class_count": 39, "compute_pyx": 39, "pyx": 39, "multiannot": 40, "assert_valid_inputs_multiannot": 40, "labels_multiannot": [40, 50], "ensembl": [40, 41, 50, 60, 78, 81, 86, 88, 90, 92], "allow_single_label": 40, "annotator_id": 40, "assert_valid_pred_prob": 40, "pred_probs_unlabel": [40, 50], "format_multiannotator_label": [40, 50, 85], "lexicograph": [40, 45], "formatted_label": [40, 45], "old": [40, 45, 73, 80], "check_consensus_label_class": 40, "consensus_label": [40, 50, 85], "consensus_method": [40, 50], "consensu": [40, 50, 72, 84, 94], "establish": [40, 90, 93], "compute_soft_cross_entropi": 40, "soft": [40, 80], "find_best_temp_scal": 40, "coarse_search_rang": [40, 62, 81], "fine_search_s": [40, 62, 81], "temperatur": [40, 41, 57, 66, 70], "scale": [40, 43, 80, 81, 88, 91, 92], "factor": [40, 41, 43, 64, 66], "minim": [40, 57, 88], "temp_scale_pred_prob": 40, "temp": 40, "sharpen": [40, 80], "smoothen": 40, "classlabelscor": 41, "enum": 41, "get_normalized_margin_for_each_label": [41, 60], "get_confidence_weighted_entropy_for_each_label": [41, 60], "75": [41, 75, 76, 80, 85, 86, 87, 90, 91, 94], "from_str": 41, "scorer": 41, "exponential_moving_averag": [41, 54], "alpha": [41, 54, 57, 75, 76, 83, 86, 90], "exponenti": 41, "ema": 41, "s_1": 41, "s_k": 41, "ema_k": 41, "accord": [41, 52, 78, 79, 83, 94], "formula": [41, 43], "_t": 41, "cdot": 41, "s_t": 41, "qquad": 41, "leq": 41, "_1": 41, "give": [41, 60, 83, 85, 91], "recent": [41, 94], "success": 41, "previou": [41, 81, 82, 87], "discount": 41, "s_ema": 41, "175": [41, 83, 87], "softmin": [41, 54, 57, 66, 70], "underflow": 41, "nan": [41, 50, 78, 85, 90, 92], "possible_method": 41, "aggregated_scor": 41, "multilabelscor": 41, "base_scor": 41, "base_scorer_kwarg": 41, "aggregator_kwarg": [41, 54], "n_sampl": 41, "n_label": 41, "binari": [41, 45, 52, 54, 83, 94], "worst": [41, 85], "class_label_quality_scor": 41, "get_class_label_quality_scor": 41, "42": [41, 80, 82, 87, 91, 94], "452": [41, 79], "new_scor": 41, "575": 41, "get_label_quality_scores_per_class": [41, 54], "ml_scorer": 41, "multilabel_pi": 41, "binar": [41, 42], "get_cross_validated_multilabel_pred_prob": 41, "reformat": [41, 74], "wider": 41, "splitter": 41, "kfold": [41, 82], "multiclass": [41, 45, 50, 86], "onevsrestclassifi": [41, 86], "randomforestclassifi": [41, 83, 86], "n_split": [41, 76, 82, 86], "stack_compl": 42, "pred_prob_slic": 42, "get_onehot_num_class": 42, "onehot": 42, "multilabel": [42, 86], "int2onehot": [42, 86], "hot": [42, 52, 58, 64, 67, 78, 80, 81, 90, 91, 92], "onehot2int": [42, 86], "onehot_matrix": 42, "transform_distances_to_scor": 43, "avg_dist": 43, "scaling_factor": 43, "exp": [43, 59, 60, 75], "dt": 43, "right": [43, 55, 58, 79, 86, 87, 88, 93], "strength": [43, 58], "pronounc": 43, "differenti": 43, "ly": 43, "rule": [43, 44, 80], "thumb": 43, "ood_features_scor": [43, 59, 88], "88988177": 43, "80519832": 43, "token_classif": [44, 68, 70, 71, 81], "get_sent": [44, 94], "sentenc": [44, 68, 70, 71, 79, 93], "readabl": 44, "filter_sent": [44, 94], "lambda": [44, 74, 75, 81, 85], "long_sent": 44, "headlin": 44, "process_token": 44, "charact": [44, 45], "s1": 44, "s2": 44, "processed_token": 44, "alecnlcb": 44, "entiti": [44, 72, 81, 94], "mapped_ent": 44, "unique_ident": 44, "loc": [44, 75, 76, 82, 94], "merge_prob": 44, "probs_merg": 44, "55": [44, 80, 87, 90, 91], "0125": [44, 70], "0375": 44, "075": 44, "025": 44, "color_sent": 44, "color": [44, 67, 75, 76, 78, 83, 86, 88, 90, 91], "red": [44, 58, 75, 76, 80, 83, 86, 87, 88, 91], "colored_sent": 44, "termcolor": 44, "31msentenc": 44, "0m": 44, "ancillari": 45, "remove_noise_from_class": 45, "class_without_nois": 45, "any_other_class": 45, "choos": [45, 60, 78, 81, 83, 90, 92], "tradition": 45, "clip_noise_r": 45, "clip_valu": 45, "new_sum": 45, "preserv": 45, "value_count": [45, 81], "fill": 45, "wherea": [45, 52, 89], "come": [45, 75, 76, 81, 82, 91], "major": [45, 50, 73, 82, 88], "versu": [45, 83], "value_counts_fill_missing_class": 45, "get_missing_class": 45, "round_preserving_sum": 45, "obviou": 45, "cgdeboer": 45, "iteround": 45, "round_preserving_row_tot": 45, "reach": 45, "estimate_pu_f1": 45, "prob_s_eq_1": 45, "claesen": 45, "f1": [45, 58, 79, 83], "confusion_matrix": 45, "BE": 45, "print_square_matrix": 45, "left_nam": 45, "top_nam": 45, "titl": [45, 75, 76, 83, 86, 88], "short_titl": 45, "round_plac": 45, "pretti": [45, 83], "print_noise_matrix": [45, 83], "print_inverse_noise_matrix": 45, "print_joint_matrix": [45, 83], "joint_matrix": 45, "compress_int_arrai": 45, "num_possible_valu": 45, "train_val_split": 45, "holdout_idx": 45, "subset_x_i": 45, "extract": [45, 59, 74, 79, 85, 88, 91, 93], "subset_label": 45, "subset_data": 45, "extract_indices_tf": 45, "allow_shuffl": 45, "turn": [45, 72, 87], "unshuffle_tensorflow_dataset": 45, "shuffledataset": 45, "histori": 45, "pre_x": 45, "buffer_s": 45, "is_torch_dataset": 45, "is_tensorflow_dataset": 45, "csr_vstack": 45, "csr_matric": 45, "append": [45, 74, 80, 81, 82, 83, 85, 86, 88, 94], "bottom": [45, 55, 58, 87], "append_extra_datapoint": 45, "to_data": 45, "from_data": 45, "taken": 45, "get_num_class": 45, "label_matrix": 45, "canon": 45, "num_unique_class": 45, "get_unique_class": 45, "format_label": 45, "smart_display_datafram": 45, "displai": [45, 58, 67, 71, 74, 79, 83, 93, 94], "jupyt": [45, 74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "notebook": [45, 50, 74, 76, 80, 81, 83, 85, 86, 87, 91, 94], "consol": 45, "force_two_dimens": 45, "html": [45, 59, 78, 81, 83], "assert_valid_input": 46, "allow_missing_class": 46, "allow_one_class": 46, "assert_valid_class_label": 46, "assert_nonempty_input": 46, "assert_indexing_work": 46, "length_x": 46, "labels_to_arrai": 46, "labellik": 46, "keraswrappermodel": [49, 72], "keraswrappersequenti": 49, "tf": [49, 74], "legaci": 49, "lack": 49, "keraswrapp": 49, "huggingface_keras_imdb": 49, "unit": [49, 94], "model_kwarg": [49, 62], "compile_kwarg": 49, "sparsecategoricalcrossentropi": 49, "layer": [49, 74, 79, 88, 93], "dens": 49, "my_keras_model": 49, "from_logit": 49, "compil": 49, "declar": 49, "apply_softmax": 49, "analysi": 50, "analyz": [50, 72, 83, 85, 86], "get_label_quality_multiannot": [50, 85], "vote": 50, "crowdsourc": [50, 72, 85], "dawid": [50, 85], "skene": [50, 85], "analog": [50, 80, 85], "chosen": [50, 60, 81, 85], "crowdlab": [50, 85], "unlabel": [50, 78, 79, 82, 85, 88, 91], "decid": [50, 79, 80, 85, 90, 93, 94], "get_active_learning_scor": [50, 85], "activelab": [50, 85], "priorit": [50, 57, 87, 91, 94], "showcas": 50, "main": 50, "best_qual": 50, "quality_method": 50, "calibrate_prob": 50, "return_detailed_qu": 50, "return_annotator_stat": 50, "return_weight": 50, "label_quality_score_kwarg": 50, "necessarili": [50, 58, 79, 83], "did": [50, 51, 74, 78, 83, 85, 90, 92, 93], "majority_vot": 50, "ti": 50, "broken": [50, 58, 80], "highest": [50, 58, 75, 82, 89], "0th": 50, "consensus_quality_scor": [50, 85], "annotator_agr": [50, 85], "reman": 50, "1st": 50, "2nd": [50, 64], "3rd": 50, "consensus_label_suffix": 50, "consensus_quality_score_suffix": 50, "suffix": 50, "emsembl": 50, "weigh": [50, 80], "agreement": [50, 85], "agre": 50, "prevent": [50, 81], "overconfid": [50, 89], "wrong": [50, 55, 57, 73, 75, 76, 79, 81, 83, 87, 93], "detailed_label_qu": [50, 85], "annotator_stat": [50, 85], "model_weight": 50, "annotator_weight": 50, "warn": [50, 75, 76], "labels_info": 50, "num_annot": [50, 85], "deriv": [50, 85], "quality_annotator_1": 50, "quality_annotator_2": 50, "quality_annotator_m": 50, "annotator_qu": [50, 85], "num_examples_label": [50, 85], "agreement_with_consensu": [50, 85], "worst_class": [50, 85], "trustworthi": [50, 85, 90], "get_label_quality_multiannotator_ensembl": 50, "weigtht": 50, "budget": 50, "retrain": [50, 90, 93], "active_learning_scor": 50, "improv": [50, 76, 80, 81, 82, 83, 90, 91, 92, 93], "active_learning_scores_unlabel": 50, "get_active_learning_scores_ensembl": 50, "henc": [50, 74, 75, 85], "get_majority_vote_label": [50, 85], "event": 50, "lastli": [50, 78], "convert_long_to_wide_dataset": 50, "labels_multiannotator_long": 50, "wide": [50, 74, 92, 93], "suitabl": [50, 78, 92], "labels_multiannotator_wid": 50, "common_multilabel_issu": 51, "mutual": [51, 86], "exclus": [51, 86], "rank_classes_by_multilabel_qu": 51, "overall_multilabel_health_scor": 51, "multilabel_health_summari": 51, "classes_by_multilabel_qu": 51, "inner": [52, 66], "find_multilabel_issues_per_class": 52, "per_class_label_issu": 52, "label_issues_list": 52, "labels_list": 52, "pred_probs_list": [52, 60, 82, 83], "anim": [53, 88], "rat": 53, "predat": 53, "pet": 53, "reptil": 53, "manner": [54, 85, 90, 92, 93], "box": [55, 57, 58, 80, 87], "object_detect": [55, 57, 58, 87], "return_indices_ranked_by_scor": [55, 87], "overlapping_label_check": [55, 57], "suboptim": [55, 57], "locat": [55, 57, 87, 91, 94], "bbox": [55, 58, 87], "image_nam": [55, 58], "y1": [55, 58, 87], "y2": [55, 58, 87], "later": [55, 58, 59, 93, 94], "mmdetect": [55, 58, 87], "corner": [55, 58, 87], "swap": [55, 57, 67, 71], "penal": [55, 57], "concern": [55, 57, 72, 76], "aggregation_weight": 57, "imperfect": [57, 81], "chose": [57, 85, 87], "imperfectli": [57, 87], "dirti": [57, 60, 63, 90], "subtyp": 57, "badloc": 57, "nonneg": 57, "issues_from_scor": [57, 66, 67, 70, 71, 87, 91, 94], "compute_overlooked_box_scor": 57, "high_probability_threshold": 57, "auxiliary_input": [57, 58], "vari": [57, 76], "iou": [57, 58], "heavili": 57, "auxiliarytypesdict": 57, "pred_label": [57, 93], "pred_label_prob": 57, "pred_bbox": 57, "lab_label": 57, "lab_bbox": 57, "similarity_matrix": 57, "min_possible_similar": 57, "scores_overlook": 57, "compute_badloc_box_scor": 57, "low_probability_threshold": 57, "scores_badloc": 57, "compute_swap_box_scor": 57, "accident": [57, 78, 79, 81, 93], "scores_swap": 57, "pool_box_scores_per_imag": 57, "box_scor": 57, "image_scor": [57, 66, 91], "object_counts_per_imag": 58, "discov": [58, 76, 94], "auxiliari": [58, 88, 91], "_get_valid_inputs_for_compute_scor": 58, "object_count": 58, "bounding_box_size_distribut": 58, "down": 58, "bbox_siz": 58, "class_label_distribut": 58, "class_distribut": 58, "get_sorted_bbox_count_idx": 58, "plot": [58, 75, 76, 83, 86, 88, 90, 91], "sorted_idx": [58, 88], "plot_class_size_distribut": 58, "class_to_show": 58, "hidden": [58, 88], "max_class_to_show": 58, "plot_class_distribut": 58, "visual": [58, 75, 76, 82, 90, 92, 94], "prediction_threshold": 58, "overlai": [58, 87], "figsiz": [58, 75, 76, 82, 83, 86, 88], "save_path": [58, 87], "blue": [58, 80, 83, 87], "overlaid": 58, "side": [58, 80, 87], "figur": [58, 83, 86, 88, 90], "extens": [58, 83, 85], "png": [58, 87], "pdf": [58, 59], "svg": 58, "matplotlib": [58, 75, 76, 82, 83, 86, 87, 88, 90], "get_average_per_class_confusion_matrix": 58, "num_proc": [58, 82], "intersect": [58, 81], "tp": 58, "fp": 58, "ground": [58, 80, 83, 85, 90], "truth": [58, 83, 85, 90], "bias": 58, "avg_metr": 58, "distionari": 58, "95": [58, 68, 70, 76, 78, 80, 83, 90, 91], "calculate_per_class_metr": 58, "per_class_metr": 58, "Of": 59, "li": 59, "smaller": [59, 86, 87], "find_top_issu": [59, 60, 88], "reli": [59, 74, 75, 76, 79, 87, 88, 93], "dist_metr": 59, "dim": [59, 82, 91], "subtract": [59, 60], "renorm": [59, 60, 81], "least_confid": 59, "sum_": 59, "log": [59, 60, 73], "softmax": [59, 66, 70, 82], "literatur": 59, "gen": 59, "liu": 59, "lochman": 59, "zach": 59, "openaccess": 59, "thecvf": 59, "content": [59, 74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "cvpr2023": 59, "liu_gen_pushing_the_limits_of_softmax": 59, "based_out": 59, "distribution_detection_cvpr_2023_pap": 59, "fit_scor": [59, 88], "ood_predictions_scor": 59, "pretrain": [59, 74, 79, 88, 93], "adjust_confident_threshold": 59, "probabilist": [59, 74, 75, 76, 78, 79, 88, 89, 92], "order_label_issu": [60, 73], "whichev": [60, 89], "argsort": [60, 79, 82, 83, 88, 90, 93], "max_": 60, "get_label_quality_ensemble_scor": [60, 81, 83], "weight_ensemble_members_bi": 60, "custom_weight": 60, "log_loss_search_t_valu": 60, "0001": [60, 80], "scheme": 60, "log_loss_search": 60, "log_loss": [60, 79], "1e0": 60, "1e1": 60, "1e2": 60, "2e2": 60, "quality_scor": [60, 88], "forth": 60, "top_issue_indic": 60, "rank_bi": [60, 73], "weird": [60, 71], "minu": 60, "prob_label": 60, "max_prob_not_label": 60, "idea": 60, "AND": [60, 79], "corrupt": [62, 90], "linearregress": [62, 81, 90], "y_with_nois": 62, "n_boot": [62, 81], "include_aleatoric_uncertainti": [62, 81], "sole": [62, 75, 85, 88, 92], "bootstrap": [62, 81, 90], "resampl": [62, 74, 81], "epistem": [62, 81, 88, 90], "aleator": [62, 81, 90], "model_final_kwarg": 62, "coars": 62, "thorough": [62, 81], "fine": [62, 74, 79, 88, 93], "grain": 62, "grid": 62, "get_epistemic_uncertainti": 62, "varianc": [62, 83], "epistemic_uncertainti": 62, "get_aleatoric_uncertainti": 62, "residu": [62, 63, 81], "deviat": [62, 90], "ie": 62, "aleatoric_uncertainti": 62, "outr": 63, "contin": 63, "raw": [63, 72, 73, 76, 80, 82, 85, 87, 88], "aka": [63, 74, 83, 94], "00323821": 63, "33692597": 63, "00191686": 63, "semant": [64, 66, 67, 84], "pixel": [64, 66, 67, 88, 91], "h": [64, 66, 67, 91], "height": [64, 66, 67, 91], "w": [64, 66, 67, 91], "width": [64, 66, 67, 91], "labels_one_hot": [64, 67, 91], "stream": [64, 88, 94], "downsampl": [64, 66, 91], "shrink": [64, 66], "divis": [64, 66, 75], "segmant": [66, 67], "num_pixel_issu": [66, 91], "product": [66, 81, 82], "pixel_scor": [66, 91], "display_issu": [66, 67, 68, 70, 71, 91, 94], "highlight": [67, 71, 75, 76, 78, 91], "enter": 67, "legend": [67, 75, 76, 86, 87, 90, 91], "colormap": 67, "background": 67, "person": [67, 81, 87, 91, 94], "common_label_issu": [67, 71, 91, 94], "ambigu": [67, 71, 74, 79, 80, 83, 93, 94], "systemat": [67, 71, 85], "misunderstood": [67, 71], "issues_df": [67, 82], "filter_by_class": [67, 91], "class_index": 67, "issues_subset": [67, 71], "token_score_method": 70, "sentence_score_method": 70, "sentence_score_kwarg": 70, "compris": [70, 71], "token_scor": [70, 94], "converg": 70, "toward": 70, "_softmin_sentence_scor": 70, "sentence_scor": [70, 94], "token_info": 70, "70": [70, 78, 90, 91], "02": [70, 75, 76, 83, 87, 91], "03": [70, 78, 80, 83, 87, 91, 94], "04": [70, 78, 82, 87, 91], "08": [70, 79, 83, 87, 91, 94], "commonli": [71, 73, 75, 76, 86, 94], "filter_by_token": [71, 94], "But": [71, 79, 83, 94], "restrict": [71, 81], "reliabl": [72, 74, 81, 85, 91, 92], "thousand": 72, "imagenet": [72, 80], "popular": [72, 85, 87], "centric": [72, 78, 