diff --git a/master/.buildinfo b/master/.buildinfo index 8ae38767d..d185ed05c 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: dbd265e93e8a90113605d387700910b2 +config: 1cb1fd526d3451bc58a819e71c315d63 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/master/.doctrees/cleanlab/benchmarking/index.doctree b/master/.doctrees/cleanlab/benchmarking/index.doctree index 1c44e2beb..6c0c2c229 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 c7f22bb80..02dd7b439 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 8e706e2fc..4a8487736 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 14f7bfd78..207baebed 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 154cc86f2..06a0bde7f 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 cabebdd09..619a8990f 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/index.doctree b/master/.doctrees/cleanlab/datalab/guide/index.doctree index 85203dc54..5dfc74bc5 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 147650d0f..ca3450aa9 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 71f71febc..77fbe06cd 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 497aee045..c63021948 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 d1b84e377..8045de0e3 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 1b42352e1..76c1ec40b 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 e784ca022..e3354edb9 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 5a052d47b..d87145e9f 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 75b25aa3b..6851f56d7 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/duplicate.doctree b/master/.doctrees/cleanlab/datalab/internal/issue_manager/duplicate.doctree index 89643a4d6..a9b575f33 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 a11c4ba79..9da155a0e 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 96d22bcb9..3ff5add77 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 55de3e759..fdb743c72 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 f1803c173..9a1f11648 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 7cb2ca350..bd85f8134 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 5ec2a2d21..ddf9c2bee 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 2aa544a8c..094aea12a 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/report.doctree b/master/.doctrees/cleanlab/datalab/internal/report.doctree index b3eeac6ab..d90266f20 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 7f541c6cc..9b8123e3b 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 530eb4dce..f0c0373ae 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 16dcc78c0..f0b2d188d 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 34a1d20c0..bf42df9f8 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 c38f9e6fa..1044b8d5a 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 fc3c9391b..79e7a67f2 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 10f520718..12ee6a21a 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 ea45159f1..e4b07eac6 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 59a6934ed..d07252ae2 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 3d892d6ec..ec6251e47 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 76c86498c..c1b615e2b 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 b15b927f5..5d7b59dc2 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 a849501a3..8a317f064 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 3d90292d8..a7be3d4c0 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 8a8a0b045..33f5da6d3 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 dfbe3fbe5..4922489de 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 c99b3c768..b6245947b 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 e61780fd2..fbd23a8c1 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 3f682dc06..9305493f8 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 0c32cee94..9f95bebcc 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 67cf9682c..b4bd63195 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 048fd0fbd..41f1f9ec6 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 b08876747..7bceec635 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 a2b8cd48d..5e6ae139c 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 351216cb0..d679a81c9 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 bf9ca4967..8fb2234e9 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 9b779a97c..7e81e81c1 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 2ef6ce78c..73025c025 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 3723179f1..04bc3bd37 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 0121c25c0..813ea35d1 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 40135f9ae..8c0072a4d 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 afc67188b..3a677c7f8 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 fbbcb5ce0..5e8ef2f74 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 321d56433..40a0f167e 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 1208a38a4..0b0b9e494 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 6f5cc518b..22cbc7cd2 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 7a288c781..ac0db87e8 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 22f9441c8..536330bc9 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 c37c1d814..8852712cf 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 b62ae68b0..8ebf06b4a 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 52327eefb..c44c76d5c 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 4dfec4362..d8dfece61 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 c533b348c..b0850cb47 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 c5c9c8bcd..7b2d45990 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 74494e1b1..04308f07c 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 fc629fc93..a59f04af9 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 a6925cc48..8e656daa1 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": "2023-11-20T20:31:49.397639Z", - "iopub.status.busy": "2023-11-20T20:31:49.397122Z", - "iopub.status.idle": "2023-11-20T20:31:52.608524Z", - "shell.execute_reply": "2023-11-20T20:31:52.607908Z" + "iopub.execute_input": "2023-11-21T08:08:30.103349Z", + "iopub.status.busy": "2023-11-21T08:08:30.103158Z", + "iopub.status.idle": "2023-11-21T08:08:33.292670Z", + "shell.execute_reply": "2023-11-21T08:08:33.292054Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:31:52.611642Z", - "iopub.status.busy": "2023-11-20T20:31:52.611058Z", - "iopub.status.idle": "2023-11-20T20:31:52.614541Z", - "shell.execute_reply": "2023-11-20T20:31:52.613936Z" + "iopub.execute_input": "2023-11-21T08:08:33.295554Z", + "iopub.status.busy": "2023-11-21T08:08:33.295189Z", + "iopub.status.idle": "2023-11-21T08:08:33.298604Z", + "shell.execute_reply": "2023-11-21T08:08:33.298109Z" }, "id": "LaEiwXUiVHCS" }, @@ -157,10 +157,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:31:52.616900Z", - "iopub.status.busy": "2023-11-20T20:31:52.616560Z", - "iopub.status.idle": "2023-11-20T20:31:52.621239Z", - "shell.execute_reply": "2023-11-20T20:31:52.620760Z" + "iopub.execute_input": "2023-11-21T08:08:33.300905Z", + "iopub.status.busy": "2023-11-21T08:08:33.300467Z", + "iopub.status.idle": "2023-11-21T08:08:33.305079Z", + "shell.execute_reply": "2023-11-21T08:08:33.304608Z" }, "nbsphinx": "hidden" }, @@ -208,10 +208,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-11-20T20:31:52.623594Z", - "iopub.status.busy": "2023-11-20T20:31:52.623291Z", - "iopub.status.idle": "2023-11-20T20:31:54.299349Z", - "shell.execute_reply": "2023-11-20T20:31:54.298617Z" + "iopub.execute_input": "2023-11-21T08:08:33.307611Z", + "iopub.status.busy": "2023-11-21T08:08:33.307170Z", + "iopub.status.idle": "2023-11-21T08:08:35.270181Z", + "shell.execute_reply": "2023-11-21T08:08:35.269422Z" }, "id": "GRDPEg7-VOQe", "outputId": "cb886220-e86e-4a77-9f3a-d7844c37c3a6" @@ -242,10 +242,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-11-20T20:31:54.302464Z", - "iopub.status.busy": "2023-11-20T20:31:54.302052Z", - "iopub.status.idle": "2023-11-20T20:31:54.314399Z", - "shell.execute_reply": "2023-11-20T20:31:54.313780Z" + "iopub.execute_input": "2023-11-21T08:08:35.273066Z", + "iopub.status.busy": "2023-11-21T08:08:35.272687Z", + "iopub.status.idle": "2023-11-21T08:08:35.284548Z", + "shell.execute_reply": "2023-11-21T08:08:35.283964Z" }, "id": "FDA5sGZwUSur", "outputId": "0cedc509-63fd-4dc3-d32f-4b537dfe3895" @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:31:54.348360Z", - "iopub.status.busy": "2023-11-20T20:31:54.347905Z", - "iopub.status.idle": "2023-11-20T20:31:54.353628Z", - "shell.execute_reply": "2023-11-20T20:31:54.352993Z" + "iopub.execute_input": "2023-11-21T08:08:35.317189Z", + "iopub.status.busy": "2023-11-21T08:08:35.316797Z", + "iopub.status.idle": "2023-11-21T08:08:35.322324Z", + "shell.execute_reply": "2023-11-21T08:08:35.321715Z" }, "nbsphinx": "hidden" }, @@ -380,10 +380,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-11-20T20:31:54.355841Z", - "iopub.status.busy": "2023-11-20T20:31:54.355643Z", - "iopub.status.idle": "2023-11-20T20:31:55.094118Z", - "shell.execute_reply": "2023-11-20T20:31:55.093412Z" + "iopub.execute_input": "2023-11-21T08:08:35.324820Z", + "iopub.status.busy": "2023-11-21T08:08:35.324362Z", + "iopub.status.idle": "2023-11-21T08:08:36.014568Z", + "shell.execute_reply": "2023-11-21T08:08:36.013885Z" }, "id": "dLBvUZLlII5w", "outputId": "c6a4917f-4a82-4a89-9193-415072e45550" @@ -435,10 +435,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:31:55.096693Z", - "iopub.status.busy": "2023-11-20T20:31:55.096486Z", - "iopub.status.idle": "2023-11-20T20:31:55.800722Z", - "shell.execute_reply": "2023-11-20T20:31:55.800017Z" + "iopub.execute_input": "2023-11-21T08:08:36.017133Z", + "iopub.status.busy": "2023-11-21T08:08:36.016910Z", + "iopub.status.idle": "2023-11-21T08:08:38.844300Z", + "shell.execute_reply": "2023-11-21T08:08:38.843595Z" }, "id": "vL9lkiKsHvKr" }, @@ -472,10 +472,10 @@ "height": 143 }, "execution": { - "iopub.execute_input": "2023-11-20T20:31:55.803844Z", - "iopub.status.busy": "2023-11-20T20:31:55.803344Z", - "iopub.status.idle": "2023-11-20T20:31:55.826092Z", - "shell.execute_reply": "2023-11-20T20:31:55.825493Z" + "iopub.execute_input": "2023-11-21T08:08:38.847210Z", + "iopub.status.busy": "2023-11-21T08:08:38.847002Z", + "iopub.status.idle": "2023-11-21T08:08:38.870036Z", + "shell.execute_reply": "2023-11-21T08:08:38.869382Z" }, "id": "obQYDKdLiUU6", "outputId": "4e923d5c-2cf4-4a5c-827b-0a4fea9d87e4" @@ -555,10 +555,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:31:55.828637Z", - "iopub.status.busy": "2023-11-20T20:31:55.828270Z", - "iopub.status.idle": "2023-11-20T20:31:55.831509Z", - "shell.execute_reply": "2023-11-20T20:31:55.830985Z" + "iopub.execute_input": "2023-11-21T08:08:38.872431Z", + "iopub.status.busy": "2023-11-21T08:08:38.872083Z", + "iopub.status.idle": "2023-11-21T08:08:38.875450Z", + "shell.execute_reply": "2023-11-21T08:08:38.874851Z" }, "id": "I8JqhOZgi94g" }, @@ -580,10 +580,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:31:55.833956Z", - "iopub.status.busy": "2023-11-20T20:31:55.833449Z", - "iopub.status.idle": "2023-11-20T20:32:14.171667Z", - "shell.execute_reply": "2023-11-20T20:32:14.171023Z" + "iopub.execute_input": "2023-11-21T08:08:38.877713Z", + "iopub.status.busy": "2023-11-21T08:08:38.877320Z", + "iopub.status.idle": "2023-11-21T08:08:57.399584Z", + "shell.execute_reply": "2023-11-21T08:08:57.398945Z" }, "id": "2FSQ2GR9R_YA" }, @@ -615,10 +615,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-11-20T20:32:14.174999Z", - "iopub.status.busy": "2023-11-20T20:32:14.174766Z", - "iopub.status.idle": "2023-11-20T20:32:14.179472Z", - "shell.execute_reply": "2023-11-20T20:32:14.178803Z" + "iopub.execute_input": "2023-11-21T08:08:57.402779Z", + "iopub.status.busy": "2023-11-21T08:08:57.402403Z", + "iopub.status.idle": "2023-11-21T08:08:57.406548Z", + "shell.execute_reply": "2023-11-21T08:08:57.405945Z" }, "id": "kAkY31IVXyr8", "outputId": "fd70d8d6-2f11-48d5-ae9c-a8c97d453632" @@ -677,10 +677,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:14.182165Z", - "iopub.status.busy": "2023-11-20T20:32:14.181959Z", - "iopub.status.idle": "2023-11-20T20:32:19.669678Z", - "shell.execute_reply": "2023-11-20T20:32:19.669011Z" + "iopub.execute_input": "2023-11-21T08:08:57.409145Z", + "iopub.status.busy": "2023-11-21T08:08:57.408716Z", + "iopub.status.idle": "2023-11-21T08:09:02.904373Z", + "shell.execute_reply": "2023-11-21T08:09:02.903653Z" }, "id": "i_drkY9YOcw4" }, @@ -714,10 +714,10 @@ "base_uri": "https://localhost:8080/" }, "execution": { - "iopub.execute_input": "2023-11-20T20:32:19.673003Z", - "iopub.status.busy": "2023-11-20T20:32:19.672516Z", - "iopub.status.idle": "2023-11-20T20:32:19.678191Z", - "shell.execute_reply": "2023-11-20T20:32:19.677552Z" + "iopub.execute_input": "2023-11-21T08:09:02.909012Z", + "iopub.status.busy": "2023-11-21T08:09:02.907864Z", + "iopub.status.idle": "2023-11-21T08:09:02.915615Z", + "shell.execute_reply": "2023-11-21T08:09:02.915029Z" }, "id": "_b-AQeoXOc7q", "outputId": "15ae534a-f517-4906-b177-ca91931a8954" @@ -764,10 +764,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:19.681145Z", - "iopub.status.busy": "2023-11-20T20:32:19.680683Z", - "iopub.status.idle": "2023-11-20T20:32:19.771347Z", - "shell.execute_reply": "2023-11-20T20:32:19.770610Z" + "iopub.execute_input": "2023-11-21T08:09:02.919993Z", + "iopub.status.busy": "2023-11-21T08:09:02.918863Z", + "iopub.status.idle": "2023-11-21T08:09:03.010955Z", + "shell.execute_reply": "2023-11-21T08:09:03.010282Z" } }, "outputs": [ @@ -804,10 +804,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:19.774088Z", - "iopub.status.busy": "2023-11-20T20:32:19.773651Z", - "iopub.status.idle": "2023-11-20T20:32:19.783865Z", - "shell.execute_reply": "2023-11-20T20:32:19.783221Z" + "iopub.execute_input": "2023-11-21T08:09:03.013611Z", + "iopub.status.busy": "2023-11-21T08:09:03.013269Z", + "iopub.status.idle": "2023-11-21T08:09:03.023523Z", + "shell.execute_reply": "2023-11-21T08:09:03.022977Z" }, "scrolled": true }, @@ -862,10 +862,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:19.786463Z", - "iopub.status.busy": "2023-11-20T20:32:19.786104Z", - "iopub.status.idle": "2023-11-20T20:32:19.794350Z", - "shell.execute_reply": "2023-11-20T20:32:19.793703Z" + "iopub.execute_input": "2023-11-21T08:09:03.026049Z", + "iopub.status.busy": "2023-11-21T08:09:03.025677Z", + "iopub.status.idle": "2023-11-21T08:09:03.033860Z", + "shell.execute_reply": "2023-11-21T08:09:03.033230Z" } }, "outputs": [ @@ -969,10 +969,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:19.796831Z", - "iopub.status.busy": "2023-11-20T20:32:19.796433Z", - "iopub.status.idle": "2023-11-20T20:32:19.800970Z", - "shell.execute_reply": "2023-11-20T20:32:19.800332Z" + "iopub.execute_input": "2023-11-21T08:09:03.036384Z", + "iopub.status.busy": "2023-11-21T08:09:03.036008Z", + "iopub.status.idle": "2023-11-21T08:09:03.040733Z", + "shell.execute_reply": "2023-11-21T08:09:03.040109Z" } }, "outputs": [ @@ -1010,10 +1010,10 @@ "height": 237 }, "execution": { - "iopub.execute_input": "2023-11-20T20:32:19.803478Z", - "iopub.status.busy": "2023-11-20T20:32:19.803119Z", - "iopub.status.idle": "2023-11-20T20:32:19.809126Z", - "shell.execute_reply": "2023-11-20T20:32:19.808509Z" + "iopub.execute_input": "2023-11-21T08:09:03.043029Z", + "iopub.status.busy": "2023-11-21T08:09:03.042687Z", + "iopub.status.idle": "2023-11-21T08:09:03.048600Z", + "shell.execute_reply": "2023-11-21T08:09:03.047983Z" }, "id": "FQwRHgbclpsO", "outputId": "fee5c335-c00e-4fcc-f22b-718705e93182" @@ -1133,10 +1133,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-11-20T20:32:19.811586Z", - "iopub.status.busy": "2023-11-20T20:32:19.811220Z", - "iopub.status.idle": "2023-11-20T20:32:19.923211Z", - "shell.execute_reply": "2023-11-20T20:32:19.922558Z" + "iopub.execute_input": "2023-11-21T08:09:03.051143Z", + "iopub.status.busy": "2023-11-21T08:09:03.050697Z", + "iopub.status.idle": "2023-11-21T08:09:03.163727Z", + "shell.execute_reply": "2023-11-21T08:09:03.163107Z" }, "id": "ff1NFVlDoysO", "outputId": "8141a036-44c1-4349-c338-880432513e37" @@ -1190,10 +1190,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-11-20T20:32:19.925646Z", - "iopub.status.busy": "2023-11-20T20:32:19.925326Z", - "iopub.status.idle": "2023-11-20T20:32:20.028982Z", - "shell.execute_reply": "2023-11-20T20:32:20.028434Z" + "iopub.execute_input": "2023-11-21T08:09:03.166184Z", + "iopub.status.busy": "2023-11-21T08:09:03.165868Z", + "iopub.status.idle": "2023-11-21T08:09:03.272319Z", + "shell.execute_reply": "2023-11-21T08:09:03.271662Z" }, "id": "GZgovGkdiaiP", "outputId": "d76b2ccf-8be2-4f3a-df4c-2c5c99150db7" @@ -1238,10 +1238,10 @@ "height": 92 }, "execution": { - "iopub.execute_input": "2023-11-20T20:32:20.031410Z", - "iopub.status.busy": "2023-11-20T20:32:20.031039Z", - "iopub.status.idle": "2023-11-20T20:32:20.135541Z", - "shell.execute_reply": "2023-11-20T20:32:20.134989Z" + "iopub.execute_input": "2023-11-21T08:09:03.274943Z", + "iopub.status.busy": "2023-11-21T08:09:03.274723Z", + "iopub.status.idle": "2023-11-21T08:09:03.380481Z", + "shell.execute_reply": "2023-11-21T08:09:03.379841Z" }, "id": "lfa2eHbMwG8R", "outputId": "6627ebe2-d439-4bf5-e2cb-44f6278ae86c" @@ -1282,10 +1282,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:20.137960Z", - "iopub.status.busy": "2023-11-20T20:32:20.137577Z", - "iopub.status.idle": "2023-11-20T20:32:20.242897Z", - "shell.execute_reply": "2023-11-20T20:32:20.242236Z" + "iopub.execute_input": "2023-11-21T08:09:03.383082Z", + "iopub.status.busy": "2023-11-21T08:09:03.382687Z", + "iopub.status.idle": "2023-11-21T08:09:03.489944Z", + "shell.execute_reply": "2023-11-21T08:09:03.489350Z" } }, "outputs": [ @@ -1333,10 +1333,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:20.245510Z", - "iopub.status.busy": "2023-11-20T20:32:20.245030Z", - "iopub.status.idle": "2023-11-20T20:32:20.248474Z", - "shell.execute_reply": "2023-11-20T20:32:20.247945Z" + "iopub.execute_input": "2023-11-21T08:09:03.492544Z", + "iopub.status.busy": "2023-11-21T08:09:03.492154Z", + "iopub.status.idle": "2023-11-21T08:09:03.495667Z", + "shell.execute_reply": "2023-11-21T08:09:03.495037Z" }, "nbsphinx": "hidden" }, @@ -1377,119 +1377,38 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "019c25665f974defb4f0d7ea6699a77c": { + "0879cf0601f34ffeb562a24aeb15358f": { "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": "" } }, - "06111c5f559a4b6a8e18aafe87a2cb49": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "0b8607a3bee7492ab6df9c7f5f1335c0": { - "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_c8c3caafdc0447e79aed51bc86b4564b", - "max": 15856877.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_548d046189c348c8ab800fb62ca6a309", - "value": 15856877.0 - } - }, - "17b2323e85a14bff92da8d96b2bb7915": { + "09feff184200482d878fb7e1437264e7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_1da693de85fb478791df4a0d1240a026", - "placeholder": "", - "style": "IPY_MODEL_4224ecb1da5a4f5b9cd2a7c194d9323a", - "value": "embedding_model.ckpt: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "1c7ae626b0d2411db267eb0d7dbae763": { + "0c90cef4df474910a99ad29ea174e5e2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1504,13 +1423,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_bedae27d7c5145fba1ad928f9581e891", + "layout": "IPY_MODEL_4133d2c1c7854e2c8301f358348909bd", "placeholder": "", - "style": "IPY_MODEL_5023f2faa26a46988778e7e93aef89bf", - "value": "label_encoder.txt: 100%" + "style": "IPY_MODEL_cb058f447e004ec989cdc09a742aadc5", + "value": " 2.04k/2.04k [00:00<00:00, 339kB/s]" } }, - "1d738af2b16e41c3ba472b787e0dd3bd": { + "1054ddfc4bd341fe8be5d89f3cad0f2a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1525,13 +1444,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_37bd18e9b3ec494180cd27203ee1a324", + "layout": "IPY_MODEL_d062e37dd664400689be938c56181af5", "placeholder": "", - "style": "IPY_MODEL_d447db2fb2b74c43a5c1f4b727319436", - "value": " 16.9M/16.9M [00:00<00:00, 175MB/s]" + "style": "IPY_MODEL_99be92b878db41398883dfcfbe9700e3", + "value": " 3.20k/3.20k [00:00<00:00, 520kB/s]" } }, - "1da693de85fb478791df4a0d1240a026": { + "155b430c3f5e410c9e947412125e7e73": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1583,7 +1502,7 @@ "width": null } }, - "23ead44f66154d3192774a95969cc308": { + "1a3b24dae2704345965167814d2f4b78": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -1598,56 +1517,36 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_fc0c0a0da7324a289c48c7f0e70d786e", - "IPY_MODEL_0b8607a3bee7492ab6df9c7f5f1335c0", - "IPY_MODEL_c853d4ef58724b22b12d8bc965283dd7" + "IPY_MODEL_4fd0ea90beb047d681cc2d5082248cb5", + "IPY_MODEL_d9e18749cfca4d5bbb8d2267da4425aa", + "IPY_MODEL_8d6ff7757dba42be820b98e35a24be11" ], - "layout": "IPY_MODEL_aec210a16c6b4eb1ba6be35035b052dc" - } - }, - "23f25472cc944d71a1d16260dd97bf9b": { - "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_da206c1c0a4c400abcdbc6bc1a9a6564", - "placeholder": "", - "style": "IPY_MODEL_f05d0a69294c4a868c0fdac2b6059db6", - "value": " 129k/129k [00:00<00:00, 10.4MB/s]" + "layout": "IPY_MODEL_155b430c3f5e410c9e947412125e7e73" } }, - "266d71fc82a64cc99eb6437d6d372ca5": { + "1e4d780fecca47d4bd604227ef207b64": { "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_3c2a9445cc414e51afbede957e739954", - "placeholder": "", - "style": "IPY_MODEL_be6d94ea58a1464f9a07f03d0544a824", - "value": " 3.20k/3.20k [00:00<00:00, 573kB/s]" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_bd32f882c04c4dddbf237f36a67696cd", + "IPY_MODEL_c68c08777d4c4e0ab5ce257122870d11", + "IPY_MODEL_ac5ab80d5a854c3faeab9a7730af35a9" + ], + "layout": "IPY_MODEL_e66ea76385584b87a824b960bd9582de" } }, - "29f0770d43cc441680a04cfb300481e4": { + "2556fb96aa8141428f5674ca21acb0c8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1699,22 +1598,59 @@ "width": null } }, - "32617792190b458f86ed7f87edac4cac": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "2886d027ecad4b9599d313c5dfef9683": { + "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 } }, - "3573595d177d49f5a4eb83c92cbf2d3d": { + "29d2eed1f3504054b208d5ebe80cdc89": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -1730,7 +1666,7 @@ "description_width": "" } }, - "37bd18e9b3ec494180cd27203ee1a324": { + "2c26a144fd0c4d4b94f19a8c1bc84efc": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1782,7 +1718,7 @@ "width": null } }, - "3c2a9445cc414e51afbede957e739954": { + "2d4febc9d9264747b42ce27918b318b6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1834,7 +1770,29 @@ "width": null } }, - "3cfa18626e734488a0b98cd65d63a274": { + "2e3f0187e68942a196eb02cae07b32e9": { + "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_341c40ec249747f584723d51ca2cb37a", + "IPY_MODEL_4bd6e6b973934921b0da98f121b9ae01", + "IPY_MODEL_1054ddfc4bd341fe8be5d89f3cad0f2a" + ], + "layout": "IPY_MODEL_2886d027ecad4b9599d313c5dfef9683" + } + }, + "2ed1f5ea9c2a429fb317fd494294faf9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1849,29 +1807,73 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_d4c13f5dd771459da2d03f657b1eb839", + "layout": "IPY_MODEL_fd7b9369599e46d181185a1d6212c70b", "placeholder": "", - "style": "IPY_MODEL_9fabcb4d350048019272a96315ff2f79", - "value": "hyperparams.yaml: 100%" + "style": "IPY_MODEL_7ca3a089c19745e084a8364d6146b173", + "value": " 129k/129k [00:00<00:00, 985kB/s]" } }, - "3f85713be6df4a34af811757c0f42838": { + "2fce82469c434e7d8fff5d91812d4378": { "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": "" } }, - "4224ecb1da5a4f5b9cd2a7c194d9323a": { + "341c40ec249747f584723d51ca2cb37a": { + "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_2d4febc9d9264747b42ce27918b318b6", + "placeholder": "", + "style": "IPY_MODEL_2fce82469c434e7d8fff5d91812d4378", + "value": "mean_var_norm_emb.ckpt: 100%" + } + }, + "376161a41b1e4038a144409108f4711d": { + "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_f50f74f340de44bd8c96659a65d891d6", + "max": 2041.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_5a653dfa24a945b7849cbbdc16ca7357", + "value": 2041.0 + } + }, + "3c627c24cd234526b70a35d10dc15324": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1886,7 +1888,7 @@ "description_width": "" } }, - "4b344df1d5f04e5b91c17ec13e2d931c": { + "3cfec9f4f9a642f1aaf86c38fe229ca0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1938,38 +1940,7 @@ "width": null } }, - "5023f2faa26a46988778e7e93aef89bf": { - "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": "" - } - }, - "548d046189c348c8ab800fb62ca6a309": { - "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": "" - } - }, - "57dd65106fc94b4c93f6fb2a4948f57b": { + "4133d2c1c7854e2c8301f358348909bd": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2021,29 +1992,22 @@ "width": null } }, - "59264d126d4546bdbc17b6e96a898a7f": { + "4b6b0808a1d14e44bb829cff693b3348": { "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_98832b282d394897817eb8ac400ea30c", - "IPY_MODEL_da5b7a8b1efc4d82918eac755bd3dcc9", - "IPY_MODEL_266d71fc82a64cc99eb6437d6d372ca5" - ], - "layout": "IPY_MODEL_57dd65106fc94b4c93f6fb2a4948f57b" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "5e2617d12f0f42e3b68337154001f647": { + "4bd6e6b973934921b0da98f121b9ae01": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -2059,37 +2023,36 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_86449095c8694887a7d758b92af28829", - "max": 128619.0, + "layout": "IPY_MODEL_3cfec9f4f9a642f1aaf86c38fe229ca0", + "max": 3201.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_3f85713be6df4a34af811757c0f42838", - "value": 128619.0 + "style": "IPY_MODEL_0879cf0601f34ffeb562a24aeb15358f", + "value": 3201.0 } }, - "635e6f284d06470989ad89bfaa9fcc25": { + "4fd0ea90beb047d681cc2d5082248cb5": { "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_1c7ae626b0d2411db267eb0d7dbae763", - "IPY_MODEL_5e2617d12f0f42e3b68337154001f647", - "IPY_MODEL_23f25472cc944d71a1d16260dd97bf9b" - ], - "layout": "IPY_MODEL_ded92d60968f4ae89f7f856c1a96be50" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2c26a144fd0c4d4b94f19a8c1bc84efc", + "placeholder": "", + "style": "IPY_MODEL_3c627c24cd234526b70a35d10dc15324", + "value": "embedding_model.ckpt: 100%" } }, - "661903adf5914df08b1e6b47f0d74157": { + "565c44d48f50496ab02de334d1f367f2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2141,7 +2104,23 @@ "width": null } }, - "66937a568a7046b194a2aba662745c6e": { + "5a653dfa24a945b7849cbbdc16ca7357": { + "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": "" + } + }, + "5db944c0bbc64b5e9e3da58b09bce3bf": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2193,46 +2172,102 @@ "width": null } }, - "66c9c3fb76c540ec9ed099c4e60e33c9": { + "63d380ab9e864103b14c8bec952fa532": { "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_29f0770d43cc441680a04cfb300481e4", - "max": 2041.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_e23fb6813b8c4f138c88dad004ada43c", - "value": 2041.0 + "layout": "IPY_MODEL_e4746dbd923144cb982b94001b51f53e", + "placeholder": "", + "style": "IPY_MODEL_90fcec82ba624123a628131cb7b8165c", + "value": "hyperparams.yaml: 100%" + } + }, + "763b783f724c4ba89ddcbf2412a0f1ae": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "8123b5034ad148d0bd3ee92db702f9d6": { + "76b798389bf7483eb5dba558c3442dde": { "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_63d380ab9e864103b14c8bec952fa532", + "IPY_MODEL_376161a41b1e4038a144409108f4711d", + "IPY_MODEL_0c90cef4df474910a99ad29ea174e5e2" + ], + "layout": "IPY_MODEL_bc2ee1304580449d8f78c10810da667d" } }, - "86449095c8694887a7d758b92af28829": { + "7ad21c48b50d49b5ae284d54b69ab925": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2284,7 +2319,131 @@ "width": null } }, - "8bc79d23274345448ee4dcb1b3c85523": { + "7ca3a089c19745e084a8364d6146b173": { + "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": "" + } + }, + "8ba46b04fbb248afb2f26f64aa3bc046": { + "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_b865aa2044794733976d4ef0f5ef18bd", + "placeholder": "", + "style": "IPY_MODEL_09feff184200482d878fb7e1437264e7", + "value": "label_encoder.txt: 100%" + } + }, + "8c0f4a66bc284847a7c5ff393f69e050": { + "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_8ba46b04fbb248afb2f26f64aa3bc046", + "IPY_MODEL_a175c07ca39c43acb297fec694d60a9e", + "IPY_MODEL_2ed1f5ea9c2a429fb317fd494294faf9" + ], + "layout": "IPY_MODEL_dd1313141412471fabb35cb898a21b20" + } + }, + "8d6ff7757dba42be820b98e35a24be11": { + "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_2556fb96aa8141428f5674ca21acb0c8", + "placeholder": "", + "style": "IPY_MODEL_e31c184ef1a048de86b5fbeeae254e4c", + "value": " 16.9M/16.9M [00:00<00:00, 56.9MB/s]" + } + }, + "90fcec82ba624123a628131cb7b8165c": { + "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": "" + } + }, + "99be92b878db41398883dfcfbe9700e3": { + "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": "" + } + }, + "9d189f1d069449c9afc98875106ae70e": { + "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": "" + } + }, + "a175c07ca39c43acb297fec694d60a9e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -2300,15 +2459,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_4b344df1d5f04e5b91c17ec13e2d931c", - "max": 16887676.0, + "layout": "IPY_MODEL_ecac085a3a6e478686abf186e3ab2a55", + "max": 128619.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_b949b7f164734ca6befc50de1c2972fc", - "value": 16887676.0 + "style": "IPY_MODEL_29d2eed1f3504054b208d5ebe80cdc89", + "value": 128619.0 } }, - "98832b282d394897817eb8ac400ea30c": { + "ac5ab80d5a854c3faeab9a7730af35a9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2323,28 +2482,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_66937a568a7046b194a2aba662745c6e", + "layout": "IPY_MODEL_763b783f724c4ba89ddcbf2412a0f1ae", "placeholder": "", - "style": "IPY_MODEL_32617792190b458f86ed7f87edac4cac", - "value": "mean_var_norm_emb.ckpt: 100%" - } - }, - "9fabcb4d350048019272a96315ff2f79": { - "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_9d189f1d069449c9afc98875106ae70e", + "value": " 15.9M/15.9M [00:00<00:00, 90.1MB/s]" } }, - "a6da529d56654aada92ed25b2d6e25b5": { + "b865aa2044794733976d4ef0f5ef18bd": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2396,7 +2540,7 @@ "width": null } }, - "ab805e32743549b6b82fea8935666fd0": { + "bc2ee1304580449d8f78c10810da667d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2448,90 +2592,68 @@ "width": null } }, - "aec210a16c6b4eb1ba6be35035b052dc": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "bd32f882c04c4dddbf237f36a67696cd": { + "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_565c44d48f50496ab02de334d1f367f2", + "placeholder": "", + "style": "IPY_MODEL_4b6b0808a1d14e44bb829cff693b3348", + "value": "classifier.ckpt: 100%" } }, - "b466d8db02874e3a893a8ef1494f79ec": { + "c244736040394055bb1acf722e812ff7": { "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": "" } }, - "b949b7f164734ca6befc50de1c2972fc": { + "c68c08777d4c4e0ab5ce257122870d11": { "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/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": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_7ad21c48b50d49b5ae284d54b69ab925", + "max": 15856877.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_c244736040394055bb1acf722e812ff7", + "value": 15856877.0 } }, - "be6d94ea58a1464f9a07f03d0544a824": { + "cb058f447e004ec989cdc09a742aadc5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -2546,7 +2668,7 @@ "description_width": "" } }, - "bedae27d7c5145fba1ad928f9581e891": { + "d062e37dd664400689be938c56181af5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2598,28 +2720,31 @@ "width": null } }, - "c853d4ef58724b22b12d8bc965283dd7": { + "d9e18749cfca4d5bbb8d2267da4425aa": { "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_06111c5f559a4b6a8e18aafe87a2cb49", - "placeholder": "", - "style": "IPY_MODEL_019c25665f974defb4f0d7ea6699a77c", - "value": " 15.9M/15.9M [00:00<00:00, 312MB/s]" + "layout": "IPY_MODEL_5db944c0bbc64b5e9e3da58b09bce3bf", + "max": 16887676.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f149adcd1c5f456c9e81a356a73f686d", + "value": 16887676.0 } }, - "c8c3caafdc0447e79aed51bc86b4564b": { + "dd1313141412471fabb35cb898a21b20": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2671,28 +2796,7 @@ "width": null } }, - "d303c15e303d483da12e7bce9d4d60a5": { - "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_e03b0c880a404023a43abb1556b62b8e", - "placeholder": "", - "style": "IPY_MODEL_b466d8db02874e3a893a8ef1494f79ec", - "value": " 2.04k/2.04k [00:00<00:00, 330kB/s]" - } - }, - "d447db2fb2b74c43a5c1f4b727319436": { + "e31c184ef1a048de86b5fbeeae254e4c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -2707,7 +2811,7 @@ "description_width": "" } }, - "d4c13f5dd771459da2d03f657b1eb839": { + "e4746dbd923144cb982b94001b51f53e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2759,7 +2863,7 @@ "width": null } }, - "d7d792cf0ed340bc845dcf5f7ca049cb": { + "e66ea76385584b87a824b960bd9582de": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2811,7 +2915,7 @@ "width": null } }, - "da206c1c0a4c400abcdbc6bc1a9a6564": { + "ecac085a3a6e478686abf186e3ab2a55": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2863,53 +2967,23 @@ "width": null } }, - "da5b7a8b1efc4d82918eac755bd3dcc9": { - "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_ab805e32743549b6b82fea8935666fd0", - "max": 3201.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3573595d177d49f5a4eb83c92cbf2d3d", - "value": 3201.0 - } - }, - "dafd85af8ddf46e3810f7b6923dbbe66": { + "f149adcd1c5f456c9e81a356a73f686d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_3cfa18626e734488a0b98cd65d63a274", - "IPY_MODEL_66c9c3fb76c540ec9ed099c4e60e33c9", - "IPY_MODEL_d303c15e303d483da12e7bce9d4d60a5" - ], - "layout": "IPY_MODEL_661903adf5914df08b1e6b47f0d74157" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "ded92d60968f4ae89f7f856c1a96be50": { + "f50f74f340de44bd8c96659a65d891d6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2961,7 +3035,7 @@ "width": null } }, - "e03b0c880a404023a43abb1556b62b8e": { + "fd7b9369599e46d181185a1d6212c70b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3012,80 +3086,6 @@ "visibility": null, "width": null } - }, - "e23fb6813b8c4f138c88dad004ada43c": { - "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": "" - } - }, - "f05d0a69294c4a868c0fdac2b6059db6": { - "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": "" - } - }, - "fc0c0a0da7324a289c48c7f0e70d786e": { - "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_a6da529d56654aada92ed25b2d6e25b5", - "placeholder": "", - "style": "IPY_MODEL_8123b5034ad148d0bd3ee92db702f9d6", - "value": "classifier.ckpt: 100%" - } - }, - "feb2125d2c584e48b80c5b56b625f863": { - "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_17b2323e85a14bff92da8d96b2bb7915", - "IPY_MODEL_8bc79d23274345448ee4dcb1b3c85523", - "IPY_MODEL_1d738af2b16e41c3ba472b787e0dd3bd" - ], - "layout": "IPY_MODEL_d7d792cf0ed340bc845dcf5f7ca049cb" - } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_advanced.ipynb index 9faf808d7..2aada4060 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": "2023-11-20T20:32:25.602824Z", - "iopub.status.busy": "2023-11-20T20:32:25.602647Z", - "iopub.status.idle": "2023-11-20T20:32:26.653811Z", - "shell.execute_reply": "2023-11-20T20:32:26.653208Z" + "iopub.execute_input": "2023-11-21T08:09:08.223217Z", + "iopub.status.busy": "2023-11-21T08:09:08.223026Z", + "iopub.status.idle": "2023-11-21T08:09:09.278940Z", + "shell.execute_reply": "2023-11-21T08:09:09.278305Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:32:26.656950Z", - "iopub.status.busy": "2023-11-20T20:32:26.656469Z", - "iopub.status.idle": "2023-11-20T20:32:26.659616Z", - "shell.execute_reply": "2023-11-20T20:32:26.659049Z" + "iopub.execute_input": "2023-11-21T08:09:09.282068Z", + "iopub.status.busy": "2023-11-21T08:09:09.281623Z", + "iopub.status.idle": "2023-11-21T08:09:09.284822Z", + "shell.execute_reply": "2023-11-21T08:09:09.284264Z" } }, "outputs": [], @@ -252,10 +252,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:26.662198Z", - "iopub.status.busy": "2023-11-20T20:32:26.661902Z", - "iopub.status.idle": "2023-11-20T20:32:26.671253Z", - "shell.execute_reply": "2023-11-20T20:32:26.670746Z" + "iopub.execute_input": "2023-11-21T08:09:09.287398Z", + "iopub.status.busy": "2023-11-21T08:09:09.287029Z", + "iopub.status.idle": "2023-11-21T08:09:09.296476Z", + "shell.execute_reply": "2023-11-21T08:09:09.295948Z" }, "nbsphinx": "hidden" }, @@ -353,10 +353,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:26.673591Z", - "iopub.status.busy": "2023-11-20T20:32:26.673221Z", - "iopub.status.idle": "2023-11-20T20:32:26.678135Z", - "shell.execute_reply": "2023-11-20T20:32:26.677501Z" + "iopub.execute_input": "2023-11-21T08:09:09.298675Z", + "iopub.status.busy": "2023-11-21T08:09:09.298477Z", + "iopub.status.idle": "2023-11-21T08:09:09.303170Z", + "shell.execute_reply": "2023-11-21T08:09:09.302677Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:26.680811Z", - "iopub.status.busy": "2023-11-20T20:32:26.680424Z", - "iopub.status.idle": "2023-11-20T20:32:26.945702Z", - "shell.execute_reply": "2023-11-20T20:32:26.945154Z" + "iopub.execute_input": "2023-11-21T08:09:09.305573Z", + "iopub.status.busy": "2023-11-21T08:09:09.305367Z", + "iopub.status.idle": "2023-11-21T08:09:09.577094Z", + "shell.execute_reply": "2023-11-21T08:09:09.576474Z" }, "nbsphinx": "hidden" }, @@ -517,10 +517,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:26.948395Z", - "iopub.status.busy": "2023-11-20T20:32:26.948148Z", - "iopub.status.idle": "2023-11-20T20:32:27.317782Z", - "shell.execute_reply": "2023-11-20T20:32:27.317130Z" + "iopub.execute_input": "2023-11-21T08:09:09.580151Z", + "iopub.status.busy": "2023-11-21T08:09:09.579752Z", + "iopub.status.idle": "2023-11-21T08:09:09.944086Z", + "shell.execute_reply": "2023-11-21T08:09:09.943423Z" } }, "outputs": [ @@ -568,10 +568,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:27.320355Z", - "iopub.status.busy": "2023-11-20T20:32:27.320146Z", - "iopub.status.idle": "2023-11-20T20:32:27.345065Z", - "shell.execute_reply": "2023-11-20T20:32:27.344572Z" + "iopub.execute_input": "2023-11-21T08:09:09.946891Z", + "iopub.status.busy": "2023-11-21T08:09:09.946494Z", + "iopub.status.idle": "2023-11-21T08:09:09.971351Z", + "shell.execute_reply": "2023-11-21T08:09:09.970860Z" } }, "outputs": [], @@ -607,10 +607,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:27.347742Z", - "iopub.status.busy": "2023-11-20T20:32:27.347186Z", - "iopub.status.idle": "2023-11-20T20:32:27.356640Z", - "shell.execute_reply": "2023-11-20T20:32:27.355991Z" + "iopub.execute_input": "2023-11-21T08:09:09.973835Z", + "iopub.status.busy": "2023-11-21T08:09:09.973443Z", + "iopub.status.idle": "2023-11-21T08:09:09.982440Z", + "shell.execute_reply": "2023-11-21T08:09:09.981937Z" } }, "outputs": [], @@ -641,10 +641,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:27.359411Z", - "iopub.status.busy": "2023-11-20T20:32:27.358930Z", - "iopub.status.idle": "2023-11-20T20:32:28.625064Z", - "shell.execute_reply": "2023-11-20T20:32:28.624342Z" + "iopub.execute_input": "2023-11-21T08:09:09.984897Z", + "iopub.status.busy": "2023-11-21T08:09:09.984532Z", + "iopub.status.idle": "2023-11-21T08:09:11.251919Z", + "shell.execute_reply": "2023-11-21T08:09:11.251264Z" } }, "outputs": [ @@ -708,10 +708,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:28.628038Z", - "iopub.status.busy": "2023-11-20T20:32:28.627539Z", - "iopub.status.idle": "2023-11-20T20:32:28.650422Z", - "shell.execute_reply": "2023-11-20T20:32:28.649876Z" + "iopub.execute_input": "2023-11-21T08:09:11.254656Z", + "iopub.status.busy": "2023-11-21T08:09:11.254299Z", + "iopub.status.idle": "2023-11-21T08:09:11.276491Z", + "shell.execute_reply": "2023-11-21T08:09:11.275853Z" } }, "outputs": [ @@ -820,10 +820,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:28.652849Z", - "iopub.status.busy": "2023-11-20T20:32:28.652498Z", - "iopub.status.idle": "2023-11-20T20:32:28.672987Z", - "shell.execute_reply": "2023-11-20T20:32:28.672307Z" + "iopub.execute_input": "2023-11-21T08:09:11.278868Z", + "iopub.status.busy": "2023-11-21T08:09:11.278672Z", + "iopub.status.idle": "2023-11-21T08:09:11.299154Z", + "shell.execute_reply": "2023-11-21T08:09:11.298509Z" } }, "outputs": [ @@ -909,7 +909,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:297: UserWarning: Overwriting columns ['outlier_score', 'is_outlier_issue'] in self.issues with columns from issue manager OutlierIssueManager.\n", + "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:297: UserWarning: Overwriting columns ['is_outlier_issue', 'outlier_score'] in self.issues with columns from issue manager OutlierIssueManager.\n", " warnings.warn(\n", "/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:327: 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": "2023-11-20T20:32:28.675536Z", - "iopub.status.busy": "2023-11-20T20:32:28.675166Z", - "iopub.status.idle": "2023-11-20T20:32:28.689417Z", - "shell.execute_reply": "2023-11-20T20:32:28.688897Z" + "iopub.execute_input": "2023-11-21T08:09:11.301636Z", + "iopub.status.busy": "2023-11-21T08:09:11.301284Z", + "iopub.status.idle": "2023-11-21T08:09:11.315897Z", + "shell.execute_reply": "2023-11-21T08:09:11.315364Z" } }, "outputs": [ @@ -1068,17 +1068,17 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:28.692050Z", - "iopub.status.busy": "2023-11-20T20:32:28.691675Z", - "iopub.status.idle": "2023-11-20T20:32:28.713585Z", - "shell.execute_reply": "2023-11-20T20:32:28.712909Z" + "iopub.execute_input": "2023-11-21T08:09:11.318361Z", + "iopub.status.busy": "2023-11-21T08:09:11.317993Z", + "iopub.status.idle": "2023-11-21T08:09:11.342250Z", + "shell.execute_reply": "2023-11-21T08:09:11.341603Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "87e593156bf743fd9a437814a5809399", + "model_id": "ce9d525f001149f0aa55835166baf79e", "version_major": 2, "version_minor": 0 }, @@ -1114,10 +1114,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:28.717270Z", - "iopub.status.busy": "2023-11-20T20:32:28.716881Z", - "iopub.status.idle": "2023-11-20T20:32:28.732190Z", - "shell.execute_reply": "2023-11-20T20:32:28.731613Z" + "iopub.execute_input": "2023-11-21T08:09:11.344913Z", + "iopub.status.busy": "2023-11-21T08:09:11.344662Z", + "iopub.status.idle": "2023-11-21T08:09:11.359407Z", + "shell.execute_reply": "2023-11-21T08:09:11.358875Z" } }, "outputs": [ @@ -1235,10 +1235,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:28.734640Z", - "iopub.status.busy": "2023-11-20T20:32:28.734313Z", - "iopub.status.idle": "2023-11-20T20:32:28.740581Z", - "shell.execute_reply": "2023-11-20T20:32:28.740041Z" + "iopub.execute_input": "2023-11-21T08:09:11.361953Z", + "iopub.status.busy": "2023-11-21T08:09:11.361548Z", + "iopub.status.idle": "2023-11-21T08:09:11.368022Z", + "shell.execute_reply": "2023-11-21T08:09:11.367448Z" } }, "outputs": [], @@ -1295,10 +1295,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:28.743064Z", - "iopub.status.busy": "2023-11-20T20:32:28.742700Z", - "iopub.status.idle": "2023-11-20T20:32:28.761255Z", - "shell.execute_reply": "2023-11-20T20:32:28.760610Z" + "iopub.execute_input": "2023-11-21T08:09:11.370480Z", + "iopub.status.busy": "2023-11-21T08:09:11.370118Z", + "iopub.status.idle": "2023-11-21T08:09:11.388477Z", + "shell.execute_reply": "2023-11-21T08:09:11.387970Z" } }, "outputs": [ @@ -1430,22 +1430,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "365449c7063b42d2b6be7bc39158f702": { - "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": "" - } - }, - "4668f725580948ce966fbff73803fbc9": { + "18934680214141d1a62d5f20beb949c5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1497,7 +1482,49 @@ "width": null } }, - "6528d3c147054d978bd816788baaafe1": { + "1a4439019778420fb3ef019debe16806": { + "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_6a92ae3d3ca34d059d479f4ab63aa8f8", + "placeholder": "", + "style": "IPY_MODEL_ebfbb11e1c4948e89a9a404923d89554", + "value": " 132/132 [00:00<00:00, 9322.09 examples/s]" + } + }, + "51e7666e42b14eee988efdcc860da85a": { + "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_f3a505fb18264d6dbf77fea37ffdd31b", + "placeholder": "", + "style": "IPY_MODEL_a8f02836dbb5419487e38bb3cebf4a25", + "value": "Saving the dataset (1/1 shards): 100%" + } + }, + "6a92ae3d3ca34d059d479f4ab63aa8f8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1549,59 +1576,84 @@ "width": null } }, - "742c82dd39e34ba9be0aca50b3d91114": { + "9ea53b69243b4c06a25b5095bfbd39e4": { "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_4668f725580948ce966fbff73803fbc9", - "placeholder": "", - "style": "IPY_MODEL_365449c7063b42d2b6be7bc39158f702", - "value": "Saving the dataset (1/1 shards): 100%" + "layout": "IPY_MODEL_d494d6147b974097b9e03eaf370f0b13", + "max": 132.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ae3b6c81ff2f4ad284499dd0e102bbb8", + "value": 132.0 } }, - "751a51e565e64021b0519db1910de2ff": { + "a8f02836dbb5419487e38bb3cebf4a25": { "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": "" } }, - "78fd35cce86a41d588398850d7901d3e": { + "ae3b6c81ff2f4ad284499dd0e102bbb8": { "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": "" } }, - "8317f15395fd4215bc5d8bce79443f67": { + "ce9d525f001149f0aa55835166baf79e": { + "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_51e7666e42b14eee988efdcc860da85a", + "IPY_MODEL_9ea53b69243b4c06a25b5095bfbd39e4", + "IPY_MODEL_1a4439019778420fb3ef019debe16806" + ], + "layout": "IPY_MODEL_18934680214141d1a62d5f20beb949c5" + } + }, + "d494d6147b974097b9e03eaf370f0b13": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1653,74 +1705,22 @@ "width": null } }, - "87e593156bf743fd9a437814a5809399": { + "ebfbb11e1c4948e89a9a404923d89554": { "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_742c82dd39e34ba9be0aca50b3d91114", - "IPY_MODEL_aec4783cd9884a46bc04779092188bb1", - "IPY_MODEL_b56f186c91194547aa64330c32cb7662" - ], - "layout": "IPY_MODEL_8317f15395fd4215bc5d8bce79443f67" - } - }, - "aec4783cd9884a46bc04779092188bb1": { - "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_6528d3c147054d978bd816788baaafe1", - "max": 132.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_751a51e565e64021b0519db1910de2ff", - "value": 132.0 - } - }, - "b56f186c91194547aa64330c32cb7662": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_b836e9148d134e31b627ca5fc416a38d", - "placeholder": "", - "style": "IPY_MODEL_78fd35cce86a41d588398850d7901d3e", - "value": " 132/132 [00:00<00:00, 10963.98 examples/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "b836e9148d134e31b627ca5fc416a38d": { + "f3a505fb18264d6dbf77fea37ffdd31b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/datalab_quickstart.ipynb index 428589054..a4d04fb3d 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": "2023-11-20T20:32:33.469542Z", - "iopub.status.busy": "2023-11-20T20:32:33.469351Z", - "iopub.status.idle": "2023-11-20T20:32:34.500487Z", - "shell.execute_reply": "2023-11-20T20:32:34.499885Z" + "iopub.execute_input": "2023-11-21T08:09:16.277922Z", + "iopub.status.busy": "2023-11-21T08:09:16.277714Z", + "iopub.status.idle": "2023-11-21T08:09:17.330413Z", + "shell.execute_reply": "2023-11-21T08:09:17.329797Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:32:34.503345Z", - "iopub.status.busy": "2023-11-20T20:32:34.502902Z", - "iopub.status.idle": "2023-11-20T20:32:34.505963Z", - "shell.execute_reply": "2023-11-20T20:32:34.505392Z" + "iopub.execute_input": "2023-11-21T08:09:17.333390Z", + "iopub.status.busy": "2023-11-21T08:09:17.332969Z", + "iopub.status.idle": "2023-11-21T08:09:17.336371Z", + "shell.execute_reply": "2023-11-21T08:09:17.335841Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:34.508592Z", - "iopub.status.busy": "2023-11-20T20:32:34.508203Z", - "iopub.status.idle": "2023-11-20T20:32:34.517536Z", - "shell.execute_reply": "2023-11-20T20:32:34.517033Z" + "iopub.execute_input": "2023-11-21T08:09:17.338978Z", + "iopub.status.busy": "2023-11-21T08:09:17.338557Z", + "iopub.status.idle": "2023-11-21T08:09:17.348157Z", + "shell.execute_reply": "2023-11-21T08:09:17.347659Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:34.519840Z", - "iopub.status.busy": "2023-11-20T20:32:34.519477Z", - "iopub.status.idle": "2023-11-20T20:32:34.523906Z", - "shell.execute_reply": "2023-11-20T20:32:34.523422Z" + "iopub.execute_input": "2023-11-21T08:09:17.350302Z", + "iopub.status.busy": "2023-11-21T08:09:17.350108Z", + "iopub.status.idle": "2023-11-21T08:09:17.354550Z", + "shell.execute_reply": "2023-11-21T08:09:17.354079Z" } }, "outputs": [], @@ -443,10 +443,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:34.526233Z", - "iopub.status.busy": "2023-11-20T20:32:34.526031Z", - "iopub.status.idle": "2023-11-20T20:32:34.800096Z", - "shell.execute_reply": "2023-11-20T20:32:34.799485Z" + "iopub.execute_input": "2023-11-21T08:09:17.356909Z", + "iopub.status.busy": "2023-11-21T08:09:17.356713Z", + "iopub.status.idle": "2023-11-21T08:09:17.628106Z", + "shell.execute_reply": "2023-11-21T08:09:17.627493Z" }, "nbsphinx": "hidden" }, @@ -515,10 +515,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:34.802853Z", - "iopub.status.busy": "2023-11-20T20:32:34.802647Z", - "iopub.status.idle": "2023-11-20T20:32:35.170934Z", - "shell.execute_reply": "2023-11-20T20:32:35.170290Z" + "iopub.execute_input": "2023-11-21T08:09:17.630816Z", + "iopub.status.busy": "2023-11-21T08:09:17.630614Z", + "iopub.status.idle": "2023-11-21T08:09:17.999037Z", + "shell.execute_reply": "2023-11-21T08:09:17.998384Z" } }, "outputs": [ @@ -554,10 +554,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:35.173355Z", - "iopub.status.busy": "2023-11-20T20:32:35.173147Z", - "iopub.status.idle": "2023-11-20T20:32:35.176045Z", - "shell.execute_reply": "2023-11-20T20:32:35.175500Z" + "iopub.execute_input": "2023-11-21T08:09:18.001717Z", + "iopub.status.busy": "2023-11-21T08:09:18.001244Z", + "iopub.status.idle": "2023-11-21T08:09:18.004303Z", + "shell.execute_reply": "2023-11-21T08:09:18.003786Z" } }, "outputs": [], @@ -596,10 +596,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:35.178459Z", - "iopub.status.busy": "2023-11-20T20:32:35.178258Z", - "iopub.status.idle": "2023-11-20T20:32:35.202236Z", - "shell.execute_reply": "2023-11-20T20:32:35.201743Z" + "iopub.execute_input": "2023-11-21T08:09:18.006766Z", + "iopub.status.busy": "2023-11-21T08:09:18.006403Z", + "iopub.status.idle": "2023-11-21T08:09:18.030659Z", + "shell.execute_reply": "2023-11-21T08:09:18.030036Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:35.204586Z", - "iopub.status.busy": "2023-11-20T20:32:35.204251Z", - "iopub.status.idle": "2023-11-20T20:32:36.458769Z", - "shell.execute_reply": "2023-11-20T20:32:36.458040Z" + "iopub.execute_input": "2023-11-21T08:09:18.033219Z", + "iopub.status.busy": "2023-11-21T08:09:18.032822Z", + "iopub.status.idle": "2023-11-21T08:09:19.343733Z", + "shell.execute_reply": "2023-11-21T08:09:19.342983Z" } }, "outputs": [ @@ -677,10 +677,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.461652Z", - "iopub.status.busy": "2023-11-20T20:32:36.461156Z", - "iopub.status.idle": "2023-11-20T20:32:36.478467Z", - "shell.execute_reply": "2023-11-20T20:32:36.477930Z" + "iopub.execute_input": "2023-11-21T08:09:19.347630Z", + "iopub.status.busy": "2023-11-21T08:09:19.346235Z", + "iopub.status.idle": "2023-11-21T08:09:19.364152Z", + "shell.execute_reply": "2023-11-21T08:09:19.363645Z" } }, "outputs": [ @@ -814,10 +814,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.481003Z", - "iopub.status.busy": "2023-11-20T20:32:36.480646Z", - "iopub.status.idle": "2023-11-20T20:32:36.487233Z", - "shell.execute_reply": "2023-11-20T20:32:36.486595Z" + "iopub.execute_input": "2023-11-21T08:09:19.366740Z", + "iopub.status.busy": "2023-11-21T08:09:19.366373Z", + "iopub.status.idle": "2023-11-21T08:09:19.373109Z", + "shell.execute_reply": "2023-11-21T08:09:19.372483Z" } }, "outputs": [ @@ -907,10 +907,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.489757Z", - "iopub.status.busy": "2023-11-20T20:32:36.489404Z", - "iopub.status.idle": "2023-11-20T20:32:36.495590Z", - "shell.execute_reply": "2023-11-20T20:32:36.494997Z" + "iopub.execute_input": "2023-11-21T08:09:19.375578Z", + "iopub.status.busy": "2023-11-21T08:09:19.375069Z", + "iopub.status.idle": "2023-11-21T08:09:19.381331Z", + "shell.execute_reply": "2023-11-21T08:09:19.380721Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.498095Z", - "iopub.status.busy": "2023-11-20T20:32:36.497725Z", - "iopub.status.idle": "2023-11-20T20:32:36.506412Z", - "shell.execute_reply": "2023-11-20T20:32:36.505889Z" + "iopub.execute_input": "2023-11-21T08:09:19.383700Z", + "iopub.status.busy": "2023-11-21T08:09:19.383268Z", + "iopub.status.idle": "2023-11-21T08:09:19.391810Z", + "shell.execute_reply": "2023-11-21T08:09:19.391208Z" } }, "outputs": [ @@ -1122,10 +1122,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.508696Z", - "iopub.status.busy": "2023-11-20T20:32:36.508333Z", - "iopub.status.idle": "2023-11-20T20:32:36.517506Z", - "shell.execute_reply": "2023-11-20T20:32:36.516962Z" + "iopub.execute_input": "2023-11-21T08:09:19.394285Z", + "iopub.status.busy": "2023-11-21T08:09:19.393921Z", + "iopub.status.idle": "2023-11-21T08:09:19.403180Z", + "shell.execute_reply": "2023-11-21T08:09:19.402579Z" } }, "outputs": [ @@ -1241,10 +1241,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.519928Z", - "iopub.status.busy": "2023-11-20T20:32:36.519565Z", - "iopub.status.idle": "2023-11-20T20:32:36.527049Z", - "shell.execute_reply": "2023-11-20T20:32:36.526537Z" + "iopub.execute_input": "2023-11-21T08:09:19.405691Z", + "iopub.status.busy": "2023-11-21T08:09:19.405303Z", + "iopub.status.idle": "2023-11-21T08:09:19.412717Z", + "shell.execute_reply": "2023-11-21T08:09:19.412127Z" }, "scrolled": true }, @@ -1357,10 +1357,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.529506Z", - "iopub.status.busy": "2023-11-20T20:32:36.529146Z", - "iopub.status.idle": "2023-11-20T20:32:36.539031Z", - "shell.execute_reply": "2023-11-20T20:32:36.538507Z" + "iopub.execute_input": "2023-11-21T08:09:19.415152Z", + "iopub.status.busy": "2023-11-21T08:09:19.414796Z", + "iopub.status.idle": "2023-11-21T08:09:19.424574Z", + "shell.execute_reply": "2023-11-21T08:09:19.423984Z" } }, "outputs": [ @@ -1453,7 +1453,7 @@ "source": [ "`Datalab` makes it very easy to check your datasets for all sorts of issues that are important to deal with for training robust models. The inputs it uses to detect issues can come from *any* model you have trained (the better your model, the more accurate the issue detection will be).