79, 82, 84], "capabl": 72, "minut": [72, 74, 78, 79, 80, 85, 86, 87, 90, 91, 92, 93, 94], "conda": 72, "feature_embed": [72, 88], "Then": [72, 81, 82, 90, 92, 93], "your_dataset": [72, 74, 75, 76, 78, 79, 81, 82], "column_name_of_label": [72, 74, 75, 76, 78, 79, 82], "plagu": [72, 76], "untrain": 72, "\u30c4": 72, "label_issues_info": [72, 76], "sklearn_compatible_model": 72, "framework": [72, 86, 87], "complianc": 72, "tag": [72, 86, 94], "sequenc": 72, "recognit": [72, 74, 81, 94], "train_data": [72, 88, 90, 92, 93], "gotten": 72, "test_data": [72, 83, 86, 88, 90, 92, 93], "deal": [72, 76], "tutori": [72, 74, 75, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "feel": [72, 74, 76, 81], "free": [72, 74, 76, 78, 79, 81, 82, 83], "ask": [72, 81], "slack": [72, 81], "project": [72, 90], "welcom": 72, "commun": [72, 81], "guidelin": [72, 87], "piec": 72, "studio": [72, 76, 78, 79, 81, 82], "platform": [72, 78, 79, 81, 82], "automl": [72, 81], "foundat": 72, "smart": [72, 78, 79, 81, 82], "edit": [72, 81], "easier": [72, 83], "unreli": [72, 74, 78, 79, 92], "older": 73, "outlin": 73, "substitut": 73, "v2": [73, 78, 92], "get_noise_indic": 73, "psx": 73, "sorted_index_method": 73, "order_label_error": 73, "label_errors_bool": 73, "latent_estim": 73, "num_label_error": 73, "learningwithnoisylabel": 73, "neatli": 73, "organ": [73, 78, 80, 92, 94], "reorgan": 73, "baseline_method": 73, "incorpor": [73, 83], "research": [73, 83], "polyplex": 73, "terminologi": 73, "label_error": 73, "quickstart": [74, 75, 76, 78, 79, 80, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "spoken": 74, "500": [74, 88, 94], "english": [74, 80], "pronunci": 74, "wav": 74, "huggingfac": [74, 75, 76, 82], "voxceleb": 74, "speech": [74, 94], "your_pred_prob": [74, 75, 76, 78, 79], "tensorflow_io": 74, "26": [74, 75, 80, 82, 83, 85, 87, 91], "huggingface_hub": 74, "12": [74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 87, 88, 90, 91, 92, 93, 94], "branch": [74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 92, 93], "reproduc": [74, 78, 83, 85], "command": 74, "wget": [74, 87, 91, 94], "navig": 74, "link": [74, 80, 87], "browser": 74, "jakobovski": 74, "archiv": [74, 94], "v1": 74, "tar": [74, 88], "gz": [74, 88], "mkdir": [74, 94], "spoken_digit": 74, "xf": 74, "6_nicolas_32": 74, "data_path": 74, "listdir": 74, "nondeterminist": 74, "file_nam": 74, "endswith": 74, "file_path": 74, "join": [74, 81, 82], "39": [74, 75, 79, 80, 81, 82, 87, 90, 91, 93, 94], "7_george_26": 74, "0_nicolas_24": 74, "0_nicolas_6": 74, "listen": 74, "display_exampl": 74, "click": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "expand": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "pulldown": [74, 75, 76, 80, 82, 83, 85, 86, 88, 90, 94], "colab": [74, 75, 76, 80, 81, 82, 83, 85, 86, 88, 90, 94], "tfio": 74, "pathlib": 74, "ipython": 74, "load_wav_16k_mono": 74, "filenam": 74, "khz": 74, "file_cont": 74, "io": [74, 80], "read_fil": 74, "sample_r": 74, "decode_wav": 74, "desired_channel": 74, "squeez": 74, "rate_in": 74, "rate_out": 74, "16000": 74, "wav_file_nam": 74, "audio_r": 74, "wav_file_exampl": 74, "plai": [74, 80, 81], "button": 74, "wav_file_name_exampl": 74, "7_jackson_43": 74, "hear": 74, "extractor": 74, "encoderclassifi": 74, "spkrec": 74, "xvect": 74, "feature_extractor": 74, "from_hparam": 74, "run_opt": 74, "uncom": 74, "wav_audio_file_path": 74, "head": [74, 76, 78, 79, 80, 82, 83, 85, 90, 92, 93], "torchaudio": 74, "extract_audio_embed": 74, "emb": [74, 82], "signal": 74, "encode_batch": 74, "embeddings_list": [74, 82], "embeddings_arrai": 74, "512": [74, 82], "14": [74, 75, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "196315": 74, "3194594": 74, "478977": 74, "2890828": 74, "8170278": 74, "892647": 74, "24": [74, 80, 83, 85, 87, 91], "898054": 74, "256194": 74, "559642": 74, "559715": 74, "620667": 74, "285246": 74, "21": [74, 75, 80, 81, 83, 87, 91, 94], "709623": 74, "5033712": 74, "913803": 74, "8198366": 74, "1831512": 74, "208761": 74, "08426": 74, "3210406": 74, "005453": 74, "2161605": 74, "478239": 74, "682179": 74, "0538025": 74, "242471": 74, "0914207": 74, "7833488": 74, "039538": 74, "23": [74, 80, 82, 83, 87, 91], "56918": 74, "19": [74, 79, 80, 81, 82, 83, 88, 90, 91, 93, 94], "761095": 74, "1258287": 74, "753235": 74, "3508894": 74, "598273": 74, "237122": 74, "2500": 74, "leverag": [74, 79, 81, 83, 85, 93], "tune": [74, 79, 80, 88, 93], "computation": [74, 79, 93], "intens": [74, 79, 93], "held": [74, 78, 79, 80, 87, 88, 89, 92], "straightforward": [74, 78, 92], "benefit": [74, 89, 91, 92], "tol": 74, "num_crossval_fold": [74, 78, 85, 92], "decreas": [74, 81], "never": [74, 83, 86, 88, 89], "accuracy_scor": [74, 79, 83, 92, 93], "cv_accuraci": 74, "9772": 74, "probabilit": [74, 93], "9980": 74, "176": [74, 80, 83, 86], "006488": 74, "2318": 74, "008269": 74, "986": 74, "010354": 74, "469": 74, "013459": 74, "516": 74, "013478": 74, "investig": 74, "100541": 74, "998729": 74, "998768": 74, "980980": 74, "998217": 74, "18": [74, 79, 80, 81, 82, 83, 87, 88, 90, 91, 93], "identified_label_issu": [74, 79], "lowest_quality_label": [74, 79, 83, 90, 93], "sort_valu": [74, 76, 78, 79, 81, 82, 83, 85], "1946": 74, "1871": 74, "1955": 74, "2132": 74, "worth": [74, 83], "iloc": [74, 78, 79, 90, 92, 93], "6_yweweler_35": 74, "6_yweweler_36": 74, "6_yweweler_14": 74, "6_theo_27": 74, "4_george_31": 74, "6_nicolas_8": 74, "sound": 74, "quit": [74, 88], "22": [74, 75, 80, 82, 83, 86, 87, 91, 94], "blindli": [74, 81, 90, 92, 93], "trust": [74, 81, 83, 85, 89, 90, 92, 93], "underneath": 75, "hood": 75, "alert": 75, "introduct": 75, "mayb": [75, 76, 79], "examin": [75, 76, 78, 92], "your_feature_matrix": [75, 76], "toi": [75, 76, 80, 82, 83, 85], "train_test_split": [75, 76, 88, 92, 93], "inf": [75, 76], "mid": [75, 76], "bins_map": [75, 76], "create_data": [75, 76], "y_bin": [75, 76], "y_i": [75, 76], "y_bin_idx": [75, 76], "y_train": [75, 76, 83, 90], "y_test": [75, 76, 83, 90], "y_train_idx": [75, 76], "y_test_idx": [75, 76], "test_siz": [75, 76, 92, 93], "slide": [75, 76, 80], "decis": [75, 76, 92], "boundari": [75, 76], "frame": [75, 76], "x_out": [75, 76], "tini": [75, 76], "concaten": [75, 76, 81, 89], "y_out": [75, 76], "y_out_bin": [75, 76], "y_out_bin_idx": [75, 76], "exact_duplicate_idx": [75, 76], "x_duplic": [75, 76], "y_duplic": [75, 76], "y_duplicate_idx": [75, 76], "noisy_labels_idx": [75, 76, 86], "scatter": [75, 76, 83, 86, 90], "black": [75, 76, 80, 90], "cyan": [75, 76], "pyplot": [75, 76, 82, 83, 86, 88, 90], "plt": [75, 76, 82, 83, 86, 88, 90], "plot_data": [75, 76, 83, 86, 90], "fig": [75, 76, 80, 82, 88, 90], "ax": [75, 76, 82, 88, 90], "subplot": [75, 76, 82, 88], "set_titl": [75, 76, 82, 88], "set_xlabel": [75, 76], "x_1": [75, 76], "fontsiz": [75, 76, 82, 83, 86], "set_ylabel": [75, 76], "x_2": [75, 76], "set_xlim": [75, 76], "set_ylim": [75, 76], "linestyl": [75, 76], "circl": [75, 76, 83, 86], "misclassifi": [75, 76], "zip": [75, 76, 82, 87, 94], "label_err": [75, 76], "180": [75, 76, 87], "marker": [75, 76], "facecolor": [75, 76], "edgecolor": [75, 76], "linewidth": [75, 76, 88], "dup": [75, 76], "first_legend": [75, 76], "align": [75, 76], "title_fontproperti": [75, 76], "semibold": [75, 76], "second_legend": [75, 76], "45": [75, 76, 80, 82, 83, 87, 91], "gca": [75, 76], "add_artist": [75, 76], "tight_layout": [75, 76], "ideal": [75, 76], "logist": [75, 76, 79, 85, 88, 93], "remaind": 75, "modal": [75, 76, 81, 85], "regardless": [75, 76], "132": [75, 76, 83, 87], "9318": 75, "77": [75, 76, 78, 87, 91, 92], "006939": 75, "007830": 75, "40": [75, 76, 79, 80, 82, 91], "014826": 75, "107": [75, 76, 83, 86], "021220": 75, "120": [75, 76, 92], "026403": 75, "notic": [75, 83, 85, 87], "3558": [75, 76], "126": [75, 76, 83, 87], "006636": [75, 76], "130": [75, 76], "012571": [75, 76], "129": [75, 76], "127": [75, 76], "014909": [75, 76], "128": [75, 76, 82], "017443": [75, 76], "6160": [75, 76], "is_near_duplicate_issu": [75, 76, 78, 79, 81, 82, 83], "131": [75, 76, 91], "000000e": [75, 76], "00": [75, 76, 78, 80, 82, 91, 92], "000002": [75, 76], "463180e": [75, 76], "07": [75, 76, 78, 82, 83, 87, 91], "51": [75, 76, 78, 80, 83, 87, 91], "161148": [75, 76], "859087e": [75, 76], "30": [75, 76, 80, 81, 82, 86, 91, 94], "3453": 75, "029542": 75, "031182": 75, "057961": 75, "058244": 75, "home": [75, 76, 79, 80, 88, 93], "runner": [75, 76, 79, 88, 93], "300": [75, 85, 94], "userwarn": [75, 76], "330": [75, 82, 87], "309": 75, "34": [75, 80, 83, 85, 87, 88, 91, 94], "54": [75, 80, 83, 87, 91], "039117": 75, "53": [75, 76, 78, 80, 86, 87, 91, 92], "044594": 75, "105": 75, "105121": 75, "133588": 75, "43": [75, 80, 83, 87, 91, 93], "168035": 75, "125": 75, "101107": 75, "37": [75, 80, 91], "183382": 75, "109": [75, 80, 87], "209259": 75, "211042": 75, "221316": 75, "average_ood_scor": 75, "34530442089193386": 75, "52": [75, 80, 87, 91, 94], "169820": 75, "087324e": 75, "89": [75, 78, 87, 90, 91], "92": [75, 83, 87, 91, 92], "259024": 75, "583757e": 75, "91": [75, 87, 91, 93], "346458": 75, "341292e": 75, "specfi": 75, "new_lab": 75, "scoring_funct": 75, "div": 75, "rem": 75, "inv_scal": 75, "49": [75, 80, 83, 87, 91], "superstitionissuemanag": 75, "unlucki": 75, "superstit": 75, "to_seri": 75, "issues_mask": 75, "summary_scor": 75, "9242": 75, "is_superstition_issu": 75, "superstition_scor": 75, "047581": 75, "090635": 75, "129591": 75, "65": [75, 82, 87, 91, 92], "164840": 75, "demo": [76, 78, 86, 92], "lurk": [76, 82, 83], "opt": 76, "hostedtoolcach": 76, "x64": 76, "lib": 76, "python3": 76, "site": 76, "_split": 76, "737": 76, "thoroughli": 76, "preprocess": [76, 78, 88, 90, 92, 93], "904": 76, "review": [76, 78, 79, 80, 81, 83, 87, 90, 91, 92, 93, 94], "8561": 76, "001894": 76, "58": [76, 78, 80, 82, 83, 87, 91, 92], "003565": 76, "007326": 76, "008974": 76, "009699": 76, "0227": 76, "is_class_imbalance_issu": [76, 78, 79, 82, 83], "022727": 76, "86": [76, 78, 82, 83, 87, 90, 91, 92], "87": [76, 82, 87, 90, 91, 93], "0000": [76, 79, 80, 82, 83], "is_null_issu": [76, 79, 82, 83], "96": [76, 78, 80, 83, 86, 87, 90, 91], "94": [76, 78, 80, 83, 87, 90, 91, 92], "93": [76, 80, 87, 90, 91, 92, 94], "8218": 76, "is_non_iid_issu": [76, 78, 79, 82, 83], "810274": 76, "826147": 76, "849587": 76, "855359": 76, "855485": 76, "821750488732925": 76, "auto": [76, 80, 81, 90, 92, 93], "conceptu": 76, "856061": 76, "355772": 76, "616034": 76, "821750": 76, "betweeen": 76, "859109": 76, "417707": 76, "664083": 76, "970324": 76, "816965": 76, "375317": 76, "641516": 76, "890575": 76, "530924": 76, "460593": 76, "601188": 76, "752776": 76, "321635": 76, "562539": 76, "948362": 76, "090224": 76, "472909": 76, "746763": 76, "878267": 76, "examples_w_issu": [76, 81], "013444": 76, "025173": 76, "026416": 76, "inde": [76, 79], "miscellan": [76, 94], "428571": 76, "111111": 76, "571429": 76, "407407": 76, "592593": 76, "337838": 76, "092593": 76, "662162": 76, "333333": [76, 80], "952381": 76, "666667": 76, "portion": 76, "huge": [76, 83], "worri": [76, 79], "critic": 76, "highli": [76, 82], "sql": [78, 92], "databas": [78, 92], "excel": [78, 92], "parquet": [78, 92], "student": [78, 90, 92, 94], "grade": [78, 90, 92], "900": [78, 90, 92], "exam": [78, 90, 92], "letter": [78, 92, 94], "hundr": [78, 92], "histgradientboostingclassifi": 78, "standardscal": [78, 88, 92], "possibli": [78, 92], "grades_data": [78, 92], "read_csv": [78, 79, 90, 92, 93], "stud_id": [78, 92], "exam_1": [78, 90, 92], "exam_2": [78, 90, 92], "exam_3": [78, 90, 92], "letter_grad": [78, 92], "f48f73": [78, 92], "0bd4e7": [78, 92], "81": [78, 79, 87, 90, 91, 92, 94], "great": [78, 80, 92], "particip": [78, 92], "cb9d7a": [78, 92], "61": [78, 83, 87, 91, 92], "78": [78, 80, 83, 87, 90, 91, 92], "9acca4": [78, 92], "48": [78, 80, 82, 83, 87, 91, 92], "x_raw": [78, 92], "cat_featur": 78, "x_encod": [78, 92], "get_dummi": [78, 90, 92], "drop_first": [78, 92], "numeric_featur": [78, 92], "scaler": [78, 88, 92], "x_process": [78, 92], "fit_transform": [78, 92], "bring": [78, 79, 82, 85, 90, 92, 93], "byod": [78, 79, 82, 85, 90, 92, 93], "boost": [78, 81, 85, 90], "xgboost": [78, 81, 90], "think": [78, 79, 81, 86, 91, 94], "carefulli": [78, 79, 82, 92], "nonzero": 78, "suspici": [78, 92], "tabl": [78, 80, 85, 92], "358": 78, "294": [78, 87], "46": [78, 80, 82, 83, 87, 91], "941": 78, "7109": 78, "000005": [78, 79, 82], "886": 78, "000059": 78, "709": 78, "000104": 78, "723": 78, "000169": 78, "689": 78, "000181": 78, "3590": 78, "051882e": 78, "683133e": 78, "536582e": 78, "406589e": 78, "324246e": 78, "6165": 78, "582": 78, "185": [78, 80, 87, 94], "187": [78, 80], "27": [78, 80, 83, 87, 91, 94], "898": 78, "637": [78, 92], "0014": [78, 80], "595": 78, "702427": 78, "147": [78, 83, 87], "711186": 78, "157": [78, 83], "721394": 78, "771": 78, "731979": 78, "740335": 78, "0014153602099278074": 78, "1562": 78, "393": 78, "156217": 78, "391": 78, "806": 78, "805": 78, "156": [78, 83], "na": [78, 79, 82, 83, 85], "issue_result": 78, "000842": 78, "555944": 78, "004374": 78, "sorted_issu": 78, "73": [78, 80, 86, 87, 90, 91], "deserv": 78, "outlier_result": 78, "sorted_outli": 78, "56": [78, 80, 90, 91], "lt": [78, 79, 80, 82, 85, 91], "style": [78, 91], "font": 78, "18px": 78, "ff00ff": 78, "bac": 78, "unintend": [78, 79], "mistak": [78, 79, 82, 92, 93], "duplicate_result": 78, "690": 78, "246": [78, 87], "perhap": [78, 83, 85], "twice": 78, "67": [78, 80, 82, 87, 90, 91], "wari": [78, 79, 81], "super": [78, 79, 82], "system": [78, 79, 82, 91], "intent": [79, 93], "servic": [79, 81, 93], "onlin": [79, 93], "bank": [79, 80, 93], "banking77": [79, 93], "oo": [79, 93], "000": [79, 80, 82, 93, 94], "categori": [79, 82, 93], "scope": [79, 93], "dive": 79, "your_featur": 79, "sentence_transform": [79, 93], "sentencetransform": [79, 93], "payment": [79, 93], "cancel_transf": [79, 93], "transfer": [79, 93], "fund": [79, 93], "cancel": [79, 93], "transact": [79, 93], "my": [79, 93], "revert": [79, 93], "morn": [79, 93], "realis": [79, 93], "yesterdai": [79, 93], "rent": [79, 93], "realli": [79, 85, 91, 93], "tomorrow": [79, 93], "raw_text": [79, 93], "card_payment_fee_charg": [79, 93], "beneficiary_not_allow": [79, 93], "card_about_to_expir": [79, 93], "apple_pay_or_google_pai": [79, 93], "lost_or_stolen_phon": [79, 93], "supported_cards_and_curr": [79, 93], "visa_or_mastercard": [79, 93], "change_pin": [79, 93], "getting_spare_card": [79, 93], "utter": [79, 93], "continu": [79, 81, 82, 85, 90, 92, 93, 94], "suit": [79, 80, 81, 93], "electra": [79, 93], "discrimin": [79, 