\n", "\n", - "To learn more, check out this [examples notebook](https://github.com/cleanlab/examples/blob/master/datalab_image_classification/datalab.ipynb) and the [advanced Datalab tutorial](datalab_advanced.html)." + "To learn more, check out this [example notebook](https://github.com/cleanlab/examples/blob/master/datalab_image_classification/datalab.ipynb) (demonstrates Datalab applied to a real dataset) and the [advanced Datalab tutorial](datalab_advanced.html) (demonstrates configuration and customization options to exert greater control)." ] } ], diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/tabular.ipynb index c640f534b..1bfc63eb7 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": "2023-11-20T20:32:41.789698Z", - "iopub.status.busy": "2023-11-20T20:32:41.789256Z", - "iopub.status.idle": "2023-11-20T20:32:42.774357Z", - "shell.execute_reply": "2023-11-20T20:32:42.773747Z" + "iopub.execute_input": "2023-11-21T08:09:24.352475Z", + "iopub.status.busy": "2023-11-21T08:09:24.352291Z", + "iopub.status.idle": "2023-11-21T08:09:25.349712Z", + "shell.execute_reply": "2023-11-21T08:09:25.349078Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:32:42.777399Z", - "iopub.status.busy": "2023-11-20T20:32:42.776926Z", - "iopub.status.idle": "2023-11-20T20:32:42.796657Z", - "shell.execute_reply": "2023-11-20T20:32:42.796058Z" + "iopub.execute_input": "2023-11-21T08:09:25.352735Z", + "iopub.status.busy": "2023-11-21T08:09:25.352254Z", + "iopub.status.idle": "2023-11-21T08:09:25.372356Z", + "shell.execute_reply": "2023-11-21T08:09:25.371799Z" } }, "outputs": [], @@ -155,10 +155,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:42.799491Z", - "iopub.status.busy": "2023-11-20T20:32:42.799011Z", - "iopub.status.idle": "2023-11-20T20:32:42.941253Z", - "shell.execute_reply": "2023-11-20T20:32:42.940652Z" + "iopub.execute_input": "2023-11-21T08:09:25.375034Z", + "iopub.status.busy": "2023-11-21T08:09:25.374665Z", + "iopub.status.idle": "2023-11-21T08:09:25.648497Z", + "shell.execute_reply": "2023-11-21T08:09:25.647858Z" } }, "outputs": [ @@ -265,10 +265,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:42.943695Z", - "iopub.status.busy": "2023-11-20T20:32:42.943215Z", - "iopub.status.idle": "2023-11-20T20:32:42.946835Z", - "shell.execute_reply": "2023-11-20T20:32:42.946242Z" + "iopub.execute_input": "2023-11-21T08:09:25.650926Z", + "iopub.status.busy": "2023-11-21T08:09:25.650733Z", + "iopub.status.idle": "2023-11-21T08:09:25.654384Z", + "shell.execute_reply": "2023-11-21T08:09:25.653798Z" } }, "outputs": [], @@ -289,10 +289,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:42.949139Z", - "iopub.status.busy": "2023-11-20T20:32:42.948787Z", - "iopub.status.idle": "2023-11-20T20:32:42.956625Z", - "shell.execute_reply": "2023-11-20T20:32:42.956039Z" + "iopub.execute_input": "2023-11-21T08:09:25.656761Z", + "iopub.status.busy": "2023-11-21T08:09:25.656298Z", + "iopub.status.idle": "2023-11-21T08:09:25.664389Z", + "shell.execute_reply": "2023-11-21T08:09:25.663763Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:42.959374Z", - "iopub.status.busy": "2023-11-20T20:32:42.958908Z", - "iopub.status.idle": "2023-11-20T20:32:42.961731Z", - "shell.execute_reply": "2023-11-20T20:32:42.961128Z" + "iopub.execute_input": "2023-11-21T08:09:25.667081Z", + "iopub.status.busy": "2023-11-21T08:09:25.666642Z", + "iopub.status.idle": "2023-11-21T08:09:25.669479Z", + "shell.execute_reply": "2023-11-21T08:09:25.668877Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:42.964166Z", - "iopub.status.busy": "2023-11-20T20:32:42.963732Z", - "iopub.status.idle": "2023-11-20T20:32:46.557261Z", - "shell.execute_reply": "2023-11-20T20:32:46.556562Z" + "iopub.execute_input": "2023-11-21T08:09:25.671825Z", + "iopub.status.busy": "2023-11-21T08:09:25.671430Z", + "iopub.status.idle": "2023-11-21T08:09:29.278265Z", + "shell.execute_reply": "2023-11-21T08:09:29.277553Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:46.560778Z", - "iopub.status.busy": "2023-11-20T20:32:46.560146Z", - "iopub.status.idle": "2023-11-20T20:32:46.569973Z", - "shell.execute_reply": "2023-11-20T20:32:46.569461Z" + "iopub.execute_input": "2023-11-21T08:09:29.281842Z", + "iopub.status.busy": "2023-11-21T08:09:29.281270Z", + "iopub.status.idle": "2023-11-21T08:09:29.291026Z", + "shell.execute_reply": "2023-11-21T08:09:29.290418Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:46.572428Z", - "iopub.status.busy": "2023-11-20T20:32:46.572088Z", - "iopub.status.idle": "2023-11-20T20:32:47.854978Z", - "shell.execute_reply": "2023-11-20T20:32:47.854244Z" + "iopub.execute_input": "2023-11-21T08:09:29.293775Z", + "iopub.status.busy": "2023-11-21T08:09:29.293265Z", + "iopub.status.idle": "2023-11-21T08:09:30.615685Z", + "shell.execute_reply": "2023-11-21T08:09:30.614966Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.858619Z", - "iopub.status.busy": "2023-11-20T20:32:47.857936Z", - "iopub.status.idle": "2023-11-20T20:32:47.879874Z", - "shell.execute_reply": "2023-11-20T20:32:47.879283Z" + "iopub.execute_input": "2023-11-21T08:09:30.620079Z", + "iopub.status.busy": "2023-11-21T08:09:30.618561Z", + "iopub.status.idle": "2023-11-21T08:09:30.642679Z", + "shell.execute_reply": "2023-11-21T08:09:30.642095Z" }, "scrolled": true }, @@ -602,10 +602,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.882808Z", - "iopub.status.busy": "2023-11-20T20:32:47.882365Z", - "iopub.status.idle": "2023-11-20T20:32:47.892330Z", - "shell.execute_reply": "2023-11-20T20:32:47.891743Z" + "iopub.execute_input": "2023-11-21T08:09:30.646969Z", + "iopub.status.busy": "2023-11-21T08:09:30.645857Z", + "iopub.status.idle": "2023-11-21T08:09:30.658265Z", + "shell.execute_reply": "2023-11-21T08:09:30.657693Z" } }, "outputs": [ @@ -709,10 +709,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.896116Z", - "iopub.status.busy": "2023-11-20T20:32:47.895000Z", - "iopub.status.idle": "2023-11-20T20:32:47.909362Z", - "shell.execute_reply": "2023-11-20T20:32:47.908782Z" + "iopub.execute_input": "2023-11-21T08:09:30.662481Z", + "iopub.status.busy": "2023-11-21T08:09:30.661346Z", + "iopub.status.idle": "2023-11-21T08:09:30.675664Z", + "shell.execute_reply": "2023-11-21T08:09:30.675085Z" } }, "outputs": [ @@ -841,10 +841,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.913675Z", - "iopub.status.busy": "2023-11-20T20:32:47.912553Z", - "iopub.status.idle": "2023-11-20T20:32:47.925127Z", - "shell.execute_reply": "2023-11-20T20:32:47.924531Z" + "iopub.execute_input": "2023-11-21T08:09:30.679946Z", + "iopub.status.busy": "2023-11-21T08:09:30.678830Z", + "iopub.status.idle": "2023-11-21T08:09:30.691299Z", + "shell.execute_reply": "2023-11-21T08:09:30.690732Z" } }, "outputs": [ @@ -958,10 +958,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.929494Z", - "iopub.status.busy": "2023-11-20T20:32:47.928365Z", - "iopub.status.idle": "2023-11-20T20:32:47.942464Z", - "shell.execute_reply": "2023-11-20T20:32:47.941879Z" + "iopub.execute_input": "2023-11-21T08:09:30.695552Z", + "iopub.status.busy": "2023-11-21T08:09:30.694443Z", + "iopub.status.idle": "2023-11-21T08:09:30.708130Z", + "shell.execute_reply": "2023-11-21T08:09:30.707674Z" } }, "outputs": [ @@ -1072,10 +1072,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.946160Z", - "iopub.status.busy": "2023-11-20T20:32:47.945557Z", - "iopub.status.idle": "2023-11-20T20:32:47.952712Z", - "shell.execute_reply": "2023-11-20T20:32:47.952176Z" + "iopub.execute_input": "2023-11-21T08:09:30.711020Z", + "iopub.status.busy": "2023-11-21T08:09:30.710537Z", + "iopub.status.idle": "2023-11-21T08:09:30.717380Z", + "shell.execute_reply": "2023-11-21T08:09:30.716812Z" } }, "outputs": [ @@ -1159,10 +1159,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.955087Z", - "iopub.status.busy": "2023-11-20T20:32:47.954876Z", - "iopub.status.idle": "2023-11-20T20:32:47.961968Z", - "shell.execute_reply": "2023-11-20T20:32:47.961177Z" + "iopub.execute_input": "2023-11-21T08:09:30.719851Z", + "iopub.status.busy": "2023-11-21T08:09:30.719376Z", + "iopub.status.idle": "2023-11-21T08:09:30.726272Z", + "shell.execute_reply": "2023-11-21T08:09:30.725649Z" } }, "outputs": [ @@ -1246,10 +1246,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.964435Z", - "iopub.status.busy": "2023-11-20T20:32:47.964067Z", - "iopub.status.idle": "2023-11-20T20:32:47.971090Z", - "shell.execute_reply": "2023-11-20T20:32:47.970455Z" + "iopub.execute_input": "2023-11-21T08:09:30.728567Z", + "iopub.status.busy": "2023-11-21T08:09:30.728237Z", + "iopub.status.idle": "2023-11-21T08:09:30.735274Z", + "shell.execute_reply": "2023-11-21T08:09:30.734633Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb b/master/.doctrees/nbsphinx/tutorials/datalab/text.ipynb index 9450c9a0e..518f19aa7 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": "2023-11-20T20:32:52.575977Z", - "iopub.status.busy": "2023-11-20T20:32:52.575528Z", - "iopub.status.idle": "2023-11-20T20:32:54.937269Z", - "shell.execute_reply": "2023-11-20T20:32:54.936714Z" + "iopub.execute_input": "2023-11-21T08:09:35.328863Z", + "iopub.status.busy": "2023-11-21T08:09:35.328235Z", + "iopub.status.idle": "2023-11-21T08:09:37.683797Z", + "shell.execute_reply": "2023-11-21T08:09:37.683197Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d54db05155764ee79fb8bed826d68136", + "model_id": "84efccf1fdb54b2fbd5ffafd72a0d2d4", "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:32:54.940299Z", - "iopub.status.busy": "2023-11-20T20:32:54.939859Z", - "iopub.status.idle": "2023-11-20T20:32:54.943455Z", - "shell.execute_reply": "2023-11-20T20:32:54.942825Z" + "iopub.execute_input": "2023-11-21T08:09:37.686963Z", + "iopub.status.busy": "2023-11-21T08:09:37.686395Z", + "iopub.status.idle": "2023-11-21T08:09:37.689918Z", + "shell.execute_reply": "2023-11-21T08:09:37.689297Z" } }, "outputs": [], @@ -167,10 +167,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:54.945857Z", - "iopub.status.busy": "2023-11-20T20:32:54.945488Z", - "iopub.status.idle": "2023-11-20T20:32:54.948798Z", - "shell.execute_reply": "2023-11-20T20:32:54.948205Z" + "iopub.execute_input": "2023-11-21T08:09:37.692242Z", + "iopub.status.busy": "2023-11-21T08:09:37.692040Z", + "iopub.status.idle": "2023-11-21T08:09:37.695169Z", + "shell.execute_reply": "2023-11-21T08:09:37.694641Z" }, "nbsphinx": "hidden" }, @@ -200,10 +200,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:54.951369Z", - "iopub.status.busy": "2023-11-20T20:32:54.951016Z", - "iopub.status.idle": "2023-11-20T20:32:55.014131Z", - "shell.execute_reply": "2023-11-20T20:32:55.013517Z" + "iopub.execute_input": "2023-11-21T08:09:37.697722Z", + "iopub.status.busy": "2023-11-21T08:09:37.697197Z", + "iopub.status.idle": "2023-11-21T08:09:37.880336Z", + "shell.execute_reply": "2023-11-21T08:09:37.879693Z" } }, "outputs": [ @@ -293,10 +293,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:55.016618Z", - "iopub.status.busy": "2023-11-20T20:32:55.016109Z", - "iopub.status.idle": "2023-11-20T20:32:55.020522Z", - "shell.execute_reply": "2023-11-20T20:32:55.019994Z" + "iopub.execute_input": "2023-11-21T08:09:37.882668Z", + "iopub.status.busy": "2023-11-21T08:09:37.882468Z", + "iopub.status.idle": "2023-11-21T08:09:37.886833Z", + "shell.execute_reply": "2023-11-21T08:09:37.886311Z" } }, "outputs": [ @@ -305,7 +305,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'change_pin', 'beneficiary_not_allowed', 'getting_spare_card', 'cancel_transfer', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_payment_fee_charged'}\n" + "Classes: {'lost_or_stolen_phone', 'beneficiary_not_allowed', 'change_pin', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'cancel_transfer', 'card_about_to_expire', 'visa_or_mastercard', 'supported_cards_and_currencies', 'getting_spare_card'}\n" ] } ], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:55.022841Z", - "iopub.status.busy": "2023-11-20T20:32:55.022499Z", - "iopub.status.idle": "2023-11-20T20:32:55.026100Z", - "shell.execute_reply": "2023-11-20T20:32:55.025481Z" + "iopub.execute_input": "2023-11-21T08:09:37.889162Z", + "iopub.status.busy": "2023-11-21T08:09:37.888966Z", + "iopub.status.idle": "2023-11-21T08:09:37.892867Z", + "shell.execute_reply": "2023-11-21T08:09:37.892357Z" } }, "outputs": [ @@ -387,17 +387,17 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:55.028777Z", - "iopub.status.busy": "2023-11-20T20:32:55.028279Z", - "iopub.status.idle": "2023-11-20T20:33:04.039924Z", - "shell.execute_reply": "2023-11-20T20:33:04.039210Z" + "iopub.execute_input": "2023-11-21T08:09:37.895400Z", + "iopub.status.busy": "2023-11-21T08:09:37.895052Z", + "iopub.status.idle": "2023-11-21T08:09:48.146709Z", + "shell.execute_reply": "2023-11-21T08:09:48.145986Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f3a80e73a7b443beb304232769a2adf5", + "model_id": "bdbfd3c9e01e4c66a7d2920c798024d3", "version_major": 2, "version_minor": 0 }, @@ -411,7 +411,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f1eac02d89ea410296effca01dd5fdf1", + "model_id": "ef9b066900fb4ced8f7b4f486b6b61a6", "version_major": 2, "version_minor": 0 }, @@ -425,7 +425,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2f6960c8c542429984f644a4477a1bbd", + "model_id": "e90c4b7b38ac41bf97dab284611fe567", "version_major": 2, "version_minor": 0 }, @@ -439,7 +439,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24defa2b39da45b489630e31a68d7a34", + "model_id": "1dbde4ffa31c4efb85eddbafeda00e66", "version_major": 2, "version_minor": 0 }, @@ -453,7 +453,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d646f3871ca44fba85ea234b692e0de", + "model_id": "f69acc9daea44e8f8636482936ec63ea", "version_major": 2, "version_minor": 0 }, @@ -467,7 +467,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "48b3ebdabbf748608b1f8b746f57cf16", + "model_id": "3d07442b240545d6bd98314771e24331", "version_major": 2, "version_minor": 0 }, @@ -481,7 +481,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5fe16afe08654d5fbbb7a8efb3b9006a", + "model_id": "2111d77e8324438fa80daa16cba4c64c", "version_major": 2, "version_minor": 0 }, @@ -535,10 +535,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:04.043353Z", - "iopub.status.busy": "2023-11-20T20:33:04.042928Z", - "iopub.status.idle": "2023-11-20T20:33:05.288496Z", - "shell.execute_reply": "2023-11-20T20:33:05.287832Z" + "iopub.execute_input": "2023-11-21T08:09:48.150018Z", + "iopub.status.busy": "2023-11-21T08:09:48.149787Z", + "iopub.status.idle": "2023-11-21T08:09:49.317159Z", + "shell.execute_reply": "2023-11-21T08:09:49.316481Z" }, "scrolled": true }, @@ -570,10 +570,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:05.292133Z", - "iopub.status.busy": "2023-11-20T20:33:05.291530Z", - "iopub.status.idle": "2023-11-20T20:33:05.295016Z", - "shell.execute_reply": "2023-11-20T20:33:05.294440Z" + "iopub.execute_input": "2023-11-21T08:09:49.320806Z", + "iopub.status.busy": "2023-11-21T08:09:49.320342Z", + "iopub.status.idle": "2023-11-21T08:09:49.323504Z", + "shell.execute_reply": "2023-11-21T08:09:49.322949Z" } }, "outputs": [], @@ -593,10 +593,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:05.297976Z", - "iopub.status.busy": "2023-11-20T20:33:05.297541Z", - "iopub.status.idle": "2023-11-20T20:33:06.602112Z", - "shell.execute_reply": "2023-11-20T20:33:06.601301Z" + "iopub.execute_input": "2023-11-21T08:09:49.326388Z", + "iopub.status.busy": "2023-11-21T08:09:49.325962Z", + "iopub.status.idle": "2023-11-21T08:09:50.636016Z", + "shell.execute_reply": "2023-11-21T08:09:50.635252Z" }, "scrolled": true }, @@ -638,10 +638,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.605661Z", - "iopub.status.busy": "2023-11-20T20:33:06.604934Z", - "iopub.status.idle": "2023-11-20T20:33:06.628259Z", - "shell.execute_reply": "2023-11-20T20:33:06.627633Z" + "iopub.execute_input": "2023-11-21T08:09:50.641239Z", + "iopub.status.busy": "2023-11-21T08:09:50.639659Z", + "iopub.status.idle": "2023-11-21T08:09:50.664791Z", + "shell.execute_reply": "2023-11-21T08:09:50.664192Z" }, "scrolled": true }, @@ -766,10 +766,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.635537Z", - "iopub.status.busy": "2023-11-20T20:33:06.635066Z", - "iopub.status.idle": "2023-11-20T20:33:06.646046Z", - "shell.execute_reply": "2023-11-20T20:33:06.645375Z" + "iopub.execute_input": "2023-11-21T08:09:50.669150Z", + "iopub.status.busy": "2023-11-21T08:09:50.667876Z", + "iopub.status.idle": "2023-11-21T08:09:50.681035Z", + "shell.execute_reply": "2023-11-21T08:09:50.680452Z" }, "scrolled": true }, @@ -879,10 +879,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.649442Z", - "iopub.status.busy": "2023-11-20T20:33:06.649024Z", - "iopub.status.idle": "2023-11-20T20:33:06.654657Z", - "shell.execute_reply": "2023-11-20T20:33:06.654046Z" + "iopub.execute_input": "2023-11-21T08:09:50.685353Z", + "iopub.status.busy": "2023-11-21T08:09:50.684227Z", + "iopub.status.idle": "2023-11-21T08:09:50.691781Z", + "shell.execute_reply": "2023-11-21T08:09:50.691207Z" } }, "outputs": [ @@ -920,10 +920,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.657989Z", - "iopub.status.busy": "2023-11-20T20:33:06.657517Z", - "iopub.status.idle": "2023-11-20T20:33:06.665476Z", - "shell.execute_reply": "2023-11-20T20:33:06.664975Z" + "iopub.execute_input": "2023-11-21T08:09:50.695017Z", + "iopub.status.busy": "2023-11-21T08:09:50.694817Z", + "iopub.status.idle": "2023-11-21T08:09:50.702157Z", + "shell.execute_reply": "2023-11-21T08:09:50.701550Z" } }, "outputs": [ @@ -1040,10 +1040,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.668159Z", - "iopub.status.busy": "2023-11-20T20:33:06.667766Z", - "iopub.status.idle": "2023-11-20T20:33:06.674700Z", - "shell.execute_reply": "2023-11-20T20:33:06.674211Z" + "iopub.execute_input": "2023-11-21T08:09:50.704614Z", + "iopub.status.busy": "2023-11-21T08:09:50.704255Z", + "iopub.status.idle": "2023-11-21T08:09:50.711038Z", + "shell.execute_reply": "2023-11-21T08:09:50.710520Z" } }, "outputs": [ @@ -1126,10 +1126,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.677235Z", - "iopub.status.busy": "2023-11-20T20:33:06.676846Z", - "iopub.status.idle": "2023-11-20T20:33:06.683494Z", - "shell.execute_reply": "2023-11-20T20:33:06.682950Z" + "iopub.execute_input": "2023-11-21T08:09:50.713287Z", + "iopub.status.busy": "2023-11-21T08:09:50.712928Z", + "iopub.status.idle": "2023-11-21T08:09:50.720090Z", + "shell.execute_reply": "2023-11-21T08:09:50.719464Z" } }, "outputs": [ @@ -1237,10 +1237,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.685859Z", - "iopub.status.busy": "2023-11-20T20:33:06.685572Z", - "iopub.status.idle": "2023-11-20T20:33:06.695914Z", - "shell.execute_reply": "2023-11-20T20:33:06.695269Z" + "iopub.execute_input": "2023-11-21T08:09:50.722513Z", + "iopub.status.busy": "2023-11-21T08:09:50.722149Z", + "iopub.status.idle": "2023-11-21T08:09:50.731346Z", + "shell.execute_reply": "2023-11-21T08:09:50.730840Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.698397Z", - "iopub.status.busy": "2023-11-20T20:33:06.698189Z", - "iopub.status.idle": "2023-11-20T20:33:06.704394Z", - "shell.execute_reply": "2023-11-20T20:33:06.703751Z" + "iopub.execute_input": "2023-11-21T08:09:50.733786Z", + "iopub.status.busy": "2023-11-21T08:09:50.733415Z", + "iopub.status.idle": "2023-11-21T08:09:50.739208Z", + "shell.execute_reply": "2023-11-21T08:09:50.738614Z" } }, "outputs": [ @@ -1422,10 +1422,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.706901Z", - "iopub.status.busy": "2023-11-20T20:33:06.706413Z", - "iopub.status.idle": "2023-11-20T20:33:06.712217Z", - "shell.execute_reply": "2023-11-20T20:33:06.711618Z" + "iopub.execute_input": "2023-11-21T08:09:50.741542Z", + "iopub.status.busy": "2023-11-21T08:09:50.741177Z", + "iopub.status.idle": "2023-11-21T08:09:50.746859Z", + "shell.execute_reply": "2023-11-21T08:09:50.746262Z" } }, "outputs": [ @@ -1503,10 +1503,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.714730Z", - "iopub.status.busy": "2023-11-20T20:33:06.714357Z", - "iopub.status.idle": "2023-11-20T20:33:06.719989Z", - "shell.execute_reply": "2023-11-20T20:33:06.719450Z" + "iopub.execute_input": "2023-11-21T08:09:50.749290Z", + "iopub.status.busy": "2023-11-21T08:09:50.748944Z", + "iopub.status.idle": "2023-11-21T08:09:50.754186Z", + "shell.execute_reply": "2023-11-21T08:09:50.753648Z" }, "nbsphinx": "hidden" }, @@ -1556,7 +1556,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "039540c4098d4867bd077a2ce39d8e39": { + "0a163ebe579b47869711b0e6f4e62c87": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1608,29 +1608,59 @@ "width": null } }, - "0d646f3871ca44fba85ea234b692e0de": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "0a1779c499014bc68a84cfc642ce1725": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_2a0716bed248445ca1e8c04bccbaf146", - "IPY_MODEL_6f84d648d19445758024085e38f78d88", - "IPY_MODEL_44114d530986407794206b2157b4c514" - ], - "layout": "IPY_MODEL_668f3b52d76340ecbf73e0d153d01388" + "_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 } }, - "0e67b54bd6804b27b18acd1fd029fcd3": { + "0b1ff99343e14f8386920438d324ef62": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -1646,30 +1676,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_ab98e93b1bbc4325bb17e181debcb6e4", - "max": 1.0, + "layout": "IPY_MODEL_591c9dd8aaac4ac69b56816710cc9aa2", + "max": 231508.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_a053aa210df54945b8918f4bae67cd52", - "value": 0.0 - } - }, - "1175a3f3a7794d018ca694be5ffa6003": { - "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_3a33834b8968499cac761ee7650c7498", + "value": 231508.0 } }, - "12ae094ce5d04f0a8b2439e257e3165b": { + "0fea425ed46b43829f77b5de1a61c2b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1721,23 +1736,28 @@ "width": null } }, - "2005f8d011dc4f3ea9a0a944e5cbdbb3": { + "107075362c9d42b480b6c546b52e7735": { "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_0fea425ed46b43829f77b5de1a61c2b0", + "placeholder": "", + "style": "IPY_MODEL_3e817664dd7a4b439a33fe50f30624c6", + "value": " 466k/466k [00:00<00:00, 3.60MB/s]" } }, - "2066ea65fcab495b98d0ec633c205c06": { + "112c6d5b0cb34133ac7cbec5d2389adb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1752,22 +1772,31 @@ "description_width": "" } }, - "20bfb16a2a934759827d6f8b51c241fd": { + "172c78277ca14877b1a3da40b9138f52": { "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_cadc44c76fb44c07a30f82ff23e67e89", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_dc5807a6220944eebc1c543ddb7c73e8", + "value": 665.0 } }, - "211e1a6b61264e52806cfdf399bd4bb6": { + "18595bd57aff40a9a0108eafcb2ad410": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1819,71 +1848,23 @@ "width": null } }, - "21fd4b0c85a242a78e4c8a0fe5191728": { - "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_4353172fa52c4daa86930ad97e72fc83", - "placeholder": "", - "style": "IPY_MODEL_2066ea65fcab495b98d0ec633c205c06", - "value": "config.json: 100%" - } - }, - "24defa2b39da45b489630e31a68d7a34": { - "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_a65959fce8f0476bbd4578941ebe290f", - "IPY_MODEL_9523201e089c4b1484304c2389d890a1", - "IPY_MODEL_3e54a9e06b3d4257a475a4f68cc0486c" - ], - "layout": "IPY_MODEL_e756314819e141ed88bcd0bc1c4bce8a" - } - }, - "250f73e3763746ccbae18521d684039d": { + "19aecc8940484ac5a737db0f0af3a58d": { "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_c41ad12ee15e45b0bed1ebd67822eb1f", - "placeholder": "", - "style": "IPY_MODEL_20bfb16a2a934759827d6f8b51c241fd", - "value": "" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "26324b4b25c04ea7818bba469a441452": { + "1b702e942afe47ebacb4d2c7211c47cd": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1935,43 +1916,53 @@ "width": null } }, - "284816cb41ea47f194b9ab0973ee79f6": { + "1b893a8a45c143eb8779f891294f0f1f": { "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_bedca6f1a4d041c69a5315ea83880c71", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ee1d278a26754d46a3d364c3b88cc77e", + "value": 2211.0 } }, - "29fa280d9abf40aaa5e6496643f73d8a": { + "1dbde4ffa31c4efb85eddbafeda00e66": { "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_fc492bb933e1425ca731e0b9e16e3c25", - "placeholder": "", - "style": "IPY_MODEL_8227fe1ff0464d6a8c247f5d9d6f5d56", - "value": " 29.0/29.0 [00:00<00:00, 3.78kB/s]" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3fabc6db74244b9c9ac3c796dba34826", + "IPY_MODEL_4ec31c7de3eb406482338591622b33ad", + "IPY_MODEL_a55cc233450248318737b37fa4c14461" + ], + "layout": "IPY_MODEL_1b702e942afe47ebacb4d2c7211c47cd" } }, - "2a0716bed248445ca1e8c04bccbaf146": { + "1e592846b61749b9a36d221e430c1b90": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1986,37 +1977,34 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_47346d3705ec42b8869269ec44935be4", + "layout": "IPY_MODEL_e1cbae967deb4f79b112b62a3b327652", "placeholder": "", - "style": "IPY_MODEL_33db3079f4134514a013b7c5842aac85", - "value": "tokenizer.json: 100%" + "style": "IPY_MODEL_67163f62148c4b41a5a499a1a3d66288", + "value": " 232k/232k [00:00<00:00, 1.75MB/s]" } }, - "2e04d555cb3d419680fe93aa2c4d77fd": { + "207a23a480a54deb924c5c17c968cc98": { "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_ccf33f86a3404b2e981f990bab45814a", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6706a6b6dece48c7b645ddae5d18f15a", - "value": 665.0 + "layout": "IPY_MODEL_c85ead9cc74541aba00a004e422e4e82", + "placeholder": "", + "style": "IPY_MODEL_ca9ac305cc49437495789a0dda9a227b", + "value": " 2.21k/2.21k [00:00<00:00, 292kB/s]" } }, - "2f6960c8c542429984f644a4477a1bbd": { + "2111d77e8324438fa80daa16cba4c64c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -2031,60 +2019,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_21fd4b0c85a242a78e4c8a0fe5191728", - "IPY_MODEL_2e04d555cb3d419680fe93aa2c4d77fd", - "IPY_MODEL_d4bc8433e74843428c969e4d10ed6f70" + "IPY_MODEL_49ce368efd6c41a68e1b59f615be2c5c", + "IPY_MODEL_0b1ff99343e14f8386920438d324ef62", + "IPY_MODEL_1e592846b61749b9a36d221e430c1b90" ], - "layout": "IPY_MODEL_9736b54786404e6d87505bf512eabc59" - } - }, - "31f68703509a4908be64b113f1722c9a": { - "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": "" - } - }, - "33264e12ba984b149e0836334b6b3690": { - "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": "" - } - }, - "33db3079f4134514a013b7c5842aac85": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "layout": "IPY_MODEL_7436e4f9268b4fd19d5d944b06fc348d" } }, - "3482d0d18bc445d1b8e392deca24c9c6": { + "2c54172478ff492d83b81da0d976e710": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2136,7 +2078,23 @@ "width": null } }, - "37687a58523a4f48bf2665a92fdaed16": { + "2d9ce6f633934cc8a757a868256a51b1": { + "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": "" + } + }, + "32b53331dede4e649b77ac43b00f78f9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2188,7 +2146,7 @@ "width": null } }, - "37e5007d690b41a29aa34b75f470b548": { + "3662a0fdf755483ea3df820923e4a269": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2203,128 +2161,66 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_26324b4b25c04ea7818bba469a441452", + "layout": "IPY_MODEL_0a1779c499014bc68a84cfc642ce1725", "placeholder": "", - "style": "IPY_MODEL_284816cb41ea47f194b9ab0973ee79f6", - "value": " 2.21k/2.21k [00:00<00:00, 300kB/s]" + "style": "IPY_MODEL_c7970c7de6db433cafa82db7c4ba55c2", + "value": "tokenizer_config.json: 100%" } }, - "3e54a9e06b3d4257a475a4f68cc0486c": { + "3a33834b8968499cac761ee7650c7498": { "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", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_3482d0d18bc445d1b8e392deca24c9c6", - "placeholder": "", - "style": "IPY_MODEL_6be4c1d8e0af45c8a8f9ab1ca1dd7a1e", - "value": " 54.2M/54.2M [00:00<00:00, 203MB/s]" - } - }, - "4353172fa52c4daa86930ad97e72fc83": { - "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_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": "" } }, - "44114d530986407794206b2157b4c514": { + "3d07442b240545d6bd98314771e24331": { "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_37687a58523a4f48bf2665a92fdaed16", - "placeholder": "", - "style": "IPY_MODEL_31f68703509a4908be64b113f1722c9a", - "value": " 466k/466k [00:00<00:00, 15.4MB/s]" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3662a0fdf755483ea3df820923e4a269", + "IPY_MODEL_ad57643441a9400cb2a24bf8a4bffbf4", + "IPY_MODEL_c4ff8f77501d44afa0533b89ce66ced6" + ], + "layout": "IPY_MODEL_0a163ebe579b47869711b0e6f4e62c87" } }, - "44bbcc32b7ee46ddb11e73e585b804b0": { + "3e817664dd7a4b439a33fe50f30624c6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_039540c4098d4867bd077a2ce39d8e39", - "placeholder": "", - "style": "IPY_MODEL_b19018911894446ab6ac0320c0c580b7", - "value": " 0/0 [00:00<?, ?it/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "47346d3705ec42b8869269ec44935be4": { + "3f803816262543999985de624aa862a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2376,29 +2272,43 @@ "width": null } }, - "48b3ebdabbf748608b1f8b746f57cf16": { + "3fabc6db74244b9c9ac3c796dba34826": { "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_a21dcc04456d4c34a486f1bfd4e02863", - "IPY_MODEL_9fd02b530c504169a3325c314f6f9356", - "IPY_MODEL_29fa280d9abf40aaa5e6496643f73d8a" - ], - "layout": "IPY_MODEL_211e1a6b61264e52806cfdf399bd4bb6" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_63121665fa8c4bd586d1adeafc28d768", + "placeholder": "", + "style": "IPY_MODEL_b55b8af54d12442eb30f5c13b43373d9", + "value": "pytorch_model.bin: 100%" + } + }, + "41c756dd41814d2c853bafb048e267b4": { + "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": "" } }, - "49a5150a59814722906cb8d36334e5ff": { + "44df00352a2443e89267d2862e274554": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2450,7 +2360,43 @@ "width": null } }, - "4c3d271520b44bfd92d342d615467f64": { + "48e63068978f4b71843894353be3f288": { + "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": "" + } + }, + "49ce368efd6c41a68e1b59f615be2c5c": { + "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_18595bd57aff40a9a0108eafcb2ad410", + "placeholder": "", + "style": "IPY_MODEL_9aad58654a4441d5b85e2f5ff161ee7a", + "value": "vocab.txt: 100%" + } + }, + "4afdaee1b7be4a4eb13b8379fa88af2b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2502,7 +2448,31 @@ "width": null } }, - "568e9cf1380c4df3b6e2cf760ad2e7d4": { + "4ec31c7de3eb406482338591622b33ad": { + "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_5b409a661584439bb06e50825f285ab2", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f66a6f6f614b4a599f9ac0cd065c14ee", + "value": 54245363.0 + } + }, + "52dd4522849c4df38e47a768855f2f49": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2517,13 +2487,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_67f21d4199244b31b947b3591d496230", + "layout": "IPY_MODEL_c12db3c77d8a4470bdf6259bfddde092", "placeholder": "", - "style": "IPY_MODEL_7b41eff5d2d7462493f0cfabd6e27ff4", - "value": " 232k/232k [00:00<00:00, 26.8MB/s]" + "style": "IPY_MODEL_78abf288375b4f029096d9c6f45c5a4f", + "value": " 391/391 [00:00<00:00, 46.9kB/s]" } }, - "5b6bf2edb533466eac4844a61d7ef1b1": { + "591c9dd8aaac4ac69b56816710cc9aa2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2575,45 +2545,7 @@ "width": null } }, - "5fe16afe08654d5fbbb7a8efb3b9006a": { - "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_b6bbde45fc1c4d37ad3934b454d1ca6d", - "IPY_MODEL_6ced4f75fee643e3b56ed72a093932dd", - "IPY_MODEL_568e9cf1380c4df3b6e2cf760ad2e7d4" - ], - "layout": "IPY_MODEL_ae2b4daf42254d41bdc0f45124112b43" - } - }, - "65ad633e62c74b8882469e75fbe3bfcf": { - "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": "" - } - }, - "668f3b52d76340ecbf73e0d153d01388": { + "5a00d1eb7d7d4ef2b1bb67dab959baaa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2665,23 +2597,22 @@ "width": null } }, - "6706a6b6dece48c7b645ddae5d18f15a": { + "5ae37c06cebd4198bfa640e1ccd9a494": { "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": "" } }, - "67f21d4199244b31b947b3591d496230": { + "5b409a661584439bb06e50825f285ab2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2733,46 +2664,28 @@ "width": null } }, - "6be4c1d8e0af45c8a8f9ab1ca1dd7a1e": { - "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": "" - } - }, - "6ced4f75fee643e3b56ed72a093932dd": { + "5f170dd530e0495ab21ece306876e5e0": { "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_5b6bf2edb533466eac4844a61d7ef1b1", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_33264e12ba984b149e0836334b6b3690", - "value": 231508.0 + "layout": "IPY_MODEL_2c54172478ff492d83b81da0d976e710", + "placeholder": "", + "style": "IPY_MODEL_112c6d5b0cb34133ac7cbec5d2389adb", + "value": ".gitattributes: 100%" } }, - "6f84d648d19445758024085e38f78d88": { + "6009a1260cae419f9469970afa1525c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -2788,15 +2701,67 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_99f7fb1de49a4b8c805d00e9d33c5266", - "max": 466062.0, + "layout": "IPY_MODEL_cec07c03b1064d5989cc246ef655e392", + "max": 1.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_65ad633e62c74b8882469e75fbe3bfcf", - "value": 466062.0 + "style": "IPY_MODEL_2d9ce6f633934cc8a757a868256a51b1", + "value": 0.0 + } + }, + "63121665fa8c4bd586d1adeafc28d768": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "782f2b13354544f785bd75906c4245e8": { + "650ae1fa0c8f479090946a949743aef0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2848,23 +2813,43 @@ "width": null } }, - "7a797f6c6b4d4d38b79f19a57ac7bfde": { + "67163f62148c4b41a5a499a1a3d66288": { "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": "" } }, - "7b41eff5d2d7462493f0cfabd6e27ff4": { + "67e14d729c4241608896687e5ab9fe5c": { + "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_4afdaee1b7be4a4eb13b8379fa88af2b", + "placeholder": "", + "style": "IPY_MODEL_41c756dd41814d2c853bafb048e267b4", + "value": "README.md: 100%" + } + }, + "68aa460054cb4cb493b54c8fe18f613b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -2879,7 +2864,7 @@ "description_width": "" } }, - "7c206cb2d08b4c0492b50e812220f447": { + "6f771a745ddc4cd6a522467e6d87b708": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2931,7 +2916,7 @@ "width": null } }, - "8221b786332842b5a52de89a56e4b56e": { + "7161bb5e334345629fb192401c57bb2c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2983,22 +2968,7 @@ "width": null } }, - "8227fe1ff0464d6a8c247f5d9d6f5d56": { - "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": "" - } - }, - "877f9b36021745209a8675e0ad738984": { + "7308bd016a67460bb70bf0955ad2ab79": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3013,31 +2983,7 @@ "description_width": "" } }, - "8c7de965c57b47de9969c023bf51cd0b": { - "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_782f2b13354544f785bd75906c4245e8", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_96e4c7ea35c94547b45868edc53831d0", - "value": 2211.0 - } - }, - "918222bc77134442ae21b7c05cec6d17": { + "7436e4f9268b4fd19d5d944b06fc348d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3084,52 +3030,27 @@ "overflow_y": null, "padding": null, "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "9523201e089c4b1484304c2389d890a1": { - "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_918222bc77134442ae21b7c05cec6d17", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c4162e61ec4747eda3a2bb153a31bd01", - "value": 54245363.0 + "top": null, + "visibility": null, + "width": null } }, - "96e4c7ea35c94547b45868edc53831d0": { + "78abf288375b4f029096d9c6f45c5a4f": { "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": "" } }, - "9736b54786404e6d87505bf512eabc59": { + "793eed7abd274693b6f5b1acb8764c9f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3181,7 +3102,7 @@ "width": null } }, - "99f7fb1de49a4b8c805d00e9d33c5266": { + "822b64cf88564502820ccaf61631a1b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3233,7 +3154,7 @@ "width": null } }, - "9d76360f5e3345d284d810048ec3cb2a": { + "8240cd8b020047e3840806ef452c2d87": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3248,95 +3169,135 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_7c206cb2d08b4c0492b50e812220f447", + "layout": "IPY_MODEL_3f803816262543999985de624aa862a8", "placeholder": "", - "style": "IPY_MODEL_c46fda5f1d6e40469032558367ab0c9e", - "value": "README.md: 100%" + "style": "IPY_MODEL_7308bd016a67460bb70bf0955ad2ab79", + "value": "config.json: 100%" } }, - "9fd02b530c504169a3325c314f6f9356": { + "82b2dcac34f1409fbec55f00318c8c04": { "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": "" + } + }, + "84efccf1fdb54b2fbd5ffafd72a0d2d4": { + "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", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a508496db5f747cab996226d174cf171", + "IPY_MODEL_6009a1260cae419f9469970afa1525c0", + "IPY_MODEL_d1274f2fc9604c7ab40d6376139fcdd4" + ], + "layout": "IPY_MODEL_32b53331dede4e649b77ac43b00f78f9" + } + }, + "8eb23f63a9f34e9ba69cb036a2b13b80": { + "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_12ae094ce5d04f0a8b2439e257e3165b", - "max": 29.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2005f8d011dc4f3ea9a0a944e5cbdbb3", - "value": 29.0 + "layout": "IPY_MODEL_a18d771a7bed461e8986e7575ed905cc", + "placeholder": "", + "style": "IPY_MODEL_e0376bb191cf4aeda10cbf66f620e9ef", + "value": " 665/665 [00:00<00:00, 87.6kB/s]" } }, - "a053aa210df54945b8918f4bae67cd52": { + "9aad58654a4441d5b85e2f5ff161ee7a": { "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": "" } }, - "a21dcc04456d4c34a486f1bfd4e02863": { + "9dc42a0110134970b5df8a2c89f8b77c": { "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_bf12b56d42454630837fda5aae752489", - "placeholder": "", - "style": "IPY_MODEL_877f9b36021745209a8675e0ad738984", - "value": "tokenizer_config.json: 100%" + "layout": "IPY_MODEL_e97995e049b44615b50176300e51d371", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_19aecc8940484ac5a737db0f0af3a58d", + "value": 466062.0 } }, - "a65959fce8f0476bbd4578941ebe290f": { + "a0bd40cc15de406e8e312d63cf9a0102": { "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_d1de1b1d4d13403bb0c3db4f0c3ba68b", - "placeholder": "", - "style": "IPY_MODEL_be51cc71dc354041ba49e1ca1c6917c3", - "value": "pytorch_model.bin: 100%" + "layout": "IPY_MODEL_7161bb5e334345629fb192401c57bb2c", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_82b2dcac34f1409fbec55f00318c8c04", + "value": 391.0 } }, - "ab98e93b1bbc4325bb17e181debcb6e4": { + "a18d771a7bed461e8986e7575ed905cc": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3385,10 +3346,76 @@ "right": null, "top": null, "visibility": null, - "width": "20px" + "width": null + } + }, + "a508496db5f747cab996226d174cf171": { + "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_aeb258677428489fba795a7bac6375ab", + "placeholder": "", + "style": "IPY_MODEL_48e63068978f4b71843894353be3f288", + "value": "" + } + }, + "a55cc233450248318737b37fa4c14461": { + "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_eff47d174257487d802fef33fde361d7", + "placeholder": "", + "style": "IPY_MODEL_68aa460054cb4cb493b54c8fe18f613b", + "value": " 54.2M/54.2M [00:00<00:00, 212MB/s]" + } + }, + "ad57643441a9400cb2a24bf8a4bffbf4": { + "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_6f771a745ddc4cd6a522467e6d87b708", + "max": 29.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ffbc5d11132b4d3f953e99c99e0cefc1", + "value": 29.0 } }, - "ae2b4daf42254d41bdc0f45124112b43": { + "aeb258677428489fba795a7bac6375ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3440,7 +3467,44 @@ "width": null } }, - "ae6d140b37134cccbe0ce004c0f895d8": { + "b55b8af54d12442eb30f5c13b43373d9": { + "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": "" + } + }, + "bdbfd3c9e01e4c66a7d2920c798024d3": { + "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_5f170dd530e0495ab21ece306876e5e0", + "IPY_MODEL_a0bd40cc15de406e8e312d63cf9a0102", + "IPY_MODEL_52dd4522849c4df38e47a768855f2f49" + ], + "layout": "IPY_MODEL_5a00d1eb7d7d4ef2b1bb67dab959baaa" + } + }, + "bedca6f1a4d041c69a5315ea83880c71": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3492,94 +3556,7 @@ "width": null } }, - "b19018911894446ab6ac0320c0c580b7": { - "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": "" - } - }, - "b6bbde45fc1c4d37ad3934b454d1ca6d": { - "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_4c3d271520b44bfd92d342d615467f64", - "placeholder": "", - "style": "IPY_MODEL_b6fe8cd016e84742b84d2a8492ed4e64", - "value": "vocab.txt: 100%" - } - }, - "b6fe8cd016e84742b84d2a8492ed4e64": { - "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": "" - } - }, - "ba70d09674694e6895ea5e47b5f2b309": { - "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_49a5150a59814722906cb8d36334e5ff", - "placeholder": "", - "style": "IPY_MODEL_ccead207e816488daf46b779a82278d6", - "value": " 391/391 [00:00<00:00, 51.7kB/s]" - } - }, - "be51cc71dc354041ba49e1ca1c6917c3": { - "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": "" - } - }, - "bf12b56d42454630837fda5aae752489": { + "c12db3c77d8a4470bdf6259bfddde092": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3631,7 +3608,28 @@ "width": null } }, - "c1a1796d01f44b4fb7d6e14772a0ae72": { + "c4ff8f77501d44afa0533b89ce66ced6": { + "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_c69b45f0b9974436b4613e30124cfb6b", + "placeholder": "", + "style": "IPY_MODEL_f4f2a8b14d2c45ff868dc735d6356e64", + "value": " 29.0/29.0 [00:00<00:00, 3.77kB/s]" + } + }, + "c69b45f0b9974436b4613e30124cfb6b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3683,23 +3681,22 @@ "width": null } }, - "c4162e61ec4747eda3a2bb153a31bd01": { + "c7970c7de6db433cafa82db7c4ba55c2": { "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": "" } }, - "c41ad12ee15e45b0bed1ebd67822eb1f": { + "c85ead9cc74541aba00a004e422e4e82": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3751,22 +3748,7 @@ "width": null } }, - "c46fda5f1d6e40469032558367ab0c9e": { - "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": "" - } - }, - "c5828a960da94f30a1b66491e789d6f5": { + "c905e4d4faba44e88810db1f13d1208f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3781,13 +3763,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_8221b786332842b5a52de89a56e4b56e", + "layout": "IPY_MODEL_650ae1fa0c8f479090946a949743aef0", "placeholder": "", - "style": "IPY_MODEL_1175a3f3a7794d018ca694be5ffa6003", - "value": ".gitattributes: 100%" + "style": "IPY_MODEL_f0ef357162ad4e09b50aafb279c26266", + "value": "tokenizer.json: 100%" } }, - "ccead207e816488daf46b779a82278d6": { + "ca9ac305cc49437495789a0dda9a227b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3802,7 +3784,7 @@ "description_width": "" } }, - "ccf33f86a3404b2e981f990bab45814a": { + "cadc44c76fb44c07a30f82ff23e67e89": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3854,7 +3836,7 @@ "width": null } }, - "d1de1b1d4d13403bb0c3db4f0c3ba68b": { + "cec07c03b1064d5989cc246ef655e392": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3903,10 +3885,10 @@ "right": null, "top": null, "visibility": null, - "width": null + "width": "20px" } }, - "d4bc8433e74843428c969e4d10ed6f70": { + "d1274f2fc9604c7ab40d6376139fcdd4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3921,59 +3903,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_f0f810fd6c4c47909c9ab85960dfc0e9", + "layout": "IPY_MODEL_d3ccfa2190244a05bacac0d4b411e713", "placeholder": "", - "style": "IPY_MODEL_f2d6b9d677ff4ecaafb373fc3bd97a5b", - "value": " 665/665 [00:00<00:00, 89.3kB/s]" - } - }, - "d54db05155764ee79fb8bed826d68136": { - "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_250f73e3763746ccbae18521d684039d", - "IPY_MODEL_0e67b54bd6804b27b18acd1fd029fcd3", - "IPY_MODEL_44bbcc32b7ee46ddb11e73e585b804b0" - ], - "layout": "IPY_MODEL_ae6d140b37134cccbe0ce004c0f895d8" - } - }, - "dcd5d8646ff048b580a8b3e3ea17d670": { - "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_df916fbe4ae84ceb943c03c23ffb85cf", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_7a797f6c6b4d4d38b79f19a57ac7bfde", - "value": 391.0 + "style": "IPY_MODEL_5ae37c06cebd4198bfa640e1ccd9a494", + "value": " 0/0 [00:00<?, ?it/s]" } }, - "df916fbe4ae84ceb943c03c23ffb85cf": { + "d3ccfa2190244a05bacac0d4b411e713": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4025,7 +3961,38 @@ "width": null } }, - "e756314819e141ed88bcd0bc1c4bce8a": { + "dc5807a6220944eebc1c543ddb7c73e8": { + "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": "" + } + }, + "e0376bb191cf4aeda10cbf66f620e9ef": { + "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": "" + } + }, + "e1cbae967deb4f79b112b62a3b327652": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4077,7 +4044,29 @@ "width": null } }, - "f0f810fd6c4c47909c9ab85960dfc0e9": { + "e90c4b7b38ac41bf97dab284611fe567": { + "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_8240cd8b020047e3840806ef452c2d87", + "IPY_MODEL_172c78277ca14877b1a3da40b9138f52", + "IPY_MODEL_8eb23f63a9f34e9ba69cb036a2b13b80" + ], + "layout": "IPY_MODEL_822b64cf88564502820ccaf61631a1b0" + } + }, + "e97995e049b44615b50176300e51d371": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4129,44 +4118,23 @@ "width": null } }, - "f1eac02d89ea410296effca01dd5fdf1": { - "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_9d76360f5e3345d284d810048ec3cb2a", - "IPY_MODEL_8c7de965c57b47de9969c023bf51cd0b", - "IPY_MODEL_37e5007d690b41a29aa34b75f470b548" - ], - "layout": "IPY_MODEL_c1a1796d01f44b4fb7d6e14772a0ae72" - } - }, - "f2d6b9d677ff4ecaafb373fc3bd97a5b": { + "ee1d278a26754d46a3d364c3b88cc77e": { "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": "" } }, - "f3a80e73a7b443beb304232769a2adf5": { + "ef9b066900fb4ced8f7b4f486b6b61a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -4181,14 +4149,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_c5828a960da94f30a1b66491e789d6f5", - "IPY_MODEL_dcd5d8646ff048b580a8b3e3ea17d670", - "IPY_MODEL_ba70d09674694e6895ea5e47b5f2b309" + "IPY_MODEL_67e14d729c4241608896687e5ab9fe5c", + "IPY_MODEL_1b893a8a45c143eb8779f891294f0f1f", + "IPY_MODEL_207a23a480a54deb924c5c17c968cc98" ], - "layout": "IPY_MODEL_f5540318e4314e51ac6554d736f409fc" + "layout": "IPY_MODEL_793eed7abd274693b6f5b1acb8764c9f" } }, - "f5540318e4314e51ac6554d736f409fc": { + "eff47d174257487d802fef33fde361d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4240,56 +4208,88 @@ "width": null } }, - "fc492bb933e1425ca731e0b9e16e3c25": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "f0ef357162ad4e09b50aafb279c26266": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" + } + }, + "f4f2a8b14d2c45ff868dc735d6356e64": { + "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": "" + } + }, + "f66a6f6f614b4a599f9ac0cd065c14ee": { + "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": "" + } + }, + "f69acc9daea44e8f8636482936ec63ea": { + "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_c905e4d4faba44e88810db1f13d1208f", + "IPY_MODEL_9dc42a0110134970b5df8a2c89f8b77c", + "IPY_MODEL_107075362c9d42b480b6c546b52e7735" + ], + "layout": "IPY_MODEL_44df00352a2443e89267d2862e274554" + } + }, + "ffbc5d11132b4d3f953e99c99e0cefc1": { + "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": "" } } }, diff --git a/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb b/master/.doctrees/nbsphinx/tutorials/dataset_health.ipynb index 720b155d1..4714f5689 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": "2023-11-20T20:33:11.763787Z", - "iopub.status.busy": "2023-11-20T20:33:11.763596Z", - "iopub.status.idle": "2023-11-20T20:33:12.758487Z", - "shell.execute_reply": "2023-11-20T20:33:12.757869Z" + "iopub.execute_input": "2023-11-21T08:09:55.619155Z", + "iopub.status.busy": "2023-11-21T08:09:55.618966Z", + "iopub.status.idle": "2023-11-21T08:09:56.625511Z", + "shell.execute_reply": "2023-11-21T08:09:56.624881Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:33:12.761409Z", - "iopub.status.busy": "2023-11-20T20:33:12.760895Z", - "iopub.status.idle": "2023-11-20T20:33:12.764033Z", - "shell.execute_reply": "2023-11-20T20:33:12.763513Z" + "iopub.execute_input": "2023-11-21T08:09:56.628711Z", + "iopub.status.busy": "2023-11-21T08:09:56.628008Z", + "iopub.status.idle": "2023-11-21T08:09:56.631272Z", + "shell.execute_reply": "2023-11-21T08:09:56.630652Z" }, "id": "_UvI80l42iyi" }, @@ -201,10 +201,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:12.766234Z", - "iopub.status.busy": "2023-11-20T20:33:12.766036Z", - "iopub.status.idle": "2023-11-20T20:33:12.778617Z", - "shell.execute_reply": "2023-11-20T20:33:12.778106Z" + "iopub.execute_input": "2023-11-21T08:09:56.633902Z", + "iopub.status.busy": "2023-11-21T08:09:56.633713Z", + "iopub.status.idle": "2023-11-21T08:09:56.646331Z", + "shell.execute_reply": "2023-11-21T08:09:56.645704Z" }, "nbsphinx": "hidden" }, @@ -283,10 +283,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:12.780967Z", - "iopub.status.busy": "2023-11-20T20:33:12.780764Z", - "iopub.status.idle": "2023-11-20T20:33:16.096993Z", - "shell.execute_reply": "2023-11-20T20:33:16.096414Z" + "iopub.execute_input": "2023-11-21T08:09:56.648794Z", + "iopub.status.busy": "2023-11-21T08:09:56.648313Z", + "iopub.status.idle": "2023-11-21T08:10:02.849654Z", + "shell.execute_reply": "2023-11-21T08:10:02.848960Z" }, "id": "dhTHOg8Pyv5G" }, @@ -2182,7 +2182,13 @@ "\n", "\n", "🎯 Cifar100_test_set 🎯\n", - "\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Loaded the 'cifar100_test_set' dataset with predicted probabilities of shape (10000, 100)\n", "\n", @@ -2557,13 +2563,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", " * Overall, about 18% (1,846 of the 10,000) labels in your dataset have potential issues.\n", " ** The overall label health score for this dataset is: 0.82.\n", "\n", diff --git a/master/.doctrees/nbsphinx/tutorials/faq.ipynb b/master/.doctrees/nbsphinx/tutorials/faq.ipynb index 679b77e78..c2c5b4103 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": "2023-11-20T20:33:20.441289Z", - "iopub.status.busy": "2023-11-20T20:33:20.441108Z", - "iopub.status.idle": "2023-11-20T20:33:21.426585Z", - "shell.execute_reply": "2023-11-20T20:33:21.425981Z" + "iopub.execute_input": "2023-11-21T08:10:07.143495Z", + "iopub.status.busy": "2023-11-21T08:10:07.143049Z", + "iopub.status.idle": "2023-11-21T08:10:08.148389Z", + "shell.execute_reply": "2023-11-21T08:10:08.147707Z" }, "nbsphinx": "hidden" }, @@ -97,10 +97,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:21.429619Z", - "iopub.status.busy": "2023-11-20T20:33:21.429307Z", - "iopub.status.idle": "2023-11-20T20:33:21.432764Z", - "shell.execute_reply": "2023-11-20T20:33:21.432211Z" + "iopub.execute_input": "2023-11-21T08:10:08.151701Z", + "iopub.status.busy": "2023-11-21T08:10:08.151099Z", + "iopub.status.idle": "2023-11-21T08:10:08.154788Z", + "shell.execute_reply": "2023-11-21T08:10:08.154185Z" } }, "outputs": [], @@ -136,10 +136,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:21.435463Z", - "iopub.status.busy": "2023-11-20T20:33:21.435025Z", - "iopub.status.idle": "2023-11-20T20:33:23.373800Z", - "shell.execute_reply": "2023-11-20T20:33:23.373111Z" + "iopub.execute_input": "2023-11-21T08:10:08.157279Z", + "iopub.status.busy": "2023-11-21T08:10:08.156798Z", + "iopub.status.idle": "2023-11-21T08:10:10.125375Z", + "shell.execute_reply": "2023-11-21T08:10:10.124702Z" } }, "outputs": [], @@ -162,10 +162,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.377241Z", - "iopub.status.busy": "2023-11-20T20:33:23.376481Z", - "iopub.status.idle": "2023-11-20T20:33:23.411041Z", - "shell.execute_reply": "2023-11-20T20:33:23.410375Z" + "iopub.execute_input": "2023-11-21T08:10:10.128981Z", + "iopub.status.busy": "2023-11-21T08:10:10.128189Z", + "iopub.status.idle": "2023-11-21T08:10:10.165781Z", + "shell.execute_reply": "2023-11-21T08:10:10.164972Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.414145Z", - "iopub.status.busy": "2023-11-20T20:33:23.413679Z", - "iopub.status.idle": "2023-11-20T20:33:23.446512Z", - "shell.execute_reply": "2023-11-20T20:33:23.445860Z" + "iopub.execute_input": "2023-11-21T08:10:10.169002Z", + "iopub.status.busy": "2023-11-21T08:10:10.168526Z", + "iopub.status.idle": "2023-11-21T08:10:10.203492Z", + "shell.execute_reply": "2023-11-21T08:10:10.202835Z" } }, "outputs": [], @@ -213,10 +213,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.449387Z", - "iopub.status.busy": "2023-11-20T20:33:23.449047Z", - "iopub.status.idle": "2023-11-20T20:33:23.452403Z", - "shell.execute_reply": "2023-11-20T20:33:23.451883Z" + "iopub.execute_input": "2023-11-21T08:10:10.206744Z", + "iopub.status.busy": "2023-11-21T08:10:10.206265Z", + "iopub.status.idle": "2023-11-21T08:10:10.209333Z", + "shell.execute_reply": "2023-11-21T08:10:10.208830Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.454792Z", - "iopub.status.busy": "2023-11-20T20:33:23.454434Z", - "iopub.status.idle": "2023-11-20T20:33:23.457158Z", - "shell.execute_reply": "2023-11-20T20:33:23.456650Z" + "iopub.execute_input": "2023-11-21T08:10:10.211796Z", + "iopub.status.busy": "2023-11-21T08:10:10.211366Z", + "iopub.status.idle": "2023-11-21T08:10:10.214088Z", + "shell.execute_reply": "2023-11-21T08:10:10.213532Z" } }, "outputs": [], @@ -298,10 +298,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.459780Z", - "iopub.status.busy": "2023-11-20T20:33:23.459437Z", - "iopub.status.idle": "2023-11-20T20:33:23.486542Z", - "shell.execute_reply": "2023-11-20T20:33:23.485851Z" + "iopub.execute_input": "2023-11-21T08:10:10.216658Z", + "iopub.status.busy": "2023-11-21T08:10:10.216204Z", + "iopub.status.idle": "2023-11-21T08:10:10.246683Z", + "shell.execute_reply": "2023-11-21T08:10:10.246035Z" } }, "outputs": [ @@ -315,7 +315,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0a3e1d0ea46429196251824fcdfc628", + "model_id": "77fe53bc0d0b46b291880f01745c2a75", "version_major": 2, "version_minor": 0 }, @@ -329,7 +329,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5efb438f2c814efbad68e107b18a4c31", + "model_id": "4718bab615de48138738b46d1327472b", "version_major": 2, "version_minor": 0 }, @@ -387,10 +387,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.494080Z", - "iopub.status.busy": "2023-11-20T20:33:23.493648Z", - "iopub.status.idle": "2023-11-20T20:33:23.500432Z", - "shell.execute_reply": "2023-11-20T20:33:23.499828Z" + "iopub.execute_input": "2023-11-21T08:10:10.251546Z", + "iopub.status.busy": "2023-11-21T08:10:10.251115Z", + "iopub.status.idle": "2023-11-21T08:10:10.258250Z", + "shell.execute_reply": "2023-11-21T08:10:10.257748Z" }, "nbsphinx": "hidden" }, @@ -421,10 +421,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.502794Z", - "iopub.status.busy": "2023-11-20T20:33:23.502429Z", - "iopub.status.idle": "2023-11-20T20:33:23.506341Z", - "shell.execute_reply": "2023-11-20T20:33:23.505802Z" + "iopub.execute_input": "2023-11-21T08:10:10.260706Z", + "iopub.status.busy": "2023-11-21T08:10:10.260350Z", + "iopub.status.idle": "2023-11-21T08:10:10.264061Z", + "shell.execute_reply": "2023-11-21T08:10:10.263511Z" }, "nbsphinx": "hidden" }, @@ -447,10 +447,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.508579Z", - "iopub.status.busy": "2023-11-20T20:33:23.508220Z", - "iopub.status.idle": "2023-11-20T20:33:23.515124Z", - "shell.execute_reply": "2023-11-20T20:33:23.514598Z" + "iopub.execute_input": "2023-11-21T08:10:10.266428Z", + "iopub.status.busy": "2023-11-21T08:10:10.266067Z", + "iopub.status.idle": "2023-11-21T08:10:10.273026Z", + "shell.execute_reply": "2023-11-21T08:10:10.272490Z" } }, "outputs": [], @@ -500,10 +500,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.517405Z", - "iopub.status.busy": "2023-11-20T20:33:23.517051Z", - "iopub.status.idle": "2023-11-20T20:33:23.552227Z", - "shell.execute_reply": "2023-11-20T20:33:23.551583Z" + "iopub.execute_input": "2023-11-21T08:10:10.275392Z", + "iopub.status.busy": "2023-11-21T08:10:10.275034Z", + "iopub.status.idle": "2023-11-21T08:10:10.312381Z", + "shell.execute_reply": "2023-11-21T08:10:10.311696Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.555173Z", - "iopub.status.busy": "2023-11-20T20:33:23.554767Z", - "iopub.status.idle": "2023-11-20T20:33:23.596520Z", - "shell.execute_reply": "2023-11-20T20:33:23.595732Z" + "iopub.execute_input": "2023-11-21T08:10:10.315533Z", + "iopub.status.busy": "2023-11-21T08:10:10.315050Z", + "iopub.status.idle": "2023-11-21T08:10:10.354208Z", + "shell.execute_reply": "2023-11-21T08:10:10.353462Z" }, "nbsphinx": "hidden" }, @@ -602,10 +602,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.599908Z", - "iopub.status.busy": "2023-11-20T20:33:23.599446Z", - "iopub.status.idle": "2023-11-20T20:33:23.717964Z", - "shell.execute_reply": "2023-11-20T20:33:23.717259Z" + "iopub.execute_input": "2023-11-21T08:10:10.357490Z", + "iopub.status.busy": "2023-11-21T08:10:10.357065Z", + "iopub.status.idle": "2023-11-21T08:10:10.477828Z", + "shell.execute_reply": "2023-11-21T08:10:10.477069Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.720938Z", - "iopub.status.busy": "2023-11-20T20:33:23.720536Z", - "iopub.status.idle": "2023-11-20T20:33:26.236639Z", - "shell.execute_reply": "2023-11-20T20:33:26.235895Z" + "iopub.execute_input": "2023-11-21T08:10:10.480835Z", + "iopub.status.busy": "2023-11-21T08:10:10.480282Z", + "iopub.status.idle": "2023-11-21T08:10:12.978160Z", + "shell.execute_reply": "2023-11-21T08:10:12.977318Z" } }, "outputs": [ @@ -761,10 +761,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:26.239543Z", - "iopub.status.busy": "2023-11-20T20:33:26.239315Z", - "iopub.status.idle": "2023-11-20T20:33:26.298278Z", - "shell.execute_reply": "2023-11-20T20:33:26.297666Z" + "iopub.execute_input": "2023-11-21T08:10:12.980830Z", + "iopub.status.busy": "2023-11-21T08:10:12.980618Z", + "iopub.status.idle": "2023-11-21T08:10:13.041260Z", + "shell.execute_reply": "2023-11-21T08:10:13.040673Z" } }, "outputs": [ @@ -874,28 +874,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "05f57ef8b7df4d99a6bd44a3c6f74434": { - "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_5f1f34b848404f10b9e408027a29f006", - "placeholder": "", - "style": "IPY_MODEL_81c8b8a85d1645b9ab152f5597e2196f", - "value": "number of examples processed for checking labels: " - } - }, - "087593326d75413093b4109a553f5bba": { + "10dfea9c77df45c58a469d8cb2783a8e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -947,59 +926,31 @@ "width": null } }, - "1716cf2233d34e9da84989eb71da9590": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "130d80a19c3944dfacb6954cc345922c": { + "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", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", "_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": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_67817907cf194da2a49ad4d49a9db689", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_be306dd80bba4e7a8027f12651829f58", + "value": 50.0 } }, - "1f8f580207554b25aa44709417d6970a": { + "33e51882eef14b66bff8fd4a70ca3d39": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1014,39 +965,50 @@ "description_width": "" } }, - "2677b8c5242b40ee91205ef5e600d8f1": { + "4718bab615de48138738b46d1327472b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6f701f3aa1914e999e0744e4bf17676e", + "IPY_MODEL_9bde0eac9d1c40b9aa2e84baf8aa576e", + "IPY_MODEL_4fce070c575d4a4a995ceb811ef4e1bd" + ], + "layout": "IPY_MODEL_b6b25cecbefb48bab68a91da8bbc59a3" } }, - "3ce6160c4c1b4e69ad84de4a2be76168": { + "4fce070c575d4a4a995ceb811ef4e1bd": { "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_d94baa0ceb6e447b9b4fd7a5faf316a0", + "placeholder": "", + "style": "IPY_MODEL_f0a9cb1779d4435594b55e4260328c86", + "value": " 10000/? [00:00<00:00, 825244.27it/s]" } }, - "3e359595dbb8450c989b68a2993f3bbb": { + "5fb27529a3714977aa04b9c5d6caf60d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1098,52 +1060,7 @@ "width": null } }, - "471a993f6a0f48dd8163ec66b01f9d13": { - "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_c57e986bf9814d16871ee13de739ac0f", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2677b8c5242b40ee91205ef5e600d8f1", - "value": 50.0 - } - }, - "48ccc93c3c2d45f997b350ee87662d8c": { - "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_1716cf2233d34e9da84989eb71da9590", - "placeholder": "", - "style": "IPY_MODEL_b12e2f9c57484a99a9602157f9b23c82", - "value": " 10000/? [00:00<00:00, 1144296.39it/s]" - } - }, - "4ee2cd8aeba64e92a91c9db6f5c167b6": { + "6274f34e4a3441879d59801cbfae2053": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1195,44 +1112,7 @@ "width": null } }, - "5c79815a4acf43e58f4f86c490f5aba9": { - "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": "" - } - }, - "5efb438f2c814efbad68e107b18a4c31": { - "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_05f57ef8b7df4d99a6bd44a3c6f74434", - "IPY_MODEL_471a993f6a0f48dd8163ec66b01f9d13", - "IPY_MODEL_48ccc93c3c2d45f997b350ee87662d8c" - ], - "layout": "IPY_MODEL_3e359595dbb8450c989b68a2993f3bbb" - } - }, - "5f1f34b848404f10b9e408027a29f006": { + "67817907cf194da2a49ad4d49a9db689": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1284,31 +1164,7 @@ "width": null } }, - "6972e04d168e4d6794070d6f948dad5f": { - "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_4ee2cd8aeba64e92a91c9db6f5c167b6", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3ce6160c4c1b4e69ad84de4a2be76168", - "value": 50.0 - } - }, - "6ad2936986e84ad2815e64333504dd68": { + "6f701f3aa1914e999e0744e4bf17676e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1323,13 +1179,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_087593326d75413093b4109a553f5bba", + "layout": "IPY_MODEL_10dfea9c77df45c58a469d8cb2783a8e", "placeholder": "", - "style": "IPY_MODEL_1f8f580207554b25aa44709417d6970a", - "value": " 10000/? [00:00<00:00, 1055436.34it/s]" + "style": "IPY_MODEL_b9bf1bf2f3fe4b68a180cf1559954b8d", + "value": "number of examples processed for checking labels: " } }, - "81c8b8a85d1645b9ab152f5597e2196f": { + "74c8fb7c3e134ce29d4889062d4861e7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1344,7 +1200,7 @@ "description_width": "" } }, - "a0a3e1d0ea46429196251824fcdfc628": { + "77fe53bc0d0b46b291880f01745c2a75": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -1359,14 +1215,38 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_aa01de61964a4d56bce43ea1cc96f29b", - "IPY_MODEL_6972e04d168e4d6794070d6f948dad5f", - "IPY_MODEL_6ad2936986e84ad2815e64333504dd68" + "IPY_MODEL_d9dcc597385445e9b83c2de0b466927d", + "IPY_MODEL_130d80a19c3944dfacb6954cc345922c", + "IPY_MODEL_9c734373e916465a91d4e8bb14587fb7" ], - "layout": "IPY_MODEL_ab1dfa12b84e48d393fb9ccc3cfa51fc" + "layout": "IPY_MODEL_bf324747cb6b4aa69d2b9eee390ab9d2" } }, - "aa01de61964a4d56bce43ea1cc96f29b": { + "9bde0eac9d1c40b9aa2e84baf8aa576e": { + "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_5fb27529a3714977aa04b9c5d6caf60d", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_bc96821286754fc68345d02fae37d734", + "value": 50.0 + } + }, + "9c734373e916465a91d4e8bb14587fb7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1381,13 +1261,65 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_e02109ca2ce34d27827d115de0afbdc2", + "layout": "IPY_MODEL_6274f34e4a3441879d59801cbfae2053", "placeholder": "", - "style": "IPY_MODEL_5c79815a4acf43e58f4f86c490f5aba9", - "value": "number of examples processed for estimating thresholds: " + "style": "IPY_MODEL_33e51882eef14b66bff8fd4a70ca3d39", + "value": " 10000/? [00:00<00:00, 1066167.77it/s]" + } + }, + "a1da34c996e84702a838a11b9fc42a3a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "ab1dfa12b84e48d393fb9ccc3cfa51fc": { + "b6b25cecbefb48bab68a91da8bbc59a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1439,7 +1371,7 @@ "width": null } }, - "b12e2f9c57484a99a9602157f9b23c82": { + "b9bf1bf2f3fe4b68a180cf1559954b8d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1454,7 +1386,39 @@ "description_width": "" } }, - "c57e986bf9814d16871ee13de739ac0f": { + "bc96821286754fc68345d02fae37d734": { + "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": "" + } + }, + "be306dd80bba4e7a8027f12651829f58": { + "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": "" + } + }, + "bf324747cb6b4aa69d2b9eee390ab9d2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1506,7 +1470,7 @@ "width": null } }, - "e02109ca2ce34d27827d115de0afbdc2": { + "d94baa0ceb6e447b9b4fd7a5faf316a0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1557,6 +1521,42 @@ "visibility": null, "width": null } + }, + "d9dcc597385445e9b83c2de0b466927d": { + "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_a1da34c996e84702a838a11b9fc42a3a", + "placeholder": "", + "style": "IPY_MODEL_74c8fb7c3e134ce29d4889062d4861e7", + "value": "number of examples processed for estimating thresholds: " + } + }, + "f0a9cb1779d4435594b55e4260328c86": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/image.ipynb b/master/.doctrees/nbsphinx/tutorials/image.ipynb index b1e4ff362..08c7524f7 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": "2023-11-20T20:33:31.629173Z", - "iopub.status.busy": "2023-11-20T20:33:31.628719Z", - "iopub.status.idle": "2023-11-20T20:33:33.764885Z", - "shell.execute_reply": "2023-11-20T20:33:33.764262Z" + "iopub.execute_input": "2023-11-21T08:10:18.258133Z", + "iopub.status.busy": "2023-11-21T08:10:18.257949Z", + "iopub.status.idle": "2023-11-21T08:10:20.366284Z", + "shell.execute_reply": "2023-11-21T08:10:20.365585Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:33.767803Z", - "iopub.status.busy": "2023-11-20T20:33:33.767385Z", - "iopub.status.idle": "2023-11-20T20:33:33.771144Z", - "shell.execute_reply": "2023-11-20T20:33:33.770649Z" + "iopub.execute_input": "2023-11-21T08:10:20.369424Z", + "iopub.status.busy": "2023-11-21T08:10:20.369024Z", + "iopub.status.idle": "2023-11-21T08:10:20.372928Z", + "shell.execute_reply": "2023-11-21T08:10:20.372374Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:33.773478Z", - "iopub.status.busy": "2023-11-20T20:33:33.773117Z", - "iopub.status.idle": "2023-11-20T20:33:46.006704Z", - "shell.execute_reply": "2023-11-20T20:33:46.006150Z" + "iopub.execute_input": "2023-11-21T08:10:20.375365Z", + "iopub.status.busy": "2023-11-21T08:10:20.375012Z", + "iopub.status.idle": "2023-11-21T08:10:36.724947Z", + "shell.execute_reply": "2023-11-21T08:10:36.724285Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "48028e2d8e864d12bcf33caebd44f169", + "model_id": "17abac331e704de9a7ebacb0a7b2d5bb", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "88232f2a0333433daf181c32d047baa5", + "model_id": "6e6755b4623a4f0a905b741ee7cb5453", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b20ae6b1e0ec42618e40f7da092950a5", + "model_id": "aeaeeb5988cb4638aadb1b0840bf3745", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "524c2b01440a4c4b8419c369e0bf7393", + "model_id": "36ec644042e74e14874a773565a61470", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b43869cf841c45c69f9946692ef05b2a", + "model_id": "f5e3565367434275816c74477904c0b2", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "551aaea6faa348c6b6974eb09fcbc1c5", + "model_id": "238ab964fff64bba8f57438714294a3e", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce8ea6b5cf584edcbdd7c21a26970e86", + "model_id": "7329880eab5847d298a65b5166a87566", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "25525fef3faa41bfa3d7fb01644654df", + "model_id": "b938a28698dd446c98758c5ae37fbbee", "version_major": 2, "version_minor": 0 }, @@ -274,7 +274,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d3e80e28ba1b48afba73110f15d0fb40", + "model_id": "2a2efa39f0394a3ba2646b507175ce6a", "version_major": 2, "version_minor": 0 }, @@ -288,7 +288,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e7fbe13ae9d4837bc4327ad2e0b8c44", + "model_id": "00d5fb535f6e4fe69370f09780bb731a", "version_major": 2, "version_minor": 0 }, @@ -302,7 +302,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f54927ed92ee4370819b6a462532901d", + "model_id": "0f05792de3bd42fc8948e91877389b10", "version_major": 2, "version_minor": 0 }, @@ -344,10 +344,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:46.009274Z", - "iopub.status.busy": "2023-11-20T20:33:46.009052Z", - "iopub.status.idle": "2023-11-20T20:33:46.013441Z", - "shell.execute_reply": "2023-11-20T20:33:46.012774Z" + "iopub.execute_input": "2023-11-21T08:10:36.727565Z", + "iopub.status.busy": "2023-11-21T08:10:36.727213Z", + "iopub.status.idle": "2023-11-21T08:10:36.731171Z", + "shell.execute_reply": "2023-11-21T08:10:36.730649Z" } }, "outputs": [ @@ -372,17 +372,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:46.016172Z", - "iopub.status.busy": "2023-11-20T20:33:46.015780Z", - "iopub.status.idle": "2023-11-20T20:33:56.992939Z", - "shell.execute_reply": "2023-11-20T20:33:56.992342Z" + "iopub.execute_input": "2023-11-21T08:10:36.733369Z", + "iopub.status.busy": "2023-11-21T08:10:36.733167Z", + "iopub.status.idle": "2023-11-21T08:10:47.327744Z", + "shell.execute_reply": "2023-11-21T08:10:47.327138Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "de9a8b3d0e42412eb75d8abc7d35ee38", + "model_id": "840bc43fd3d340428a779ceef8112f22", "version_major": 2, "version_minor": 0 }, @@ -420,10 +420,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:56.995688Z", - "iopub.status.busy": "2023-11-20T20:33:56.995263Z", - "iopub.status.idle": "2023-11-20T20:34:18.070931Z", - "shell.execute_reply": "2023-11-20T20:34:18.070202Z" + "iopub.execute_input": "2023-11-21T08:10:47.330620Z", + "iopub.status.busy": "2023-11-21T08:10:47.330286Z", + "iopub.status.idle": "2023-11-21T08:11:09.239929Z", + "shell.execute_reply": "2023-11-21T08:11:09.239191Z" } }, "outputs": [], @@ -456,10 +456,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:18.074102Z", - "iopub.status.busy": "2023-11-20T20:34:18.073666Z", - "iopub.status.idle": "2023-11-20T20:34:18.079589Z", - "shell.execute_reply": "2023-11-20T20:34:18.079082Z" + "iopub.execute_input": "2023-11-21T08:11:09.243446Z", + "iopub.status.busy": "2023-11-21T08:11:09.242919Z", + "iopub.status.idle": "2023-11-21T08:11:09.248274Z", + "shell.execute_reply": "2023-11-21T08:11:09.247640Z" } }, "outputs": [], @@ -497,10 +497,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:18.082138Z", - "iopub.status.busy": "2023-11-20T20:34:18.081605Z", - "iopub.status.idle": "2023-11-20T20:34:18.086103Z", - "shell.execute_reply": "2023-11-20T20:34:18.085599Z" + "iopub.execute_input": "2023-11-21T08:11:09.250718Z", + "iopub.status.busy": "2023-11-21T08:11:09.250348Z", + "iopub.status.idle": "2023-11-21T08:11:09.254490Z", + "shell.execute_reply": "2023-11-21T08:11:09.253930Z" }, "nbsphinx": "hidden" }, @@ -637,10 +637,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:18.088377Z", - "iopub.status.busy": "2023-11-20T20:34:18.088180Z", - "iopub.status.idle": "2023-11-20T20:34:18.097740Z", - "shell.execute_reply": "2023-11-20T20:34:18.097240Z" + "iopub.execute_input": "2023-11-21T08:11:09.256878Z", + "iopub.status.busy": "2023-11-21T08:11:09.256559Z", + "iopub.status.idle": "2023-11-21T08:11:09.266476Z", + "shell.execute_reply": "2023-11-21T08:11:09.265868Z" }, "nbsphinx": "hidden" }, @@ -765,10 +765,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:18.100298Z", - "iopub.status.busy": "2023-11-20T20:34:18.099861Z", - "iopub.status.idle": "2023-11-20T20:34:18.127203Z", - "shell.execute_reply": "2023-11-20T20:34:18.126716Z" + "iopub.execute_input": "2023-11-21T08:11:09.268829Z", + "iopub.status.busy": "2023-11-21T08:11:09.268471Z", + "iopub.status.idle": "2023-11-21T08:11:09.303768Z", + "shell.execute_reply": "2023-11-21T08:11:09.303199Z" } }, "outputs": [], @@ -805,10 +805,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:18.129759Z", - "iopub.status.busy": "2023-11-20T20:34:18.129316Z", - "iopub.status.idle": "2023-11-20T20:34:51.336048Z", - "shell.execute_reply": "2023-11-20T20:34:51.335316Z" + "iopub.execute_input": "2023-11-21T08:11:09.306400Z", + "iopub.status.busy": "2023-11-21T08:11:09.306095Z", + "iopub.status.idle": "2023-11-21T08:11:41.507278Z", + "shell.execute_reply": "2023-11-21T08:11:41.506420Z" } }, "outputs": [ @@ -824,14 +824,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 5.139\n" + "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.542\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.609\n", + "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.524\n", "Computing feature embeddings ...