93], "googl": [79, 93], "text_embed": 79, "No": [79, 81, 93], "google_electra": [79, 93], "pool": [79, 81, 88, 93], "400": [79, 93], "data_dict": [79, 83, 85], "84": [79, 87, 91], "41": [79, 80, 87, 90, 91], "38": [79, 80, 87, 91], "9720": 79, "981": 79, "974": 79, "000150": 79, "982": [79, 80], "000218": 79, "971": 79, "000512": 79, "980": [79, 80], "000947": 79, "3584": 79, "994": 79, "009642": 79, "999": 79, "013067": 79, "013841": 79, "433": 79, "014722": 79, "989": 79, "018224": 79, "6070": 79, "160": [79, 83], "095724": 79, "148": 79, "006237": 79, "546": 79, "099340": 79, "514": 79, "006485": 79, "481": 79, "123416": 79, "008165": 79, "313": [79, 87], "564102": 79, "572258": 79, "28": [79, 80, 82, 83, 85, 91, 94], "574915": 79, "31": [79, 80, 83, 85, 87, 91, 94], "575507": 79, "575874": 79, "658": 79, "659": [79, 90], "660": 79, "661": 79, "0800": 79, "454": 79, "453": 79, "455": 79, "791961": 79, "258508": 79, "699010": 79, "183136": 79, "771112": 79, "to_numpi": [79, 81, 90, 93], "data_with_suggested_label": 79, "suggested_label": 79, "charg": [79, 93], "cash": [79, 93], "holidai": [79, 93], "sent": [79, 93, 94], "card": [79, 80, 93], "mine": [79, 93], "expir": [79, 93], "me": [79, 93], "withdraw": 79, "monei": 79, "whoever": [79, 93], "outlier_issu": [79, 82], "lowest_quality_outli": 79, "OR": 79, "636c65616e6c616220697320617765736f6d6521": 79, "phone": [79, 80], "gone": 79, "gt": [79, 85, 94], "samp": 79, "br": 79, "press": [79, 94], "nonsens": 79, "sens": 79, "detriment": 79, "duplicate_issu": 79, "fee": 79, "pai": 79, "go": [79, 80, 83], "strongli": 79, "p_valu": 79, "benign": 79, "shortlist": [79, 90, 93], "curat": [79, 84], "mnist_test_set": 80, "imagenet_val_set": 80, "tench": 80, "goldfish": 80, "white": [80, 94], "shark": 80, "tiger": 80, "hammerhead": 80, "electr": 80, "rai": 80, "stingrai": 80, "cock": 80, "hen": 80, "ostrich": 80, "brambl": 80, "goldfinch": 80, "hous": 80, "finch": 80, "junco": 80, "indigo": 80, "bunt": 80, "american": [80, 94], "robin": 80, "bulbul": 80, "jai": 80, "magpi": 80, "chickade": 80, "dipper": 80, "kite": 80, "bald": 80, "eagl": 80, "vultur": 80, "grei": 80, "owl": 80, "fire": 80, "salamand": 80, "smooth": 80, "newt": 80, "spot": [80, 87], "axolotl": 80, "bullfrog": 80, "tree": 80, "frog": [80, 88], "tail": 80, "loggerhead": 80, "sea": 80, "turtl": 80, "leatherback": 80, "mud": 80, "terrapin": 80, "band": 80, "gecko": 80, "green": [80, 94], "iguana": 80, "carolina": 80, "anol": 80, "desert": 80, "grassland": 80, "whiptail": 80, "lizard": 80, "agama": 80, "frill": 80, "neck": 80, "allig": 80, "gila": 80, "monster": 80, "european": 80, "chameleon": 80, "komodo": 80, "dragon": 80, "nile": 80, "crocodil": 80, "triceratop": 80, "worm": 80, "snake": 80, "ring": 80, "eastern": 80, "hog": 80, "nose": 80, "kingsnak": 80, "garter": 80, "water": 80, "vine": 80, "night": 80, "boa": 80, "constrictor": 80, "african": 80, "rock": 80, "indian": 80, "cobra": 80, "mamba": 80, "saharan": 80, "horn": 80, "viper": 80, "diamondback": 80, "rattlesnak": 80, "sidewind": 80, "trilobit": 80, "harvestman": 80, "scorpion": 80, "yellow": 80, "garden": 80, "spider": 80, "barn": 80, "southern": 80, "widow": 80, "tarantula": 80, "wolf": 80, "tick": 80, "centiped": 80, "grous": 80, "ptarmigan": 80, "ruf": 80, "prairi": 80, "peacock": 80, "quail": 80, "partridg": 80, "parrot": 80, "macaw": 80, "sulphur": 80, "crest": 80, "cockatoo": 80, "lorikeet": 80, "coucal": 80, "bee": 80, "eater": 80, "hornbil": 80, "hummingbird": 80, "jacamar": 80, "toucan": 80, "duck": [80, 93], "breast": 80, "mergans": 80, "goos": 80, "swan": 80, "tusker": 80, "echidna": 80, "platypu": 80, "wallabi": 80, "koala": 80, "wombat": 80, "jellyfish": 80, "anemon": 80, "brain": 80, "coral": 80, "flatworm": 80, "nematod": 80, "conch": 80, "snail": 80, "slug": 80, "chiton": 80, "chamber": 80, "nautilu": 80, "dung": 80, "crab": 80, "fiddler": 80, "king": 80, "lobster": 80, "spini": 80, "crayfish": 80, "hermit": 80, "isopod": 80, "stork": 80, "spoonbil": 80, "flamingo": 80, "heron": 80, "egret": 80, "bittern": 80, "crane": 80, "bird": [80, 88], "limpkin": 80, "gallinul": 80, "coot": 80, "bustard": 80, "ruddi": 80, "turnston": 80, "dunlin": 80, "redshank": 80, "dowitch": 80, "oystercatch": 80, "pelican": 80, "penguin": 80, "albatross": 80, "whale": 80, "killer": 80, "dugong": 80, "lion": 80, "chihuahua": 80, "japanes": 80, "chin": 80, "maltes": 80, "pekinges": 80, "shih": 80, "tzu": 80, "charl": 80, "spaniel": 80, "papillon": 80, "terrier": 80, "rhodesian": 80, "ridgeback": 80, "afghan": [80, 94], "hound": 80, "basset": 80, "beagl": 80, "bloodhound": 80, "bluetick": 80, "coonhound": 80, "tan": 80, "walker": 80, "foxhound": 80, "redbon": 80, "borzoi": 80, "irish": 80, "wolfhound": 80, "italian": 80, "greyhound": 80, "whippet": 80, "ibizan": 80, "norwegian": 80, "elkhound": 80, "otterhound": 80, "saluki": 80, "scottish": 80, "deerhound": 80, "weimaran": 80, "staffordshir": 80, "bull": 80, "bedlington": 80, "border": 80, "kerri": 80, "norfolk": 80, "norwich": 80, "yorkshir": 80, "wire": 80, "fox": 80, "lakeland": 80, "sealyham": 80, "airedal": 80, "cairn": 80, "australian": 80, "dandi": 80, "dinmont": 80, "boston": 80, "miniatur": 80, "schnauzer": 80, "giant": 80, "tibetan": 80, "silki": 80, "coat": [80, 82], "wheaten": 80, "west": 80, "highland": 80, "lhasa": 80, "apso": 80, "flat": 80, "retriev": 80, "curli": 80, "golden": 80, "labrador": 80, "chesapeak": 80, "bai": 80, "german": [80, 94], "shorthair": 80, "pointer": 80, "vizsla": 80, "setter": 80, "gordon": 80, "brittani": 80, "clumber": 80, "springer": 80, "welsh": 80, "cocker": 80, "sussex": 80, "kuvasz": 80, "schipperk": 80, "groenendael": 80, "malinoi": 80, "briard": 80, "kelpi": 80, "komondor": 80, "sheepdog": 80, "shetland": 80, "colli": 80, "bouvier": 80, "de": 80, "flandr": 80, "rottweil": 80, "shepherd": 80, "dobermann": 80, "pinscher": 80, "swiss": [80, 94], "mountain": 80, "bernes": 80, "appenzel": 80, "sennenhund": 80, "entlebuch": 80, "boxer": 80, "bullmastiff": 80, "mastiff": 80, "french": 80, "bulldog": 80, "dane": 80, "st": 80, "bernard": 80, "huski": 80, "alaskan": 80, "malamut": 80, "siberian": 80, "dalmatian": 80, "affenpinsch": 80, "basenji": 80, "pug": 80, "leonberg": 80, "newfoundland": 80, "pyrenean": 80, "samoi": 80, "pomeranian": 80, "chow": 80, "keeshond": 80, "griffon": 80, "bruxelloi": 80, "pembrok": 80, "corgi": 80, "cardigan": 80, "poodl": 80, "mexican": 80, "hairless": 80, "tundra": 80, "coyot": 80, "dingo": 80, "dhole": 80, "wild": 80, "hyena": 80, "kit": 80, "arctic": 80, "tabbi": 80, "persian": 80, "siames": 80, "egyptian": 80, "mau": 80, "cougar": 80, "lynx": 80, "leopard": 80, "snow": 80, "jaguar": 80, "cheetah": 80, "brown": [80, 91], "bear": 80, "polar": 80, "sloth": 80, "mongoos": 80, "meerkat": 80, "beetl": 80, "ladybug": 80, "longhorn": 80, "leaf": 80, "rhinocero": 80, "weevil": 80, "fly": 80, "ant": 80, "grasshopp": 80, "cricket": 80, "stick": 80, "insect": 80, "cockroach": 80, "manti": 80, "cicada": 80, "leafhopp": 80, "lacew": 80, "dragonfli": 80, "damselfli": 80, "admir": 80, "ringlet": 80, "monarch": 80, "butterfli": 80, "gossam": 80, "wing": 80, "starfish": 80, "urchin": 80, "cucumb": 80, "cottontail": 80, "rabbit": 80, "hare": 80, "angora": 80, "hamster": 80, "porcupin": 80, "squirrel": 80, "marmot": 80, "beaver": 80, "guinea": 80, "pig": 80, "sorrel": 80, "zebra": 80, "boar": 80, "warthog": 80, "hippopotamu": 80, "ox": 80, "buffalo": 80, "bison": 80, "bighorn": 80, "sheep": 80, "alpin": 80, "ibex": 80, "hartebeest": 80, "impala": 80, "gazel": 80, "dromedari": 80, "llama": 80, "weasel": 80, "mink": 80, "polecat": 80, "foot": 80, "ferret": 80, "otter": 80, "skunk": 80, "badger": 80, "armadillo": 80, "toed": 80, "orangutan": 80, "gorilla": 80, "chimpanze": 80, "gibbon": 80, "siamang": 80, "guenon": 80, "pata": 80, "monkei": 80, "baboon": 80, "macaqu": 80, "langur": 80, "colobu": 80, "probosci": 80, "marmoset": 80, "capuchin": 80, "howler": 80, "titi": 80, "geoffroi": 80, "lemur": 80, "indri": 80, "asian": 80, "eleph": 80, "bush": 80, "snoek": 80, "eel": 80, "coho": 80, "salmon": 80, "beauti": 80, "clownfish": 80, "sturgeon": 80, "garfish": 80, "lionfish": 80, "pufferfish": 80, "abacu": 80, "abaya": 80, "academ": 80, "gown": 80, "accordion": 80, "acoust": 80, "guitar": 80, "aircraft": 80, "carrier": 80, "airlin": 80, "airship": 80, "altar": 80, "ambul": 80, "amphibi": 80, "clock": [80, 94], "apiari": 80, "apron": 80, "wast": 80, "assault": 80, "rifl": 80, "backpack": 80, "bakeri": 80, "balanc": 80, "beam": 80, "balloon": 80, "ballpoint": 80, "pen": 80, "aid": 80, "banjo": 80, "balust": 80, "barbel": 80, "barber": 80, "chair": [80, 87], "barbershop": 80, "baromet": 80, "barrel": 80, "wheelbarrow": 80, "basebal": 80, "basketbal": 80, "bassinet": 80, "bassoon": 80, "swim": 80, "cap": 80, "bath": 80, "towel": 80, "bathtub": 80, "station": 80, "wagon": 80, "lighthous": 80, "beaker": 80, "militari": 80, "beer": 80, "bottl": 80, "glass": 80, "bell": 80, "cot": 80, "bib": 80, "bicycl": [80, 91], "bikini": 80, "binder": 80, "binocular": 80, "birdhous": 80, "boathous": 80, "bobsleigh": 80, "bolo": 80, "tie": 80, "poke": 80, "bonnet": 80, "bookcas": 80, "bookstor": 80, "bow": 80, "brass": 80, "bra": 80, "breakwat": 80, "breastplat": 80, "broom": 80, "bucket": 80, "buckl": 80, "bulletproof": 80, "vest": 80, "butcher": 80, "shop": 80, "taxicab": 80, "cauldron": 80, "candl": 80, "cannon": 80, "cano": 80, "mirror": [80, 87], "carousel": 80, "tool": [80, 83, 85], "carton": 80, "wheel": 80, "teller": 80, "cassett": 80, "player": 80, "castl": 80, "catamaran": 80, "cd": 80, "cello": 80, "mobil": [80, 94], "chain": 80, "fenc": [80, 91], "mail": 80, "chainsaw": 80, "chest": 80, "chiffoni": 80, "chime": 80, "china": 80, "cabinet": 80, "christma": 80, "stock": 80, "church": 80, "movi": 80, "theater": 80, "cleaver": 80, "cliff": 80, "dwell": 80, "cloak": 80, "clog": 80, "cocktail": 80, "shaker": 80, "coffe": 80, "mug": 80, "coffeemak": 80, "coil": 80, "lock": 80, "keyboard": 80, "confectioneri": 80, "ship": [80, 88], "corkscrew": 80, "cornet": 80, "cowboi": 80, "boot": 80, "hat": 80, "cradl": 80, "crash": 80, "helmet": 80, "crate": 80, "infant": 80, "bed": 80, "crock": 80, "pot": 80, "croquet": 80, "crutch": 80, "cuirass": 80, "dam": 80, "desk": 80, "desktop": 80, "rotari": 80, "dial": 80, "telephon": 80, "diaper": 80, "watch": 80, "dine": 80, "dishcloth": 80, "dishwash": 80, "disc": 80, "brake": 80, "dock": 80, "sled": 80, "dome": 80, "doormat": 80, "drill": 80, "rig": 80, "drum": 80, "drumstick": 80, "dumbbel": 80, "dutch": 80, "oven": 80, "fan": 80, "locomot": 80, "entertain": 80, "center": 80, "envelop": 80, "espresso": 80, "powder": 80, "feather": 80, "fireboat": 80, "engin": [80, 91], "screen": 80, "sheet": 80, "flagpol": 80, "flute": 80, "footbal": 80, "forklift": 80, "fountain": 80, "poster": 80, "freight": 80, "fry": 80, "pan": 80, "fur": 80, "garbag": 80, "ga": 80, "pump": 80, "goblet": 80, "kart": 80, "golf": 80, "cart": 80, "gondola": 80, "gong": 80, "grand": 80, "piano": 80, "greenhous": 80, "grill": 80, "groceri": 80, "guillotin": 80, "barrett": 80, "hair": 80, "sprai": 80, "hammer": 80, "dryer": 80, "hand": [80, 83], "handkerchief": 80, "drive": 80, "harmonica": 80, "harp": 80, "harvest": 80, "hatchet": 80, "holster": 80, "honeycomb": 80, "hoop": 80, "skirt": 80, "horizont": 80, "bar": 80, "hors": [80, 88, 93], "drawn": 80, "hourglass": 80, "ipod": 80, "cloth": 80, "iron": 80, "jack": 80, "lantern": 80, "jean": 80, "jeep": 80, "shirt": [80, 82], "jigsaw": 80, "puzzl": 80, "pull": 80, "rickshaw": 80, "joystick": 80, "kimono": 80, "knee": 80, "pad": 80, "knot": 80, "ladl": 80, "lampshad": 80, "laptop": 80, "lawn": 80, "mower": 80, "knife": 80, "lifeboat": 80, "lighter": 80, "limousin": 80, "ocean": 80, "liner": 80, "lipstick": 80, "slip": 80, "shoe": 80, "lotion": 80, "speaker": 80, "loup": 80, "sawmil": 80, "magnet": 80, "compass": 80, "bag": [80, 82, 88, 89], "mailbox": 80, "tight": 80, "tank": 80, "manhol": 80, "maraca": 80, "marimba": 80, "maypol": 80, "maze": 80, "cup": [80, 87], "medicin": 80, "megalith": 80, "microphon": 80, "microwav": 80, "milk": 80, "minibu": 80, "miniskirt": 80, "minivan": 80, "missil": 80, "mitten": 80, "mix": 80, "bowl": 80, "modem": 80, "monasteri": 80, "monitor": 80, "mope": 80, "mortar": 80, "mosqu": 80, "mosquito": 80, "scooter": 80, "bike": 80, "tent": 80, "mous": [80, 81], "mousetrap": 80, "van": 80, "muzzl": 80, "nail": 80, "brace": 80, "necklac": 80, "nippl": 80, "obelisk": 80, "obo": 80, "ocarina": 80, "odomet": 80, "oil": 80, "oscilloscop": 80, "overskirt": 80, "bullock": 80, "oxygen": 80, "packet": 80, "paddl": 80, "padlock": 80, "paintbrush": 80, "pajama": 80, "palac": [80, 94], "parachut": 80, "park": 80, "bench": 80, "meter": 80, "passeng": 80, "patio": 80, "payphon": 80, "pedest": 80, "pencil": 80, "perfum": 80, "petri": 80, "dish": 80, "photocopi": 80, "plectrum": 80, "pickelhaub": 80, "picket": 80, "pickup": 80, "pier": 80, "piggi": 80, "pill": 80, "pillow": 80, "ping": 80, "pong": 80, "pinwheel": 80, "pirat": 80, "pitcher": 80, "plane": 80, "planetarium": 80, "plastic": 80, "plate": 80, "rack": 80, "plow": 80, "plunger": 80, "polaroid": 80, "camera": 80, "pole": [80, 91], "polic": 80, "poncho": 80, "billiard": 80, "soda": 80, "potter": 80, "prayer": 80, "rug": 80, "printer": 80, "prison": 80, "projectil": 80, "projector": 80, "hockei": 80, "puck": 80, "punch": 80, "purs": 80, "quill": 80, "quilt": 80, "race": 80, "racket": 80, "radiat": 80, "radio": 80, "telescop": 80, "rain": 80, "recreat": 80, "reel": 80, "reflex": 80, "refriger": 80, "remot": 80, "restaur": 80, "revolv": 80, "rotisseri": 80, "eras": 80, "rugbi": 80, "ruler": 80, "safe": 80, "safeti": 80, "salt": 80, "sandal": [80, 82], "sarong": 80, "saxophon": 80, "scabbard": 80, "school": 80, "bu": [80, 91], "schooner": 80, "scoreboard": 80, "crt": 80, "screw": 80, "screwdriv": 80, "seat": 80, "belt": 80, "sew": 80, "shield": 80, "shoji": 80, "basket": 80, "shovel": 80, "shower": 80, "curtain": 80, "ski": 80, "sleep": 80, "door": 80, "slot": 80, "snorkel": 80, "snowmobil": 80, "snowplow": 80, "soap": 80, "dispens": 80, "soccer": [80, 94], "sock": 80, "solar": 80, "thermal": 80, "collector": 80, "sombrero": 80, "soup": 80, "heater": 80, "shuttl": 80, "spatula": 80, "motorboat": 80, "web": 80, "spindl": 80, "sport": [80, 94], "spotlight": 80, "stage": 80, "steam": 80, "arch": 80, "bridg": 80, "steel": 80, "stethoscop": 80, "scarf": 80, "stone": 80, "wall": [80, 91], "stopwatch": 80, "stove": 80, "strainer": 80, "tram": 80, "stretcher": 80, "couch": 