\n" ] }, @@ -848,7 +848,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:04, 8.47it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 16.46it/s]" ] }, { @@ -856,7 +856,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 44.69it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 48.22it/s]" ] }, { @@ -864,7 +864,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 55.85it/s]" + " 42%|████▎ | 17/40 [00:00<00:00, 54.86it/s]" ] }, { @@ -872,7 +872,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 62.81it/s]" + " 60%|██████ | 24/40 [00:00<00:00, 58.05it/s]" ] }, { @@ -880,7 +880,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 67.44it/s]" + " 78%|███████▊ | 31/40 [00:00<00:00, 59.76it/s]" ] }, { @@ -888,7 +888,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 61.51it/s]" + "100%|██████████| 40/40 [00:00<00:00, 58.83it/s]" ] }, { @@ -918,7 +918,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:04, 9.18it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 18.80it/s]" ] }, { @@ -926,7 +926,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 48.34it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 50.33it/s]" ] }, { @@ -934,7 +934,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 58.97it/s]" + " 42%|████▎ | 17/40 [00:00<00:00, 58.48it/s]" ] }, { @@ -942,7 +942,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 65.22it/s]" + " 62%|██████▎ | 25/40 [00:00<00:00, 64.34it/s]" ] }, { @@ -950,7 +950,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 69.42it/s]" + " 80%|████████ | 32/40 [00:00<00:00, 66.01it/s]" ] }, { @@ -958,7 +958,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 63.84it/s]" + "100%|██████████| 40/40 [00:00<00:00, 62.99it/s]" ] }, { @@ -980,14 +980,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.692\n" + "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.852\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 5.033\n", + "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.727\n", "Computing feature embeddings ...\n" ] }, @@ -1004,7 +1004,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 2/40 [00:00<00:01, 19.25it/s]" + " 2%|▎ | 1/40 [00:00<00:04, 9.13it/s]" ] }, { @@ -1012,7 +1012,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 10/40 [00:00<00:00, 50.59it/s]" + " 20%|██ | 8/40 [00:00<00:00, 42.65it/s]" ] }, { @@ -1020,7 +1020,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 18/40 [00:00<00:00, 59.95it/s]" + " 40%|████ | 16/40 [00:00<00:00, 57.04it/s]" ] }, { @@ -1028,7 +1028,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 26/40 [00:00<00:00, 64.42it/s]" + " 60%|██████ | 24/40 [00:00<00:00, 63.24it/s]" ] }, { @@ -1036,7 +1036,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 34/40 [00:00<00:00, 67.05it/s]" + " 78%|███████▊ | 31/40 [00:00<00:00, 65.36it/s]" ] }, { @@ -1044,7 +1044,15 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 60.32it/s]" + "100%|██████████| 40/40 [00:00<00:00, 72.89it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 40/40 [00:00<00:00, 60.78it/s]" ] }, { @@ -1074,7 +1082,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 2/40 [00:00<00:02, 18.26it/s]" + " 2%|▎ | 1/40 [00:00<00:04, 7.96it/s]" ] }, { @@ -1082,7 +1090,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 10/40 [00:00<00:00, 49.74it/s]" + " 22%|██▎ | 9/40 [00:00<00:00, 43.54it/s]" ] }, { @@ -1090,7 +1098,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 58.44it/s]" + " 42%|████▎ | 17/40 [00:00<00:00, 56.73it/s]" ] }, { @@ -1098,7 +1106,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 63.85it/s]" + " 62%|██████▎ | 25/40 [00:00<00:00, 62.96it/s]" ] }, { @@ -1106,7 +1114,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 67.64it/s]" + " 82%|████████▎ | 33/40 [00:00<00:00, 67.62it/s]" ] }, { @@ -1114,7 +1122,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 63.03it/s]" + "100%|██████████| 40/40 [00:00<00:00, 60.83it/s]" ] }, { @@ -1136,14 +1144,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.994\n" + "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.868\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.830\n", + "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.710\n", "Computing feature embeddings ...\n" ] }, @@ -1160,7 +1168,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:04, 8.02it/s]" + " 5%|▌ | 2/40 [00:00<00:01, 19.05it/s]" ] }, { @@ -1168,7 +1176,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 44.23it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 51.29it/s]" ] }, { @@ -1176,7 +1184,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 56.79it/s]" + " 45%|████▌ | 18/40 [00:00<00:00, 61.74it/s]" ] }, { @@ -1184,7 +1192,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 24/40 [00:00<00:00, 61.36it/s]" + " 65%|██████▌ | 26/40 [00:00<00:00, 65.73it/s]" ] }, { @@ -1192,7 +1200,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 32/40 [00:00<00:00, 65.62it/s]" + " 85%|████████▌ | 34/40 [00:00<00:00, 69.56it/s]" ] }, { @@ -1200,7 +1208,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 60.07it/s]" + "100%|██████████| 40/40 [00:00<00:00, 61.52it/s]" ] }, { @@ -1230,7 +1238,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:04, 8.48it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 18.34it/s]" ] }, { @@ -1238,7 +1246,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 46.26it/s]" + " 22%|██▎ | 9/40 [00:00<00:00, 45.04it/s]" ] }, { @@ -1246,7 +1254,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 59.87it/s]" + " 42%|████▎ | 17/40 [00:00<00:00, 57.87it/s]" ] }, { @@ -1254,7 +1262,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 66.53it/s]" + " 62%|██████▎ | 25/40 [00:00<00:00, 64.27it/s]" ] }, { @@ -1262,7 +1270,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 34/40 [00:00<00:00, 72.24it/s]" + " 82%|████████▎ | 33/40 [00:00<00:00, 68.45it/s]" ] }, { @@ -1270,21 +1278,21 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 64.05it/s]" + "100%|██████████| 40/40 [00:00<00:00, 61.19it/s]" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "\n" + "Finished Training\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Finished Training\n" + "\n" ] } ], @@ -1347,10 +1355,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:51.338922Z", - "iopub.status.busy": "2023-11-20T20:34:51.338483Z", - "iopub.status.idle": "2023-11-20T20:34:51.352766Z", - "shell.execute_reply": "2023-11-20T20:34:51.352234Z" + "iopub.execute_input": "2023-11-21T08:11:41.510269Z", + "iopub.status.busy": "2023-11-21T08:11:41.509984Z", + "iopub.status.idle": "2023-11-21T08:11:41.524878Z", + "shell.execute_reply": "2023-11-21T08:11:41.524297Z" } }, "outputs": [], @@ -1375,10 +1383,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:51.355205Z", - "iopub.status.busy": "2023-11-20T20:34:51.354733Z", - "iopub.status.idle": "2023-11-20T20:34:51.787258Z", - "shell.execute_reply": "2023-11-20T20:34:51.786555Z" + "iopub.execute_input": "2023-11-21T08:11:41.528191Z", + "iopub.status.busy": "2023-11-21T08:11:41.527593Z", + "iopub.status.idle": "2023-11-21T08:11:41.987616Z", + "shell.execute_reply": "2023-11-21T08:11:41.986890Z" } }, "outputs": [], @@ -1398,10 +1406,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:51.790267Z", - "iopub.status.busy": "2023-11-20T20:34:51.790011Z", - "iopub.status.idle": "2023-11-20T20:38:12.567008Z", - "shell.execute_reply": "2023-11-20T20:38:12.566283Z" + "iopub.execute_input": "2023-11-21T08:11:41.990825Z", + "iopub.status.busy": "2023-11-21T08:11:41.990293Z", + "iopub.status.idle": "2023-11-21T08:15:03.947350Z", + "shell.execute_reply": "2023-11-21T08:15:03.946586Z" } }, "outputs": [ @@ -1438,7 +1446,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f51acd0294d4c5787f936f144a867e6", + "model_id": "383b11e2af1e4e5db72eaf0ad28d4f90", "version_major": 2, "version_minor": 0 }, @@ -1477,10 +1485,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:12.569843Z", - "iopub.status.busy": "2023-11-20T20:38:12.569281Z", - "iopub.status.idle": "2023-11-20T20:38:13.042273Z", - "shell.execute_reply": "2023-11-20T20:38:13.041616Z" + "iopub.execute_input": "2023-11-21T08:15:03.950477Z", + "iopub.status.busy": "2023-11-21T08:15:03.949762Z", + "iopub.status.idle": "2023-11-21T08:15:04.427867Z", + "shell.execute_reply": "2023-11-21T08:15:04.427187Z" } }, "outputs": [ @@ -1652,10 +1660,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.045809Z", - "iopub.status.busy": "2023-11-20T20:38:13.045204Z", - "iopub.status.idle": "2023-11-20T20:38:13.108468Z", - "shell.execute_reply": "2023-11-20T20:38:13.107909Z" + "iopub.execute_input": "2023-11-21T08:15:04.431207Z", + "iopub.status.busy": "2023-11-21T08:15:04.430759Z", + "iopub.status.idle": "2023-11-21T08:15:04.495822Z", + "shell.execute_reply": "2023-11-21T08:15:04.495180Z" } }, "outputs": [ @@ -1759,10 +1767,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.111229Z", - "iopub.status.busy": "2023-11-20T20:38:13.110714Z", - "iopub.status.idle": "2023-11-20T20:38:13.119875Z", - "shell.execute_reply": "2023-11-20T20:38:13.119369Z" + "iopub.execute_input": "2023-11-21T08:15:04.498580Z", + "iopub.status.busy": "2023-11-21T08:15:04.498099Z", + "iopub.status.idle": "2023-11-21T08:15:04.507308Z", + "shell.execute_reply": "2023-11-21T08:15:04.506702Z" } }, "outputs": [ @@ -1892,10 +1900,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.122319Z", - "iopub.status.busy": "2023-11-20T20:38:13.121811Z", - "iopub.status.idle": "2023-11-20T20:38:13.126716Z", - "shell.execute_reply": "2023-11-20T20:38:13.126100Z" + "iopub.execute_input": "2023-11-21T08:15:04.509744Z", + "iopub.status.busy": "2023-11-21T08:15:04.509374Z", + "iopub.status.idle": "2023-11-21T08:15:04.514121Z", + "shell.execute_reply": "2023-11-21T08:15:04.513592Z" }, "nbsphinx": "hidden" }, @@ -1941,10 +1949,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.129019Z", - "iopub.status.busy": "2023-11-20T20:38:13.128682Z", - "iopub.status.idle": "2023-11-20T20:38:13.803701Z", - "shell.execute_reply": "2023-11-20T20:38:13.803024Z" + "iopub.execute_input": "2023-11-21T08:15:04.516530Z", + "iopub.status.busy": "2023-11-21T08:15:04.516175Z", + "iopub.status.idle": "2023-11-21T08:15:05.189600Z", + "shell.execute_reply": "2023-11-21T08:15:05.188909Z" } }, "outputs": [ @@ -1979,10 +1987,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.806179Z", - "iopub.status.busy": "2023-11-20T20:38:13.805928Z", - "iopub.status.idle": "2023-11-20T20:38:13.814630Z", - "shell.execute_reply": "2023-11-20T20:38:13.814012Z" + "iopub.execute_input": "2023-11-21T08:15:05.192320Z", + "iopub.status.busy": "2023-11-21T08:15:05.191958Z", + "iopub.status.idle": "2023-11-21T08:15:05.200786Z", + "shell.execute_reply": "2023-11-21T08:15:05.200302Z" } }, "outputs": [ @@ -2149,10 +2157,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.817092Z", - "iopub.status.busy": "2023-11-20T20:38:13.816668Z", - "iopub.status.idle": "2023-11-20T20:38:13.824337Z", - "shell.execute_reply": "2023-11-20T20:38:13.823853Z" + "iopub.execute_input": "2023-11-21T08:15:05.203307Z", + "iopub.status.busy": "2023-11-21T08:15:05.202947Z", + "iopub.status.idle": "2023-11-21T08:15:05.210602Z", + "shell.execute_reply": "2023-11-21T08:15:05.210107Z" }, "nbsphinx": "hidden" }, @@ -2228,10 +2236,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.826642Z", - "iopub.status.busy": "2023-11-20T20:38:13.826217Z", - "iopub.status.idle": "2023-11-20T20:38:14.286779Z", - "shell.execute_reply": "2023-11-20T20:38:14.286121Z" + "iopub.execute_input": "2023-11-21T08:15:05.212912Z", + "iopub.status.busy": "2023-11-21T08:15:05.212544Z", + "iopub.status.idle": "2023-11-21T08:15:05.673620Z", + "shell.execute_reply": "2023-11-21T08:15:05.672940Z" } }, "outputs": [ @@ -2268,10 +2276,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.289664Z", - "iopub.status.busy": "2023-11-20T20:38:14.289163Z", - "iopub.status.idle": "2023-11-20T20:38:14.305949Z", - "shell.execute_reply": "2023-11-20T20:38:14.305417Z" + "iopub.execute_input": "2023-11-21T08:15:05.676360Z", + "iopub.status.busy": "2023-11-21T08:15:05.675969Z", + "iopub.status.idle": "2023-11-21T08:15:05.691810Z", + "shell.execute_reply": "2023-11-21T08:15:05.691269Z" } }, "outputs": [ @@ -2428,10 +2436,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.308506Z", - "iopub.status.busy": "2023-11-20T20:38:14.308086Z", - "iopub.status.idle": "2023-11-20T20:38:14.314237Z", - "shell.execute_reply": "2023-11-20T20:38:14.313629Z" + "iopub.execute_input": "2023-11-21T08:15:05.694566Z", + "iopub.status.busy": "2023-11-21T08:15:05.694143Z", + "iopub.status.idle": "2023-11-21T08:15:05.700241Z", + "shell.execute_reply": "2023-11-21T08:15:05.699627Z" }, "nbsphinx": "hidden" }, @@ -2476,10 +2484,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.316507Z", - "iopub.status.busy": "2023-11-20T20:38:14.316142Z", - "iopub.status.idle": "2023-11-20T20:38:14.758875Z", - "shell.execute_reply": "2023-11-20T20:38:14.758194Z" + "iopub.execute_input": "2023-11-21T08:15:05.702534Z", + "iopub.status.busy": "2023-11-21T08:15:05.702182Z", + "iopub.status.idle": "2023-11-21T08:15:06.094537Z", + "shell.execute_reply": "2023-11-21T08:15:06.093700Z" } }, "outputs": [ @@ -2554,10 +2562,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.761675Z", - "iopub.status.busy": "2023-11-20T20:38:14.761433Z", - "iopub.status.idle": "2023-11-20T20:38:14.771424Z", - "shell.execute_reply": "2023-11-20T20:38:14.770770Z" + "iopub.execute_input": "2023-11-21T08:15:06.097459Z", + "iopub.status.busy": "2023-11-21T08:15:06.097244Z", + "iopub.status.idle": "2023-11-21T08:15:06.108363Z", + "shell.execute_reply": "2023-11-21T08:15:06.107909Z" } }, "outputs": [ @@ -2685,10 +2693,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.774306Z", - "iopub.status.busy": "2023-11-20T20:38:14.773957Z", - "iopub.status.idle": "2023-11-20T20:38:14.780518Z", - "shell.execute_reply": "2023-11-20T20:38:14.779868Z" + "iopub.execute_input": "2023-11-21T08:15:06.111763Z", + "iopub.status.busy": "2023-11-21T08:15:06.110901Z", + "iopub.status.idle": "2023-11-21T08:15:06.116651Z", + "shell.execute_reply": "2023-11-21T08:15:06.116210Z" }, "nbsphinx": "hidden" }, @@ -2725,10 +2733,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.783274Z", - "iopub.status.busy": "2023-11-20T20:38:14.783039Z", - "iopub.status.idle": "2023-11-20T20:38:14.982193Z", - "shell.execute_reply": "2023-11-20T20:38:14.981516Z" + "iopub.execute_input": "2023-11-21T08:15:06.119199Z", + "iopub.status.busy": "2023-11-21T08:15:06.118957Z", + "iopub.status.idle": "2023-11-21T08:15:06.288857Z", + "shell.execute_reply": "2023-11-21T08:15:06.288048Z" } }, "outputs": [ @@ -2770,10 +2778,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.984816Z", - "iopub.status.busy": "2023-11-20T20:38:14.984441Z", - "iopub.status.idle": "2023-11-20T20:38:14.992903Z", - "shell.execute_reply": "2023-11-20T20:38:14.992290Z" + "iopub.execute_input": "2023-11-21T08:15:06.291781Z", + "iopub.status.busy": "2023-11-21T08:15:06.291565Z", + "iopub.status.idle": "2023-11-21T08:15:06.299967Z", + "shell.execute_reply": "2023-11-21T08:15:06.299328Z" } }, "outputs": [ @@ -2859,10 +2867,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.995574Z", - "iopub.status.busy": "2023-11-20T20:38:14.995074Z", - "iopub.status.idle": "2023-11-20T20:38:15.184538Z", - "shell.execute_reply": "2023-11-20T20:38:15.184019Z" + "iopub.execute_input": "2023-11-21T08:15:06.302392Z", + "iopub.status.busy": "2023-11-21T08:15:06.301954Z", + "iopub.status.idle": "2023-11-21T08:15:06.469345Z", + "shell.execute_reply": "2023-11-21T08:15:06.468821Z" } }, "outputs": [ @@ -2893,10 +2901,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:15.187015Z", - "iopub.status.busy": "2023-11-20T20:38:15.186672Z", - "iopub.status.idle": "2023-11-20T20:38:15.191266Z", - "shell.execute_reply": "2023-11-20T20:38:15.190670Z" + "iopub.execute_input": "2023-11-21T08:15:06.472313Z", + "iopub.status.busy": "2023-11-21T08:15:06.471690Z", + "iopub.status.idle": "2023-11-21T08:15:06.476832Z", + "shell.execute_reply": "2023-11-21T08:15:06.476400Z" }, "nbsphinx": "hidden" }, @@ -2933,139 +2941,29 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01614767a83948fa8e86ee4304c4d4ba": { - "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_c779fc2871664020998850299a7ddd3e", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_44928fac3f7147dfb1dc6df1d280ccaa", - "value": 10000.0 - } - }, - "01641a2ad627457ca1997504835d5ea6": { - "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": "" - } - }, - "01be6e03a73e439bb56bb60d38339e89": { - "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_693298b1d2794b6fa3754cb8d30e3ddd", - "placeholder": "", - "style": "IPY_MODEL_05589418ec204505b69108ab1e3e3fcf", - "value": " 5.15k/5.15k [00:00<00:00, 638kB/s]" - } - }, - "03ff247338ad48baa1c669032b7da351": { - "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": "" - } - }, - "05589418ec204505b69108ab1e3e3fcf": { - "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": "" - } - }, - "07113d7cf4e846f8870c58c42cfaa24f": { - "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_84a17f651ff94283bbd80920a2108119", - "placeholder": "", - "style": "IPY_MODEL_adc8913a3f014e889e245e96addb29a5", - "value": " 3.13k/3.13k [00:00<00:00, 417kB/s]" - } - }, - "0774373388fa46558225957d6f8ef740": { + "00d5fb535f6e4fe69370f09780bb731a": { "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_f967c00757204de787c61238ce385d32", - "placeholder": "", - "style": "IPY_MODEL_84fc08030cf640bdb8903d2b88b629f0", - "value": " 8.85k/8.85k [00:00<00:00, 1.07MB/s]" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_62aba93e6d1443ef944cba728bfb075f", + "IPY_MODEL_5a8329ec53324bf09bd8446efc66e09a", + "IPY_MODEL_907c6bc8381f4c0398777b883338efbe" + ], + "layout": "IPY_MODEL_3c3ab5f2163d44718a15427d139e2b96" } }, - "0837969dc9a4456ba48fee4d28c99201": { + "05bf72f17f66450db11ab0343e2f4a92": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3117,7 +3015,37 @@ "width": null } }, - "0b5c12ff7e5f4272b05312a7c947f42d": { + "0706c7ac0f014b07b29b0f00f4fe6563": { + "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": "" + } + }, + "0b3226e253954bffa9d4152c174f78cb": { + "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": "" + } + }, + "0d9e50cc326048f89d4ba45f99df9574": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3169,7 +3097,29 @@ "width": null } }, - "0c654c2ed5254dd2a06412cc243827cb": { + "0f05792de3bd42fc8948e91877389b10": { + "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_d639341d172948d9877d1c6c3602eb27", + "IPY_MODEL_ae0ec41b943d4815b0364787ed43a132", + "IPY_MODEL_4ce58997facf45a68f3272c8e25476a1" + ], + "layout": "IPY_MODEL_601fd080e58b47cfbf5afa2073934a46" + } + }, + "10a144afd941436686e5eb0abdfb647b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3221,22 +3171,7 @@ "width": null } }, - "0d0ec3c5c87044a88631bbc17caddd4d": { - "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": "" - } - }, - "0dbf24fb54d442b18a2afb64da344cf5": { + "135d7ed31fa04b328a6a522a17a75f9e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3288,7 +3223,7 @@ "width": null } }, - "0e9fd9b7e2c04cb3b27ae6a5b39c6643": { + "178301fe093c465fad01b1c698b40db4": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3340,7 +3275,7 @@ "width": null } }, - "0f51acd0294d4c5787f936f144a867e6": { + "17abac331e704de9a7ebacb0a7b2d5bb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -3355,56 +3290,53 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_21ef8862f77e43b3b903eed5bc3dd00a", - "IPY_MODEL_db5f8379f5f645209fdd1c01702db84c", - "IPY_MODEL_10dcd97c0eaa41f68e57b9d9b8ab10ec" + "IPY_MODEL_3ff1eb63a7d343fca37329c3fd49b84c", + "IPY_MODEL_4e880784e0de4a1a87c4919ac54382b5", + "IPY_MODEL_316572d7b0df4dcea2874066929f5fc6" ], - "layout": "IPY_MODEL_323b616232d34d2c981fa30aff426319" + "layout": "IPY_MODEL_8572c70172c64f9bb9f6916497c70410" } }, - "10889a4c08de4efe8696ecf09185e3f8": { + "17f0aabf25cd4ababf0d602d3a3aa3f6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_39f41133e33244798f1fd5308f8338a5", - "placeholder": "", - "style": "IPY_MODEL_0d0ec3c5c87044a88631bbc17caddd4d", - "value": "Downloading data: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "10dcd97c0eaa41f68e57b9d9b8ab10ec": { + "18b47621656348409b5d33b3d5da0cd9": { "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_0837969dc9a4456ba48fee4d28c99201", - "placeholder": "", - "style": "IPY_MODEL_f4aff97d336f4e5c96f81cf80fdee564", - "value": " 60000/60000 [00:28<00:00, 2127.44it/s]" + "layout": "IPY_MODEL_e652e5327e1d47638b302b6cd670be79", + "max": 8845.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a5e0c89448bf4656a32cd7784f9bf7fc", + "value": 8845.0 } }, - "12c62f98b1614d299c05d2a04c586766": { + "1ac6d55a17dd4dddb69df64307db835b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3419,23 +3351,22 @@ "description_width": "" } }, - "149959a2af424f009c160ab889b46a60": { + "1f4c1fb893ac40a9a5ab56ed2d344467": { "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": "" } }, - "18e830b21c694e79a6883824bea40014": { + "200b37b64d154fa8a2732dddbd48ce7c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3450,7 +3381,7 @@ "description_width": "" } }, - "1b1f2df01673466e8ad13f05d189e913": { + "2067cc0813434dc89eb9a86fe60e326c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3502,7 +3433,50 @@ "width": null } }, - "1d9b3f85beac49a5a57fd9e17e5bd9a5": { + "238ab964fff64bba8f57438714294a3e": { + "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_2fecc5d665444b07bf33bfe7da19f6ab", + "IPY_MODEL_9e0dea7fc84441be9c0246e93cc8be8c", + "IPY_MODEL_f759cd46ed8b4426a1cb436ee60f0928" + ], + "layout": "IPY_MODEL_e6001b3466b548dba925b562602b9082" + } + }, + "23e2cbf074c441369576d3ff1bf3d379": { + "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_05bf72f17f66450db11ab0343e2f4a92", + "placeholder": "", + "style": "IPY_MODEL_36c34f6c5a0743339e98b7996941a077", + "value": "Downloading metadata: 100%" + } + }, + "25e0f6166ffb4ca89ed4d9d4e68d425e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3554,11 +3528,57 @@ "width": null } }, - "201226d4835447c4b62158c0339cb597": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { + "2a2efa39f0394a3ba2646b507175ce6a": { + "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_37aed048505443e39625a1cc738de353", + "IPY_MODEL_49579578b8d945ebbe20752e30903d75", + "IPY_MODEL_a9c13dc210a549879c84f5cc6f3525db" + ], + "layout": "IPY_MODEL_6fdf9247d4294ded907d918a8770e898" + } + }, + "2b7c918a38494cbcb981f5fbb885adc1": { + "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_682a1d7a400e4bc09bb9f9c90d12ceaa", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e409101d664e415db7273d1d977d0a2d", + "value": 60000.0 + } + }, + "2cdb1028439d4fff829be37812d60a8e": { + "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", @@ -3606,7 +3626,52 @@ "width": null } }, - "202954e727c944819895beb546171b0d": { + "2fecc5d665444b07bf33bfe7da19f6ab": { + "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_5ff95972494c48f68ec493214e223d05", + "placeholder": "", + "style": "IPY_MODEL_dd62db19398244a29b8193eca299e34c", + "value": "Downloading data: 100%" + } + }, + "3022c14082c9454da81cd52274b97751": { + "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_c99e527f010141b7bf2c727d6523d516", + "max": 4.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_77834d748c6340bc910f8c05d5d78931", + "value": 4.0 + } + }, + "304d0ae7f4fb4e7299927d7c24d4425a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3658,22 +3723,7 @@ "width": null } }, - "211130be421244f6b3714ce1807bde58": { - "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": "" - } - }, - "21ef8862f77e43b3b903eed5bc3dd00a": { + "316572d7b0df4dcea2874066929f5fc6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3688,59 +3738,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_0b5c12ff7e5f4272b05312a7c947f42d", + "layout": "IPY_MODEL_e71917cbe6ec459bbbcb3b8bca2fc195", "placeholder": "", - "style": "IPY_MODEL_2b87b33c19694669a346dd38e62d3a87", - "value": "100%" - } - }, - "2390619468d34af396b893ce54a6b26c": { - "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_758b14a88c104520b726e1f0b9d30ab5", - "max": 4.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5146f2e01fed451ab61b4dbf5f09393f", - "value": 4.0 - } - }, - "25525fef3faa41bfa3d7fb01644654df": { - "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_f6ad2e1179124d2ab72069d848575efa", - "IPY_MODEL_d905b794fbff44a496ea697372fa71f4", - "IPY_MODEL_01be6e03a73e439bb56bb60d38339e89" - ], - "layout": "IPY_MODEL_a26e1015d1e84175802529f355278b86" + "style": "IPY_MODEL_0706c7ac0f014b07b29b0f00f4fe6563", + "value": " 4.83k/4.83k [00:00<00:00, 561kB/s]" } }, - "258cc8e320a04b3dbebacd49719336d4": { + "31b820fbb02b43ecb0e283d960cb3bc7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3755,13 +3759,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_2a8e5d6aef3340678ded6e574ab89670", + "layout": "IPY_MODEL_f412500c28c644b8a1e441588b43428d", "placeholder": "", - "style": "IPY_MODEL_211130be421244f6b3714ce1807bde58", - "value": " 10000/10000 [00:01<00:00, 7430.74 examples/s]" + "style": "IPY_MODEL_1f4c1fb893ac40a9a5ab56ed2d344467", + "value": " 26.4M/26.4M [00:00<00:00, 102MB/s]" } }, - "269ac21b2a074052b587d17101e024ed": { + "33c33ea69bcb4d399d0a06381305beca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3776,7 +3780,7 @@ "description_width": "" } }, - "26c420a5563b49839677e1197d3e99f1": { + "343b6ab90ce54c2ca7cf316d5f2f16de": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3828,7 +3832,22 @@ "width": null } }, - "2932c05c5dfd49ff816cfd4becc97c29": { + "355fe27e55b640bc8e02dbfd47196146": { + "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": "" + } + }, + "36c34f6c5a0743339e98b7996941a077": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3843,7 +3862,93 @@ "description_width": "" } }, - "2a8e5d6aef3340678ded6e574ab89670": { + "36ec644042e74e14874a773565a61470": { + "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_96cea967e91946809f69e283650f3cfa", + "IPY_MODEL_ab5fa6577cfc4343acdcb9a8ab0e727c", + "IPY_MODEL_b96ae38d17ac4e8aa05c91d02b41299c" + ], + "layout": "IPY_MODEL_a8c4e4f9bd9448faa8fccb0e764ae544" + } + }, + "36f9f77c6285402d812cdb63ca806652": { + "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_f7fbd9dd7b464b40924c26865d5e9681", + "placeholder": "", + "style": "IPY_MODEL_c29858bcd50d47429d581dde84247d4b", + "value": "Downloading readme: 100%" + } + }, + "37aed048505443e39625a1cc738de353": { + "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_bf8ec1f5fa20443899c855bfe1c12eb3", + "placeholder": "", + "style": "IPY_MODEL_355fe27e55b640bc8e02dbfd47196146", + "value": "Extracting data files: 100%" + } + }, + "383b11e2af1e4e5db72eaf0ad28d4f90": { + "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_8ab720dfbd6a462fb08368b3d9839d7d", + "IPY_MODEL_dffa766b9fcd46038c8d200644bf8b7c", + "IPY_MODEL_cb251949a9cd4ac08a2cc55bc7a674cd" + ], + "layout": "IPY_MODEL_6b4023a1ea4f4deda95d9bf97f8c7cd8" + } + }, + "3bf38eef814041e4b752768880cf7579": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3895,7 +4000,7 @@ "width": null } }, - "2ab495a687184b419a060dc882882111": { + "3c3ab5f2163d44718a15427d139e2b96": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3947,22 +4052,7 @@ "width": null } }, - "2b87b33c19694669a346dd38e62d3a87": { - "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": "" - } - }, - "2cadd1d327464866afa5782070311337": { + "3c9a30da8dab4b158596efeb727c2f35": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4014,7 +4104,7 @@ "width": null } }, - "323b616232d34d2c981fa30aff426319": { + "3e10f9d6e8fb4d7cbd2f12501209ddd2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4066,83 +4156,70 @@ "width": null } }, - "32608dbbeef04240877642ecc80be120": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "3f4636f46f4843de9d7fa780de83b077": { + "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_343b6ab90ce54c2ca7cf316d5f2f16de", + "placeholder": "", + "style": "IPY_MODEL_b4ede8281fbd4c0c8892e794a3ce4b21", + "value": "Downloading data: 100%" } }, - "32e66cc00d5c4aee80424466c15f077c": { + "3ff1eb63a7d343fca37329c3fd49b84c": { "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_0c654c2ed5254dd2a06412cc243827cb", - "max": 4.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9004cb5dad534950ae97a4b4509d248d", - "value": 4.0 + "layout": "IPY_MODEL_25e0f6166ffb4ca89ed4d9d4e68d425e", + "placeholder": "", + "style": "IPY_MODEL_d1678c4a444448f78cf6faf9e88de31c", + "value": "Downloading builder script: 100%" + } + }, + "418ada9017a240f2af05c0e27592ba7b": { + "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_58aafbb38ce44e65bd1e1688b81e5cb3", + "placeholder": "", + "style": "IPY_MODEL_33c33ea69bcb4d399d0a06381305beca", + "value": " 3.13k/3.13k [00:00<00:00, 417kB/s]" } }, - "33a829d4a6124bbaa8266503d677d6ac": { + "41e95f2819d04888807bd8eab71799b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -4158,15 +4235,73 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_9fd213954a9741cab513ca0a5d8a10e9", - "max": 4422102.0, + "layout": "IPY_MODEL_fe2d322a45aa49fa960d5cb543a823ae", + "max": 3126.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_e6ed001c028f4d7c81f00a0930b18ba7", - "value": 4422102.0 + "style": "IPY_MODEL_afb6f3242d3a40cda4ed8edc06b38b8a", + "value": 3126.0 } }, - "3728a4de8022495a827f3655079dcd46": { + "44b74f9e842a4b9e8369a69109792a23": { + "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_7a38fe3433cc40b79c739437d901f9ec", + "placeholder": "", + "style": "IPY_MODEL_5c83a3995a5c42a084cdd0764336918f", + "value": "Map (num_proc=4): 100%" + } + }, + "47944801fdbc4336b1f1042d884d88ab": { + "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": "" + } + }, + "4847c86d46074c699d0ac99c342e0cec": { + "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_7140bb2211794e1ca9f8c688a96d8d52", + "placeholder": "", + "style": "IPY_MODEL_8161c47587dd4325bf5320cdb3e3307d", + "value": " 8.85k/8.85k [00:00<00:00, 1.12MB/s]" + } + }, + "49579578b8d945ebbe20752e30903d75": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -4182,15 +4317,52 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_fd57481a82b14bee9778af0988c28884", + "layout": "IPY_MODEL_9a57f0a473114b8682bf41684295ba93", "max": 4.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_e3bda5c528804abca61f24b1f7926116", + "style": "IPY_MODEL_605cde6aa66b4a218b4f7c7c4ed203b0", "value": 4.0 } }, - "39f41133e33244798f1fd5308f8338a5": { + "4a1038ba5ca34ac7aa5ce69c8232162a": { + "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": "" + } + }, + "4ae53e1419d24fffbcb9d4fdd38f865c": { + "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_f90a38a1a205402da89c93919685a73d", + "IPY_MODEL_3022c14082c9454da81cd52274b97751", + "IPY_MODEL_fb8fe6ad653f459f92e86d630912eb12" + ], + "layout": "IPY_MODEL_6afbf50701544bbbac9e9504800ad4da" + } + }, + "4c47fc6d8139452aab032090d30e3e0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4242,7 +4414,28 @@ "width": null } }, - "3c53d6e6289d484a8fb160cc0b438f37": { + "4ce58997facf45a68f3272c8e25476a1": { + "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_178301fe093c465fad01b1c698b40db4", + "placeholder": "", + "style": "IPY_MODEL_0b3226e253954bffa9d4152c174f78cb", + "value": " 10000/10000 [00:01<00:00, 7408.20 examples/s]" + } + }, + "4e6af7162845419ba6f1d7fd006379bf": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4294,7 +4487,31 @@ "width": null } }, - "3ddfc9c0bbc945a1a7e8d1652c660bc4": { + "4e880784e0de4a1a87c4919ac54382b5": { + "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_3c9a30da8dab4b158596efeb727c2f35", + "max": 4833.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6f6be57a9fef422fad4ddb9d9b40d293", + "value": 4833.0 + } + }, + "4ebc0c652f3f4de59738117def507e2b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4346,65 +4563,22 @@ "width": null } }, - "44227b492d524436ad162cff2208b767": { - "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_2cadd1d327464866afa5782070311337", - "placeholder": "", - "style": "IPY_MODEL_47be2a974001421895885c6bf602e7b2", - "value": "Downloading data: 100%" - } - }, - "44928fac3f7147dfb1dc6df1d280ccaa": { + "4edfe39b139c4989a38f9747e8b10610": { "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": "" } }, - "453f3dc04e4d431fa0ea8abb408674d9": { - "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_0e9fd9b7e2c04cb3b27ae6a5b39c6643", - "placeholder": "", - "style": "IPY_MODEL_502b15340dd84accba6e1ac814a769f9", - "value": " 4.83k/4.83k [00:00<00:00, 421kB/s]" - } - }, - "47b3d8daf0f7497693fd67eacb621870": { + "4fbf87f19aea459c84c330ad8902ac6a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4456,7 +4630,7 @@ "width": null } }, - "47be2a974001421895885c6bf602e7b2": { + "51cee4accf5c486181474bab1553406a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4471,149 +4645,74 @@ "description_width": "" } }, - "48028e2d8e864d12bcf33caebd44f169": { - "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_b0302d36fa39486a9f28512d4ab15e25", - "IPY_MODEL_b0749091ca6f4d28981083f8fce9bddc", - "IPY_MODEL_453f3dc04e4d431fa0ea8abb408674d9" - ], - "layout": "IPY_MODEL_32608dbbeef04240877642ecc80be120" - } - }, - "4ac5d5f7897343b39185fc9ec9cb96d6": { - "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_b335dc67ea8d4747b6641fffb470c472", - "max": 8845.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5b5e20425ad04af49557176e01a5fe60", - "value": 8845.0 - } - }, - "4d4d971616cf4f37b26a87cb61ccce1a": { - "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_e804ed8ce44641cba3198712d0d56201", - "placeholder": "", - "style": "IPY_MODEL_03ff247338ad48baa1c669032b7da351", - "value": " 4.42M/4.42M [00:00<00:00, 74.9MB/s]" - } - }, - "502b15340dd84accba6e1ac814a769f9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "54fac451fc6143cb9755bc0306cbf2ea": { + "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 } }, - "5146f2e01fed451ab61b4dbf5f09393f": { + "569feec053884b3dba76ed37d94408ab": { "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": "" } }, - "524c2b01440a4c4b8419c369e0bf7393": { - "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_89682759652f47629df772e755e56104", - "IPY_MODEL_3728a4de8022495a827f3655079dcd46", - "IPY_MODEL_e0a88a82ced749ba9d7cf4a76817105d" - ], - "layout": "IPY_MODEL_47b3d8daf0f7497693fd67eacb621870" - } - }, - "551aaea6faa348c6b6974eb09fcbc1c5": { - "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_44227b492d524436ad162cff2208b767", - "IPY_MODEL_7e3b9aa5f9d047b3ba8dd4e5c4d37987", - "IPY_MODEL_6852c1a863dc410eabef5430d3fc6264" - ], - "layout": "IPY_MODEL_e8e2782be53e40caa2354669e657b533" - } - }, - "58e5fe0e48ee41eaa6d07db906fda880": { + "56de4a4423784dd5ac7bbb5cd0ee4026": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4628,7 +4727,7 @@ "description_width": "" } }, - "5a593af6f357451788fb2065bd7ddb3a": { + "5707ae6f19fd449caabccffd73d63fc6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4680,23 +4779,31 @@ "width": null } }, - "5b5e20425ad04af49557176e01a5fe60": { + "581c0319c71f4ef898a4b8987964c3da": { "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/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": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_304d0ae7f4fb4e7299927d7c24d4425a", + "max": 4422102.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_67f112b3346247bc9be2ae6c5b40aaed", + "value": 4422102.0 } }, - "5d672c887e9f476c8529e6a2e66e1d71": { + "58aafbb38ce44e65bd1e1688b81e5cb3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4748,65 +4855,46 @@ "width": null } }, - "5d967541646841d8b5dd1496807fc7ad": { + "5a8329ec53324bf09bd8446efc66e09a": { "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_e7a502585e244930a84158ac3e03c208", - "placeholder": "", - "style": "IPY_MODEL_58e5fe0e48ee41eaa6d07db906fda880", - "value": " 26.4M/26.4M [00:00<00:00, 112MB/s]" - } - }, - "6004d4cbf844405abb4f4b90b2b46cd4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "layout": "IPY_MODEL_54fac451fc6143cb9755bc0306cbf2ea", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_47944801fdbc4336b1f1042d884d88ab", + "value": 60000.0 } }, - "6852c1a863dc410eabef5430d3fc6264": { + "5c83a3995a5c42a084cdd0764336918f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_7a3de7072363429db143ce63167fc8ac", - "placeholder": "", - "style": "IPY_MODEL_9c0a25d03fe84904a5272422c280ddcd", - "value": " 29.5k/29.5k [00:00<00:00, 3.22MB/s]" + "_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": "" } }, - "693298b1d2794b6fa3754cb8d30e3ddd": { + "5df54a79704945ffb870cae91223f3dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4858,69 +4946,59 @@ "width": null } }, - "6aef84bbda1349a09ead1b964cf97707": { - "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": "" - } - }, - "6bb33f94379547448de2a4fb81857dde": { - "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": "" - } - }, - "6cd7ba9f007b42f1a799e610cb223c6a": { - "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": "" - } - }, - "72d6a12c9dce40deaf1f072febb2b139": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "5ff95972494c48f68ec493214e223d05": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "758b14a88c104520b726e1f0b9d30ab5": { + "601fd080e58b47cfbf5afa2073934a46": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4972,43 +5050,39 @@ "width": null } }, - "792c8badbbcf455a8cb318e8dd8c5476": { + "605cde6aa66b4a218b4f7c7c4ed203b0": { "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_1d9b3f85beac49a5a57fd9e17e5bd9a5", - "placeholder": "", - "style": "IPY_MODEL_79656e33f9cd45bfa0b1b6ce55f178b6", - "value": "Extracting data files: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "79656e33f9cd45bfa0b1b6ce55f178b6": { + "606e2ec41f774e10b3216e9985e8c3ee": { "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": "" } }, - "7a03d60be7ea4fe798f89923738a0141": { + "60f1f1a34cd743a99f862fb9bd8275c2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5060,7 +5134,7 @@ "width": null } }, - "7a3de7072363429db143ce63167fc8ac": { + "61216e82298d4011a94865a1a2e8511f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5112,22 +5186,28 @@ "width": null } }, - "7d0d02c45d3f4315b639e21b13471e94": { + "62aba93e6d1443ef944cba728bfb075f": { "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_135d7ed31fa04b328a6a522a17a75f9e", + "placeholder": "", + "style": "IPY_MODEL_1ac6d55a17dd4dddb69df64307db835b", + "value": "Generating train split: 100%" } }, - "7e3b9aa5f9d047b3ba8dd4e5c4d37987": { + "64786a1aacd042ef91cbf08e381a87a9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -5143,37 +5223,31 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_26c420a5563b49839677e1197d3e99f1", - "max": 29515.0, + "layout": "IPY_MODEL_2cdb1028439d4fff829be37812d60a8e", + "max": 5148.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_88a2264c8acf4d51857377b85f5e2e5a", - "value": 29515.0 + "style": "IPY_MODEL_c646352ad02146689668d52d23584062", + "value": 5148.0 } }, - "7e7fbe13ae9d4837bc4327ad2e0b8c44": { + "67f112b3346247bc9be2ae6c5b40aaed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c4dec09a365b435db5f8a35a8e9caac7", - "IPY_MODEL_f26058b640c243cb9ff437136ea514b0", - "IPY_MODEL_e8d3dbe311c94456b19ca4f524b8d671" - ], - "layout": "IPY_MODEL_3c53d6e6289d484a8fb160cc0b438f37" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "84a17f651ff94283bbd80920a2108119": { + "682a1d7a400e4bc09bb9f9c90d12ceaa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5225,96 +5299,59 @@ "width": null } }, - "84fc08030cf640bdb8903d2b88b629f0": { - "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": "" - } - }, - "88232f2a0333433daf181c32d047baa5": { - "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_cf807421f8ba46cf83291a5fc1c986c4", - "IPY_MODEL_a7d831278d6c4302a0b905439cc613f1", - "IPY_MODEL_07113d7cf4e846f8870c58c42cfaa24f" - ], - "layout": "IPY_MODEL_a30b8ec39a4c492cbd38d703138bd328" - } - }, - "88a2264c8acf4d51857377b85f5e2e5a": { - "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": "" - } - }, - "89682759652f47629df772e755e56104": { - "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_ab52c57c736a4d01b48719366a91c755", - "placeholder": "", - "style": "IPY_MODEL_89f8121e955241cc9eeb9c582d55dd54", - "value": "Downloading data files: 100%" - } - }, - "89f8121e955241cc9eeb9c582d55dd54": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "69ce5f9ce1b94195a0f33220a60fa670": { + "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 } }, - "8fdcb4c6596c43d6b6cd045f74a1e7c8": { + "6afbf50701544bbbac9e9504800ad4da": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5366,44 +5403,7 @@ "width": null } }, - "9004cb5dad534950ae97a4b4509d248d": { - "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": "" - } - }, - "909d804677f74aa680f28c70c86362f1": { - "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_dc509f7ef1cb42d8a16edd756c66cbcf", - "placeholder": "", - "style": "IPY_MODEL_93b33ae952124a7f945d1dafbf81809d", - "value": "Map (num_proc=4): 100%" - } - }, - "915530c2968f4c44baa11933c20920c9": { + "6b4023a1ea4f4deda95d9bf97f8c7cd8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5455,22 +5455,7 @@ "width": null } }, - "91b1f9d101914e39af45b58d21128e0c": { - "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": "" - } - }, - "92e3972554604f2ab22d517a7c7f79c3": { + "6d336b84fee942e9aa7b71fc49c3e337": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5522,7 +5507,7 @@ "width": null } }, - "93b33ae952124a7f945d1dafbf81809d": { + "6e0569b029c84213b1cf767843c7dc04": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -5537,7 +5522,45 @@ "description_width": "" } }, - "93b922cf8c414b80bef502f26bebd232": { + "6e6755b4623a4f0a905b741ee7cb5453": { + "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_23e2cbf074c441369576d3ff1bf3d379", + "IPY_MODEL_41e95f2819d04888807bd8eab71799b7", + "IPY_MODEL_418ada9017a240f2af05c0e27592ba7b" + ], + "layout": "IPY_MODEL_81e9ef8b8429496f8d9a868246ab3abe" + } + }, + "6f6be57a9fef422fad4ddb9d9b40d293": { + "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": "" + } + }, + "6fdf9247d4294ded907d918a8770e898": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5589,23 +5612,7 @@ "width": null } }, - "94a89a5ef94645138137c1821fa0adc9": { - "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": "" - } - }, - "970002fca31842488c16c21f274526b8": { + "7140bb2211794e1ca9f8c688a96d8d52": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5657,37 +5664,66 @@ "width": null } }, - "990b476ad51140478ed290f8546081bf": { + "7329880eab5847d298a65b5166a87566": { "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_e3fd2ce3378240fcb497451cc53f0af2", + "IPY_MODEL_581c0319c71f4ef898a4b8987964c3da", + "IPY_MODEL_ca1b6d1298bd4bedb6d54ab041ed1568" + ], + "layout": "IPY_MODEL_86e0c44fe8bf445785d7c00123bbb74d" } }, - "9c0a25d03fe84904a5272422c280ddcd": { + "74360a7e484f420ba0cb857affbfcc21": { "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/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6d336b84fee942e9aa7b71fc49c3e337", + "placeholder": "", + "style": "IPY_MODEL_56de4a4423784dd5ac7bbb5cd0ee4026", + "value": " 5.15k/5.15k [00:00<00:00, 604kB/s]" + } + }, + "747f7277cf55435fb4eeb18604789631": { + "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": "" } }, - "9fd213954a9741cab513ca0a5d8a10e9": { + "74f66b76509b4c27a3af13f7e48971ff": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5739,43 +5775,75 @@ "width": null } }, - "a1bd1b98aa8d4e439e7038a3386b0dd3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "757eb8f93d074944928213c74cdd5876": { + "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 } }, - "a24569cb00d7446fbf013c519735f2b4": { + "77834d748c6340bc910f8c05d5d78931": { "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_f4ea967b03694affb97c1c821d5fa82f", - "placeholder": "", - "style": "IPY_MODEL_6bb33f94379547448de2a4fb81857dde", - "value": "Generating test split: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "a26e1015d1e84175802529f355278b86": { + "7a38fe3433cc40b79c739437d901f9ec": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5827,29 +5895,22 @@ "width": null } }, - "a27cbf95e9124302ad3381f88bf96687": { + "8161c47587dd4325bf5320cdb3e3307d": { "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_ad3bec5e5d7448b3b47bf9fd566c10bd", - "IPY_MODEL_32e66cc00d5c4aee80424466c15f077c", - "IPY_MODEL_ded0d9490ad0464a9ea173deb658884d" - ], - "layout": "IPY_MODEL_202954e727c944819895beb546171b0d" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "a30b8ec39a4c492cbd38d703138bd328": { + "81e9ef8b8429496f8d9a868246ab3abe": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5901,31 +5962,29 @@ "width": null } }, - "a7d831278d6c4302a0b905439cc613f1": { + "840bc43fd3d340428a779ceef8112f22": { "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_93b922cf8c414b80bef502f26bebd232", - "max": 3126.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_d7edc2c692e446b2808dc209aafe6aac", - "value": 3126.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_44b74f9e842a4b9e8369a69109792a23", + "IPY_MODEL_2b7c918a38494cbcb981f5fbb885adc1", + "IPY_MODEL_87dadaf567524fa2b5095ca8713cadd4" + ], + "layout": "IPY_MODEL_3e10f9d6e8fb4d7cbd2f12501209ddd2" } }, - "aa836377b0644584a8ee8fddb0770e86": { + "8572c70172c64f9bb9f6916497c70410": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5977,28 +6036,22 @@ "width": null } }, - "ab3bcf26cabd4d628a3e6bbd88cbd8a8": { + "866bf7e31f8840feb77c866ad6be2355": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_970002fca31842488c16c21f274526b8", - "placeholder": "", - "style": "IPY_MODEL_990b476ad51140478ed290f8546081bf", - "value": "Downloading readme: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "ab52c57c736a4d01b48719366a91c755": { + "86e0c44fe8bf445785d7c00123bbb74d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6050,7 +6103,7 @@ "width": null } }, - "ad3bec5e5d7448b3b47bf9fd566c10bd": { + "87dadaf567524fa2b5095ca8713cadd4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -6065,28 +6118,50 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_d365c14db178402bb34b0be706cf6d05", + "layout": "IPY_MODEL_60f1f1a34cd743a99f862fb9bd8275c2", "placeholder": "", - "style": "IPY_MODEL_269ac21b2a074052b587d17101e024ed", - "value": "Computing checksums: 100%" + "style": "IPY_MODEL_ed01dc5ecdf5453eb5dbc7933f840eee", + "value": " 60000/60000 [00:10<00:00, 8360.10 examples/s]" } }, - "adc8913a3f014e889e245e96addb29a5": { + "8ab720dfbd6a462fb08368b3d9839d7d": { "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/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c74936d51f0740ed921a9700622a4a89", + "placeholder": "", + "style": "IPY_MODEL_866bf7e31f8840feb77c866ad6be2355", + "value": "100%" + } + }, + "8ba20bad13d342f1a72b0c8fd4402bb9": { + "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": "" } }, - "b0302d36fa39486a9f28512d4ab15e25": { + "907c6bc8381f4c0398777b883338efbe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -6101,59 +6176,49 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_aa836377b0644584a8ee8fddb0770e86", + "layout": "IPY_MODEL_0d9e50cc326048f89d4ba45f99df9574", "placeholder": "", - "style": "IPY_MODEL_12c62f98b1614d299c05d2a04c586766", - "value": "Downloading builder script: 100%" + "style": "IPY_MODEL_6e0569b029c84213b1cf767843c7dc04", + "value": " 60000/60000 [00:08<00:00, 7468.00 examples/s]" } }, - "b0749091ca6f4d28981083f8fce9bddc": { + "96cea967e91946809f69e283650f3cfa": { "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_2ab495a687184b419a060dc882882111", - "max": 4833.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_72d6a12c9dce40deaf1f072febb2b139", - "value": 4833.0 + "layout": "IPY_MODEL_9db392828b6542b8a1c55b8ee519a2e2", + "placeholder": "", + "style": "IPY_MODEL_17f0aabf25cd4ababf0d602d3a3aa3f6", + "value": "Downloading data files: 100%" } }, - "b20ae6b1e0ec42618e40f7da092950a5": { + "98f7bd253cee4c3fa731747eefe6d4fc": { "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_ab3bcf26cabd4d628a3e6bbd88cbd8a8", - "IPY_MODEL_4ac5d5f7897343b39185fc9ec9cb96d6", - "IPY_MODEL_0774373388fa46558225957d6f8ef740" - ], - "layout": "IPY_MODEL_de8eaca934674764a720c4df0e744316" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "b335dc67ea8d4747b6641fffb470c472": { + "9a57f0a473114b8682bf41684295ba93": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6205,29 +6270,7 @@ "width": null } }, - "b43869cf841c45c69f9946692ef05b2a": { - "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_10889a4c08de4efe8696ecf09185e3f8", - "IPY_MODEL_f99629b380e4462a8550a52edaa77d72", - "IPY_MODEL_5d967541646841d8b5dd1496807fc7ad" - ], - "layout": "IPY_MODEL_eb679a35e2404cd4b4dbf14f669d61d8" - } - }, - "be703ed8d63b4d37aac4ba0d78f88a04": { + "9db392828b6542b8a1c55b8ee519a2e2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6279,28 +6322,47 @@ "width": null } }, - "c4dec09a365b435db5f8a35a8e9caac7": { + "9e0dea7fc84441be9c0246e93cc8be8c": { "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_5a593af6f357451788fb2065bd7ddb3a", - "placeholder": "", - "style": "IPY_MODEL_ed3cefdbf7374abbb10dae18f7e8495f", - "value": "Generating train split: 100%" + "layout": "IPY_MODEL_757eb8f93d074944928213c74cdd5876", + "max": 29515.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_eb00096028b146cba10bd44c7165be0f", + "value": 29515.0 + } + }, + "a5e0c89448bf4656a32cd7784f9bf7fc": { + "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": "" } }, - "c779fc2871664020998850299a7ddd3e": { + "a8c4e4f9bd9448faa8fccb0e764ae544": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6352,23 +6414,91 @@ "width": null } }, - "c7e1a6ec7f054efab5206037087be6fb": { + "a9c13dc210a549879c84f5cc6f3525db": { "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/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c692bc7715ba4f168b9786293efb4715", + "placeholder": "", + "style": "IPY_MODEL_200b37b64d154fa8a2732dddbd48ce7c", + "value": " 4/4 [00:00<00:00, 3.82it/s]" + } + }, + "ab5fa6577cfc4343acdcb9a8ab0e727c": { + "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_4e6af7162845419ba6f1d7fd006379bf", + "max": 4.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_baa811997184433b93e8f74b07145b46", + "value": 4.0 + } + }, + "ae0ec41b943d4815b0364787ed43a132": { + "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_5707ae6f19fd449caabccffd73d63fc6", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_606e2ec41f774e10b3216e9985e8c3ee", + "value": 10000.0 + } + }, + "ae1e9e16acf9437eb4b8d0844ef00632": { + "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": "" } }, - "ce8ea6b5cf584edcbdd7c21a26970e86": { + "aeaeeb5988cb4638aadb1b0840bf3745": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -6383,139 +6513,60 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_fecc45267966445c932534eb2bb35ce4", - "IPY_MODEL_33a829d4a6124bbaa8266503d677d6ac", - "IPY_MODEL_4d4d971616cf4f37b26a87cb61ccce1a" + "IPY_MODEL_36f9f77c6285402d812cdb63ca806652", + "IPY_MODEL_18b47621656348409b5d33b3d5da0cd9", + "IPY_MODEL_4847c86d46074c699d0ac99c342e0cec" ], - "layout": "IPY_MODEL_eaa8f8640aef4221acfc59cbfff1289e" + "layout": "IPY_MODEL_69ce5f9ce1b94195a0f33220a60fa670" } }, - "cf807421f8ba46cf83291a5fc1c986c4": { + "afb6f3242d3a40cda4ed8edc06b38b8a": { "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_0dbf24fb54d442b18a2afb64da344cf5", - "placeholder": "", - "style": "IPY_MODEL_7d0d02c45d3f4315b639e21b13471e94", - "value": "Downloading metadata: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "d34b5b7ccc3143248409e3c3821854a6": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "b3c5fabf52194e3491a45effd0b8c9f0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" } }, - "d365c14db178402bb34b0be706cf6d05": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "b4ede8281fbd4c0c8892e794a3ce4b21": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" } }, - "d3e80e28ba1b48afba73110f15d0fb40": { + "b938a28698dd446c98758c5ae37fbbee": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -6530,14 +6581,75 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_792c8badbbcf455a8cb318e8dd8c5476", - "IPY_MODEL_2390619468d34af396b893ce54a6b26c", - "IPY_MODEL_f203f58903084ca5844e4dd7f95ef930" + "IPY_MODEL_fe2fa3e58ddc4b249fa7ea4d5663b952", + "IPY_MODEL_64786a1aacd042ef91cbf08e381a87a9", + "IPY_MODEL_74360a7e484f420ba0cb857affbfcc21" ], - "layout": "IPY_MODEL_fa4c777bec884573b66325b782d20cbe" + "layout": "IPY_MODEL_61216e82298d4011a94865a1a2e8511f" + } + }, + "b96ae38d17ac4e8aa05c91d02b41299c": { + "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_10a144afd941436686e5eb0abdfb647b", + "placeholder": "", + "style": "IPY_MODEL_f431a961370144c9b7e31682cd0263aa", + "value": " 4/4 [00:05<00:00, 1.24s/it]" + } + }, + "baa811997184433b93e8f74b07145b46": { + "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": "" + } + }, + "bc60cda6e0614f61b3d44bf44f48defa": { + "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_74f66b76509b4c27a3af13f7e48971ff", + "max": 26421880.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_747f7277cf55435fb4eeb18604789631", + "value": 26421880.0 } }, - "d402495ce6d9485dad1649ef6a5e6198": { + "bf8ec1f5fa20443899c855bfe1c12eb3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6589,7 +6701,22 @@ "width": null } }, - "d4dd8310053d46eb88540bfec436fe60": { + "c29858bcd50d47429d581dde84247d4b": { + "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": "" + } + }, + "c5f29035128845fcbb2a4fcb9e041167": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6641,7 +6768,7 @@ "width": null } }, - "d7edc2c692e446b2808dc209aafe6aac": { + "c646352ad02146689668d52d23584062": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -6657,7 +6784,7 @@ "description_width": "" } }, - "d879a2e95cbc438e8493d7702aa3d529": { + "c692bc7715ba4f168b9786293efb4715": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6709,55 +6836,7 @@ "width": null } }, - "d905b794fbff44a496ea697372fa71f4": { - "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_5d672c887e9f476c8529e6a2e66e1d71", - "max": 5148.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6aef84bbda1349a09ead1b964cf97707", - "value": 5148.0 - } - }, - "db5f8379f5f645209fdd1c01702db84c": { - "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_1b1f2df01673466e8ad13f05d189e913", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6004d4cbf844405abb4f4b90b2b46cd4", - "value": 60000.0 - } - }, - "dc509f7ef1cb42d8a16edd756c66cbcf": { + "c74936d51f0740ed921a9700622a4a89": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6809,22 +6888,7 @@ "width": null } }, - "dd7fd75fcdcd4369833b938332c3023e": { - "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": "" - } - }, - "de8eaca934674764a720c4df0e744316": { + "c99e527f010141b7bf2c727d6523d516": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6876,29 +6940,7 @@ "width": null } }, - "de9a8b3d0e42412eb75d8abc7d35ee38": { - "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_909d804677f74aa680f28c70c86362f1", - "IPY_MODEL_f8fff61f71d24c38b2f558913fbc8e35", - "IPY_MODEL_f2c39cf53e60411c942c69aebac75140" - ], - "layout": "IPY_MODEL_8fdcb4c6596c43d6b6cd045f74a1e7c8" - } - }, - "ded0d9490ad0464a9ea173deb658884d": { + "ca1b6d1298bd4bedb6d54ab041ed1568": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -6913,13 +6955,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_3ddfc9c0bbc945a1a7e8d1652c660bc4", + "layout": "IPY_MODEL_4c47fc6d8139452aab032090d30e3e0a", "placeholder": "", - "style": "IPY_MODEL_01641a2ad627457ca1997504835d5ea6", - "value": " 4/4 [00:00<00:00, 733.49it/s]" + "style": "IPY_MODEL_98f7bd253cee4c3fa731747eefe6d4fc", + "value": " 4.42M/4.42M [00:00<00:00, 55.1MB/s]" } }, - "e0a88a82ced749ba9d7cf4a76817105d": { + "cb251949a9cd4ac08a2cc55bc7a674cd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -6934,97 +6976,88 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_d4dd8310053d46eb88540bfec436fe60", + "layout": "IPY_MODEL_c5f29035128845fcbb2a4fcb9e041167", "placeholder": "", - "style": "IPY_MODEL_2932c05c5dfd49ff816cfd4becc97c29", - "value": " 4/4 [00:01<00:00, 2.23it/s]" + "style": "IPY_MODEL_4edfe39b139c4989a38f9747e8b10610", + "value": " 60000/60000 [00:28<00:00, 2141.56it/s]" } }, - "e3bda5c528804abca61f24b1f7926116": { + "d1678c4a444448f78cf6faf9e88de31c": { "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": "" } }, - "e6ed001c028f4d7c81f00a0930b18ba7": { + "d639341d172948d9877d1c6c3602eb27": { "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_4ebc0c652f3f4de59738117def507e2b", + "placeholder": "", + "style": "IPY_MODEL_4a1038ba5ca34ac7aa5ce69c8232162a", + "value": "Generating test split: 100%" } }, - "e7a502585e244930a84158ac3e03c208": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "dd62db19398244a29b8193eca299e34c": { + "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": "" + } + }, + "dffa766b9fcd46038c8d200644bf8b7c": { + "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_2067cc0813434dc89eb9a86fe60e326c", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_8ba20bad13d342f1a72b0c8fd4402bb9", + "value": 60000.0 } }, - "e804ed8ce44641cba3198712d0d56201": { + "e1e630f8a8af4b73bc6555384939dd3a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7076,7 +7109,22 @@ "width": null } }, - "e8d3dbe311c94456b19ca4f524b8d671": { + "e3fc21908c4c4246862f128f67b934ca": { + "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": "" + } + }, + "e3fd2ce3378240fcb497451cc53f0af2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -7091,13 +7139,29 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_d34b5b7ccc3143248409e3c3821854a6", + "layout": "IPY_MODEL_fabd302ba8a5447aa734842dc751cdfe", "placeholder": "", - "style": "IPY_MODEL_a1bd1b98aa8d4e439e7038a3386b0dd3", - "value": " 60000/60000 [00:08<00:00, 7387.28 examples/s]" + "style": "IPY_MODEL_569feec053884b3dba76ed37d94408ab", + "value": "Downloading data: 100%" + } + }, + "e409101d664e415db7273d1d977d0a2d": { + "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": "" } }, - "e8e2782be53e40caa2354669e657b533": { + "e6001b3466b548dba925b562602b9082": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7149,7 +7213,7 @@ "width": null } }, - "eaa8f8640aef4221acfc59cbfff1289e": { + "e652e5327e1d47638b302b6cd670be79": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7201,7 +7265,7 @@ "width": null } }, - "eb679a35e2404cd4b4dbf14f669d61d8": { + "e71917cbe6ec459bbbcb3b8bca2fc195": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7253,88 +7317,23 @@ "width": null } }, - "ed3cefdbf7374abbb10dae18f7e8495f": { + "eb00096028b146cba10bd44c7165be0f": { "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": "" } }, - "f203f58903084ca5844e4dd7f95ef930": { - "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_d879a2e95cbc438e8493d7702aa3d529", - "placeholder": "", - "style": "IPY_MODEL_91b1f9d101914e39af45b58d21128e0c", - "value": " 4/4 [00:00<00:00, 3.87it/s]" - } - }, - "f26058b640c243cb9ff437136ea514b0": { - "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_7a03d60be7ea4fe798f89923738a0141", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_94a89a5ef94645138137c1821fa0adc9", - "value": 60000.0 - } - }, - "f2c39cf53e60411c942c69aebac75140": { - "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_d402495ce6d9485dad1649ef6a5e6198", - "placeholder": "", - "style": "IPY_MODEL_6cd7ba9f007b42f1a799e610cb223c6a", - "value": " 60000/60000 [00:10<00:00, 7986.28 examples/s]" - } - }, - "f4aff97d336f4e5c96f81cf80fdee564": { + "ed01dc5ecdf5453eb5dbc7933f840eee": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -7349,7 +7348,7 @@ "description_width": "" } }, - "f4ea967b03694affb97c1c821d5fa82f": { + "f1d37505db474911a8f7429decf11b2a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7401,74 +7400,7 @@ "width": null } }, - "f54927ed92ee4370819b6a462532901d": { - "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_a24569cb00d7446fbf013c519735f2b4", - "IPY_MODEL_01614767a83948fa8e86ee4304c4d4ba", - "IPY_MODEL_258cc8e320a04b3dbebacd49719336d4" - ], - "layout": "IPY_MODEL_915530c2968f4c44baa11933c20920c9" - } - }, - "f6ad2e1179124d2ab72069d848575efa": { - "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_92e3972554604f2ab22d517a7c7f79c3", - "placeholder": "", - "style": "IPY_MODEL_18e830b21c694e79a6883824bea40014", - "value": "Downloading data: 100%" - } - }, - "f8fff61f71d24c38b2f558913fbc8e35": { - "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_be703ed8d63b4d37aac4ba0d78f88a04", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c7e1a6ec7f054efab5206037087be6fb", - "value": 60000.0 - } - }, - "f967c00757204de787c61238ce385d32": { + "f412500c28c644b8a1e441588b43428d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7520,31 +7452,65 @@ "width": null } }, - "f99629b380e4462a8550a52edaa77d72": { + "f431a961370144c9b7e31682cd0263aa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f5e3565367434275816c74477904c0b2": { + "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", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3f4636f46f4843de9d7fa780de83b077", + "IPY_MODEL_bc60cda6e0614f61b3d44bf44f48defa", + "IPY_MODEL_31b820fbb02b43ecb0e283d960cb3bc7" + ], + "layout": "IPY_MODEL_e1e630f8a8af4b73bc6555384939dd3a" + } + }, + "f759cd46ed8b4426a1cb436ee60f0928": { + "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_fa19246e2a844f19858891c7bf54f4d5", - "max": 26421880.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_149959a2af424f009c160ab889b46a60", - "value": 26421880.0 + "layout": "IPY_MODEL_3bf38eef814041e4b752768880cf7579", + "placeholder": "", + "style": "IPY_MODEL_ae1e9e16acf9437eb4b8d0844ef00632", + "value": " 29.5k/29.5k [00:00<00:00, 3.46MB/s]" } }, - "fa19246e2a844f19858891c7bf54f4d5": { + "f7fbd9dd7b464b40924c26865d5e9681": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7596,7 +7562,28 @@ "width": null } }, - "fa4c777bec884573b66325b782d20cbe": { + "f90a38a1a205402da89c93919685a73d": { + "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_4fbf87f19aea459c84c330ad8902ac6a", + "placeholder": "", + "style": "IPY_MODEL_51cee4accf5c486181474bab1553406a", + "value": "Computing checksums: 100%" + } + }, + "fabd302ba8a5447aa734842dc751cdfe": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7648,7 +7635,28 @@ "width": null } }, - "fd57481a82b14bee9778af0988c28884": { + "fb8fe6ad653f459f92e86d630912eb12": { + "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_5df54a79704945ffb870cae91223f3dd", + "placeholder": "", + "style": "IPY_MODEL_e3fc21908c4c4246862f128f67b934ca", + "value": " 4/4 [00:00<00:00, 737.40it/s]" + } + }, + "fe2d322a45aa49fa960d5cb543a823ae": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7700,7 +7708,7 @@ "width": null } }, - "fecc45267966445c932534eb2bb35ce4": { + "fe2fa3e58ddc4b249fa7ea4d5663b952": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -7715,9 +7723,9 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_201226d4835447c4b62158c0339cb597", + "layout": "IPY_MODEL_f1d37505db474911a8f7429decf11b2a", "placeholder": "", - "style": "IPY_MODEL_dd7fd75fcdcd4369833b938332c3023e", + "style": "IPY_MODEL_b3c5fabf52194e3491a45effd0b8c9f0", "value": "Downloading data: 100%" } } diff --git a/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb b/master/.doctrees/nbsphinx/tutorials/indepth_overview.ipynb index d8353ce43..27113b1e6 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": "2023-11-20T20:38:20.150459Z", - "iopub.status.busy": "2023-11-20T20:38:20.150273Z", - "iopub.status.idle": "2023-11-20T20:38:21.186823Z", - "shell.execute_reply": "2023-11-20T20:38:21.186228Z" + "iopub.execute_input": "2023-11-21T08:15:11.719976Z", + "iopub.status.busy": "2023-11-21T08:15:11.719778Z", + "iopub.status.idle": "2023-11-21T08:15:12.834495Z", + "shell.execute_reply": "2023-11-21T08:15:12.833860Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:38:21.189772Z", - "iopub.status.busy": "2023-11-20T20:38:21.189238Z", - "iopub.status.idle": "2023-11-20T20:38:21.449576Z", - "shell.execute_reply": "2023-11-20T20:38:21.448981Z" + "iopub.execute_input": "2023-11-21T08:15:12.837917Z", + "iopub.status.busy": "2023-11-21T08:15:12.837269Z", + "iopub.status.idle": "2023-11-21T08:15:13.124645Z", + "shell.execute_reply": "2023-11-21T08:15:13.124000Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:21.452723Z", - "iopub.status.busy": "2023-11-20T20:38:21.452279Z", - "iopub.status.idle": "2023-11-20T20:38:21.464191Z", - "shell.execute_reply": "2023-11-20T20:38:21.463707Z" + "iopub.execute_input": "2023-11-21T08:15:13.127838Z", + "iopub.status.busy": "2023-11-21T08:15:13.127399Z", + "iopub.status.idle": "2023-11-21T08:15:13.139862Z", + "shell.execute_reply": "2023-11-21T08:15:13.139315Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:21.466510Z", - "iopub.status.busy": "2023-11-20T20:38:21.466143Z", - "iopub.status.idle": "2023-11-20T20:38:21.665081Z", - "shell.execute_reply": "2023-11-20T20:38:21.664393Z" + "iopub.execute_input": "2023-11-21T08:15:13.142524Z", + "iopub.status.busy": "2023-11-21T08:15:13.142088Z", + "iopub.status.idle": "2023-11-21T08:15:13.377180Z", + "shell.execute_reply": "2023-11-21T08:15:13.376435Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:21.667868Z", - "iopub.status.busy": "2023-11-20T20:38:21.667489Z", - "iopub.status.idle": "2023-11-20T20:38:21.693950Z", - "shell.execute_reply": "2023-11-20T20:38:21.693453Z" + "iopub.execute_input": "2023-11-21T08:15:13.380165Z", + "iopub.status.busy": "2023-11-21T08:15:13.379678Z", + "iopub.status.idle": "2023-11-21T08:15:13.406667Z", + "shell.execute_reply": "2023-11-21T08:15:13.406122Z" } }, "outputs": [], @@ -427,10 +427,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:21.696417Z", - "iopub.status.busy": "2023-11-20T20:38:21.696018Z", - "iopub.status.idle": "2023-11-20T20:38:22.956921Z", - "shell.execute_reply": "2023-11-20T20:38:22.956297Z" + "iopub.execute_input": "2023-11-21T08:15:13.409323Z", + "iopub.status.busy": "2023-11-21T08:15:13.408955Z", + "iopub.status.idle": "2023-11-21T08:15:14.790664Z", + "shell.execute_reply": "2023-11-21T08:15:14.789923Z" } }, "outputs": [ @@ -471,10 +471,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:22.959749Z", - "iopub.status.busy": "2023-11-20T20:38:22.959209Z", - "iopub.status.idle": "2023-11-20T20:38:22.975650Z", - "shell.execute_reply": "2023-11-20T20:38:22.975113Z" + "iopub.execute_input": "2023-11-21T08:15:14.793579Z", + "iopub.status.busy": "2023-11-21T08:15:14.793155Z", + "iopub.status.idle": "2023-11-21T08:15:14.810437Z", + "shell.execute_reply": "2023-11-21T08:15:14.809795Z" }, "scrolled": true }, @@ -599,10 +599,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:22.978112Z", - "iopub.status.busy": "2023-11-20T20:38:22.977722Z", - "iopub.status.idle": "2023-11-20T20:38:23.838592Z", - "shell.execute_reply": "2023-11-20T20:38:23.837879Z" + "iopub.execute_input": "2023-11-21T08:15:14.812980Z", + "iopub.status.busy": "2023-11-21T08:15:14.812528Z", + "iopub.status.idle": "2023-11-21T08:15:15.733701Z", + "shell.execute_reply": "2023-11-21T08:15:15.732755Z" }, "id": "AaHC5MRKjruT" }, @@ -721,10 +721,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:23.841571Z", - "iopub.status.busy": "2023-11-20T20:38:23.841034Z", - "iopub.status.idle": "2023-11-20T20:38:23.855078Z", - "shell.execute_reply": "2023-11-20T20:38:23.854563Z" + "iopub.execute_input": "2023-11-21T08:15:15.736588Z", + "iopub.status.busy": "2023-11-21T08:15:15.736305Z", + "iopub.status.idle": "2023-11-21T08:15:15.752119Z", + "shell.execute_reply": "2023-11-21T08:15:15.751494Z" }, "id": "Wy27rvyhjruU" }, @@ -773,10 +773,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:23.857342Z", - "iopub.status.busy": "2023-11-20T20:38:23.857144Z", - "iopub.status.idle": "2023-11-20T20:38:23.937856Z", - "shell.execute_reply": "2023-11-20T20:38:23.937144Z" + "iopub.execute_input": "2023-11-21T08:15:15.755040Z", + "iopub.status.busy": "2023-11-21T08:15:15.754590Z", + "iopub.status.idle": "2023-11-21T08:15:15.848572Z", + "shell.execute_reply": "2023-11-21T08:15:15.847827Z" }, "id": "Db8YHnyVjruU" }, @@ -883,10 +883,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:23.940411Z", - "iopub.status.busy": "2023-11-20T20:38:23.940143Z", - "iopub.status.idle": "2023-11-20T20:38:24.143549Z", - "shell.execute_reply": "2023-11-20T20:38:24.142879Z" + "iopub.execute_input": "2023-11-21T08:15:15.851408Z", + "iopub.status.busy": "2023-11-21T08:15:15.851106Z", + "iopub.status.idle": "2023-11-21T08:15:16.056551Z", + "shell.execute_reply": "2023-11-21T08:15:16.055832Z" }, "id": "iJqAHuS2jruV" }, @@ -923,10 +923,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.146126Z", - "iopub.status.busy": "2023-11-20T20:38:24.145916Z", - "iopub.status.idle": "2023-11-20T20:38:24.163337Z", - "shell.execute_reply": "2023-11-20T20:38:24.162741Z" + "iopub.execute_input": "2023-11-21T08:15:16.059090Z", + "iopub.status.busy": "2023-11-21T08:15:16.058867Z", + "iopub.status.idle": "2023-11-21T08:15:16.077082Z", + "shell.execute_reply": "2023-11-21T08:15:16.076518Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -988,10 +988,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.165767Z", - "iopub.status.busy": "2023-11-20T20:38:24.165399Z", - "iopub.status.idle": "2023-11-20T20:38:24.175238Z", - "shell.execute_reply": "2023-11-20T20:38:24.174690Z" + "iopub.execute_input": "2023-11-21T08:15:16.079742Z", + "iopub.status.busy": "2023-11-21T08:15:16.079348Z", + "iopub.status.idle": "2023-11-21T08:15:16.090092Z", + "shell.execute_reply": "2023-11-21T08:15:16.089457Z" }, "id": "0lonvOYvjruV" }, @@ -1138,10 +1138,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.177445Z", - "iopub.status.busy": "2023-11-20T20:38:24.177244Z", - "iopub.status.idle": "2023-11-20T20:38:24.269534Z", - "shell.execute_reply": "2023-11-20T20:38:24.268810Z" + "iopub.execute_input": "2023-11-21T08:15:16.092732Z", + "iopub.status.busy": "2023-11-21T08:15:16.092272Z", + "iopub.status.idle": "2023-11-21T08:15:16.196365Z", + "shell.execute_reply": "2023-11-21T08:15:16.195626Z" }, "id": "MfqTCa3kjruV" }, @@ -1222,10 +1222,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.272709Z", - "iopub.status.busy": "2023-11-20T20:38:24.271997Z", - "iopub.status.idle": "2023-11-20T20:38:24.405467Z", - "shell.execute_reply": "2023-11-20T20:38:24.404768Z" + "iopub.execute_input": "2023-11-21T08:15:16.199332Z", + "iopub.status.busy": "2023-11-21T08:15:16.199047Z", + "iopub.status.idle": "2023-11-21T08:15:16.362845Z", + "shell.execute_reply": "2023-11-21T08:15:16.362037Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1285,10 +1285,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.408652Z", - "iopub.status.busy": "2023-11-20T20:38:24.408153Z", - "iopub.status.idle": "2023-11-20T20:38:24.412192Z", - "shell.execute_reply": "2023-11-20T20:38:24.411658Z" + "iopub.execute_input": "2023-11-21T08:15:16.365931Z", + "iopub.status.busy": "2023-11-21T08:15:16.365427Z", + "iopub.status.idle": "2023-11-21T08:15:16.370349Z", + "shell.execute_reply": "2023-11-21T08:15:16.369688Z" }, "id": "0rXP3ZPWjruW" }, @@ -1326,10 +1326,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.414783Z", - "iopub.status.busy": "2023-11-20T20:38:24.414291Z", - "iopub.status.idle": "2023-11-20T20:38:24.418847Z", - "shell.execute_reply": "2023-11-20T20:38:24.418307Z" + "iopub.execute_input": "2023-11-21T08:15:16.372940Z", + "iopub.status.busy": "2023-11-21T08:15:16.372566Z", + "iopub.status.idle": "2023-11-21T08:15:16.377656Z", + "shell.execute_reply": "2023-11-21T08:15:16.377086Z" }, "id": "-iRPe8KXjruW" }, @@ -1384,10 +1384,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.421186Z", - "iopub.status.busy": "2023-11-20T20:38:24.420846Z", - "iopub.status.idle": "2023-11-20T20:38:24.459580Z", - "shell.execute_reply": "2023-11-20T20:38:24.459063Z" + "iopub.execute_input": "2023-11-21T08:15:16.380165Z", + "iopub.status.busy": "2023-11-21T08:15:16.379759Z", + "iopub.status.idle": "2023-11-21T08:15:16.420635Z", + "shell.execute_reply": "2023-11-21T08:15:16.419986Z" }, "id": "ZpipUliyjruW" }, @@ -1438,10 +1438,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.461924Z", - "iopub.status.busy": "2023-11-20T20:38:24.461625Z", - "iopub.status.idle": "2023-11-20T20:38:24.506928Z", - "shell.execute_reply": "2023-11-20T20:38:24.506291Z" + "iopub.execute_input": "2023-11-21T08:15:16.423373Z", + "iopub.status.busy": "2023-11-21T08:15:16.422988Z", + "iopub.status.idle": "2023-11-21T08:15:16.470618Z", + "shell.execute_reply": "2023-11-21T08:15:16.469941Z" }, "id": "SLq-3q4xjruX" }, @@ -1510,10 +1510,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.509339Z", - "iopub.status.busy": "2023-11-20T20:38:24.508971Z", - "iopub.status.idle": "2023-11-20T20:38:24.613358Z", - "shell.execute_reply": "2023-11-20T20:38:24.612701Z" + "iopub.execute_input": "2023-11-21T08:15:16.473194Z", + "iopub.status.busy": "2023-11-21T08:15:16.472993Z", + "iopub.status.idle": "2023-11-21T08:15:16.581508Z", + "shell.execute_reply": "2023-11-21T08:15:16.580685Z" }, "id": "g5LHhhuqFbXK" }, @@ -1545,10 +1545,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.616337Z", - "iopub.status.busy": "2023-11-20T20:38:24.615939Z", - "iopub.status.idle": "2023-11-20T20:38:24.715217Z", - "shell.execute_reply": "2023-11-20T20:38:24.714499Z" + "iopub.execute_input": "2023-11-21T08:15:16.584891Z", + "iopub.status.busy": "2023-11-21T08:15:16.584471Z", + "iopub.status.idle": "2023-11-21T08:15:16.707437Z", + "shell.execute_reply": "2023-11-21T08:15:16.706682Z" }, "id": "p7w8F8ezBcet" }, @@ -1605,10 +1605,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.718090Z", - "iopub.status.busy": "2023-11-20T20:38:24.717615Z", - "iopub.status.idle": "2023-11-20T20:38:24.920898Z", - "shell.execute_reply": "2023-11-20T20:38:24.920382Z" + "iopub.execute_input": "2023-11-21T08:15:16.710848Z", + "iopub.status.busy": "2023-11-21T08:15:16.710210Z", + "iopub.status.idle": "2023-11-21T08:15:16.915769Z", + "shell.execute_reply": "2023-11-21T08:15:16.915049Z" }, "id": "WETRL74tE_sU" }, @@ -1643,10 +1643,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.923493Z", - "iopub.status.busy": "2023-11-20T20:38:24.923242Z", - "iopub.status.idle": "2023-11-20T20:38:25.122566Z", - "shell.execute_reply": "2023-11-20T20:38:25.121841Z" + "iopub.execute_input": "2023-11-21T08:15:16.918636Z", + "iopub.status.busy": "2023-11-21T08:15:16.918166Z", + "iopub.status.idle": "2023-11-21T08:15:17.183079Z", + "shell.execute_reply": "2023-11-21T08:15:17.182318Z" }, "id": "kCfdx2gOLmXS" }, @@ -1808,10 +1808,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:25.125525Z", - "iopub.status.busy": "2023-11-20T20:38:25.125035Z", - "iopub.status.idle": "2023-11-20T20:38:25.131807Z", - "shell.execute_reply": "2023-11-20T20:38:25.131299Z" + "iopub.execute_input": "2023-11-21T08:15:17.186072Z", + "iopub.status.busy": "2023-11-21T08:15:17.185779Z", + "iopub.status.idle": "2023-11-21T08:15:17.192835Z", + "shell.execute_reply": "2023-11-21T08:15:17.192204Z" }, "id": "-uogYRWFYnuu" }, @@ -1865,10 +1865,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:25.134200Z", - "iopub.status.busy": "2023-11-20T20:38:25.133787Z", - "iopub.status.idle": "2023-11-20T20:38:25.340624Z", - "shell.execute_reply": "2023-11-20T20:38:25.339968Z" + "iopub.execute_input": "2023-11-21T08:15:17.195417Z", + "iopub.status.busy": "2023-11-21T08:15:17.194913Z", + "iopub.status.idle": "2023-11-21T08:15:17.410311Z", + "shell.execute_reply": "2023-11-21T08:15:17.409666Z" }, "id": "pG-ljrmcYp9Q" }, @@ -1915,10 +1915,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:25.343473Z", - "iopub.status.busy": "2023-11-20T20:38:25.342949Z", - "iopub.status.idle": "2023-11-20T20:38:26.431732Z", - "shell.execute_reply": "2023-11-20T20:38:26.431022Z" + "iopub.execute_input": "2023-11-21T08:15:17.412922Z", + "iopub.status.busy": "2023-11-21T08:15:17.412708Z", + "iopub.status.idle": "2023-11-21T08:15:18.508447Z", + "shell.execute_reply": "2023-11-21T08:15:18.507802Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb b/master/.doctrees/nbsphinx/tutorials/multiannotator.ipynb index 44a7f7fca..f1504888c 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": "2023-11-20T20:38:31.640100Z", - "iopub.status.busy": "2023-11-20T20:38:31.639920Z", - "iopub.status.idle": "2023-11-20T20:38:32.631874Z", - "shell.execute_reply": "2023-11-20T20:38:32.631286Z" + "iopub.execute_input": "2023-11-21T08:15:24.378820Z", + "iopub.status.busy": "2023-11-21T08:15:24.378616Z", + "iopub.status.idle": "2023-11-21T08:15:25.432429Z", + "shell.execute_reply": "2023-11-21T08:15:25.431696Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:38:32.635082Z", - "iopub.status.busy": "2023-11-20T20:38:32.634595Z", - "iopub.status.idle": "2023-11-20T20:38:32.637765Z", - "shell.execute_reply": "2023-11-20T20:38:32.637212Z" + "iopub.execute_input": "2023-11-21T08:15:25.435719Z", + "iopub.status.busy": "2023-11-21T08:15:25.435333Z", + "iopub.status.idle": "2023-11-21T08:15:25.438874Z", + "shell.execute_reply": "2023-11-21T08:15:25.438345Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.640266Z", - "iopub.status.busy": "2023-11-20T20:38:32.639901Z", - "iopub.status.idle": "2023-11-20T20:38:32.648191Z", - "shell.execute_reply": "2023-11-20T20:38:32.647689Z" + "iopub.execute_input": "2023-11-21T08:15:25.441327Z", + "iopub.status.busy": "2023-11-21T08:15:25.440964Z", + "iopub.status.idle": "2023-11-21T08:15:25.449290Z", + "shell.execute_reply": "2023-11-21T08:15:25.448715Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.650579Z", - "iopub.status.busy": "2023-11-20T20:38:32.650227Z", - "iopub.status.idle": "2023-11-20T20:38:32.698591Z", - "shell.execute_reply": "2023-11-20T20:38:32.698044Z" + "iopub.execute_input": "2023-11-21T08:15:25.451621Z", + "iopub.status.busy": "2023-11-21T08:15:25.451279Z", + "iopub.status.idle": "2023-11-21T08:15:25.500928Z", + "shell.execute_reply": "2023-11-21T08:15:25.500221Z" } }, "outputs": [], @@ -380,10 +380,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.701083Z", - "iopub.status.busy": "2023-11-20T20:38:32.700687Z", - "iopub.status.idle": "2023-11-20T20:38:32.719687Z", - "shell.execute_reply": "2023-11-20T20:38:32.719195Z" + "iopub.execute_input": "2023-11-21T08:15:25.504188Z", + "iopub.status.busy": "2023-11-21T08:15:25.503666Z", + "iopub.status.idle": "2023-11-21T08:15:25.523895Z", + "shell.execute_reply": "2023-11-21T08:15:25.523338Z" } }, "outputs": [ @@ -598,10 +598,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.721941Z", - "iopub.status.busy": "2023-11-20T20:38:32.721718Z", - "iopub.status.idle": "2023-11-20T20:38:32.725737Z", - "shell.execute_reply": "2023-11-20T20:38:32.725161Z" + "iopub.execute_input": "2023-11-21T08:15:25.526417Z", + "iopub.status.busy": "2023-11-21T08:15:25.526200Z", + "iopub.status.idle": "2023-11-21T08:15:25.530488Z", + "shell.execute_reply": "2023-11-21T08:15:25.529870Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.728016Z", - "iopub.status.busy": "2023-11-20T20:38:32.727822Z", - "iopub.status.idle": "2023-11-20T20:38:32.754978Z", - "shell.execute_reply": "2023-11-20T20:38:32.754493Z" + "iopub.execute_input": "2023-11-21T08:15:25.532779Z", + "iopub.status.busy": "2023-11-21T08:15:25.532584Z", + "iopub.status.idle": "2023-11-21T08:15:25.561936Z", + "shell.execute_reply": "2023-11-21T08:15:25.561258Z" } }, "outputs": [], @@ -699,10 +699,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.757402Z", - "iopub.status.busy": "2023-11-20T20:38:32.756969Z", - "iopub.status.idle": "2023-11-20T20:38:32.784302Z", - "shell.execute_reply": "2023-11-20T20:38:32.783820Z" + "iopub.execute_input": "2023-11-21T08:15:25.564837Z", + "iopub.status.busy": "2023-11-21T08:15:25.564361Z", + "iopub.status.idle": "2023-11-21T08:15:25.592631Z", + "shell.execute_reply": "2023-11-21T08:15:25.591897Z" } }, "outputs": [], @@ -739,10 +739,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.786534Z", - "iopub.status.busy": "2023-11-20T20:38:32.786336Z", - "iopub.status.idle": "2023-11-20T20:38:34.060313Z", - "shell.execute_reply": "2023-11-20T20:38:34.059698Z" + "iopub.execute_input": "2023-11-21T08:15:25.596085Z", + "iopub.status.busy": "2023-11-21T08:15:25.595635Z", + "iopub.status.idle": "2023-11-21T08:15:26.972849Z", + "shell.execute_reply": "2023-11-21T08:15:26.972131Z" } }, "outputs": [], @@ -772,10 +772,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.063384Z", - "iopub.status.busy": "2023-11-20T20:38:34.063009Z", - "iopub.status.idle": "2023-11-20T20:38:34.070262Z", - "shell.execute_reply": "2023-11-20T20:38:34.069650Z" + "iopub.execute_input": "2023-11-21T08:15:26.976578Z", + "iopub.status.busy": "2023-11-21T08:15:26.975887Z", + "iopub.status.idle": "2023-11-21T08:15:26.984119Z", + "shell.execute_reply": "2023-11-21T08:15:26.983468Z" }, "scrolled": true }, @@ -886,10 +886,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.072942Z", - "iopub.status.busy": "2023-11-20T20:38:34.072742Z", - "iopub.status.idle": "2023-11-20T20:38:34.086541Z", - "shell.execute_reply": "2023-11-20T20:38:34.086024Z" + "iopub.execute_input": "2023-11-21T08:15:26.986683Z", + "iopub.status.busy": "2023-11-21T08:15:26.986466Z", + "iopub.status.idle": "2023-11-21T08:15:27.001920Z", + "shell.execute_reply": "2023-11-21T08:15:27.001225Z" } }, "outputs": [ @@ -1139,10 +1139,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.089094Z", - "iopub.status.busy": "2023-11-20T20:38:34.088637Z", - "iopub.status.idle": "2023-11-20T20:38:34.095575Z", - "shell.execute_reply": "2023-11-20T20:38:34.095074Z" + "iopub.execute_input": "2023-11-21T08:15:27.004786Z", + "iopub.status.busy": "2023-11-21T08:15:27.004326Z", + "iopub.status.idle": "2023-11-21T08:15:27.012030Z", + "shell.execute_reply": "2023-11-21T08:15:27.011408Z" }, "scrolled": true }, @@ -1316,10 +1316,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.097774Z", - "iopub.status.busy": "2023-11-20T20:38:34.097586Z", - "iopub.status.idle": "2023-11-20T20:38:34.100490Z", - "shell.execute_reply": "2023-11-20T20:38:34.099989Z" + "iopub.execute_input": "2023-11-21T08:15:27.014805Z", + "iopub.status.busy": "2023-11-21T08:15:27.014405Z", + "iopub.status.idle": "2023-11-21T08:15:27.017405Z", + "shell.execute_reply": "2023-11-21T08:15:27.016841Z" } }, "outputs": [], @@ -1341,10 +1341,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.102966Z", - "iopub.status.busy": "2023-11-20T20:38:34.102538Z", - "iopub.status.idle": "2023-11-20T20:38:34.106883Z", - "shell.execute_reply": "2023-11-20T20:38:34.106357Z" + "iopub.execute_input": "2023-11-21T08:15:27.019942Z", + "iopub.status.busy": "2023-11-21T08:15:27.019577Z", + "iopub.status.idle": "2023-11-21T08:15:27.023670Z", + "shell.execute_reply": "2023-11-21T08:15:27.023047Z" }, "scrolled": true }, @@ -1396,10 +1396,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.109241Z", - "iopub.status.busy": "2023-11-20T20:38:34.109044Z", - "iopub.status.idle": "2023-11-20T20:38:34.111767Z", - "shell.execute_reply": "2023-11-20T20:38:34.111249Z" + "iopub.execute_input": "2023-11-21T08:15:27.026330Z", + "iopub.status.busy": "2023-11-21T08:15:27.025956Z", + "iopub.status.idle": "2023-11-21T08:15:27.028856Z", + "shell.execute_reply": "2023-11-21T08:15:27.028309Z" } }, "outputs": [], @@ -1423,10 +1423,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.114042Z", - "iopub.status.busy": "2023-11-20T20:38:34.113845Z", - "iopub.status.idle": "2023-11-20T20:38:34.118272Z", - "shell.execute_reply": "2023-11-20T20:38:34.117638Z" + "iopub.execute_input": "2023-11-21T08:15:27.031319Z", + "iopub.status.busy": "2023-11-21T08:15:27.030934Z", + "iopub.status.idle": "2023-11-21T08:15:27.035820Z", + "shell.execute_reply": "2023-11-21T08:15:27.035111Z" } }, "outputs": [ @@ -1481,10 +1481,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.120666Z", - "iopub.status.busy": "2023-11-20T20:38:34.120431Z", - "iopub.status.idle": "2023-11-20T20:38:34.153210Z", - "shell.execute_reply": "2023-11-20T20:38:34.152719Z" + "iopub.execute_input": "2023-11-21T08:15:27.038466Z", + "iopub.status.busy": "2023-11-21T08:15:27.037996Z", + "iopub.status.idle": "2023-11-21T08:15:27.073000Z", + "shell.execute_reply": "2023-11-21T08:15:27.072281Z" } }, "outputs": [], @@ -1527,10 +1527,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.155415Z", - "iopub.status.busy": "2023-11-20T20:38:34.155217Z", - "iopub.status.idle": "2023-11-20T20:38:34.160137Z", - "shell.execute_reply": "2023-11-20T20:38:34.159603Z" + "iopub.execute_input": "2023-11-21T08:15:27.075947Z", + "iopub.status.busy": "2023-11-21T08:15:27.075739Z", + "iopub.status.idle": "2023-11-21T08:15:27.081029Z", + "shell.execute_reply": "2023-11-21T08:15:27.080493Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/multilabel_classification.ipynb index b4beb5f24..5d3f11c93 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": "2023-11-20T20:38:39.787603Z", - "iopub.status.busy": "2023-11-20T20:38:39.787410Z", - "iopub.status.idle": "2023-11-20T20:38:40.825707Z", - "shell.execute_reply": "2023-11-20T20:38:40.825106Z" + "iopub.execute_input": "2023-11-21T08:15:32.578754Z", + "iopub.status.busy": "2023-11-21T08:15:32.578565Z", + "iopub.status.idle": "2023-11-21T08:15:33.654723Z", + "shell.execute_reply": "2023-11-21T08:15:33.654142Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:38:40.828834Z", - "iopub.status.busy": "2023-11-20T20:38:40.828178Z", - "iopub.status.idle": "2023-11-20T20:38:41.109068Z", - "shell.execute_reply": "2023-11-20T20:38:41.108470Z" + "iopub.execute_input": "2023-11-21T08:15:33.657555Z", + "iopub.status.busy": "2023-11-21T08:15:33.657242Z", + "iopub.status.idle": "2023-11-21T08:15:33.956445Z", + "shell.execute_reply": "2023-11-21T08:15:33.955866Z" } }, "outputs": [], @@ -269,10 +269,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:41.112088Z", - "iopub.status.busy": "2023-11-20T20:38:41.111861Z", - "iopub.status.idle": "2023-11-20T20:38:41.125669Z", - "shell.execute_reply": "2023-11-20T20:38:41.125099Z" + "iopub.execute_input": "2023-11-21T08:15:33.959430Z", + "iopub.status.busy": "2023-11-21T08:15:33.959201Z", + "iopub.status.idle": "2023-11-21T08:15:33.973318Z", + "shell.execute_reply": "2023-11-21T08:15:33.972674Z" }, "nbsphinx": "hidden" }, @@ -408,10 +408,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:41.127975Z", - "iopub.status.busy": "2023-11-20T20:38:41.127767Z", - "iopub.status.idle": "2023-11-20T20:38:43.805848Z", - "shell.execute_reply": "2023-11-20T20:38:43.805147Z" + "iopub.execute_input": "2023-11-21T08:15:33.975907Z", + "iopub.status.busy": "2023-11-21T08:15:33.975514Z", + "iopub.status.idle": "2023-11-21T08:15:36.603383Z", + "shell.execute_reply": "2023-11-21T08:15:36.602721Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:43.808737Z", - "iopub.status.busy": "2023-11-20T20:38:43.808274Z", - "iopub.status.idle": "2023-11-20T20:38:45.357918Z", - "shell.execute_reply": "2023-11-20T20:38:45.357272Z" + "iopub.execute_input": "2023-11-21T08:15:36.606154Z", + "iopub.status.busy": "2023-11-21T08:15:36.605745Z", + "iopub.status.idle": "2023-11-21T08:15:38.142526Z", + "shell.execute_reply": "2023-11-21T08:15:38.141907Z" } }, "outputs": [], @@ -498,10 +498,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:45.360730Z", - "iopub.status.busy": "2023-11-20T20:38:45.360316Z", - "iopub.status.idle": "2023-11-20T20:38:45.380519Z", - "shell.execute_reply": "2023-11-20T20:38:45.380000Z" + "iopub.execute_input": "2023-11-21T08:15:38.145534Z", + "iopub.status.busy": "2023-11-21T08:15:38.145096Z", + "iopub.status.idle": "2023-11-21T08:15:38.164240Z", + "shell.execute_reply": "2023-11-21T08:15:38.163696Z" } }, "outputs": [ @@ -543,10 +543,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:45.382837Z", - "iopub.status.busy": "2023-11-20T20:38:45.382454Z", - "iopub.status.idle": "2023-11-20T20:38:46.654412Z", - "shell.execute_reply": "2023-11-20T20:38:46.653634Z" + "iopub.execute_input": "2023-11-21T08:15:38.166941Z", + "iopub.status.busy": "2023-11-21T08:15:38.166419Z", + "iopub.status.idle": "2023-11-21T08:15:39.571311Z", + "shell.execute_reply": "2023-11-21T08:15:39.570521Z" } }, "outputs": [ @@ -584,10 +584,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:46.657833Z", - "iopub.status.busy": "2023-11-20T20:38:46.657031Z", - "iopub.status.idle": "2023-11-20T20:38:49.459236Z", - "shell.execute_reply": "2023-11-20T20:38:49.458579Z" + "iopub.execute_input": "2023-11-21T08:15:39.574558Z", + "iopub.status.busy": "2023-11-21T08:15:39.573924Z", + "iopub.status.idle": "2023-11-21T08:15:42.383577Z", + "shell.execute_reply": "2023-11-21T08:15:42.382897Z" } }, "outputs": [ @@ -622,10 +622,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:49.461861Z", - "iopub.status.busy": "2023-11-20T20:38:49.461457Z", - "iopub.status.idle": "2023-11-20T20:38:49.466204Z", - "shell.execute_reply": "2023-11-20T20:38:49.465690Z" + "iopub.execute_input": "2023-11-21T08:15:42.386103Z", + "iopub.status.busy": "2023-11-21T08:15:42.385897Z", + "iopub.status.idle": "2023-11-21T08:15:42.390826Z", + "shell.execute_reply": "2023-11-21T08:15:42.390322Z" } }, "outputs": [ @@ -662,10 +662,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:49.468685Z", - "iopub.status.busy": "2023-11-20T20:38:49.468288Z", - "iopub.status.idle": "2023-11-20T20:38:49.472340Z", - "shell.execute_reply": "2023-11-20T20:38:49.471796Z" + "iopub.execute_input": "2023-11-21T08:15:42.392998Z", + "iopub.status.busy": "2023-11-21T08:15:42.392802Z", + "iopub.status.idle": "2023-11-21T08:15:42.397827Z", + "shell.execute_reply": "2023-11-21T08:15:42.397297Z" } }, "outputs": [], @@ -688,10 +688,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:49.474467Z", - "iopub.status.busy": "2023-11-20T20:38:49.474268Z", - "iopub.status.idle": "2023-11-20T20:38:49.477495Z", - "shell.execute_reply": "2023-11-20T20:38:49.476953Z" + "iopub.execute_input": "2023-11-21T08:15:42.400263Z", + "iopub.status.busy": "2023-11-21T08:15:42.399900Z", + "iopub.status.idle": "2023-11-21T08:15:42.403128Z", + "shell.execute_reply": "2023-11-21T08:15:42.402588Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb b/master/.doctrees/nbsphinx/tutorials/object_detection.ipynb index 9b93f7c73..b95d60dd2 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": "2023-11-20T20:38:54.444917Z", - "iopub.status.busy": "2023-11-20T20:38:54.444469Z", - "iopub.status.idle": "2023-11-20T20:38:55.485336Z", - "shell.execute_reply": "2023-11-20T20:38:55.484706Z" + "iopub.execute_input": "2023-11-21T08:15:47.401029Z", + "iopub.status.busy": "2023-11-21T08:15:47.400584Z", + "iopub.status.idle": "2023-11-21T08:15:48.489258Z", + "shell.execute_reply": "2023-11-21T08:15:48.488641Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:38:55.488330Z", - "iopub.status.busy": "2023-11-20T20:38:55.487876Z", - "iopub.status.idle": "2023-11-20T20:38:56.762123Z", - "shell.execute_reply": "2023-11-20T20:38:56.761235Z" + "iopub.execute_input": "2023-11-21T08:15:48.492205Z", + "iopub.status.busy": "2023-11-21T08:15:48.491715Z", + "iopub.status.idle": "2023-11-21T08:15:51.011067Z", + "shell.execute_reply": "2023-11-21T08:15:51.010298Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:56.765024Z", - "iopub.status.busy": "2023-11-20T20:38:56.764810Z", - "iopub.status.idle": "2023-11-20T20:38:56.768061Z", - "shell.execute_reply": "2023-11-20T20:38:56.767513Z" + "iopub.execute_input": "2023-11-21T08:15:51.014160Z", + "iopub.status.busy": "2023-11-21T08:15:51.013682Z", + "iopub.status.idle": "2023-11-21T08:15:51.017086Z", + "shell.execute_reply": "2023-11-21T08:15:51.016578Z" } }, "outputs": [], @@ -165,10 +165,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:56.770246Z", - "iopub.status.busy": "2023-11-20T20:38:56.770052Z", - "iopub.status.idle": "2023-11-20T20:38:56.776059Z", - "shell.execute_reply": "2023-11-20T20:38:56.775588Z" + "iopub.execute_input": "2023-11-21T08:15:51.019420Z", + "iopub.status.busy": "2023-11-21T08:15:51.019039Z", + "iopub.status.idle": "2023-11-21T08:15:51.025076Z", + "shell.execute_reply": "2023-11-21T08:15:51.024608Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:56.778573Z", - "iopub.status.busy": "2023-11-20T20:38:56.778194Z", - "iopub.status.idle": "2023-11-20T20:38:57.383984Z", - "shell.execute_reply": "2023-11-20T20:38:57.383326Z" + "iopub.execute_input": "2023-11-21T08:15:51.027345Z", + "iopub.status.busy": "2023-11-21T08:15:51.027007Z", + "iopub.status.idle": "2023-11-21T08:15:51.652011Z", + "shell.execute_reply": "2023-11-21T08:15:51.651278Z" }, "scrolled": true }, @@ -237,10 +237,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:57.386941Z", - "iopub.status.busy": "2023-11-20T20:38:57.386529Z", - "iopub.status.idle": "2023-11-20T20:38:57.392644Z", - "shell.execute_reply": "2023-11-20T20:38:57.392107Z" + "iopub.execute_input": "2023-11-21T08:15:51.655362Z", + "iopub.status.busy": "2023-11-21T08:15:51.654925Z", + "iopub.status.idle": "2023-11-21T08:15:51.661068Z", + "shell.execute_reply": "2023-11-21T08:15:51.660496Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:57.395127Z", - "iopub.status.busy": "2023-11-20T20:38:57.394751Z", - "iopub.status.idle": "2023-11-20T20:38:57.398794Z", - "shell.execute_reply": "2023-11-20T20:38:57.398304Z" + "iopub.execute_input": "2023-11-21T08:15:51.663268Z", + "iopub.status.busy": "2023-11-21T08:15:51.663066Z", + "iopub.status.idle": "2023-11-21T08:15:51.667807Z", + "shell.execute_reply": "2023-11-21T08:15:51.667250Z" } }, "outputs": [ @@ -552,10 +552,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:57.400954Z", - "iopub.status.busy": "2023-11-20T20:38:57.400753Z", - "iopub.status.idle": "2023-11-20T20:38:57.954146Z", - "shell.execute_reply": "2023-11-20T20:38:57.953514Z" + "iopub.execute_input": "2023-11-21T08:15:51.670229Z", + "iopub.status.busy": "2023-11-21T08:15:51.670002Z", + "iopub.status.idle": "2023-11-21T08:15:52.293210Z", + "shell.execute_reply": "2023-11-21T08:15:52.292463Z" } }, "outputs": [ @@ -611,10 +611,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:57.956735Z", - "iopub.status.busy": "2023-11-20T20:38:57.956514Z", - "iopub.status.idle": "2023-11-20T20:38:58.056910Z", - "shell.execute_reply": "2023-11-20T20:38:58.056378Z" + "iopub.execute_input": "2023-11-21T08:15:52.296313Z", + "iopub.status.busy": "2023-11-21T08:15:52.295886Z", + "iopub.status.idle": "2023-11-21T08:15:52.396285Z", + "shell.execute_reply": "2023-11-21T08:15:52.395704Z" } }, "outputs": [ @@ -655,10 +655,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:58.059275Z", - "iopub.status.busy": "2023-11-20T20:38:58.059070Z", - "iopub.status.idle": "2023-11-20T20:38:58.063560Z", - "shell.execute_reply": "2023-11-20T20:38:58.063053Z" + "iopub.execute_input": "2023-11-21T08:15:52.398792Z", + "iopub.status.busy": "2023-11-21T08:15:52.398476Z", + "iopub.status.idle": "2023-11-21T08:15:52.403250Z", + "shell.execute_reply": "2023-11-21T08:15:52.402727Z" } }, "outputs": [ @@ -695,10 +695,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:58.066036Z", - "iopub.status.busy": "2023-11-20T20:38:58.065634Z", - "iopub.status.idle": "2023-11-20T20:38:58.439535Z", - "shell.execute_reply": "2023-11-20T20:38:58.438845Z" + "iopub.execute_input": "2023-11-21T08:15:52.405758Z", + "iopub.status.busy": "2023-11-21T08:15:52.405299Z", + "iopub.status.idle": "2023-11-21T08:15:52.779773Z", + "shell.execute_reply": "2023-11-21T08:15:52.779062Z" } }, "outputs": [ @@ -757,10 +757,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:58.442297Z", - "iopub.status.busy": "2023-11-20T20:38:58.442065Z", - "iopub.status.idle": "2023-11-20T20:38:58.778074Z", - "shell.execute_reply": "2023-11-20T20:38:58.777460Z" + "iopub.execute_input": "2023-11-21T08:15:52.783064Z", + "iopub.status.busy": "2023-11-21T08:15:52.782575Z", + "iopub.status.idle": "2023-11-21T08:15:53.120065Z", + "shell.execute_reply": "2023-11-21T08:15:53.119356Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:58.780900Z", - "iopub.status.busy": "2023-11-20T20:38:58.780681Z", - "iopub.status.idle": "2023-11-20T20:38:59.130514Z", - "shell.execute_reply": "2023-11-20T20:38:59.129867Z" + "iopub.execute_input": "2023-11-21T08:15:53.122843Z", + "iopub.status.busy": "2023-11-21T08:15:53.122445Z", + "iopub.status.idle": "2023-11-21T08:15:53.476855Z", + "shell.execute_reply": "2023-11-21T08:15:53.476168Z" } }, "outputs": [ @@ -857,10 +857,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:59.133788Z", - "iopub.status.busy": "2023-11-20T20:38:59.133566Z", - "iopub.status.idle": "2023-11-20T20:38:59.594058Z", - "shell.execute_reply": "2023-11-20T20:38:59.593376Z" + "iopub.execute_input": "2023-11-21T08:15:53.480499Z", + "iopub.status.busy": "2023-11-21T08:15:53.480093Z", + "iopub.status.idle": "2023-11-21T08:15:53.916301Z", + "shell.execute_reply": "2023-11-21T08:15:53.915516Z" } }, "outputs": [ @@ -920,10 +920,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:59.598715Z", - "iopub.status.busy": "2023-11-20T20:38:59.598088Z", - "iopub.status.idle": "2023-11-20T20:39:00.045563Z", - "shell.execute_reply": "2023-11-20T20:39:00.044889Z" + "iopub.execute_input": "2023-11-21T08:15:53.920866Z", + "iopub.status.busy": "2023-11-21T08:15:53.920421Z", + "iopub.status.idle": "2023-11-21T08:15:54.377542Z", + "shell.execute_reply": "2023-11-21T08:15:54.376830Z" } }, "outputs": [ @@ -966,10 +966,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:00.048935Z", - "iopub.status.busy": "2023-11-20T20:39:00.048424Z", - "iopub.status.idle": "2023-11-20T20:39:00.247419Z", - "shell.execute_reply": "2023-11-20T20:39:00.246733Z" + "iopub.execute_input": "2023-11-21T08:15:54.380958Z", + "iopub.status.busy": "2023-11-21T08:15:54.380730Z", + "iopub.status.idle": "2023-11-21T08:15:54.610045Z", + "shell.execute_reply": "2023-11-21T08:15:54.609290Z" } }, "outputs": [ @@ -1012,10 +1012,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:00.250058Z", - "iopub.status.busy": "2023-11-20T20:39:00.249849Z", - "iopub.status.idle": "2023-11-20T20:39:00.430193Z", - "shell.execute_reply": "2023-11-20T20:39:00.429502Z" + "iopub.execute_input": "2023-11-21T08:15:54.612981Z", + "iopub.status.busy": "2023-11-21T08:15:54.612669Z", + "iopub.status.idle": "2023-11-21T08:15:54.794417Z", + "shell.execute_reply": "2023-11-21T08:15:54.793786Z" } }, "outputs": [ @@ -1050,10 +1050,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:00.432787Z", - "iopub.status.busy": "2023-11-20T20:39:00.432341Z", - "iopub.status.idle": "2023-11-20T20:39:00.436217Z", - "shell.execute_reply": "2023-11-20T20:39:00.435594Z" + "iopub.execute_input": "2023-11-21T08:15:54.797675Z", + "iopub.status.busy": "2023-11-21T08:15:54.797104Z", + "iopub.status.idle": "2023-11-21T08:15:54.801103Z", + "shell.execute_reply": "2023-11-21T08:15:54.800490Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/outliers.ipynb b/master/.doctrees/nbsphinx/tutorials/outliers.ipynb index aebe89c2a..f76ad0d0a 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": "2023-11-20T20:39:02.679556Z", - "iopub.status.busy": "2023-11-20T20:39:02.679032Z", - "iopub.status.idle": "2023-11-20T20:39:04.560943Z", - "shell.execute_reply": "2023-11-20T20:39:04.560337Z" + "iopub.execute_input": "2023-11-21T08:15:57.093965Z", + "iopub.status.busy": "2023-11-21T08:15:57.093777Z", + "iopub.status.idle": "2023-11-21T08:15:59.059149Z", + "shell.execute_reply": "2023-11-21T08:15:59.058531Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:39:04.563824Z", - "iopub.status.busy": "2023-11-20T20:39:04.563509Z", - "iopub.status.idle": "2023-11-20T20:39:04.868128Z", - "shell.execute_reply": "2023-11-20T20:39:04.867528Z" + "iopub.execute_input": "2023-11-21T08:15:59.062280Z", + "iopub.status.busy": "2023-11-21T08:15:59.061863Z", + "iopub.status.idle": "2023-11-21T08:15:59.388859Z", + "shell.execute_reply": "2023-11-21T08:15:59.388172Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "3792f82e", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:04.871099Z", - "iopub.status.busy": "2023-11-20T20:39:04.870584Z", - "iopub.status.idle": "2023-11-20T20:39:04.875381Z", - "shell.execute_reply": "2023-11-20T20:39:04.874907Z" + "iopub.execute_input": "2023-11-21T08:15:59.392002Z", + "iopub.status.busy": "2023-11-21T08:15:59.391533Z", + "iopub.status.idle": "2023-11-21T08:15:59.395807Z", + "shell.execute_reply": "2023-11-21T08:15:59.395317Z" }, "nbsphinx": "hidden" }, @@ -225,10 +225,10 @@ "id": "fd853a54", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:04.877900Z", - "iopub.status.busy": "2023-11-20T20:39:04.877428Z", - "iopub.status.idle": "2023-11-20T20:39:09.935948Z", - "shell.execute_reply": "2023-11-20T20:39:09.935367Z" + "iopub.execute_input": "2023-11-21T08:15:59.398320Z", + "iopub.status.busy": "2023-11-21T08:15:59.397961Z", + "iopub.status.idle": "2023-11-21T08:16:11.004531Z", + "shell.execute_reply": "2023-11-21T08:16:11.003839Z" } }, "outputs": [ @@ -242,7 +242,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1c68f3d2b96f42ed828dab7eb7c9cc3d", + "model_id": "ee378258a68244cda413ea601cd4397b", "version_major": 2, "version_minor": 0 }, @@ -361,10 +361,10 @@ "id": "9b64e0aa", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:09.938796Z", - "iopub.status.busy": "2023-11-20T20:39:09.938306Z", - "iopub.status.idle": "2023-11-20T20:39:09.943479Z", - "shell.execute_reply": "2023-11-20T20:39:09.942971Z" + "iopub.execute_input": "2023-11-21T08:16:11.007512Z", + "iopub.status.busy": "2023-11-21T08:16:11.007100Z", + "iopub.status.idle": "2023-11-21T08:16:11.012334Z", + "shell.execute_reply": "2023-11-21T08:16:11.011724Z" }, "nbsphinx": "hidden" }, @@ -415,10 +415,10 @@ "id": "a00aa3ed", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:09.945714Z", - "iopub.status.busy": "2023-11-20T20:39:09.945369Z", - "iopub.status.idle": "2023-11-20T20:39:10.491881Z", - "shell.execute_reply": "2023-11-20T20:39:10.491226Z" + "iopub.execute_input": "2023-11-21T08:16:11.014988Z", + "iopub.status.busy": "2023-11-21T08:16:11.014660Z", + "iopub.status.idle": "2023-11-21T08:16:11.571451Z", + "shell.execute_reply": "2023-11-21T08:16:11.570804Z" } }, "outputs": [ @@ -451,10 +451,10 @@ "id": "41e5cb6b", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:10.494515Z", - "iopub.status.busy": "2023-11-20T20:39:10.494111Z", - "iopub.status.idle": "2023-11-20T20:39:11.121144Z", - "shell.execute_reply": "2023-11-20T20:39:11.120501Z" + "iopub.execute_input": "2023-11-21T08:16:11.574253Z", + "iopub.status.busy": "2023-11-21T08:16:11.573856Z", + "iopub.status.idle": "2023-11-21T08:16:12.237955Z", + "shell.execute_reply": "2023-11-21T08:16:12.237235Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "1cf25354", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:11.123866Z", - "iopub.status.busy": "2023-11-20T20:39:11.123491Z", - "iopub.status.idle": "2023-11-20T20:39:11.127058Z", - "shell.execute_reply": "2023-11-20T20:39:11.126523Z" + "iopub.execute_input": "2023-11-21T08:16:12.240806Z", + "iopub.status.busy": "2023-11-21T08:16:12.240409Z", + "iopub.status.idle": "2023-11-21T08:16:12.244282Z", + "shell.execute_reply": "2023-11-21T08:16:12.243597Z" } }, "outputs": [], @@ -518,10 +518,10 @@ "id": "85a58d41", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:11.129435Z", - "iopub.status.busy": "2023-11-20T20:39:11.129083Z", - "iopub.status.idle": "2023-11-20T20:39:23.064606Z", - "shell.execute_reply": "2023-11-20T20:39:23.063900Z" + "iopub.execute_input": "2023-11-21T08:16:12.246972Z", + "iopub.status.busy": "2023-11-21T08:16:12.246519Z", + "iopub.status.idle": "2023-11-21T08:16:25.108308Z", + "shell.execute_reply": "2023-11-21T08:16:25.107565Z" } }, "outputs": [ @@ -580,10 +580,10 @@ "id": "feb0f519", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:23.067543Z", - "iopub.status.busy": "2023-11-20T20:39:23.067087Z", - "iopub.status.idle": "2023-11-20T20:39:24.634123Z", - "shell.execute_reply": "2023-11-20T20:39:24.633443Z" + "iopub.execute_input": "2023-11-21T08:16:25.111259Z", + "iopub.status.busy": "2023-11-21T08:16:25.110823Z", + "iopub.status.idle": "2023-11-21T08:16:26.698926Z", + "shell.execute_reply": "2023-11-21T08:16:26.698301Z" } }, "outputs": [ @@ -627,10 +627,10 @@ "id": "089d5860", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:24.637381Z", - "iopub.status.busy": "2023-11-20T20:39:24.636837Z", - "iopub.status.idle": "2023-11-20T20:39:24.898043Z", - "shell.execute_reply": "2023-11-20T20:39:24.896891Z" + "iopub.execute_input": "2023-11-21T08:16:26.702219Z", + "iopub.status.busy": "2023-11-21T08:16:26.701647Z", + "iopub.status.idle": "2023-11-21T08:16:26.963994Z", + "shell.execute_reply": "2023-11-21T08:16:26.963251Z" } }, "outputs": [ @@ -666,10 +666,10 @@ "id": "78b1951c", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:24.900931Z", - "iopub.status.busy": "2023-11-20T20:39:24.900678Z", - "iopub.status.idle": "2023-11-20T20:39:25.562288Z", - "shell.execute_reply": "2023-11-20T20:39:25.561649Z" + "iopub.execute_input": "2023-11-21T08:16:26.967546Z", + "iopub.status.busy": "2023-11-21T08:16:26.966989Z", + "iopub.status.idle": "2023-11-21T08:16:27.626816Z", + "shell.execute_reply": "2023-11-21T08:16:27.626249Z" } }, "outputs": [ @@ -719,10 +719,10 @@ "id": "e9dff81b", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:25.565749Z", - "iopub.status.busy": "2023-11-20T20:39:25.565243Z", - "iopub.status.idle": "2023-11-20T20:39:26.069451Z", - "shell.execute_reply": "2023-11-20T20:39:26.068796Z" + "iopub.execute_input": "2023-11-21T08:16:27.629964Z", + "iopub.status.busy": "2023-11-21T08:16:27.629413Z", + "iopub.status.idle": "2023-11-21T08:16:28.171733Z", + "shell.execute_reply": "2023-11-21T08:16:28.171035Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "id": "616769f8", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:26.072134Z", - "iopub.status.busy": "2023-11-20T20:39:26.071722Z", - "iopub.status.idle": "2023-11-20T20:39:26.316578Z", - "shell.execute_reply": "2023-11-20T20:39:26.315869Z" + "iopub.execute_input": "2023-11-21T08:16:28.174469Z", + "iopub.status.busy": "2023-11-21T08:16:28.174062Z", + "iopub.status.idle": "2023-11-21T08:16:28.422987Z", + "shell.execute_reply": "2023-11-21T08:16:28.422281Z" } }, "outputs": [ @@ -829,10 +829,10 @@ "id": "40fed4ef", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:26.319778Z", - "iopub.status.busy": "2023-11-20T20:39:26.319407Z", - "iopub.status.idle": "2023-11-20T20:39:26.401198Z", - "shell.execute_reply": "2023-11-20T20:39:26.400631Z" + "iopub.execute_input": "2023-11-21T08:16:28.426345Z", + "iopub.status.busy": "2023-11-21T08:16:28.425813Z", + "iopub.status.idle": "2023-11-21T08:16:28.512014Z", + "shell.execute_reply": "2023-11-21T08:16:28.511432Z" } }, "outputs": [], @@ -853,10 +853,10 @@ "id": "89f9db72", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:26.404013Z", - "iopub.status.busy": "2023-11-20T20:39:26.403629Z", - "iopub.status.idle": "2023-11-20T20:40:04.295725Z", - "shell.execute_reply": "2023-11-20T20:40:04.295104Z" + "iopub.execute_input": "2023-11-21T08:16:28.515038Z", + "iopub.status.busy": "2023-11-21T08:16:28.514598Z", + "iopub.status.idle": "2023-11-21T08:17:07.304506Z", + "shell.execute_reply": "2023-11-21T08:17:07.303823Z" } }, "outputs": [ @@ -893,10 +893,10 @@ "id": "874c885a", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:04.298509Z", - "iopub.status.busy": "2023-11-20T20:40:04.298088Z", - "iopub.status.idle": "2023-11-20T20:40:05.458698Z", - "shell.execute_reply": "2023-11-20T20:40:05.458117Z" + "iopub.execute_input": "2023-11-21T08:17:07.307388Z", + "iopub.status.busy": "2023-11-21T08:17:07.306967Z", + "iopub.status.idle": "2023-11-21T08:17:08.490581Z", + "shell.execute_reply": "2023-11-21T08:17:08.489951Z" } }, "outputs": [ @@ -927,10 +927,10 @@ "id": "e110fc4b", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:05.461706Z", - "iopub.status.busy": "2023-11-20T20:40:05.461246Z", - "iopub.status.idle": "2023-11-20T20:40:05.646802Z", - "shell.execute_reply": "2023-11-20T20:40:05.646223Z" + "iopub.execute_input": "2023-11-21T08:17:08.493922Z", + "iopub.status.busy": "2023-11-21T08:17:08.493188Z", + "iopub.status.idle": "2023-11-21T08:17:08.676897Z", + "shell.execute_reply": "2023-11-21T08:17:08.676295Z" } }, "outputs": [], @@ -944,10 +944,10 @@ "id": "85b60cbf", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:05.649758Z", - "iopub.status.busy": "2023-11-20T20:40:05.649365Z", - "iopub.status.idle": "2023-11-20T20:40:05.652678Z", - "shell.execute_reply": "2023-11-20T20:40:05.652177Z" + "iopub.execute_input": "2023-11-21T08:17:08.679884Z", + "iopub.status.busy": "2023-11-21T08:17:08.679441Z", + "iopub.status.idle": "2023-11-21T08:17:08.682886Z", + "shell.execute_reply": "2023-11-21T08:17:08.682316Z" } }, "outputs": [], @@ -969,10 +969,10 @@ "id": "17f96fa6", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:05.655056Z", - "iopub.status.busy": "2023-11-20T20:40:05.654690Z", - "iopub.status.idle": "2023-11-20T20:40:05.663588Z", - "shell.execute_reply": "2023-11-20T20:40:05.663113Z" + "iopub.execute_input": "2023-11-21T08:17:08.685412Z", + "iopub.status.busy": "2023-11-21T08:17:08.685045Z", + "iopub.status.idle": "2023-11-21T08:17:08.694865Z", + "shell.execute_reply": "2023-11-21T08:17:08.694200Z" }, "nbsphinx": "hidden" }, @@ -1017,68 +1017,28 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "07eebd3a6bf14a1698df6cbeed688d7d": { + "117451eb21e94da2b8affd2c0b7ab4dc": { "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_8c840edca45d4ab5a3283d2af43cc943", - "max": 170498071.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_f7fd73f587e346459568c54930eb1dde", - "value": 170498071.0 - } - }, - "0e0f323e6570419a9e18e1cdb04f5bdc": { - "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": "" - } - }, - "1c68f3d2b96f42ed828dab7eb7c9cc3d": { - "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_48477467cf544459bff9d092994b4bf6", - "IPY_MODEL_07eebd3a6bf14a1698df6cbeed688d7d", - "IPY_MODEL_8c773f15009f48ce990eb7cc6a1faae0" - ], - "layout": "IPY_MODEL_ee355b920d2f4b969876f34e138957eb" + "layout": "IPY_MODEL_da32e4a81c0444b9be557f342b2123d1", + "placeholder": "", + "style": "IPY_MODEL_dfbcf30225444066bcc6deb612503ffc", + "value": " 170498071/170498071 [00:04<00:00, 45189414.78it/s]" } }, - "316f4d21ecc140658b10788179d8c8cc": { + "60ca8e211ab24937b6617b6c77e1c45f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1130,7 +1090,7 @@ "width": null } }, - "48477467cf544459bff9d092994b4bf6": { + "66efbc83bd894898851660c821870e8e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1145,34 +1105,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_316f4d21ecc140658b10788179d8c8cc", + "layout": "IPY_MODEL_60ca8e211ab24937b6617b6c77e1c45f", "placeholder": "", - "style": "IPY_MODEL_0e0f323e6570419a9e18e1cdb04f5bdc", + "style": "IPY_MODEL_d196bfc491c043c6b5187ed613247f08", "value": "100%" } }, - "8c773f15009f48ce990eb7cc6a1faae0": { - "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_fe00db2b7a4d4860b66a14b292499f35", - "placeholder": "", - "style": "IPY_MODEL_ef4d2118c4634e1c91ae67842502fbfe", - "value": " 170498071/170498071 [00:02<00:00, 77195293.64it/s]" - } - }, - "8c840edca45d4ab5a3283d2af43cc943": { + "6ca6da019e3e4ce58433e4328c90e657": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1224,7 +1163,47 @@ "width": null } }, - "ee355b920d2f4b969876f34e138957eb": { + "6da8458cf90a45cdb1a3825523aa9a28": { + "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_b870b382e8a04ee4a23d060c120a150d", + "max": 170498071.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a43c94b16ea248af827b7200c30c1dbc", + "value": 170498071.0 + } + }, + "a43c94b16ea248af827b7200c30c1dbc": { + "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": "" + } + }, + "b870b382e8a04ee4a23d060c120a150d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1276,7 +1255,7 @@ "width": null } }, - "ef4d2118c4634e1c91ae67842502fbfe": { + "d196bfc491c043c6b5187ed613247f08": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1291,23 +1270,7 @@ "description_width": "" } }, - "f7fd73f587e346459568c54930eb1dde": { - "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": "" - } - }, - "fe00db2b7a4d4860b66a14b292499f35": { + "da32e4a81c0444b9be557f342b2123d1": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1358,6 +1321,43 @@ "visibility": null, "width": null } + }, + "dfbcf30225444066bcc6deb612503ffc": { + "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": "" + } + }, + "ee378258a68244cda413ea601cd4397b": { + "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_66efbc83bd894898851660c821870e8e", + "IPY_MODEL_6da8458cf90a45cdb1a3825523aa9a28", + "IPY_MODEL_117451eb21e94da2b8affd2c0b7ab4dc" + ], + "layout": "IPY_MODEL_6ca6da019e3e4ce58433e4328c90e657" + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/regression.ipynb b/master/.doctrees/nbsphinx/tutorials/regression.ipynb index 70c378053..858705f29 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": "2023-11-20T20:40:10.686452Z", - "iopub.status.busy": "2023-11-20T20:40:10.685935Z", - "iopub.status.idle": "2023-11-20T20:40:11.733748Z", - "shell.execute_reply": "2023-11-20T20:40:11.733094Z" + "iopub.execute_input": "2023-11-21T08:17:14.170883Z", + "iopub.status.busy": "2023-11-21T08:17:14.170350Z", + "iopub.status.idle": "2023-11-21T08:17:15.310118Z", + "shell.execute_reply": "2023-11-21T08:17:15.309533Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:40:11.736603Z", - "iopub.status.busy": "2023-11-20T20:40:11.736134Z", - "iopub.status.idle": "2023-11-20T20:40:11.756127Z", - "shell.execute_reply": "2023-11-20T20:40:11.755623Z" + "iopub.execute_input": "2023-11-21T08:17:15.313344Z", + "iopub.status.busy": "2023-11-21T08:17:15.312742Z", + "iopub.status.idle": "2023-11-21T08:17:15.334534Z", + "shell.execute_reply": "2023-11-21T08:17:15.333922Z" } }, "outputs": [], @@ -157,10 +157,10 @@ "id": "284dc264", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:11.758457Z", - "iopub.status.busy": "2023-11-20T20:40:11.758079Z", - "iopub.status.idle": "2023-11-20T20:40:11.761246Z", - "shell.execute_reply": "2023-11-20T20:40:11.760733Z" + "iopub.execute_input": "2023-11-21T08:17:15.337686Z", + "iopub.status.busy": "2023-11-21T08:17:15.337075Z", + "iopub.status.idle": "2023-11-21T08:17:15.340599Z", + "shell.execute_reply": "2023-11-21T08:17:15.339973Z" }, "nbsphinx": "hidden" }, @@ -191,10 +191,10 @@ "id": "0f7450db", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:11.763587Z", - "iopub.status.busy": "2023-11-20T20:40:11.763221Z", - "iopub.status.idle": "2023-11-20T20:40:11.850100Z", - "shell.execute_reply": "2023-11-20T20:40:11.849484Z" + "iopub.execute_input": "2023-11-21T08:17:15.343302Z", + "iopub.status.busy": "2023-11-21T08:17:15.342897Z", + "iopub.status.idle": "2023-11-21T08:17:15.646848Z", + "shell.execute_reply": "2023-11-21T08:17:15.646199Z" } }, "outputs": [ @@ -367,10 +367,10 @@ "id": "55513fed", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:11.852559Z", - "iopub.status.busy": "2023-11-20T20:40:11.852195Z", - "iopub.status.idle": "2023-11-20T20:40:12.109012Z", - "shell.execute_reply": "2023-11-20T20:40:12.108415Z" + "iopub.execute_input": "2023-11-21T08:17:15.649467Z", + "iopub.status.busy": "2023-11-21T08:17:15.649094Z", + "iopub.status.idle": "2023-11-21T08:17:15.927876Z", + "shell.execute_reply": "2023-11-21T08:17:15.927257Z" }, "nbsphinx": "hidden" }, @@ -410,10 +410,10 @@ "id": "df5a0f59", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:12.111808Z", - "iopub.status.busy": "2023-11-20T20:40:12.111595Z", - "iopub.status.idle": "2023-11-20T20:40:12.325983Z", - "shell.execute_reply": "2023-11-20T20:40:12.325297Z" + "iopub.execute_input": "2023-11-21T08:17:15.930938Z", + "iopub.status.busy": "2023-11-21T08:17:15.930534Z", + "iopub.status.idle": "2023-11-21T08:17:16.186549Z", + "shell.execute_reply": "2023-11-21T08:17:16.185886Z" } }, "outputs": [ @@ -449,10 +449,10 @@ "id": "7af78a8a", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:12.328481Z", - "iopub.status.busy": "2023-11-20T20:40:12.328270Z", - "iopub.status.idle": "2023-11-20T20:40:12.332701Z", - "shell.execute_reply": "2023-11-20T20:40:12.332165Z" + "iopub.execute_input": "2023-11-21T08:17:16.189349Z", + "iopub.status.busy": "2023-11-21T08:17:16.188925Z", + "iopub.status.idle": "2023-11-21T08:17:16.193620Z", + "shell.execute_reply": "2023-11-21T08:17:16.193041Z" } }, "outputs": [], @@ -470,10 +470,10 @@ "id": "9556c624", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:12.335142Z", - "iopub.status.busy": "2023-11-20T20:40:12.334762Z", - "iopub.status.idle": "2023-11-20T20:40:12.341746Z", - "shell.execute_reply": "2023-11-20T20:40:12.341266Z" + "iopub.execute_input": "2023-11-21T08:17:16.195955Z", + "iopub.status.busy": "2023-11-21T08:17:16.195603Z", + "iopub.status.idle": "2023-11-21T08:17:16.202532Z", + "shell.execute_reply": "2023-11-21T08:17:16.202024Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "3c2f1ccc", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:12.344326Z", - "iopub.status.busy": "2023-11-20T20:40:12.343874Z", - "iopub.status.idle": "2023-11-20T20:40:12.346841Z", - "shell.execute_reply": "2023-11-20T20:40:12.346219Z" + "iopub.execute_input": "2023-11-21T08:17:16.205030Z", + "iopub.status.busy": "2023-11-21T08:17:16.204591Z", + "iopub.status.idle": "2023-11-21T08:17:16.207521Z", + "shell.execute_reply": "2023-11-21T08:17:16.206879Z" } }, "outputs": [], @@ -538,10 +538,10 @@ "id": "7e1b7860", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:12.349421Z", - "iopub.status.busy": "2023-11-20T20:40:12.348933Z", - "iopub.status.idle": "2023-11-20T20:40:22.245228Z", - "shell.execute_reply": "2023-11-20T20:40:22.244491Z" + "iopub.execute_input": "2023-11-21T08:17:16.209919Z", + "iopub.status.busy": "2023-11-21T08:17:16.209520Z", + "iopub.status.idle": "2023-11-21T08:17:26.466089Z", + "shell.execute_reply": "2023-11-21T08:17:26.465323Z" } }, "outputs": [], @@ -565,10 +565,10 @@ "id": "f407bd69", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:22.248403Z", - "iopub.status.busy": "2023-11-20T20:40:22.247753Z", - "iopub.status.idle": "2023-11-20T20:40:22.255976Z", - "shell.execute_reply": "2023-11-20T20:40:22.255386Z" + "iopub.execute_input": "2023-11-21T08:17:26.469956Z", + "iopub.status.busy": "2023-11-21T08:17:26.469149Z", + "iopub.status.idle": "2023-11-21T08:17:26.477079Z", + "shell.execute_reply": "2023-11-21T08:17:26.476459Z" } }, "outputs": [ @@ -671,10 +671,10 @@ "id": "f7385336", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:22.258538Z", - "iopub.status.busy": "2023-11-20T20:40:22.258075Z", - "iopub.status.idle": "2023-11-20T20:40:22.262035Z", - "shell.execute_reply": "2023-11-20T20:40:22.261398Z" + "iopub.execute_input": "2023-11-21T08:17:26.479467Z", + "iopub.status.busy": "2023-11-21T08:17:26.479259Z", + "iopub.status.idle": "2023-11-21T08:17:26.483437Z", + "shell.execute_reply": "2023-11-21T08:17:26.482812Z" } }, "outputs": [], @@ -689,10 +689,10 @@ "id": "59fc3091", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:22.264434Z", - "iopub.status.busy": "2023-11-20T20:40:22.264096Z", - "iopub.status.idle": "2023-11-20T20:40:22.267724Z", - "shell.execute_reply": "2023-11-20T20:40:22.267103Z" + "iopub.execute_input": "2023-11-21T08:17:26.486078Z", + "iopub.status.busy": "2023-11-21T08:17:26.485678Z", + "iopub.status.idle": "2023-11-21T08:17:26.489450Z", + "shell.execute_reply": "2023-11-21T08:17:26.488828Z" } }, "outputs": [ @@ -727,10 +727,10 @@ "id": "00949977", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:22.270130Z", - "iopub.status.busy": "2023-11-20T20:40:22.269650Z", - "iopub.status.idle": "2023-11-20T20:40:22.273040Z", - "shell.execute_reply": "2023-11-20T20:40:22.272417Z" + "iopub.execute_input": "2023-11-21T08:17:26.491825Z", + "iopub.status.busy": "2023-11-21T08:17:26.491472Z", + "iopub.status.idle": "2023-11-21T08:17:26.494862Z", + "shell.execute_reply": "2023-11-21T08:17:26.494206Z" } }, "outputs": [], @@ -749,10 +749,10 @@ "id": "b6c1ae3a", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:22.275362Z", - "iopub.status.busy": "2023-11-20T20:40:22.274901Z", - "iopub.status.idle": "2023-11-20T20:40:22.283566Z", - "shell.execute_reply": "2023-11-20T20:40:22.282940Z" + "iopub.execute_input": "2023-11-21T08:17:26.497169Z", + "iopub.status.busy": "2023-11-21T08:17:26.496737Z", + "iopub.status.idle": "2023-11-21T08:17:26.505671Z", + "shell.execute_reply": "2023-11-21T08:17:26.505031Z" } }, "outputs": [ @@ -894,10 +894,10 @@ "id": "31c704e7", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:22.286077Z", - "iopub.status.busy": "2023-11-20T20:40:22.285622Z", - "iopub.status.idle": "2023-11-20T20:40:22.428809Z", - "shell.execute_reply": "2023-11-20T20:40:22.428254Z" + "iopub.execute_input": "2023-11-21T08:17:26.508301Z", + "iopub.status.busy": "2023-11-21T08:17:26.507927Z", + "iopub.status.idle": "2023-11-21T08:17:26.662104Z", + "shell.execute_reply": "2023-11-21T08:17:26.661364Z" } }, "outputs": [ @@ -936,10 +936,10 @@ "id": "0bcc43db", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:22.431398Z", - "iopub.status.busy": "2023-11-20T20:40:22.430993Z", - "iopub.status.idle": "2023-11-20T20:40:22.562349Z", - "shell.execute_reply": "2023-11-20T20:40:22.561731Z" + "iopub.execute_input": "2023-11-21T08:17:26.664959Z", + "iopub.status.busy": "2023-11-21T08:17:26.664729Z", + "iopub.status.idle": "2023-11-21T08:17:26.794360Z", + "shell.execute_reply": "2023-11-21T08:17:26.793629Z" } }, "outputs": [ @@ -995,10 +995,10 @@ "id": "7021bd68", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:22.565185Z", - "iopub.status.busy": "2023-11-20T20:40:22.564856Z", - "iopub.status.idle": "2023-11-20T20:40:23.139075Z", - "shell.execute_reply": "2023-11-20T20:40:23.138513Z" + "iopub.execute_input": "2023-11-21T08:17:26.797191Z", + "iopub.status.busy": "2023-11-21T08:17:26.796961Z", + "iopub.status.idle": "2023-11-21T08:17:27.385744Z", + "shell.execute_reply": "2023-11-21T08:17:27.384964Z" } }, "outputs": [], @@ -1014,10 +1014,10 @@ "id": "d49c990b", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:23.141586Z", - "iopub.status.busy": "2023-11-20T20:40:23.141386Z", - "iopub.status.idle": "2023-11-20T20:40:23.224411Z", - "shell.execute_reply": "2023-11-20T20:40:23.223797Z" + "iopub.execute_input": "2023-11-21T08:17:27.389323Z", + "iopub.status.busy": "2023-11-21T08:17:27.388649Z", + "iopub.status.idle": "2023-11-21T08:17:27.472540Z", + "shell.execute_reply": "2023-11-21T08:17:27.471876Z" } }, "outputs": [ @@ -1056,10 +1056,10 @@ "id": "95531cda", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:23.227045Z", - "iopub.status.busy": "2023-11-20T20:40:23.226805Z", - "iopub.status.idle": "2023-11-20T20:40:23.236770Z", - "shell.execute_reply": "2023-11-20T20:40:23.236249Z" + "iopub.execute_input": "2023-11-21T08:17:27.475535Z", + "iopub.status.busy": "2023-11-21T08:17:27.475144Z", + "iopub.status.idle": "2023-11-21T08:17:27.485012Z", + "shell.execute_reply": "2023-11-21T08:17:27.484510Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb b/master/.doctrees/nbsphinx/tutorials/segmentation.ipynb index 1e5c77500..df73dacdb 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": "2023-11-20T20:40:28.480077Z", - "iopub.status.busy": "2023-11-20T20:40:28.479565Z", - "iopub.status.idle": "2023-11-20T20:40:30.385026Z", - "shell.execute_reply": "2023-11-20T20:40:30.384228Z" + "iopub.execute_input": "2023-11-21T08:17:32.007539Z", + "iopub.status.busy": "2023-11-21T08:17:32.007135Z", + "iopub.status.idle": "2023-11-21T08:17:34.244281Z", + "shell.execute_reply": "2023-11-21T08:17:34.243536Z" } }, "outputs": [], @@ -79,10 +79,10 @@ "id": "58fd4c55", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:40:30.388072Z", - "iopub.status.busy": "2023-11-20T20:40:30.387818Z", - "iopub.status.idle": "2023-11-20T20:41:30.062355Z", - "shell.execute_reply": "2023-11-20T20:41:30.061586Z" + "iopub.execute_input": "2023-11-21T08:17:34.247394Z", + "iopub.status.busy": "2023-11-21T08:17:34.246967Z", + "iopub.status.idle": "2023-11-21T08:18:45.523100Z", + "shell.execute_reply": "2023-11-21T08:18:45.522365Z" } }, "outputs": [], @@ -97,10 +97,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:41:30.065152Z", - "iopub.status.busy": "2023-11-20T20:41:30.064759Z", - "iopub.status.idle": "2023-11-20T20:41:31.051054Z", - "shell.execute_reply": "2023-11-20T20:41:31.050396Z" + "iopub.execute_input": "2023-11-21T08:18:45.526206Z", + "iopub.status.busy": "2023-11-21T08:18:45.525795Z", + "iopub.status.idle": "2023-11-21T08:18:46.580895Z", + "shell.execute_reply": "2023-11-21T08:18:46.580187Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:41:31.053855Z", - "iopub.status.busy": "2023-11-20T20:41:31.053417Z", - "iopub.status.idle": "2023-11-20T20:41:31.057115Z", - "shell.execute_reply": "2023-11-20T20:41:31.056600Z" + "iopub.execute_input": "2023-11-21T08:18:46.583972Z", + "iopub.status.busy": "2023-11-21T08:18:46.583594Z", + "iopub.status.idle": "2023-11-21T08:18:46.587265Z", + "shell.execute_reply": "2023-11-21T08:18:46.586736Z" } }, "outputs": [], @@ -203,10 +203,10 @@ "id": "07dc5678", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:41:31.059610Z", - "iopub.status.busy": "2023-11-20T20:41:31.059249Z", - "iopub.status.idle": "2023-11-20T20:41:31.063338Z", - "shell.execute_reply": "2023-11-20T20:41:31.062740Z" + "iopub.execute_input": "2023-11-21T08:18:46.589833Z", + "iopub.status.busy": "2023-11-21T08:18:46.589441Z", + "iopub.status.idle": "2023-11-21T08:18:46.593419Z", + "shell.execute_reply": "2023-11-21T08:18:46.592913Z" } }, "outputs": [ @@ -247,10 +247,10 @@ "id": "25ebe22a", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:41:31.065539Z", - "iopub.status.busy": "2023-11-20T20:41:31.065343Z", - "iopub.status.idle": "2023-11-20T20:41:31.069500Z", - "shell.execute_reply": "2023-11-20T20:41:31.068977Z" + "iopub.execute_input": "2023-11-21T08:18:46.595725Z", + "iopub.status.busy": "2023-11-21T08:18:46.595526Z", + "iopub.status.idle": "2023-11-21T08:18:46.599308Z", + "shell.execute_reply": "2023-11-21T08:18:46.598816Z" } }, "outputs": [ @@ -290,10 +290,10 @@ "id": "3faedea9", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:41:31.071970Z", - "iopub.status.busy": "2023-11-20T20:41:31.071534Z", - "iopub.status.idle": "2023-11-20T20:41:31.074538Z", - "shell.execute_reply": "2023-11-20T20:41:31.073999Z" + "iopub.execute_input": "2023-11-21T08:18:46.601637Z", + "iopub.status.busy": "2023-11-21T08:18:46.601270Z", + "iopub.status.idle": "2023-11-21T08:18:46.604252Z", + "shell.execute_reply": "2023-11-21T08:18:46.603714Z" } }, "outputs": [], @@ -333,10 +333,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:41:31.076915Z", - "iopub.status.busy": "2023-11-20T20:41:31.076551Z", - "iopub.status.idle": "2023-11-20T20:42:22.794051Z", - "shell.execute_reply": "2023-11-20T20:42:22.793250Z" + "iopub.execute_input": "2023-11-21T08:18:46.606690Z", + "iopub.status.busy": "2023-11-21T08:18:46.606329Z", + "iopub.status.idle": "2023-11-21T08:19:38.074281Z", + "shell.execute_reply": "2023-11-21T08:19:38.073568Z" } }, "outputs": [ @@ -350,7 +350,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1cb7db243abb49e2bab0c3517c8cad4c", + "model_id": "1ceadfc5823641dea9edbed3de1d9151", "version_major": 2, "version_minor": 0 }, @@ -364,7 +364,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f00bc4af99e44d07a674745b8952af3a", + "model_id": "3c1e1c10507b4c01a882d07bc848f8f3", "version_major": 2, "version_minor": 0 }, @@ -407,10 +407,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:42:22.796960Z", - "iopub.status.busy": "2023-11-20T20:42:22.796737Z", - "iopub.status.idle": "2023-11-20T20:42:23.533307Z", - "shell.execute_reply": "2023-11-20T20:42:23.532711Z" + "iopub.execute_input": "2023-11-21T08:19:38.077734Z", + "iopub.status.busy": "2023-11-21T08:19:38.077219Z", + "iopub.status.idle": "2023-11-21T08:19:38.844577Z", + "shell.execute_reply": "2023-11-21T08:19:38.843987Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "57fed473", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:42:23.535843Z", - "iopub.status.busy": "2023-11-20T20:42:23.535513Z", - "iopub.status.idle": "2023-11-20T20:42:25.630404Z", - "shell.execute_reply": "2023-11-20T20:42:25.629718Z" + "iopub.execute_input": "2023-11-21T08:19:38.847437Z", + "iopub.status.busy": "2023-11-21T08:19:38.846897Z", + "iopub.status.idle": "2023-11-21T08:19:40.987778Z", + "shell.execute_reply": "2023-11-21T08:19:40.987101Z" } }, "outputs": [ @@ -526,10 +526,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:42:25.633313Z", - "iopub.status.busy": "2023-11-20T20:42:25.632914Z", - "iopub.status.idle": "2023-11-20T20:42:54.900865Z", - "shell.execute_reply": "2023-11-20T20:42:54.900204Z" + "iopub.execute_input": "2023-11-21T08:19:40.990597Z", + "iopub.status.busy": "2023-11-21T08:19:40.990210Z", + "iopub.status.idle": "2023-11-21T08:20:09.052348Z", + "shell.execute_reply": "2023-11-21T08:20:09.051672Z" } }, "outputs": [ @@ -546,7 +546,7 @@ "output_type": "stream", "text": [ "\r", - " 0%| | 17095/4997436 [00:00<00:29, 170942.83it/s]" + " 0%| | 17766/4997436 [00:00<00:28, 177641.96it/s]" ] }, { @@ -554,7 +554,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 34244/4997436 [00:00<00:28, 171259.26it/s]" + " 1%| | 35577/4997436 [00:00<00:27, 177907.63it/s]" ] }, { @@ -562,7 +562,7 @@ "output_type": "stream", "text": [ "\r", - " 1%| | 51547/4997436 [00:00<00:28, 172061.88it/s]" + " 1%| | 53396/4997436 [00:00<00:27, 178028.69it/s]" ] }, { @@ -570,7 +570,7 @@ "output_type": "stream", "text": [ "\r", - " 1%|▏ | 68850/4997436 [00:00<00:28, 172441.37it/s]" + " 1%|▏ | 71222/4997436 [00:00<00:27, 178113.37it/s]" ] }, { @@ -578,7 +578,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 86095/4997436 [00:00<00:28, 171941.21it/s]" + " 2%|▏ | 89162/4997436 [00:00<00:27, 178571.69it/s]" ] }, { @@ -586,7 +586,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 103290/4997436 [00:00<00:28, 171711.69it/s]" + " 2%|▏ | 107020/4997436 [00:00<00:27, 177935.49it/s]" ] }, { @@ -594,7 +594,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▏ | 120493/4997436 [00:00<00:28, 171806.89it/s]" + " 2%|▏ | 124854/4997436 [00:00<00:27, 178062.20it/s]" ] }, { @@ -602,7 +602,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 137674/4997436 [00:00<00:28, 171612.85it/s]" + " 3%|▎ | 142661/4997436 [00:00<00:27, 177944.33it/s]" ] }, { @@ -610,7 +610,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 154836/4997436 [00:00<00:28, 171539.45it/s]" + " 3%|▎ | 160501/4997436 [00:00<00:27, 178082.49it/s]" ] }, { @@ -618,7 +618,7 @@ "output_type": "stream", "text": [ "\r", - " 3%|▎ | 171991/4997436 [00:01<00:28, 171092.38it/s]" + " 4%|▎ | 178400/4997436 [00:01<00:27, 178358.98it/s]" ] }, { @@ -626,7 +626,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 189195/4997436 [00:01<00:28, 171379.48it/s]" + " 4%|▍ | 196376/4997436 [00:01<00:26, 178783.30it/s]" ] }, { @@ -634,7 +634,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 206463/4997436 [00:01<00:27, 171770.38it/s]" + " 4%|▍ | 214364/4997436 [00:01<00:26, 179111.59it/s]" ] }, { @@ -642,7 +642,7 @@ "output_type": "stream", "text": [ "\r", - " 4%|▍ | 223732/4997436 [00:01<00:27, 172047.12it/s]" + " 5%|▍ | 232318/4997436 [00:01<00:26, 179238.26it/s]" ] }, { @@ -650,7 +650,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▍ | 240943/4997436 [00:01<00:27, 172062.68it/s]" + " 5%|▌ | 250397/4997436 [00:01<00:26, 179702.21it/s]" ] }, { @@ -658,7 +658,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 258152/4997436 [00:01<00:27, 172067.87it/s]" + " 5%|▌ | 268368/4997436 [00:01<00:26, 179454.09it/s]" ] }, { @@ -666,7 +666,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 275359/4997436 [00:01<00:27, 172035.61it/s]" + " 6%|▌ | 286429/4997436 [00:01<00:26, 179797.42it/s]" ] }, { @@ -674,7 +674,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 292563/4997436 [00:01<00:27, 170154.06it/s]" + " 6%|▌ | 304409/4997436 [00:01<00:26, 179595.36it/s]" ] }, { @@ -682,7 +682,7 @@ "output_type": "stream", "text": [ "\r", - " 6%|▌ | 309695/4997436 [00:01<00:27, 170500.44it/s]" + " 6%|▋ | 322369/4997436 [00:01<00:26, 178729.83it/s]" ] }, { @@ -690,7 +690,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 326870/4997436 [00:01<00:27, 170870.26it/s]" + " 7%|▋ | 340243/4997436 [00:01<00:26, 178692.41it/s]" ] }, { @@ -698,7 +698,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 344095/4997436 [00:02<00:27, 171280.11it/s]" + " 7%|▋ | 358538/4997436 [00:02<00:25, 179962.44it/s]" ] }, { @@ -706,7 +706,7 @@ "output_type": "stream", "text": [ "\r", - " 7%|▋ | 361226/4997436 [00:02<00:27, 171032.42it/s]" + " 8%|▊ | 376705/4997436 [00:02<00:25, 180468.87it/s]" ] }, { @@ -714,7 +714,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 378331/4997436 [00:02<00:27, 170744.78it/s]" + " 8%|▊ | 394872/4997436 [00:02<00:25, 180824.82it/s]" ] }, { @@ -722,7 +722,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 395501/4997436 [00:02<00:26, 171022.60it/s]" + " 8%|▊ | 413111/4997436 [00:02<00:25, 181290.26it/s]" ] }, { @@ -730,7 +730,7 @@ "output_type": "stream", "text": [ "\r", - " 8%|▊ | 412711/4997436 [00:02<00:26, 171343.71it/s]" + " 9%|▊ | 431298/4997436 [00:02<00:25, 181460.76it/s]" ] }, { @@ -738,7 +738,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▊ | 429847/4997436 [00:02<00:26, 171284.29it/s]" + " 9%|▉ | 449445/4997436 [00:02<00:25, 181158.31it/s]" ] }, { @@ -746,7 +746,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 446976/4997436 [00:02<00:26, 171116.40it/s]" + " 9%|▉ | 467625/4997436 [00:02<00:24, 181346.31it/s]" ] }, { @@ -754,7 +754,7 @@ "output_type": "stream", "text": [ "\r", - " 9%|▉ | 464133/4997436 [00:02<00:26, 171249.74it/s]" + " 10%|▉ | 485819/4997436 [00:02<00:24, 181521.81it/s]" ] }, { @@ -762,7 +762,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 481259/4997436 [00:02<00:26, 171247.60it/s]" + " 10%|█ | 503972/4997436 [00:02<00:24, 180916.20it/s]" ] }, { @@ -770,7 +770,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|▉ | 498419/4997436 [00:02<00:26, 171352.18it/s]" + " 10%|█ | 522085/4997436 [00:02<00:24, 180977.11it/s]" ] }, { @@ -778,7 +778,7 @@ "output_type": "stream", "text": [ "\r", - " 10%|█ | 515555/4997436 [00:03<00:26, 171133.76it/s]" + " 11%|█ | 540264/4997436 [00:03<00:24, 181216.27it/s]" ] }, { @@ -786,7 +786,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 532756/4997436 [00:03<00:26, 171389.24it/s]" + " 11%|█ | 558386/4997436 [00:03<00:24, 180535.45it/s]" ] }, { @@ -794,7 +794,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█ | 549896/4997436 [00:03<00:26, 170943.60it/s]" + " 12%|█▏ | 576441/4997436 [00:03<00:24, 179644.83it/s]" ] }, { @@ -802,7 +802,7 @@ "output_type": "stream", "text": [ "\r", - " 11%|█▏ | 566991/4997436 [00:03<00:25, 170829.05it/s]" + " 12%|█▏ | 594590/4997436 [00:03<00:24, 180191.13it/s]" ] }, { @@ -810,7 +810,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 584075/4997436 [00:03<00:25, 170806.53it/s]" + " 12%|█▏ | 612629/4997436 [00:03<00:24, 180247.00it/s]" ] }, { @@ -818,7 +818,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 601156/4997436 [00:03<00:25, 170555.03it/s]" + " 13%|█▎ | 630771/4997436 [00:03<00:24, 180595.32it/s]" ] }, { @@ -826,7 +826,7 @@ "output_type": "stream", "text": [ "\r", - " 12%|█▏ | 618212/4997436 [00:03<00:25, 170219.33it/s]" + " 13%|█▎ | 649047/4997436 [00:03<00:23, 181240.05it/s]" ] }, { @@ -834,7 +834,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 635235/4997436 [00:03<00:25, 168395.82it/s]" + " 13%|█▎ | 667381/4997436 [00:03<00:23, 181866.42it/s]" ] }, { @@ -842,7 +842,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 652181/4997436 [00:03<00:25, 168709.80it/s]" + " 14%|█▎ | 685607/4997436 [00:03<00:23, 181980.52it/s]" ] }, { @@ -850,7 +850,7 @@ "output_type": "stream", "text": [ "\r", - " 13%|█▎ | 669161/4997436 [00:03<00:25, 169032.54it/s]" + " 14%|█▍ | 703870/4997436 [00:03<00:23, 182170.46it/s]" ] }, { @@ -858,7 +858,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▎ | 686213/4997436 [00:04<00:25, 169473.94it/s]" + " 14%|█▍ | 722088/4997436 [00:04<00:23, 181756.03it/s]" ] }, { @@ -866,7 +866,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 703344/4997436 [00:04<00:25, 170020.19it/s]" + " 15%|█▍ | 740264/4997436 [00:04<00:23, 181122.45it/s]" ] }, { @@ -874,7 +874,7 @@ "output_type": "stream", "text": [ "\r", - " 14%|█▍ | 720374/4997436 [00:04<00:25, 170099.98it/s]" + " 15%|█▌ | 758377/4997436 [00:04<00:23, 180696.41it/s]" ] }, { @@ -882,7 +882,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▍ | 737392/4997436 [00:04<00:25, 170122.17it/s]" + " 16%|█▌ | 776461/4997436 [00:04<00:23, 180736.52it/s]" ] }, { @@ -890,7 +890,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 754405/4997436 [00:04<00:24, 169835.55it/s]" + " 16%|█▌ | 794655/4997436 [00:04<00:23, 181092.77it/s]" ] }, { @@ -898,7 +898,7 @@ "output_type": "stream", "text": [ "\r", - " 15%|█▌ | 771485/4997436 [00:04<00:24, 170102.25it/s]" + " 16%|█▋ | 812765/4997436 [00:04<00:23, 180850.52it/s]" ] }, { @@ -906,7 +906,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 788620/4997436 [00:04<00:24, 170472.17it/s]" + " 17%|█▋ | 830944/4997436 [00:04<00:23, 181126.42it/s]" ] }, { @@ -914,7 +914,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▌ | 805769/4997436 [00:04<00:24, 170773.95it/s]" + " 17%|█▋ | 849069/4997436 [00:04<00:22, 181159.99it/s]" ] }, { @@ -922,7 +922,7 @@ "output_type": "stream", "text": [ "\r", - " 16%|█▋ | 822850/4997436 [00:04<00:24, 170782.24it/s]" + " 17%|█▋ | 867186/4997436 [00:04<00:22, 180415.38it/s]" ] }, { @@ -930,7 +930,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 839996/4997436 [00:04<00:24, 170982.57it/s]" + " 18%|█▊ | 885229/4997436 [00:04<00:22, 180359.01it/s]" ] }, { @@ -938,7 +938,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 857095/4997436 [00:05<00:24, 170848.04it/s]" + " 18%|█▊ | 903395/4997436 [00:05<00:22, 180745.02it/s]" ] }, { @@ -946,7 +946,7 @@ "output_type": "stream", "text": [ "\r", - " 17%|█▋ | 874302/4997436 [00:05<00:24, 171212.29it/s]" + " 18%|█▊ | 921470/4997436 [00:05<00:22, 180450.18it/s]" ] }, { @@ -954,7 +954,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 891479/4997436 [00:05<00:23, 171377.59it/s]" + " 19%|█▉ | 939568/4997436 [00:05<00:22, 180606.19it/s]" ] }, { @@ -962,7 +962,7 @@ "output_type": "stream", "text": [ "\r", - " 18%|█▊ | 908730/4997436 [00:05<00:23, 171714.64it/s]" + " 19%|█▉ | 957629/4997436 [00:05<00:22, 179980.18it/s]" ] }, { @@ -970,7 +970,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▊ | 925944/4997436 [00:05<00:23, 171839.31it/s]" + " 20%|█▉ | 975653/4997436 [00:05<00:22, 180054.42it/s]" ] }, { @@ -978,7 +978,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 943128/4997436 [00:05<00:23, 171673.84it/s]" + " 20%|█▉ | 993659/4997436 [00:05<00:22, 179895.85it/s]" ] }, { @@ -986,7 +986,7 @@ "output_type": "stream", "text": [ "\r", - " 19%|█▉ | 960313/4997436 [00:05<00:23, 171723.75it/s]" + " 20%|██ | 1011672/4997436 [00:05<00:22, 179960.84it/s]" ] }, { @@ -994,7 +994,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 977486/4997436 [00:05<00:23, 171550.43it/s]" + " 21%|██ | 1029669/4997436 [00:05<00:22, 179648.09it/s]" ] }, { @@ -1002,7 +1002,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|█▉ | 994642/4997436 [00:05<00:23, 171022.69it/s]" + " 21%|██ | 1047635/4997436 [00:05<00:22, 179511.20it/s]" ] }, { @@ -1010,7 +1010,7 @@ "output_type": "stream", "text": [ "\r", - " 20%|██ | 1011799/4997436 [00:05<00:23, 171182.66it/s]" + " 21%|██▏ | 1065592/4997436 [00:05<00:21, 179525.26it/s]" ] }, { @@ -1018,7 +1018,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1028918/4997436 [00:06<00:23, 170097.33it/s]" + " 22%|██▏ | 1083619/4997436 [00:06<00:21, 179743.76it/s]" ] }, { @@ -1026,7 +1026,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██ | 1046024/4997436 [00:06<00:23, 170381.68it/s]" + " 22%|██▏ | 1101594/4997436 [00:06<00:21, 179377.62it/s]" ] }, { @@ -1034,7 +1034,7 @@ "output_type": "stream", "text": [ "\r", - " 21%|██▏ | 1063171/4997436 [00:06<00:23, 170704.92it/s]" + " 22%|██▏ | 1119532/4997436 [00:06<00:21, 179253.53it/s]" ] }, { @@ -1042,7 +1042,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1080389/4997436 [00:06<00:22, 171143.52it/s]" + " 23%|██▎ | 1137501/4997436 [00:06<00:21, 179379.00it/s]" ] }, { @@ -1050,7 +1050,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1097605/4997436 [00:06<00:22, 171445.19it/s]" + " 23%|██▎ | 1155440/4997436 [00:06<00:21, 179100.42it/s]" ] }, { @@ -1058,7 +1058,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▏ | 1114751/4997436 [00:06<00:22, 171272.67it/s]" + " 23%|██▎ | 1173351/4997436 [00:06<00:21, 178978.92it/s]" ] }, { @@ -1066,7 +1066,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1131879/4997436 [00:06<00:22, 171110.83it/s]" + " 24%|██▍ | 1191249/4997436 [00:06<00:21, 178950.29it/s]" ] }, { @@ -1074,7 +1074,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1148991/4997436 [00:06<00:22, 170323.38it/s]" + " 24%|██▍ | 1209246/4997436 [00:06<00:21, 179252.09it/s]" ] }, { @@ -1082,7 +1082,7 @@ "output_type": "stream", "text": [ "\r", - " 23%|██▎ | 1166030/4997436 [00:06<00:22, 170341.75it/s]" + " 25%|██▍ | 1227172/4997436 [00:06<00:21, 178963.74it/s]" ] }, { @@ -1090,7 +1090,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▎ | 1183111/4997436 [00:06<00:22, 170480.12it/s]" + " 25%|██▍ | 1245069/4997436 [00:06<00:21, 178446.40it/s]" ] }, { @@ -1098,7 +1098,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1200191/4997436 [00:07<00:22, 170574.21it/s]" + " 25%|██▌ | 1262914/4997436 [00:07<00:20, 178066.84it/s]" ] }, { @@ -1106,7 +1106,7 @@ "output_type": "stream", "text": [ "\r", - " 24%|██▍ | 1217249/4997436 [00:07<00:22, 170385.09it/s]" + " 26%|██▌ | 1280721/4997436 [00:07<00:20, 177920.78it/s]" ] }, { @@ -1114,7 +1114,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▍ | 1234288/4997436 [00:07<00:22, 170272.95it/s]" + " 26%|██▌ | 1298783/4997436 [00:07<00:20, 178722.73it/s]" ] }, { @@ -1122,7 +1122,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 1251366/4997436 [00:07<00:21, 170421.07it/s]" + " 26%|██▋ | 1316841/4997436 [00:07<00:20, 179275.35it/s]" ] }, { @@ -1130,7 +1130,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 1268409/4997436 [00:07<00:21, 169938.46it/s]" + " 27%|██▋ | 1334908/4997436 [00:07<00:20, 179690.03it/s]" ] }, { @@ -1138,7 +1138,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1285421/4997436 [00:07<00:21, 169990.14it/s]" + " 27%|██▋ | 1352949/4997436 [00:07<00:20, 179902.87it/s]" ] }, { @@ -1146,7 +1146,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▌ | 1302462/4997436 [00:07<00:21, 170112.25it/s]" + " 27%|██▋ | 1371030/4997436 [00:07<00:20, 180171.10it/s]" ] }, { @@ -1154,7 +1154,7 @@ "output_type": "stream", "text": [ "\r", - " 26%|██▋ | 1319480/4997436 [00:07<00:21, 170126.40it/s]" + " 28%|██▊ | 1389048/4997436 [00:07<00:20, 180153.73it/s]" ] }, { @@ -1162,7 +1162,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1336646/4997436 [00:07<00:21, 170583.01it/s]" + " 28%|██▊ | 1407137/4997436 [00:07<00:19, 180371.59it/s]" ] }, { @@ -1170,7 +1170,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1353705/4997436 [00:07<00:21, 170528.48it/s]" + " 29%|██▊ | 1425256/4997436 [00:07<00:19, 180612.56it/s]" ] }, { @@ -1178,7 +1178,7 @@ "output_type": "stream", "text": [ "\r", - " 27%|██▋ | 1370758/4997436 [00:08<00:21, 170363.51it/s]" + " 29%|██▉ | 1443419/4997436 [00:08<00:19, 180915.05it/s]" ] }, { @@ -1186,7 +1186,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1387852/4997436 [00:08<00:21, 170534.10it/s]" + " 29%|██▉ | 1461623/4997436 [00:08<00:19, 181248.50it/s]" ] }, { @@ -1194,7 +1194,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1404906/4997436 [00:08<00:21, 170413.65it/s]" + " 30%|██▉ | 1479851/4997436 [00:08<00:19, 181542.24it/s]" ] }, { @@ -1202,7 +1202,7 @@ "output_type": "stream", "text": [ "\r", - " 28%|██▊ | 1421959/4997436 [00:08<00:20, 170444.42it/s]" + " 30%|██▉ | 1498025/4997436 [00:08<00:19, 181597.59it/s]" ] }, { @@ -1210,7 +1210,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1439128/4997436 [00:08<00:20, 170815.29it/s]" + " 30%|███ | 1516185/4997436 [00:08<00:19, 181424.37it/s]" ] }, { @@ -1218,7 +1218,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1456333/4997436 [00:08<00:20, 171184.32it/s]" + " 31%|███ | 1534356/4997436 [00:08<00:19, 181506.54it/s]" ] }, { @@ -1226,7 +1226,7 @@ "output_type": "stream", "text": [ "\r", - " 29%|██▉ | 1473452/4997436 [00:08<00:20, 170937.00it/s]" + " 31%|███ | 1552507/4997436 [00:08<00:18, 181443.30it/s]" ] }, { @@ -1234,7 +1234,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|██▉ | 1490546/4997436 [00:08<00:20, 170737.44it/s]" + " 31%|███▏ | 1570652/4997436 [00:08<00:18, 180923.66it/s]" ] }, { @@ -1242,7 +1242,7 @@ "output_type": "stream", "text": [ "\r", - " 30%|███ | 1507649/4997436 [00:08<00:20, 170817.42it/s]" + " 32%|███▏ | 1588921/4997436 [00:08<00:18, 181449.59it/s]" ] }, { @@ -1250,7 +1250,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1524731/4997436 [00:08<00:20, 167322.01it/s]" + " 32%|███▏ | 1607067/4997436 [00:08<00:18, 178873.61it/s]" ] }, { @@ -1258,7 +1258,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1542115/4997436 [00:09<00:20, 169244.24it/s]" + " 33%|███▎ | 1625177/4997436 [00:09<00:18, 179528.64it/s]" ] }, { @@ -1266,7 +1266,7 @@ "output_type": "stream", "text": [ "\r", - " 31%|███ | 1559535/4997436 [00:09<00:20, 170712.24it/s]" + " 33%|███▎ | 1643222/4997436 [00:09<00:18, 179797.56it/s]" ] }, { @@ -1274,7 +1274,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1576840/4997436 [00:09<00:19, 171404.42it/s]" + " 33%|███▎ | 1661474/4997436 [00:09<00:18, 180604.03it/s]" ] }, { @@ -1282,7 +1282,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1594132/4997436 [00:09<00:19, 171855.26it/s]" + " 34%|███▎ | 1679655/4997436 [00:09<00:18, 180958.78it/s]" ] }, { @@ -1290,7 +1290,7 @@ "output_type": "stream", "text": [ "\r", - " 32%|███▏ | 1611474/4997436 [00:09<00:19, 172319.53it/s]" + " 34%|███▍ | 1697754/4997436 [00:09<00:18, 180777.21it/s]" ] }, { @@ -1298,7 +1298,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1628731/4997436 [00:09<00:19, 172391.57it/s]" + " 34%|███▍ | 1715881/4997436 [00:09<00:18, 180919.54it/s]" ] }, { @@ -1306,7 +1306,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1646030/4997436 [00:09<00:19, 172567.86it/s]" + " 35%|███▍ | 1733996/4997436 [00:09<00:18, 180985.63it/s]" ] }, { @@ -1314,7 +1314,7 @@ "output_type": "stream", "text": [ "\r", - " 33%|███▎ | 1663290/4997436 [00:09<00:19, 172210.35it/s]" + " 35%|███▌ | 1752109/4997436 [00:09<00:17, 181025.05it/s]" ] }, { @@ -1322,7 +1322,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▎ | 1680557/4997436 [00:09<00:19, 172343.04it/s]" + " 35%|███▌ | 1770213/4997436 [00:09<00:17, 180805.94it/s]" ] }, { @@ -1330,7 +1330,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1697793/4997436 [00:09<00:19, 171981.85it/s]" + " 36%|███▌ | 1788295/4997436 [00:09<00:17, 180251.39it/s]" ] }, { @@ -1338,7 +1338,7 @@ "output_type": "stream", "text": [ "\r", - " 34%|███▍ | 1715030/4997436 [00:10<00:19, 172095.46it/s]" + " 36%|███▌ | 1806432/4997436 [00:10<00:17, 180582.21it/s]" ] }, { @@ -1346,7 +1346,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▍ | 1732409/4997436 [00:10<00:18, 172600.94it/s]" + " 37%|███▋ | 1824626/4997436 [00:10<00:17, 180984.92it/s]" ] }, { @@ -1354,7 +1354,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1749773/4997436 [00:10<00:18, 172909.08it/s]" + " 37%|███▋ | 1842725/4997436 [00:10<00:17, 180536.82it/s]" ] }, { @@ -1362,7 +1362,7 @@ "output_type": "stream", "text": [ "\r", - " 35%|███▌ | 1767151/4997436 [00:10<00:18, 173169.04it/s]" + " 37%|███▋ | 1860852/4997436 [00:10<00:17, 180752.84it/s]" ] }, { @@ -1370,7 +1370,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1784476/4997436 [00:10<00:18, 173189.77it/s]" + " 38%|███▊ | 1878928/4997436 [00:10<00:17, 180749.62it/s]" ] }, { @@ -1378,7 +1378,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▌ | 1801796/4997436 [00:10<00:18, 173013.66it/s]" + " 38%|███▊ | 1897004/4997436 [00:10<00:17, 180349.09it/s]" ] }, { @@ -1386,7 +1386,7 @@ "output_type": "stream", "text": [ "\r", - " 36%|███▋ | 1819098/4997436 [00:10<00:18, 172965.56it/s]" + " 38%|███▊ | 1915100/4997436 [00:10<00:17, 180526.66it/s]" ] }, { @@ -1394,7 +1394,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1836454/4997436 [00:10<00:18, 173140.31it/s]" + " 39%|███▊ | 1933170/4997436 [00:10<00:16, 180575.78it/s]" ] }, { @@ -1402,7 +1402,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1853769/4997436 [00:10<00:18, 171932.40it/s]" + " 39%|███▉ | 1951233/4997436 [00:10<00:16, 180589.24it/s]" ] }, { @@ -1410,7 +1410,7 @@ "output_type": "stream", "text": [ "\r", - " 37%|███▋ | 1870965/4997436 [00:10<00:18, 171889.57it/s]" + " 39%|███▉ | 1969400/4997436 [00:10<00:16, 180909.73it/s]" ] }, { @@ -1418,7 +1418,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1888302/4997436 [00:11<00:18, 172328.30it/s]" + " 40%|███▉ | 1987492/4997436 [00:11<00:16, 180679.49it/s]" ] }, { @@ -1426,7 +1426,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1905592/4997436 [00:11<00:17, 172495.56it/s]" + " 40%|████ | 2005561/4997436 [00:11<00:16, 180223.64it/s]" ] }, { @@ -1434,7 +1434,7 @@ "output_type": "stream", "text": [ "\r", - " 38%|███▊ | 1922951/4997436 [00:11<00:17, 172820.83it/s]" + " 40%|████ | 2023631/4997436 [00:11<00:16, 180361.45it/s]" ] }, { @@ -1442,7 +1442,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1940269/4997436 [00:11<00:17, 172925.88it/s]" + " 41%|████ | 2041668/4997436 [00:11<00:16, 180224.85it/s]" ] }, { @@ -1450,7 +1450,7 @@ "output_type": "stream", "text": [ "\r", - " 39%|███▉ | 1957599/4997436 [00:11<00:17, 173034.84it/s]" + " 41%|████ | 2059691/4997436 [00:11<00:16, 179514.54it/s]" ] }, { @@ -1458,7 +1458,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 1974903/4997436 [00:11<00:17, 172926.68it/s]" + " 42%|████▏ | 2077644/4997436 [00:11<00:16, 179428.34it/s]" ] }, { @@ -1466,7 +1466,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|███▉ | 1992196/4997436 [00:11<00:17, 172760.08it/s]" + " 42%|████▏ | 2095632/4997436 [00:11<00:16, 179560.76it/s]" ] }, { @@ -1474,7 +1474,7 @@ "output_type": "stream", "text": [ "\r", - " 40%|████ | 2009473/4997436 [00:11<00:17, 172686.75it/s]" + " 42%|████▏ | 2113676/4997436 [00:11<00:16, 179818.34it/s]" ] }, { @@ -1482,7 +1482,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2026742/4997436 [00:11<00:17, 172379.91it/s]" + " 43%|████▎ | 2131659/4997436 [00:11<00:15, 179485.31it/s]" ] }, { @@ -1490,7 +1490,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2043981/4997436 [00:11<00:17, 172312.27it/s]" + " 43%|████▎ | 2149608/4997436 [00:11<00:15, 178867.53it/s]" ] }, { @@ -1498,7 +1498,7 @@ "output_type": "stream", "text": [ "\r", - " 41%|████ | 2061213/4997436 [00:12<00:17, 172119.48it/s]" + " 43%|████▎ | 2167496/4997436 [00:12<00:15, 178697.81it/s]" ] }, { @@ -1506,7 +1506,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2078486/4997436 [00:12<00:16, 172298.50it/s]" + " 44%|████▎ | 2185367/4997436 [00:12<00:15, 178558.72it/s]" ] }, { @@ -1514,7 +1514,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2095770/4997436 [00:12<00:16, 172457.93it/s]" + " 44%|████▍ | 2203251/4997436 [00:12<00:15, 178636.78it/s]" ] }, { @@ -1522,7 +1522,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▏ | 2113024/4997436 [00:12<00:16, 172480.97it/s]" + " 44%|████▍ | 2221115/4997436 [00:12<00:15, 178050.96it/s]" ] }, { @@ -1530,7 +1530,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2130314/4997436 [00:12<00:16, 172604.79it/s]" + " 45%|████▍ | 2238921/4997436 [00:12<00:15, 177999.18it/s]" ] }, { @@ -1538,7 +1538,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2147575/4997436 [00:12<00:16, 172251.88it/s]" + " 45%|████▌ | 2256872/4997436 [00:12<00:15, 178445.18it/s]" ] }, { @@ -1546,7 +1546,7 @@ "output_type": "stream", "text": [ "\r", - " 43%|████▎ | 2164801/4997436 [00:12<00:16, 172182.20it/s]" + " 46%|████▌ | 2274717/4997436 [00:12<00:15, 177450.35it/s]" ] }, { @@ -1554,7 +1554,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▎ | 2182020/4997436 [00:12<00:16, 172167.99it/s]" + " 46%|████▌ | 2292464/4997436 [00:12<00:15, 177442.12it/s]" ] }, { @@ -1562,7 +1562,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2199237/4997436 [00:12<00:16, 171895.76it/s]" + " 46%|████▌ | 2310210/4997436 [00:12<00:15, 177227.54it/s]" ] }, { @@ -1570,7 +1570,7 @@ "output_type": "stream", "text": [ "\r", - " 44%|████▍ | 2216427/4997436 [00:12<00:16, 171102.75it/s]" + " 47%|████▋ | 2328012/4997436 [00:12<00:15, 177460.94it/s]" ] }, { @@ -1578,7 +1578,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▍ | 2233555/4997436 [00:13<00:16, 171154.39it/s]" + " 47%|████▋ | 2345759/4997436 [00:13<00:15, 175164.40it/s]" ] }, { @@ -1586,7 +1586,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 2250760/4997436 [00:13<00:16, 171419.94it/s]" + " 47%|████▋ | 2363672/4997436 [00:13<00:14, 176337.25it/s]" ] }, { @@ -1594,7 +1594,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 2267920/4997436 [00:13<00:15, 171469.97it/s]" + " 48%|████▊ | 2381589/4997436 [00:13<00:14, 177176.49it/s]" ] }, { @@ -1602,7 +1602,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2285068/4997436 [00:13<00:15, 171379.52it/s]" + " 48%|████▊ | 2399352/4997436 [00:13<00:14, 177307.57it/s]" ] }, { @@ -1610,7 +1610,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▌ | 2302207/4997436 [00:13<00:15, 171218.97it/s]" + " 48%|████▊ | 2417269/4997436 [00:13<00:14, 177860.31it/s]" ] }, { @@ -1618,7 +1618,7 @@ "output_type": "stream", "text": [ "\r", - " 46%|████▋ | 2319332/4997436 [00:13<00:15, 171225.38it/s]" + " 49%|████▊ | 2435271/4997436 [00:13<00:14, 178502.21it/s]" ] }, { @@ -1626,7 +1626,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2336591/4997436 [00:13<00:15, 171630.27it/s]" + " 49%|████▉ | 2453139/4997436 [00:13<00:14, 178551.17it/s]" ] }, { @@ -1634,7 +1634,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2353899/4997436 [00:13<00:15, 172061.91it/s]" + " 49%|████▉ | 2470996/4997436 [00:13<00:14, 178446.09it/s]" ] }, { @@ -1642,7 +1642,7 @@ "output_type": "stream", "text": [ "\r", - " 47%|████▋ | 2371106/4997436 [00:13<00:15, 171976.37it/s]" + " 50%|████▉ | 2489237/4997436 [00:13<00:13, 179632.15it/s]" ] }, { @@ -1650,7 +1650,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2388324/4997436 [00:13<00:15, 172034.63it/s]" + " 50%|█████ | 2507433/4997436 [00:13<00:13, 180327.18it/s]" ] }, { @@ -1658,7 +1658,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2405620/4997436 [00:14<00:15, 172309.30it/s]" + " 51%|█████ | 2525599/4997436 [00:14<00:13, 180723.05it/s]" ] }, { @@ -1666,7 +1666,7 @@ "output_type": "stream", "text": [ "\r", - " 48%|████▊ | 2422942/4997436 [00:14<00:14, 172581.34it/s]" + " 51%|█████ | 2543672/4997436 [00:14<00:13, 180661.52it/s]" ] }, { @@ -1674,7 +1674,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2440222/4997436 [00:14<00:14, 172645.61it/s]" + " 51%|█████▏ | 2561757/4997436 [00:14<00:13, 180713.43it/s]" ] }, { @@ -1682,7 +1682,7 @@ "output_type": "stream", "text": [ "\r", - " 49%|████▉ | 2457528/4997436 [00:14<00:14, 172767.11it/s]" + " 52%|█████▏ | 2579842/4997436 [00:14<00:13, 180750.82it/s]" ] }, { @@ -1690,7 +1690,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|████▉ | 2474838/4997436 [00:14<00:14, 172865.53it/s]" + " 52%|█████▏ | 2597918/4997436 [00:14<00:13, 180644.99it/s]" ] }, { @@ -1698,7 +1698,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|████▉ | 2492215/4997436 [00:14<00:14, 173135.65it/s]" + " 52%|█████▏ | 2615983/4997436 [00:14<00:13, 180338.72it/s]" ] }, { @@ -1706,7 +1706,7 @@ "output_type": "stream", "text": [ "\r", - " 50%|█████ | 2509565/4997436 [00:14<00:14, 173241.65it/s]" + " 53%|█████▎ | 2634018/4997436 [00:14<00:13, 180132.10it/s]" ] }, { @@ -1714,7 +1714,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2526890/4997436 [00:14<00:14, 173202.95it/s]" + " 53%|█████▎ | 2652032/4997436 [00:14<00:13, 179400.96it/s]" ] }, { @@ -1722,7 +1722,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████ | 2544211/4997436 [00:14<00:14, 172873.81it/s]" + " 53%|█████▎ | 2669973/4997436 [00:14<00:12, 179194.27it/s]" ] }, { @@ -1730,7 +1730,7 @@ "output_type": "stream", "text": [ "\r", - " 51%|█████▏ | 2561499/4997436 [00:14<00:14, 172617.44it/s]" + " 54%|█████▍ | 2687893/4997436 [00:14<00:12, 178443.31it/s]" ] }, { @@ -1738,7 +1738,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2578821/4997436 [00:15<00:13, 172794.67it/s]" + " 54%|█████▍ | 2705739/4997436 [00:15<00:13, 175824.40it/s]" ] }, { @@ -1746,7 +1746,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2596286/4997436 [00:15<00:13, 173347.77it/s]" + " 54%|█████▍ | 2723565/4997436 [00:15<00:12, 176539.75it/s]" ] }, { @@ -1754,7 +1754,7 @@ "output_type": "stream", "text": [ "\r", - " 52%|█████▏ | 2613872/4997436 [00:15<00:13, 174098.84it/s]" + " 55%|█████▍ | 2741347/4997436 [00:15<00:12, 176915.01it/s]" ] }, { @@ -1762,7 +1762,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2631353/4997436 [00:15<00:13, 174309.09it/s]" + " 55%|█████▌ | 2759122/4997436 [00:15<00:12, 177160.90it/s]" ] }, { @@ -1770,7 +1770,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2648795/4997436 [00:15<00:13, 174340.52it/s]" + " 56%|█████▌ | 2776926/4997436 [00:15<00:12, 177418.95it/s]" ] }, { @@ -1778,7 +1778,7 @@ "output_type": "stream", "text": [ "\r", - " 53%|█████▎ | 2666298/4997436 [00:15<00:13, 174546.09it/s]" + " 56%|█████▌ | 2794703/4997436 [00:15<00:12, 177520.28it/s]" ] }, { @@ -1786,7 +1786,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▎ | 2683762/4997436 [00:15<00:13, 174570.56it/s]" + " 56%|█████▋ | 2812566/4997436 [00:15<00:12, 177849.76it/s]" ] }, { @@ -1794,7 +1794,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2701220/4997436 [00:15<00:13, 174395.48it/s]" + " 57%|█████▋ | 2830362/4997436 [00:15<00:12, 177880.67it/s]" ] }, { @@ -1802,7 +1802,7 @@ "output_type": "stream", "text": [ "\r", - " 54%|█████▍ | 2718660/4997436 [00:15<00:13, 174147.73it/s]" + " 57%|█████▋ | 2848161/4997436 [00:15<00:12, 177909.79it/s]" ] }, { @@ -1810,7 +1810,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▍ | 2736136/4997436 [00:15<00:12, 174326.68it/s]" + " 57%|█████▋ | 2865976/4997436 [00:15<00:11, 177978.15it/s]" ] }, { @@ -1818,7 +1818,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2753569/4997436 [00:16<00:12, 174049.44it/s]" + " 58%|█████▊ | 2883775/4997436 [00:16<00:11, 177584.31it/s]" ] }, { @@ -1826,7 +1826,7 @@ "output_type": "stream", "text": [ "\r", - " 55%|█████▌ | 2770975/4997436 [00:16<00:12, 174021.84it/s]" + " 58%|█████▊ | 2901534/4997436 [00:16<00:11, 175974.18it/s]" ] }, { @@ -1834,7 +1834,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2788385/4997436 [00:16<00:12, 174041.57it/s]" + " 58%|█████▊ | 2919201/4997436 [00:16<00:11, 176177.35it/s]" ] }, { @@ -1842,7 +1842,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▌ | 2805830/4997436 [00:16<00:12, 174161.54it/s]" + " 59%|█████▉ | 2936879/4997436 [00:16<00:11, 176354.82it/s]" ] }, { @@ -1850,7 +1850,7 @@ "output_type": "stream", "text": [ "\r", - " 56%|█████▋ | 2823271/4997436 [00:16<00:12, 174232.46it/s]" + " 59%|█████▉ | 2954517/4997436 [00:16<00:11, 175797.86it/s]" ] }, { @@ -1858,7 +1858,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2840695/4997436 [00:16<00:12, 174171.27it/s]" + " 59%|█████▉ | 2972194/4997436 [00:16<00:11, 176083.60it/s]" ] }, { @@ -1866,7 +1866,7 @@ "output_type": "stream", "text": [ "\r", - " 57%|█████▋ | 2858113/4997436 [00:16<00:12, 174039.58it/s]" + " 60%|█████▉ | 2989882/4997436 [00:16<00:11, 176319.10it/s]" ] }, { @@ -1874,7 +1874,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2875583/4997436 [00:16<00:12, 174234.20it/s]" + " 60%|██████ | 3007545/4997436 [00:16<00:11, 176409.43it/s]" ] }, { @@ -1882,7 +1882,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2893050/4997436 [00:16<00:12, 174361.91it/s]" + " 61%|██████ | 3025258/4997436 [00:16<00:11, 176622.50it/s]" ] }, { @@ -1890,7 +1890,7 @@ "output_type": "stream", "text": [ "\r", - " 58%|█████▊ | 2910526/4997436 [00:16<00:11, 174477.96it/s]" + " 61%|██████ | 3042984/4997436 [00:16<00:11, 176808.77it/s]" ] }, { @@ -1898,7 +1898,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▊ | 2927974/4997436 [00:17<00:11, 173990.46it/s]" + " 61%|██████ | 3060738/4997436 [00:17<00:10, 177023.19it/s]" ] }, { @@ -1906,7 +1906,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2945374/4997436 [00:17<00:11, 173395.05it/s]" + " 62%|██████▏ | 3078623/4997436 [00:17<00:10, 177567.77it/s]" ] }, { @@ -1914,7 +1914,7 @@ "output_type": "stream", "text": [ "\r", - " 59%|█████▉ | 2962715/4997436 [00:17<00:11, 173385.73it/s]" + " 62%|██████▏ | 3096413/4997436 [00:17<00:10, 177662.11it/s]" ] }, { @@ -1922,7 +1922,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2980159/4997436 [00:17<00:11, 173697.82it/s]" + " 62%|██████▏ | 3114325/4997436 [00:17<00:10, 178096.58it/s]" ] }, { @@ -1930,7 +1930,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|█████▉ | 2997632/4997436 [00:17<00:11, 174005.17it/s]" + " 63%|██████▎ | 3132135/4997436 [00:17<00:10, 177750.15it/s]" ] }, { @@ -1938,7 +1938,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 3015033/4997436 [00:17<00:11, 173943.06it/s]" + " 63%|██████▎ | 3149932/4997436 [00:17<00:10, 177811.35it/s]" ] }, { @@ -1946,7 +1946,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3032441/4997436 [00:17<00:11, 173983.14it/s]" + " 63%|██████▎ | 3167714/4997436 [00:17<00:10, 177765.86it/s]" ] }, { @@ -1954,7 +1954,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████ | 3049915/4997436 [00:17<00:11, 174207.19it/s]" + " 64%|██████▎ | 3185491/4997436 [00:17<00:10, 177559.20it/s]" ] }, { @@ -1962,7 +1962,7 @@ "output_type": "stream", "text": [ "\r", - " 61%|██████▏ | 3067379/4997436 [00:17<00:11, 174335.38it/s]" + " 64%|██████▍ | 3203275/4997436 [00:17<00:10, 177638.29it/s]" ] }, { @@ -1970,7 +1970,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3084832/4997436 [00:17<00:10, 174389.80it/s]" + " 64%|██████▍ | 3221039/4997436 [00:17<00:10, 176949.18it/s]" ] }, { @@ -1978,7 +1978,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3102272/4997436 [00:18<00:10, 174071.17it/s]" + " 65%|██████▍ | 3238735/4997436 [00:18<00:10, 170726.90it/s]" ] }, { @@ -1986,7 +1986,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▏ | 3119743/4997436 [00:18<00:10, 174258.72it/s]" + " 65%|██████▌ | 3256751/4997436 [00:18<00:10, 173475.09it/s]" ] }, { @@ -1994,7 +1994,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3137170/4997436 [00:18<00:10, 174228.54it/s]" + " 66%|██████▌ | 3274614/4997436 [00:18<00:09, 174988.61it/s]" ] }, { @@ -2002,7 +2002,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3154596/4997436 [00:18<00:10, 174236.77it/s]" + " 66%|██████▌ | 3292761/4997436 [00:18<00:09, 176901.55it/s]" ] }, { @@ -2010,7 +2010,7 @@ "output_type": "stream", "text": [ "\r", - " 63%|██████▎ | 3172020/4997436 [00:18<00:10, 174022.34it/s]" + " 66%|██████▌ | 3310756/4997436 [00:18<00:09, 177802.48it/s]" ] }, { @@ -2018,7 +2018,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3189423/4997436 [00:18<00:10, 173942.43it/s]" + " 67%|██████▋ | 3328746/4997436 [00:18<00:09, 178424.38it/s]" ] }, { @@ -2026,7 +2026,7 @@ "output_type": "stream", "text": [ "\r", - " 64%|██████▍ | 3206827/4997436 [00:18<00:10, 173970.47it/s]" + " 67%|██████▋ | 3346696/4997436 [00:18<00:09, 178742.02it/s]" ] }, { @@ -2034,7 +2034,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 3224225/4997436 [00:18<00:10, 173906.64it/s]" + " 67%|██████▋ | 3364581/4997436 [00:18<00:09, 178771.43it/s]" ] }, { @@ -2042,7 +2042,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▍ | 3241616/4997436 [00:18<00:10, 173745.91it/s]" + " 68%|██████▊ | 3382629/4997436 [00:18<00:09, 179279.65it/s]" ] }, { @@ -2050,7 +2050,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 3258991/4997436 [00:18<00:10, 173105.28it/s]" + " 68%|██████▊ | 3400660/4997436 [00:18<00:08, 179583.81it/s]" ] }, { @@ -2058,7 +2058,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3276303/4997436 [00:19<00:09, 172876.84it/s]" + " 68%|██████▊ | 3418922/4997436 [00:19<00:08, 180489.78it/s]" ] }, { @@ -2066,7 +2066,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▌ | 3293770/4997436 [00:19<00:09, 173410.18it/s]" + " 69%|██████▉ | 3436974/4997436 [00:19<00:08, 180480.44it/s]" ] }, { @@ -2074,7 +2074,7 @@ "output_type": "stream", "text": [ "\r", - " 66%|██████▋ | 3311112/4997436 [00:19<00:09, 173373.70it/s]" + " 69%|██████▉ | 3455126/4997436 [00:19<00:08, 180788.28it/s]" ] }, { @@ -2082,7 +2082,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3328450/4997436 [00:19<00:09, 173291.51it/s]" + " 70%|██████▉ | 3473271/4997436 [00:19<00:08, 180982.74it/s]" ] }, { @@ -2090,7 +2090,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3345907/4997436 [00:19<00:09, 173671.51it/s]" + " 70%|██████▉ | 3491371/4997436 [00:19<00:08, 180569.14it/s]" ] }, { @@ -2098,7 +2098,7 @@ "output_type": "stream", "text": [ "\r", - " 67%|██████▋ | 3363303/4997436 [00:19<00:09, 173756.39it/s]" + " 70%|███████ | 3509429/4997436 [00:19<00:08, 180492.04it/s]" ] }, { @@ -2106,7 +2106,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3380778/4997436 [00:19<00:09, 174053.04it/s]" + " 71%|███████ | 3527479/4997436 [00:19<00:08, 180489.71it/s]" ] }, { @@ -2114,7 +2114,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3398193/4997436 [00:19<00:09, 174078.93it/s]" + " 71%|███████ | 3545741/4997436 [00:19<00:08, 181123.71it/s]" ] }, { @@ -2122,7 +2122,7 @@ "output_type": "stream", "text": [ "\r", - " 68%|██████▊ | 3415601/4997436 [00:19<00:09, 174047.82it/s]" + " 71%|███████▏ | 3563854/4997436 [00:19<00:07, 181030.69it/s]" ] }, { @@ -2130,7 +2130,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▊ | 3433006/4997436 [00:19<00:09, 173824.20it/s]" + " 72%|███████▏ | 3582117/4997436 [00:19<00:07, 181504.89it/s]" ] }, { @@ -2138,7 +2138,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3450389/4997436 [00:20<00:08, 172911.26it/s]" + " 72%|███████▏ | 3600268/4997436 [00:20<00:07, 181080.39it/s]" ] }, { @@ -2146,7 +2146,7 @@ "output_type": "stream", "text": [ "\r", - " 69%|██████▉ | 3467748/4997436 [00:20<00:08, 173110.68it/s]" + " 72%|███████▏ | 3618377/4997436 [00:20<00:07, 180596.98it/s]" ] }, { @@ -2154,7 +2154,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|██████▉ | 3485166/4997436 [00:20<00:08, 173426.55it/s]" + " 73%|███████▎ | 3636544/4997436 [00:20<00:07, 180913.86it/s]" ] }, { @@ -2162,7 +2162,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3502510/4997436 [00:20<00:08, 173406.37it/s]" + " 73%|███████▎ | 3654651/4997436 [00:20<00:07, 180959.09it/s]" ] }, { @@ -2170,7 +2170,7 @@ "output_type": "stream", "text": [ "\r", - " 70%|███████ | 3519852/4997436 [00:20<00:08, 173140.61it/s]" + " 73%|███████▎ | 3672748/4997436 [00:20<00:07, 180875.36it/s]" ] }, { @@ -2178,7 +2178,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3537167/4997436 [00:20<00:08, 173028.39it/s]" + " 74%|███████▍ | 3690836/4997436 [00:20<00:07, 180649.47it/s]" ] }, { @@ -2186,7 +2186,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████ | 3554603/4997436 [00:20<00:08, 173423.88it/s]" + " 74%|███████▍ | 3708902/4997436 [00:20<00:07, 180500.63it/s]" ] }, { @@ -2194,7 +2194,7 @@ "output_type": "stream", "text": [ "\r", - " 71%|███████▏ | 3572048/4997436 [00:20<00:08, 173728.47it/s]" + " 75%|███████▍ | 3727000/4997436 [00:20<00:07, 180640.47it/s]" ] }, { @@ -2202,7 +2202,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3589617/4997436 [00:20<00:08, 174313.88it/s]" + " 75%|███████▍ | 3745065/4997436 [00:20<00:06, 180519.21it/s]" ] }, { @@ -2210,7 +2210,7 @@ "output_type": "stream", "text": [ "\r", - " 72%|███████▏ | 3607049/4997436 [00:20<00:08, 173378.07it/s]" + " 75%|███████▌ | 3763118/4997436 [00:20<00:06, 180266.92it/s]" ] }, { @@ -2218,7 +2218,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3624389/4997436 [00:21<00:07, 173114.54it/s]" + " 76%|███████▌ | 3781150/4997436 [00:21<00:06, 180277.59it/s]" ] }, { @@ -2226,7 +2226,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3641702/4997436 [00:21<00:07, 172963.97it/s]" + " 76%|███████▌ | 3799178/4997436 [00:21<00:06, 180192.13it/s]" ] }, { @@ -2234,7 +2234,7 @@ "output_type": "stream", "text": [ "\r", - " 73%|███████▎ | 3658999/4997436 [00:21<00:07, 172692.44it/s]" + " 76%|███████▋ | 3817249/4997436 [00:21<00:06, 180342.70it/s]" ] }, { @@ -2242,7 +2242,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▎ | 3676269/4997436 [00:21<00:07, 172400.72it/s]" + " 77%|███████▋ | 3835284/4997436 [00:21<00:06, 179961.98it/s]" ] }, { @@ -2250,7 +2250,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3693510/4997436 [00:21<00:07, 172036.73it/s]" + " 77%|███████▋ | 3853329/4997436 [00:21<00:06, 180104.14it/s]" ] }, { @@ -2258,7 +2258,7 @@ "output_type": "stream", "text": [ "\r", - " 74%|███████▍ | 3710714/4997436 [00:21<00:07, 171541.63it/s]" + " 77%|███████▋ | 3871340/4997436 [00:21<00:06, 179787.13it/s]" ] }, { @@ -2266,7 +2266,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 3727869/4997436 [00:21<00:07, 171232.78it/s]" + " 78%|███████▊ | 3889365/4997436 [00:21<00:06, 179921.23it/s]" ] }, { @@ -2274,7 +2274,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▍ | 3745048/4997436 [00:21<00:07, 171397.75it/s]" + " 78%|███████▊ | 3907358/4997436 [00:21<00:06, 179591.63it/s]" ] }, { @@ -2282,7 +2282,7 @@ "output_type": "stream", "text": [ "\r", - " 75%|███████▌ | 3762188/4997436 [00:21<00:07, 171388.57it/s]" + " 79%|███████▊ | 3925364/4997436 [00:21<00:05, 179726.98it/s]" ] }, { @@ -2290,7 +2290,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3779328/4997436 [00:21<00:07, 171030.43it/s]" + " 79%|███████▉ | 3943337/4997436 [00:21<00:05, 179595.08it/s]" ] }, { @@ -2298,7 +2298,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▌ | 3796432/4997436 [00:22<00:07, 167757.08it/s]" + " 79%|███████▉ | 3961297/4997436 [00:22<00:05, 178722.59it/s]" ] }, { @@ -2306,7 +2306,7 @@ "output_type": "stream", "text": [ "\r", - " 76%|███████▋ | 3813556/4997436 [00:22<00:07, 168784.22it/s]" + " 80%|███████▉ | 3979171/4997436 [00:22<00:05, 178540.98it/s]" ] }, { @@ -2314,7 +2314,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3830707/4997436 [00:22<00:06, 169589.88it/s]" + " 80%|███████▉ | 3997317/4997436 [00:22<00:05, 179409.30it/s]" ] }, { @@ -2322,7 +2322,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3847684/4997436 [00:22<00:06, 169638.49it/s]" + " 80%|████████ | 4015310/4997436 [00:22<00:05, 179562.18it/s]" ] }, { @@ -2330,7 +2330,7 @@ "output_type": "stream", "text": [ "\r", - " 77%|███████▋ | 3864654/4997436 [00:22<00:06, 169613.69it/s]" + " 81%|████████ | 4033352/4997436 [00:22<00:05, 179814.07it/s]" ] }, { @@ -2338,7 +2338,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3881704/4997436 [00:22<00:06, 169876.51it/s]" + " 81%|████████ | 4051359/4997436 [00:22<00:05, 179887.83it/s]" ] }, { @@ -2346,7 +2346,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3898702/4997436 [00:22<00:06, 169905.34it/s]" + " 81%|████████▏ | 4069349/4997436 [00:22<00:05, 179613.29it/s]" ] }, { @@ -2354,7 +2354,7 @@ "output_type": "stream", "text": [ "\r", - " 78%|███████▊ | 3915695/4997436 [00:22<00:06, 169762.99it/s]" + " 82%|████████▏ | 4087311/4997436 [00:22<00:05, 179014.62it/s]" ] }, { @@ -2362,7 +2362,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▊ | 3932772/4997436 [00:22<00:06, 170060.89it/s]" + " 82%|████████▏ | 4105214/4997436 [00:22<00:04, 178750.22it/s]" ] }, { @@ -2370,7 +2370,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3949780/4997436 [00:22<00:06, 169981.80it/s]" + " 83%|████████▎ | 4123187/4997436 [00:22<00:04, 179039.58it/s]" ] }, { @@ -2378,7 +2378,7 @@ "output_type": "stream", "text": [ "\r", - " 79%|███████▉ | 3966853/4997436 [00:23<00:06, 170201.75it/s]" + " 83%|████████▎ | 4141092/4997436 [00:23<00:04, 178708.44it/s]" ] }, { @@ -2386,7 +2386,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|███████▉ | 3983895/4997436 [00:23<00:05, 170264.05it/s]" + " 83%|████████▎ | 4158964/4997436 [00:23<00:04, 178119.45it/s]" ] }, { @@ -2394,7 +2394,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 4001056/4997436 [00:23<00:05, 170664.56it/s]" + " 84%|████████▎ | 4176777/4997436 [00:23<00:04, 177799.81it/s]" ] }, { @@ -2402,7 +2402,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 4018123/4997436 [00:23<00:05, 170373.19it/s]" + " 84%|████████▍ | 4194622/4997436 [00:23<00:04, 177990.89it/s]" ] }, { @@ -2410,7 +2410,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4035214/4997436 [00:23<00:05, 170523.31it/s]" + " 84%|████████▍ | 4212503/4997436 [00:23<00:04, 178232.86it/s]" ] }, { @@ -2418,7 +2418,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████ | 4052305/4997436 [00:23<00:05, 170637.65it/s]" + " 85%|████████▍ | 4230415/4997436 [00:23<00:04, 178494.40it/s]" ] }, { @@ -2426,7 +2426,7 @@ "output_type": "stream", "text": [ "\r", - " 81%|████████▏ | 4069369/4997436 [00:23<00:05, 170504.12it/s]" + " 85%|████████▌ | 4248366/4997436 [00:23<00:04, 178793.42it/s]" ] }, { @@ -2434,7 +2434,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4086420/4997436 [00:23<00:05, 169993.27it/s]" + " 85%|████████▌ | 4266427/4997436 [00:23<00:04, 179333.11it/s]" ] }, { @@ -2442,7 +2442,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4103516/4997436 [00:23<00:05, 170281.17it/s]" + " 86%|████████▌ | 4284469/4997436 [00:23<00:03, 179657.04it/s]" ] }, { @@ -2450,7 +2450,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▏ | 4120545/4997436 [00:23<00:05, 170011.54it/s]" + " 86%|████████▌ | 4302606/4997436 [00:23<00:03, 180166.83it/s]" ] }, { @@ -2458,7 +2458,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4137547/4997436 [00:24<00:05, 169732.57it/s]" + " 86%|████████▋ | 4320708/4997436 [00:24<00:03, 180419.89it/s]" ] }, { @@ -2466,7 +2466,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4154745/4997436 [00:24<00:04, 170403.90it/s]" + " 87%|████████▋ | 4338801/4997436 [00:24<00:03, 180568.80it/s]" ] }, { @@ -2474,7 +2474,7 @@ "output_type": "stream", "text": [ "\r", - " 83%|████████▎ | 4171876/4997436 [00:24<00:04, 170671.44it/s]" + " 87%|████████▋ | 4356979/4997436 [00:24<00:03, 180929.02it/s]" ] }, { @@ -2482,7 +2482,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4189027/4997436 [00:24<00:04, 170920.13it/s]" + " 88%|████████▊ | 4375072/4997436 [00:24<00:03, 180778.24it/s]" ] }, { @@ -2490,7 +2490,7 @@ "output_type": "stream", "text": [ "\r", - " 84%|████████▍ | 4206120/4997436 [00:24<00:04, 170614.22it/s]" + " 88%|████████▊ | 4393202/4997436 [00:24<00:03, 180930.44it/s]" ] }, { @@ -2498,7 +2498,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▍ | 4223263/4997436 [00:24<00:04, 170855.71it/s]" + " 88%|████████▊ | 4411296/4997436 [00:24<00:03, 180736.19it/s]" ] }, { @@ -2506,7 +2506,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▍ | 4240487/4997436 [00:24<00:04, 171266.92it/s]" + " 89%|████████▊ | 4429370/4997436 [00:24<00:03, 180667.29it/s]" ] }, { @@ -2514,7 +2514,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 4257813/4997436 [00:24<00:04, 171862.70it/s]" + " 89%|████████▉ | 4447437/4997436 [00:24<00:03, 180514.93it/s]" ] }, { @@ -2522,7 +2522,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4275000/4997436 [00:24<00:04, 171779.24it/s]" + " 89%|████████▉ | 4465489/4997436 [00:24<00:02, 180090.45it/s]" ] }, { @@ -2530,7 +2530,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4292179/4997436 [00:24<00:04, 171444.35it/s]" + " 90%|████████▉ | 4483499/4997436 [00:24<00:02, 180062.01it/s]" ] }, { @@ -2538,7 +2538,7 @@ "output_type": "stream", "text": [ "\r", - " 86%|████████▌ | 4309324/4997436 [00:25<00:04, 165350.97it/s]" + " 90%|█████████ | 4501506/4997436 [00:25<00:02, 179868.00it/s]" ] }, { @@ -2546,7 +2546,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4326674/4997436 [00:25<00:03, 167727.87it/s]" + " 90%|█████████ | 4519493/4997436 [00:25<00:02, 179734.72it/s]" ] }, { @@ -2554,7 +2554,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4344026/4997436 [00:25<00:03, 169431.84it/s]" + " 91%|█████████ | 4537658/4997436 [00:25<00:02, 180304.86it/s]" ] }, { @@ -2562,7 +2562,7 @@ "output_type": "stream", "text": [ "\r", - " 87%|████████▋ | 4361352/4997436 [00:25<00:03, 170562.91it/s]" + " 91%|█████████ | 4555689/4997436 [00:25<00:02, 180104.36it/s]" ] }, { @@ -2570,7 +2570,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4378694/4997436 [00:25<00:03, 171408.49it/s]" + " 92%|█████████▏| 4573700/4997436 [00:25<00:02, 179438.10it/s]" ] }, { @@ -2578,7 +2578,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4395954/4997436 [00:25<00:03, 171760.37it/s]" + " 92%|█████████▏| 4591799/4997436 [00:25<00:02, 179897.58it/s]" ] }, { @@ -2586,7 +2586,7 @@ "output_type": "stream", "text": [ "\r", - " 88%|████████▊ | 4413142/4997436 [00:25<00:03, 171667.81it/s]" + " 92%|█████████▏| 4609840/4997436 [00:25<00:02, 180045.93it/s]" ] }, { @@ -2594,7 +2594,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▊ | 4430508/4997436 [00:25<00:03, 172261.56it/s]" + " 93%|█████████▎| 4627846/4997436 [00:25<00:02, 180018.07it/s]" ] }, { @@ -2602,7 +2602,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4447803/4997436 [00:25<00:03, 172465.26it/s]" + " 93%|█████████▎| 4645849/4997436 [00:25<00:01, 179692.51it/s]" ] }, { @@ -2610,7 +2610,7 @@ "output_type": "stream", "text": [ "\r", - " 89%|████████▉ | 4465127/4997436 [00:25<00:03, 172694.71it/s]" + " 93%|█████████▎| 4663819/4997436 [00:25<00:01, 179617.00it/s]" ] }, { @@ -2618,7 +2618,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|████████▉ | 4482400/4997436 [00:26<00:02, 171944.44it/s]" + " 94%|█████████▎| 4681836/4997436 [00:26<00:01, 179779.17it/s]" ] }, { @@ -2626,7 +2626,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 4499653/4997436 [00:26<00:02, 172117.74it/s]" + " 94%|█████████▍| 4699815/4997436 [00:26<00:01, 179720.46it/s]" ] }, { @@ -2634,7 +2634,7 @@ "output_type": "stream", "text": [ "\r", - " 90%|█████████ | 4516867/4997436 [00:26<00:02, 172047.02it/s]" + " 94%|█████████▍| 4717972/4997436 [00:26<00:01, 180269.30it/s]" ] }, { @@ -2642,7 +2642,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4534261/4997436 [00:26<00:02, 172609.97it/s]" + " 95%|█████████▍| 4736196/4997436 [00:26<00:01, 180855.66it/s]" ] }, { @@ -2650,7 +2650,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████ | 4551737/4997436 [00:26<00:02, 173252.63it/s]" + " 95%|█████████▌| 4754282/4997436 [00:26<00:01, 180301.28it/s]" ] }, { @@ -2658,7 +2658,7 @@ "output_type": "stream", "text": [ "\r", - " 91%|█████████▏| 4569217/4997436 [00:26<00:02, 173713.75it/s]" + " 95%|█████████▌| 4772313/4997436 [00:26<00:01, 180000.50it/s]" ] }, { @@ -2666,7 +2666,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4586697/4997436 [00:26<00:02, 174037.05it/s]" + " 96%|█████████▌| 4790314/4997436 [00:26<00:01, 179915.12it/s]" ] }, { @@ -2674,7 +2674,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4604102/4997436 [00:26<00:02, 173130.35it/s]" + " 96%|█████████▌| 4808306/4997436 [00:26<00:01, 179786.04it/s]" ] }, { @@ -2682,7 +2682,7 @@ "output_type": "stream", "text": [ "\r", - " 92%|█████████▏| 4621417/4997436 [00:26<00:02, 172784.87it/s]" + " 97%|█████████▋| 4826331/4997436 [00:26<00:00, 179920.86it/s]" ] }, { @@ -2690,7 +2690,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4638697/4997436 [00:26<00:02, 171978.23it/s]" + " 97%|█████████▋| 4844330/4997436 [00:26<00:00, 179936.23it/s]" ] }, { @@ -2698,7 +2698,7 @@ "output_type": "stream", "text": [ "\r", - " 93%|█████████▎| 4655897/4997436 [00:27<00:01, 171798.19it/s]" + " 97%|█████████▋| 4862324/4997436 [00:27<00:00, 179418.51it/s]" ] }, { @@ -2706,7 +2706,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▎| 4673248/4997436 [00:27<00:01, 172304.59it/s]" + " 98%|█████████▊| 4880267/4997436 [00:27<00:00, 179258.98it/s]" ] }, { @@ -2714,7 +2714,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 4690517/4997436 [00:27<00:01, 172416.53it/s]" + " 98%|█████████▊| 4898194/4997436 [00:27<00:00, 178941.02it/s]" ] }, { @@ -2722,7 +2722,7 @@ "output_type": "stream", "text": [ "\r", - " 94%|█████████▍| 4707887/4997436 [00:27<00:01, 172797.92it/s]" + " 98%|█████████▊| 4916175/4997436 [00:27<00:00, 179197.51it/s]" ] }, { @@ -2730,7 +2730,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▍| 4725248/4997436 [00:27<00:01, 173038.56it/s]" + " 99%|█████████▊| 4934406/4997436 [00:27<00:00, 180125.94it/s]" ] }, { @@ -2738,7 +2738,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▍| 4742563/4997436 [00:27<00:01, 173070.51it/s]" + " 99%|█████████▉| 4952419/4997436 [00:27<00:00, 179948.96it/s]" ] }, { @@ -2746,7 +2746,7 @@ "output_type": "stream", "text": [ "\r", - " 95%|█████████▌| 4759875/4997436 [00:27<00:01, 173084.41it/s]" + " 99%|█████████▉| 4970468/4997436 [00:27<00:00, 180106.54it/s]" ] }, { @@ -2754,7 +2754,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4777184/4997436 [00:27<00:01, 172958.30it/s]" + "100%|█████████▉| 4988519/4997436 [00:27<00:00, 180223.23it/s]" ] }, { @@ -2762,103 +2762,7 @@ "output_type": "stream", "text": [ "\r", - " 96%|█████████▌| 4794527/4997436 [00:27<00:01, 173096.63it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 96%|█████████▋| 4811837/4997436 [00:27<00:01, 172883.91it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 97%|█████████▋| 4829126/4997436 [00:28<00:00, 172879.98it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 97%|█████████▋| 4846447/4997436 [00:28<00:00, 172975.23it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 97%|█████████▋| 4863745/4997436 [00:28<00:00, 172736.82it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 98%|█████████▊| 4881149/4997436 [00:28<00:00, 173121.94it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 98%|█████████▊| 4898573/4997436 [00:28<00:00, 173455.57it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 98%|█████████▊| 4915969/4997436 [00:28<00:00, 173603.98it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 99%|█████████▊| 4933372/4997436 [00:28<00:00, 173728.54it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 99%|█████████▉| 4950745/4997436 [00:28<00:00, 173721.20it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - " 99%|█████████▉| 4968131/4997436 [00:28<00:00, 173761.64it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|█████████▉| 4985508/4997436 [00:28<00:00, 173713.64it/s]" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\r", - "100%|██████████| 4997436/4997436 [00:29<00:00, 171961.37it/s]" + "100%|██████████| 4997436/4997436 [00:27<00:00, 179461.57it/s]" ] }, { @@ -3097,10 +3001,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:42:54.903373Z", - "iopub.status.busy": "2023-11-20T20:42:54.903153Z", - "iopub.status.idle": "2023-11-20T20:43:02.110265Z", - "shell.execute_reply": "2023-11-20T20:43:02.109514Z" + "iopub.execute_input": "2023-11-21T08:20:09.055049Z", + "iopub.status.busy": "2023-11-21T08:20:09.054648Z", + "iopub.status.idle": "2023-11-21T08:20:16.521274Z", + "shell.execute_reply": "2023-11-21T08:20:16.520555Z" } }, "outputs": [], @@ -3114,10 +3018,10 @@ "id": "716c74f3", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:02.113041Z", - "iopub.status.busy": "2023-11-20T20:43:02.112807Z", - "iopub.status.idle": "2023-11-20T20:43:05.187337Z", - "shell.execute_reply": "2023-11-20T20:43:05.186664Z" + "iopub.execute_input": "2023-11-21T08:20:16.524425Z", + "iopub.status.busy": "2023-11-21T08:20:16.523958Z", + "iopub.status.idle": "2023-11-21T08:20:19.770965Z", + "shell.execute_reply": "2023-11-21T08:20:19.770306Z" } }, "outputs": [ @@ -3186,17 +3090,17 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:05.190013Z", - "iopub.status.busy": "2023-11-20T20:43:05.189620Z", - "iopub.status.idle": "2023-11-20T20:43:06.491127Z", - "shell.execute_reply": "2023-11-20T20:43:06.490499Z" + "iopub.execute_input": "2023-11-21T08:20:19.773655Z", + "iopub.status.busy": "2023-11-21T08:20:19.773224Z", + "iopub.status.idle": "2023-11-21T08:20:21.112316Z", + "shell.execute_reply": "2023-11-21T08:20:21.111609Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "136633953e17405aafdb252818b51aab", + "model_id": "5daa97516d5c4713bd99d9f6ece69a74", "version_major": 2, "version_minor": 0 }, @@ -3226,10 +3130,10 @@ "id": "390780a1", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:06.494093Z", - "iopub.status.busy": "2023-11-20T20:43:06.493655Z", - "iopub.status.idle": "2023-11-20T20:43:06.690278Z", - "shell.execute_reply": "2023-11-20T20:43:06.689625Z" + "iopub.execute_input": "2023-11-21T08:20:21.115267Z", + "iopub.status.busy": "2023-11-21T08:20:21.115055Z", + "iopub.status.idle": "2023-11-21T08:20:21.311072Z", + "shell.execute_reply": "2023-11-21T08:20:21.310355Z" } }, "outputs": [], @@ -3243,10 +3147,10 @@ "id": "933d6ef0", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:06.693296Z", - "iopub.status.busy": "2023-11-20T20:43:06.692805Z", - "iopub.status.idle": "2023-11-20T20:43:11.304590Z", - "shell.execute_reply": "2023-11-20T20:43:11.303941Z" + "iopub.execute_input": "2023-11-21T08:20:21.314129Z", + "iopub.status.busy": "2023-11-21T08:20:21.313873Z", + "iopub.status.idle": "2023-11-21T08:20:25.935014Z", + "shell.execute_reply": "2023-11-21T08:20:25.934356Z" } }, "outputs": [ @@ -3319,10 +3223,10 @@ "id": "86bac686", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:11.307027Z", - "iopub.status.busy": "2023-11-20T20:43:11.306826Z", - "iopub.status.idle": "2023-11-20T20:43:11.364240Z", - "shell.execute_reply": "2023-11-20T20:43:11.363666Z" + "iopub.execute_input": "2023-11-21T08:20:25.937780Z", + "iopub.status.busy": "2023-11-21T08:20:25.937373Z", + "iopub.status.idle": "2023-11-21T08:20:25.994267Z", + "shell.execute_reply": "2023-11-21T08:20:25.993645Z" }, "nbsphinx": "hidden" }, @@ -3366,74 +3270,23 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "08f66a396ea34a5b990ad7885ff0479e": { - "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_b51402cad01a4f91a04bf055c2ef8e2f", - "placeholder": "", - "style": "IPY_MODEL_f697babbbec949e39ac36fcd028d39f7", - "value": " 300000/? [00:02<00:00, 122904.46it/s]" - } - }, - "0ccc1733c7a249ea94e608567a6d1ce5": { - "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_b2b60ee84c1c404e96b78be48b4b07ab", - "max": 244800.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_4e12479963e34558bd2dcba54c258b77", - "value": 244800.0 - } - }, - "136633953e17405aafdb252818b51aab": { + "11be4939db224f8c8bad0c26361c98bf": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_5b60d638d014409aae4c5b8e0e79a662", - "IPY_MODEL_ce47372b51db483692da527f8a19e41a", - "IPY_MODEL_ec65b92d09ca493891f7e950792cf084" - ], - "layout": "IPY_MODEL_2359f598b4f54a7297838faf6b5b9e65" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "1cb1266271884d8190f07168c25c79b1": { + "12068cf21c424309a8ebe406d0f6260f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3485,7 +3338,22 @@ "width": null } }, - "1cb7db243abb49e2bab0c3517c8cad4c": { + "1bc498d4b3714e859ae2538fa82ae87c": { + "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": "" + } + }, + "1ceadfc5823641dea9edbed3de1d9151": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -3500,66 +3368,53 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_1da3da00504b4a27918834e68f554659", - "IPY_MODEL_3d8ff6b0813c45318b46390dc91a9ff6", - "IPY_MODEL_b9feafaf8f3440248972d5c51064f54e" + "IPY_MODEL_c07e7ab0e6a345c8828c0d68c4de11a4", + "IPY_MODEL_1fec6bff1cd14468b9817dfeca68019f", + "IPY_MODEL_606fb7a3727e404783e5ac7190c69468" ], - "layout": "IPY_MODEL_ddf21e4fb1854161bbabce98ef55ff3d" + "layout": "IPY_MODEL_12068cf21c424309a8ebe406d0f6260f" } }, - "1cf99ceb6c934b678ff47e12bc8506f8": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "1fec6bff1cd14468b9817dfeca68019f": { + "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", + "_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_b5cd713f3c16480fa61697479ab461a5", + "max": 244800.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_3f5ed3a8432e43599c3fe634bc354866", + "value": 244800.0 + } + }, + "2eacf7c5c7cc4e85962222806e228a18": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" } }, - "1da3da00504b4a27918834e68f554659": { + "35de23cb84a24217bf9a9f9bdcaf3f3f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3574,13 +3429,35 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_41567d3526c94d4d93c0e04d9e6af4f6", + "layout": "IPY_MODEL_c7318af075044df1bdc9f6e48b6521da", "placeholder": "", - "style": "IPY_MODEL_88d1309b5e0b4453bc9fd5c0e476891e", - "value": "number of examples processed for estimating thresholds: " + "style": "IPY_MODEL_1bc498d4b3714e859ae2538fa82ae87c", + "value": "images processed using softmin: 100%" + } + }, + "3c1e1c10507b4c01a882d07bc848f8f3": { + "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_6b506e9bdaae42a0836f7e56d63840da", + "IPY_MODEL_4173720adf0140ebaf75139888add436", + "IPY_MODEL_b01cffaebc8e4c2cbbccab1162d1659e" + ], + "layout": "IPY_MODEL_cd21087abc2540f1a72cc2d3d2b9f4ef" } }, - "2359f598b4f54a7297838faf6b5b9e65": { + "3d4e43f098c14feda149e10aa7c9c699": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3632,59 +3509,23 @@ "width": null } }, - "3cca747fee264d338395c9f61bc1e61d": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "3f5ed3a8432e43599c3fe634bc354866": { + "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", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", + "_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": "" } }, - "3d8ff6b0813c45318b46390dc91a9ff6": { + "4173720adf0140ebaf75139888add436": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -3700,15 +3541,45 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_1cb1266271884d8190f07168c25c79b1", + "layout": "IPY_MODEL_3d4e43f098c14feda149e10aa7c9c699", "max": 244800.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_9b993d62c2dc41929add03bb8491ea9e", + "style": "IPY_MODEL_11be4939db224f8c8bad0c26361c98bf", "value": 244800.0 } }, - "3dd110c0fa0f43408f2d5dd59087a73f": { + "5060aa237bb1499e84231c655cc082b7": { + "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": "" + } + }, + "58e868fcc8f44af8b35d89f0f83ae225": { + "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": "" + } + }, + "5b0a5748d59144349e210099789384cf": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3760,7 +3631,7 @@ "width": null } }, - "41567d3526c94d4d93c0e04d9e6af4f6": { + "5bfcea58b0ca49699bf288b2f097dad8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3812,23 +3683,29 @@ "width": null } }, - "4e12479963e34558bd2dcba54c258b77": { + "5daa97516d5c4713bd99d9f6ece69a74": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_35de23cb84a24217bf9a9f9bdcaf3f3f", + "IPY_MODEL_87f20cabce4145818604cec592499dbb", + "IPY_MODEL_ae2b46984b8047b78267d0d1b9c73a4a" + ], + "layout": "IPY_MODEL_f3d7935ac9154a55b2990e4e2d11c29c" } }, - "5b60d638d014409aae4c5b8e0e79a662": { + "606fb7a3727e404783e5ac7190c69468": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3843,44 +3720,34 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_3dd110c0fa0f43408f2d5dd59087a73f", + "layout": "IPY_MODEL_9ae011fea0ec4565bbbaaa6dda14ae7d", "placeholder": "", - "style": "IPY_MODEL_bb72c8b5c7724240b1bc79d98f19ad83", - "value": "images processed using softmin: 100%" + "style": "IPY_MODEL_d5151201473445fc871d91ff3ede8af4", + "value": " 300000/? [00:00<00:00, 5347331.20it/s]" } }, - "6ae90725490a443a94bf188885d0bffd": { + "6b506e9bdaae42a0836f7e56d63840da": { "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": "" - } - }, - "74c08e98539f4defabfbff4b9e64abb0": { - "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_70b88511812d42d09e7a8ba35a7aa85d", + "placeholder": "", + "style": "IPY_MODEL_2eacf7c5c7cc4e85962222806e228a18", + "value": "number of examples processed for checking labels: " } }, - "77974905e80840acb73788a147e93db6": { + "70b88511812d42d09e7a8ba35a7aa85d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3932,7 +3799,7 @@ "width": null } }, - "88d1309b5e0b4453bc9fd5c0e476891e": { + "7783bc22b55a41b98619ce1cd20cfd34": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3947,23 +3814,31 @@ "description_width": "" } }, - "9b993d62c2dc41929add03bb8491ea9e": { + "87f20cabce4145818604cec592499dbb": { "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/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": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_d841650b1ba24bb8837eeb2f332c128f", + "max": 30.