80, "stupa": 80, "submarin": 80, "sundial": 80, "sunglass": 80, "sunscreen": 80, "suspens": 80, "mop": 80, "sweatshirt": 80, "swimsuit": 80, "swing": 80, "switch": 80, "syring": 80, "lamp": 80, "tape": 80, "teapot": 80, "teddi": 80, "televis": [80, 94], "tenni": 80, "thatch": 80, "roof": 80, "front": 80, "thimbl": 80, "thresh": 80, "throne": 80, "tile": 80, "toaster": 80, "tobacco": 80, "toilet": 80, "totem": 80, "tow": 80, "tractor": 80, "semi": 80, "trailer": 80, "trai": 80, "trench": 80, "tricycl": 80, "trimaran": 80, "tripod": 80, "triumphal": 80, "trolleybu": 80, "trombon": 80, "tub": 80, "turnstil": 80, "typewrit": 80, "umbrella": 80, "unicycl": 80, "upright": 80, "vacuum": 80, "cleaner": 80, "vase": 80, "vault": 80, "velvet": 80, "vend": 80, "vestment": 80, "viaduct": 80, "violin": 80, "volleybal": 80, "waffl": 80, "wallet": 80, "wardrob": 80, "sink": 80, "wash": 80, "jug": 80, "tower": 80, "whiskei": 80, "whistl": 80, "wig": 80, "shade": [80, 91], "windsor": 80, "wine": 80, "wok": 80, "wooden": 80, "spoon": 80, "wool": 80, "rail": 80, "shipwreck": 80, "yawl": 80, "yurt": 80, "websit": 80, "comic": 80, "book": 80, "crossword": 80, "traffic": [80, 87, 91], "sign": [80, 91, 94], "dust": 80, "jacket": [80, 87], "menu": 80, "guacamol": 80, "consomm": 80, "trifl": 80, "ic": 80, "cream": 80, "pop": 80, "baguett": 80, "bagel": 80, "pretzel": 80, "cheeseburg": 80, "mash": 80, "potato": 80, "cabbag": 80, "broccoli": 80, "cauliflow": 80, "zucchini": 80, "spaghetti": 80, "squash": 80, "acorn": 80, "butternut": 80, "artichok": 80, "pepper": 80, "cardoon": 80, "mushroom": 80, "granni": 80, "smith": 80, "strawberri": 80, "orang": 80, "lemon": 80, "pineappl": 80, "banana": 80, "jackfruit": 80, "custard": 80, "appl": 80, "pomegran": 80, "hai": 80, "carbonara": 80, "chocol": 80, "syrup": 80, "dough": 80, "meatloaf": 80, "pizza": 80, "pie": 80, "burrito": 80, "eggnog": 80, "alp": 80, "bubbl": 80, "reef": 80, "geyser": 80, "lakeshor": 80, "promontori": 80, "shoal": 80, "seashor": 80, "vallei": 80, "volcano": 80, "bridegroom": 80, "scuba": 80, "diver": 80, "rapese": 80, "daisi": 80, "ladi": 80, "slipper": 80, "corn": 80, "rose": 80, "hip": 80, "chestnut": 80, "fungu": 80, "agar": 80, "gyromitra": 80, "stinkhorn": 80, "earth": 80, "star": 80, "wood": 80, "bolet": 80, "ear": 80, "cifar10_test_set": 80, "airplan": [80, 88], "automobil": [80, 88], "deer": [80, 88], "cifar100_test_set": 80, "aquarium_fish": 80, "babi": 80, "boi": 80, "camel": 80, "caterpillar": 80, "cattl": [80, 94], "cloud": 80, "dinosaur": 80, "dolphin": 80, "flatfish": 80, "forest": 80, "girl": 80, "kangaroo": 80, "lawn_mow": 80, "man": 80, "maple_tre": 80, "motorcycl": [80, 91], "oak_tre": 80, "orchid": 80, "palm_tre": 80, "pear": 80, "pickup_truck": 80, "pine_tre": 80, "plain": 80, "poppi": 80, "possum": 80, "raccoon": 80, "road": [80, 91], "rocket": 80, "seal": 80, "shrew": 80, "skyscrap": 80, "streetcar": 80, "sunflow": 80, "sweet_pepp": 80, "trout": 80, "tulip": 80, "willow_tre": 80, "woman": [80, 87], "caltech256": 80, "ak47": 80, "bat": 80, "glove": 80, "birdbath": 80, "blimp": 80, "bonsai": 80, "boom": 80, "breadmak": 80, "buddha": 80, "bulldoz": 80, "cactu": 80, "cake": 80, "tire": 80, "cartman": 80, "cereal": 80, "chandeli": 80, "chess": 80, "board": 80, "chimp": 80, "chopstick": 80, "coffin": 80, "coin": 80, "comet": 80, "cormor": 80, "globe": 80, "diamond": 80, "dice": 80, "doorknob": 80, "drink": 80, "straw": 80, "dumb": 80, "eiffel": 80, "elk": 80, "ewer": 80, "eyeglass": 80, "fern": 80, "fighter": 80, "jet": [80, 90], "extinguish": 80, "hydrant": 80, "firework": 80, "flashlight": 80, "floppi": 80, "fri": 80, "frisbe": 80, "galaxi": 80, "giraff": 80, "goat": 80, "gate": 80, "grape": 80, "pick": [80, 81], "hamburg": 80, "hammock": 80, "harpsichord": 80, "hawksbil": 80, "helicopt": 80, "hibiscu": 80, "homer": 80, "simpson": 80, "horsesho": 80, "air": 80, "skeleton": 80, "ibi": 80, "cone": 80, "iri": 80, "jesu": 80, "christ": 80, "joi": 80, "kayak": 80, "ketch": 80, "ladder": 80, "lath": 80, "licens": 80, "lightbulb": 80, "lightn": 80, "mandolin": 80, "mar": 80, "mattress": 80, "megaphon": 80, "menorah": 80, "microscop": 80, "minaret": 80, "minotaur": 80, "motorbik": 80, "mussel": 80, "neckti": 80, "octopu": 80, "palm": 80, "pilot": 80, "paperclip": 80, "shredder": 80, "pci": 80, "peopl": [80, 87], "pez": 80, "picnic": 80, "pram": 80, "prai": 80, "pyramid": 80, "rainbow": 80, "roulett": 80, "saddl": 80, "saturn": 80, "segwai": 80, "propel": 80, "sextant": 80, "music": 80, "skateboard": 80, "smokestack": 80, "sneaker": 80, "boat": 80, "stain": 80, "steer": 80, "stirrup": 80, "superman": 80, "sushi": 80, "armi": [80, 94], "sword": 80, "tambourin": 80, "teepe": 80, "court": 80, "theodolit": 80, "tomato": 80, "tombston": 80, "tour": 80, "pisa": 80, "treadmil": 80, "fork": 80, "tweezer": 80, "unicorn": 80, "vcr": 80, "waterfal": 80, "watermelon": 80, "weld": 80, "windmil": 80, "xylophon": 80, "yarmulk": 80, "yo": 80, "toad": 80, "twenty_news_test_set": 80, "alt": 80, "atheism": 80, "comp": 80, "graphic": [80, 91], "misc": [80, 94], "sy": 80, "ibm": 80, "pc": 80, "hardwar": 80, "mac": 80, "forsal": 80, "rec": 80, "sci": 80, "crypt": 80, "electron": 80, "med": 80, "soc": 80, "religion": 80, "christian": [80, 94], "talk": [80, 94], "polit": 80, "gun": 80, "mideast": 80, "amazon": 80, "neutral": 80, "imdb_test_set": 80, "all_class": 80, "20news_test_set": 80, "_load_classes_predprobs_label": 80, "dataset_nam": 80, "labelerror": 80, "url_bas": 80, "5392f6c71473055060be3044becdde1cbc18284d": 80, "url_label": 80, "original_test_label": 80, "_original_label": 80, "url_prob": 80, "cross_validated_predicted_prob": 80, "_pyx": 80, "num_part": 80, "datatset": 80, "bytesio": 80, "allow_pickl": 80, "pred_probs_part": 80, "url": 80, "_of_": 80, "nload": 80, "imdb": 80, "ve": [80, 81, 83, 85, 87], "interpret": [80, 81, 83], "capit": 80, "29780": 80, "256": [80, 81, 87], "780": 80, "medic": [80, 94], "doctor": 80, "254": [80, 87], "359223": 80, "640777": 80, "184": [80, 83], "258427": 80, "341176": 80, "263158": 80, "658824": 80, "337349": 80, "246575": 80, "662651": 80, "248": 80, "330000": 80, "355769": 80, "670000": 80, "251": [80, 87, 94], "167": [80, 83, 87], "252": 80, "112": 80, "253": [80, 87], "022989": 80, "255": [80, 82], "049505": 80, "190": [80, 83, 87], "66": [80, 91], "002216": 80, "000974": 80, "59": [80, 82, 87, 91], "88": [80, 82, 83, 86, 87, 90, 91], "000873": 80, "000739": 80, "79": [80, 87, 91, 92], "32635": 80, "32636": 80, "47": [80, 82, 87, 91], "32637": 80, "32638": 80, "32639": 80, "32640": 80, "051": 80, "002242": 80, "997758": 80, "002088": 80, "001045": 80, "997912": 80, "002053": 80, "997947": 80, "001980": 80, "000991": 80, "998020": 80, "001946": 80, "002915": 80, "998054": 80, "001938": 80, "002904": 80, "998062": 80, "001020": 80, "998980": 80, "001018": 80, "002035": 80, "998982": 80, "999009": 80, "0003": 80, "0002": 80, "36": [80, 91, 94], "44": [80, 86, 87, 91], "71": [80, 83, 87, 91], "071": 80, "067269": 80, "929": 80, "046": 80, "058243": 80, "954": 80, "035": 80, "032096": 80, "965": 80, "031": 80, "012232": 80, "969": 80, "022": 80, "025896": 80, "978": 80, "020": [80, 83], "013092": 80, "018": 80, "013065": 80, "016": 80, "030542": 80, "984": 80, "013": 80, "020833": 80, "987": 80, "012": 80, "010020": 80, "988": 80, "0073": 80, "0020": 80, "0016": 80, "0015": 80, "0013": 80, "0012": 80, "0010": 80, "0008": 80, "0007": 80, "0006": 80, "0005": 80, "0004": 80, "244": [80, 87], "98": [80, 81, 90, 91], "452381": 80, "459770": 80, "72": [80, 83, 86, 90, 91], "523364": 80, "460784": 80, "446602": 80, "57": [80, 83, 91], "68": [80, 82, 83, 87, 91, 92, 94], "103774": 80, "030612": 80, "97": [80, 81, 83, 87, 90, 91, 92, 94], "110092": 80, "049020": 80, "99": [80, 83, 91, 92], "0034": 80, "0032": 80, "0026": 80, "0025": 80, "4945": 80, "4946": 80, "4947": 80, "4948": 80, "4949": 80, "4950": 80, "846": 80, "82": [80, 82, 83, 87, 91], "7532": 80, "532": 80, "034483": 80, "009646": 80, "965517": 80, "030457": 80, "020513": 80, "969543": 80, "028061": 80, "035443": 80, "971939": 80, "025316": 80, "005168": 80, "974684": 80, "049751": 80, "979487": 80, "019920": 80, "042802": 80, "980080": 80, "017677": 80, "005115": 80, "982323": 80, "012987": 80, "005236": 80, "987013": 80, "012723": 80, "025126": 80, "987277": 80, "010989": 80, "008264": 80, "989011": 80, "010283": 80, "027778": 80, "989717": 80, "009677": 80, "990323": 80, "007614": 80, "010127": 80, "992386": 80, "005051": 80, "994949": 80, "005025": 80, "994975": 80, "005013": 80, "994987": 80, "001859": 80, "001328": 80, "000929": 80, "000664": 80, "186": [80, 83], "188": [80, 83, 86, 94], "189": [80, 83], "snippet": 81, "nlp": [81, 94], "mind": [81, 83], "number_of_class": 81, "total_number_of_data_point": 81, "drop": [81, 85, 90, 93], "feed": 81, "alphabet": 81, "labels_proper_format": 81, "your_classifi": 81, "issues_datafram": 81, "class_predicted_for_flagged_exampl": 81, "class_predicted_for_all_exampl": 81, "grant": 81, "datataset": 81, "fair": [81, 83], "game": 81, "speedup": [81, 88], "flexibl": 81, "tempfil": 81, "mkdtemp": 81, "sped": 81, "anywai": 81, "pred_probs_merg": 81, "merge_rare_class": 81, "count_threshold": 81, "class_mapping_orig2new": 81, "heath_summari": 81, "num_examples_per_class": 81, "rare_class": 81, "num_classes_merg": 81, "other_class": 81, "labels_merg": 81, "new_c": 81, "merged_prob": 81, "hstack": [81, 82, 83, 85], "new_class": 81, "original_class": 81, "num_check": 81, "ones_array_ref": 81, "isclos": 81, "though": [81, 83, 94], "successfulli": 81, "meaning": [81, 88], "virtuou": [81, 85], "cycl": [81, 85], "jointli": 81, "junk": 81, "clutter": 81, "unknown": 81, "caltech": 81, "combined_boolean_mask": 81, "mask1": 81, "mask2": 81, "gradientboostingclassifi": [81, 83], "true_error": [81, 83, 86], "101": [81, 87], "102": [81, 86, 87], "104": [81, 83, 87], "model_to_find_error": 81, "model_to_return": 81, "cl0": 81, "randomizedsearchcv": 81, "expens": 81, "param_distribut": 81, "learning_r": [81, 83], "max_depth": [81, 83], "magnitud": 81, "coeffici": [81, 90], "optin": 81, "environ": [81, 83], "rerun": [81, 83], "cell": [81, 83], "On": [81, 83, 87], "unabl": [81, 83], "render": [81, 83], "nbviewer": [81, 83], "cleanlearningcleanlearn": [81, 83], "linearregressionlinearregress": 81, "n_init": 81, "fit_predict": 81, "continuous_column": 81, "categorical_column": 81, "data_df": 81, "feature_a": 81, "feature_b": 81, "unexpectedli": 81, "emphas": 81, "especi": [81, 82, 90, 92, 93], "crucial": 81, "merge_duplicate_set": 81, "merge_kei": 81, "construct_group_kei": 81, "merged_set": 81, "consolidate_set": 81, "tolist": [81, 86], "issubset": 81, "frozenset": 81, "sets_list": 81, "mutabl": 81, "new_set": 81, "current_set": 81, "intersecting_set": 81, "lowest_score_strategi": 81, "sub_df": 81, "idxmin": 81, "filter_near_dupl": 81, "strategy_fn": 81, "strategy_kwarg": 81, "duplicate_row": 81, "group_kei": 81, "to_keep_indic": 81, "groupbi": 81, "explod": 81, "to_remov": 81, "isin": [81, 88], "kept": 81, "near_duplicate_issu": [81, 82], "ids_to_remove_seri": 81, "assist": 81, "streamlin": 81, "ux": 81, "agpl": 81, "compani": 81, "commerci": 81, "alter": 81, "email": 81, "discuss": 81, "anywher": 81, "profession": 81, "expert": 81, "60": [82, 83, 91], "excess": 82, "torchvis": [82, 88], "tensordataset": 82, "stratifiedkfold": [82, 86], "tqdm": 82, "fashion_mnist": 82, "num_row": 82, "60000": 82, "pil": 82, "transformed_dataset": 82, "with_format": 82, "unsqueez": 82, "cpu_count": 82, "torch_dataset": 82, "quick": [82, 86], "relu": 82, "batchnorm2d": 82, "maxpool2d": 82, "lazylinear": 82, "flatten": 82, "get_test_accuraci": 82, "testload": [82, 88], "energi": 82, "trainload": [82, 88], "n_epoch": 82, "patienc": 82, "criterion": 82, "crossentropyloss": 82, "adamw": 82, "best_test_accuraci": 82, "start_epoch": 82, "running_loss": 82, "best_epoch": 82, "end_epoch": 82, "3f": [82, 90], "acc": [82, 83], "time_taken": 82, "compute_embed": 82, "compute_pred_prob": 82, "train_batch_s": 82, "num_work": 82, "worker": [82, 94], "train_id_list": 82, "test_id_list": 82, "train_id": 82, "test_id": 82, "embeddings_model": 82, "ntrain": 82, "trainset": 82, "testset": 82, "pin_memori": 82, "fold_embed": 82, "fold_pred_prob": 82, "finish": 82, "483": 82, "835": 82, "725": 82, "331": 82, "310": 82, "378": 82, "stderr": [82, 91], "sphinxverbatim": [82, 91, 94], "53it": [82, 91], "61it": [82, 91], "10it": [82, 91], "62": [82, 83, 87, 90, 91], "63": [82, 83, 87, 91], "58it": [82, 91], "04it": [82, 91], "85it": [82, 91], "65it": [82, 91], "20it": 82, "50it": [82, 91], "69": [82, 83, 90, 91], "42it": [82, 91], "21it": [82, 91], "492": 82, "085": 82, "585": 82, "290": [82, 87], "446": 82, "06it": [82, 91], "84it": [82, 91], "46it": [82, 91], "55it": [82, 91], "76it": [82, 91], "27it": [82, 91], "72it": [82, 91], "32it": [82, 91], "476": 82, "305": [82, 90], "577": 82, "328": [82, 87], "335": 82, "419": 82, "11it": [82, 91], "41it": [82, 91], "35it": [82, 91], "62it": [82, 91], "79it": [82, 91], "64it": [82, 91], "01it": [82, 91], "70it": [82, 91], "26it": [82, 91], "77it": [82, 91], "40it": [82, 91], "reorder": 82, "vision": 82, "grayscal": 82, "exce": 82, "max_preval": 82, "7620": 82, "3692": 82, "3521": 82, "225": [82, 86], "166": 82, "3691": 82, "40378": 82, "943831e": 82, "54473": 82, "066211e": 82, "06": [82, 83, 87, 91, 94], "29412": 82, "899069e": 82, "25316": 82, "984817e": 82, "52247": 82, "245879e": 82, "9581": 82, "19228": 82, "dress": 82, "54078": 82, "000010": 82, "pullov": 82, "32657": 82, "21282": 82, "000011": 82, "11262": 82, "000014": 82, "6294": 82, "30659": 82, "000798": 82, "30968": 82, "000015": 82, "258": 82, "000907": 82, "9762": 82, "54565": 82, "47139": 82, "000017": 82, "001423": 82, "000026": 82, "39992": 82, "39993": 82, "39994": 82, "39995": 82, "7834": 82, "42819": 82, "629362": 82, "51431": 82, "654330": 82, "55548": 82, "658364": 82, "51191": 82, "668572": 82, "50081": 82, "669703": 82, "7834321613629787": 82, "13732": 82, "13733": 82, "13734": 82, "47635": 82, "110901": 82, "974390": 82, "998733": 82, "937117": 82, "998755": 82, "53564": 82, "5473": 82, "trouser": 82, "plot_label_issue_exampl": 82, "ncol": [82, 88], "nrow": [82, 88], "ceil": 82, "axes_list": 82, "label_issue_indic": 82, "gl": 82, "sl": 82, "fontdict": 82, "imshow": [82, 88], "cmap": [82, 90], "grai": 82, "subplots_adjust": 82, "hspace": 82, "outsiz": 82, "outlier_issues_df": 82, "depict": [82, 86, 87, 88, 89, 91], "plot_outlier_issues_exampl": 82, "n_comparison_imag": 82, "sample_from_class": 