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_de230bb444ea417c9c220652ae4e705a", + "value": 30.0 } }, - "b1e0f9650f084b4baaf4f8487c78f579": { + "8a570f9b7cb7418a99db062a49228d5f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4015,7 +3890,7 @@ "width": null } }, - "b2b60ee84c1c404e96b78be48b4b07ab": { + "9ae011fea0ec4565bbbaaa6dda14ae7d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4067,22 +3942,49 @@ "width": null } }, - "b35e79d14db546d981653f6a6242a574": { + "ae2b46984b8047b78267d0d1b9c73a4a": { "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_8a570f9b7cb7418a99db062a49228d5f", + "placeholder": "", + "style": "IPY_MODEL_5060aa237bb1499e84231c655cc082b7", + "value": " 30/30 [00:01<00:00, 22.98it/s]" } }, - "b51402cad01a4f91a04bf055c2ef8e2f": { + "b01cffaebc8e4c2cbbccab1162d1659e": { + "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_5b0a5748d59144349e210099789384cf", + "placeholder": "", + "style": "IPY_MODEL_58e868fcc8f44af8b35d89f0f83ae225", + "value": " 300000/? [00:03<00:00, 89948.11it/s]" + } + }, + "b5cd713f3c16480fa61697479ab461a5": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4134,7 +4036,7 @@ "width": null } }, - "b9feafaf8f3440248972d5c51064f54e": { + "c07e7ab0e6a345c8828c0d68c4de11a4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -4149,52 +4051,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_dc1cf2025ae643ef8ceb4f18ce6b182f", + "layout": "IPY_MODEL_5bfcea58b0ca49699bf288b2f097dad8", "placeholder": "", - "style": "IPY_MODEL_6ae90725490a443a94bf188885d0bffd", - "value": " 300000/? [00:00<00:00, 5612760.95it/s]" - } - }, - "bb72c8b5c7724240b1bc79d98f19ad83": { - "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": "" - } - }, - "ce47372b51db483692da527f8a19e41a": { - "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_b1e0f9650f084b4baaf4f8487c78f579", - "max": 30.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_74c08e98539f4defabfbff4b9e64abb0", - "value": 30.0 + "style": "IPY_MODEL_7783bc22b55a41b98619ce1cd20cfd34", + "value": "number of examples processed for estimating thresholds: " } }, - "dc1cf2025ae643ef8ceb4f18ce6b182f": { + "c7318af075044df1bdc9f6e48b6521da": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4246,7 +4109,7 @@ "width": null } }, - "ddf21e4fb1854161bbabce98ef55ff3d": { + "cd21087abc2540f1a72cc2d3d2b9f4ef": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4298,49 +4161,7 @@ "width": null } }, - "e7199c2cf9d14e13bb691c8ac11652f4": { - "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_3cca747fee264d338395c9f61bc1e61d", - "placeholder": "", - "style": "IPY_MODEL_b35e79d14db546d981653f6a6242a574", - "value": "number of examples processed for checking labels: " - } - }, - "ec65b92d09ca493891f7e950792cf084": { - "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_77974905e80840acb73788a147e93db6", - "placeholder": "", - "style": "IPY_MODEL_ee2458ca75da4af2ade7852edf06fa5b", - "value": " 30/30 [00:01<00:00, 23.39it/s]" - } - }, - "ee2458ca75da4af2ade7852edf06fa5b": { + "d5151201473445fc871d91ff3ede8af4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4355,42 +4176,125 @@ "description_width": "" } }, - "f00bc4af99e44d07a674745b8952af3a": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "d841650b1ba24bb8837eeb2f332c128f": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_e7199c2cf9d14e13bb691c8ac11652f4", - "IPY_MODEL_0ccc1733c7a249ea94e608567a6d1ce5", - "IPY_MODEL_08f66a396ea34a5b990ad7885ff0479e" - ], - "layout": "IPY_MODEL_1cf99ceb6c934b678ff47e12bc8506f8" + "_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 } }, - "f697babbbec949e39ac36fcd028d39f7": { + "de230bb444ea417c9c220652ae4e705a": { "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": "" } + }, + "f3d7935ac9154a55b2990e4e2d11c29c": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } } }, "version_major": 2, diff --git a/master/.doctrees/nbsphinx/tutorials/tabular.ipynb b/master/.doctrees/nbsphinx/tutorials/tabular.ipynb index ee5d6271b..7c0b06323 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": "2023-11-20T20:43:14.200880Z", - "iopub.status.busy": "2023-11-20T20:43:14.200345Z", - "iopub.status.idle": "2023-11-20T20:43:15.207598Z", - "shell.execute_reply": "2023-11-20T20:43:15.206959Z" + "iopub.execute_input": "2023-11-21T08:20:28.993946Z", + "iopub.status.busy": "2023-11-21T08:20:28.993744Z", + "iopub.status.idle": "2023-11-21T08:20:30.066310Z", + "shell.execute_reply": "2023-11-21T08:20:30.065673Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:43:15.210610Z", - "iopub.status.busy": "2023-11-20T20:43:15.210052Z", - "iopub.status.idle": "2023-11-20T20:43:15.229920Z", - "shell.execute_reply": "2023-11-20T20:43:15.229398Z" + "iopub.execute_input": "2023-11-21T08:20:30.069472Z", + "iopub.status.busy": "2023-11-21T08:20:30.069081Z", + "iopub.status.idle": "2023-11-21T08:20:30.090080Z", + "shell.execute_reply": "2023-11-21T08:20:30.089487Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:15.232292Z", - "iopub.status.busy": "2023-11-20T20:43:15.231926Z", - "iopub.status.idle": "2023-11-20T20:43:15.278321Z", - "shell.execute_reply": "2023-11-20T20:43:15.277773Z" + "iopub.execute_input": "2023-11-21T08:20:30.092917Z", + "iopub.status.busy": "2023-11-21T08:20:30.092704Z", + "iopub.status.idle": "2023-11-21T08:20:30.249338Z", + "shell.execute_reply": "2023-11-21T08:20:30.248737Z" } }, "outputs": [ @@ -304,10 +304,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:15.280779Z", - "iopub.status.busy": "2023-11-20T20:43:15.280417Z", - "iopub.status.idle": "2023-11-20T20:43:15.284117Z", - "shell.execute_reply": "2023-11-20T20:43:15.283598Z" + "iopub.execute_input": "2023-11-21T08:20:30.252086Z", + "iopub.status.busy": "2023-11-21T08:20:30.251579Z", + "iopub.status.idle": "2023-11-21T08:20:30.255523Z", + "shell.execute_reply": "2023-11-21T08:20:30.255028Z" } }, "outputs": [], @@ -328,10 +328,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:15.286529Z", - "iopub.status.busy": "2023-11-20T20:43:15.286069Z", - "iopub.status.idle": "2023-11-20T20:43:15.294401Z", - "shell.execute_reply": "2023-11-20T20:43:15.293903Z" + "iopub.execute_input": "2023-11-21T08:20:30.258099Z", + "iopub.status.busy": "2023-11-21T08:20:30.257656Z", + "iopub.status.idle": "2023-11-21T08:20:30.266636Z", + "shell.execute_reply": "2023-11-21T08:20:30.266153Z" } }, "outputs": [], @@ -383,10 +383,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:15.296804Z", - "iopub.status.busy": "2023-11-20T20:43:15.296432Z", - "iopub.status.idle": "2023-11-20T20:43:15.299186Z", - "shell.execute_reply": "2023-11-20T20:43:15.298633Z" + "iopub.execute_input": "2023-11-21T08:20:30.268938Z", + "iopub.status.busy": "2023-11-21T08:20:30.268740Z", + "iopub.status.idle": "2023-11-21T08:20:30.271490Z", + "shell.execute_reply": "2023-11-21T08:20:30.270934Z" } }, "outputs": [], @@ -408,10 +408,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:15.301381Z", - "iopub.status.busy": "2023-11-20T20:43:15.301045Z", - "iopub.status.idle": "2023-11-20T20:43:15.884062Z", - "shell.execute_reply": "2023-11-20T20:43:15.883356Z" + "iopub.execute_input": "2023-11-21T08:20:30.273915Z", + "iopub.status.busy": "2023-11-21T08:20:30.273542Z", + "iopub.status.idle": "2023-11-21T08:20:30.868773Z", + "shell.execute_reply": "2023-11-21T08:20:30.868130Z" } }, "outputs": [], @@ -445,10 +445,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:15.887173Z", - "iopub.status.busy": "2023-11-20T20:43:15.886793Z", - "iopub.status.idle": "2023-11-20T20:43:17.093417Z", - "shell.execute_reply": "2023-11-20T20:43:17.092673Z" + "iopub.execute_input": "2023-11-21T08:20:30.872018Z", + "iopub.status.busy": "2023-11-21T08:20:30.871544Z", + "iopub.status.idle": "2023-11-21T08:20:32.207286Z", + "shell.execute_reply": "2023-11-21T08:20:32.206501Z" } }, "outputs": [ @@ -480,10 +480,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:17.096317Z", - "iopub.status.busy": "2023-11-20T20:43:17.095782Z", - "iopub.status.idle": "2023-11-20T20:43:17.106360Z", - "shell.execute_reply": "2023-11-20T20:43:17.105669Z" + "iopub.execute_input": "2023-11-21T08:20:32.210278Z", + "iopub.status.busy": "2023-11-21T08:20:32.209680Z", + "iopub.status.idle": "2023-11-21T08:20:32.220521Z", + "shell.execute_reply": "2023-11-21T08:20:32.219905Z" } }, "outputs": [ @@ -604,10 +604,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:17.108968Z", - "iopub.status.busy": "2023-11-20T20:43:17.108510Z", - "iopub.status.idle": "2023-11-20T20:43:17.112893Z", - "shell.execute_reply": "2023-11-20T20:43:17.112356Z" + "iopub.execute_input": "2023-11-21T08:20:32.223328Z", + "iopub.status.busy": "2023-11-21T08:20:32.222879Z", + "iopub.status.idle": "2023-11-21T08:20:32.227813Z", + "shell.execute_reply": "2023-11-21T08:20:32.227194Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:17.115272Z", - "iopub.status.busy": "2023-11-20T20:43:17.114911Z", - "iopub.status.idle": "2023-11-20T20:43:17.122566Z", - "shell.execute_reply": "2023-11-20T20:43:17.122056Z" + "iopub.execute_input": "2023-11-21T08:20:32.230376Z", + "iopub.status.busy": "2023-11-21T08:20:32.229863Z", + "iopub.status.idle": "2023-11-21T08:20:32.237989Z", + "shell.execute_reply": "2023-11-21T08:20:32.237408Z" } }, "outputs": [], @@ -657,10 +657,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:17.125013Z", - "iopub.status.busy": "2023-11-20T20:43:17.124671Z", - "iopub.status.idle": "2023-11-20T20:43:17.245608Z", - "shell.execute_reply": "2023-11-20T20:43:17.245066Z" + "iopub.execute_input": "2023-11-21T08:20:32.240638Z", + "iopub.status.busy": "2023-11-21T08:20:32.240255Z", + "iopub.status.idle": "2023-11-21T08:20:32.364322Z", + "shell.execute_reply": "2023-11-21T08:20:32.363633Z" } }, "outputs": [ @@ -690,10 +690,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:17.248078Z", - "iopub.status.busy": "2023-11-20T20:43:17.247669Z", - "iopub.status.idle": "2023-11-20T20:43:17.250727Z", - "shell.execute_reply": "2023-11-20T20:43:17.250195Z" + "iopub.execute_input": "2023-11-21T08:20:32.366994Z", + "iopub.status.busy": "2023-11-21T08:20:32.366631Z", + "iopub.status.idle": "2023-11-21T08:20:32.369631Z", + "shell.execute_reply": "2023-11-21T08:20:32.369044Z" } }, "outputs": [], @@ -714,10 +714,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:17.253081Z", - "iopub.status.busy": "2023-11-20T20:43:17.252713Z", - "iopub.status.idle": "2023-11-20T20:43:18.668008Z", - "shell.execute_reply": "2023-11-20T20:43:18.667333Z" + "iopub.execute_input": "2023-11-21T08:20:32.372115Z", + "iopub.status.busy": "2023-11-21T08:20:32.371653Z", + "iopub.status.idle": "2023-11-21T08:20:33.844796Z", + "shell.execute_reply": "2023-11-21T08:20:33.844069Z" } }, "outputs": [], @@ -737,10 +737,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:18.671253Z", - "iopub.status.busy": "2023-11-20T20:43:18.670795Z", - "iopub.status.idle": "2023-11-20T20:43:18.684529Z", - "shell.execute_reply": "2023-11-20T20:43:18.683998Z" + "iopub.execute_input": "2023-11-21T08:20:33.848328Z", + "iopub.status.busy": "2023-11-21T08:20:33.847858Z", + "iopub.status.idle": "2023-11-21T08:20:33.862042Z", + "shell.execute_reply": "2023-11-21T08:20:33.861342Z" } }, "outputs": [ @@ -770,10 +770,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:18.687071Z", - "iopub.status.busy": "2023-11-20T20:43:18.686703Z", - "iopub.status.idle": "2023-11-20T20:43:18.729349Z", - "shell.execute_reply": "2023-11-20T20:43:18.728835Z" + "iopub.execute_input": "2023-11-21T08:20:33.864719Z", + "iopub.status.busy": "2023-11-21T08:20:33.864225Z", + "iopub.status.idle": "2023-11-21T08:20:34.015710Z", + "shell.execute_reply": "2023-11-21T08:20:34.014999Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/text.ipynb b/master/.doctrees/nbsphinx/tutorials/text.ipynb index 52353e359..ac69e5f6b 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": "2023-11-20T20:43:23.187464Z", - "iopub.status.busy": "2023-11-20T20:43:23.187274Z", - "iopub.status.idle": "2023-11-20T20:43:25.201961Z", - "shell.execute_reply": "2023-11-20T20:43:25.201245Z" + "iopub.execute_input": "2023-11-21T08:20:39.063885Z", + "iopub.status.busy": "2023-11-21T08:20:39.063687Z", + "iopub.status.idle": "2023-11-21T08:20:41.191252Z", + "shell.execute_reply": "2023-11-21T08:20:41.190618Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:43:25.204847Z", - "iopub.status.busy": "2023-11-20T20:43:25.204532Z", - "iopub.status.idle": "2023-11-20T20:43:25.208039Z", - "shell.execute_reply": "2023-11-20T20:43:25.207506Z" + "iopub.execute_input": "2023-11-21T08:20:41.194339Z", + "iopub.status.busy": "2023-11-21T08:20:41.193729Z", + "iopub.status.idle": "2023-11-21T08:20:41.197335Z", + "shell.execute_reply": "2023-11-21T08:20:41.196796Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.210217Z", - "iopub.status.busy": "2023-11-20T20:43:25.210013Z", - "iopub.status.idle": "2023-11-20T20:43:25.213166Z", - "shell.execute_reply": "2023-11-20T20:43:25.212675Z" + "iopub.execute_input": "2023-11-21T08:20:41.199795Z", + "iopub.status.busy": "2023-11-21T08:20:41.199409Z", + "iopub.status.idle": "2023-11-21T08:20:41.202679Z", + "shell.execute_reply": "2023-11-21T08:20:41.202149Z" }, "nbsphinx": "hidden" }, @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.215621Z", - "iopub.status.busy": "2023-11-20T20:43:25.215261Z", - "iopub.status.idle": "2023-11-20T20:43:25.261440Z", - "shell.execute_reply": "2023-11-20T20:43:25.260939Z" + "iopub.execute_input": "2023-11-21T08:20:41.205116Z", + "iopub.status.busy": "2023-11-21T08:20:41.204764Z", + "iopub.status.idle": "2023-11-21T08:20:41.352639Z", + "shell.execute_reply": "2023-11-21T08:20:41.352015Z" } }, "outputs": [ @@ -311,10 +311,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.263750Z", - "iopub.status.busy": "2023-11-20T20:43:25.263386Z", - "iopub.status.idle": "2023-11-20T20:43:25.267013Z", - "shell.execute_reply": "2023-11-20T20:43:25.266504Z" + "iopub.execute_input": "2023-11-21T08:20:41.355219Z", + "iopub.status.busy": "2023-11-21T08:20:41.354814Z", + "iopub.status.idle": "2023-11-21T08:20:41.358579Z", + "shell.execute_reply": "2023-11-21T08:20:41.358003Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.269464Z", - "iopub.status.busy": "2023-11-20T20:43:25.268982Z", - "iopub.status.idle": "2023-11-20T20:43:25.273116Z", - "shell.execute_reply": "2023-11-20T20:43:25.272512Z" + "iopub.execute_input": "2023-11-21T08:20:41.360711Z", + "iopub.status.busy": "2023-11-21T08:20:41.360525Z", + "iopub.status.idle": "2023-11-21T08:20:41.364213Z", + "shell.execute_reply": "2023-11-21T08:20:41.363607Z" } }, "outputs": [ @@ -341,7 +341,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'apple_pay_or_google_pay', 'getting_spare_card', 'change_pin', 'beneficiary_not_allowed', 'visa_or_mastercard', 'supported_cards_and_currencies', 'cancel_transfer', 'lost_or_stolen_phone', 'card_payment_fee_charged'}\n" + "Classes: {'supported_cards_and_currencies', 'getting_spare_card', 'card_payment_fee_charged', 'card_about_to_expire', 'change_pin', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'lost_or_stolen_phone', 'cancel_transfer'}\n" ] } ], @@ -364,10 +364,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.275646Z", - "iopub.status.busy": "2023-11-20T20:43:25.275283Z", - "iopub.status.idle": "2023-11-20T20:43:25.278664Z", - "shell.execute_reply": "2023-11-20T20:43:25.278028Z" + "iopub.execute_input": "2023-11-21T08:20:41.366690Z", + "iopub.status.busy": "2023-11-21T08:20:41.366325Z", + "iopub.status.idle": "2023-11-21T08:20:41.369774Z", + "shell.execute_reply": "2023-11-21T08:20:41.369111Z" } }, "outputs": [ @@ -408,10 +408,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.281350Z", - "iopub.status.busy": "2023-11-20T20:43:25.280847Z", - "iopub.status.idle": "2023-11-20T20:43:25.284399Z", - "shell.execute_reply": "2023-11-20T20:43:25.283842Z" + "iopub.execute_input": "2023-11-21T08:20:41.372187Z", + "iopub.status.busy": "2023-11-21T08:20:41.371828Z", + "iopub.status.idle": "2023-11-21T08:20:41.375202Z", + "shell.execute_reply": "2023-11-21T08:20:41.374661Z" } }, "outputs": [], @@ -452,10 +452,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.286755Z", - "iopub.status.busy": "2023-11-20T20:43:25.286549Z", - "iopub.status.idle": "2023-11-20T20:43:33.803067Z", - "shell.execute_reply": "2023-11-20T20:43:33.802442Z" + "iopub.execute_input": "2023-11-21T08:20:41.377614Z", + "iopub.status.busy": "2023-11-21T08:20:41.377254Z", + "iopub.status.idle": "2023-11-21T08:20:50.551012Z", + "shell.execute_reply": "2023-11-21T08:20:50.550364Z" } }, "outputs": [ @@ -502,10 +502,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:33.806235Z", - "iopub.status.busy": "2023-11-20T20:43:33.805786Z", - "iopub.status.idle": "2023-11-20T20:43:33.809004Z", - "shell.execute_reply": "2023-11-20T20:43:33.808489Z" + "iopub.execute_input": "2023-11-21T08:20:50.554295Z", + "iopub.status.busy": "2023-11-21T08:20:50.553853Z", + "iopub.status.idle": "2023-11-21T08:20:50.557026Z", + "shell.execute_reply": "2023-11-21T08:20:50.556496Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:33.811254Z", - "iopub.status.busy": "2023-11-20T20:43:33.810957Z", - "iopub.status.idle": "2023-11-20T20:43:33.813903Z", - "shell.execute_reply": "2023-11-20T20:43:33.813243Z" + "iopub.execute_input": "2023-11-21T08:20:50.559369Z", + "iopub.status.busy": "2023-11-21T08:20:50.559002Z", + "iopub.status.idle": "2023-11-21T08:20:50.561910Z", + "shell.execute_reply": "2023-11-21T08:20:50.561284Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:33.816216Z", - "iopub.status.busy": "2023-11-20T20:43:33.815868Z", - "iopub.status.idle": "2023-11-20T20:43:36.007450Z", - "shell.execute_reply": "2023-11-20T20:43:36.006723Z" + "iopub.execute_input": "2023-11-21T08:20:50.564416Z", + "iopub.status.busy": "2023-11-21T08:20:50.563970Z", + "iopub.status.idle": "2023-11-21T08:20:52.818503Z", + "shell.execute_reply": "2023-11-21T08:20:52.817716Z" }, "scrolled": true }, @@ -571,10 +571,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.010959Z", - "iopub.status.busy": "2023-11-20T20:43:36.010206Z", - "iopub.status.idle": "2023-11-20T20:43:36.018250Z", - "shell.execute_reply": "2023-11-20T20:43:36.017605Z" + "iopub.execute_input": "2023-11-21T08:20:52.822285Z", + "iopub.status.busy": "2023-11-21T08:20:52.821480Z", + "iopub.status.idle": "2023-11-21T08:20:52.829951Z", + "shell.execute_reply": "2023-11-21T08:20:52.829244Z" } }, "outputs": [ @@ -675,10 +675,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.020799Z", - "iopub.status.busy": "2023-11-20T20:43:36.020366Z", - "iopub.status.idle": "2023-11-20T20:43:36.024683Z", - "shell.execute_reply": "2023-11-20T20:43:36.024190Z" + "iopub.execute_input": "2023-11-21T08:20:52.832248Z", + "iopub.status.busy": "2023-11-21T08:20:52.831944Z", + "iopub.status.idle": "2023-11-21T08:20:52.836232Z", + "shell.execute_reply": "2023-11-21T08:20:52.835707Z" } }, "outputs": [], @@ -692,10 +692,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.027211Z", - "iopub.status.busy": "2023-11-20T20:43:36.026782Z", - "iopub.status.idle": "2023-11-20T20:43:36.030395Z", - "shell.execute_reply": "2023-11-20T20:43:36.029770Z" + "iopub.execute_input": "2023-11-21T08:20:52.838696Z", + "iopub.status.busy": "2023-11-21T08:20:52.838309Z", + "iopub.status.idle": "2023-11-21T08:20:52.841937Z", + "shell.execute_reply": "2023-11-21T08:20:52.841296Z" } }, "outputs": [ @@ -730,10 +730,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.032806Z", - "iopub.status.busy": "2023-11-20T20:43:36.032373Z", - "iopub.status.idle": "2023-11-20T20:43:36.035704Z", - "shell.execute_reply": "2023-11-20T20:43:36.035083Z" + "iopub.execute_input": "2023-11-21T08:20:52.844498Z", + "iopub.status.busy": "2023-11-21T08:20:52.844007Z", + "iopub.status.idle": "2023-11-21T08:20:52.847322Z", + "shell.execute_reply": "2023-11-21T08:20:52.846720Z" } }, "outputs": [], @@ -753,10 +753,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.038093Z", - "iopub.status.busy": "2023-11-20T20:43:36.037728Z", - "iopub.status.idle": "2023-11-20T20:43:36.044815Z", - "shell.execute_reply": "2023-11-20T20:43:36.044199Z" + "iopub.execute_input": "2023-11-21T08:20:52.849824Z", + "iopub.status.busy": "2023-11-21T08:20:52.849464Z", + "iopub.status.idle": "2023-11-21T08:20:52.856744Z", + "shell.execute_reply": "2023-11-21T08:20:52.856146Z" } }, "outputs": [ @@ -881,10 +881,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.047272Z", - "iopub.status.busy": "2023-11-20T20:43:36.046938Z", - "iopub.status.idle": "2023-11-20T20:43:36.287961Z", - "shell.execute_reply": "2023-11-20T20:43:36.287384Z" + "iopub.execute_input": "2023-11-21T08:20:52.859409Z", + "iopub.status.busy": "2023-11-21T08:20:52.858913Z", + "iopub.status.idle": "2023-11-21T08:20:53.106683Z", + "shell.execute_reply": "2023-11-21T08:20:53.105724Z" }, "scrolled": true }, @@ -923,10 +923,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.290865Z", - "iopub.status.busy": "2023-11-20T20:43:36.290435Z", - "iopub.status.idle": "2023-11-20T20:43:36.565315Z", - "shell.execute_reply": "2023-11-20T20:43:36.564731Z" + "iopub.execute_input": "2023-11-21T08:20:53.110056Z", + "iopub.status.busy": "2023-11-21T08:20:53.109593Z", + "iopub.status.idle": "2023-11-21T08:20:53.410426Z", + "shell.execute_reply": "2023-11-21T08:20:53.409728Z" }, "scrolled": true }, @@ -959,10 +959,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.568258Z", - "iopub.status.busy": "2023-11-20T20:43:36.567827Z", - "iopub.status.idle": "2023-11-20T20:43:36.571859Z", - "shell.execute_reply": "2023-11-20T20:43:36.571281Z" + "iopub.execute_input": "2023-11-21T08:20:53.414751Z", + "iopub.status.busy": "2023-11-21T08:20:53.413542Z", + "iopub.status.idle": "2023-11-21T08:20:53.419270Z", + "shell.execute_reply": "2023-11-21T08:20:53.418671Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb b/master/.doctrees/nbsphinx/tutorials/token_classification.ipynb index 00916da21..b320f7a2d 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": "2023-11-20T20:43:41.206191Z", - "iopub.status.busy": "2023-11-20T20:43:41.206003Z", - "iopub.status.idle": "2023-11-20T20:43:42.320526Z", - "shell.execute_reply": "2023-11-20T20:43:42.319901Z" + "iopub.execute_input": "2023-11-21T08:20:58.123913Z", + "iopub.status.busy": "2023-11-21T08:20:58.123719Z", + "iopub.status.idle": "2023-11-21T08:20:59.900707Z", + "shell.execute_reply": "2023-11-21T08:20:59.900015Z" } }, "outputs": [ @@ -86,7 +86,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2023-11-20 20:43:41-- https://data.deepai.org/conll2003.zip\r\n", + "--2023-11-21 08:20:58-- https://data.deepai.org/conll2003.zip\r\n", "Resolving data.deepai.org (data.deepai.org)... " ] }, @@ -94,8 +94,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "185.93.1.247, 2400:52e0:1a00::1029:1\r\n", - "Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443... " + "169.150.249.166, 2400:52e0:1a01::999:1\r\n", + "Connecting to data.deepai.org (data.deepai.org)|169.150.249.166|:443... " ] }, { @@ -103,14 +103,7 @@ "output_type": "stream", "text": [ "connected.\r\n", - "HTTP request sent, awaiting response... " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "200 OK\r\n", + "HTTP request sent, awaiting response... 200 OK\r\n", "Length: 982975 (960K) [application/zip]\r\n", "Saving to: ‘conll2003.zip’\r\n", "\r\n", @@ -125,7 +118,7 @@ "\r", "conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s \r\n", "\r\n", - "2023-11-20 20:43:41 (6.45 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", + "2023-11-21 08:20:58 (6.35 MB/s) - ‘conll2003.zip’ saved [982975/982975]\r\n", "\r\n", "mkdir: cannot create directory ‘data’: File exists\r\n" ] @@ -145,9 +138,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "--2023-11-20 20:43:41-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", - "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 16.182.72.145, 52.217.117.113, 52.217.198.121, ...\r\n", - "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|16.182.72.145|:443... connected.\r\n", + "--2023-11-21 08:20:58-- https://cleanlab-public.s3.amazonaws.com/TokenClassification/pred_probs.npz\r\n", + "Resolving cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)... 52.217.195.121, 52.216.209.185, 3.5.29.144, ...\r\n", + "Connecting to cleanlab-public.s3.amazonaws.com (cleanlab-public.s3.amazonaws.com)|52.217.195.121|:443... " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "connected.\r\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "HTTP request sent, awaiting response... " ] }, @@ -168,9 +174,26 @@ "output_type": "stream", "text": [ "\r", - "pred_probs.npz 100%[===================>] 16.26M 99.1MB/s in 0.2s \r\n", + "pred_probs.npz 1%[ ] 261.11K 1.21MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 27%[====> ] 4.51M 10.7MB/s " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "pred_probs.npz 96%[==================> ] 15.65M 24.8MB/s \r", + "pred_probs.npz 100%[===================>] 16.26M 25.6MB/s in 0.6s \r\n", "\r\n", - "2023-11-20 20:43:42 (99.1 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", + "2023-11-21 08:20:59 (25.6 MB/s) - ‘pred_probs.npz’ saved [17045998/17045998]\r\n", "\r\n" ] } @@ -187,10 +210,10 @@ "id": "439b0305", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:42.323270Z", - "iopub.status.busy": "2023-11-20T20:43:42.322900Z", - "iopub.status.idle": "2023-11-20T20:43:43.305709Z", - "shell.execute_reply": "2023-11-20T20:43:43.305048Z" + "iopub.execute_input": "2023-11-21T08:20:59.903840Z", + "iopub.status.busy": "2023-11-21T08:20:59.903413Z", + "iopub.status.idle": "2023-11-21T08:21:00.919888Z", + "shell.execute_reply": "2023-11-21T08:21:00.919295Z" }, "nbsphinx": "hidden" }, @@ -201,7 +224,7 @@ "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -227,10 +250,10 @@ "id": "a1349304", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:43.308811Z", - "iopub.status.busy": "2023-11-20T20:43:43.308351Z", - "iopub.status.idle": "2023-11-20T20:43:43.311899Z", - "shell.execute_reply": "2023-11-20T20:43:43.311399Z" + "iopub.execute_input": "2023-11-21T08:21:00.923212Z", + "iopub.status.busy": "2023-11-21T08:21:00.922638Z", + "iopub.status.idle": "2023-11-21T08:21:00.926515Z", + "shell.execute_reply": "2023-11-21T08:21:00.925837Z" } }, "outputs": [], @@ -280,10 +303,10 @@ "id": "ab9d59a0", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:43.314423Z", - "iopub.status.busy": "2023-11-20T20:43:43.314068Z", - "iopub.status.idle": "2023-11-20T20:43:43.317121Z", - "shell.execute_reply": "2023-11-20T20:43:43.316583Z" + "iopub.execute_input": "2023-11-21T08:21:00.929223Z", + "iopub.status.busy": "2023-11-21T08:21:00.928838Z", + "iopub.status.idle": "2023-11-21T08:21:00.932061Z", + "shell.execute_reply": "2023-11-21T08:21:00.931510Z" }, "nbsphinx": "hidden" }, @@ -301,10 +324,10 @@ "id": "519cb80c", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:43.319383Z", - "iopub.status.busy": "2023-11-20T20:43:43.319024Z", - "iopub.status.idle": "2023-11-20T20:43:51.115739Z", - "shell.execute_reply": "2023-11-20T20:43:51.115099Z" + "iopub.execute_input": "2023-11-21T08:21:00.934563Z", + "iopub.status.busy": "2023-11-21T08:21:00.934197Z", + "iopub.status.idle": "2023-11-21T08:21:08.839030Z", + "shell.execute_reply": "2023-11-21T08:21:08.838406Z" } }, "outputs": [], @@ -378,10 +401,10 @@ "id": "202f1526", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:51.118642Z", - "iopub.status.busy": "2023-11-20T20:43:51.118282Z", - "iopub.status.idle": "2023-11-20T20:43:51.124194Z", - "shell.execute_reply": "2023-11-20T20:43:51.123611Z" + "iopub.execute_input": "2023-11-21T08:21:08.842131Z", + "iopub.status.busy": "2023-11-21T08:21:08.841746Z", + "iopub.status.idle": "2023-11-21T08:21:08.847608Z", + "shell.execute_reply": "2023-11-21T08:21:08.847074Z" }, "nbsphinx": "hidden" }, @@ -421,10 +444,10 @@ "id": "a4381f03", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:51.126567Z", - "iopub.status.busy": "2023-11-20T20:43:51.126209Z", - "iopub.status.idle": "2023-11-20T20:43:51.521671Z", - "shell.execute_reply": "2023-11-20T20:43:51.521063Z" + "iopub.execute_input": "2023-11-21T08:21:08.849942Z", + "iopub.status.busy": "2023-11-21T08:21:08.849651Z", + "iopub.status.idle": "2023-11-21T08:21:09.268086Z", + "shell.execute_reply": "2023-11-21T08:21:09.267457Z" } }, "outputs": [], @@ -461,10 +484,10 @@ "id": "7842e4a3", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:51.524560Z", - "iopub.status.busy": "2023-11-20T20:43:51.524130Z", - "iopub.status.idle": "2023-11-20T20:43:51.529811Z", - "shell.execute_reply": "2023-11-20T20:43:51.529315Z" + "iopub.execute_input": "2023-11-21T08:21:09.271317Z", + "iopub.status.busy": "2023-11-21T08:21:09.270841Z", + "iopub.status.idle": "2023-11-21T08:21:09.276811Z", + "shell.execute_reply": "2023-11-21T08:21:09.276280Z" } }, "outputs": [ @@ -536,10 +559,10 @@ "id": "2c2ad9ad", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:51.532292Z", - "iopub.status.busy": "2023-11-20T20:43:51.531933Z", - "iopub.status.idle": "2023-11-20T20:43:53.398998Z", - "shell.execute_reply": "2023-11-20T20:43:53.398264Z" + "iopub.execute_input": "2023-11-21T08:21:09.279444Z", + "iopub.status.busy": "2023-11-21T08:21:09.279069Z", + "iopub.status.idle": "2023-11-21T08:21:11.227685Z", + "shell.execute_reply": "2023-11-21T08:21:11.226912Z" } }, "outputs": [], @@ -561,10 +584,10 @@ "id": "95dc7268", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:53.402450Z", - "iopub.status.busy": "2023-11-20T20:43:53.401685Z", - "iopub.status.idle": "2023-11-20T20:43:53.408828Z", - "shell.execute_reply": "2023-11-20T20:43:53.408276Z" + "iopub.execute_input": "2023-11-21T08:21:11.231351Z", + "iopub.status.busy": "2023-11-21T08:21:11.230603Z", + "iopub.status.idle": "2023-11-21T08:21:11.237722Z", + "shell.execute_reply": "2023-11-21T08:21:11.237077Z" } }, "outputs": [ @@ -600,10 +623,10 @@ "id": "e13de188", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:53.411294Z", - "iopub.status.busy": "2023-11-20T20:43:53.410924Z", - "iopub.status.idle": "2023-11-20T20:43:53.428260Z", - "shell.execute_reply": "2023-11-20T20:43:53.427753Z" + "iopub.execute_input": "2023-11-21T08:21:11.240328Z", + "iopub.status.busy": "2023-11-21T08:21:11.239937Z", + "iopub.status.idle": "2023-11-21T08:21:11.257362Z", + "shell.execute_reply": "2023-11-21T08:21:11.256849Z" } }, "outputs": [ @@ -781,10 +804,10 @@ "id": "e4a006bd", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:53.430648Z", - "iopub.status.busy": "2023-11-20T20:43:53.430281Z", - "iopub.status.idle": "2023-11-20T20:43:53.462163Z", - "shell.execute_reply": "2023-11-20T20:43:53.461635Z" + "iopub.execute_input": "2023-11-21T08:21:11.259944Z", + "iopub.status.busy": "2023-11-21T08:21:11.259487Z", + "iopub.status.idle": "2023-11-21T08:21:11.291321Z", + "shell.execute_reply": "2023-11-21T08:21:11.290786Z" } }, "outputs": [ @@ -886,10 +909,10 @@ "id": "c8f4e163", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:53.464461Z", - "iopub.status.busy": "2023-11-20T20:43:53.464266Z", - "iopub.status.idle": "2023-11-20T20:43:53.472010Z", - "shell.execute_reply": "2023-11-20T20:43:53.471498Z" + "iopub.execute_input": "2023-11-21T08:21:11.293746Z", + "iopub.status.busy": "2023-11-21T08:21:11.293367Z", + "iopub.status.idle": "2023-11-21T08:21:11.302099Z", + "shell.execute_reply": "2023-11-21T08:21:11.301573Z" } }, "outputs": [ @@ -963,10 +986,10 @@ "id": "db0b5179", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:53.474260Z", - "iopub.status.busy": "2023-11-20T20:43:53.474067Z", - "iopub.status.idle": "2023-11-20T20:43:55.230223Z", - "shell.execute_reply": "2023-11-20T20:43:55.229602Z" + "iopub.execute_input": "2023-11-21T08:21:11.304483Z", + "iopub.status.busy": "2023-11-21T08:21:11.304126Z", + "iopub.status.idle": "2023-11-21T08:21:13.155407Z", + "shell.execute_reply": "2023-11-21T08:21:13.154764Z" } }, "outputs": [ @@ -1138,10 +1161,10 @@ "id": "a18795eb", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:55.232804Z", - "iopub.status.busy": "2023-11-20T20:43:55.232597Z", - "iopub.status.idle": "2023-11-20T20:43:55.236862Z", - "shell.execute_reply": "2023-11-20T20:43:55.236332Z" + "iopub.execute_input": "2023-11-21T08:21:13.158219Z", + "iopub.status.busy": "2023-11-21T08:21:13.157793Z", + "iopub.status.idle": "2023-11-21T08:21:13.162226Z", + "shell.execute_reply": "2023-11-21T08:21:13.161662Z" }, "nbsphinx": "hidden" }, diff --git a/master/.doctrees/tutorials/audio.doctree b/master/.doctrees/tutorials/audio.doctree index d55fbacff..3721a66e0 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 553396818..d1c1df9bc 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 01d52c84b..659ad3e48 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 99301b777..969ceaf95 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 9833d2d1c..aa44c8fd8 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 69004dcc0..0041419a2 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 0c3ff9583..c4fe5a9d5 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 84148055a..5da5f548d 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 c86499904..24a299707 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 de4914827..726c033de 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 b4283c6ee..6ea36944e 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 8d2bc140f..b2266df6c 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 2f7534285..1e619f11b 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 1bd78eeea..9fee948be 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 286054c4b..785d4722e 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 048247e3a..308680682 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 57362af9a..d1b5f3e48 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 a50f248c9..27a1cfb7c 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 08d7e6ba6..abf989758 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 dc16382f5..1d0f412ad 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 6a4e81698..71210adb2 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 73d499fdc..8a5e8b6e5 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 c03933dff..21bdddd18 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 330757665..601f7ec39 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", @@ -736,7 +736,7 @@ "source": [ "`Datalab` makes it very easy to check your datasets for all sorts of issues that are important to deal with for training robust models. The inputs it uses to detect issues can come from *any* model you have trained (the better your model, the more accurate the issue detection will be).\n", "\n", - "To learn more, check out this [examples notebook](https://github.com/cleanlab/examples/blob/master/datalab_image_classification/datalab.ipynb) and the [advanced Datalab tutorial](datalab_advanced.html)." + "To learn more, check out this [example notebook](https://github.com/cleanlab/examples/blob/master/datalab_image_classification/datalab.ipynb) (demonstrates Datalab applied to a real dataset) and the [advanced Datalab tutorial](datalab_advanced.html) (demonstrates configuration and customization options to exert greater control)." ] } ], diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb index eafe33df8..7be78abf2 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 a9ab23993..41c3f353e 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 317df9feb..aa5763ec8 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 602bbd434..a4751002d 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 2a214d808..449c408e5 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 15807b244..0a998ccb5 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 73dd7607d..51be355bb 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 eba7a0726..30c6379db 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 96cd237f6..7c27ed281 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 9d6a7f0cd..108ec0eca 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 ce838dda3..d98ae2b83 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 a06940a7c..b17671a50 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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 f6b73d5c0..30f79080e 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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\n", " cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n", " %pip install $cmd\n", "else:\n", diff --git a/master/searchindex.js b/master/searchindex.js index eb5010220..9d0ecda1a 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/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "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/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/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/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", "Datalab guides", "Datalab Issue Types", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "noniid", "null", "outlier", "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, 68, 70, 71, 78, 80, 81], "helper": [1, 13, 29, 33, 35, 36, 37, 38, 39, 40, 52, 75, 77, 89], "method": [1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 70, 71, 73, 74, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "ar": [1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20, 25, 26, 28, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 88, 89], "us": [1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 67, 68, 70, 75, 79, 84], "benchmark": [1, 26, 67, 68, 70, 71, 78, 80, 81], "cleanlab": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 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, 43, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 68, 70, 71, 75, 79, 84], "": [1, 2, 3, 7, 25, 26, 30, 33, 36, 38, 40, 45, 46, 50, 52, 53, 54, 55, 57, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "core": [1, 4, 29, 31, 59, 61, 86], "algorithm": [1, 2, 7, 27, 40, 45, 54, 63, 65, 67, 76, 78, 80, 89], "These": [1, 2, 3, 7, 17, 28, 31, 32, 43, 45, 46, 49, 54, 58, 62, 63, 65, 66, 69, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "introduc": [1, 69, 76, 78], "synthet": [1, 80, 81, 86], "nois": [1, 2, 3, 25, 31, 34, 40, 46, 70, 71, 75, 80], "label": [1, 2, 3, 4, 5, 6, 9, 13, 17, 18, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 40, 41, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 70, 75, 79, 83, 84], "classif": [1, 3, 4, 7, 11, 23, 25, 29, 31, 34, 36, 37, 40, 45, 46, 47, 48, 49, 54, 55, 63, 64, 65, 66, 67, 68, 70, 71, 79, 80, 83, 84, 85, 86], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 16, 18, 20, 21, 22, 29, 30, 31, 34, 36, 40, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 70, 72, 73, 79, 80, 84, 87], "specif": [1, 3, 4, 6, 11, 13, 23, 28, 43, 47, 50, 53, 62, 66, 71, 73, 74, 77, 78, 89], "thi": [1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "modul": [1, 3, 10, 11, 12, 13, 17, 23, 25, 26, 27, 28, 29, 30, 31, 40, 43, 45, 54, 55, 67, 76, 77, 81], "provid": [1, 2, 3, 4, 5, 7, 11, 13, 19, 25, 26, 27, 29, 30, 31, 34, 40, 44, 45, 46, 47, 52, 53, 54, 55, 57, 59, 61, 62, 65, 66, 67, 69, 70, 71, 74, 76, 77, 78, 80, 83, 84, 85, 86, 87, 88, 89], "gener": [1, 2, 3, 5, 7, 19, 23, 25, 36, 40, 41, 54, 55, 57, 62, 69, 70, 71, 75, 76, 77, 78, 80, 81, 83, 84, 85, 86, 88, 89], "valid": [1, 2, 3, 4, 7, 9, 25, 31, 32, 34, 35, 36, 40, 45, 47, 50, 53, 55, 57, 58, 66, 68, 69, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89], "matric": [1, 3, 34, 76], "which": [1, 2, 3, 4, 7, 9, 10, 11, 13, 18, 20, 23, 25, 26, 30, 31, 34, 36, 39, 40, 45, 46, 47, 50, 52, 53, 54, 55, 57, 58, 61, 62, 63, 65, 67, 68, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 88, 89], "learn": [1, 2, 3, 4, 7, 11, 13, 18, 23, 27, 28, 29, 30, 31, 33, 35, 40, 43, 45, 47, 54, 56, 58, 61, 65, 67, 69, 70, 73, 74, 75, 79, 80, 85, 88], "i": [1, 2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "possibl": [1, 2, 3, 7, 25, 26, 30, 31, 33, 34, 36, 47, 48, 49, 50, 52, 53, 54, 55, 57, 63, 65, 66, 71, 76, 78, 80, 81, 82, 85, 86, 89], "noisi": [1, 2, 3, 25, 27, 30, 31, 34, 40, 46, 47, 49, 55, 57, 58, 59, 61, 62, 68, 70, 71, 73, 74, 76, 79, 80], "given": [1, 2, 3, 7, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 39, 40, 45, 46, 47, 50, 52, 53, 54, 55, 57, 58, 62, 63, 65, 66, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 86, 87, 88, 89], "matrix": [1, 2, 3, 4, 7, 13, 25, 31, 33, 34, 37, 40, 41, 47, 52, 54, 55, 73, 83], "trace": [1, 70, 71, 78, 80, 81], "valu": [1, 2, 3, 4, 7, 9, 10, 13, 18, 20, 21, 25, 26, 27, 29, 30, 31, 33, 34, 36, 40, 45, 46, 47, 49, 50, 52, 54, 55, 57, 58, 59, 61, 62, 63, 66, 69, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 86, 88, 89], "more": [1, 2, 3, 4, 5, 7, 10, 13, 20, 25, 26, 29, 30, 33, 36, 40, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 61, 62, 63, 65, 67, 69, 70, 73, 74, 75, 76, 77, 80, 81, 82, 83, 86, 89], "function": [1, 2, 3, 4, 5, 10, 11, 13, 19, 20, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 69, 71, 75, 76, 78, 80, 81, 82, 86, 87, 88, 89], "noise_matrix_is_valid": 1, "noise_matrix": [1, 2, 3, 7, 34, 40, 70, 71, 78, 80, 81], "py": [1, 3, 23, 26, 27, 31, 34, 36, 45, 70, 71, 78, 80, 81], "verbos": [1, 2, 4, 5, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 29, 31, 45, 46, 47, 52, 54, 55, 57, 59, 61, 62, 66, 70, 78, 80], "fals": [1, 2, 3, 4, 5, 9, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 35, 39, 40, 41, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 63, 69, 70, 71, 73, 74, 76, 77, 78, 80, 82, 83, 85, 86, 88], "sourc": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66], "prior": [1, 2, 3, 25, 31, 34, 36], "repres": [1, 2, 3, 4, 5, 7, 9, 13, 20, 25, 29, 31, 34, 37, 40, 45, 46, 47, 50, 52, 53, 54, 55, 57, 59, 61, 62, 66, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 87, 88, 89], "p": [1, 2, 3, 7, 25, 31, 33, 34, 40, 45, 53, 54, 55, 59, 71, 73, 74, 77, 78, 80, 89], "true_label": [1, 2, 3, 25, 34, 40, 78, 80], "k": [1, 2, 3, 4, 7, 9, 13, 15, 19, 20, 22, 25, 29, 31, 33, 34, 35, 36, 37, 38, 39, 40, 45, 46, 47, 48, 49, 50, 53, 54, 55, 57, 59, 61, 62, 63, 65, 66, 69, 70, 71, 76, 78, 80, 81, 82, 83, 86, 87, 89], "check": [1, 2, 4, 6, 7, 9, 13, 26, 29, 30, 35, 41, 44, 50, 53, 57, 67, 69, 70, 71, 76, 77, 78, 80, 81, 85, 87, 88], "learnabl": 1, "mean": [1, 2, 5, 9, 10, 18, 20, 27, 30, 34, 36, 52, 57, 71, 74, 76, 78, 80, 81, 83, 85, 88], "achiev": [1, 2, 26, 27, 30, 57, 80, 89], "better": [1, 4, 31, 45, 47, 55, 57, 58, 67, 69, 71, 73, 74, 76, 78, 81, 82, 83, 88, 89], "than": [1, 2, 3, 5, 7, 20, 22, 25, 31, 40, 44, 45, 50, 52, 54, 55, 57, 61, 65, 69, 76, 77, 78, 80, 81, 82, 83, 84, 86, 87, 89], "random": [1, 2, 3, 5, 7, 29, 36, 45, 55, 57, 69, 70, 71, 73, 76, 77, 78, 80, 81, 83, 87], "perform": [1, 2, 5, 7, 20, 22, 26, 30, 36, 57, 67, 70, 76, 78, 80, 81, 84, 85, 87, 88], "averag": [1, 3, 18, 22, 25, 26, 30, 36, 38, 45, 46, 54, 55, 76, 80, 83], "amount": [1, 3, 77], "paramet": [1, 2, 3, 4, 6, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 69, 71, 74, 77, 87, 88], "np": [1, 2, 3, 4, 5, 13, 25, 27, 29, 31, 33, 34, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 62, 63, 65, 66, 69, 70, 71, 73, 75, 76, 77, 78, 80, 81, 83, 85, 86, 87, 88, 89], "ndarrai": [1, 2, 3, 4, 13, 19, 20, 25, 27, 29, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 65, 89], "an": [1, 2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 38, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "arrai": [1, 2, 3, 4, 5, 9, 13, 20, 25, 27, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 69, 70, 71, 74, 76, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "shape": [1, 2, 3, 4, 13, 25, 27, 29, 31, 33, 34, 35, 36, 38, 39, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 69, 75, 76, 78, 81, 82, 83, 86, 89], "condit": [1, 2, 3, 34, 39, 40, 55, 77, 78, 89], "probabl": [1, 2, 3, 4, 7, 13, 19, 22, 25, 29, 30, 31, 33, 34, 36, 37, 39, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 63, 65, 66, 67, 68, 75, 76, 78, 79, 81, 82, 83, 86, 89], "k_": [1, 2, 3, 34, 40], "k_y": [1, 2, 3, 34, 40], "contain": [1, 2, 3, 4, 9, 10, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 38, 39, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 61, 62, 63, 65, 66, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88], "fraction": [1, 2, 3, 7, 16, 27, 34, 40, 45, 57, 73, 76], "exampl": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 36, 37, 38, 39, 40, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 80, 81, 82, 84, 85, 86, 87, 88, 89], "everi": [1, 2, 3, 4, 13, 26, 30, 31, 34, 39, 40, 47, 55, 57, 58, 69, 70, 71, 73, 74, 76, 77, 80, 82, 84, 86, 87, 89], "class": [1, 2, 3, 4, 5, 6, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 87, 88, 89], "other": [1, 2, 3, 4, 7, 13, 18, 25, 26, 28, 29, 30, 31, 34, 37, 40, 41, 43, 45, 46, 49, 54, 55, 57, 62, 69, 70, 71, 73, 74, 76, 77, 78, 81, 83, 86, 89], "assum": [1, 2, 3, 9, 31, 34, 38, 39, 40, 55, 59, 62, 76, 83, 86, 89], "column": [1, 2, 3, 4, 7, 9, 10, 25, 29, 31, 34, 36, 37, 39, 40, 45, 46, 47, 49, 50, 53, 54, 55, 57, 62, 63, 65, 66, 69, 70, 71, 74, 75, 76, 77, 80, 82, 85, 86, 87, 88, 89], "sum": [1, 2, 3, 20, 25, 34, 36, 40, 46, 47, 49, 52, 57, 70, 71, 76, 77, 78, 80, 81, 86, 89], "1": [1, 2, 3, 4, 5, 7, 9, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 75, 76, 84], "each": [1, 2, 3, 4, 5, 6, 10, 11, 13, 16, 18, 19, 20, 23, 25, 26, 27, 29, 30, 31, 33, 34, 36, 37, 38, 40, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "true": [1, 2, 3, 4, 5, 7, 9, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 34, 36, 39, 40, 41, 44, 45, 46, 47, 50, 52, 53, 54, 55, 57, 59, 61, 62, 66, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "return": [1, 2, 3, 4, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 88, 89], "type": [1, 2, 3, 4, 5, 8, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 68, 69, 70, 71, 73, 74, 77, 81, 82, 86, 87, 89], "bool": [1, 2, 3, 4, 9, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 36, 39, 40, 45, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 66], "is_valid": 1, "whether": [1, 3, 4, 7, 9, 10, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 30, 31, 40, 45, 46, 47, 49, 50, 66, 69, 71, 73, 74, 75, 77, 78, 85, 88, 89], "generate_noisy_label": [1, 70, 71, 78, 80, 81], "from": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 18, 19, 23, 24, 25, 26, 27, 29, 30, 31, 34, 36, 37, 38, 39, 40, 45, 47, 49, 52, 53, 54, 55, 57, 58, 63, 65, 66, 67, 69, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 86, 89], "perfect": [1, 2, 25, 57, 78, 82], "exactli": [1, 3, 7, 25, 26, 30, 31, 48, 54, 70, 71, 73, 74, 77, 78], "yield": [1, 26, 30], "between": [1, 4, 7, 12, 13, 17, 18, 20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 38, 43, 45, 46, 49, 52, 54, 55, 57, 58, 61, 65, 66, 68, 69, 70, 71, 73, 74, 77, 78, 80, 81, 82, 83, 85, 86, 88, 89], "below": [1, 3, 7, 25, 26, 29, 30, 31, 33, 36, 45, 46, 47, 52, 53, 61, 65, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "we": [1, 2, 3, 4, 5, 7, 10, 18, 26, 29, 30, 31, 36, 40, 41, 45, 52, 55, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "loop": [1, 3, 34, 40, 77], "implement": [1, 2, 3, 4, 6, 11, 18, 26, 27, 29, 30, 34, 40, 57, 67, 69, 70, 73, 83, 84, 87], "what": [1, 4, 6, 7, 13, 23, 25, 27, 29, 31, 45, 46, 50, 52, 69, 70, 71, 73, 74, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "doe": [1, 2, 3, 7, 29, 30, 31, 36, 41, 52, 57, 59, 61, 65, 69, 70, 71, 73, 74, 77, 81, 85, 86, 88], "do": [1, 2, 4, 7, 25, 29, 30, 40, 41, 54, 55, 59, 69, 70, 71, 73, 74, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "fast": 1, "explain": [1, 7], "python": [1, 2, 30, 44, 57, 70, 71, 75, 83], "pseudocod": [1, 84], "happen": [1, 7, 31, 47, 80, 86], "n": [1, 2, 3, 4, 5, 25, 26, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 65, 69, 74, 75, 76, 77, 80, 81, 85, 86, 87, 88, 89], "without": [1, 2, 4, 7, 9, 11, 16, 26, 30, 49, 57, 67, 69, 74, 78, 82, 83, 88], "ani": [1, 2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 29, 30, 31, 33, 35, 39, 40, 44, 45, 47, 49, 50, 52, 53, 55, 57, 59, 61, 62, 67, 69, 70, 71, 73, 74, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88], "distinct": [1, 40, 89], "natur": [1, 7, 80, 83], "number": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 65, 66, 68, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 86, 89], "0": [1, 2, 3, 4, 5, 7, 9, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "count_joint": 1, "len": [1, 2, 3, 5, 25, 29, 34, 39, 40, 41, 54, 55, 57, 70, 71, 74, 75, 76, 77, 78, 80, 81, 83, 85, 87, 88, 89], "y": [1, 2, 3, 4, 30, 34, 36, 40, 41, 44, 53, 57, 58, 69, 70, 71, 73, 76, 78, 80, 81, 83, 85, 88], "round": [1, 29, 31, 40, 57, 76, 85], "astyp": [1, 80], "int": [1, 2, 3, 4, 5, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 36, 37, 38, 39, 40, 46, 47, 49, 53, 54, 55, 57, 59, 61, 62, 63, 66, 69, 70, 77, 83], "rang": [1, 3, 5, 9, 34, 36, 38, 40, 57, 58, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 89], "idx_flip": 1, "where": [1, 2, 3, 4, 5, 7, 9, 10, 13, 18, 25, 29, 31, 34, 35, 36, 37, 38, 39, 40, 41, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 69, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 88, 89], "pragma": 1, "cover": [1, 3, 68, 75], "choic": [1, 31, 77, 81, 83], "replac": [1, 39, 44, 55, 70, 71, 74, 75, 77, 80, 83, 87, 88], "generate_noise_matrix_from_trac": [1, 70, 71, 78, 80, 81], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 55, 69, 70, 71], "05": [1, 20, 39, 57, 63, 65, 75, 76, 78, 82], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 70, 71, 78, 80, 81], "none": [1, 2, 3, 4, 5, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 44, 45, 46, 47, 48, 49, 52, 53, 54, 55, 57, 59, 61, 62, 65, 66, 70, 71, 76, 77, 78, 80, 81, 86], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 7, 20, 30, 36, 57, 69, 70, 71, 73, 75, 78, 80, 81, 87], "max_it": [1, 69, 74, 83, 88], "10000": [1, 29, 75, 76], "x": [1, 2, 3, 4, 7, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 30, 31, 33, 34, 36, 39, 40, 41, 44, 45, 47, 53, 54, 55, 57, 59, 69, 70, 71, 73, 75, 76, 77, 78, 80, 81, 83, 85, 87, 88], "diagon": [1, 3, 4, 13, 31, 34, 40], "equal": [1, 3, 7, 9, 47, 52, 62, 84], "creat": [1, 2, 6, 13, 26, 29, 30, 31, 40, 57, 67, 69, 73, 74, 76, 77, 86, 88, 89], "impli": [1, 25, 46], "float": [1, 2, 7, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 33, 35, 36, 39, 40, 45, 46, 47, 49, 52, 53, 57, 61, 65, 69, 70, 71, 78, 80, 81], "entri": [1, 3, 4, 13, 25, 26, 30, 31, 33, 37, 40, 45, 46, 47, 50, 73, 74, 78, 81, 82, 87, 88], "maximum": [1, 7, 54, 62, 66, 86], "minimum": [1, 7, 16, 31, 33, 47, 52, 65], "noise_r": 1, "non": [1, 2, 3, 4, 6, 13, 20, 26, 30, 31, 52, 57, 70, 76, 78, 80, 82, 83], "default": [1, 2, 3, 4, 7, 11, 13, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 36, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 70, 76, 77, 86], "If": [1, 2, 3, 4, 7, 9, 10, 13, 20, 22, 25, 26, 29, 30, 31, 33, 34, 36, 39, 40, 44, 45, 46, 47, 50, 52, 53, 54, 57, 58, 59, 61, 62, 65, 66, 67, 68, 69, 70, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "have": [1, 2, 3, 4, 7, 13, 17, 20, 25, 26, 28, 29, 30, 31, 34, 36, 40, 44, 45, 46, 47, 50, 52, 53, 54, 55, 57, 58, 62, 66, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "all": [1, 2, 3, 4, 5, 7, 10, 11, 13, 18, 23, 25, 26, 29, 30, 31, 34, 36, 37, 39, 40, 44, 45, 46, 47, 48, 49, 52, 53, 54, 55, 57, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "necessari": [1, 2, 3, 5, 7, 9, 39, 70], "In": [1, 2, 3, 7, 25, 26, 29, 30, 45, 46, 48, 69, 70, 71, 73, 74, 75, 76, 77, 78, 81, 82, 83, 84, 85, 86, 87, 88, 89], "particular": [1, 4, 7, 10, 11, 13, 15, 16, 18, 20, 21, 22, 26, 30, 40, 45, 49, 53, 57, 62, 66, 67, 69, 71, 74, 76, 80, 81, 83, 85, 87, 88], "satisfi": [1, 3, 25], "requir": [1, 2, 4, 5, 6, 7, 8, 9, 24, 26, 27, 28, 29, 30, 31, 34, 40, 43, 44, 47, 54, 55, 57, 59, 67, 68, 69, 75, 76, 78, 84], "argument": [1, 2, 3, 4, 7, 13, 19, 26, 29, 30, 31, 36, 41, 44, 45, 46, 47, 49, 52, 53, 54, 55, 57, 61, 62, 63, 65, 71, 74, 75, 76, 77, 82, 85, 88, 89], "when": [1, 2, 3, 4, 7, 9, 11, 19, 20, 26, 30, 31, 34, 36, 40, 44, 47, 49, 50, 52, 54, 55, 57, 58, 70, 71, 73, 74, 76, 77, 80, 84, 85, 86, 87, 88, 89], "The": [1, 2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 36, 37, 38, 40, 44, 45, 46, 47, 50, 52, 53, 54, 55, 57, 59, 62, 63, 65, 67, 69, 70, 71, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "rate": [1, 2, 3, 7, 27, 40, 69, 89], "set": [1, 2, 3, 4, 6, 7, 9, 10, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 35, 36, 40, 44, 45, 47, 50, 52, 53, 54, 55, 57, 59, 61, 62, 70, 71, 73, 74, 76, 80, 81, 83, 84, 85, 86, 87, 88, 89], "note": [1, 2, 3, 5, 7, 26, 29, 30, 31, 36, 40, 45, 50, 52, 53, 54, 55, 57, 58, 62, 68, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "you": [1, 2, 3, 4, 5, 7, 11, 13, 25, 26, 28, 29, 30, 31, 36, 43, 44, 45, 47, 50, 52, 53, 54, 55, 57, 58, 59, 62, 63, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "high": [1, 2, 13, 29, 31, 40, 52, 55, 57, 70, 71, 75, 77, 78, 82, 85, 86, 87, 88, 89], "mai": [1, 2, 3, 4, 7, 10, 17, 18, 25, 26, 28, 29, 30, 31, 34, 36, 40, 45, 46, 50, 52, 53, 54, 55, 57, 59, 62, 66, 68, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 84, 85, 86, 88, 89], "imposs": [1, 7, 78], "also": [1, 2, 3, 4, 5, 7, 18, 25, 26, 29, 30, 31, 39, 44, 45, 54, 57, 62, 65, 66, 67, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89], "low": [1, 7, 40, 45, 67, 70, 71, 74, 78, 82, 86], "zero": [1, 3, 4, 13, 26, 30, 33, 40, 41, 70, 77, 81, 82, 83], "forc": [1, 2, 3, 4, 30, 70, 89], "instead": [1, 2, 3, 7, 10, 13, 23, 25, 26, 29, 30, 31, 34, 40, 44, 45, 47, 49, 54, 55, 57, 58, 61, 63, 65, 68, 69, 73, 76, 77, 78, 81, 82, 83, 85, 86, 87, 88, 89], "onli": [1, 2, 3, 4, 7, 13, 19, 20, 25, 26, 29, 30, 31, 33, 34, 39, 40, 44, 45, 54, 55, 57, 59, 61, 65, 66, 67, 69, 70, 71, 74, 77, 80, 81, 82, 83, 84, 85, 86, 88, 89], "guarante": [1, 3, 4, 12, 17, 26, 28, 30, 32, 34, 43, 68], "produc": [1, 2, 4, 7, 13, 36, 45, 55, 57, 59, 61, 67, 69, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 86, 87, 88, 89], "higher": [1, 4, 7, 25, 31, 33, 34, 36, 45, 46, 57, 71, 74, 76, 82], "opposit": [1, 89], "occur": [1, 3, 7, 25, 39, 52, 70, 71, 76, 77, 83], "small": [1, 3, 7, 25, 29, 36, 40, 46, 53, 74, 75, 77, 81, 83, 88], "numpi": [1, 3, 4, 5, 7, 9, 29, 30, 36, 38, 39, 41, 44, 49, 52, 57, 58, 63, 65, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "max": [1, 31, 54, 55, 77, 83], "tri": [1, 26, 30, 84], "befor": [1, 2, 3, 26, 30, 40, 54, 57, 62, 74, 76, 78, 80, 83, 85, 87, 88], "option": [1, 2, 3, 4, 5, 6, 9, 10, 13, 19, 20, 25, 26, 29, 30, 31, 34, 36, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 65, 66, 67, 69, 70, 71, 73, 76, 77, 78, 85, 86, 87], "left": [1, 2, 31, 33, 38, 40, 47, 50, 53, 70, 71, 81, 82, 83, 86], "stochast": 1, "exceed": 1, "generate_n_rand_probabilities_that_sum_to_m": 1, "m": [1, 26, 30, 35, 36, 45, 50, 52, 53, 54, 70, 71, 75, 80, 81, 82, 89], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 26, 30, 44, 76, 78, 86], "length": [1, 4, 9, 20, 25, 27, 31, 40, 47, 50, 54, 55, 57, 59, 62, 66, 69, 81, 83, 86, 87, 89], "must": [1, 2, 3, 4, 13, 25, 26, 27, 28, 30, 31, 34, 36, 37, 40, 43, 44, 45, 46, 47, 54, 55, 57, 59, 61, 62, 63, 65, 66, 69, 80, 84, 86, 89], "randomly_distribute_n_balls_into_k_bin": 1, "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 7, 9, 25, 29, 31, 37, 40, 41, 45, 47, 53, 59, 61, 62, 63, 65, 66, 69, 76, 80, 81, 82, 86, 87, 88, 89], "ball": [1, 75], "bin": [1, 3, 47, 70, 71, 83], "ensur": [1, 2, 7, 26, 30, 40, 41, 52, 55, 57, 69, 70, 71, 74, 77, 78, 83, 84, 85, 87, 88], "most": [1, 3, 4, 5, 7, 13, 25, 29, 31, 36, 44, 45, 46, 47, 50, 52, 53, 54, 55, 58, 61, 65, 66, 67, 68, 69, 70, 71, 73, 74, 76, 78, 80, 81, 82, 83, 85, 86, 87, 88], "least": [1, 7, 25, 29, 45, 46, 52, 55, 65, 76, 77, 80, 83, 86], "int_arrai": [1, 40], "can": [2, 3, 4, 5, 6, 10, 11, 13, 23, 25, 26, 27, 28, 29, 30, 31, 35, 36, 37, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 58, 59, 62, 63, 66, 67, 68, 69, 70, 73, 74, 77, 81, 82, 83, 84, 85, 86, 87, 88, 89], "model": [2, 3, 4, 7, 13, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 39, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 68, 70, 71, 75, 79, 84, 86, 89], "For": [2, 3, 4, 5, 6, 7, 8, 13, 18, 24, 25, 26, 29, 30, 31, 34, 36, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 63, 65, 66, 67, 69, 71, 73, 75, 76, 77, 78, 80, 81, 82, 83, 84, 86, 87, 88, 89], "regular": [2, 3, 29, 44], "multi": [2, 3, 7, 25, 26, 29, 30, 31, 35, 36, 37, 40, 41, 46, 47, 48, 49, 54, 55, 67, 76, 78, 79], "task": [2, 4, 9, 11, 13, 23, 25, 29, 34, 36, 37, 38, 40, 45, 47, 55, 57, 67, 69, 74, 75, 76, 78, 81, 83, 86, 88, 89], "cleanlearn": [2, 3, 7, 19, 26, 40, 44, 57, 58, 67, 68, 85, 87, 88], "wrap": [2, 26, 30, 44, 54, 57, 67, 70, 71, 73, 74, 78, 85, 87, 88], "instanc": [2, 3, 4, 5, 7, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 30, 36, 44, 53, 54, 57, 62, 69, 70, 71, 73, 74, 77, 78, 87], "sklearn": [2, 3, 4, 7, 25, 30, 36, 40, 44, 54, 57, 58, 67, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 83, 84, 85, 87, 88], "classifi": [2, 3, 30, 36, 40, 45, 48, 54, 55, 67, 68, 69, 73, 74, 76, 80, 81, 83, 84, 86, 87, 88, 89], "adher": [2, 30, 57], "estim": [2, 3, 4, 6, 10, 18, 25, 29, 30, 31, 34, 40, 45, 46, 47, 52, 54, 57, 59, 61, 65, 67, 68, 69, 70, 71, 73, 74, 76, 77, 79, 81, 82, 83, 84, 85, 86, 89], "api": [2, 3, 11, 44, 54, 57, 68, 76, 85], "defin": [2, 3, 4, 5, 7, 11, 18, 25, 26, 27, 29, 30, 31, 55, 57, 59, 70, 71, 73, 80, 83, 89], "four": [2, 7, 75, 78, 89], "clf": [2, 3, 4, 36, 57, 67, 73, 76, 78, 81, 87], "fit": [2, 3, 4, 7, 30, 44, 54, 57, 67, 70, 71, 73, 74, 76, 77, 78, 80, 81, 83, 84, 85, 87, 88, 89], "sample_weight": [2, 30, 57, 78], "predict_proba": [2, 4, 25, 30, 36, 44, 69, 70, 71, 73, 74, 76, 78, 80, 81, 83, 87], "predict": [2, 3, 4, 7, 13, 18, 19, 22, 25, 29, 30, 31, 33, 34, 36, 37, 39, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 75, 76, 78, 79, 83, 85, 86, 88, 89], "score": [2, 3, 4, 5, 7, 10, 15, 16, 18, 19, 20, 21, 22, 25, 29, 31, 33, 36, 38, 45, 46, 47, 49, 50, 52, 54, 55, 57, 58, 61, 63, 65, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 83, 85, 87, 88], "data": [2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29, 30, 31, 36, 37, 40, 43, 44, 45, 46, 47, 48, 52, 54, 55, 56, 57, 62, 63, 64, 65, 66, 68, 72, 74, 77, 79, 84, 88], "e": [2, 3, 4, 7, 9, 13, 18, 25, 26, 29, 30, 31, 34, 36, 37, 40, 41, 45, 46, 47, 48, 54, 55, 57, 59, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 88], "featur": [2, 3, 4, 7, 13, 15, 19, 20, 21, 22, 36, 40, 54, 57, 67, 70, 71, 73, 74, 78, 80, 85, 87], "element": [2, 3, 4, 25, 31, 33, 40, 45, 47, 55, 62, 63, 65, 69, 74, 76, 88, 89], "first": [2, 7, 14, 20, 21, 25, 29, 36, 40, 45, 46, 50, 53, 55, 57, 69, 70, 73, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "index": [2, 7, 20, 25, 31, 38, 39, 40, 41, 46, 55, 57, 62, 65, 66, 69, 70, 71, 73, 75, 77, 78, 80, 82, 83, 85, 86, 88, 89], "should": [2, 3, 4, 5, 7, 11, 13, 18, 20, 25, 26, 29, 30, 31, 33, 34, 36, 39, 40, 44, 45, 46, 49, 50, 52, 53, 54, 55, 57, 58, 62, 63, 65, 66, 69, 70, 71, 73, 74, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "correspond": [2, 3, 4, 7, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 29, 30, 31, 33, 34, 36, 39, 40, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 62, 63, 65, 66, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "differ": [2, 4, 5, 7, 10, 12, 17, 20, 25, 26, 28, 29, 30, 31, 32, 36, 40, 41, 43, 45, 50, 52, 54, 57, 69, 70, 71, 73, 74, 77, 78, 80, 83, 84, 87], "sampl": [2, 3, 4, 7, 13, 16, 31, 33, 36, 47, 50, 53, 55, 57, 58, 67, 68, 75, 76, 78, 79, 81, 82, 85, 86, 88, 89], "size": [2, 7, 26, 29, 30, 31, 36, 47, 52, 53, 57, 59, 61, 73, 76, 77, 78, 80, 81, 84, 86, 88], "here": [2, 4, 5, 7, 11, 29, 31, 34, 44, 45, 46, 47, 49, 50, 53, 54, 65, 67, 68, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "re": [2, 4, 26, 30, 39, 45, 57, 67, 69, 70, 73, 74, 76, 85, 86, 87, 88, 89], "weight": [2, 7, 26, 27, 30, 36, 45, 52, 55, 57, 69, 70, 71, 74, 83, 88], "loss": [2, 27, 44, 55, 57, 77], "while": [2, 3, 7, 26, 29, 30, 35, 36, 40, 50, 53, 57, 67, 76, 77, 78, 80, 85], "train": [2, 3, 4, 7, 13, 26, 27, 30, 36, 40, 44, 45, 50, 53, 54, 57, 58, 68, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 86, 89], "support": [2, 3, 4, 9, 29, 36, 40, 41, 54, 55, 65, 67, 68, 69, 70, 71, 76, 77], "your": [2, 3, 4, 6, 7, 13, 25, 26, 28, 29, 30, 31, 36, 40, 43, 44, 45, 46, 47, 49, 54, 55, 57, 58, 59, 61, 62, 68, 69, 73, 75, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "recommend": [2, 4, 7, 10, 13, 29, 31, 45, 70, 71, 76, 77, 84, 85], "furthermor": 2, "correctli": [2, 3, 7, 25, 26, 30, 31, 34, 41, 46, 47, 52, 57, 59, 74, 76, 81, 82, 85, 86, 88], "clonabl": [2, 57], "via": [2, 4, 7, 10, 13, 18, 25, 27, 29, 30, 36, 40, 45, 50, 53, 54, 55, 57, 58, 61, 65, 69, 70, 71, 73, 74, 75, 76, 77, 81, 82, 83, 84, 85, 86, 87, 88, 89], "base": [2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 29, 30, 31, 34, 35, 36, 38, 39, 40, 41, 44, 45, 46, 47, 49, 52, 54, 55, 57, 58, 61, 63, 65, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 89], "clone": [2, 57, 81], "intern": [2, 3, 5, 7, 8, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 29, 33, 34, 35, 36, 37, 38, 39, 40, 41, 49, 53, 57, 63, 68, 70, 76, 78, 80, 81, 83, 89], "multipl": [2, 3, 4, 9, 10, 25, 31, 39, 45, 46, 47, 49, 52, 53, 57, 67, 70, 71, 76, 77, 79, 81, 82, 85], "g": [2, 3, 4, 7, 9, 18, 25, 26, 30, 31, 37, 40, 47, 48, 54, 55, 57, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 88], "manual": [2, 57, 69, 76, 83, 84, 85, 87, 88, 89], "pytorch": [2, 26, 27, 30, 57, 67, 69, 76, 79, 81, 86], "call": [2, 3, 4, 7, 10, 11, 15, 16, 18, 19, 20, 21, 22, 26, 30, 36, 40, 44, 54, 57, 69, 70, 71, 74, 76, 78, 83, 84, 86, 88, 89], "__init__": [2, 27, 57, 77], "independ": [2, 3, 7, 46, 57, 84, 89], "compat": [2, 26, 29, 30, 44, 57, 58, 61, 65, 67, 76, 84, 85, 87, 88], "neural": [2, 27, 44, 54, 57, 69, 76, 77, 81, 83], "network": [2, 26, 27, 30, 44, 54, 57, 69, 74, 76, 77, 81, 83, 88], "typic": [2, 26, 30, 54, 57, 69, 71, 73, 74, 77, 83, 84, 87, 88], "initi": [2, 3, 10, 26, 30, 45, 57, 74, 76, 87], "insid": [2, 30, 57, 76, 78], "There": [2, 3, 67, 78, 80, 81], "two": [2, 3, 7, 20, 25, 26, 29, 30, 37, 40, 50, 52, 53, 68, 70, 71, 73, 74, 76, 77, 78, 81, 85, 86, 88, 89], "new": [2, 5, 11, 18, 26, 29, 30, 35, 39, 40, 45, 57, 69, 70, 74, 75, 76, 83, 84, 88, 89], "notion": 2, "confid": [2, 3, 7, 18, 25, 29, 31, 34, 36, 40, 45, 46, 47, 50, 52, 53, 54, 55, 57, 61, 65, 67, 78, 80, 81, 82, 84, 86, 87, 89], "packag": [2, 4, 5, 6, 7, 8, 12, 24, 28, 31, 32, 40, 43, 50, 53, 57, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "prune": [2, 3, 31, 47, 57, 68, 82], "everyth": [2, 78], "els": [2, 70, 75, 76, 77, 80, 81], "mathemat": [2, 3, 7, 34], "keep": [2, 10, 11, 40, 67, 70, 75, 76, 86], "belong": [2, 3, 7, 25, 31, 33, 34, 46, 47, 48, 49, 54, 55, 59, 63, 65, 66, 77, 78, 81, 83, 86, 89], "2": [2, 3, 4, 5, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 44, 46, 47, 49, 54, 55, 57, 58, 62, 63, 65, 66, 75, 76, 84], "error": [2, 3, 4, 7, 26, 30, 31, 33, 34, 38, 40, 46, 47, 49, 50, 52, 53, 55, 57, 59, 61, 62, 65, 68, 69, 70, 71, 73, 74, 75, 79, 87], "erron": [2, 3, 25, 31, 34, 40, 46, 47, 55, 57, 58, 59, 83, 85], "import": [2, 3, 4, 5, 9, 10, 11, 15, 16, 18, 19, 20, 21, 22, 23, 25, 29, 36, 38, 39, 45, 49, 52, 57, 58, 63, 65, 66, 67, 73, 74, 76, 81, 82, 83, 85, 86, 87, 88, 89], "linear_model": [2, 4, 25, 40, 57, 67, 69, 70, 71, 74, 76, 78, 80, 83, 88], "logisticregress": [2, 3, 4, 25, 40, 67, 69, 70, 71, 74, 76, 78, 80, 83, 88], "logreg": 2, "cl": [2, 11, 57, 67, 76, 78, 85, 87, 88], "pass": [2, 3, 4, 7, 9, 10, 11, 13, 19, 23, 26, 29, 30, 31, 35, 36, 40, 44, 45, 47, 54, 55, 57, 63, 67, 69, 70, 71, 74, 75, 76, 78, 80, 82, 83, 85, 88], "x_train": [2, 70, 71, 78, 80, 81, 85, 87], "labels_maybe_with_error": 2, "had": [2, 3, 57, 82], "issu": [2, 3, 4, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 43, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 72, 79, 80, 84, 85, 88], "pred": [2, 31, 40, 84, 85, 87, 88], "x_test": [2, 70, 71, 78, 81, 85, 87], "might": [2, 45, 57, 62, 70, 71, 76, 77, 87, 88], "case": [2, 3, 10, 25, 36, 45, 57, 69, 70, 71, 73, 75, 77, 78, 83, 85, 87, 88, 89], "standard": [2, 3, 4, 25, 31, 44, 46, 47, 49, 55, 57, 67, 70, 71, 73, 75, 78, 87], "adapt": [2, 26, 28, 40, 43, 57, 83], "skorch": [2, 57, 67, 76], "kera": [2, 43, 57, 67, 76], "scikera": [2, 44, 57, 76], "open": [2, 29, 75, 82, 89], "doesn": [2, 57, 67], "t": [2, 3, 7, 14, 21, 26, 27, 29, 