82, "number_of_sampl": 82, "non_outlier_indic": 82, "isnul": 82, "non_outlier_indices_excluding_curr": 82, "sampled_indic": 82, "label_scores_of_sampl": 82, "top_score_indic": 82, "top_label_indic": 82, "sampled_imag": 82, "get_image_given_label_and_sampl": 82, "image_from_dataset": 82, "corresponding_label": 82, "comparison_imag": 82, "images_to_plot": 82, "idlist": 82, "iterrow": 82, "closest": 82, "counterpart": 82, "near_duplicate_issues_df": 82, "plot_near_duplicate_issue_exampl": 82, "seen_id_pair": 82, "get_image_and_given_label_and_predicted_label": 82, "duplicate_imag": 82, "nd_set": 82, "challeng": 82, "dark_issu": 82, "reveal": [82, 91], "dark_scor": 82, "dark_issues_df": 82, "is_dark_issu": 82, "34848": 82, "203922": 82, "50270": 82, "204588": 82, "3936": 82, "213098": 82, "733": 82, "217686": 82, "8094": 82, "230118": 82, "plot_image_issue_exampl": 82, "difficult": 82, "disproportion": 82, "lowinfo_issu": 82, "low_information_scor": 82, "lowinfo_issues_df": 82, "is_low_information_issu": 82, "53050": 82, "067975": 82, "40875": 82, "089929": 82, "9594": 82, "092601": 82, "34825": 82, "107744": 82, "37530": 82, "108516": 82, "lot": 82, "depth": 83, "survei": [83, 94], "focus": [83, 85], "scienc": 83, "multivariate_norm": [83, 85, 86], "make_data": [83, 85], "cov": [83, 85, 86], "avg_trac": [83, 86], "test_label": [83, 86, 88, 93], "py_tru": 83, "noise_matrix_tru": 83, "noise_marix": 83, "s_test": 83, "noisy_test_label": 83, "purpl": 83, "val": 83, "namespac": 83, "exec": 83, "markerfacecolor": [83, 86], "markeredgecolor": [83, 86, 90], "markers": [83, 86, 90], "markeredgewidth": [83, 86, 90], "realist": 83, "7560": 83, "638483e": 83, "897052e": 83, "548986e": 83, "924634e": 83, "374580e": 83, "3454": 83, "014051": 83, "020451": 83, "249": [83, 87], "042594": 83, "043859": 83, "045954": 83, "6120": 83, "023714": 83, "007136": 83, "119": [83, 87], "107266": 83, "103": [83, 87, 94], "033738": 83, "238": [83, 87], "119505": 83, "236": [83, 87], "037843": 83, "222": 83, "614915": 83, "122": [83, 87], "624422": 83, "625965": 83, "626079": 83, "118": 83, "627675": 83, "158": 83, "159": [83, 86, 87], "161": 83, "1960": 83, "196": [83, 87], "223": [83, 87], "221": 83, "219": [83, 87], "695174": 83, "323529": 83, "522929": 83, "013722": 83, "675606": 83, "646438": 83, "anyth": 83, "enhanc": [83, 85, 87], "magic": 83, "83": [83, 87, 90, 91, 92, 94], "liter": 83, "identif": 83, "x27": 83, "logisticregressionlogisticregress": 83, "ever": 83, "092": 83, "040": 83, "024": 83, "004": 83, "surpris": 83, "1705": 83, "01936": 83, "ton": 83, "yourfavoritemodel1": 83, "merged_label": 83, "merged_test_label": 83, "newli": [83, 85], "yourfavoritemodel2": 83, "yourfavoritemodel3": 83, "cl3": 83, "takeawai": 83, "That": [83, 86], "randomli": 83, "my_test_pred_prob": 83, "my_test_pr": 83, "issues_test": 83, "corrected_test_label": 83, "pretend": 83, "cl_test_pr": 83, "fairli": 83, "label_acc": 83, "percentag": 83, "offset": 83, "nquestion": 83, "overestim": 83, "answer": 83, "experienc": 83, "76": [83, 86, 87, 90, 91, 92], "knowledg": 83, "quantiti": [83, 90], "prioiri": 83, "known": 83, "versatil": 83, "label_issues_indic": 83, "213": [83, 87], "212": [83, 92], "218": [83, 87], "152": 83, "197": [83, 87], "170": 83, "214": 83, "164": [83, 86], "198": [83, 87], "191": [83, 87], "121": [83, 93], "117": [83, 90], "206": [83, 87], "115": [83, 87], "193": 83, "194": 83, "201": [83, 87], "174": 83, "163": 83, "150": [83, 85, 87], "169": 83, "151": [83, 87], "168": 83, "precision_scor": 83, "recall_scor": 83, "f1_score": 83, "true_label_issu": 83, "filter_by_list": 83, "718750": [83, 85], "807018": 83, "912": 83, "733333": 83, "800000": 83, "721311": 83, "792793": 83, "908": 83, "676923": 83, "765217": 83, "892": 83, "567901": 83, "702290": 83, "844": 83, "gaug": 83, "label_issues_count": 83, "155": [83, 87], "172": [83, 86], "easiest": 83, "modular": 83, "penalti": 83, "l2": 83, "model3": 83, "n_estim": 83, "cv_pred_probs_1": 83, "cv_pred_probs_2": 83, "cv_pred_probs_3": 83, "label_quality_scores_best": 83, "cv_pred_probs_ensembl": 83, "label_quality_scores_bett": 83, "superior": [83, 89], "workflow": [84, 90], "speechbrain": 84, "timm": 84, "glad": 85, "multiannotator_label": 85, "noisier": 85, "111": [85, 90], "local_data": [85, 86], "true_labels_train": [85, 86], "noise_matrix_bett": 85, "noise_matrix_wors": 85, "transpos": [85, 88], "dropna": 85, "zfill": 85, "row_na_check": 85, "notna": 85, "reset_index": 85, "a0001": 85, "a0002": 85, "a0003": 85, "a0004": 85, "a0005": 85, "a0006": 85, "a0007": 85, "a0008": 85, "a0009": 85, "a0010": 85, "a0041": 85, "a0042": 85, "a0043": 85, "a0044": 85, "a0045": 85, "a0046": 85, "a0047": 85, "a0048": 85, "a0049": 85, "a0050": 85, "60856743": 85, "41693214": 85, "40908785": 85, "87147629": 85, "64941785": 85, "10774851": 85, "0524466": 85, "71853246": 85, "37169848": 85, "66031048": 85, "multiannotator_util": 85, "crude": 85, "straight": 85, "majority_vote_label": 85, "736157": 85, "757738": 85, "782255": 85, "715585": 85, "824273": 85, "quality_annotator_a0001": 85, "quality_annotator_a0002": 85, "quality_annotator_a0003": 85, "quality_annotator_a0004": 85, "quality_annotator_a0005": 85, "quality_annotator_a0006": 85, "quality_annotator_a0007": 85, "quality_annotator_a0008": 85, "quality_annotator_a0009": 85, "quality_annotator_a0010": 85, "quality_annotator_a0041": 85, "quality_annotator_a0042": 85, "quality_annotator_a0043": 85, "quality_annotator_a0044": 85, "quality_annotator_a0045": 85, "quality_annotator_a0046": 85, "quality_annotator_a0047": 85, "quality_annotator_a0048": 85, "quality_annotator_a0049": 85, "quality_annotator_a0050": 85, "070551": 85, "216064": 85, "119178": 85, "alongisd": 85, "244982": 85, "208333": 85, "295978": 85, "294118": 85, "324194": 85, "310345": 85, "355315": 85, "346154": 85, "439728": 85, "480000": 85, "a0031": 85, "523205": 85, "580645": 85, "a0034": 85, "535313": 85, "607143": 85, "a0021": 85, "607002": 85, "a0015": 85, "609527": 85, "678571": 85, "a0011": 85, "621101": 85, "692308": 85, "wors": 85, "improved_consensus_label": 85, "majority_vote_accuraci": 85, "cleanlab_label_accuraci": 85, "8581081081081081": 85, "9797297297297297": 85, "besid": 85, "sorted_consensus_quality_scor": 85, "worst_qual": 85, "better_qu": 85, "worst_quality_accuraci": 85, "better_quality_accuraci": 85, "9893238434163701": 85, "improved_pred_prob": 85, "treat": [85, 86, 90, 94], "analzi": 85, "copyright": 86, "advertis": 86, "violenc": 86, "nsfw": 86, "ranked_label_issu": [86, 92, 93], "multioutput": 86, "multioutputclassifi": 86, "celeba": 86, "make_multilabel_data": 86, "boxes_coordin": 86, "box_multilabel": 86, "make_multi": 86, "bx1": 86, "by1": 86, "bx2": 86, "by2": 86, "label_list": 86, "ur": 86, "upper": 86, "inidx": 86, "logical_and": 86, "inv_d": 86, "labels_idx": 86, "true_labels_test": 86, "dict_unique_label": 86, "get_color_arrai": 86, "dcolor": 86, "aa4400": 86, "55227f": 86, "55a100": 86, "00ff00": 86, "007f7f": 86, "386b55": 86, "0000ff": 86, "simplic": 86, "advis": 86, "y_onehot": 86, "single_class_label": 86, "stratifi": [86, 89], "kf": 86, "train_index": 86, "test_index": 86, "clf_cv": 86, "x_train_cv": 86, "x_test_cv": 86, "y_train_cv": 86, "y_test_cv": 86, "y_pred_cv": 86, "saw": 86, "num_to_displai": 86, "09": [86, 87, 91], "275": 86, "267": 86, "171": 86, "234": 86, "165": 86, "227": [86, 87], "262": [86, 87], "263": [86, 87], "266": [86, 87], "139": 86, "143": [86, 87], "216": [86, 87, 94], "265": 86, "despit": [86, 94], "suspect": 86, "888": 86, "8224": 86, "9632": 86, "968": 86, "6512": 86, "0444": 86, "774": 86, "labels_binary_format": 86, "labels_list_format": 86, "surround": 87, "scene": 87, "coco": 87, "everydai": 87, "has_label_issu": 87, "insal": 87, "nc": [87, 91, 94], "s3": [87, 91, 94], "amazonaw": [87, 91, 94], "objectdetectionbenchmark": 87, "tutorial_obj": 87, "pkl": 87, "example_imag": 87, "unzip": [87, 94], "begin": 87, "detectron2": 87, "image_path": 87, "rb": 87, "image_to_visu": 87, "seg_map": 87, "334": 87, "float32": 87, "bboxes_ignor": 87, "286": 87, "285": 87, "224": 87, "231": 87, "293": 87, "235": 87, "289": [87, 90], "282": 87, "74": [87, 90, 91, 92], "281": 87, "271": 87, "280": 87, "277": 87, "279": 87, "287": 87, "299": 87, "276": 87, "307": 87, "321": 87, "326": 87, "333": 87, "261": 87, "319": 87, "257": 87, "295": 87, "283": 87, "243": 87, "303": 87, "316": 87, "247": 87, "323": 87, "327": 87, "226": 87, "228": 87, "232": 87, "239": 87, "240": 87, "209": 87, "242": 87, "202": 87, "230": 87, "215": 87, "220": 87, "229": 87, "85": [87, 90, 91], "217": [87, 94], "237": 87, "207": 87, "204": 87, "205": 87, "153": 87, "149": 87, "140": 87, "124": 87, "268": 87, "273": 87, "108": [87, 94], "284": 87, "110": 87, "136": 87, "145": 87, "173": 87, "297": 87, "317": 87, "192": 87, "329": 87, "332": 87, "324": 87, "203": 87, "320": 87, "314": 87, "199": 87, "291": 87, "000000481413": 87, "jpg": 87, "42398": 87, "44503": 87, "337": [87, 93], "29968": 87, "336": 87, "21005": 87, "9978472": 87, "forgot": 87, "drew": 87, "label_issue_idx": 87, "num_examples_to_show": 87, "113": [87, 90], "candid": 87, "97489622": 87, "70610878": 87, "98764951": 87, "88899237": 87, "99085805": 87, "issue_idx": 87, "95569726e": 87, "03354841e": 87, "57510169e": 87, "58447666e": 87, "39755858e": 87, "suppli": 87, "issue_to_visu": 87, "000000009483": 87, "95569726168054e": 87, "addition": [87, 91], "visibl": 87, "missmatch": 87, "likelei": 87, "agnost": 87, "vaidat": 87, "inconsist": 87, "000000395701": 87, "033548411774308e": 87, "armchair": 87, "tv": 87, "000000154004": 87, "38300759625496356": 87, "foreground": 87, "000000448410": 87, "0008575101690203273": 87, "crowd": 87, "alon": 87, "explor": [87, 88], "resembl": [87, 88], "000000499768": 87, "9748962231208227": 87, "000000521141": 87, "8889923658893665": 87, "000000143931": 87, "9876495074395956": 87, "train_feature_embed": 88, "ood_train_feature_scor": 88, "test_feature_embed": 88, "ood_test_feature_scor": 88, "ood_train_predictions_scor": 88, "train_pred_prob": 88, "ood_test_predictions_scor": 88, "test_pred_prob": 88, "pylab": 88, "rcparam": 88, "baggingclassifi": 88, "therebi": 88, "rescal": 88, "transform_norm": 88, "totensor": 88, "root": 88, "animal_class": 88, "non_animal_class": 88, "animal_idx": 88, "test_idx": 88, "toronto": 88, "edu": 88, "kriz": 88, "5000": 88, "plot_imag": 88, "visualize_outli": 88, "txt_class": 88, "img": [88, 90], "npimg": 88, "show_label": 88, "data_subset": 88, "resnet50": 88, "corpu": 88, "2048": 88, "embed_imag": 88, "create_model": 88, "rwightman": 88, "v0": 88, "rsb": 88, "resnet50_a1_0": 88, "14fe96d1": 88, "pth": 88, "checkpoint": 88, "strang": 88, "odd": 88, "train_ood_features_scor": 88, "top_train_ood_features_idx": 88, "fun": 88, "negat": 88, "homogen": 88, "bottom_train_ood_features_idx": 88, "test_ood_features_scor": 88, "top_ood_features_idx": 88, "inevit": 88, "trade": 88, "5th": 88, "percentil": 88, "fifth_percentil": 88, "plt_rang": 88, "hist": 88, "train_outlier_scor": 88, "ylabel": 88, "axvlin": 88, "test_outlier_scor": 88, "ood_features_indic": 88, "revisit": 88, "unusu": 88, "return_invers": 88, "train_feature_embeddings_sc": 88, "test_feature_embeddings_sc": 88, "train_pred_label": 88, "9702": 88, "train_ood_predictions_scor": 88, "test_ood_predictions_scor": 88, "mainli": [88, 94], "lost": 88, "unsuit": 89, "ok": [89, 94], "convention": 89, "aforement": 89, "hypothet": 89, "contrast": 89, "tradit": 89, "disjoint": 89, "out_of_sample_pred_probs_for_a": 89, "out_of_sample_pred_probs_for_b": 89, "out_of_sample_pred_probs_for_c": 89, "out_of_sample_pred_prob": 89, "price": 90, "incom": 90, "ag": 90, "histgradientboostingregressor": 90, "r2_score": 90, "student_grades_r": 90, "final_scor": 90, "true_final_scor": 90, "homework": 90, "3d": 90, "hue": 90, "mpl_toolkit": 90, "mplot3d": 90, "axes3d": 90, "errors_idx": 90, "add_subplot": 90, "z": 90, "colorbar": 90, "errors_mask": 90, "feature_column": 90, "predicted_column": 90, "x_train_raw": 90, "x_test_raw": 90, "categorical_featur": [90, 92], "randomforestregressor": 90, "629763": 90, "521450": 90, "954607": 90, "547234": 90, "338296": 90, "754531": 90, "619090": 90, "312295": 90, "806626": 90, "784048": 90, "identified_issu": [90, 93], "367": 90, "560": 90, "318": 90, "688": 90, "657": 90, "view_datapoint": 90, "concat": 90, "consum": [90, 93], "baseline_model": [90, 93], "preds_og": 90, "r2_og": 90, "838": 90, "robustli": [90, 92, 93], "acceler": [90, 93], "found_label_issu": 90, "preds_cl": 90, "r2_cl": 90, "925": 90, "effort": [90, 92, 93], "favorit": 90, "13091885": 90, "48412548": 90, "00695165": 90, "44421119": 90, "43029854": 90, "synthia": 91, "imagesegment": 91, "given_mask": 91, "predicted_mask": 91, "set_printopt": [91, 94], "sky": 91, "sidewalk": 91, "veget": 91, "terrain": 91, "rider": 91, "pred_probs_filepath": 91, "1088": 91, "1920": 91, "label_filepath": 91, "synthia_class": 91, "maunal": 91, "100000": 91, "244800": 91, "leftmost": 91, "area": 91, "middl": [91, 94], "infact": 91, "rightmost": 91, "discrep": 91, "4997817": 91, "17086": 91, "170847": 91, "54it": 91, "34440": 91, "172423": 91, "89it": 91, "51683": 91, "172348": 91, "69010": 91, "172708": 91, "80it": 91, "86281": 91, "172699": 91, "103551": 91, "172696": 91, "13it": 91, "120836": 91, "172742": 91, "57it": 91, "138126": 91, "172788": 91, "155405": 91, "172451": 91, "81it": 91, "172651": 91, "172202": 91, "189891": 91, "172258": 91, "29it": 91, "207171": 91, "172419": 91, "78it": 91, "224449": 91, "172523": 91, "241702": 91, "172233": 91, "259049": 91, "172601": 91, "276310": 91, "172489": 91, "293563": 91, "172497": 91, "33it": 91, "310813": 91, "172337": 91, "95it": 91, "328047": 91, "172170": 91, "345265": 91, "171966": 91, "362537": 91, "172189": 91, "92it": 91, "379879": 91, "172555": 91, "05it": 91, "397213": 91, "172785": 91, "48it": 91, "414506": 91, "172826": 91, "02it": 91, "431820": 91, "172915": 91, "66it": 91, "449112": 91, "172764": 91, "44it": 91, "466414": 91, "172836": 91, "90it": 91, "483698": 91, "172730": 91, "45it": 91, "500972": 91, "172466": 91, "518219": 91, "172446": 91, "24it": 91, "535464": 91, "172438": 91, "552708": 91, "172043": 91, "07it": 91, "569913": 91, "172004": 91, "587114": 91, "171923": 91, "43it": 91, "604307": 91, "171654": 91, "621473": 91, "171589": 91, "638633": 91, "171509": 91, "655785": 91, "171228": 91, "88it": 91, "672908": 91, "170161": 91, "690003": 91, "170392": 91, "25it": 91, "707173": 91, "170780": 91, "724317": 91, "170972": 91, "99it": 91, "741430": 91, "171016": 91, "38it": 91, "758533": 91, "167182": 91, "47it": 91, "776036": 91, "169494": 91, "74it": 91, "793560": 91, "171193": 91, "69it": 91, "810976": 91, "172071": 91, "828224": 91, "172188": 91, "845496": 91, "172343": 91, "08it": 91, "862736": 91, "172024": 91, "98it": 91, "879997": 91, "172194": 91, "897274": 91, "172364": 91, "52it": 91, "914513": 91, "172254": 91, "49it": 91, "931740": 91, "169929": 91, "949055": 91, "170883": 91, "37it": 91, "966340": 91, "171465": 91, "82it": 91, "983583": 91, "171749": 91, "93it": 91, "1000769": 91, "171780": 91, "19it": 91, "1017998": 91, "171928": 91, "73it": 91, "1035235": 91, "172056": 91, "86it": 91, "1052442": 91, "172014": 91, "1069690": 91, "172149": 91, "67it": 91, "1086906": 91, "172109": 91, "1104148": 91, "172198": 91, "83it": 91, "1121369": 91, "165344": 91, "1138453": 91, "166943": 91, "1155552": 91, "168130": 91, "23it": 91, "1172616": 91, "168868": 91, "1189678": 91, "169384": 91, "87it": 91, "1206756": 91, "169795": 91, "56it": 91, "1223863": 91, "170171": 91, "1240930": 91, "170314": 91, "1258042": 91, "170553": 91, "51it": 91, "1275102": 91, "170438": 91, "1292201": 91, "170599": 91, "16it": 91, "1309373": 91, "170931": 91, "1326571": 91, "171243": 91, "1343697": 91, "170805": 91, "1360802": 91, "170874": 91, "1377979": 91, "171139": 91, "1395094": 91, "170088": 91, "34it": 91, "1412105": 91, "169658": 91, "17it": 91, "1429228": 91, "170122": 91, "1446318": 91, "170351": 91, "1463355": 91, "164217": 91, "1480456": 91, "166196": 91, "1497358": 91, "167024": 91, "1514556": 91, "168486": 91, "1531721": 91, "169424": 91, "1548911": 91, "170159": 91, "1566094": 91, "170655": 91, "03it": 91, "1583242": 91, "170897": 91, "97it": 91, "1600338": 91, "170597": 91, "1617403": 91, "170590": 91, "1634553": 91, "170859": 91, "1651677": 91, "170970": 91, "1668835": 91, "171148": 91, "1686094": 91, "171576": 91, "1703331": 91, "171811": 91, "94it": 91, "1720557": 91, "171943": 91, "1737819": 91, "172141": 91, "68it": 91, "1755034": 91, "172091": 91, "31it": 91, "1772420": 91, "172616": 91, "1789723": 91, "172735": 91, "1806997": 91, "169044": 91, "1824418": 91, "170567": 91, "09it": 91, "1841773": 91, "171448": 91, "30it": 91, "1859140": 91, "172107": 91, "1876360": 91, "172090": 91, "1893635": 91, "172285": 91, "1910941": 91, "172514": 91, "18it": 91, "1928231": 91, "172626": 91, "1945536": 91, "172751": 91, "1962813": 91, "172740": 91, "1980089": 91, "172371": 91, "1997328": 91, "171941": 91, "2014523": 91, "171574": 91, "2031682": 91, "171369": 91, "2048829": 91, "171395": 91, "2065969": 91, "171282": 91, "2083130": 91, "171378": 91, "2100431": 91, "171863": 91, "75it": 91, "2117705": 91, "172124": 91, "2134918": 91, "172104": 91, "2152129": 91, "171791": 91, "60it": 91, "2169309": 91, "164877": 91, "71it": 91, "2186442": 91, "166753": 91, "2203549": 91, "168018": 91, "2220689": 91, "169015": 91, "2237815": 91, "169679": 91, "2254844": 91, "169856": 91, "2271879": 91, "170000": 91, "2288907": 91, "170080": 91, "2305964": 91, "170224": 91, "2323041": 91, "170385": 91, "2340083": 91, "170382": 91, "2357190": 91, "170585": 91, "2374382": 91, "170982": 91, "2391535": 91, "171143": 91, "2408651": 91, "171036": 91, "96it": 91, "2425756": 91, "170904": 91, "2442871": 91, "170975": 91, "2460006": 91, "171084": 91, "91it": 91, "2477172": 91, "171255": 91, "2494379": 91, "171495": 91, "2511529": 91, "170654": 91, "2528643": 91, "170797": 91, "2545739": 91, "170841": 91, "2562824": 91, "170493": 91, "2579953": 91, "170728": 91, "2597105": 91, "170961": 91, "2614202": 91, "170940": 91, "2631309": 91, "170974": 91, "2648407": 91, "170962": 91, "2665526": 91, "171027": 91, "2682658": 91, "171111": 91, "2699770": 91, "2716873": 91, "170989": 91, "2734011": 91, "171103": 91, "39it": 91, "2751181": 91, "171278": 91, "2768309": 91, "171162": 91, "63it": 91, "2785426": 91, "171065": 91, "2802570": 91, "171176": 91, "28it": 91, "2819713": 91, "171250": 91, "2836839": 91, "2853957": 91, "171024": 91, "2871241": 91, "171565": 91, "2888648": 91, "172312": 91, "2905883": 91, "172319": 91, "2923116": 91, "172179": 91, "2940335": 91, "171700": 91, "2957506": 91, "171411": 91, "2974693": 91, "171544": 91, "2991852": 91, "171555": 91, "3009024": 91, "171601": 91, "3026185": 91, "171560": 91, "3043342": 91, "171322": 91, "3060482": 91, "171342": 91, "3077630": 91, "171380": 91, "3094806": 91, "171490": 91, "3112003": 91, "171629": 91, "3129202": 91, "171732": 91, "3146417": 91, "171853": 91, "3163656": 91, "172010": 91, "3180858": 91, "171988": 91, "3198078": 91, "172048": 91, "3215283": 91, "169778": 91, "3232268": 91, "169721": 91, "3249541": 91, "170613": 91, "3266864": 91, "171390": 91, "3284229": 91, "172060": 91, "3301452": 91, "3318696": 91, "172205": 91, "3336026": 91, "172528": 91, "3353385": 91, "172841": 91, "3370761": 91, "173114": 91, "3388087": 91, "173153": 91, "3405403": 91, "172969": 91, "3422701": 91, "172546": 91, "3439957": 91, "172327": 91, "3457191": 91, "172085": 91, "3474400": 91, "171804": 91, "3491581": 91, "171606": 91, "3508742": 91, "171311": 91, "3525874": 91, "170939": 91, "3543043": 91, "171160": 91, "3560160": 91, "171067": 91, "3577419": 91, "171518": 91, "3594624": 91, "171675": 91, "3611792": 91, "3629022": 91, "171767": 91, "3646274": 91, "171989": 91, "3663474": 91, "171964": 91, "3680719": 91, "172105": 91, "3697930": 91, "172046": 91, "3715230": 91, "172328": 91, "3732463": 91, "172130": 91, "3749677": 91, "171467": 91, "15it": 91, "3766854": 91, "3784058": 91, "171695": 91, "3801268": 91, "171812": 91, "3818508": 91, "171985": 91, "3835708": 91, "3852981": 91, "172207": 91, "3870202": 91, "172135": 91, "3887458": 91, "172260": 91, "3904685": 91, "171946": 91, "3921880": 91, "171690": 91, "22it": 91, "3939050": 91, "171373": 91, "3956200": 91, "171406": 91, "3973341": 91, "171115": 91, "3990459": 91, "171130": 91, "4007594": 91, "4024747": 91, "171289": 91, "4041877": 91, "171288": 91, "4059428": 91, "172552": 91, "4076757": 91, "172770": 91, "4094035": 91, "172749": 91, "4111311": 91, "172590": 91, "4128626": 91, "172755": 91, "4145965": 91, "172941": 91, "4163390": 91, "173331": 91, "4180724": 91, "173224": 91, "4198100": 91, "173382": 91, "4215467": 91, "173465": 91, "4232814": 91, "173187": 91, "4250164": 91, "173276": 91, "4267492": 91, "171087": 91, "4284847": 91, "171815": 91, "4302136": 91, "172133": 91, "4319480": 91, "172520": 91, "4336926": 91, "173095": 91, "4354434": 91, "173685": 91, "4371978": 91, "174208": 91, "4389480": 91, "174447": 91, "4406926": 91, "174264": 91, "4424354": 91, "173694": 91, "4441725": 91, "173476": 91, "4459108": 91, "173579": 91, "4476523": 91, "173746": 91, "4493902": 91, "173756": 91, "4511278": 91, "173434": 91, "4528660": 91, "173546": 91, "4546015": 91, "173215": 91, "4563337": 91, "173046": 91, "4580642": 91, "173036": 91, "4597946": 91, "172707": 91, "12it": 91, "4615217": 91, "171945": 91, "4632413": 91, "171736": 91, "4649630": 91, "171861": 91, "4666868": 91, "4684070": 91, "4701269": 91, "171722": 91, "4718442": 91, "171694": 91, "00it": 91, "4735612": 91, "171538": 91, "4752766": 91, "171290": 91, "4769896": 91, "171233": 91, "4787020": 91, "171187": 91, "4804139": 91, "167381": 91, "4821271": 91, "168539": 91, "4838412": 91, "169387": 91, "4855548": 91, "169969": 91, "4872639": 91, "170246": 91, "4889670": 91, "169986": 91, "4906852": 91, "170529": 91, "4924033": 91, "170909": 91, "4941127": 91, "170913": 91, "59it": 91, "4958234": 91, "170956": 91, "4975331": 91, "170614": 91, "4992485": 91, "170886": 91, "171429": 91, "3263230": 91, "783379": 91, "275110": 91, "255792": 91, "78225": 91, "55990": 91, "54427": 91, "33591": 91, "24645": 91, "21308": 91, "15045": 91, "14171": 91, "13832": 91, "13498": 91, "11490": 91, "9164": 91, "8769": 91, "6999": 91, "6031": 91, "5011": 91, "mistakenli": 91, "class_issu": 91, "aim": [91, 94], "domin": 91, "extratreesclassifi": 92, "extratre": 92, "labelencod": [92, 93], "labels_raw": 92, "interg": [92, 93], "tress": 92, "827": 92, "cheat": 92, "0pt": 92, "233": 92, "labels_train": 92, "labels_test": 92, "acc_og": [92, 93], "783068783068783": 92, "acc_cl": [92, 93], "8095238095238095": 92, "earlier": [93, 94], "raw_label": 93, "raw_train_text": 93, "raw_test_text": 93, "raw_train_label": 93, "raw_test_label": 93, "encond": 93, "train_text": 93, "test_text": 93, "858050": 93, "545854": 93, "826194": 93, "965814": 93, "791923": 93, "646": 93, "390": 93, "628": 93, "702": 93, "863": 93, "135": 93, "735": 93, "print_as_df": 93, "inverse_transform": 93, "fight": 93, "bunch": 94, "conll": 94, "2003": 94, "love": 94, "n_i": 94, "optional_list_of_ordered_class_nam": 94, "deepai": 94, "conll2003": 94, "rm": 94, "tokenclassif": 94, "2024": 94, "2400": 94, "52e0": 94, "1a00": 94, "845": 94, "connect": 94, "443": 94, "await": 94, "982975": 94, "960k": 94, "kb": 94, "959": 94, "94k": 94, "68mb": 94, "mb": 94, "directori": 94, "inflat": 94, "17045998": 94, "16m": 94, "octet": 94, "26m": 94, "108mb": 94, "bert": 94, "read_npz": 94, "filepath": 94, "corrsespond": 94, "iob2": 94, "given_ent": 94, "entity_map": 94, "readfil": 94, "sep": 94, "startswith": 94, "docstart": 94, "isalpha": 94, "isupp": 94, "indices_to_preview": 94, "nsentenc": 94, "eu": 94, "reject": 94, "boycott": 94, "british": 94, "lamb": 94, "00030412": 94, "00023826": 94, "99936208": 94, "00007009": 94, "00002545": 94, "99998795": 94, "00000401": 94, "00000218": 94, "00000455": 94, "00000131": 94, "00000749": 94, "99996115": 94, "00001371": 94, "0000087": 94, "00000895": 94, "99998936": 94, "00000382": 94, "00000178": 94, "00000366": 94, "00000137": 94, "99999101": 94, "00000266": 94, "00000174": 94, "0000035": 94, "00000109": 94, "99998768": 94, "00000482": 94, "00000202": 94, "00000438": 94, "0000011": 94, "00000465": 94, "99996392": 94, "00001105": 94, "0000116": 94, "00000878": 94, "99998671": 94, "00000364": 94, "00000213": 94, "00000472": 94, "00000281": 94, "99999073": 94, "00000211": 94, "00000159": 94, "00000442": 94, "00000115": 94, "peter": 94, "blackburn": 94, "00000358": 94, "00000529": 94, "99995623": 94, "000022": 94, "0000129": 94, "0000024": 94, "00001812": 94, "99994141": 94, "00001645": 94, "00002162": 94, "brussel": 94, "1996": 94, "00001172": 94, "00000821": 94, "00004661": 94, "0000618": 94, "99987167": 94, "99999061": 94, "00000201": 94, "00000195": 94, "00000408": 94, "00000135": 94, "2254": 94, "2907": 94, "19392": 94, "9962": 94, "8904": 94, "19303": 94, "12918": 94, "9256": 94, "11855": 94, "18392": 94, "20426": 94, "19402": 94, "14744": 94, "19371": 94, "4645": 94, "10331": 94, "9430": 94, "6143": 94, "18367": 94, "12914": 94, "todai": 94, "weather": 94, "march": 94, "scalfaro": 94, "northern": 94, "himself": 94, "said": 94, "germani": 94, "nastja": 94, "rysich": 94, "north": 94, "spla": 94, "fought": 94, "khartoum": 94, "govern": 94, "south": 94, "1983": 94, "autonomi": 94, "animist": 94, "region": 94, "moslem": 94, "arabis": 94, "mayor": 94, "antonio": 94, "gonzalez": 94, "garcia": 94, "revolutionari": 94, "parti": 94, "wednesdai": 94, "troop": 94, "raid": 94, "farm": 94, "stole": 94, "rape": 94, "women": 94, "spring": 94, "chg": 94, "hrw": 94, "12pct": 94, "princ": 94, "photo": 94, "moment": 94, "spokeswoman": 94, "rainier": 94, "told": 94, "reuter": 94, "danila": 94, "carib": 94, "w224": 94, "equip": 94, "radiomet": 94, "earn": 94, "19996": 94, "london": 94, "denom": 94, "sale": 94, "uk": 94, "jp": 94, "fr": 94, "maccabi": 94, "hapoel": 94, "haifa": 94, "tel": 94, "aviv": 94, "hospit": 94, "rever": 94, "roman": 94, "cathol": 94, "nun": 94, "admit": 94, "calcutta": 94, "week": 94, "ago": 94, "fever": 94, "vomit": 94, "allianc": 94, "embattl": 94, "kabul": 94, "salang": 94, "highwai": 94, "mondai": 94, "tuesdai": 94, "suprem": 94, "council": 94, "led": 94, "jumbish": 94, "milli": 94, "movement": 94, "warlord": 94, "abdul": 94, "rashid": 94, "dostum": 94, "dollar": 94, "exchang": 94, "3570": 94, "12049": 94, "born": 94, "1937": 94, "provinc": 94, "anhui": 94, "dai": 94, "came": 94, "shanghai": 94, "citi": 94, "prolif": 94, "author": 94, "teacher": 94, "chines": 94, "16764": 94, "1990": 94, "historian": 94, "alan": 94, "john": 94, "percival": 94, "taylor": 94, "di": 94, "20446": 94, "pace": 94, "bowler": 94, "ian": 94, "harvei": 94, "claim": 94, "victoria": 94, "15514": 94, "cotti": 94, "osc": 94, "foreign": 94, "minist": 94, "7525": 94, "sultan": 94, "specter": 94, "met": 94, "crown": 94, "abdullah": 94, "defenc": 94, "aviat": 94, "jeddah": 94, "saudi": 94, "agenc": 94, "2288": 94, "hi": 94, "customari": 94, "outfit": 94, "champion": 94, "damp": 94, "scalp": 94, "canada": 94, "reign": 94, "olymp": 94, "donovan": 94, "bailei": 94, "1992": 94, "linford": 94, "christi": 94, "britain": 94, "1984": 94, "1988": 94, "carl": 94, "lewi": 94, "ambigi": 94, "punctuat": 94, "chicago": 94, "digest": 94, "philadelphia": 94, "usda": 94, "york": 94, "token_issu": 94, "471": 94, "kean": 94, "year": 94, "contract": 94, "manchest": 94, "19072": 94, "societi": 94, "million": 94, "bite": 94, "deliv": 94, "19910": 94, "father": 94, "clarenc": 94, "woolmer": 94, "renam": 94, "uttar": 94, "pradesh": 94, "india": 94, "ranji": 94, "trophi": 94, "nation": 94, "championship": 94, "captain": 94, "1949": 94, "15658": 94, "19879": 94, "iii": 94, "brian": 94, "shimer": 94, "randi": 94, "jone": 94, "19104": 94}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [9, 0, 0, "-", "datalab"], [30, 0, 0, "-", "dataset"], [33, 0, 0, "-", "experimental"], [36, 0, 0, "-", "filter"], [37, 0, 0, "-", "internal"], [48, 0, 0, "-", "models"], [50, 0, 0, "-", "multiannotator"], [53, 0, 0, "-", "multilabel_classification"], [56, 0, 0, "-", "object_detection"], [59, 0, 0, "-", "outlier"], [60, 0, 0, "-", "rank"], [61, 0, 0, "-", "regression"], [65, 0, 0, "-", "segmentation"], [69, 0, 0, "-", "token_classification"]], "cleanlab.benchmarking": [[1, 0, 0, "-", "noise_generation"]], "cleanlab.benchmarking.noise_generation": [[1, 1, 1, "", "generate_n_rand_probabilities_that_sum_to_m"], [1, 1, 1, "", "generate_noise_matrix_from_trace"], [1, 1, 1, "", "generate_noisy_labels"], [1, 1, 1, "", "noise_matrix_is_valid"], [1, 1, 1, "", "randomly_distribute_N_balls_into_K_bins"]], "cleanlab.classification": [[2, 2, 1, "", "CleanLearning"]], "cleanlab.classification.CleanLearning": [[2, 3, 1, "", "__init_subclass__"], [2, 3, 1, "", "find_label_issues"], [2, 3, 1, "", "fit"], [2, 3, 1, "", "get_label_issues"], [2, 3, 1, "", "get_metadata_routing"], [2, 3, 1, "", "get_params"], [2, 3, 1, "", "predict"], [2, 3, 1, "", "predict_proba"], [2, 3, 