30, 31, 36, 38, 39, 49, 54, 55, 57, 63, 65, 66, 67, 70, 71, 75, 77, 78, 81, 82, 89], "alreadi": [2, 4, 13, 26, 29, 30, 34, 44, 45, 57, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 87, 88], "exist": [2, 4, 7, 9, 26, 29, 30, 39, 44, 50, 52, 54, 57, 67, 68, 70, 71, 74, 80, 81, 88, 89], "made": [2, 4, 13, 57, 74, 77, 80, 82, 84, 85, 87, 88], "easi": [2, 34, 57, 70, 71, 75, 76, 78, 81], "inherit": [2, 5, 27, 57], "baseestim": [2, 30, 57], "yourmodel": [2, 57], "def": [2, 5, 11, 26, 30, 44, 57, 69, 70, 71, 75, 76, 77, 78, 80, 81, 83, 85, 88, 89], "self": [2, 3, 4, 5, 9, 10, 11, 13, 26, 27, 29, 30, 31, 36, 54, 55, 57, 70, 75, 77, 81, 86, 87, 89], "refer": [2, 7, 26, 30, 46, 47, 49, 50, 52, 53, 57, 61, 62, 70, 71, 73, 74, 76, 77, 78, 84, 85], "origin": [2, 4, 7, 30, 31, 39, 40, 44, 46, 47, 50, 53, 54, 57, 58, 61, 63, 65, 70, 73, 74, 76, 77, 78, 82, 83, 85, 87, 88, 89], "total": [2, 3, 25, 29, 40, 46, 66, 76, 77, 86], "state": [2, 3, 4, 26, 27, 30, 35, 57, 78, 81, 82, 89], "art": [2, 27, 78, 81], "northcutt": [2, 3, 25, 54, 55], "et": [2, 3, 25, 27, 54, 55], "al": [2, 3, 25, 27, 54, 55], "2021": [2, 3, 25, 54, 55], "weak": 2, "supervis": [2, 7, 70, 71, 76, 80], "find": [2, 4, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 28, 29, 30, 31, 35, 39, 40, 43, 50, 53, 54, 55, 57, 59, 63, 65, 68, 70, 79, 84], "uncertainti": [2, 7, 33, 54, 57, 76, 83, 85], "It": [2, 3, 4, 5, 7, 9, 10, 13, 18, 23, 26, 30, 31, 34, 36, 38, 45, 52, 53, 57, 67, 70, 71, 76, 77, 78, 81, 84], "work": [2, 3, 4, 5, 7, 9, 25, 26, 29, 30, 31, 34, 39, 40, 41, 44, 45, 55, 57, 67, 68, 70, 71, 75, 83, 85, 88], "includ": [2, 3, 4, 5, 7, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 39, 40, 43, 45, 46, 49, 50, 54, 55, 57, 61, 62, 63, 65, 67, 68, 70, 71, 73, 74, 76, 77, 78, 81, 82, 83, 89], "deep": [2, 28, 30, 43, 44, 57, 74], "see": [2, 3, 4, 10, 25, 26, 29, 30, 31, 36, 40, 44, 46, 47, 49, 50, 53, 54, 55, 57, 63, 65, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "subfield": 2, "theori": [2, 78], "machin": [2, 4, 11, 13, 23, 28, 43, 57, 70, 71, 75, 80], "across": [2, 3, 4, 5, 7, 10, 13, 18, 25, 29, 36, 46, 53, 54, 70, 71, 73, 74, 75, 76, 77, 78, 82, 84], "varieti": [2, 87, 88], "like": [2, 3, 4, 5, 7, 11, 13, 23, 25, 26, 29, 30, 31, 34, 40, 44, 45, 46, 49, 50, 52, 55, 57, 58, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 76, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "pu": [2, 40], "input": [2, 3, 4, 7, 13, 20, 25, 26, 29, 30, 34, 36, 39, 40, 41, 44, 53, 57, 67, 68, 71, 74, 75, 76, 77, 78, 80, 81, 82, 85, 86, 88, 89], "discret": [2, 31, 34, 40, 54, 55, 59, 61, 62], "vector": [2, 3, 4, 7, 13, 31, 34, 36, 37, 40, 54, 55, 67, 69, 70, 71, 73, 74, 77, 78, 81, 82, 83, 86, 88, 89], "would": [2, 3, 4, 26, 29, 30, 31, 40, 47, 57, 67, 70, 76, 77, 78, 83, 85, 88, 89], "obtain": [2, 4, 7, 13, 31, 45, 47, 50, 53, 55, 58, 69, 71, 74, 76, 80, 82, 84, 86, 89], "been": [2, 25, 31, 34, 39, 40, 45, 46, 50, 52, 54, 55, 57, 69, 70, 73, 76, 78, 80, 81, 82, 83, 86, 89], "dure": [2, 13, 54, 57, 69, 73, 74, 76, 78, 81, 84, 85, 87, 88, 89], "denot": [2, 3, 34, 36, 40, 47, 54, 55, 65], "tild": 2, "paper": [2, 7, 45, 54, 63, 65, 75, 78, 80, 83, 85, 89], "cv_n_fold": [2, 3, 57, 88], "5": [2, 3, 4, 15, 16, 18, 19, 20, 21, 22, 23, 25, 30, 31, 33, 35, 36, 40, 45, 46, 49, 50, 53, 57, 58, 65, 70, 74, 75, 76, 81, 82, 83, 84, 86, 88, 89], "converge_latent_estim": [2, 3], "pulearn": [2, 40], "find_label_issues_kwarg": [2, 7, 57, 68, 76, 78], "label_quality_scores_kwarg": [2, 7], "low_memori": [2, 47, 63, 76], "clean": [2, 52, 55, 57, 58, 67, 70, 71, 75, 85, 87, 88], "even": [2, 3, 25, 29, 33, 34, 40, 57, 69, 76, 78, 80, 81, 82], "messi": [2, 57, 78], "ridden": [2, 57], "autom": [2, 57, 67, 71, 75, 76], "robust": [2, 34, 57, 71, 76], "prone": [2, 57], "out": [2, 3, 4, 7, 13, 22, 26, 30, 31, 36, 44, 47, 48, 50, 53, 54, 55, 57, 58, 66, 67, 68, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 89], "current": [2, 3, 7, 10, 11, 18, 26, 30, 31, 36, 45, 52, 57, 70, 71, 76, 80], "intend": [2, 10, 11, 12, 13, 23, 32, 45, 61, 65, 69, 70, 71, 74, 78], "A": [2, 3, 4, 5, 7, 9, 10, 11, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 30, 31, 34, 35, 36, 37, 39, 40, 44, 45, 46, 49, 52, 53, 54, 55, 57, 59, 61, 62, 66, 68, 69, 70, 73, 74, 75, 76, 77, 78, 80, 82, 84, 87, 88, 89], "follow": [2, 3, 7, 11, 25, 26, 29, 30, 36, 38, 45, 46, 50, 52, 53, 54, 57, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "experiment": [2, 26, 27, 29, 30, 47, 68, 76], "wrapper": [2, 4, 44, 69, 85, 87, 88], "around": [2, 4, 52, 70, 71, 82, 83, 89], "fasttext": [2, 43], "store": [2, 4, 7, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 30, 54, 57, 73, 74, 75, 86, 87, 88, 89], "along": [2, 36, 47, 65, 70, 71, 76, 77, 83], "dimens": [2, 38, 40, 59, 62, 76, 77, 83, 86], "select": [2, 6, 20, 45, 55, 77, 80, 83], "split": [2, 3, 4, 7, 9, 29, 36, 39, 40, 57, 69, 70, 71, 73, 74, 75, 77, 78, 81, 84, 87, 89], "cross": [2, 3, 7, 25, 31, 34, 35, 36, 47, 50, 53, 55, 57, 58, 68, 69, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89], "fold": [2, 3, 25, 31, 34, 57, 69, 73, 75, 76, 82, 86, 87], "By": [2, 4, 25, 46, 47, 57, 70, 86], "need": [2, 3, 7, 25, 26, 29, 30, 31, 46, 47, 49, 54, 57, 67, 69, 70, 71, 74, 76, 78, 80, 81, 82, 86, 88], "holdout": [2, 3, 57], "comput": [2, 3, 4, 5, 7, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 38, 40, 45, 46, 47, 49, 52, 53, 54, 55, 57, 58, 59, 61, 67, 68, 70, 71, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88], "them": [2, 3, 4, 5, 6, 7, 8, 9, 24, 26, 28, 29, 30, 31, 43, 45, 54, 57, 68, 70, 71, 73, 74, 76, 77, 80, 81, 83, 85, 86, 87, 88, 89], "numer": [2, 3, 4, 7, 10, 18, 36, 52, 54, 57, 62, 67, 68, 69, 70, 71, 72, 74, 77, 78, 80, 83, 85, 87, 88], "consist": [2, 3, 26, 30, 40, 45, 86, 89], "latent": [2, 3, 34], "thei": [2, 3, 4, 12, 17, 20, 26, 27, 28, 30, 31, 32, 40, 44, 47, 52, 55, 57, 58, 61, 65, 67, 69, 70, 71, 73, 74, 76, 77, 78, 80, 83, 85, 88, 89], "relat": [2, 3, 10, 15, 16, 20, 21, 22, 34, 40, 46, 57, 71], "close": [2, 3, 7, 29, 34, 54, 69, 70, 71, 73, 74, 76, 77, 78, 82], "form": [2, 3, 7, 26, 27, 30, 34, 39, 40, 55, 57, 76], "equival": [2, 3, 26, 30, 34, 54, 83], "iter": [2, 3, 25, 26, 30, 31, 40, 46, 47, 57, 80, 86], "enforc": [2, 26, 30, 40], "perfectli": [2, 25, 46, 78], "certain": [2, 3, 4, 13, 26, 30, 44, 57, 70, 71, 75, 83], "dict": [2, 3, 4, 7, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 29, 30, 31, 35, 36, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 65, 70, 71, 76, 77, 89], "keyword": [2, 3, 4, 7, 13, 19, 26, 29, 30, 31, 33, 36, 39, 44, 45, 47, 54, 55, 57, 63, 65, 70], "filter": [2, 3, 7, 29, 39, 46, 48, 49, 51, 60, 61, 62, 64, 65, 66, 67, 68, 69, 74, 75, 76, 77, 81, 82, 85, 86, 87, 88, 89], "find_label_issu": [2, 3, 7, 29, 31, 46, 47, 49, 50, 52, 57, 59, 61, 62, 63, 65, 66, 67, 68, 76, 81, 82, 85, 86, 87, 88, 89], "particularli": [2, 67, 80, 83], "filter_bi": [2, 3, 29, 31, 47, 68, 76], "frac_nois": [2, 31, 47, 63, 76], "min_examples_per_class": [2, 31, 47, 76, 78], "impact": [2, 7, 70, 71, 77], "ml": [2, 4, 7, 57, 67, 70, 71, 73, 74, 77, 80, 87, 88], "accuraci": [2, 27, 55, 69, 76, 77, 78, 80, 83, 85, 86, 87, 88], "n_job": [2, 29, 31, 47, 59, 61, 63, 76, 83, 86], "disabl": [2, 26, 30, 31, 83], "process": [2, 3, 5, 10, 13, 29, 31, 39, 45, 47, 59, 61, 63, 69, 70, 80, 84, 88], "caus": [2, 31, 36, 70, 71], "rank": [2, 3, 25, 29, 31, 36, 46, 47, 48, 50, 51, 53, 54, 56, 60, 62, 63, 64, 66, 67, 68, 70, 71, 75, 76, 81, 82, 83, 85, 86, 87, 88, 89], "get_label_quality_scor": [2, 29, 31, 36, 45, 47, 49, 50, 52, 55, 58, 61, 63, 65, 68, 78, 81, 82, 85, 86, 89], "adjust_pred_prob": [2, 7, 49, 54, 55, 78], "control": [2, 4, 6, 7, 13, 29, 31, 38, 45, 53, 54, 57, 63, 65, 70, 71, 75, 76], "how": [2, 3, 4, 7, 10, 11, 13, 18, 25, 26, 27, 29, 30, 34, 40, 45, 46, 49, 50, 52, 54, 55, 57, 61, 65, 67, 70, 71, 73, 74, 75, 77, 82, 83, 84, 85, 86, 87, 88], "much": [2, 7, 25, 29, 31, 57, 76, 78, 80, 83], "output": [2, 3, 4, 7, 13, 26, 27, 30, 34, 40, 44, 45, 46, 50, 52, 53, 54, 57, 61, 62, 65, 66, 67, 68, 69, 70, 74, 75, 76, 77, 82, 83, 84, 85, 88], "print": [2, 4, 5, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 40, 45, 46, 47, 52, 54, 55, 57, 59, 61, 62, 66, 68, 69, 71, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "suppress": [2, 29, 45, 52, 54, 55, 57, 59, 61, 62, 86, 89], "statement": [2, 29, 45, 52, 54, 55, 57, 59, 61, 62], "big": [2, 29, 47, 53, 57, 78], "limit": [2, 4, 13, 29, 47, 82, 86, 89], "memori": [2, 26, 29, 30, 47, 53, 59, 61, 70, 86], "label_issues_batch": [2, 28, 47, 76], "find_label_issues_batch": [2, 29, 47, 76], "pred_prob": [2, 3, 4, 7, 13, 19, 20, 22, 25, 29, 31, 33, 34, 35, 36, 37, 40, 41, 45, 46, 47, 49, 50, 53, 54, 55, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 87, 88], "threshold": [2, 3, 5, 7, 15, 16, 18, 22, 29, 52, 53, 54, 55, 61, 65, 70, 82, 83, 86, 89], "inverse_noise_matrix": [2, 3, 7, 34, 40, 68, 78], "label_issu": [2, 29, 31, 47, 50, 57, 59, 68, 69, 74, 76, 77, 78, 85, 87, 88], "clf_kwarg": [2, 3, 7, 57], "clf_final_kwarg": [2, 57], "validation_func": [2, 3, 7], "correct": [2, 7, 25, 29, 31, 33, 45, 46, 47, 49, 50, 52, 53, 55, 57, 58, 61, 65, 67, 69, 73, 74, 77, 78, 80, 82, 84, 85], "result": [2, 3, 7, 10, 13, 15, 16, 18, 19, 20, 21, 22, 26, 29, 30, 31, 33, 40, 47, 49, 50, 53, 55, 57, 58, 59, 61, 65, 69, 70, 71, 73, 74, 76, 77, 78, 80, 85, 86, 87, 88, 89], "identifi": [2, 3, 4, 5, 7, 9, 13, 23, 25, 29, 31, 47, 50, 55, 57, 58, 59, 62, 63, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 81, 83, 85, 86, 87, 88, 89], "final": [2, 7, 57, 73, 82, 84, 85, 87], "remain": [2, 57, 68, 77, 85, 87, 88, 89], "datasetlik": [2, 40, 57], "beyond": [2, 4, 5, 6, 8, 24, 67, 86], "pd": [2, 3, 4, 5, 10, 15, 16, 18, 19, 20, 21, 22, 25, 35, 44, 45, 46, 57, 65, 69, 70, 71, 73, 74, 76, 78, 80, 85, 87, 88, 89], "datafram": [2, 3, 4, 5, 9, 10, 15, 16, 18, 19, 20, 21, 22, 25, 29, 35, 40, 41, 44, 45, 46, 57, 62, 66, 68, 69, 70, 71, 73, 74, 76, 77, 78, 80, 85, 86, 88, 89], "scipi": [2, 10, 40], "spars": [2, 4, 7, 10, 13, 40, 41, 73], "csr_matrix": [2, 4, 10, 13], "torch": [2, 26, 27, 30, 69, 74, 75, 77, 83, 88], "util": [2, 4, 13, 23, 26, 27, 30, 32, 45, 57, 67, 68, 69, 70, 71, 76, 77, 78, 83], "tensorflow": [2, 40, 44, 67, 69, 76], "object": [2, 4, 9, 10, 13, 23, 26, 27, 29, 30, 36, 40, 41, 44, 47, 50, 51, 52, 53, 54, 57, 65, 67, 69, 71, 73, 77, 78, 79, 85, 88], "list": [2, 3, 4, 9, 11, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 37, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 61, 62, 63, 65, 66, 68, 69, 70, 71, 75, 77, 78, 81, 82, 85, 88, 89], "index_list": 2, "subset": [2, 3, 4, 13, 25, 29, 31, 40, 55, 62, 66, 69, 73, 74, 76, 77, 81, 82, 83, 84, 85, 87, 88, 89], "wa": [2, 3, 9, 11, 29, 40, 45, 46, 52, 54, 66, 69, 70, 71, 73, 74, 76, 78, 81, 82, 84, 86, 87, 88, 89], "abl": [2, 3, 7, 57, 69, 76, 78, 80, 81], "format": [2, 3, 4, 7, 9, 26, 29, 30, 31, 34, 35, 36, 37, 40, 41, 44, 45, 46, 47, 50, 53, 54, 55, 57, 59, 61, 62, 65, 66, 70, 71, 73, 75, 77, 80, 85, 86, 87, 89], "make": [2, 3, 26, 29, 30, 36, 44, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 87, 88], "sure": [2, 29, 31, 36, 69, 70, 71, 73, 74, 75, 77, 80, 81, 82, 83, 85, 87, 88], "shuffl": [2, 7, 40, 69, 77, 81, 83], "ha": [2, 3, 4, 7, 15, 16, 17, 18, 19, 20, 21, 22, 26, 30, 34, 36, 39, 40, 45, 50, 52, 57, 63, 65, 66, 67, 69, 70, 71, 73, 74, 78, 80, 81, 82, 83, 84, 85, 87, 88, 89], "batch": [2, 29, 40, 44, 45, 59, 61, 76, 77, 83], "order": [2, 7, 25, 26, 30, 31, 34, 35, 36, 38, 40, 45, 46, 47, 50, 53, 54, 55, 59, 62, 63, 65, 66, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 82, 85, 86, 88, 89], "destroi": [2, 40], "oper": [2, 26, 29, 30, 40, 44, 55, 67, 74, 83, 87, 88], "eg": [2, 7, 40, 50, 53, 70, 71, 76], "repeat": [2, 40, 45, 80, 83], "appli": [2, 26, 30, 31, 36, 37, 39, 40, 49, 54, 63, 69, 70, 73, 77, 80, 81, 83, 84, 85, 86, 87, 88], "array_lik": [2, 3, 25, 31, 40, 47, 54, 58], "some": [2, 3, 4, 7, 11, 18, 25, 26, 28, 30, 31, 34, 39, 40, 43, 45, 46, 47, 49, 50, 53, 54, 55, 57, 59, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89], "seri": [2, 3, 29, 40, 41, 57, 65], "row": [2, 3, 4, 10, 13, 21, 25, 29, 31, 33, 34, 38, 40, 45, 46, 47, 49, 54, 55, 57, 62, 63, 65, 66, 69, 70, 73, 74, 75, 76, 77, 80, 81, 83, 87, 89], "rather": [2, 3, 20, 25, 40, 44, 45, 52, 61, 65, 80, 84, 86, 88, 89], "leav": [2, 31], "per": [2, 3, 10, 25, 29, 31, 36, 39, 45, 46, 47, 49, 52, 55, 58, 59, 61, 65, 71, 76, 82, 89], "determin": [2, 3, 7, 13, 18, 20, 25, 29, 31, 36, 40, 45, 47, 50, 52, 55, 61, 65, 70, 80, 83, 85], "cutoff": [2, 3, 83], "consid": [2, 3, 4, 7, 10, 13, 19, 20, 22, 25, 26, 30, 31, 40, 45, 52, 54, 55, 58, 61, 65, 69, 71, 73, 74, 76, 77, 78, 82, 83, 84, 85, 86, 87, 88], "section": [2, 3, 5, 7, 68, 73, 77], "3": [2, 3, 4, 5, 25, 26, 30, 31, 34, 35, 36, 37, 38, 39, 40, 44, 47, 54, 55, 57, 58, 63, 65, 75, 76, 84], "equat": [2, 3, 34], "advanc": [2, 3, 4, 6, 13, 52, 54, 65, 68, 71, 72, 78], "user": [2, 3, 4, 11, 13, 23, 26, 30, 31, 52, 54, 55, 57, 61, 65, 78], "specifi": [2, 3, 4, 7, 10, 11, 13, 23, 26, 29, 30, 31, 36, 39, 45, 46, 47, 50, 52, 54, 55, 57, 58, 66, 68, 69, 71, 74, 76, 77, 80, 82, 85, 88], "automat": [2, 3, 4, 20, 25, 67, 73, 74, 75, 76, 77, 80, 82, 85, 86, 87, 88, 89], "greater": [2, 3, 4, 6, 7, 22, 29, 38, 40, 52, 71, 75, 76, 89], "count": [2, 18, 20, 25, 29, 31, 34, 40, 46, 47, 53, 68, 76, 77], "observ": [2, 3, 34, 69, 70, 71, 80, 83, 85], "mislabel": [2, 7, 25, 29, 31, 34, 45, 46, 47, 50, 52, 55, 61, 63, 65, 67, 69, 73, 74, 76, 77, 78, 81, 82, 85, 87, 88], "one": [2, 3, 4, 7, 20, 25, 26, 29, 30, 31, 36, 40, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 73, 74, 77, 80, 83, 84, 85, 87, 88, 89], "get_label_issu": [2, 29, 57, 78, 85, 87, 88], "either": [2, 3, 5, 7, 26, 29, 30, 31, 45, 47, 52, 54, 55, 59, 61, 71, 81, 82], "boolean": [2, 5, 7, 18, 29, 31, 39, 45, 47, 50, 55, 57, 59, 61, 62, 67, 69, 71, 74, 76, 77, 82, 85, 86, 88], "label_issues_mask": [2, 31, 55, 57, 68], "indic": [2, 3, 4, 5, 7, 10, 18, 25, 29, 30, 31, 33, 36, 40, 44, 45, 46, 47, 49, 50, 52, 54, 55, 57, 58, 61, 63, 65, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "its": [2, 4, 6, 7, 13, 26, 29, 30, 31, 38, 39, 47, 50, 53, 54, 55, 57, 59, 63, 65, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 84, 85, 86, 88, 89], "return_indices_ranked_bi": [2, 29, 31, 47, 63, 68, 76, 78, 81, 87, 88], "significantli": [2, 77, 78, 80, 84], "reduc": [2, 29, 31, 40, 69, 76], "time": [2, 7, 26, 29, 30, 40, 45, 68, 70, 75, 76, 77, 78, 82, 83, 85, 86, 87, 88, 89], "take": [2, 4, 7, 13, 25, 26, 30, 35, 36, 40, 44, 55, 73, 77, 80, 87, 89], "run": [2, 4, 5, 6, 8, 11, 13, 20, 24, 26, 29, 30, 57, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 87, 88, 89], "skip": [2, 7, 26, 30, 57, 69, 76, 81, 89], "slow": [2, 3], "step": [2, 5, 20, 36, 76, 77, 78, 80, 84], "caution": [2, 4, 76], "previous": [2, 4, 10, 40, 54, 57, 68, 69, 70, 73, 74, 80, 84, 87], "assign": [2, 5, 15, 16, 18, 19, 20, 21, 22, 35, 36, 40, 57, 70, 73, 76, 77, 85, 86, 87, 89], "individu": [2, 10, 20, 26, 30, 45, 49, 52, 55, 57, 63, 65, 68, 71, 73, 76, 80, 81, 82, 87, 89], "still": [2, 29, 30, 40, 54, 76, 77, 83, 87], "extra": [2, 26, 30, 40, 44, 45, 46, 57, 74, 76, 77, 80, 83], "receiv": [2, 7, 26, 30, 46, 49, 50, 57, 59, 63, 71, 82], "overwritten": [2, 57], "callabl": [2, 3, 36, 39, 44, 49], "x_val": 2, "y_val": 2, "map": [2, 3, 9, 29, 30, 35, 39, 40, 53, 55, 57, 62, 69, 70, 71, 76, 77, 78, 81, 89], "appropri": [2, 7, 13, 47, 55, 70, 73, 81, 82], "earli": [2, 77], "stop": [2, 77], "x_valid": 2, "y_valid": 2, "could": [2, 18, 25, 40, 54, 70, 73, 77, 81, 85, 87, 89], "f": [2, 5, 69, 70, 73, 74, 75, 76, 77, 78, 80, 81, 83, 85, 87, 88], "ignor": [2, 26, 30, 39, 44, 57, 62, 66, 69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "allow": [2, 25, 26, 29, 30, 33, 40, 45, 53, 54, 57, 59, 61, 69, 76, 77, 84, 86, 88], "access": [2, 7, 10, 26, 30, 57, 71, 77, 81], "hyperparamet": [2, 49, 54, 77], "purpos": [2, 70, 71, 76, 81, 85], "want": [2, 4, 25, 29, 41, 45, 47, 57, 70, 74, 75, 77, 80, 82, 83, 84, 86, 88, 89], "explicitli": [2, 30, 57], "yourself": [2, 4, 29, 71], "altern": [2, 7, 36, 40, 44, 45, 55, 68, 69, 73, 74, 76, 77, 78, 80, 81, 83, 85, 88], "same": [2, 3, 4, 5, 7, 9, 11, 20, 26, 29, 30, 31, 40, 44, 45, 47, 54, 55, 57, 61, 62, 65, 66, 67, 70, 71, 73, 74, 76, 77, 82, 83, 84, 85, 86, 87, 88], "effect": [2, 7, 26, 30, 45, 54, 57, 73, 74, 76, 83], "offer": [2, 4, 69, 70, 71, 74, 76, 78, 81, 88], "after": [2, 3, 4, 10, 15, 16, 18, 19, 20, 21, 22, 26, 30, 40, 45, 57, 70, 74, 76, 77, 78, 80, 82, 83, 84, 85, 86, 88], "attribut": [2, 4, 5, 7, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 30, 36, 54, 57, 70, 87], "label_issues_df": [2, 57, 77], "similar": [2, 7, 25, 26, 30, 38, 40, 45, 49, 50, 52, 54, 57, 61, 65, 70, 71, 73, 74, 76, 77, 78, 82, 83, 86], "document": [2, 3, 4, 7, 11, 25, 26, 29, 30, 31, 36, 39, 44, 46, 47, 49, 52, 53, 54, 57, 61, 62, 63, 65, 68, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 87, 88, 89], "descript": [2, 4, 5, 7, 15, 16, 18, 19, 20, 21, 22, 23, 25, 40, 50, 57, 70, 71], "were": [2, 3, 4, 25, 30, 46, 52, 65, 69, 73, 76, 78, 80, 82, 84, 86, 87], "present": [2, 3, 4, 7, 9, 10, 16, 25, 40, 54, 62, 67, 73, 76, 77, 83], "actual": [2, 3, 4, 25, 45, 46, 55, 71, 76, 78, 89], "num_class": [2, 25, 29, 40, 44, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 83, 87, 88], "uniqu": [2, 40, 62, 70, 76, 81, 83], "given_label": [2, 4, 25, 34, 57, 62, 66, 69, 70, 71, 73, 74, 77, 78, 85, 86, 88, 89], "normal": [2, 3, 20, 31, 33, 36, 39, 40, 55, 76, 78, 83], "trick": [2, 76], "distribut": [2, 3, 4, 7, 13, 20, 22, 25, 30, 31, 35, 45, 53, 54, 55, 67, 70, 71, 73, 74, 77, 83], "account": [2, 25, 45, 49, 54, 55, 74, 76, 78, 80, 81, 83, 85, 88], "word": [2, 3, 39, 65, 66, 76], "remov": [2, 7, 25, 26, 30, 31, 57, 67, 74, 75, 76, 77, 83, 85, 87, 88], "so": [2, 3, 5, 7, 11, 20, 25, 26, 29, 30, 31, 40, 45, 46, 52, 55, 57, 61, 65, 69, 70, 71, 74, 77, 78, 83, 86], "proportion": [2, 7, 31], "just": [2, 3, 4, 7, 10, 25, 27, 29, 40, 44, 55, 57, 59, 67, 68, 69, 71, 73, 74, 76, 77, 78, 81, 82, 83, 84, 86, 87, 88], "procedur": 2, "get": [2, 3, 4, 10, 26, 27, 30, 31, 36, 39, 40, 45, 47, 49, 54, 55, 57, 58, 59, 67, 69, 74, 75, 76, 77, 78, 83, 84, 85, 87, 88], "detect": [2, 4, 5, 6, 10, 11, 13, 18, 22, 38, 48, 50, 51, 52, 53, 54, 55, 56, 57, 60, 64, 67, 70, 72, 77, 79, 81, 85, 86, 87, 88, 89], "arg": [2, 9, 18, 26, 27, 30, 36, 40, 55, 57], "kwarg": [2, 5, 7, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 30, 36, 44, 57, 59, 61, 63, 76], "test": [2, 7, 20, 30, 36, 44, 57, 67, 70, 71, 73, 74, 77, 84, 85, 87, 88, 89], "expect": [2, 3, 26, 30, 31, 36, 45, 54, 55, 57, 76, 78, 80, 81, 82, 85, 87, 88, 89], "class_predict": 2, "evalu": [2, 7, 26, 27, 29, 30, 57, 69, 70, 71, 76, 77, 78, 80, 84, 85, 86, 87, 88], "simpli": [2, 25, 55, 70, 71, 73, 74, 76, 78, 85, 86, 88, 89], "quantifi": [2, 4, 5, 7, 10, 31, 49, 54, 57, 67, 71, 73, 74, 77, 78, 82], "save_spac": [2, 7, 57], "potenti": [2, 7, 25, 31, 39, 47, 50, 55, 57, 59, 61, 68, 69, 70, 71, 73, 74, 75, 77, 78, 81, 82, 86, 87, 89], "cach": [2, 74, 83, 88], "panda": [2, 4, 5, 9, 15, 16, 18, 19, 20, 21, 22, 25, 40, 41, 44, 45, 46, 68, 69, 70, 71, 73, 74, 75, 78, 80, 85, 86, 87, 88], "unlik": [2, 31, 33, 36, 44, 46, 47, 49, 65, 70, 80, 81, 83, 85], "both": [2, 4, 7, 13, 20, 25, 26, 30, 31, 40, 45, 47, 55, 59, 61, 66, 67, 70, 76, 77, 78, 80, 89], "mask": [2, 29, 31, 39, 40, 47, 50, 55, 57, 59, 61, 62, 67, 75, 76, 80, 82, 86, 89], "prefer": [2, 55, 63], "plan": 2, "subsequ": [2, 3, 26, 30, 74, 76, 78, 82, 88], "invok": [2, 26, 30, 78, 84], "scratch": [2, 57], "To": [2, 4, 5, 6, 7, 8, 10, 13, 20, 24, 26, 29, 30, 31, 44, 45, 47, 49, 53, 54, 55, 57, 58, 59, 61, 67, 69, 70, 71, 73, 74, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "share": [2, 55, 57], "mostli": [2, 40, 52, 57], "longer": [2, 35, 39, 57, 68, 74, 76, 82, 88], "info": [2, 4, 5, 10, 15, 16, 18, 19, 20, 21, 22, 25, 46, 57, 65, 70, 71, 75, 76, 89], "about": [2, 3, 4, 5, 7, 10, 15, 16, 18, 19, 20, 21, 22, 23, 25, 27, 29, 33, 45, 46, 49, 57, 62, 65, 69, 70, 73, 74, 75, 76, 77, 78, 80, 83], "docstr": [2, 25, 26, 30, 40, 57, 75, 78], "unless": [2, 26, 30, 57, 76], "our": [2, 3, 7, 44, 45, 55, 57, 67, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "is_label_issu": [2, 57, 69, 70, 71, 73, 74, 77, 78, 85, 88], "entir": [2, 7, 20, 29, 31, 34, 46, 47, 52, 55, 57, 59, 61, 62, 67, 70, 71, 76, 82, 83, 84, 86, 89], "accur": [2, 3, 4, 7, 13, 25, 29, 31, 45, 46, 47, 50, 53, 55, 57, 58, 59, 61, 62, 68, 71, 73, 74, 76, 77, 80, 85], "label_qu": [2, 45, 57, 78, 80, 85, 88], "measur": [2, 25, 45, 46, 57, 67, 75, 76, 78, 80, 81, 86, 87, 89], "qualiti": [2, 3, 4, 5, 7, 10, 25, 29, 31, 33, 36, 45, 46, 47, 49, 50, 52, 55, 57, 58, 61, 63, 65, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 79, 85, 87, 88], "lower": [2, 4, 5, 7, 10, 22, 29, 36, 45, 46, 49, 52, 55, 57, 58, 61, 65, 69, 71, 73, 74, 77, 80, 81, 82, 83, 85, 86, 88, 89], "eas": 2, "comparison": [2, 26, 30, 78, 80, 85], "against": [2, 26, 30, 70, 73, 76, 80, 81], "predicted_label": [2, 4, 57, 62, 66, 69, 70, 71, 73, 74, 77, 78, 85, 86, 88], "ad": [2, 26, 30, 71, 80, 85], "precis": [2, 47, 50, 76, 78, 86, 89], "definit": [2, 5, 57, 73, 87], "accessor": [2, 57], "describ": [2, 7, 45, 54, 55, 57, 63, 65, 78, 80, 81, 82, 84, 89], "precomput": [2, 34, 57, 75], "clear": [2, 57, 74, 85, 88], "save": [2, 4, 13, 26, 29, 30, 53, 57, 76, 82, 86, 89], "space": [2, 7, 54, 57, 73, 75, 77], "place": [2, 26, 30, 40, 57, 80, 87], "larg": [2, 29, 57, 76, 83, 86, 89], "deploi": [2, 57, 76], "care": [2, 7, 26, 30, 57, 76, 78], "avail": [2, 4, 5, 9, 11, 23, 30, 57, 78, 80, 82, 85], "cannot": [2, 4, 9, 11, 40, 84, 89], "anymor": 2, "classmethod": [2, 15, 16, 18, 19, 20, 21, 22, 30, 36, 57], "__init_subclass__": [2, 30, 57], "set_": [2, 30, 57], "_request": [2, 30, 57], "pep": [2, 30, 57], "487": [2, 30, 57], "look": [2, 4, 5, 13, 26, 30, 40, 57, 62, 70, 71, 73, 76, 78, 80, 81, 82, 83, 86, 87, 89], "inform": [2, 4, 5, 7, 10, 13, 23, 26, 30, 40, 45, 46, 50, 53, 57, 62, 65, 66, 67, 69, 70, 73, 74, 78, 80, 83, 86, 89], "__metadata_request__": [2, 30, 57], "infer": [2, 30, 40, 57, 62, 66, 77, 80, 81, 85, 87, 88], "signatur": [2, 26, 30, 57], "accept": [2, 26, 30, 55, 57, 70, 71], "metadata": [2, 30, 57, 89], "through": [2, 4, 5, 30, 57, 69, 71, 74, 75, 80, 83, 85, 88], "develop": [2, 6, 30, 57, 76, 78, 89], "request": [2, 30, 57, 71, 74, 75, 81, 87, 88, 89], "those": [2, 3, 7, 29, 30, 31, 44, 45, 47, 57, 61, 65, 66, 67, 69, 76, 77, 82, 86], "http": [2, 4, 5, 6, 7, 8, 24, 26, 27, 29, 30, 33, 40, 54, 57, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "www": [2, 30, 57, 83], "org": [2, 26, 27, 30, 40, 54, 57, 76, 78, 89], "dev": [2, 30, 57], "0487": [2, 30, 57], "get_metadata_rout": [2, 30, 57], "rout": [2, 30, 57], "pleas": [2, 26, 30, 44, 57, 67, 69, 70, 71, 74, 75, 76, 77, 78, 80, 81, 83, 85, 88, 89], "guid": [2, 5, 30, 57, 68, 77], "mechan": [2, 26, 30, 57], "metadatarequest": [2, 30, 57], "encapsul": [2, 13, 30, 52, 57], "get_param": [2, 30, 44, 57], "subobject": [2, 30, 57], "param": [2, 7, 26, 30, 44, 54, 57], "name": [2, 4, 5, 7, 9, 10, 25, 26, 30, 35, 36, 40, 44, 45, 46, 53, 57, 62, 66, 69, 71, 74, 75, 77, 78, 81, 86, 88, 89], "set_fit_request": [2, 30, 57], "union": [2, 3, 4, 9, 29, 30, 36, 40, 41, 47, 53, 57, 61, 65], "str": [2, 3, 4, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 34, 36, 39, 40, 44, 45, 46, 50, 52, 53, 55, 57, 62, 66, 69, 70, 80, 81, 89], "unchang": [2, 26, 30, 57, 89], "relev": [2, 13, 20, 30, 57, 77], "enable_metadata_rout": [2, 30, 57], "set_config": [2, 30, 57], "meta": [2, 30, 57], "rais": [2, 4, 9, 10, 26, 30, 33, 36, 57, 76], "alia": [2, 26, 30, 57], "metadata_rout": [2, 30, 57], "retain": [2, 30, 40, 57], "chang": [2, 26, 29, 30, 33, 57, 65, 69, 70, 74, 76, 82, 83, 88, 89], "version": [2, 4, 5, 6, 7, 8, 12, 17, 24, 26, 28, 30, 32, 33, 40, 43, 44, 55, 57, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 87, 88, 89], "sub": [2, 30, 52, 57], "pipelin": [2, 30, 57], "otherwis": [2, 7, 25, 26, 29, 30, 31, 37, 39, 40, 47, 54, 57, 59, 61, 62, 66, 74, 76, 88], "updat": [2, 10, 26, 29, 30, 57, 68, 70, 77], "set_param": [2, 30, 44, 57], "simpl": [2, 26, 30, 31, 45, 55, 57, 70, 71, 73, 74, 77, 80, 83, 85, 87, 88], "well": [2, 3, 7, 26, 30, 33, 34, 45, 47, 55, 57, 62, 65, 66, 68, 70, 71, 73, 74, 76, 77, 78, 80, 82, 83], "nest": [2, 26, 30, 57, 63, 65, 66, 89], "latter": [2, 26, 30, 57, 83], "compon": [2, 30, 57], "__": [2, 30, 57], "set_score_request": [2, 57], "structur": [3, 54, 73, 87], "unobserv": 3, "less": [3, 4, 7, 29, 36, 45, 54, 55, 59, 61, 65, 73, 75, 76, 77, 78, 82, 89], "channel": [3, 69, 78], "character": 3, "flip": 3, "nm": 3, "invers": [3, 7, 25, 34, 40, 46, 71, 75, 88], "inv": 3, "confident_joint": [3, 18, 25, 31, 40, 46, 47, 68, 76, 78], "un": 3, "under": [3, 7, 26, 30, 46, 53, 54, 83], "joint": [3, 25, 31, 34, 40, 46, 47, 75], "num_label_issu": [3, 29, 31, 47, 62, 66, 68], "estimation_method": [3, 29], "off_diagon": 3, "multi_label": [3, 25, 31, 40, 41, 47, 81], "don": [3, 67, 71, 78, 82], "statis": 3, "compute_confident_joint": [3, 25, 31, 40, 47, 78], "off": [3, 31, 40, 52, 77, 78, 82, 83], "j": [3, 4, 13, 25, 26, 30, 31, 47, 50, 53, 54, 63, 65, 66, 70, 71, 78, 86, 89], "confident_learn": [3, 31, 47, 78], "off_diagonal_calibr": 3, "calibr": [3, 31, 40, 45, 80], "cj": [3, 34, 40], "axi": [3, 34, 36, 59, 62, 69, 70, 71, 76, 77, 78, 80, 81, 83, 85, 86], "bincount": [3, 70, 71, 78, 80, 81], "alwai": [3, 7, 26, 30, 40, 69, 78, 85, 87, 88], "estimate_issu": 3, "over": [3, 7, 26, 29, 30, 52, 53, 59, 61, 71, 73, 75, 76, 77, 78, 83, 85, 87], "As": [3, 5, 67, 70, 71, 74, 78, 85, 89], "add": [3, 4, 5, 10, 26, 30, 44, 53, 69, 70, 71, 74, 76, 77, 78, 81, 88], "approach": [3, 25, 29, 31, 73, 78, 81, 83, 85, 87], "custom": [3, 5, 8, 26, 29, 30, 36, 39, 55, 74, 78, 88], "know": [3, 70, 71, 76, 78, 80], "cut": [3, 52, 67, 78], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 83, 89], "underestim": 3, "few": [3, 53, 67, 76, 80, 81, 82, 83, 89], "4": [3, 4, 15, 16, 18, 19, 20, 21, 22, 35, 36, 39, 49, 50, 52, 53, 55, 58, 65, 75, 76, 81, 86, 89], "detail": [3, 11, 25, 26, 30, 36, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 61, 62, 63, 67, 68, 69, 81, 83, 89], "num_issu": [3, 5, 29, 69, 70, 71, 73, 74, 77, 78], "calibrate_confident_joint": 3, "up": [3, 14, 20, 21, 31, 36, 45, 75, 76, 82, 85, 88, 89], "p_": [3, 25, 31], "pair": [3, 7, 25, 31, 78], "v": [3, 7, 29, 46, 47, 49, 55, 70, 71, 81, 83, 84], "rest": [3, 4, 5, 6, 7, 8, 24, 46, 47, 49, 57, 70, 71, 73, 74, 77, 78, 80, 85, 87, 88], "fashion": [3, 59, 87], "2x2": 3, "incorrectli": [3, 25, 46, 47, 50, 73, 89], "calibrated_cj": 3, "c": [3, 7, 39, 47, 55, 67, 69, 70, 71, 73, 74, 76, 78, 81, 83, 84, 85, 87], "whose": [3, 4, 7, 13, 22, 26, 30, 34, 39, 45, 49, 52, 58, 61, 65, 66, 69, 70, 71, 73, 74, 76, 77, 78, 81, 82, 83, 86, 89], "truli": [3, 83, 86], "estimate_joint": [3, 25, 78], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 47, 78, 82, 84, 86, 89], "return_indices_of_off_diagon": 3, "frequenc": [3, 20, 45, 46, 53, 62, 83], "done": [3, 7, 57, 70, 76, 78, 81, 83, 84], "overfit": [3, 7, 50, 53, 69, 70, 71, 73, 74, 77, 84, 87], "classifict": 3, "singl": [3, 4, 20, 25, 26, 30, 36, 37, 40, 45, 46, 52, 53, 54, 55, 65, 69, 70, 76, 78, 81, 82, 87], "baselin": [3, 26, 31, 83, 85, 88], "proxi": 3, "tupl": [3, 26, 30, 34, 35, 37, 39, 40, 45, 47, 53, 61, 63, 65, 66, 69, 89], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 29, 34, 45, 59, 61, 67, 76, 77, 86, 88], "practic": [3, 71, 77, 78, 83, 85, 87, 88], "complet": [3, 69, 70, 71, 73, 74, 77, 78, 82], "gist": 3, "cj_ish": 3, "guess": [3, 34, 78, 80], "8": [3, 4, 5, 35, 36, 37, 39, 49, 63, 65, 69, 70, 71, 73, 74, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "parallel": [3, 31, 63, 75], "again": [3, 44, 83, 87], "simplifi": [3, 11], "understand": [3, 6, 25, 46, 53, 71, 78, 85, 86, 89], "100": [3, 26, 30, 55, 70, 71, 73, 75, 76, 77, 78, 81, 86, 87, 88, 89], "optim": [3, 26, 27, 30, 44, 77, 80], "speed": [3, 31, 75, 76, 85, 88], "dtype": [3, 19, 20, 26, 30, 39, 40, 49, 65, 69, 82], "enumer": [3, 26, 30, 69, 70, 71, 77, 89], "s_label": 3, "confident_bin": 3, "6": [3, 4, 30, 36, 40, 65, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "num_confident_bin": 3, "argmax": [3, 31, 55, 59, 62, 69, 76, 78, 83, 86], "elif": 3, "estimate_lat": 3, "py_method": [3, 34], "cnt": [3, 34], "1d": [3, 29, 31, 36, 37, 40, 41, 49, 58, 69, 87], "eqn": [3, 34], "margin": [3, 31, 34, 36, 55], "marginal_p": [3, 34], "shorthand": [3, 10], "proport": [3, 7, 25, 46, 78, 84], "poorli": [3, 34, 87], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 78], "variabl": [3, 5, 11, 40, 57, 58, 69, 70, 73, 78, 81, 85], "exact": [3, 34, 70, 71, 73, 77, 87], "within": [3, 4, 12, 26, 27, 30, 32, 47, 52, 61, 63, 65, 70, 71, 77, 82, 86], "percent": 3, "often": [3, 25, 34, 46, 76, 78, 84, 86], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 7, 40, 41, 53, 69, 70, 73, 74, 76, 77, 82, 83, 88], "wai": [3, 4, 44, 67, 68, 69, 70, 71, 73, 74, 76, 78, 80, 81, 82, 84, 87, 88], "pro": 3, "con": 3, "pred_proba": [3, 84], "combin": [3, 25, 70, 75, 76, 77, 78, 84, 85], "becaus": [3, 34, 40, 52, 76, 78, 80, 82], "littl": [3, 29, 75, 82, 89], "uniform": [3, 55, 75, 76, 78], "20": [3, 5, 66, 69, 71, 75, 77, 78, 86, 89], "Such": [3, 77, 83], "bound": [3, 19, 26, 30, 50, 52, 53, 82], "reason": [3, 18, 26, 30], "comment": [3, 39, 89], "end": [3, 26, 30, 53], "file": [3, 4, 9, 28, 29, 43, 53, 69, 70, 73, 74, 75, 76, 82, 83, 86, 87, 89], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 78], "handl": [3, 4, 5, 7, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 30, 68, 70, 71, 78, 86, 87, 89], "five": [3, 50, 53, 78, 82], "estimate_cv_predicted_prob": [3, 78], "estimate_noise_matric": 3, "get_confident_threshold": [3, 29], "amongst": [3, 7], "confident_threshold": [3, 7, 18, 29, 54], "unifi": 4, "audit": [4, 6, 9, 10, 13, 69, 72, 73, 74, 77, 78, 82], "kind": [4, 5, 69, 70, 73, 74, 75, 77, 78], "addit": [4, 5, 6, 7, 8, 10, 23, 24, 26, 30, 36, 41, 45, 63, 69, 70, 73, 74, 77, 78, 80, 83, 84, 87, 88], "depend": [4, 5, 6, 7, 8, 9, 10, 24, 28, 31, 33, 40, 43, 47, 54, 57, 58, 67], "instal": [4, 5, 6, 7, 8, 24, 26, 28, 29, 30, 31, 43, 44, 59, 61], "pip": [4, 5, 6, 8, 24, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "development": [4, 5, 6, 8, 24], "git": [4, 5, 6, 8, 24, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 87, 88], "github": [4, 5, 6, 8, 24, 26, 27, 40, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 87, 88], "com": [4, 5, 6, 8, 24, 26, 27, 29, 33, 40, 54, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "egg": [4, 5, 6, 8, 24, 67, 75], "label_nam": [4, 5, 7, 9, 67, 69, 70, 71, 73, 74, 77, 78], "image_kei": [4, 77], "interfac": [4, 67, 76, 78], "librari": [4, 7, 30, 50, 53, 54, 67, 70, 74, 75, 76, 88], "goal": 4, "track": [4, 10, 11, 67, 70, 75, 76, 78], "intermedi": [4, 6, 71], "statist": [4, 7, 10, 18, 20, 25, 45, 46, 71, 73, 74, 77, 78], "convert": [4, 9, 26, 30, 37, 41, 45, 52, 61, 65, 68, 69, 74, 75, 77, 80, 81, 82, 88], "hug": [4, 9, 77], "face": [4, 9, 13, 75, 77, 81], "kei": [4, 5, 7, 9, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 30, 36, 45, 46, 52, 54, 70, 71, 76, 77, 78, 80, 82], "string": [4, 7, 9, 11, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 30, 40, 45, 46, 58, 62, 65, 66, 73, 74, 76, 80, 81, 88, 89], "dictionari": [4, 5, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 30, 35, 40, 45, 46, 49, 50, 52, 53, 70, 71, 73, 74, 78, 80, 81, 82], "path": [4, 9, 26, 29, 30, 53, 69, 70, 76, 82], "local": [4, 9, 26, 27, 30, 69, 70, 71, 75, 76, 77, 78, 80, 81, 83, 85, 89], "text": [4, 5, 7, 9, 15, 16, 18, 19, 20, 21, 22, 36, 54, 63, 65, 66, 67, 70, 71, 72, 75, 76, 78, 79, 80, 83], "txt": [4, 9, 89], "csv": [4, 9, 73, 74, 85, 87, 88], "json": [4, 9], "hub": [4, 9, 83], "regress": [4, 9, 11, 23, 70, 71, 74, 79, 80, 83, 88], "imag": [4, 6, 25, 30, 50, 52, 53, 54, 59, 61, 62, 67, 70, 71, 75, 76, 79, 80, 81, 82, 84, 86], "point": [4, 5, 20, 26, 30, 70, 71, 76, 78, 80], "field": [4, 7, 26, 30], "themselv": [4, 85, 87, 88], "cleanvis": [4, 7], "level": [4, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 39, 63, 65, 71, 77, 79, 86], "load_dataset": [4, 9, 77], "glue": 4, "sst2": 4, "properti": [4, 9, 10], "has_label": [4, 9], "class_nam": [4, 9, 25, 46, 53, 62, 66, 67, 75, 78, 82, 86, 89], "empti": [4, 9, 34, 45, 71, 81], "find_issu": [4, 5, 7, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 67, 69, 70, 71, 73, 74, 77, 78], "knn_graph": [4, 7, 13, 15, 20, 22, 73], "issue_typ": [4, 5, 7, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 69, 70, 71, 73, 74, 77, 78], "sort": [4, 13, 29, 31, 36, 38, 45, 47, 50, 52, 53, 55, 61, 63, 65, 69, 71, 73, 74, 76, 77, 78, 80, 81, 82, 85, 86, 87, 88, 89], "common": [4, 10, 13, 71, 72, 75, 76, 78, 81, 82, 86], "real": [4, 13, 67, 70, 71, 76, 78, 80, 81, 85, 86], "world": [4, 13, 67, 70, 71, 76, 78, 80, 85, 86], "interact": [4, 13, 76], "embed": [4, 7, 13, 54, 67, 69, 70, 71, 73, 74, 78, 88], "thereof": [4, 13], "insight": [4, 13, 80], "act": [4, 7, 52, 70], "issuefind": [4, 13, 23], "logic": [4, 11, 29, 31, 59, 61, 86], "best": [4, 13, 35, 45, 55, 70, 71, 73, 76, 80, 81, 83, 85, 87, 88, 89], "2d": [4, 13, 29, 36, 37, 39, 40, 45, 69, 81, 87], "num_exampl": [4, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 46, 69, 70, 71, 73, 74, 77, 78], "represent": [4, 7, 13, 26, 30, 37, 47, 67, 69, 70, 71, 74, 76, 77, 78, 83, 88], "num_featur": [4, 13, 26, 30, 44], "distanc": [4, 7, 13, 20, 22, 38, 52, 54, 73, 83], "nearest": [4, 7, 13, 19, 20, 22, 38, 54, 71, 74, 83], "neighbor": [4, 7, 13, 19, 20, 22, 38, 54, 70, 71, 73, 74, 77, 83], "graph": [4, 7, 10, 13, 20], "squar": [4, 13, 40, 57, 75, 85], "csr": [4, 13], "evenli": [4, 13], "omit": [4, 13, 52, 53, 77, 82], "itself": [4, 13, 26, 30, 82], "duplic": [4, 6, 13, 17, 18, 26, 30, 67, 70, 78], "explicit": [4, 13], "precend": [4, 13], "construct": [4, 5, 7, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 30, 36, 44], "neither": [4, 7, 11, 13, 82], "nor": [4, 7, 11, 13], "collect": [4, 7, 10, 13, 15, 16, 18, 19, 20, 21, 22, 45, 80, 89], "unspecifi": [4, 13, 31, 47], "interest": [4, 13, 18, 62, 66, 74, 78, 86, 87, 88, 89], "constructor": [4, 7, 13, 19], "issuemanag": [4, 6, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23], "respons": [4, 13, 18, 57, 58, 75, 85, 89], "random_st": [4, 69, 70, 71, 77, 78, 81, 83, 87], "lab": [4, 15, 16, 18, 19, 20, 21, 22, 29, 67, 69, 70, 71, 73, 74, 75, 77, 78, 81], "nearestneighbor": [4, 7, 54, 73, 83], "comprehens": [4, 67, 77], "nbr": 4, "n_neighbor": [4, 7, 54], "metric": [4, 7, 15, 20, 40, 44, 54, 69, 73, 74, 77, 78, 85, 87, 88], "euclidean": [4, 7, 52, 54, 73], "kneighbors_graph": [4, 73], "mode": [4, 26, 29, 30, 73, 83], "4x4": 4, "float64": [4, 20, 26, 30, 65], "compress": [4, 7, 40, 59, 61], "toarrai": 4, "NOT": [4, 29, 74], "23606798": 4, "41421356": 4, "configur": [4, 13, 36], "suppos": [4, 50, 83, 85, 87, 88], "who": [4, 52, 73, 78, 87, 89], "manag": [4, 6, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 70], "clean_learning_kwarg": [4, 7, 19], "labelissuemanag": [4, 7, 19], "prune_method": [4, 68], "prune_by_noise_r": [4, 31, 47, 78], "report": [4, 5, 8, 12, 15, 16, 18, 19, 20, 21, 22, 25, 46, 66, 67, 69, 70, 71, 73, 74, 78, 89], "include_descript": [4, 15, 16, 18, 19, 20, 21, 22, 23], "show_summary_scor": [4, 23], "summari": [4, 5, 10, 15, 16, 18, 19, 20, 21, 22, 25, 44, 46, 51, 60, 61, 63, 64, 65, 68, 69, 70, 71, 73, 74, 75, 77, 78, 82, 86, 89], "show": [4, 20, 26, 30, 35, 40, 53, 62, 66, 71, 73, 74, 75, 76, 77, 78, 80, 83, 85, 86, 87, 89], "top": [4, 25, 29, 31, 40, 47, 50, 53, 55, 62, 66, 67, 69, 70, 71, 73, 74, 75, 76, 78, 82, 83, 85, 88, 89], "suffer": [4, 7, 10, 18, 47, 55, 66, 89], "onc": [4, 18, 25, 26, 30, 70, 76, 78, 81, 82, 87], "familiar": 4, "usag": [4, 29, 44], "found": [4, 5, 7, 10, 11, 15, 16, 18, 19, 20, 21, 22, 26, 30, 40, 67, 69, 70, 71, 73, 74, 76, 77, 83, 85, 87, 88, 89], "issue_summari": [4, 7, 10, 70], "overal": [4, 5, 7, 10, 15, 16, 18, 19, 20, 21, 22, 25, 36, 45, 46, 49, 52, 53, 57, 61, 62, 63, 65, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 82, 89], "sever": [4, 5, 7, 9, 10, 18, 26, 29, 30, 31, 49, 52, 54, 55, 61, 65, 67, 69, 70, 71, 73, 74, 75, 76, 78, 82, 83, 87, 88, 89], "dataissu": [4, 10, 13, 23], "outlier": [4, 6, 11, 17, 18, 32, 55, 67, 70, 71, 78, 79], "someth": [4, 5, 26, 30, 55], "123": [4, 70, 71], "456": [4, 69, 87, 88], "nearest_neighbor": 4, "7": [4, 36, 37, 44, 63, 65, 69, 70, 71, 73, 74, 75, 76, 80, 81, 82, 83, 85, 86, 87, 88, 89], "9": [4, 15, 16, 18, 19, 20, 21, 22, 36, 37, 49, 63, 65, 69, 70, 71, 73, 74, 75, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "distance_to_nearest_neighbor": [4, 70, 71, 73, 74, 77, 78], "789": 4, "get_issu": [4, 7, 10, 69, 71, 73, 74, 77], "issue_nam": [4, 5, 7, 10, 11, 15, 16, 18, 19, 20, 21, 22, 70, 71], "focu": [4, 10, 74, 86, 89], "full": [4, 7, 10, 29, 53, 77, 89], "summar": [4, 10, 15, 16, 18, 19, 20, 21, 22, 25, 46, 62, 66, 67, 86], "valueerror": [4, 9, 10, 33, 36, 76], "specific_issu": [4, 10], "exhibit": [4, 7, 10, 62, 71, 73, 74, 77, 78, 82], "lie": [4, 7, 38, 54, 55, 69, 70, 71, 73, 74, 77, 78, 88], "directli": [4, 11, 13, 23, 29, 44, 45, 71, 74, 81, 82, 85, 88], "compar": [4, 45, 54, 65, 70, 71, 73, 78], "get_issue_summari": [4, 10, 71], "get_info": [4, 10, 71], "yet": [4, 14, 17, 21, 75, 80], "list_possible_issue_typ": [4, 11], "regist": [4, 5, 11, 12, 14, 21, 26, 30, 70], "registri": [4, 11], "list_default_issue_typ": [4, 11], "folder": [4, 69, 70, 77], "load": [4, 9, 29, 53, 75, 76, 77, 78, 82, 83, 86, 89], "futur": [4, 7, 18, 26, 30, 45, 67, 70], "overwrit": [4, 70], "separ": [4, 25, 36, 49, 70, 71, 76, 77, 82, 84], "static": 4, "rememb": [4, 76, 78], "part": [4, 7, 26, 30, 31, 50, 52, 53, 69, 70, 75, 86, 89], "ident": [4, 7, 18, 40], "walk": 5, "alongsid": [5, 26, 30, 70, 76], "pre": [5, 7, 26, 30, 70, 71], "runtim": [5, 26, 29, 30, 57, 59, 61, 69, 76, 77], "issue_manager_factori": [5, 11, 70], "myissuemanag": [5, 11], "decor": [5, 11], "start": [5, 26, 27, 30, 67, 73, 81, 89], "ll": [5, 36, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 88, 89], "thing": [5, 30, 78, 85, 88], "next": [5, 45, 67, 69, 73, 74, 80, 82, 85, 87, 88, 89], "dummi": 5, "randint": [5, 36, 70, 71], "mark": [5, 68, 82, 83, 85], "regard": [5, 71, 78], "rand": [5, 36, 70, 71], "is_": [5, 7, 70], "_issu": [5, 7, 70], "issue_score_kei": [5, 15, 16, 18, 19, 20, 21, 22, 70], "whole": [5, 20, 26, 30, 71], "make_summari": [5, 15, 16, 18, 19, 20, 21, 22, 70], "popul": 5, "verbosity_level": [5, 15, 16, 18, 19, 20, 21, 22], "std": 5, "raw_scor": 5, "bit": 5, "involv": [5, 29, 62, 66, 76, 81], "intermediate_arg": 5, "min": [5, 36, 52, 65, 70, 76, 83], "sin_filt": 5, "sin": 5, "arang": 5, "kernel": 5, "wip": 5, "progress": 5, "issue_manag": [5, 7, 8, 10, 12, 15, 16, 19, 20, 21, 22, 70], "instanti": [5, 13, 29, 44, 54, 69, 71, 73, 88], "477762": 5, "286455": 5, "term": [5, 7, 34, 40, 69, 70, 71, 73, 74, 77, 78], "4778": 5, "is_basic_issu": 5, "basic_scor": 5, "13": [5, 15, 22, 69, 70, 71, 73, 74, 75, 77, 78, 80, 82, 83, 85, 86, 87, 88, 89], "003042": 5, "058117": 5, "11": [5, 44, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 86, 87, 88, 89], "121908": 5, "15": [5, 38, 57, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 86, 87, 88, 89], "169312": 5, "17": [5, 69, 73, 74, 75, 77, 78, 80, 82, 83, 85, 86, 88, 89], "229044": 5, "2865": 5, "is_intermediate_issu": 5, "intermediate_scor": 5, "000000": [5, 70, 75, 78], "007059": 5, "009967": 5, "010995": 5, "087332": 5, "016296": 5, "03947": 5, "019459": 5, "794251": 5, "search": [6, 7, 16, 20, 21, 39, 57, 76, 84], "nondefault": 6, "Near": 6, "iid": [6, 20, 71, 73, 74, 77, 78], "imbal": [6, 17, 49, 54, 55], "togeth": [6, 7, 34, 70, 71, 73, 74, 77, 78, 85, 88, 89], "built": [6, 36], "own": [6, 26, 28, 30, 43, 49, 50, 53, 59, 63, 69, 71, 73, 74, 77, 80, 81, 85, 86, 87, 88, 89], "prerequisit": 6, "basic": [6, 30, 44, 83], "page": [7, 71, 76, 78], "variou": [7, 10, 28, 41, 43, 67, 70, 71, 73, 74, 75, 78, 80, 82, 87], "sai": [7, 26, 30, 81, 86], "why": [7, 74], "matter": [7, 25, 46], "three": [7, 25, 45, 46, 57, 62, 69, 70, 71, 73, 75, 78, 80, 84, 85, 86, 87, 89], "_score": 7, "flag": [7, 18, 20, 31, 36, 46, 47, 50, 57, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 83, 85, 86, 88], "badli": [7, 52, 89], "code": [7, 26, 30, 34, 40, 44, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "issue_scor": 7, "outlier_scor": [7, 22, 70, 71, 73, 74, 77, 78, 83], "atyp": [7, 54, 70, 71, 73, 74, 77, 78, 83], "datapoint": [7, 31, 36, 40, 55, 58, 67, 69, 70, 71, 73, 74, 76, 84, 85, 87, 88], "is_issu": [7, 18], "is_outlier_issu": [7, 70, 71, 73, 74, 77, 78], "annot": [7, 25, 35, 45, 46, 47, 49, 50, 52, 53, 62, 65, 66, 67, 69, 70, 71, 73, 74, 76, 77, 78, 79, 82, 86], "transform": [7, 36, 38, 40, 54, 55, 71, 74, 77, 83, 87, 88, 89], "dissimilar": [7, 73, 74], "preced": 7, "cosin": [7, 54, 83], "incorrect": [7, 52, 55, 58, 69, 70, 71, 73, 74, 77, 78, 82, 85, 87], "due": [7, 29, 31, 55, 59, 61, 69, 70, 71, 73, 74, 77, 78], "appear": [7, 25, 35, 46, 47, 50, 58, 71, 73, 74, 77, 85, 86], "likelihood": [7, 29, 31, 47, 52, 54, 55, 59, 63], "now": [7, 29, 68, 69, 71, 80, 82, 83, 85, 87, 88, 89], "u": [7, 69, 70, 73, 76, 77, 78, 80, 81, 84, 85, 86, 87, 88, 89], "token": [7, 39, 61, 62, 63, 64, 65, 66, 76, 78, 79], "etc": [7, 18, 26, 30, 34, 44, 45, 63, 67, 70, 71, 73, 74, 76, 77, 78], "calcul": [7, 20, 29, 36, 45, 49, 50, 52, 54, 57, 61, 75, 77], "hamper": [7, 75, 77], "analyt": [7, 67, 80], "lead": [7, 52, 55, 77, 82], "draw": [7, 70, 71], "conclus": 7, "try": [7, 29, 31, 44, 45, 59, 61, 67, 71, 76, 78, 86], "veri": [7, 25, 46, 50, 52, 70, 71, 73, 74, 77, 78, 80, 83, 85, 88], "rare": [7, 31, 53, 70, 71, 73, 74, 76, 77, 78], "anomal": [7, 55, 70, 71, 73, 74, 77, 78], "articl": [7, 29, 76], "ai": [7, 67, 69, 70, 71, 73, 74, 75, 76, 77, 79, 80, 81, 83, 85, 87, 88, 89], "blog": 7, "unexpect": [7, 26, 30], "consequ": 7, "inspect": [7, 69, 71, 77, 78, 82, 85, 88], "neg": [7, 52, 70, 71, 75], "affect": [7, 26, 30, 59, 65, 74, 76], "extrem": [7, 70, 71, 73, 74, 76, 77, 78], "rel": [7, 25, 45, 46, 54, 70, 71, 73, 74, 77, 78, 83], "record": [7, 26, 30, 69, 73, 85], "abbrevi": 7, "misspel": 7, "typo": [7, 66], "resolut": 7, "video": [7, 75], "audio": [7, 70, 71, 76, 79], "minor": [7, 39], "variat": 7, "translat": 7, "d": [7, 38, 73, 74, 78, 81, 87, 89], "constant": [7, 57], "median": 7, "question": [7, 18, 67, 78], "nearli": [7, 18, 71, 73, 74, 77], "awar": [7, 68, 78], "presenc": [7, 78], "signific": [7, 71, 73, 74, 77, 78], "violat": [7, 71, 73, 74, 77, 78], "assumpt": [7, 71, 73, 74, 77, 78], "changepoint": [7, 71, 73, 74, 77, 78], "shift": [7, 71, 73, 74, 77, 78], "drift": [7, 71, 73, 74, 77, 78], "autocorrel": [7, 71, 73, 74, 77, 78], "almost": [7, 71, 73, 74, 77, 78], "adjac": [7, 71, 73, 74, 77, 78], "tend": [7, 25, 34, 71, 73, 74, 77, 78, 86, 89], "sequenti": [7, 26, 30, 44, 77], "gap": 7, "group": [7, 20, 75, 82, 89], "b": [7, 15, 16, 18, 19, 20, 21, 22, 25, 39, 40, 65, 73, 74, 75, 78, 84, 87, 89], "x1": [7, 50, 53, 82], "x2": [7, 50, 53, 82], "10th": 7, "100th": 7, "90": [7, 65, 73, 78, 84, 87], "similarli": [7, 26, 30, 70, 73, 76, 77, 82], "math": [7, 77], "behind": [7, 54, 78], "fundament": 7, "proper": [7, 40, 45, 50, 53, 74, 77, 80, 82, 87], "closer": [7, 52, 82], "scenario": [7, 55, 70, 71], "underli": [7, 54, 63, 65, 89], "stem": [7, 54, 83], "evolv": 7, "influenc": 7, "accordingli": 7, "emploi": [7, 81, 83], "partit": [7, 84], "ahead": 7, "good": [7, 26, 30, 44, 46, 52, 55, 59, 61, 62, 67, 77], "fix": [7, 45, 74, 78, 85, 88], "problem": [7, 29, 36, 62, 67, 70, 71, 74, 76, 77], "deploy": [7, 78, 85, 87, 88], "overlook": [7, 52, 82], "fact": 7, "thu": [7, 25, 30, 46, 69, 73, 74, 78, 84, 87, 89], "diagnos": [7, 71, 76], "rarest": 7, "q": [7, 82], "fall": [7, 52, 61, 65, 78, 83], "subpar": 7, "special": [7, 39], "techniqu": 7, "smote": 7, "asymmetr": [7, 25], "properli": [7, 29, 35, 40, 41, 59, 76, 81, 83, 85, 86], "too": [7, 31, 36, 54, 76, 77, 82], "dark": [7, 86], "bright": [7, 89], "blurri": [7, 77], "abnorm": [7, 53, 77], "exert": 7, "possible_issue_typ": 7, "label_kwarg": 7, "outlier_kwarg": 7, "near_dupl": [7, 11, 15, 70, 71, 73, 74, 77, 78], "near_duplicate_kwarg": 7, "non_iid": [7, 11, 20, 71, 73, 74, 77, 78], "non_iid_kwarg": 7, "health_summary_paramet": [7, 19], "health_summari": [7, 19, 25, 67, 75], "health_summary_kwarg": 7, "tandem": [7, 75], "view": [7, 26, 30, 31, 61, 63, 65, 67, 69, 70, 71, 73, 74, 75, 78, 80, 81, 82, 83, 84, 85, 87, 88, 89], "sensit": 7, "ood_kwarg": 7, "outofdistribut": [7, 22, 54, 83], "outsid": 7, "knn": [7, 10, 20, 54, 73, 83], "outlierissuemanag": [7, 11, 22, 70], "nearduplicateissuemanag": [7, 11, 15], "noniidissuemanag": [7, 11, 20], "num_permut": [7, 20], "permut": [7, 20], "significance_threshold": [7, 20], "signic": 7, "noniid": [7, 17], "class_imbalance_kwarg": 7, "classimbalanceissuemanag": [7, 16], "data_issu": [8, 12, 13, 23, 70], "issue_find": [8, 12], "factori": [8, 12, 13], "datalab": [9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 67, 69, 77, 80, 87, 88], "except": [9, 44, 55, 70, 71, 77, 80], "dataformaterror": 9, "with_traceback": 9, "tb": 9, "__traceback__": 9, "datasetdicterror": 9, "datasetdict": 9, "usual": [9, 23, 77, 80, 85], "datasetloaderror": 9, "dataset_typ": 9, "fail": 9, "map_to_int": 9, "hold": 9, "is_avail": [9, 77], "serv": [10, 13, 80], "central": [10, 89], "repositori": 10, "strategi": [10, 36], "being": [10, 25, 26, 30, 31, 36, 39, 40, 55, 73, 78, 85, 86, 87], "_infostrategi": 10, "basi": 10, "collect_statist": 10, "reus": [10, 18], "avoid": [10, 26, 29, 30, 31, 38, 40, 47, 50, 53, 57, 59, 61, 70, 71, 76], "recomput": [10, 88], "weighted_knn_graph": 10, "issue_manager_that_computes_knn_graph": 10, "collect_issues_from_issue_manag": 10, "collect_issues_from_imagelab": 10, "imagelab": 10, "set_health_scor": 10, "health": [10, 19, 25, 46, 67], "get_data_statist": 10, "concret": 11, "subclass": [11, 26, 30, 54, 70], "my_issu": 11, "stabl": [12, 17, 28, 32, 40, 43, 54, 68], "unregist": 12, "instati": 13, "public": [13, 78, 82, 86, 89], "creation": [13, 30], "execut": [13, 26, 30, 70, 76, 82], "coordin": [13, 50, 52, 53, 82, 89], "behavior": [13, 25, 26, 30], "At": [13, 76], "associ": [13, 26, 30, 53, 80], "get_available_issue_typ": 13, "isn": [14, 21], "direct": [14, 21, 26, 30], "10": [15, 19, 20, 26, 27, 53, 54, 55, 66, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "_": [15, 18, 19, 20, 21, 36, 39, 40, 69, 70, 75, 77, 78, 81, 87], "classvar": [15, 16, 18, 19, 20, 21, 22], "short": [15, 16, 18, 19, 20, 21, 22, 39, 40], "item": [15, 16, 18, 19, 20, 21, 22, 40, 70, 71, 77, 78, 80, 81], "some_info_kei": [15, 16, 18, 19, 20, 21, 22], "additional_info_kei": [15, 16, 18, 19, 20, 21, 22], "near_duplicate_set": [15, 70, 71, 73, 74, 77, 78], "occurr": [15, 16, 18, 20, 21, 22, 39], "collect_info": [15, 16, 18, 19, 20, 21, 22], "near_duplicate_scor": [15, 70, 71, 73, 74, 77, 78], "info_to_omit": [15, 16, 18, 19, 20, 21, 22], "compos": [15, 16, 18, 19, 20, 21, 22, 26, 30, 74, 83, 88], "is_x_issu": [15, 16, 18, 19, 20, 21, 22], "x_score": [15, 16, 18, 19, 20, 21, 22], "val_a": [15, 16, 18, 19, 20, 21, 22], "val_b1": [15, 16, 18, 19, 20, 21, 22], "val_b2": [15, 16, 18, 19, 20, 21, 22], "report_str": [15, 16, 18, 19, 20, 21, 22, 23], "class_imbal": 16, "class_imbalance_scor": 16, "bleed": [17, 28], "edg": [17, 28, 52, 67, 78, 89], "sharp": [17, 28], "null": 17, "abc": 18, "believ": [18, 86], "priori": [18, 78], "global": 18, "anoth": [18, 25, 29, 39, 52, 55, 73, 74, 76, 78, 80, 83, 88], "abstract": 18, "applic": [19, 45, 78, 80, 81, 89], "typevar": [19, 26, 30, 52, 53], "_scalartype_co": 19, "covari": [19, 57, 85], "get_health_summari": 19, "summary_dict": 19, "label_scor": [19, 69, 70, 71, 73, 74, 77, 78], "simplified_kolmogorov_smirnov_test": 20, "neighbor_histogram": 20, "non_neighbor_histogram": 20, "kolmogorov": 20, "smirnov": 20, "largest": [20, 29, 36, 55, 59, 61, 86], "empir": [20, 35, 45], "cumul": 20, "ecdf": 20, "histogram": [20, 73, 85], "absolut": 20, "25": [20, 26, 36, 38, 75, 77, 78, 80, 81, 82, 89], "dimension": [20, 40, 69, 78, 83], "trial": 20, "non_iid_scor": [20, 71, 73, 74, 77, 78], "nullissuemanag": 21, "miss": [21, 26, 30, 40, 50, 52, 73, 76, 82, 85], "null_track": 21, "null_scor": 21, "default_threshold": 22, "37037": 22, "q3_avg_dist": 22, "iqr_avg_dist": 22, "median_outlier_scor": 22, "ood": [22, 54, 55, 70, 71, 74, 77, 78, 83], "exclud": [23, 62, 66, 70, 89], "get_report": 23, "overview": [25, 69, 71, 73, 74, 77, 80, 82, 83, 85, 87, 88, 89], "modifi": [25, 26, 29, 30, 40, 76, 78], "help": [25, 26, 30, 53, 67, 68, 69, 70, 73, 74, 75, 76, 80, 81, 85, 86, 87, 88, 89], "rank_classes_by_label_qu": [25, 71], "merg": [25, 39, 67, 75, 76, 89], "find_overlapping_class": [25, 76, 78], "ascend": [25, 38, 46, 77, 78], "problemat": [25, 46, 62, 66, 69, 82, 89], "unnorm": [25, 46, 78], "abov": [25, 26, 29, 30, 40, 45, 52, 55, 61, 65, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89], "model_select": [25, 36, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 83, 85, 87, 88], "cross_val_predict": [25, 30, 69, 70, 71, 73, 74, 78, 80, 84, 85, 87, 88], "get_data_labels_from_dataset": 25, "yourfavoritemodel": [25, 78], "cv": [25, 36, 69, 70, 71, 73, 78, 80, 87], "df": [25, 40, 66, 69], "overall_label_qu": [25, 46], "col": 25, "prob": [25, 39, 78, 84], "divid": [25, 46, 55], "label_nois": [25, 46], "human": [25, 75, 86, 89], "clearli": [25, 55, 77, 82, 86], "num": [25, 46, 75, 78], "overlap": [25, 67, 75, 76, 78], "ontolog": 25, "publish": [25, 89], "therefor": [25, 55], "vehicl": [25, 75], "truck": [25, 75, 83, 86], "intuit": [25, 46], "car": [25, 75, 82, 86], "frequent": [25, 45, 73, 76, 85], "confus": [25, 26, 30, 31, 40, 88, 89], "characterist": 25, "l": [25, 26, 30, 50, 52, 53], "class1": 25, "class2": 25, "relationship": 25, "arbitrari": [25, 61, 65, 70, 83, 85], "match": [25, 26, 30, 31, 45, 46, 55, 70, 71, 75, 77, 82, 84, 86], "dog": [25, 40, 46, 48, 62, 75, 76, 83, 84, 89], "cat": [25, 40, 46, 48, 75, 76, 83, 84], "captur": [25, 69, 82, 83, 86], "co": [25, 26, 27], "noisy_label": [25, 70, 71, 81], "overlapping_class": 25, "descend": [25, 26, 30, 36, 46, 53], "overall_label_health_scor": [25, 46, 78], "suggest": [25, 45, 46, 52, 74, 76, 77, 85, 88], "half": [25, 26, 30, 46, 75, 89], "health_scor": [25, 46], "classes_by_label_qu": [25, 71], "cnn": [26, 30, 77], "cifar": [26, 27, 75, 83], "teach": [26, 27], "bhanml": 26, "blob": 26, "master": [26, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 87, 88], "call_bn": 26, "bn": 26, "input_channel": 26, "n_output": 26, "dropout_r": 26, "top_bn": 26, "architectur": [26, 30], "shown": [26, 53, 70, 76, 80, 83, 84, 86, 89], "forward": [26, 27, 30, 77, 80], "overridden": [26, 30], "although": [26, 30, 54, 73, 87], "recip": [26, 30], "afterward": [26, 30], "sinc": [26, 30, 33, 41, 46, 61, 65, 76, 80, 81, 82, 84, 89], "former": [26, 30], "hook": [26, 30, 75], "silent": [26, 29, 30], "t_destin": [26, 30], "__call__": [26, 30, 36], "add_modul": [26, 30], "child": [26, 30], "fn": [26, 30], "recurs": [26, 30, 36], "submodul": [26, 30], "children": [26, 30, 89], "nn": [26, 27, 30, 77], "init": [26, 30, 78], "doc": [26, 30, 69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "no_grad": [26, 30, 77, 83], "init_weight": [26, 30], "linear": [26, 30, 74, 77, 88], "fill_": [26, 30], "net": [26, 30, 69, 75, 77], "in_featur": [26, 30], "out_featur": [26, 30], "bia": [26, 30, 77], "tensor": [26, 27, 30, 69, 77, 83], "requires_grad": [26, 30], "bfloat16": [26, 30], "cast": [26, 30, 69], "buffer": [26, 30], "datatyp": [26, 30], "member": [26, 30, 70], "xdoctest": [26, 30], "undefin": [26, 30], "var": [26, 30], "buf": [26, 30], "20l": [26, 30], "1l": [26, 30], "5l": [26, 30], "immedi": [26, 30, 83], "cpu": [26, 30, 31, 69, 77], "move": [26, 30, 36, 68, 75], "cuda": [26, 30, 69, 77], "devic": [26, 30, 69, 77], "gpu": [26, 30, 69, 74, 88], "live": [26, 30], "copi": [26, 30, 57, 69, 70, 71, 73, 76, 81, 84, 85, 87], "doubl": [26, 30], "dump_patch": [26, 30], "eval": [26, 30, 77, 81, 83], "dropout": [26, 30], "batchnorm": [26, 30], "grad": [26, 30], "extra_repr": [26, 30], "line": [26, 30, 67, 70, 75, 80, 83, 89], "get_buff": [26, 30], "target": [26, 27, 30, 57, 58, 83, 85], "throw": [26, 30], "get_submodul": [26, 30], "explan": [26, 30], "fulli": [26, 30, 44, 76], "qualifi": [26, 30], "referenc": [26, 30], "attributeerror": [26, 30], "invalid": [26, 30], "resolv": [26, 30, 89], "get_extra_st": [26, 30], "state_dict": [26, 30], "set_extra_st": [26, 30], "build": [26, 30, 77, 86], "pickleabl": [26, 30], "serial": [26, 30], "backward": [26, 30, 77], "break": [26, 30, 77], "pickl": [26, 30, 82], "get_paramet": [26, 30], "let": [26, 30, 54, 55, 69, 71, 73, 74, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "net_b": [26, 30], "net_c": [26, 30], "conv": [26, 30], "conv2d": [26, 30, 77], "16": [26, 30, 36, 61, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 86, 88, 89], "33": [26, 30, 75, 82], "kernel_s": [26, 30], "stride": [26, 30], "200": [26, 30, 55, 75, 82, 89], "diagram": [26, 30, 84], "degre": [26, 30, 85], "queri": [26, 30, 71, 77], "named_modul": [26, 30], "o": [26, 30, 38, 39, 69, 70, 71, 75, 76, 78, 81, 82, 89], "transit": [26, 30], "ipu": [26, 30], "load_state_dict": [26, 30], "strict": [26, 30, 36], "persist": [26, 30], "strictli": [26, 30], "namedtupl": [26, 30], "missing_kei": [26, 30], "unexpected_kei": [26, 30], "runtimeerror": [26, 30], "idx": [26, 30, 40, 41, 53, 70, 76, 77, 78, 80, 82, 83], "named_buff": [26, 30], "prefix": [26, 30, 69, 89], "prepend": [26, 30], "running_var": [26, 30], "named_children": [26, 30], "conv4": [26, 30], "conv5": [26, 30], "memo": [26, 30], "remove_dupl": [26, 30], "named_paramet": [26, 30], "register_backward_hook": [26, 30], "deprec": [26, 30, 33], "favor": [26, 30], "register_full_backward_hook": [26, 30], "removablehandl": [26, 30], "register_buff": [26, 30], "running_mean": [26, 30], "register_forward_hook": [26, 30], "posit": [26, 30, 40, 75, 83], "won": [26, 30, 70, 71, 76, 81], "inplac": [26, 30, 80], "register_forward_pre_hook": [26, 30], "gradient": [26, 30, 73, 77, 85], "respect": [26, 30, 53, 78], "grad_input": [26, 30], "grad_output": [26, 30], "technic": [26, 30], "caller": [26, 30], "register_load_state_dict_post_hook": [26, 30], "post": [26, 30], "incompatible_kei": [26, 30], "modif": [26, 30], "thrown": [26, 30], "clearn": [26, 30], "register_modul": [26, 30], "register_paramet": [26, 30], "requires_grad_": [26, 30], "autograd": [26, 30], "freez": [26, 30, 69, 74, 88], "finetun": [26, 30], "gan": [26, 30], "share_memori": [26, 30], "share_memory_": [26, 30], "destin": [26, 30], "keep_var": [26, 30], "shallow": [26, 30], "howev": [26, 30, 40, 69, 73, 74, 77, 80, 84, 86, 87, 88], "releas": [26, 30, 68, 76, 83], "design": [26, 30], "ordereddict": [26, 30], "detach": [26, 30, 77], "non_block": [26, 30], "memory_format": [26, 30], "channels_last": [26, 30], "Its": [26, 30, 36, 46, 52], "complex": [26, 30], "integr": [26, 30, 67], "asynchron": [26, 30], "host": [26, 30], "pin": [26, 30, 74, 75, 88], "desir": [26, 30, 39, 53], "4d": [26, 30], "ignore_w": [26, 30], "determinist": [26, 30, 69], "1913": [26, 30], "3420": [26, 30], "5113": [26, 30], "2325": [26, 30], "env": [26, 30], "torch_doctest_cuda1": [26, 30], "gpu1": [26, 30], "1914": [26, 30], "5112": [26, 30], "2324": [26, 30], "float16": [26, 30], "cdoubl": [26, 30], "3741": [26, 30], "2382": [26, 30], "5593": [26, 30], "4443": [26, 30], "complex128": [26, 30], "6122": [26, 30], "1150": [26, 30], "to_empti": [26, 30], "storag": [26, 30], "dst_type": [26, 30], "xpu": [26, 30], "zero_grad": [26, 30, 77], "set_to_non": [26, 30], "context": [26, 30, 82], "noisili": [27, 78], "han": 27, "2018": 27, "cifar_cnn": [27, 28], "loss_coteach": 27, "y_1": 27, "y_2": 27, "forget_r": 27, "class_weight": 27, "logit": [27, 44, 77], "decim": [27, 40], "quickli": [27, 69, 73, 74, 76, 77, 81, 83, 86, 87, 89], "forget": [27, 36, 89], "rate_schedul": 27, "epoch": [27, 30, 76, 77], "initialize_lr_schedul": 27, "lr": [27, 30], "001": [27, 55, 76], "250": [27, 70, 71, 78, 82], "epoch_decay_start": 27, "80": [27, 73, 81, 85, 87], "schedul": 27, "adjust": [27, 31, 49, 54, 55, 67, 78], "beta": 27, "adam": 27, "adjust_learning_r": 27, "alpha_plan": 27, "beta1_plan": 27, "forget_rate_schedul": 27, "num_gradu": 27, "expon": 27, "tell": [27, 74, 77, 78, 88], "train_load": [27, 30], "model1": [27, 78], "optimizer1": 27, "model2": [27, 78], "optimizer2": 27, "dataload": [27, 77, 83], "parser": 27, "parse_arg": 27, "num_iter_per_epoch": 27, "print_freq": 27, "topk": 27, "top1": 27, "top5": 27, "test_load": 27, "offici": [28, 43, 89], "wish": [28, 43, 83, 86, 89], "mnist_pytorch": 28, "coteach": [28, 68], "mini": [29, 59, 61, 76], "With": [29, 74, 78, 80, 85, 86, 88, 89], "approxim": [29, 54, 80], "low_self_confid": [29, 31, 47], "self_confid": [29, 31, 36, 47, 49, 55, 63, 65, 76, 78, 81, 87, 88], "conveni": [29, 69, 74, 88], "script": 29, "labelinspector": [29, 76], "adj_confident_thresholds_shar": 29, "labels_shar": 29, "pred_probs_shar": 29, "labels_fil": [29, 76], "pred_probs_fil": [29, 76], "batch_siz": [29, 30, 59, 61, 76, 77, 83, 86], "quality_score_kwarg": 29, "num_issue_kwarg": 29, "return_mask": 29, "variant": [29, 45, 86], "read": [29, 33, 71, 76, 78, 83, 89], "zarr": [29, 76], "memmap": [29, 86], "pythonspe": 29, "mmap": [29, 76], "hdf5": 29, "further": [29, 46, 47, 49, 52, 53, 61, 62, 69, 76], "yourfil": 29, "r": [29, 57, 70, 71, 85, 86], "npy": [29, 75, 76, 86], "mmap_mod": [29, 86], "tip": [29, 31, 44, 76], "save_arrai": 29, "your_arrai": 29, "disk": [29, 75, 76], "npz": [29, 89], "maxim": [29, 45, 59, 61, 86], "multiprocess": [29, 31, 47, 59, 61, 76, 77, 86], "linux": [29, 59, 61], "physic": [29, 31, 59, 61, 82, 86], "psutil": [29, 31, 59, 61, 86], "demonstr": [29, 70, 74, 76, 77, 78, 80, 81, 82, 85, 86], "labels_arrai": [29, 41], "predprob": 29, "pred_probs_arrai": 29, "back": [29, 53, 70, 76, 82, 83], "store_result": 29, "becom": [29, 83], "verifi": [29, 76, 80, 83], "long": [29, 45, 54, 80], "enough": [29, 40, 76], "chunk": [29, 84], "ram": [29, 75], "faster": [29, 54, 57, 59, 61, 76, 78], "end_index": 29, "labels_batch": 29, "pred_probs_batch": 29, "update_confident_threshold": 29, "batch_result": 29, "score_label_qu": 29, "indices_of_examples_with_issu": [29, 76], "shortcut": 29, "encount": [29, 31, 59], "1000": [29, 69, 74, 76, 83], "aggreg": [29, 36, 45, 49, 52, 55, 65, 76, 78, 80], "get_num_issu": 29, "fetch": [29, 69, 71], "seen": [29, 76, 83, 89], "far": [29, 45], "get_quality_scor": 29, "label_quality_scor": [29, 49, 52, 55, 58, 78, 82, 85], "method1": 29, "method2": 29, "normalized_margin": [29, 31, 36, 47, 49, 55, 63, 65], "low_normalized_margin": [29, 31, 47], "issue_indic": [29, 52, 77], "update_num_issu": 29, "split_arr": 29, "arr": [29, 76], "chunksiz": 29, "convnet": 30, "bespok": [30, 44], "get_mnist_dataset": 30, "loader": [30, 77], "download": [30, 69, 76, 83], "mnist": [30, 67, 69, 75], "get_sklearn_digits_dataset": 30, "handwritten": 30, "digit": [30, 69, 75], "last": [30, 36, 50, 53, 70, 71, 76, 80, 89], "sklearn_digits_test_s": 30, "hard": [30, 75, 83], "simplenet": 30, "64": [30, 73, 77, 78, 82, 87], "log_interv": 30, "50": [30, 76, 78, 80, 82, 83], "01": [30, 55, 57, 69, 78, 81, 82, 85], "momentum": 30, "no_cuda": 30, "test_batch_s": [30, 77], "templat": 30, "enabl": 30, "flexibli": 30, "among": [30, 45, 78], "test_set": 30, "Be": 30, "overrid": 30, "train_idx": [30, 40, 83], "train_label": [30, 83, 88], "scikit": [30, 40, 54, 67, 69, 70, 71, 73, 74, 76, 79, 85, 88], "set_predict_proba_request": 30, "set_predict_request": 30, "encourag": [31, 47, 55, 58], "multilabel_classif": [31, 46, 47, 49, 55, 76, 81], "pred_probs_by_class": 31, "prune_count_matrix_col": 31, "rank_by_kwarg": [31, 47, 55, 78], "num_to_remove_per_class": [31, 47], "bad": [31, 47, 52, 55, 74, 76, 88], "seem": [31, 78, 81], "fewer": [31, 40, 82], "aren": 31, "confidence_weighted_entropi": [31, 36, 47, 49, 55, 63, 65], "label_issues_idx": [31, 55], "entropi": [31, 33, 35, 36, 54, 55], "prune_by_class": [31, 47, 78], "predicted_neq_given": [31, 47, 78], "prune_counts_matrix": 31, "smallest": [31, 55], "unus": 31, "number_of_mislabeled_examples_in_class_k": 31, "delet": [31, 67, 76, 88], "thread": [31, 47], "window": [31, 75], "shorter": [31, 50], "find_predicted_neq_given": 31, "find_label_issues_using_argmax_confusion_matrix": 31, "latent_algebra": [32, 68], "label_quality_util": 32, "multilabel_util": [32, 81], "multilabel_scor": [32, 49], "token_classification_util": [32, 89], "get_normalized_entropi": 33, "min_allowed_prob": 33, "wikipedia": 33, "activ": [33, 35, 45, 67, 80], "towardsdatasci": 33, "cheatsheet": 33, "ec57bc067c0b": 33, "clip": [33, 40, 69], "behav": 33, "unnecessari": [33, 76], "slightli": [33, 87, 88], "interv": [33, 36, 83], "herein": 34, "inexact": 34, "cours": 34, "propag": 34, "throughout": [34, 40, 57, 69, 80, 86, 89], "compute_ps_py_inv_noise_matrix": 34, "compute_py_inv_noise_matrix": 34, "compute_inv_noise_matrix": 34, "easili": [34, 68, 69, 71, 73, 74, 78, 80, 81, 83, 84, 85, 86, 87, 88], "increas": [34, 52, 54, 55, 69, 70, 76, 80, 81, 89], "dot": [34, 65, 76], "compute_noise_matrix_from_invers": 34, "compute_pi": 34, "true_labels_class_count": 34, "compute_pyx": 34, "pyx": 34, "multiannot": 35, "assert_valid_inputs_multiannot": 35, "labels_multiannot": [35, 45], "ensembl": [35, 36, 45, 55, 73, 76, 81, 83, 85, 87], "allow_single_label": 35, "annotator_id": 35, "assert_valid_pred_prob": 35, "pred_probs_unlabel": [35, 45], "format_multiannotator_label": [35, 45, 80], "lexicograph": [35, 40], "formatted_label": [35, 40], "old": [35, 40, 68, 75], "th": [35, 39, 40, 45, 47, 50, 52, 53, 54, 63, 65, 66, 74, 81, 82, 89], "check_consensus_label_class": 35, "consensus_label": [35, 45, 80], "consensus_method": [35, 45], "consensu": [35, 45, 67, 79, 89], "establish": [35, 85, 88], "compute_soft_cross_entropi": 35, "soft": [35, 75], "find_best_temp_scal": 35, "coarse_search_rang": [35, 57, 76], "fine_search_s": [35, 57, 76], "temperatur": [35, 36, 52, 61, 65], "scale": [35, 38, 75, 76, 83, 86, 87], "factor": [35, 36, 59, 61], "minim": [35, 52, 83], "temp_scale_pred_prob": 35, "temp": 35, "sharpen": [35, 75], "smoothen": 35, "classlabelscor": 36, "enum": 36, "get_self_confidence_for_each_label": [36, 55], "get_normalized_margin_for_each_label": [36, 55], "get_confidence_weighted_entropy_for_each_label": [36, 55], "75": [36, 70, 71, 75, 80, 81, 82, 85, 89], "from_str": 36, "scorer": 36, "exponential_moving_averag": [36, 49], "alpha": [36, 49, 52, 70, 71, 78, 81, 85], "exponenti": 36, "ema": 36, "s_1": 36, "s_k": 36, "ema_k": 36, "accord": [36, 47, 73, 74, 78, 89], "formula": [36, 38], "_t": 36, "cdot": 36, "s_t": 36, "qquad": 36, "leq": 36, "_1": 36, "give": [36, 55, 78, 80, 86], "recent": [36, 89], "success": 36, "previou": [36, 77, 82], "discount": 36, "s_ema": 36, "175": [36, 78, 82], "softmin": [36, 49, 52, 61, 65], "underflow": 36, "nan": [36, 45, 73, 80, 85, 87], "possible_method": 36, "aggregated_scor": 36, "multilabelscor": 36, "base_scor": 36, "base_scorer_kwarg": 36, "aggregator_kwarg": [36, 49], "n_sampl": 36, "n_label": 36, "binari": [36, 40, 47, 49, 78, 89], "worst": [36, 80], "class_label_quality_scor": 36, "get_class_label_quality_scor": 36, "42": [36, 75, 82, 89], "452": 36, "new_scor": 36, "575": 36, "get_label_quality_scores_per_class": [36, 49], "ml_scorer": 36, "multilabel_pi": 36, "binar": [36, 37], "second": [36, 38, 40, 53, 55, 70, 76, 78, 89], "get_cross_validated_multilabel_pred_prob": 36, "reformat": [36, 69], "wider": 36, "splitter": 36, "kfold": [36, 77], "multiclass": [36, 40, 45, 81], "onevsrestclassifi": [36, 81], "randomforestclassifi": [36, 