1, "", "save_space"], [2, 3, 1, "", "score"], [2, 3, 1, "", "set_fit_request"], [2, 3, 1, "", "set_params"], [2, 3, 1, "", "set_score_request"]], "cleanlab.count": [[3, 1, 1, "", "calibrate_confident_joint"], [3, 1, 1, "", "compute_confident_joint"], [3, 1, 1, "", "estimate_confident_joint_and_cv_pred_proba"], [3, 1, 1, "", "estimate_cv_predicted_probabilities"], [3, 1, 1, "", "estimate_joint"], [3, 1, 1, "", "estimate_latent"], [3, 1, 1, "", "estimate_noise_matrices"], [3, 1, 1, "", "estimate_py_and_noise_matrices_from_probabilities"], [3, 1, 1, "", "estimate_py_noise_matrices_and_cv_pred_proba"], [3, 1, 1, "", "get_confident_thresholds"], [3, 1, 1, "", "num_label_issues"]], "cleanlab.datalab": [[4, 0, 0, "-", "datalab"], [13, 0, 0, "-", "internal"]], "cleanlab.datalab.datalab": [[4, 2, 1, "", "Datalab"]], "cleanlab.datalab.datalab.Datalab": [[4, 4, 1, "", "class_names"], [4, 3, 1, "", "find_issues"], [4, 3, 1, "", "get_info"], [4, 3, 1, "", "get_issue_summary"], [4, 3, 1, "", "get_issues"], [4, 4, 1, "", "has_labels"], [4, 4, 1, "", "info"], [4, 4, 1, "", "issue_summary"], [4, 4, 1, "", "issues"], [4, 4, 1, "", "labels"], [4, 3, 1, "", "list_default_issue_types"], [4, 3, 1, "", "list_possible_issue_types"], [4, 3, 1, "", "load"], [4, 3, 1, "", "report"], [4, 3, 1, "", "save"]], "cleanlab.datalab.internal": [[10, 0, 0, "-", "data"], [11, 0, 0, "-", "data_issues"], [14, 0, 0, "-", "issue_finder"], [12, 0, 0, "-", "issue_manager_factory"], [28, 0, 0, "-", "report"]], "cleanlab.datalab.internal.data": [[10, 2, 1, "", "Data"], [10, 5, 1, "", "DataFormatError"], [10, 5, 1, "", "DatasetDictError"], [10, 5, 1, "", "DatasetLoadError"], [10, 2, 1, "", "Label"]], "cleanlab.datalab.internal.data.Data": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[10, 6, 1, "", "args"], [10, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[10, 4, 1, "", "class_names"], [10, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[11, 2, 1, "", "DataIssues"], [11, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[11, 3, 1, "", "collect_issues_from_imagelab"], [11, 3, 1, "", "collect_issues_from_issue_manager"], [11, 3, 1, "", "collect_statistics"], [11, 3, 1, "", "get_info"], [11, 3, 1, "", "get_issue_summary"], [11, 3, 1, "", "get_issues"], [11, 6, 1, "", "info"], [11, 6, 1, "", "issue_summary"], [11, 6, 1, "", "issues"], [11, 3, 1, "", "set_health_score"], [11, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[14, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[14, 3, 1, "", "find_issues"], [14, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[16, 0, 0, "-", "data_valuation"], [17, 0, 0, "-", "duplicate"], [18, 0, 0, "-", "imbalance"], [20, 0, 0, "-", "issue_manager"], [21, 0, 0, "-", "label"], [22, 0, 0, "-", "noniid"], [23, 0, 0, "-", "null"], [24, 0, 0, "-", "outlier"], [27, 0, 0, "-", "underperforming_group"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, 2, 1, "", "DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager": [[16, 6, 1, "", "DEFAULT_THRESHOLD"], [16, 3, 1, "", "collect_info"], [16, 6, 1, "", "description"], [16, 3, 1, "", "find_issues"], [16, 6, 1, "", "info"], [16, 6, 1, "", "issue_name"], [16, 6, 1, "", "issue_score_key"], [16, 6, 1, "", "issues"], [16, 3, 1, "", "make_summary"], [16, 3, 1, "", "report"], [16, 6, 1, "", "summary"], [16, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[17, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[17, 3, 1, "", "collect_info"], [17, 6, 1, "", "description"], [17, 3, 1, "", "find_issues"], [17, 6, 1, "", "info"], [17, 6, 1, "", "issue_name"], [17, 6, 1, "", "issue_score_key"], [17, 6, 1, "", "issues"], [17, 3, 1, "", "make_summary"], [17, 6, 1, "", "near_duplicate_sets"], [17, 3, 1, "", "report"], [17, 6, 1, "", "summary"], [17, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[18, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[18, 3, 1, "", "collect_info"], [18, 6, 1, "", "description"], [18, 3, 1, "", "find_issues"], [18, 6, 1, "", "info"], [18, 6, 1, "", "issue_name"], [18, 6, 1, "", "issue_score_key"], [18, 6, 1, "", "issues"], [18, 3, 1, "", "make_summary"], [18, 3, 1, "", "report"], [18, 6, 1, "", "summary"], [18, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[20, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[20, 3, 1, "", "collect_info"], [20, 6, 1, "", "description"], [20, 3, 1, "", "find_issues"], [20, 6, 1, "", "info"], [20, 6, 1, "", "issue_name"], [20, 6, 1, "", "issue_score_key"], [20, 6, 1, "", "issues"], [20, 3, 1, "", "make_summary"], [20, 3, 1, "", "report"], [20, 6, 1, "", "summary"], [20, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.label": [[21, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [21, 3, 1, "", "get_health_summary"], [21, 6, 1, "", "health_summary_parameters"], [21, 6, 1, "", "info"], [21, 6, 1, "", "issue_name"], [21, 6, 1, "", "issue_score_key"], [21, 6, 1, "", "issues"], [21, 3, 1, "", "make_summary"], [21, 3, 1, "", "report"], [21, 6, 1, "", "summary"], [21, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[22, 2, 1, "", "NonIIDIssueManager"], [22, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[22, 3, 1, "", "collect_info"], [22, 6, 1, "", "description"], [22, 3, 1, "", "find_issues"], [22, 6, 1, "", "info"], [22, 6, 1, "", "issue_name"], [22, 6, 1, "", "issue_score_key"], [22, 6, 1, "", "issues"], [22, 3, 1, "", "make_summary"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.null": [[23, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[23, 3, 1, "", "collect_info"], [23, 6, 1, "", "description"], [23, 3, 1, "", "find_issues"], [23, 6, 1, "", "info"], [23, 6, 1, "", "issue_name"], [23, 6, 1, "", "issue_score_key"], [23, 6, 1, "", "issues"], [23, 3, 1, "", "make_summary"], [23, 3, 1, "", "report"], [23, 6, 1, "", "summary"], [23, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.outlier": [[24, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[24, 6, 1, "", "DEFAULT_THRESHOLDS"], [24, 3, 1, "", "collect_info"], [24, 6, 1, "", "description"], [24, 3, 1, "", "find_issues"], [24, 6, 1, "", "info"], [24, 6, 1, "", "issue_name"], [24, 6, 1, "", "issue_score_key"], [24, 6, 1, "", "issues"], [24, 3, 1, "", "make_summary"], [24, 6, 1, "", "ood"], [24, 3, 1, "", "report"], [24, 6, 1, "", "summary"], [24, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.regression": [[26, 0, 0, "-", "label"]], "cleanlab.datalab.internal.issue_manager.regression.label": [[26, 2, 1, "", "RegressionLabelIssueManager"], [26, 1, 1, "", "find_issues_with_features"], [26, 1, 1, "", "find_issues_with_predictions"]], "cleanlab.datalab.internal.issue_manager.regression.label.RegressionLabelIssueManager": [[26, 3, 1, "", "collect_info"], [26, 6, 1, "", "description"], [26, 3, 1, "", "find_issues"], [26, 6, 1, "", "info"], [26, 6, 1, "", "issue_name"], [26, 6, 1, "", "issue_score_key"], [26, 6, 1, "", "issues"], [26, 3, 1, "", "make_summary"], [26, 3, 1, "", "report"], [26, 6, 1, "", "summary"], [26, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.underperforming_group": [[27, 2, 1, "", "UnderperformingGroupIssueManager"]], "cleanlab.datalab.internal.issue_manager.underperforming_group.UnderperformingGroupIssueManager": [[27, 6, 1, "", "NO_UNDERPERFORMING_CLUSTER_ID"], [27, 6, 1, "", "OUTLIER_CLUSTER_LABELS"], [27, 3, 1, "", "collect_info"], [27, 6, 1, "", "description"], [27, 3, 1, "", "filter_cluster_ids"], [27, 3, 1, "", "find_issues"], [27, 3, 1, "", "get_worst_cluster"], [27, 6, 1, "", "info"], [27, 6, 1, "", "issue_name"], [27, 6, 1, "", "issue_score_key"], [27, 6, 1, "", "issues"], [27, 3, 1, "", "make_summary"], [27, 3, 1, "", "perform_clustering"], [27, 3, 1, "", "report"], [27, 3, 1, "", "set_knn_graph"], [27, 6, 1, "", "summary"], [27, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[12, 7, 1, "", "REGISTRY"], [12, 1, 1, "", "list_default_issue_types"], [12, 1, 1, "", "list_possible_issue_types"], [12, 1, 1, "", "register"]], "cleanlab.datalab.internal.report": [[28, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[28, 3, 1, "", "get_report"], [28, 3, 1, "", "report"]], "cleanlab.dataset": [[30, 1, 1, "", "find_overlapping_classes"], [30, 1, 1, "", "health_summary"], [30, 1, 1, "", "overall_label_health_score"], [30, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[31, 0, 0, "-", "cifar_cnn"], [32, 0, 0, "-", "coteaching"], [34, 0, 0, "-", "label_issues_batched"], [35, 0, 0, "-", "mnist_pytorch"]], "cleanlab.experimental.cifar_cnn": [[31, 2, 1, "", "CNN"], [31, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[31, 6, 1, "", "T_destination"], [31, 3, 1, "", "__call__"], [31, 3, 1, "", "add_module"], [31, 3, 1, "", "apply"], [31, 3, 1, "", "bfloat16"], [31, 3, 1, "", "buffers"], [31, 3, 1, "", "children"], [31, 3, 1, "", "cpu"], [31, 3, 1, "", "cuda"], [31, 3, 1, "", "double"], [31, 6, 1, "", "dump_patches"], [31, 3, 1, "", "eval"], [31, 3, 1, "", "extra_repr"], [31, 3, 1, "", "float"], [31, 3, 1, "id0", "forward"], [31, 3, 1, "", "get_buffer"], [31, 3, 1, "", "get_extra_state"], [31, 3, 1, "", "get_parameter"], [31, 3, 1, "", "get_submodule"], [31, 3, 1, "", "half"], [31, 3, 1, "", "ipu"], [31, 3, 1, "", "load_state_dict"], [31, 3, 1, "", "modules"], [31, 3, 1, "", "named_buffers"], [31, 3, 1, "", "named_children"], [31, 3, 1, "", "named_modules"], [31, 3, 1, "", "named_parameters"], [31, 3, 1, "", "parameters"], [31, 3, 1, "", "register_backward_hook"], [31, 3, 1, "", "register_buffer"], [31, 3, 1, "", "register_forward_hook"], [31, 3, 1, "", "register_forward_pre_hook"], [31, 3, 1, "", "register_full_backward_hook"], [31, 3, 1, "", "register_load_state_dict_post_hook"], [31, 3, 1, "", "register_module"], [31, 3, 1, "", "register_parameter"], [31, 3, 1, "", "requires_grad_"], [31, 3, 1, "", "set_extra_state"], [31, 3, 1, "", "share_memory"], [31, 3, 1, "", "state_dict"], [31, 3, 1, "", "to"], [31, 3, 1, "", "to_empty"], [31, 3, 1, "", "train"], [31, 6, 1, "", "training"], [31, 3, 1, "", "type"], [31, 3, 1, "", "xpu"], [31, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[32, 1, 1, "", "adjust_learning_rate"], [32, 1, 1, "", "evaluate"], [32, 1, 1, "", "forget_rate_scheduler"], [32, 1, 1, "", "initialize_lr_scheduler"], [32, 1, 1, "", "loss_coteaching"], [32, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[34, 2, 1, "", "LabelInspector"], [34, 7, 1, "", "adj_confident_thresholds_shared"], [34, 1, 1, "", "find_label_issues_batched"], [34, 7, 1, "", "labels_shared"], [34, 7, 1, "", "pred_probs_shared"], [34, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[34, 3, 1, "", "get_confident_thresholds"], [34, 3, 1, "", "get_label_issues"], [34, 3, 1, "", "get_num_issues"], [34, 3, 1, "", "get_quality_scores"], [34, 3, 1, "", "score_label_quality"], [34, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[35, 2, 1, "", "CNN"], [35, 2, 1, "", "SimpleNet"], [35, 1, 1, "", "get_mnist_dataset"], [35, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[35, 3, 1, "", "__init_subclass__"], [35, 6, 1, "", "batch_size"], [35, 6, 1, "", "dataset"], [35, 6, 1, "", "epochs"], [35, 3, 1, "id0", "fit"], [35, 3, 1, "", "get_metadata_routing"], [35, 3, 1, "", "get_params"], [35, 6, 1, "", "loader"], [35, 6, 1, "", "log_interval"], [35, 6, 1, "", "lr"], [35, 6, 1, "", "momentum"], [35, 6, 1, "", "no_cuda"], [35, 3, 1, "id1", "predict"], [35, 3, 1, "id4", "predict_proba"], [35, 6, 1, "", "seed"], [35, 3, 1, "", "set_fit_request"], [35, 3, 1, "", "set_params"], [35, 3, 1, "", "set_predict_proba_request"], [35, 3, 1, "", "set_predict_request"], [35, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[35, 6, 1, "", "T_destination"], [35, 3, 1, "", "__call__"], [35, 3, 1, "", "add_module"], [35, 3, 1, "", "apply"], [35, 3, 1, "", "bfloat16"], [35, 3, 1, "", "buffers"], [35, 3, 1, "", "children"], [35, 3, 1, "", "cpu"], [35, 3, 1, "", "cuda"], [35, 3, 1, "", "double"], [35, 6, 1, "", "dump_patches"], [35, 3, 1, "", "eval"], [35, 3, 1, "", "extra_repr"], [35, 3, 1, "", "float"], [35, 3, 1, "", "forward"], [35, 3, 1, "", "get_buffer"], [35, 3, 1, "", "get_extra_state"], [35, 3, 1, "", "get_parameter"], [35, 3, 1, "", "get_submodule"], [35, 3, 1, "", "half"], [35, 3, 1, "", "ipu"], [35, 3, 1, "", "load_state_dict"], [35, 3, 1, "", "modules"], [35, 3, 1, "", "named_buffers"], [35, 3, 1, "", "named_children"], [35, 3, 1, "", "named_modules"], [35, 3, 1, "", "named_parameters"], [35, 3, 1, "", "parameters"], [35, 3, 1, "", "register_backward_hook"], [35, 3, 1, "", "register_buffer"], [35, 3, 1, "", "register_forward_hook"], [35, 3, 1, "", "register_forward_pre_hook"], [35, 3, 1, "", "register_full_backward_hook"], [35, 3, 1, "", "register_load_state_dict_post_hook"], [35, 3, 1, "", "register_module"], [35, 3, 1, "", "register_parameter"], [35, 3, 1, "", "requires_grad_"], [35, 3, 1, "", "set_extra_state"], [35, 3, 1, "", "share_memory"], [35, 3, 1, "", "state_dict"], [35, 3, 1, "", "to"], [35, 3, 1, "", "to_empty"], [35, 3, 1, "", "train"], [35, 6, 1, "", "training"], [35, 3, 1, "", "type"], [35, 3, 1, "", "xpu"], [35, 3, 1, "", "zero_grad"]], "cleanlab.filter": [[36, 1, 1, "", "find_label_issues"], [36, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [36, 1, 1, "", "find_predicted_neq_given"], [36, 7, 1, "", "pred_probs_by_class"], [36, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[38, 0, 0, "-", "label_quality_utils"], [39, 0, 0, "-", "latent_algebra"], [40, 0, 0, "-", "multiannotator_utils"], [41, 0, 0, "-", "multilabel_scorer"], [42, 0, 0, "-", "multilabel_utils"], [43, 0, 0, "-", "outlier"], [44, 0, 0, "-", "token_classification_utils"], [45, 0, 0, "-", "util"], [46, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[38, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[39, 1, 1, "", "compute_inv_noise_matrix"], [39, 1, 1, "", "compute_noise_matrix_from_inverse"], [39, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [39, 1, 1, "", "compute_py"], [39, 1, 1, "", "compute_py_inv_noise_matrix"], [39, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[40, 1, 1, "", "assert_valid_inputs_multiannotator"], [40, 1, 1, "", "assert_valid_pred_probs"], [40, 1, 1, "", "check_consensus_label_classes"], [40, 1, 1, "", "compute_soft_cross_entropy"], [40, 1, 1, "", "find_best_temp_scaler"], [40, 1, 1, "", "format_multiannotator_labels"], [40, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[41, 2, 1, "", "Aggregator"], [41, 2, 1, "", "ClassLabelScorer"], [41, 2, 1, "", "MultilabelScorer"], [41, 1, 1, "", "exponential_moving_average"], [41, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [41, 1, 1, "", "get_label_quality_scores"], [41, 1, 1, "", "multilabel_py"], [41, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[41, 3, 1, "", "__call__"], [41, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[41, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [41, 6, 1, "", "NORMALIZED_MARGIN"], [41, 6, 1, "", "SELF_CONFIDENCE"], [41, 3, 1, "", "__call__"], [41, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[41, 3, 1, "", "__call__"], [41, 3, 1, "", "aggregate"], [41, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[42, 1, 1, "", "get_onehot_num_classes"], [42, 1, 1, "", "int2onehot"], [42, 1, 1, "", "onehot2int"], [42, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[43, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[44, 1, 1, "", "color_sentence"], [44, 1, 1, "", "filter_sentence"], [44, 1, 1, "", "get_sentence"], [44, 1, 1, "", "mapping"], [44, 1, 1, "", "merge_probs"], [44, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[45, 1, 1, "", "append_extra_datapoint"], [45, 1, 1, "", "clip_noise_rates"], [45, 1, 1, "", "clip_values"], [45, 1, 1, "", "compress_int_array"], [45, 1, 1, "", "confusion_matrix"], [45, 1, 1, "", "csr_vstack"], [45, 1, 1, "", "estimate_pu_f1"], [45, 1, 1, "", "extract_indices_tf"], [45, 1, 1, "", "force_two_dimensions"], [45, 1, 1, "", "format_labels"], [45, 1, 1, "", "get_missing_classes"], [45, 1, 1, "", "get_num_classes"], [45, 1, 1, "", "get_unique_classes"], [45, 1, 1, "", "is_tensorflow_dataset"], [45, 1, 1, "", "is_torch_dataset"], [45, 1, 1, "", "num_unique_classes"], [45, 1, 1, "", "print_inverse_noise_matrix"], [45, 1, 1, "", "print_joint_matrix"], [45, 1, 1, "", "print_noise_matrix"], [45, 1, 1, "", "print_square_matrix"], [45, 1, 1, "", "remove_noise_from_class"], [45, 1, 1, "", "round_preserving_row_totals"], [45, 1, 1, "", "round_preserving_sum"], [45, 1, 1, "", "smart_display_dataframe"], [45, 1, 1, "", "subset_X_y"], [45, 1, 1, "", "subset_data"], [45, 1, 1, "", "subset_labels"], [45, 1, 1, "", "train_val_split"], [45, 1, 1, "", "unshuffle_tensorflow_dataset"], [45, 1, 1, "", "value_counts"], [45, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[46, 1, 1, "", "assert_indexing_works"], [46, 1, 1, "", "assert_nonempty_input"], [46, 1, 1, "", "assert_valid_class_labels"], [46, 1, 1, "", "assert_valid_inputs"], [46, 1, 1, "", "labels_to_array"]], "cleanlab.models": [[49, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[49, 2, 1, "", "KerasWrapperModel"], [49, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[49, 3, 1, "", "fit"], [49, 3, 1, "", "get_params"], [49, 3, 1, "", "predict"], [49, 3, 1, "", "predict_proba"], [49, 3, 1, "", "set_params"], [49, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[49, 3, 1, "", "fit"], [49, 3, 1, "", "get_params"], [49, 3, 1, "", "predict"], [49, 3, 1, "", "predict_proba"], [49, 3, 1, "", "set_params"], [49, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[50, 1, 1, "", "convert_long_to_wide_dataset"], [50, 1, 1, "", "get_active_learning_scores"], [50, 1, 1, "", "get_active_learning_scores_ensemble"], [50, 1, 1, "", "get_label_quality_multiannotator"], [50, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [50, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[51, 0, 0, "-", "dataset"], [52, 0, 0, "-", "filter"], [54, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[51, 1, 1, "", "common_multilabel_issues"], [51, 1, 1, "", "multilabel_health_summary"], [51, 1, 1, "", "overall_multilabel_health_score"], [51, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[52, 1, 1, "", "find_label_issues"], [52, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[54, 1, 1, "", "get_label_quality_scores"], [54, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[55, 0, 0, "-", "filter"], [57, 0, 0, "-", "rank"], [58, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[55, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[57, 1, 1, "", "compute_badloc_box_scores"], [57, 1, 1, "", "compute_overlooked_box_scores"], [57, 1, 1, "", "compute_swap_box_scores"], [57, 1, 1, "", "get_label_quality_scores"], [57, 1, 1, "", "issues_from_scores"], [57, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[58, 1, 1, "", "bounding_box_size_distribution"], [58, 1, 1, "", "calculate_per_class_metrics"], [58, 1, 1, "", "class_label_distribution"], [58, 1, 1, "", "get_average_per_class_confusion_matrix"], [58, 1, 1, "", "get_sorted_bbox_count_idxs"], [58, 1, 1, "", "object_counts_per_image"], [58, 1, 1, "", "plot_class_distribution"], [58, 1, 1, "", "plot_class_size_distributions"], [58, 1, 1, "", "visualize"]], "cleanlab.outlier": [[59, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[59, 3, 1, "", "fit"], [59, 3, 1, "", "fit_score"], [59, 3, 1, "", "score"]], "cleanlab.rank": [[60, 1, 1, "", "find_top_issues"], [60, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [60, 1, 1, "", "get_label_quality_ensemble_scores"], [60, 1, 1, "", "get_label_quality_scores"], [60, 1, 1, "", "get_normalized_margin_for_each_label"], [60, 1, 1, "", "get_self_confidence_for_each_label"], [60, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[62, 0, 0, "-", "learn"], [63, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[62, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[62, 3, 1, "", "__init_subclass__"], [62, 3, 1, "", "find_label_issues"], [62, 3, 1, "", "fit"], [62, 3, 1, "", "get_aleatoric_uncertainty"], [62, 3, 1, "", "get_epistemic_uncertainty"], [62, 3, 1, "", "get_label_issues"], [62, 3, 1, "", "get_metadata_routing"], [62, 3, 1, "", "get_params"], [62, 3, 1, "", "predict"], [62, 3, 1, "", "save_space"], [62, 3, 1, "", "score"], [62, 3, 1, "", "set_fit_request"], [62, 3, 1, "", "set_params"], [62, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[63, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[64, 0, 0, "-", "filter"], [66, 0, 0, "-", "rank"], [67, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[64, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[66, 1, 1, "", "get_label_quality_scores"], [66, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[67, 1, 1, "", "common_label_issues"], [67, 1, 1, "", "display_issues"], [67, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[68, 0, 0, "-", "filter"], [70, 0, 0, "-", "rank"], [71, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[68, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[70, 1, 1, "", "get_label_quality_scores"], [70, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[71, 1, 1, "", "common_label_issues"], [71, 1, 1, "", "display_issues"], [71, 1, 1, "", "filter_by_token"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method", "4": "py:property", "5": "py:exception", "6": "py:attribute", "7": "py:data"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "exception", "Python exception"], "6": ["py", "attribute", "Python attribute"], "7": ["py", "data", "Python data"]}, "titleterms": {"benchmark": 0, "noise_gener": 1, "classif": [2, 74, 78, 79, 81, 82, 83, 86, 92, 93, 94], "count": [3, 83], "datalab": [4, 5, 7, 8, 9, 75, 76, 77, 78, 79, 83], "creat": [5, 75, 76, 83, 85], "your": [5, 72, 75, 76, 79, 81, 83], "own": 5, "issu": [5, 7, 8, 19, 26, 72, 74, 75, 76, 78, 79, 80, 81, 82, 83, 86, 87, 91, 92, 94], "manag": [5, 19], "prerequisit": 5, "implement": 5, "issuemanag": [5, 75], "basic": 5, "check": 5, "intermedi": 5, "advanc": [5, 75], "us": [5, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "gener": 6, "cluster": [6, 81], "id": 6, "guid": [7, 9], "type": [7, 8, 83], "custom": [7, 75], "can": [8, 76, 80, 81, 83, 85], "detect": [8, 76, 78, 79, 81, 83, 87, 88], "estim": [8, 83, 85], "each": 8, "label": [8, 21, 26, 72, 74, 76, 78, 79, 81, 82, 83, 85, 86, 87, 90, 91, 92, 93, 94], "outlier": [8, 24, 43, 59, 78, 79, 82, 88], "Near": [8, 76, 78, 79, 82], "duplic": [8, 17, 76, 78, 79, 81, 82], "non": [8, 79], "iid": [8, 79], "class": [8, 73, 83, 91], "imbal": [8, 18], "imag": [8, 82, 88], "specif": [8, 19, 91], "underperform": [8, 81], "group": [8, 81], "null": [8, 23], "data": [8, 10, 72, 74, 75, 76, 78, 79, 80, 81, 83, 85, 86, 87, 88, 90, 91, 92, 94], "valuat": 8, "option": 8, "paramet": [8, 83], "get": [9, 75, 76, 85, 86, 87, 91, 94], "start": [9, 80], "api": 9, "refer": 9, "data_issu": 11, "factori": 12, "intern": [13, 37], "issue_find": 14, "data_valu": 16, "issue_manag": [19, 20], "regist": 19, "unregist": 19, "ml": [19, 81, 83], "task": 19, "noniid": 22, "regress": [25, 61, 62, 63, 81, 90], "prioriti": 26, "order": 26, "find": [26, 72, 74, 76, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "underperforming_group": 27, "report": [28, 82], "dataset": [30, 51, 72, 76, 79, 80, 81, 82, 83, 86, 87, 88, 90, 91, 93, 94], "cifar_cnn": 31, "coteach": 32, "experiment": 33, "label_issues_batch": 34, "mnist_pytorch": 35, "filter": [36, 52, 55, 64, 68, 83], "label_quality_util": 38, "latent_algebra": 39, "multiannotator_util": 40, "multilabel_scor": 41, "multilabel_util": 42, "token_classification_util": 44, "util": 45, "valid": [46, 82, 89], "fasttext": 47, "model": [48, 72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 92, 93], "kera": 49, "multiannot": [50, 85], "multilabel_classif": 53, "rank": [54, 57, 60, 63, 66, 70, 83], "object_detect": 56, "summari": [58, 67, 71], "learn": [62, 76, 81, 83, 92], "segment": [65, 91], "token_classif": [69, 94], "cleanlab": [72, 74, 78, 79, 81, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "open": [72, 81], "sourc": [72, 81], "document": 72, "quickstart": 72, "1": [72, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "instal": [72, 74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "2": [72, 73, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "common": [72, 73, 94], "3": [72, 74, 75, 76, 78, 79, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "handl": [72, 81], "error": [72, 81, 82, 83, 85, 86, 87, 90, 91, 93, 94], "train": [72, 74, 81, 88, 90, 92, 93], "robust": [72, 83, 90, 92, 93], "noisi": [72, 83, 90, 92, 93], "4": [72, 74, 75, 76, 78, 79, 82, 83, 85, 87, 88, 90, 92, 93], "curat": [72, 80], "fix": [72, 81], "level": [72, 80, 83, 94], "5": [72, 74, 76, 78, 82, 83, 85, 90, 92], "improv": [72, 85], "via": [72, 83, 85], "mani": [72, 83], "other": [72, 85, 87, 90], "techniqu": 72, "contribut": 72, "easi": [72, 78, 79, 82], "mode": [72, 78, 79, 82], "how": [73, 81, 83, 85, 86, 94], "migrat": 73, "version": 73, "0": 73, "from": [73, 75, 76, 83, 90, 92, 93], "pre": [73, 74, 81, 88], "function": [73, 75], "name": 73, "chang": 73, "modul": [73, 83], "new": 73, "remov": 73, "argument": [73, 75], "variabl": 73, "audio": 74, "speechbrain": 74, "depend": [74, 75, 76, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 94], "import": [74, 75, 76, 80, 82, 83, 85], "them": [74, 80, 83], "load": [74, 75, 76, 78, 79, 90, 92, 93], "featur": [74, 82, 88], "fit": 74, "linear": 74, "comput": [74, 78, 79, 81, 82, 85, 89, 92], "out": [74, 75, 76, 78, 79, 82, 85, 89, 92], "sampl": [74, 75, 76, 78, 79, 82, 85, 89, 92], "predict": [74, 75, 76, 78, 79, 82, 85, 86, 87, 89, 92], "probabl": [74, 75, 76, 78, 79, 82, 85, 89, 92], "workflow": [75, 83], "audit": [75, 76], "requir": [75, 76, 78, 79, 82, 85, 86, 87, 88, 90, 91, 92, 93, 94], "classifi": [75, 76], "instanti": 75, "object": [75, 87], "increment": 75, "search": 75, "specifi": [75, 81], "nondefault": 75, "save": 75, "ad": 75, "A": 76, "unifi": 76, "all": [76, 83], "kind": [76, 87], "skip": [76, 80, 83, 85], "detail": [76, 80, 83, 85], "more": [76, 83, 90, 92, 93], "about": 76, "addit": 76, "inform": [76, 82], "tutori": [77, 80, 84], "tabular": [78, 92], "numer": 78, "categor": 78, "column": 78, "process": [78, 88, 90, 92], "select": [78, 92], "construct": 78, "k": [78, 82, 89], "nearest": 78, "neighbour": 78, "graph": 78, "text": [79, 93, 94], "format": [79, 81, 86, 87, 93], "defin": [79, 82, 90, 93], "drift": 79, "fetch": [80, 82], "evalu": 80, "health": [80, 83], "8": [80, 83], "popular": 80, "faq": 81, "what": [81, 83, 89], "do": [81, 83], "i": [81, 83, 89], "infer": 81, "correct": 81, "exampl": [81, 82, 83, 88], "ha": 81, "flag": 81, "should": 81, "v": 81, "test": [81, 83, 88], "big": 81, "limit": 81, "memori": 81, "why": 81, "isn": 81, "t": 81, "cleanlearn": [81, 83], "work": [81, 83, 85, 94], "me": 81, "differ": [81, 87], "clean": [81, 83], "final": 81, "hyperparamet": 81, "tune": 81, "onli": 81, "one": [81, 83, 86, 91], "doe": [81, 85, 94], "take": 81, "so": 81, "long": 81, "slice": 81, "when": [81, 83], "identifi": [81, 87], "run": 81, "licens": 81, "under": 81, "an": 81, "answer": 81, "question": 81, "pytorch": [82, 88], "normal": 82, "fashion": 82, "mnist": 82, "prepar": 82, "fold": [82, 89], "cross": [82, 89], "embed": [82, 88], "7": [82, 83], "view": 82, "most": [82, 94], "like": 82, "sever": 82, "set": [82, 83], "dark": 82, "top": [82, 91], "low": 82, "The": 83, "centric": 83, "ai": 83, "machin": 83, "find_label_issu": 83, "line": 83, "code": 83, "visual": [83, 87, 88, 91], "twenti": 83, "lowest": 83, "qualiti": [83, 85, 86, 87, 91, 94], "see": 83, "now": 83, "let": 83, "": 83, "happen": 83, "we": 83, "merg": 83, "seafoam": 83, "green": 83, "yellow": 83, "too": 83, "you": 83, "re": 83, "6": 83, "One": 83, "score": [83, 85, 86, 87, 91, 94], "rule": 83, "overal": [83, 91], "accur": 83, "thi": 83, "directli": 83, "fulli": 83, "character": 83, "nois": 83, "matrix": [83, 86], "joint": 83, "prior": 83, "true": 83, "distribut": 83, "flip": 83, "rate": 83, "ani": 83, "again": 83, "support": 83, "lot": 83, "method": 83, "filter_bi": 83, "automat": 83, "everi": 83, "uniqu": 83, "num_label_issu": 83, "threshold": 83, "found": 83, "Not": 83, "sure": 83, "ensembl": 83, "multipl": [83, 85], "predictor": 83, "consensu": 85, "annot": 85, "initi": 85, "major": 85, "vote": 85, "better": 85, "statist": 85, "compar": 85, "inspect": 85, "potenti": [85, 90, 93], "retrain": 85, "further": 85, "multi": 86, "given": 86, "hot": 86, "binari": 86, "download": [87, 91, 94], "objectlab": 87, "timm": 88, "cifar10": 88, "some": 88, "pred_prob": [88, 91, 94], "wai": 90, "semant": 91, "which": 91, "ar": 91, "commonli": 91, "mislabel": [91, 94], "focus": 91, "scikit": 92, "token": 94, "word": 94, "sentenc": 94, "contain": 94, "particular": 94}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 6, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx.ext.viewcode": 1, "sphinx.ext.todo": 2, "sphinx": 56}})
\ No newline at end of file
diff --git a/master/tutorials/audio.html b/master/tutorials/audio.html
index ee7af433b..4620d3fe3 100644
--- a/master/tutorials/audio.html
+++ b/master/tutorials/audio.html
@@ -15,7 +15,7 @@
-
+/tutorials/audio.html" />
@@ -1504,7 +1504,7 @@ 5. Use cleanlab to find label issues