78, 81], "n_split": [36, 77, 81], "stack_compl": 37, "pred_prob_slic": 37, "extend": [37, 77, 83, 89], "get_onehot_num_class": 37, "onehot": 37, "encod": [37, 53, 59, 62, 73, 74, 76, 85, 86, 87, 88], "multilabel": [37, 81], "int2onehot": [37, 81], "hot": [37, 47, 53, 59, 62, 73, 75, 76, 85, 86, 87], "onehot2int": [37, 81], "onehot_matrix": 37, "transform_distances_to_scor": 38, "exp": [38, 54, 55, 70], "dt": 38, "right": [38, 50, 53, 74, 81, 82, 83, 88], "num_neighbor": 38, "slice": 38, "ood_features_scor": [38, 54, 83], "95122942": 38, "83945702": 38, "token_classif": [39, 63, 65, 66, 76], "get_sent": [39, 89], "sentenc": [39, 63, 65, 66, 74, 88], "readabl": 39, "filter_sent": [39, 89], "lambda": [39, 69, 70, 80], "long_sent": 39, "headlin": 39, "process_token": 39, "charact": [39, 40], "s1": 39, "s2": 39, "processed_token": 39, "rule": [39, 75], "alecnlcb": 39, "entiti": [39, 67, 76, 89], "mapped_ent": 39, "unique_ident": 39, "loc": [39, 70, 71, 77, 89], "merge_prob": 39, "probs_merg": 39, "55": [39, 75, 82, 85], "0125": [39, 65], "0375": 39, "075": 39, "025": 39, "color_sent": 39, "color": [39, 62, 70, 71, 73, 78, 81, 83, 85, 86], "red": [39, 53, 70, 71, 75, 78, 81, 82, 83, 86], "colored_sent": 39, "termcolor": 39, "31msentenc": 39, "0m": 39, "ancillari": 40, "remove_noise_from_class": 40, "class_without_nois": 40, "any_other_class": 40, "choos": [40, 55, 73, 76, 78, 85, 87], "tradition": 40, "clip_noise_r": 40, "clip_valu": 40, "new_sum": 40, "preserv": 40, "value_count": [40, 76], "fill": 40, "wherea": [40, 47, 84], "come": [40, 70, 71, 76, 86], "major": [40, 45, 68, 77, 83], "versu": [40, 78], "value_counts_fill_missing_class": 40, "get_missing_class": 40, "round_preserving_sum": 40, "obviou": 40, "cgdeboer": 40, "iteround": 40, "round_preserving_row_tot": 40, "reach": 40, "estimate_pu_f1": 40, "prob_s_eq_1": 40, "claesen": 40, "f1": [40, 74, 78], "confusion_matrix": 40, "BE": 40, "print_square_matrix": 40, "left_nam": 40, "top_nam": 40, "titl": [40, 70, 71, 78, 81, 83], "short_titl": 40, "round_plac": 40, "pretti": [40, 78], "print_noise_matrix": [40, 78], "print_inverse_noise_matrix": 40, "print_joint_matrix": [40, 78], "joint_matrix": 40, "compress_int_arrai": 40, "num_possible_valu": 40, "train_val_split": 40, "holdout_idx": 40, "subset_x_i": 40, "extract": [40, 54, 69, 74, 80, 83, 86, 88], "subset_label": 40, "subset_data": 40, "extract_indices_tf": 40, "allow_shuffl": 40, "turn": [40, 67, 82], "unshuffle_tensorflow_dataset": 40, "shuffledataset": 40, "histori": 40, "pre_x": 40, "buffer_s": 40, "is_torch_dataset": 40, "is_tensorflow_dataset": 40, "csr_vstack": 40, "csr_matric": 40, "append": [40, 69, 75, 77, 78, 80, 81, 83, 89], "bottom": [40, 50, 53, 82], "vstack": [40, 75, 76, 77, 78, 80, 81], "append_extra_datapoint": 40, "to_data": 40, "from_data": 40, "taken": 40, "One": [40, 54, 76], "get_num_class": 40, "label_matrix": 40, "canon": 40, "num_unique_class": 40, "get_unique_class": 40, "format_label": 40, "smart_display_datafram": 40, "displai": [40, 53, 62, 66, 69, 74, 78, 88, 89], "jupyt": [40, 69, 70, 71, 75, 76, 77, 78, 80, 81, 83, 85, 89], "notebook": [40, 45, 69, 71, 75, 76, 78, 80, 81, 82, 86, 89], "consol": 40, "force_two_dimens": 40, "html": [40, 54, 73, 76, 78], "assert_valid_input": 41, "allow_missing_class": 41, "allow_one_class": 41, "assert_valid_class_label": 41, "assert_nonempty_input": 41, "assert_indexing_work": 41, "length_x": 41, "labels_to_arrai": 41, "labellik": 41, "keraswrappermodel": [44, 67], "keraswrappersequenti": 44, "tf": [44, 69], "legaci": 44, "lack": 44, "keraswrapp": 44, "huggingface_keras_imdb": 44, "unit": [44, 89], "model_kwarg": [44, 57], "compile_kwarg": 44, "sparsecategoricalcrossentropi": 44, "layer": [44, 69, 74, 83, 88], "dens": 44, "my_keras_model": 44, "from_logit": 44, "compil": 44, "declar": 44, "apply_softmax": 44, "analysi": 45, "analyz": [45, 67, 78, 80, 81], "get_label_quality_multiannot": [45, 80], "vote": 45, "crowdsourc": [45, 67, 80], "dawid": [45, 80], "skene": [45, 80], "analog": [45, 75, 80], "chosen": [45, 55, 80], "crowdlab": [45, 80], "unlabel": [45, 80, 83, 86], "decid": [45, 74, 75, 80, 85, 88, 89], "get_active_learning_scor": [45, 80], "activelab": [45, 80], "priorit": [45, 52, 82, 86, 89], "showcas": 45, "main": 45, "best_qual": 45, "quality_method": 45, "calibrate_prob": 45, "return_detailed_qu": 45, "return_annotator_stat": 45, "return_weight": 45, "label_quality_score_kwarg": 45, "necessarili": [45, 74, 78], "did": [45, 46, 69, 73, 78, 80, 85, 87, 88], "id": [45, 70, 77, 80], "majority_vot": 45, "ti": 45, "broken": [45, 53, 75], "highest": [45, 53, 70, 77, 84], "0th": 45, "consensus_quality_scor": [45, 80], "annotator_agr": [45, 80], "reman": 45, "1st": 45, "2nd": [45, 59], "3rd": 45, "consensus_label_suffix": 45, "consensus_quality_score_suffix": 45, "suffix": 45, "emsembl": 45, "weigh": [45, 75], "agreement": [45, 80], "agre": 45, "prevent": 45, "overconfid": [45, 84], "wrong": [45, 50, 52, 68, 70, 71, 74, 76, 78, 82, 88], "detailed_label_qu": [45, 80], "annotator_stat": [45, 80], "model_weight": 45, "annotator_weight": 45, "warn": [45, 70], "labels_info": 45, "num_annot": [45, 80], "deriv": [45, 80], "quality_annotator_1": 45, "quality_annotator_2": 45, "quality_annotator_m": 45, "lowest": [45, 53, 71, 77, 80, 81, 82, 86], "annotator_qu": [45, 80], "num_examples_label": [45, 80], "agreement_with_consensu": [45, 80], "worst_class": [45, 80], "trustworthi": [45, 80, 85], "get_label_quality_multiannotator_ensembl": 45, "func": 45, "weigtht": 45, "budget": 45, "retrain": [45, 85, 88], "active_learning_scor": 45, "improv": [45, 71, 75, 76, 77, 78, 85, 86, 87, 88], "active_learning_scores_unlabel": 45, "get_active_learning_scores_ensembl": 45, "henc": [45, 69, 70, 80], "get_majority_vote_label": [45, 80], "event": 45, "lastli": [45, 73], "convert_long_to_wide_dataset": 45, "labels_multiannotator_long": 45, "wide": [45, 69, 87, 88], "suitabl": [45, 73, 87], "labels_multiannotator_wid": 45, "common_multilabel_issu": 46, "mutual": [46, 81], "exclus": [46, 81], "vice": 46, "versa": 46, "rank_classes_by_multilabel_qu": 46, "overall_multilabel_health_scor": 46, "multilabel_health_summari": 46, "classes_by_multilabel_qu": 46, "inner": [47, 61], "find_multilabel_issues_per_class": 47, "per_class_label_issu": 47, "label_issues_list": 47, "labels_list": 47, "pred_probs_list": [47, 55, 77, 78], "anim": [48, 83], "rat": 48, "predat": 48, "pet": 48, "reptil": 48, "manner": [49, 80, 85, 87, 88], "box": [50, 52, 53, 75, 82], "object_detect": [50, 52, 53, 82], "return_indices_ranked_by_scor": [50, 82], "overlapping_label_check": [50, 52], "suboptim": [50, 52], "locat": [50, 52, 82, 86, 89], "bbox": [50, 53, 82], "image_nam": [50, 53], "y1": [50, 53, 82], "y2": [50, 53, 82], "later": [50, 53, 54, 88, 89], "mmdetect": [50, 53, 82], "corner": [50, 53, 82], "swap": [50, 52, 62, 66], "penal": [50, 52], "concern": [50, 52, 67, 71], "aggregation_weight": 52, "imperfect": [52, 76], "chose": [52, 80, 82], "imperfectli": [52, 82], "dirti": [52, 55, 58, 85], "subtyp": 52, "badloc": 52, "nonneg": 52, "issues_from_scor": [52, 61, 62, 65, 66, 82, 86, 89], "compute_overlooked_box_scor": 52, "high_probability_threshold": 52, "auxiliary_input": [52, 53], "vari": [52, 71], "iou": 52, "heavili": 52, "auxiliarytypesdict": 52, "pred_label": [52, 88], "pred_label_prob": 52, "pred_bbox": 52, "lab_label": 52, "lab_bbox": 52, "similarity_matrix": 52, "min_possible_similar": 52, "scores_overlook": 52, "compute_badloc_box_scor": 52, "low_probability_threshold": 52, "scores_badloc": 52, "compute_swap_box_scor": 52, "accident": [52, 73, 74, 88], "scores_swap": 52, "pool_box_scores_per_imag": 52, "box_scor": 52, "image_scor": [52, 61, 86], "object_counts_per_imag": 53, "discov": [53, 71, 89], "auxiliari": [53, 83, 86], "_get_valid_inputs_for_compute_scor": 53, "object_count": 53, "bounding_box_size_distribut": 53, "down": 53, "bbox_siz": 53, "class_label_distribut": 53, "class_distribut": 53, "get_sorted_bbox_count_idx": 53, "plot": [53, 70, 71, 78, 81, 83, 85, 86], "sorted_idx": [53, 83], "plot_class_size_distribut": 53, "class_to_show": 53, "hidden": [53, 83], "max_class_to_show": 53, "plot_class_distribut": 53, "visual": [53, 70, 71, 77, 85, 87, 89], "prediction_threshold": 53, "overlai": [53, 82], "figsiz": [53, 70, 71, 77, 78, 81, 83], "save_path": [53, 82], "blue": [53, 75, 78, 82], "overlaid": 53, "side": [53, 75, 82], "figur": [53, 78, 81, 83, 85], "extens": [53, 78, 80], "png": [53, 82], "pdf": [53, 54], "ep": 53, "svg": 53, "matplotlib": [53, 70, 71, 77, 78, 81, 82, 83, 85], "Of": 54, "li": 54, "smaller": [54, 81, 82], "find_top_issu": [54, 55, 83], "reli": [54, 69, 70, 71, 74, 82, 83, 88], "dist_metr": 54, "dim": [54, 77, 86], "subtract": [54, 55], "renorm": [54, 55, 76], "least_confid": 54, "sum_": 54, "log": [54, 55, 68], "softmax": [54, 61, 65, 77], "literatur": 54, "gen": 54, "liu": 54, "lochman": 54, "zach": 54, "openaccess": 54, "thecvf": 54, "content": [54, 69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "cvpr2023": 54, "liu_gen_pushing_the_limits_of_softmax": 54, "based_out": 54, "distribution_detection_cvpr_2023_pap": 54, "fit_scor": [54, 83], "ood_predictions_scor": 54, "categor": [54, 70, 71, 72, 85, 87], "pretrain": [54, 69, 74, 83, 88], "adjust_confident_threshold": 54, "probabilist": [54, 69, 70, 71, 73, 74, 83, 84, 87], "order_label_issu": [55, 68], "whichev": [55, 84], "argsort": [55, 74, 77, 78, 83, 85, 88], "max_": 55, "get_label_quality_ensemble_scor": [55, 76, 78], "weight_ensemble_members_bi": 55, "custom_weight": 55, "log_loss_search_t_valu": 55, "0001": [55, 75], "scheme": 55, "log_loss_search": 55, "log_loss": [55, 74], "1e0": 55, "1e1": 55, "1e2": 55, "2e2": 55, "quality_scor": [55, 83], "forth": 55, "top_issue_indic": 55, "rank_bi": [55, 68], "weird": [55, 66], "minu": 55, "prob_label": 55, "max_prob_not_label": 55, "idea": 55, "AND": [55, 74], "corrupt": [57, 85], "linearregress": [57, 76, 85], "y_with_nois": 57, "n_boot": [57, 76], "include_aleatoric_uncertainti": [57, 76], "sole": [57, 70, 80, 83, 87], "larger": [57, 59, 61, 75, 76, 77], "bootstrap": [57, 76, 85], "resampl": [57, 69, 76], "epistem": [57, 76, 83, 85], "aleator": [57, 76, 85], "model_final_kwarg": 57, "coars": 57, "thorough": [57, 76], "fine": [57, 69, 74, 83, 88], "grain": 57, "grid": 57, "get_epistemic_uncertainti": 57, "varianc": [57, 78], "epistemic_uncertainti": 57, "get_aleatoric_uncertainti": 57, "residu": [57, 58, 76], "deviat": [57, 85], "ie": 57, "aleatoric_uncertainti": 57, "outr": 58, "contin": 58, "raw": [58, 67, 68, 71, 75, 77, 80, 82, 83], "aka": [58, 69, 78, 89], "00323821": 58, "33692597": 58, "00191686": 58, "semant": [59, 61, 62, 79], "segment": [59, 61, 62, 79], "pixel": [59, 61, 62, 83, 86], "h": [59, 61, 62, 86], "height": [59, 61, 62, 86], "w": [59, 61, 62, 86], "width": [59, 61, 62, 86], "labels_one_hot": [59, 62, 86], "stream": [59, 83, 89], "downsampl": [59, 61, 86], "shrink": [59, 61], "divis": [59, 61, 70], "segmant": [61, 62], "num_pixel_issu": [61, 86], "product": [61, 76, 77], "pixel_scor": [61, 86], "display_issu": [61, 62, 63, 65, 66, 86, 89], "highlight": [62, 66, 70, 71, 73, 86], "enter": 62, "legend": [62, 70, 71, 81, 82, 85, 86], "colormap": 62, "background": 62, "person": [62, 76, 82, 86, 89], "common_label_issu": [62, 66, 86, 89], "ambigu": [62, 66, 69, 74, 75, 78, 88, 89], "systemat": [62, 66, 80], "misunderstood": [62, 66], "issues_df": [62, 77], "filter_by_class": [62, 86], "class_index": 62, "issues_subset": [62, 66], "95": [63, 65, 73, 75, 78, 85], "token_score_method": 65, "sentence_score_method": 65, "sentence_score_kwarg": 65, "compris": [65, 66], "token_scor": [65, 89], "converg": 65, "toward": 65, "_softmin_sentence_scor": 65, "sentence_scor": [65, 89], "token_info": 65, "70": [65, 73, 85], "02": [65, 70, 71, 78, 82, 85], "03": [65, 75, 78, 82, 89], "04": [65, 82, 85], "08": [65, 78, 82, 89], "commonli": [66, 68, 70, 71, 81, 89], "filter_by_token": [66, 89], "But": [66, 78, 89], "restrict": [66, 76], "reliabl": [67, 69, 76, 80, 86, 87], "thousand": 67, "imagenet": [67, 75], "popular": [67, 80, 82], "centric": [67, 79], "capabl": 67, "minut": [67, 69, 73, 74, 75, 80, 81, 82, 85, 86, 87, 88, 89], "conda": 67, "feature_embed": [67, 83], "Then": [67, 76, 77, 85, 87, 88], "your_dataset": [67, 69, 70, 71, 73, 74, 77], "column_name_of_label": [67, 69, 70, 71, 73, 74, 77], "plagu": [67, 71], "untrain": 67, "\u30c4": 67, "label_issues_info": [67, 71], "sklearn_compatible_model": 67, "framework": [67, 81, 82], "complianc": 67, "tag": [67, 81, 89], "sequenc": 67, "recognit": [67, 69, 76, 89], "train_data": [67, 83, 85, 87, 88], "gotten": 67, "test_data": [67, 78, 81, 83, 85, 87, 88], "deal": [67, 71], "tutori": [67, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "faq": [67, 79], "feel": [67, 69, 71, 76], "free": [67, 69, 71, 76, 78], "ask": [67, 76], "slack": [67, 76], "project": [67, 85], "welcom": 67, "commun": [67, 76], "guidelin": [67, 82], "piec": 67, "studio": [67, 71, 76], "platform": [67, 76], "tabular": [67, 70, 71, 72, 76, 79, 80], "automl": [67, 76], "foundat": 67, "smart": [67, 76], "edit": [67, 76], "easier": [67, 78], "unreli": [67, 69, 73, 74, 87], "older": 68, "outlin": 68, "substitut": 68, "v2": [68, 73, 87], "get_noise_indic": 68, "psx": 68, "sorted_index_method": 68, "order_label_error": 68, "label_errors_bool": 68, "latent_estim": 68, "num_label_error": 68, "learningwithnoisylabel": 68, "neatli": 68, "organ": [68, 73, 75, 87, 89], "reorgan": 68, "baseline_method": 68, "incorpor": [68, 78], "research": [68, 78], "polyplex": 68, "terminologi": 68, "label_error": 68, "quickstart": [69, 70, 71, 73, 74, 75, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "spoken": 69, "500": [69, 83, 89], "english": [69, 75], "pronunci": 69, "wav": 69, "huggingfac": [69, 70, 71, 77], "voxceleb": 69, "speech": [69, 89], "your_pred_prob": [69, 70, 71, 73, 74], "tensorflow_io": 69, "26": [69, 70, 75, 77, 78, 80, 82], "huggingface_hub": 69, "12": [69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 86, 87, 88, 89], "branch": [69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 87, 88], "reproduc": [69, 73, 78, 80], "command": 69, "wget": [69, 82, 86, 89], "navig": 69, "link": [69, 75, 82], "browser": 69, "jakobovski": 69, "archiv": [69, 89], "v1": 69, "tar": [69, 83], "gz": [69, 83], "mkdir": [69, 89], "spoken_digit": 69, "xf": 69, "6_nicolas_32": 69, "data_path": 69, "listdir": 69, "nondeterminist": 69, "file_nam": 69, "endswith": 69, "file_path": 69, "join": [69, 76, 77], "39": [69, 70, 74, 75, 76, 77, 82, 85, 86, 88, 89], "7_george_26": 69, "0_nicolas_24": 69, "0_nicolas_6": 69, "listen": 69, "display_exampl": 69, "click": [69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "expand": [69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "pulldown": [69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "colab": [69, 70, 71, 75, 76, 77, 78, 80, 81, 83, 85, 89], "tfio": 69, "pathlib": 69, "ipython": 69, "load_wav_16k_mono": 69, "filenam": 69, "khz": 69, "file_cont": 69, "io": [69, 75], "read_fil": 69, "sample_r": 69, "decode_wav": 69, "desired_channel": 69, "squeez": 69, "int64": [69, 80], "rate_in": 69, "rate_out": 69, "16000": 69, "wav_file_nam": 69, "audio_r": 69, "wav_file_exampl": 69, "plai": [69, 75, 76], "button": 69, "wav_file_name_exampl": 69, "7_jackson_43": 69, "hear": 69, "extractor": 69, "encoderclassifi": 69, "spkrec": 69, "xvect": 69, "feature_extractor": 69, "from_hparam": 69, "run_opt": 69, "uncom": 69, "wav_audio_file_path": 69, "head": [69, 71, 73, 74, 75, 77, 78, 80, 85, 87, 88], "torchaudio": 69, "extract_audio_embed": 69, "emb": [69, 77], "signal": 69, "encode_batch": 69, "embeddings_list": [69, 77], "embeddings_arrai": 69, "512": [69, 77], "14": [69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "196315": 69, "3194594": 69, "478977": 69, "2890828": 69, "8170278": 69, "892647": 69, "24": [69, 75, 78, 80, 82], "898054": 69, "256194": 69, "559642": 69, "559715": 69, "620667": 69, "285246": 69, "21": [69, 70, 71, 75, 78, 82, 89], "709623": 69, "5033712": 69, "913803": 69, "8198366": 69, "1831512": 69, "208761": 69, "08426": 69, "3210406": 69, "005453": 69, "2161605": 69, "478239": 69, "682179": 69, "0538025": 69, "242471": 69, "0914207": 69, "7833488": 69, "039538": 69, "23": [69, 75, 77, 78, 82], "56918": 69, "19": [69, 74, 75, 77, 78, 83, 85, 86, 88], "761095": 69, "1258287": 69, "753235": 69, "3508894": 69, "598273": 69, "237122": 69, "2500": 69, "leverag": [69, 74, 76, 78, 80, 88], "tune": [69, 74, 75, 83, 88], "computation": [69, 74, 88], "intens": [69, 74, 88], "held": [69, 73, 74, 75, 82, 83, 84, 87], "straightforward": [69, 73, 87], "benefit": [69, 84, 86, 87], "tol": 69, "num_crossval_fold": [69, 73, 80, 87], "decreas": [69, 76], "never": [69, 78, 81, 83, 84], "accuracy_scor": [69, 74, 78, 87, 88], "cv_accuraci": 69, "9772": 69, "probabilit": [69, 88], "9980": 69, "176": [69, 75, 78, 81], "006488": 69, "2318": 69, "008269": 69, "986": 69, "010354": 69, "469": 69, "013459": 69, "516": 69, "013478": 69, "investig": 69, "100541": 69, "998729": 69, "998768": 69, "980980": 69, "998217": 69, "18": [69, 74, 75, 78, 82, 83, 85, 86, 88], "identified_label_issu": [69, 74], "lowest_quality_label": [69, 74, 78, 85, 88], "sort_valu": [69, 71, 73, 74, 77, 78, 80], "1946": 69, "1871": 69, "1955": 69, "2132": 69, "worth": [69, 78], "iloc": [69, 73, 74, 85, 87, 88], "6_yweweler_35": 69, "6_yweweler_36": 69, "6_yweweler_14": 69, "6_theo_27": 69, "4_george_31": 69, "6_nicolas_8": 69, "sound": 69, "quit": [69, 83], "22": [69, 70, 75, 77, 78, 81, 82, 89], "blindli": [69, 76, 85, 87, 88], "trust": [69, 76, 78, 80, 84, 85, 87, 88], "address": [70, 71, 74, 76, 88], "underneath": 70, "hood": 70, "alert": 70, "introduct": 70, "mayb": [70, 71, 74], "examin": [70, 71, 73, 87], "your_feature_matrix": [70, 71], "toi": [70, 71, 75, 77, 78, 80], "train_test_split": [70, 71, 83, 87, 88], "inf": [70, 71], "mid": [70, 71], "bins_map": [70, 71], "create_data": [70, 71], "y_bin": [70, 71], "y_i": [70, 71], "y_bin_idx": [70, 71], "y_train": [70, 71, 78, 85], "y_test": [70, 71, 78, 85], "y_train_idx": [70, 71], "y_test_idx": [70, 71], "test_siz": [70, 71, 87, 88], "slide": [70, 71, 75], "decis": [70, 71, 87], "boundari": [70, 71], "frame": [70, 71], "x_out": [70, 71], "tini": [70, 71], "concaten": [70, 71, 84], "y_out": [70, 71], "y_out_bin": [70, 71], "y_out_bin_idx": [70, 71], "exact_duplicate_idx": [70, 71], "x_duplic": [70, 71], "y_duplic": [70, 71], "y_duplicate_idx": [70, 71], "noisy_labels_idx": [70, 71, 81], "scatter": [70, 71, 78, 81, 85], "black": [70, 71, 75, 85], "cyan": [70, 71], "pyplot": [70, 71, 77, 78, 81, 83, 85], "plt": [70, 71, 77, 78, 81, 83, 85], "plot_data": [70, 71, 78, 81, 85], "fig": [70, 71, 75, 77, 83, 85], "ax": [70, 71, 77, 83, 85], "subplot": [70, 71, 77, 83], "set_titl": [70, 71, 77, 83], "set_xlabel": [70, 71], "x_1": [70, 71], "fontsiz": [70, 71, 77, 78, 81], "set_ylabel": [70, 71], "x_2": [70, 71], "set_xlim": [70, 71], "set_ylim": [70, 71], "linestyl": [70, 71], "circl": [70, 71, 78, 81], "misclassifi": [70, 71], "zip": [70, 71, 77, 82, 89], "label_err": [70, 71], "180": [70, 71, 82], "marker": [70, 71], "facecolor": [70, 71], "edgecolor": [70, 71], "linewidth": [70, 71, 83], "dup": [70, 71], "first_legend": [70, 71], "align": [70, 71], "title_fontproperti": [70, 71], "semibold": [70, 71], "second_legend": [70, 71], "45": [70, 71, 75, 78, 82, 89], "gca": [70, 71], "add_artist": [70, 71], "tight_layout": [70, 71], "ideal": [70, 71], "logist": [70, 71, 74, 80, 83, 88], "remaind": 70, "modal": [70, 71, 76, 80], "regardless": [70, 71], "132": [70, 71, 78, 82], "9318": [70, 71], "77": [70, 71, 73, 82, 87], "006939": [70, 71], "007830": [70, 71], "40": [70, 71, 74, 75, 77], "014826": [70, 71], "107": [70, 71, 78, 81], "021220": [70, 71], "120": [70, 71, 87], "026403": [70, 71], "notic": [70, 78, 80, 82], "5221": [70, 71], "126": [70, 71, 78, 82], "046465": [70, 71], "130": [70, 71], "068695": [70, 71], "129": [70, 71], "127": [70, 71], "076251": [70, 71], "128": [70, 71, 77], "083941": [70, 71], "2465": [70, 71], "is_near_duplicate_issu": [70, 71, 73, 74, 77, 78], "131": [70, 71, 86], "000000e": [70, 71], "00": [70, 71, 73, 75, 77, 86, 87], "463180e": [70, 71], "07": [70, 71, 78, 82], "51": [70, 71, 73, 75, 78, 82], "857172e": [70, 71], "859087e": [70, 71], "30": [70, 75, 76, 77, 81, 86, 89], "3293": 70, "025076": 70, "026534": 70, "050766": 70, "051025": 70, "home": [70, 74, 75, 83, 88], "runner": [70, 74, 83, 88], "297": [70, 82], "userwarn": 70, "327": [70, 82], "306": 70, "34": [70, 75, 78, 80, 82, 83, 89], "54": [70, 75, 78, 82], "039117": 70, "53": [70, 73, 75, 81, 82, 87], "044594": 70, "105": 70, "105121": 70, "133588": [70, 71], "43": [70, 75, 78, 82, 88, 89], "168035": 70, "125": 70, "090878": 70, "37": [70, 75], "169462": 70, "109": [70, 75, 82], "194566": 70, "35": [70, 75, 80, 81, 82], "196302": 70, "206314": 70, "average_ood_scor": 70, "32933380816554325": 70, "52": [70, 75, 82, 89], "085049e": 70, "087324e": 70, "89": [70, 73, 82, 85], "92": [70, 78, 82, 87], "574261e": 70, "583757e": 70, "91": [70, 82, 88], "314215e": 70, "341292e": 70, "specfi": 70, "new_lab": 70, "scoring_funct": 70, "div": 70, "rem": 70, "inv_scal": 70, "49": [70, 75, 78, 82], "superstitionissuemanag": 70, "unlucki": 70, "superstit": 70, "to_seri": 70, "issues_mask": 70, "summary_scor": 70, "32": [70, 75, 80, 82], "9242": 70, "is_superstition_issu": 70, "superstition_scor": 70, "047581": 70, "090635": 70, "129591": 70, "65": [70, 82, 87], "164840": 70, "demo": [71, 73, 81, 87], "lurk": [71, 77, 78], "thoroughli": 71, "preprocess": [71, 73, 83, 85, 87, 88], "review": [71, 73, 74, 75, 76, 78, 82, 85, 86, 87, 88, 89], "8218": 71, "is_non_iid_issu": [71, 73, 74, 77, 78], "810274": 71, "826147": 71, "849587": 71, "855359": 71, "855485": 71, "821750488732925": 71, "auto": [71, 75, 76, 85, 87, 88], "conceptu": 71, "931818": 71, "522080": 71, "246459": 71, "821750": 71, "betweeen": 71, "864232": 71, "586131": 71, "235095": 71, "970324": 71, "825563": 71, "548979": 71, "221560": 71, "890575": 71, "533367": 71, "622256": 71, "199185": 71, "755724": 71, "499498": 71, "179601": 71, "948362": 71, "632385": 71, "292800": 71, "878267": 71, "examples_w_issu": [71, 76], "inde": [71, 74], "miscellan": [71, 89], "206897": 71, "041667": 71, "793103": 71, "071429": 71, "103448": 71, "928571": 71, "053333": 71, "101266": 71, "946667": 71, "portion": 71, "huge": [71, 78], "worri": 71, "critic": 71, "highli": [71, 77], "sql": [73, 87], "databas": [73, 87], "excel": [73, 87], "parquet": [73, 87], "student": [73, 85, 87, 89], "grade": [73, 85, 87], "900": [73, 85, 87], "exam": [73, 85, 87], "letter": [73, 87, 89], "hundr": [73, 87], "histgradientboostingclassifi": 73, "reflect": [73, 74, 80, 82, 83, 85, 87, 88], "standardscal": [73, 83, 87], "possibli": [73, 87], "grades_data": [73, 87], "read_csv": [73, 74, 85, 87, 88], "stud_id": [73, 87], "exam_1": [73, 85, 87], "exam_2": [73, 85, 87], "exam_3": [73, 85, 87], "letter_grad": [73, 87], "f48f73": [73, 87], "0bd4e7": [73, 87], "81": [73, 74, 82, 85, 87, 89], "great": [73, 75, 87], "particip": [73, 87], "cb9d7a": [73, 87], "61": [73, 77, 78, 82, 87], "94": [73, 75, 78, 82, 85, 87], "78": [73, 75, 78, 82, 85, 87], "9acca4": [73, 87], "48": [73, 75, 78, 82, 87], "x_raw": [73, 87], "cat_featur": 73, "x_encod": [73, 87], "get_dummi": [73, 85, 87], "drop_first": [73, 87], "numeric_featur": [73, 87], "scaler": [73, 83, 87], "x_process": [73, 87], "fit_transform": [73, 87], "bring": [73, 74, 77, 80, 85, 87, 88], "byod": [73, 74, 77, 80, 85, 87, 88], "boost": [73, 76, 80, 85], "xgboost": [73, 76, 85], "think": [73, 76, 81, 86, 89], "carefulli": [73, 74, 77, 87], "nonzero": 73, "suspici": [73, 87], "tabl": [73, 75, 80, 87], "358": 73, "294": [73, 82], "46": [73, 75, 78, 82], "941": 73, "7109": 73, "000005": [73, 74, 77], "886": 73, "000059": 73, "709": 73, "000104": 73, "723": 73, "000169": 73, "689": 73, "000181": 73, "7154": 73, "012085": 73, "061510": 73, "115512": 73, "124391": 73, "214163": 73, "2169": 73, "690": 73, "246": [73, 82], "185": [73, 75, 82, 89], "582": 73, "691": 73, "168": [73, 78], "915": 73, "187": [73, 75], "27": [73, 75, 78, 82, 89], "0014": [73, 75], "595": 73, "702427": 73, "147": [73, 78, 82], "711186": 73, "157": [73, 78], "721394": 73, "771": 73, "731979": 73, "898": 73, "740335": 73, "0014153602099278074": 73, "issue_result": 73, "000842": 73, "555944": 73, "004374": 73, "sorted_issu": 73, "73": [73, 75, 81, 82, 85], "86": [73, 77, 78, 82, 85, 87], "deserv": 73, "outlier_result": 73, "sorted_outli": 73, "56": [73, 75, 85], "96": [73, 75, 78, 81, 82, 85], "lt": [73, 74, 75, 77, 80, 86], "style": [73, 86], "font": 73, "18px": 73, "ff00ff": 73, "bac": 73, "unintend": [73, 74], "mistak": [73, 74, 77, 87, 88], "duplicate_result": 73, "58": [73, 75, 78, 82, 87], "perhap": [73, 78, 80], "twice": 73, "67": [73, 75, 82, 85], "wari": [73, 74, 76], "intent": [74, 88], "servic": [74, 88], "onlin": [74, 88], "bank": [74, 75, 88], "banking77": [74, 88], "oo": [74, 88], "000": [74, 75, 77, 88, 89], "categori": [74, 77, 88], "scope": [74, 88], "dive": 74, "your_featur": 74, "sentence_transform": [74, 88], "sentencetransform": [74, 88], "payment": [74, 88], "cancel_transf": [74, 88], "transfer": [74, 88], "fund": [74, 88], "cancel": [74, 88], "transact": [74, 88], "my": [74, 88], "revert": [74, 88], "morn": [74, 88], "realis": [74, 88], "yesterdai": [74, 88], "rent": [74, 88], "realli": [74, 80, 86, 88], "tomorrow": [74, 88], "raw_text": [74, 88], "change_pin": [74, 88], "beneficiary_not_allow": [74, 88], "getting_spare_card": [74, 88], "supported_cards_and_curr": [74, 88], "apple_pay_or_google_pai": [74, 88], "card_about_to_expir": [74, 88], "visa_or_mastercard": [74, 88], "lost_or_stolen_phon": [74, 88], "card_payment_fee_charg": [74, 88], "utter": [74, 88], "continu": [74, 77, 80, 85, 87, 88, 89], "suit": [74, 75, 88], "electra": [74, 88], "discrimin": [74, 88], "googl": [74, 88], "text_embed": 74, "No": [74, 76, 88], "google_electra": [74, 88], "pool": [74, 76, 83, 88], "400": [74, 88], "data_dict": [74, 78, 80], "84": [74, 82], "41": [74, 75, 82, 85, 89], "38": [74, 75, 82], "9720": 74, "981": 74, "974": 74, "000150": 74, "982": [74, 75], "000218": 74, "971": 74, "000512": 74, "980": [74, 75], "000947": 74, "9122": 74, "994": [74, 77], "676322": 74, "999": 74, "693868": 74, "697240": 74, "433": 74, "700874": 74, "989": 74, "713590": 74, "0656": 74, "160": 74, "006237": 74, "148": 74, "546": 74, "006485": 74, "514": 74, "481": 74, "008165": 74, "0000": [74, 75, 78], "313": [74, 82], "564102": 74, "572258": 74, "28": [74, 75, 77, 78, 80, 89], "574915": 74, "31": [74, 75, 78, 80, 82], "575507": 74, "575874": 74, "791961": 74, "258508": 74, "699010": 74, "183136": 74, "771112": 74, "to_numpi": [74, 85, 88], "data_with_suggested_label": 74, "suggested_label": 74, "charg": [74, 88], "cash": [74, 88], "holidai": [74, 88], "sent": [74, 88, 89], "card": [74, 75, 88], "mine": [74, 88], "expir": [74, 88], "me": [74, 88], "withdraw": 74, "monei": 74, "whoever": [74, 88], "outlier_issu": [74, 77], "lowest_quality_outli": 74, "OR": 74, "636c65616e6c616220697320617765736f6d6521": 74, "phone": [74, 75], "gone": 74, "gt": [74, 80, 89], "samp": 74, "br": 74, "press": [74, 89], "nonsens": 74, "sens": 74, "detriment": 74, "duplicate_issu": 74, "fee": 74, "pai": 74, "go": [74, 75, 78], "shortlist": [74, 85, 88], "curat": [74, 79], "mnist_test_set": 75, "imagenet_val_set": 75, "tench": 75, "goldfish": 75, "white": [75, 89], "shark": 75, "tiger": 75, "hammerhead": 75, "electr": 75, "rai": 75, "stingrai": 75, "cock": 75, "hen": 75, "ostrich": 75, "brambl": 75, "goldfinch": 75, "hous": 75, "finch": 75, "junco": 75, "indigo": 75, "bunt": 75, "american": [75, 89], "robin": 75, "bulbul": 75, "jai": 75, "magpi": 75, "chickade": 75, "dipper": 75, "kite": 75, "bald": 75, "eagl": 75, "vultur": 75, "grei": 75, "owl": 75, "fire": 75, "salamand": 75, "smooth": 75, "newt": 75, "spot": [75, 82], "axolotl": 75, "bullfrog": 75, "tree": 75, "frog": [75, 83], "tail": 75, "loggerhead": 75, "sea": 75, "turtl": 75, "leatherback": 75, "mud": 75, "terrapin": 75, "band": 75, "gecko": 75, "green": [75, 89], "iguana": 75, "carolina": 75, "anol": 75, "desert": 75, "grassland": 75, "whiptail": 75, "lizard": 75, "agama": 75, "frill": 75, "neck": 75, "allig": 75, "gila": 75, "monster": 75, "european": 75, "chameleon": 75, "komodo": 75, "dragon": 75, "nile": 75, "crocodil": 75, "triceratop": 75, "worm": 75, "snake": 75, "ring": 75, "eastern": 75, "hog": 75, "nose": 75, "kingsnak": 75, "garter": 75, "water": 75, "vine": 75, "night": 75, "boa": 75, "constrictor": 75, "african": 75, "rock": 75, "indian": 75, "cobra": 75, "mamba": 75, "saharan": 75, "horn": 75, "viper": 75, "diamondback": 75, "rattlesnak": 75, "sidewind": 75, "trilobit": 75, "harvestman": 75, "scorpion": 75, "yellow": 75, "garden": 75, "spider": 75, "barn": 75, "southern": 75, "widow": 75, "tarantula": 75, "wolf": 75, "tick": 75, "centiped": 75, "grous": 75, "ptarmigan": 75, "ruf": 75, "prairi": 75, "peacock": 75, "quail": 75, "partridg": 75, "parrot": 75, "macaw": 75, "sulphur": 75, "crest": 75, "cockatoo": 75, "lorikeet": 75, "coucal": 75, "bee": 75, "eater": 75, "hornbil": 75, "hummingbird": 75, "jacamar": 75, "toucan": 75, "duck": [75, 88], "breast": 75, "mergans": 75, "goos": 75, "swan": 75, "tusker": 75, "echidna": 75, "platypu": 75, "wallabi": 75, "koala": 75, "wombat": 75, "jellyfish": 75, "anemon": 75, "brain": 75, "coral": 75, "flatworm": 75, "nematod": 75, "conch": 75, "snail": 75, "slug": 75, "chiton": 75, "chamber": 75, "nautilu": 75, "dung": 75, "crab": 75, "fiddler": 75, "king": 75, "lobster": 75, "spini": 75, "crayfish": 75, "hermit": 75, "isopod": 75, "stork": 75, "spoonbil": 75, "flamingo": 75, "heron": 75, "egret": 75, "bittern": 75, "crane": 75, "bird": [75, 83], "limpkin": 75, "gallinul": 75, "coot": 75, "bustard": 75, "ruddi": 75, "turnston": 75, "dunlin": 75, "redshank": 75, "dowitch": 75, "oystercatch": 75, "pelican": 75, "penguin": 75, "albatross": 75, "whale": 75, "killer": 75, "dugong": 75, "lion": 75, "chihuahua": 75, "japanes": 75, "chin": 75, "maltes": 75, "pekinges": 75, "shih": 75, "tzu": 75, "charl": 75, "spaniel": 75, "papillon": 75, "terrier": 75, "rhodesian": 75, "ridgeback": 75, "afghan": [75, 89], "hound": 75, "basset": 75, "beagl": 75, "bloodhound": 75, "bluetick": 75, "coonhound": 75, "tan": 75, "walker": 75, "foxhound": 75, "redbon": 75, "borzoi": 75, "irish": 75, "wolfhound": 75, "italian": 75, "greyhound": 75, "whippet": 75, "ibizan": 75, "norwegian": 75, "elkhound": 75, "otterhound": 75, "saluki": 75, "scottish": 75, "deerhound": 75, "weimaran": 75, "staffordshir": 75, "bull": 75, "bedlington": 75, "border": 75, "kerri": 75, "norfolk": 75, "norwich": 75, "yorkshir": 75, "wire": 75, "fox": 75, "lakeland": 75, "sealyham": 75, "airedal": 75, "cairn": 75, "australian": 75, "dandi": 75, "dinmont": 75, "boston": 75, "miniatur": 75, "schnauzer": 75, "giant": 75, "tibetan": 75, "silki": 75, "coat": [75, 77], "wheaten": 75, "west": 75, "highland": 75, "lhasa": 75, "apso": 75, "flat": 75, "retriev": 75, "curli": 75, "golden": 75, "labrador": 75, "chesapeak": 75, "bai": 75, "german": [75, 89], "shorthair": 75, "pointer": 75, "vizsla": 75, "setter": 75, "gordon": 75, "brittani": 75, "clumber": 75, "springer": 75, "welsh": 75, "cocker": 75, "sussex": 75, "kuvasz": 75, "schipperk": 75, "groenendael": 75, "malinoi": 75, "briard": 75, "kelpi": 75, "komondor": 75, "sheepdog": 75, "shetland": 75, "colli": 75, "bouvier": 75, "de": 75, "flandr": 75, "rottweil": 75, "shepherd": 75, "dobermann": 75, "pinscher": 75, "swiss": [75, 89], "mountain": 75, "bernes": 75, "appenzel": 75, "sennenhund": 75, "entlebuch": 75, "boxer": 75, "bullmastiff": 75, "mastiff": 75, "french": 75, "bulldog": 75, "dane": 75, "st": 75, "bernard": 75, "huski": 75, "alaskan": 75, "malamut": 75, "siberian": 75, "dalmatian": 75, "affenpinsch": 75, "basenji": 75, "pug": 75, "leonberg": 75, "newfoundland": 75, "pyrenean": 75, "samoi": 75, "pomeranian": 75, "chow": 75, "keeshond": 75, "griffon": 75, "bruxelloi": 75, "pembrok": 75, "corgi": 75, "cardigan": 75, "poodl": 75, "mexican": 75, "hairless": 75, "tundra": 75, "coyot": 75, "dingo": 75, "dhole": 75, "wild": 75, "hyena": 75, "kit": 75, "arctic": 75, "tabbi": 75, "persian": 75, "siames": 75, "egyptian": 75, "mau": 75, "cougar": 75, "lynx": 75, "leopard": 75, "snow": 75, "jaguar": 75, "cheetah": 75, "brown": [75, 86], "bear": 75, "polar": 75, "sloth": 75, "mongoos": 75, "meerkat": 75, "beetl": 75, "ladybug": 75, "ground": [75, 78, 80, 85], "longhorn": 75, "leaf": 75, "rhinocero": 75, "weevil": 75, "fly": 75, "ant": 75, "grasshopp": 75, "cricket": 75, "stick": 75, "insect": 75, "cockroach": 75, "manti": 75, "cicada": 75, "leafhopp": 75, "lacew": 75, "dragonfli": 75, "damselfli": 75, "admir": 75, "ringlet": 75, "monarch": 75, "butterfli": 75, "gossam": 75, "wing": 75, "starfish": 75, "urchin": 75, "cucumb": 75, "cottontail": 75, "rabbit": 75, "hare": 75, "angora": 75, "hamster": 75, "porcupin": 75, "squirrel": 75, "marmot": 75, "beaver": 75, "guinea": 75, "pig": 75, "sorrel": 75, "zebra": 75, "boar": 75, "warthog": 75, "hippopotamu": 75, "ox": 75, "buffalo": 75, "bison": 75, "bighorn": 75, "sheep": 75, "alpin": 75, "ibex": 75, "hartebeest": 75, "impala": 75, "gazel": 75, "dromedari": 75, "llama": 75, "weasel": 75, "mink": 75, "polecat": 75, "foot": 75, "ferret": 75, "otter": 75, "skunk": 75, "badger": 75, "armadillo": 75, "toed": 75, "orangutan": 75, "gorilla": 75, "chimpanze": 75, "gibbon": 75, "siamang": 75, "guenon": 75, "pata": 75, "monkei": 75, "baboon": 75, "macaqu": 75, "langur": 75, "colobu": 75, "probosci": 75, "marmoset": 75, "capuchin": 75, "howler": 75, "titi": 75, "geoffroi": 75, "lemur": 75, "indri": 75, "asian": 75, "eleph": 75, "bush": 75, "snoek": 75, "eel": 75, "coho": 75, "salmon": 75, "beauti": 75, "clownfish": 75, "sturgeon": 75, "garfish": 75, "lionfish": 75, "pufferfish": 75, "abacu": 75, "abaya": 75, "academ": 75, "gown": 75, "accordion": 75, "acoust": 75, "guitar": 75, "aircraft": 75, "carrier": 75, "airlin": 75, "airship": 75, "altar": 75, "ambul": 75, "amphibi": 75, "clock": [75, 89], "apiari": 75, "apron": 75, "wast": 75, "assault": 75, "rifl": 75, "backpack": 75, "bakeri": 75, "balanc": 75, "beam": 75, "balloon": 75, "ballpoint": 75, "pen": 75, "aid": 75, "banjo": 75, "balust": 75, "barbel": 75, "barber": 75, "chair": [75, 82], "barbershop": 75, "baromet": 75, "barrel": 75, "wheelbarrow": 75, "basebal": 75, "basketbal": 75, "bassinet": 75, "bassoon": 75, "swim": 75, "cap": 75, "bath": 75, "towel": 75, "bathtub": 75, "station": 75, "wagon": 75, "lighthous": 75, "beaker": 75, "militari": 75, "beer": 75, "bottl": 75, "glass": 75, "bell": 75, "cot": 75, "bib": 75, "bicycl": [75, 86], "bikini": 75, "binder": 75, "binocular": 75, "birdhous": 75, "boathous": 75, "bobsleigh": 75, "bolo": 75, "tie": 75, "poke": 75, "bonnet": 75, "bookcas": 75, "bookstor": 75, "bow": 75, "brass": 75, "bra": 75, "breakwat": 75, "breastplat": 75, "broom": 75, "bucket": 75, "buckl": 75, "bulletproof": 75, "vest": 75, "butcher": 75, "shop": 75, "taxicab": 75, "cauldron": 75, "candl": 75, "cannon": 75, "cano": 75, "mirror": [75, 82], "carousel": 75, "tool": [75, 78, 80], "carton": 75, "wheel": 75, "teller": 75, "cassett": 75, "player": 75, "castl": 75, "catamaran": 75, "cd": 75, "cello": 75, "mobil": [75, 89], "chain": 75, "fenc": [75, 86], "mail": 75, "chainsaw": 75, "chest": 75, "chiffoni": 75, "chime": 75, "china": 75, "cabinet": 75, "christma": 75, "stock": 75, "church": 75, "movi": 75, "theater": 75, "cleaver": 75, "cliff": 75, "dwell": 75, "cloak": 75, "clog": 75, "cocktail": 75, "shaker": 75, "coffe": 75, "mug": 75, "coffeemak": 75, "coil": 75, "lock": 75, "keyboard": 75, "confectioneri": 75, "ship": [75, 83], "corkscrew": 75, "cornet": 75, "cowboi": 75, "boot": 75, "hat": 75, "cradl": 75, "crash": 75, "helmet": 75, "crate": 75, "infant": 75, "bed": 75, "crock": 75, "pot": 75, "croquet": 75, "crutch": 75, "cuirass": 75, "dam": 75, "desk": 75, "desktop": 75, "rotari": 75, "dial": 75, "telephon": 75, "diaper": 75, "watch": 75, "dine": 75, "dishcloth": 75, "dishwash": 75, "disc": 75, "brake": 75, "dock": 75, "sled": 75, "dome": 75, "doormat": 75, "drill": 75, "rig": 75, "drum": 75, "drumstick": 75, "dumbbel": 75, "dutch": 75, "oven": 75, "fan": 75, "locomot": 75, "entertain": 75, "center": 75, "envelop": 75, "espresso": 75, "powder": 75, "feather": 75, "fireboat": 75, "engin": [75, 86], "screen": 75, "sheet": 75, "flagpol": 75, "flute": 75, "footbal": 75, "forklift": 75, "fountain": 75, "poster": 75, "freight": 75, "fry": 75, "pan": 75, "fur": 75, "garbag": 75, "ga": 75, "pump": 75, "goblet": 75, "kart": 75, "golf": 75, "cart": 75, "gondola": 75, "gong": 75, "grand": 75, "piano": 75, "greenhous": 75, "grill": 75, "groceri": 75, "guillotin": 75, "barrett": 75, "hair": 75, "sprai": 75, "hammer": 75, "dryer": 75, "hand": [75, 78], "handkerchief": 75, "drive": 75, "harmonica": 75, "harp": 75, "harvest": 75, "hatchet": 75, "holster": 75, "honeycomb": 75, "hoop": 75, "skirt": 75, "horizont": 75, "bar": 75, "hors": [75, 83, 88], "drawn": 75, "hourglass": 75, "ipod": 75, "cloth": 75, "iron": 75, "jack": 75, "lantern": 75, "jean": 75, "jeep": 75, "shirt": [75, 77], "jigsaw": 75, "puzzl": 75, "pull": 75, "rickshaw": 75, "joystick": 75, "kimono": 75, "knee": 75, "pad": 75, "knot": 75, "ladl": 75, "lampshad": 75, "laptop": 75, "lawn": 75, "mower": 75, "knife": 75, "lifeboat": 75, "lighter": 75, "limousin": 75, "ocean": 75, "liner": 75, "lipstick": 75, "slip": 75, "shoe": 75, "lotion": 75, "speaker": 75, "loup": 75, "sawmil": 75, "magnet": 75, "compass": 75, "bag": [75, 77, 83, 84], "mailbox": 75, "tight": 75, "tank": 75, "manhol": 75, "maraca": 75, "marimba": 75, "maypol": 75, "maze": 75, "cup": [75, 82], "medicin": 75, "megalith": 75, "microphon": 75, "microwav": 75, "milk": 75, "minibu": 75, "miniskirt": 75, "minivan": 75, "missil": 75, "mitten": 75, "mix": 75, "bowl": 75, "modem": 75, "monasteri": 75, "monitor": 75, "mope": 75, "mortar": 75, "mosqu": 75, "mosquito": 75, "scooter": 75, "bike": 75, "tent": 75, "mous": [75, 76], "mousetrap": 75, "van": 75, "muzzl": 75, "nail": 75, "brace": 75, "necklac": 75, "nippl": 75, "obelisk": 75, "obo": 75, "ocarina": 75, "odomet": 75, "oil": 75, "oscilloscop": 75, "overskirt": 75, "bullock": 75, "oxygen": 75, "packet": 75, "paddl": 75, "padlock": 75, "paintbrush": 75, "pajama": 75, "palac": [75, 89], "parachut": 75, "park": 75, "bench": 75, "meter": 75, "passeng": 75, "patio": 75, "payphon": 75, "pedest": 75, "pencil": 75, "perfum": 75, "petri": 75, "dish": 75, "photocopi": 75, "plectrum": 75, "pickelhaub": 75, "picket": 75, "pickup": 75, "pier": 75, "piggi": 75, "pill": 75, "pillow": 75, "ping": 75, "pong": 75, "pinwheel": 75, "pirat": 75, "pitcher": 75, "plane": 75, "planetarium": 75, "plastic": 75, "plate": 75, "rack": 75, "plow": 75, "plunger": 75, "polaroid": 75, "camera": 75, "pole": [75, 86], "polic": 75, "poncho": 75, "billiard": 75, "soda": 75, "potter": 75, "power": [75, 78, 89], "prayer": 75, "rug": 75, "printer": 75, "prison": 75, "projectil": 75, "projector": 75, "hockei": 75, "puck": 75, "punch": 75, "purs": 75, "quill": 75, "quilt": 75, "race": 75, "racket": 75, "radiat": 75, "radio": 75, "telescop": 75, "rain": 75, "recreat": 75, "reel": 75, "reflex": 75, "refriger": 75, "remot": 75, "restaur": 75, "revolv": 75, "rotisseri": 75, "eras": 75, "rugbi": 75, "ruler": 75, "safe": 75, "safeti": 75, "salt": 75, "sandal": [75, 77], "sarong": 75, "saxophon": 75, "scabbard": 75, "school": 75, "bu": [75, 86], "schooner": 75, "scoreboard": 75, "crt": 75, "screw": 75, "screwdriv": 75, "seat": 75, "belt": 75, "sew": 75, "shield": 75, "shoji": 75, "basket": 75, "shovel": 75, "shower": 75, "curtain": 75, "ski": 75, "sleep": 75, "door": 75, "slot": 75, "snorkel": 75, "snowmobil": 75, "snowplow": 75, "soap": 75, "dispens": 75, "soccer": [75, 89], "sock": 75, "solar": 75, "thermal": 75, "collector": 75, "sombrero": 75, "soup": 75, "heater": 75, "shuttl": 75, "spatula": 75, "motorboat": 75, "web": 75, "spindl": 75, "sport": [75, 89], "spotlight": 75, "stage": 75, "steam": 75, "arch": 75, "bridg": 75, "steel": 75, "stethoscop": 75, "scarf": 75, "stone": 75, "wall": [75, 86], "stopwatch": 75, "stove": 75, "strainer": 75, "tram": 75, "stretcher": 75, "couch": 75, "stupa": 75, "submarin": 75, "sundial": 75, "sunglass": 75, "sunscreen": 75, "suspens": 75, "mop": 75, "sweatshirt": 75, "swimsuit": 75, "swing": 75, "switch": 75, "syring": 75, "lamp": 75, "tape": 75, "teapot": 75, "teddi": 75, "televis": [75, 89], "tenni": 75, "thatch": 75, "roof": 75, "front": 75, "thimbl": 75, "thresh": 75, "throne": 75, "tile": 75, "toaster": 75, "tobacco": 75, "toilet": 75, "totem": 75, "tow": 75, "tractor": 75, "semi": 75, "trailer": 75, "trai": 75, "trench": 75, "tricycl": 75, "trimaran": 75, "tripod": 75, "triumphal": 75, "trolleybu": 75, "trombon": 75, "tub": 75, "turnstil": 75, "typewrit": 75, "umbrella": 75, "unicycl": 75, "upright": 75, "vacuum": 75, "cleaner": 75, "vase": 75, "vault": 75, "velvet": 75, "vend": 75, "vestment": 75, "viaduct": 75, "violin": 75, "volleybal": 75, "waffl": 75, "wallet": 75, "wardrob": 75, "sink": 75, "wash": 75, "jug": 75, "tower": 75, "whiskei": 75, "whistl": 75, "wig": 75, "shade": [75, 86], "windsor": 75, "wine": 75, "wok": 75, "wooden": 75, "spoon": 75, "wool": 75, "rail": 75, "shipwreck": 75, "yawl": 75, "yurt": 75, "websit": 75, "comic": 75, "book": 75, "crossword": 75, "traffic": [75, 82, 86], "sign": [75, 86, 89], "light": [75, 77, 82, 86], "dust": 75, "jacket": [75, 82], "menu": 75, "guacamol": 75, "consomm": 75, "trifl": 75, "ic": 75, "cream": 75, "pop": 75, "baguett": 75, "bagel": 75, "pretzel": 75, "cheeseburg": 75, "mash": 75, "potato": 75, "cabbag": 75, "broccoli": 75, "cauliflow": 75, "zucchini": 75, "spaghetti": 75, "squash": 75, "acorn": 75, "butternut": 75, "artichok": 75, "pepper": 75, "cardoon": 75, "mushroom": 75, "granni": 75, "smith": 75, "strawberri": 75, "orang": 75, "lemon": 75, "pineappl": 75, "banana": 75, "jackfruit": 75, "custard": 75, "appl": 75, "pomegran": 75, "hai": 75, "carbonara": 75, "chocol": 75, "syrup": 75, "dough": 75, "meatloaf": 75, "pizza": 75, "pie": 75, "burrito": 75, "eggnog": 75, "alp": 75, "bubbl": 75, "reef": 75, "geyser": 75, "lakeshor": 75, "promontori": 75, "shoal": 75, "seashor": 75, "vallei": 75, "volcano": 75, "bridegroom": 75, "scuba": 75, "diver": 75, "rapese": 75, "daisi": 75, "ladi": 75, "slipper": 75, "corn": 75, "rose": 75, "hip": 75, "chestnut": 75, "fungu": 75, "agar": 75, "gyromitra": 75, "stinkhorn": 75, "earth": 75, "star": 75, "wood": 75, "bolet": 75, "ear": 75, "cifar10_test_set": 75, "airplan": [75, 83], "automobil": [75, 83], "deer": [75, 83], "cifar100_test_set": 75, "aquarium_fish": 75, "babi": 75, "boi": 75, "camel": 75, "caterpillar": 75, "cattl": [75, 89], "cloud": 75, "dinosaur": 75, "dolphin": 75, "flatfish": 75, "forest": 75, "girl": 75, "kangaroo": 75, "lawn_mow": 75, "man": 75, "maple_tre": 75, "motorcycl": [75, 86], "oak_tre": 75, "orchid": 75, "palm_tre": 75, "pear": 75, "pickup_truck": 75, "pine_tre": 75, "plain": 75, "poppi": 75, "possum": 75, "raccoon": 75, "road": [75, 86], "rocket": 75, "seal": 75, "shrew": 75, "skyscrap": 75, "streetcar": 75, "sunflow": 75, "sweet_pepp": 75, "trout": 75, "tulip": 75, "willow_tre": 75, "woman": [75, 82], "caltech256": 75, "ak47": 75, "bat": 75, "glove": 75, "birdbath": 75, "blimp": 75, "bonsai": 75, "boom": 75, "breadmak": 75, "buddha": 75, "bulldoz": 75, "cactu": 75, "cake": 75, "tire": 75, "cartman": 75, "cereal": 75, "chandeli": 75, "chess": 75, "board": 75, "chimp": 75, "chopstick": 75, "coffin": 75, "coin": 75, "comet": 75, "cormor": 75, "globe": 75, "diamond": 75, "dice": 75, "doorknob": 75, "drink": 75, "straw": 75, "dumb": 75, "eiffel": 75, "elk": 75, "ewer": 75, "eyeglass": 75, "fern": 75, "fighter": 75, "jet": [75, 85], "extinguish": 75, "hydrant": 75, "firework": 75, "flashlight": 75, "floppi": 75, "fri": 75, "frisbe": 75, "galaxi": 75, "giraff": 75, "goat": 75, "gate": 75, "grape": 75, "pick": 75, "hamburg": 75, "hammock": 75, "harpsichord": 75, "hawksbil": 75, "helicopt": 75, "hibiscu": 75, "homer": 75, "simpson": 75, "horsesho": 75, "air": 75, "skeleton": 75, "ibi": 75, "cone": 75, "iri": 75, "jesu": 75, "christ": 75, "joi": 75, "kayak": 75, "ketch": 75, "ladder": 75, "lath": 75, "licens": 75, "lightbulb": 75, "lightn": 75, "mandolin": 75, "mar": 75, "mattress": 75, "megaphon": 75, "menorah": 75, "microscop": 75, "minaret": 75, "minotaur": 75, "motorbik": 75, "mussel": 75, "neckti": 75, "octopu": 75, "palm": 75, "pilot": 75, "paperclip": 75, "shredder": 75, "pci": 75, "peopl": [75, 82], "pez": 75, "picnic": 75, "pram": 75, "prai": 75, "pyramid": 75, "rainbow": 75, "roulett": 75, "saddl": 75, "saturn": 75, "segwai": 75, "propel": 75, "sextant": 75, "music": 75, "skateboard": 75, "smokestack": 75, "sneaker": 75, "boat": 75, "stain": 75, "steer": 75, "stirrup": 75, "superman": 75, "sushi": 75, "armi": [75, 89], "sword": 75, "tambourin": 75, "teepe": 75, "court": 75, "theodolit": 75, "tomato": 75, "tombston": 75, "tour": 75, "pisa": 75, "treadmil": 75, "fork": 75, "tweezer": 75, "unicorn": 75, "vcr": 75, "waterfal": 75, "watermelon": 75, "weld": 75, "windmil": 75, "xylophon": 75, "yarmulk": 75, "yo": 75, "toad": 75, "twenty_news_test_set": 75, "alt": 75, "atheism": 75, "comp": 75, "graphic": [75, 86], "misc": [75, 89], "sy": 75, "ibm": 75, "pc": 75, "hardwar": 75, "mac": 75, "forsal": 75, "rec": 75, "sci": 75, "crypt": 75, "electron": 75, "med": 75, "soc": 75, "religion": 75, "christian": [75, 89], "talk": [75, 89], "polit": 75, "gun": 75, "mideast": 75, "amazon": 75, "neutral": 75, "imdb_test_set": 75, "all_class": 75, "20news_test_set": 75, "_load_classes_predprobs_label": 75, "dataset_nam": 75, "labelerror": 75, "url_bas": 75, "5392f6c71473055060be3044becdde1cbc18284d": 75, "url_label": 75, "original_test_label": 75, "_original_label": 75, "url_prob": 75, "cross_validated_predicted_prob": 75, "_pyx": 75, "num_part": 75, "datatset": 75, "bytesio": 75, "allow_pickl": 75, "pred_probs_part": 75, "url": 75, "_of_": 75, "nload": 75, "imdb": 75, "ve": [75, 78, 80, 82], "interpret": [75, 76, 78], "capit": 75, "29780": 75, "256": [75, 76, 82], "29": [75, 77, 80, 81, 82, 86, 89], "780": 75, "medic": [75, 89], "doctor": 75, "254": [75, 82], "359223": 75, "333333": 75, "640777": 75, "184": [75, 78], "258427": 75, "341176": 75, "263158": 75, "658824": 75, "337349": 75, "246575": 75, "662651": 75, "248": 75, "330000": 75, "355769": 75, "670000": 75, "251": [75, 82], "167": [75, 78, 82], "252": 75, "112": 75, "253": [75, 82], "022989": 75, "255": [75, 77], "049505": 75, "190": [75, 78, 82], "66": 75, "002216": 75, "000974": 75, "59": [75, 82], "88": [75, 77, 78, 81, 82, 85], "000873": 75, "000739": 75, "79": [75, 82, 87], "32635": 75, "32636": 75, "47": [75, 82], "32637": 75, "32638": 75, "32639": 75, "32640": 75, "051": 75, "93": [75, 82, 85, 87, 89], "002242": 75, "997758": 75, "002088": 75, "001045": 75, "997912": 75, "002053": 75, "997947": 75, "001980": 75, "000991": 75, "998020": 75, "001946": 75, "002915": 75, "998054": 75, "001938": 75, "002904": 75, "998062": 75, "001020": 75, "998980": 75, "001018": 75, "002035": 75, "998982": 75, "999009": 75, "0003": 75, "0002": 75, "36": [75, 89], "44": [75, 81, 82], "71": [75, 78, 82], "071": 75, "067269": 75, "929": 75, "046": 75, "058243": 75, "954": 75, "035": 75, "032096": 75, "965": 75, "031": 75, "012232": 75, "969": 75, "022": 75, "025896": 75, "978": 75, "020": [75, 78], "013092": 75, "018": 75, "013065": 75, "016": 75, "030542": 75, "984": 75, "013": 75, "020833": 75, "987": 75, "012": 75, "010020": 75, "988": 75, "0073": 75, "0020": 75, "0016": 75, "0015": 75, "0013": 75, "0012": 75, "0010": 75, "0008": 75, "0007": 75, "0006": 75, "0005": 75, "0004": 75, "244": [75, 82], "98": [75, 76, 85], "452381": 75, "459770": 75, "72": [75, 78, 81, 85, 89], "523364": 75, "460784": 75, "446602": 75, "57": [75, 78], "68": [75, 78, 82, 87], "103774": 75, "030612": 75, "97": [75, 76, 78, 82, 85, 87, 89], "110092": 75, "049020": 75, "99": [75, 78, 87, 89], "0034": 75, "0032": 75, "0026": 75, "0025": 75, "4945": 75, "4946": 75, "4947": 75, "4948": 75, "4949": 75, "4950": 75, "846": 75, "82": [75, 78, 82], "7532": 75, "532": 75, "034483": 75, "009646": 75, "965517": 75, "030457": 75, "020513": 75, "969543": 75, "028061": 75, "035443": 75, "971939": 75, "025316": 75, "005168": 75, "974684": 75, "049751": 75, "979487": 75, "019920": 75, "042802": 75, "980080": 75, "017677": 75, "005115": 75, "982323": 75, "012987": 75, "005236": 75, "987013": 75, "012723": 75, "025126": 75, "987277": 75, "010989": 75, "008264": 75, "989011": 75, "010283": 75, "027778": 75, "989717": 75, "009677": 75, "990323": 75, "007614": 75, "010127": 75, "992386": 75, "005051": 75, "994949": 75, "005025": 75, "994975": 75, "005013": 75, "994987": 75, "001859": 75, "001328": 75, "000929": 75, "000664": 75, "186": [75, 78], "188": [75, 78, 81], "189": [75, 78], "snippet": 76, "nlp": [76, 89], "mind": [76, 78], "number_of_class": 76, "total_number_of_data_point": 76, "drop": [76, 80, 85, 88], "feed": 76, "alphabet": 76, "labels_proper_format": 76, "your_classifi": 76, "issues_datafram": 76, "class_predicted_for_flagged_exampl": 76, "class_predicted_for_all_exampl": 76, "grant": 76, "datataset": 76, "fair": [76, 78], "game": 76, "speedup": [76, 83], "flexibl": 76, "tempfil": 76, "mkdtemp": 76, "sped": 76, "anywai": 76, "pred_probs_merg": 76, "merge_rare_class": 76, "count_threshold": 76, "class_mapping_orig2new": 76, "heath_summari": 76, "num_examples_per_class": 76, "rare_class": 76, "num_classes_merg": 76, "other_class": 76, "labels_merg": 76, "new_c": 76, "merged_prob": 76, "keepdim": 76, "hstack": [76, 77, 78, 80], "new_class": 76, "original_class": 76, "num_check": 76, "ones_array_ref": 76, "isclos": 76, "though": [76, 78, 89], "successfulli": 76, "meaning": [76, 83], "virtuou": [76, 80], "cycl": [76, 80], "jointli": 76, "junk": 76, "clutter": 76, "unknown": 76, "caltech": 76, "intersect": 76, "combined_boolean_mask": 76, "mask1": 76, "mask2": 76, "gradientboostingclassifi": [76, 78], "true_error": [76, 78, 81], "101": [76, 82], "102": [76, 81, 82], "104": [76, 78, 82], "model_to_find_error": 76, "model_to_return": 76, "cl0": 76, "randomizedsearchcv": 76, "expens": 76, "param_distribut": 76, "learning_r": [76, 78], "max_depth": [76, 78], "magnitud": 76, "coeffici": [76, 85], "optin": 76, "environ": [76, 78], "rerun": [76, 78], "cell": [76, 78], "On": [76, 78, 82], "unabl": [76, 78], "render": [76, 78], "nbviewer": [76, 78], "cleanlearningcleanlearn": [76, 78], "linearregressionlinearregress": 76, "assist": 76, "streamlin": 76, "ux": 76, "agpl": 76, "compani": 76, "commerci": 76, "alter": 76, "email": 76, "discuss": 76, "anywher": 76, "60": [77, 78], "excess": 77, "torchvis": [77, 83], "tensordataset": 77, "stratifiedkfold": [77, 81], "tqdm": 77, "fashion_mnist": 77, "num_row": 77, "60000": 77, "pil": 77, "transformed_dataset": 77, "with_format": 77, "unsqueez": 77, "num_proc": 77, "cpu_count": 77, "torch_dataset": 77, "quick": [77, 81], "super": 77, "relu": 77, "batchnorm2d": 77, "maxpool2d": 77, "lazylinear": 77, "flatten": 77, "get_test_accuraci": 77, "testload": [77, 83], "energi": 77, "trainload": [77, 83], "n_epoch": 77, "patienc": 77, "criterion": 77, "crossentropyloss": 77, "adamw": 77, "best_test_accuraci": 77, "start_epoch": 77, "running_loss": 77, "best_epoch": 77, "end_epoch": 77, "3f": [77, 85], "acc": [77, 78], "time_taken": 77, "compute_embed": 77, "compute_pred_prob": 77, "train_batch_s": 77, "num_work": 77, "worker": [77, 89], "train_id_list": 77, "test_id_list": 77, "train_id": 77, "test_id": 77, "embeddings_model": 77, "ntrain": 77, "trainset": 77, "testset": 77, "pin_memori": 77, "fold_embed": 77, "fold_pred_prob": 77, "finish": 77, "483": 77, "835": 77, "139": [77, 81], "331": 77, "310": 77, "609": 77, "51it": 77, "63": [77, 78, 82], "84it": 77, "492": 77, "87": [77, 82, 85, 88], "085": 77, "692": 77, "330": [77, 82], "290": [77, 82], "033": 77, "32it": 77, "03it": 77, "476": 77, "305": [77, 85], "328": [77, 82], "335": 77, "830": 77, "07it": 77, "05it": 77, "reorder": 77, "vision": 77, "low_inform": 77, "odd_aspect_ratio": 77, "odd_siz": 77, "grayscal": 77, "exce": 77, "max_preval": 77, "7620": 77, "3692": 77, "3521": 77, "225": [77, 81], "166": 77, "9661": 77, "40378": 77, "687452": 77, "54473": 77, "705050": 77, "29412": 77, "715470": 77, "25316": 77, "716273": 77, "52247": 77, "725283": 77, "9581": 77, "19228": 77, "dress": 77, "54078": 77, "000010": 77, "pullov": 77, "32657": 77, "21282": 77, "000011": 77, "11262": 77, "000014": 77, "0268": 77, "30659": 77, "000015": 77, "30968": 77, "258": 77, "000017": 77, "9762": 77, "54565": 77, "47139": 77, "000026": 77, "7834": 77, "42819": 77, "629362": 77, "51431": 77, "654330": 77, "55548": 77, "658364": 77, "51191": 77, "668572": 77, "50081": 77, "669703": 77, "7834321613629787": 77, "110901": 77, "974390": 77, "998733": 77, "937117": 77, "998755": 77, "53564": 77, "5473": 77, "trouser": 77, "plot_label_issue_exampl": 77, "ncol": [77, 83], "nrow": [77, 83], "ceil": 77, "axes_list": 77, "label_issue_indic": 77, "gl": 77, "sl": 77, "fontdict": 77, "imshow": [77, 83], "cmap": [77, 85], "grai": 77, "subplots_adjust": 77, "hspace": 77, "outsiz": 77, "outlier_issues_df": 77, "depict": [77, 81, 82, 83, 84, 86], "plot_outlier_issues_exampl": 77, "n_comparison_imag": 77, "sample_from_class": 77, "number_of_sampl": 77, "non_outlier_indic": 77, "isnul": 77, "non_outlier_indices_excluding_curr": 77, "sampled_indic": 77, "label_scores_of_sampl": 77, "top_score_indic": 77, "top_label_indic": 77, "sampled_imag": 77, "get_image_given_label_and_sampl": 77, "image_from_dataset": 77, "corresponding_label": 77, "comparison_imag": 77, "images_to_plot": 77, "idlist": 77, "iterrow": 77, "especi": [77, 85, 87, 88], "near_duplicate_issu": 77, "closest": 77, "counterpart": 77, "near_duplicate_issues_df": 77, "plot_near_duplicate_issue_exampl": 77, "seen_id_pair": 77, "get_image_and_given_label_and_predicted_label": 77, "duplicate_imag": 77, "nd_set": 77, "challeng": 77, "dark_issu": 77, "reveal": [77, 86], "dark_scor": 77, "dark_issues_df": 77, "is_dark_issu": 77, "34848": 77, "203922": 77, "50270": 77, "204588": 77, "3936": 77, "213098": 77, "733": 77, "217686": 77, "8094": 77, "230118": 77, "plot_image_issue_exampl": 77, "difficult": 77, "disproportion": 77, "lowinfo_issu": 77, "low_information_scor": 77, "lowinfo_issues_df": 77, "is_low_information_issu": 77, "53050": 77, "067975": 77, "40875": 77, "089929": 77, "9594": 77, "092601": 77, "34825": 77, "107744": 77, "37530": 77, "108516": 77, "lot": 77, "depth": 78, "survei": [78, 89], "focus": [78, 80], "scienc": 78, "multivariate_norm": [78, 80, 81], "make_data": [78, 80], "cov": [78, 80, 81], "avg_trac": [78, 81], "test_label": [78, 81, 83, 88], "py_tru": 78, "noise_matrix_tru": 78, "noise_marix": 78, "s_test": 78, "noisy_test_label": 78, "purpl": 78, "val": 78, "namespac": 78, "exec": 78, "markerfacecolor": [78, 81], "markeredgecolor": [78, 81, 85], "markers": [78, 81, 85], "markeredgewidth": [78, 81, 85], "realist": 78, "7560": 78, "638483e": 78, "897052e": 78, "548986e": 78, "924634e": 78, "374580e": 78, "4643": 78, "050286": 78, "065420": 78, "249": [78, 82], "109420": 78, "111687": 78, "115403": 78, "3312": 78, "007136": 78, "119": [78, 82], "033725": 78, "103": [78, 82], "033738": 78, "238": [78, 82], "037825": 78, "236": [78, 82], "037843": 78, "222": 78, "614915": 78, "122": [78, 82], "624422": 78, "625965": 78, "626079": 78, "118": 78, "627675": 78, "695174": 78, "323529": 78, "522929": 78, "013722": 78, "675606": 78, "646438": 78, "anyth": 78, "enhanc": [78, 80, 82], "magic": 78, "83": [78, 82, 85, 87, 89], "liter": 78, "identif": 78, "x27": 78, "logisticregressionlogisticregress": 78, "ever": 78, "truth": [78, 80, 85], "092": 78, "040": 78, "024": 78, "004": 78, "surpris": 78, "arxiv": 78, "ab": 78, "1705": 78, "01936": 78, "ton": 78, "yourfavoritemodel1": 78, "merged_label": 78, "merged_test_label": 78, "newli": [78, 80], "yourfavoritemodel2": 78, "yourfavoritemodel3": 78, "cl3": 78, "takeawai": 78, "That": [78, 81], "randomli": 78, "my_test_pred_prob": 78, "my_test_pr": 78, "issues_test": 78, "corrected_test_label": 78, "pretend": 78, "cl_test_pr": 78, "69": [78, 85], "fairli": 78, "label_acc": 78, "percentag": 78, "offset": 78, "nquestion": 78, "overestim": 78, "answer": 78, "experienc": 78, "06": [78, 82, 89], "76": [78, 81, 82, 85, 87], "knowledg": 78, "quantiti": [78, 85], "prioiri": 78, "known": 78, "versatil": 78, "label_issues_indic": 78, "213": [78, 82], "212": [78, 87], "218": [78, 82], "152": 78, "197": [78, 82], "196": [78, 82], "170": 78, "214": 78, "164": [78, 81], "198": [78, 82, 89], "191": [78, 82], "121": [78, 88, 89], "117": [78, 85, 89], "62": [78, 82, 85], "206": [78, 82], "115": [78, 82], "193": 78, "194": 78, "201": [78, 82], "174": 78, "163": 78, "150": [78, 80, 82], "169": 78, "151": [78, 82], "precision_scor": 78, "recall_scor": 78, "f1_score": 78, "true_label_issu": 78, "filter_by_list": 78, "718750": [78, 80], "807018": 78, "912": 78, "733333": 78, "800000": 78, "721311": 78, "792793": 78, "908": 78, "676923": 78, "765217": 78, "892": 78, "567901": 78, "702290": 78, "844": 78, "gaug": 78, "label_issues_count": 78, "155": [78, 82], "156": 78, "172": [78, 81], "easiest": 78, "modular": 78, "penalti": 78, "l2": 78, "model3": 78, "n_estim": 78, "cv_pred_probs_1": 78, "cv_pred_probs_2": 78, "cv_pred_probs_3": 78, "label_quality_scores_best": 78, "cv_pred_probs_ensembl": 78, "label_quality_scores_bett": 78, "superior": [78, 84], "workflow": [79, 85], "speechbrain": 79, "timm": 79, "glad": 80, "multiannotator_label": 80, "300": [80, 89], "noisier": 80, "111": [80, 85], "local_data": [80, 81], "true_labels_train": [80, 81], "noise_matrix_bett": 80, "noise_matrix_wors": 80, "transpos": [80, 83], "dropna": 80, "zfill": 80, "row_na_check": 80, "notna": 80, "reset_index": 80, "a0001": 80, "a0002": 80, "a0003": 80, "a0004": 80, "a0005": 80, "a0006": 80, "a0007": 80, "a0008": 80, "a0009": 80, "a0010": 80, "a0041": 80, "a0042": 80, "a0043": 80, "a0044": 80, "a0045": 80, "a0046": 80, "a0047": 80, "a0048": 80, "a0049": 80, "a0050": 80, "na": 80, "60856743": 80, "41693214": 80, "40908785": 80, "87147629": 80, "64941785": 80, "10774851": 80, "0524466": 80, "71853246": 80, "37169848": 80, "66031048": 80, "multiannotator_util": 80, "crude": 80, "straight": 80, "majority_vote_label": 80, "736157": 80, "757738": 80, "782255": 80, "715585": 80, "824273": 80, "quality_annotator_a0001": 80, "quality_annotator_a0002": 80, "quality_annotator_a0003": 80, "quality_annotator_a0004": 80, "quality_annotator_a0005": 80, "quality_annotator_a0006": 80, "quality_annotator_a0007": 80, "quality_annotator_a0008": 80, "quality_annotator_a0009": 80, "quality_annotator_a0010": 80, "quality_annotator_a0041": 80, "quality_annotator_a0042": 80, "quality_annotator_a0043": 80, "quality_annotator_a0044": 80, "quality_annotator_a0045": 80, "quality_annotator_a0046": 80, "quality_annotator_a0047": 80, "quality_annotator_a0048": 80, "quality_annotator_a0049": 80, "quality_annotator_a0050": 80, "070551": 80, "216064": 80, "119178": 80, "alongisd": 80, "244982": 80, "208333": 80, "295978": 80, "294118": 80, "324194": 80, "310345": 80, "355315": 80, "346154": 80, "439728": 80, "480000": 80, "a0031": 80, "523205": 80, "580645": 80, "a0034": 80, "535313": 80, "607143": 80, "a0021": 80, "607002": 80, "a0015": 80, "609527": 80, "678571": 80, "a0011": 80, "621101": 80, "692308": 80, "wors": 80, "improved_consensus_label": 80, "majority_vote_accuraci": 80, "cleanlab_label_accuraci": 80, "8581081081081081": 80, "9797297297297297": 80, "besid": 80, "sorted_consensus_quality_scor": 80, "worst_qual": 80, "better_qu": 80, "worst_quality_accuraci": 80, "better_quality_accuraci": 80, "9893238434163701": 80, "improved_pred_prob": 80, "treat": [80, 81, 85, 89], "analzi": 80, "copyright": 81, "advertis": 81, "violenc": 81, "nsfw": 81, "ranked_label_issu": [81, 87, 88], "multioutput": 81, "multioutputclassifi": 81, "celeba": 81, "make_multilabel_data": 81, "boxes_coordin": 81, "box_multilabel": 81, "make_multi": 81, "bx1": 81, "by1": 81, "bx2": 81, "by2": 81, "label_list": 81, "ur": 81, "upper": 81, "inidx": 81, "logical_and": 81, "tolist": 81, "inv_d": 81, "labels_idx": 81, "true_labels_test": 81, "dict_unique_label": 81, "get_color_arrai": 81, "dcolor": 81, "aa4400": 81, "55227f": 81, "55a100": 81, "00ff00": 81, "007f7f": 81, "386b55": 81, "0000ff": 81, "simplic": 81, "advis": 81, "y_onehot": 81, "single_class_label": 81, "stratifi": [81, 84], "kf": 81, "train_index": 81, "test_index": 81, "clf_cv": 81, "x_train_cv": 81, "x_test_cv": 81, "y_train_cv": 81, "y_test_cv": 81, "y_pred_cv": 81, "saw": 81, "num_to_displai": 81, "09": [81, 82], "275": 81, "267": 81, "171": 81, "234": 81, "165": 81, "227": [81, 82], "262": [81, 82], "263": [81, 82], "266": [81, 82], "143": [81, 82], "216": [81, 82], "265": 81, "159": [81, 82], "despit": [81, 89], "suspect": 81, "888": 81, "8224": 81, "9632": 81, "968": 81, "6512": 81, "0444": 81, "774": 81, "labels_binary_format": 81, "labels_list_format": 81, "surround": 82, "scene": 82, "coco": 82, "everydai": 82, "has_label_issu": 82, "insal": 82, "nc": [82, 86, 89], "s3": [82, 86, 89], "amazonaw": [82, 86, 89], "objectdetectionbenchmark": 82, "tutorial_obj": 82, "pkl": 82, "example_imag": 82, "unzip": [82, 89], "begin": 82, "detectron2": 82, "image_path": 82, "rb": 82, "image_to_visu": 82, "seg_map": 82, "334": 82, "float32": 82, "bboxes_ignor": 82, "286": 82, "285": 82, "224": 82, "231": 82, "293": 82, "235": 82, "289": [82, 85], "282": 82, "74": [82, 85, 87], "281": 82, "271": 82, "280": 82, "277": 82, "279": 82, "287": 82, "299": 82, "276": 82, "307": 82, "321": 82, "326": 82, "333": 82, "261": 82, "319": 82, "257": 82, "295": 82, "283": 82, "243": 82, "303": 82, "316": 82, "247": [82, 89], "323": 82, "226": 82, "228": 82, "232": 82, "219": 82, "239": 82, "240": 82, "209": 82, "242": 82, "202": 82, "230": 82, "215": 82, "220": 82, "229": 82, "85": [82, 85], "217": [82, 89], "237": 82, "207": 82, "204": 82, "205": 82, "223": 82, "153": 82, "149": 82, "140": 82, "124": 82, "268": 82, "273": 82, "108": 82, "284": 82, "110": 82, "136": 82, "145": [82, 89], "173": 82, "317": 82, "192": 82, "329": 82, "332": 82, "324": 82, "203": 82, "320": 82, "314": 82, "199": 82, "291": 82, "000000481413": 82, "jpg": 82, "42398": 82, "44503": 82, "337": [82, 88], "29968": 82, "336": 82, "21005": 82, "9978472": 82, "forgot": 82, "drew": 82, "label_issue_idx": 82, "num_examples_to_show": 82, "113": [82, 85, 89], "candid": 82, "97489622": 82, "70610878": 82, "98764951": 82, "88899237": 82, "99085805": 82, "issue_idx": 82, "95569726e": 82, "03354841e": 82, "57510169e": 82, "58447666e": 82, "39755858e": 82, "suppli": 82, "issue_to_visu": 82, "000000009483": 82, "95569726168054e": 82, "addition": [82, 86], "visibl": 82, "missmatch": 82, "likelei": 82, "agnost": 82, "vaidat": 82, "inconsist": 82, "000000395701": 82, "033548411774308e": 82, "armchair": 82, "tv": 82, "000000154004": 82, "38300759625496356": 82, "foreground": 82, "000000448410": 82, "0008575101690203273": 82, "crowd": 82, "alon": 82, "explor": [82, 83], "resembl": [82, 83], "contribut": 82, "000000499768": 82, "9748962231208227": 82, "000000521141": 82, "8889923658893665": 82, "000000143931": 82, "9876495074395956": 82, "train_feature_embed": 83, "ood_train_feature_scor": 83, "test_feature_embed": 83, "ood_test_feature_scor": 83, "ood_train_predictions_scor": 83, "train_pred_prob": 83, "ood_test_predictions_scor": 83, "test_pred_prob": 83, "pylab": 83, "rcparam": 83, "baggingclassifi": 83, "therebi": 83, "rescal": 83, "transform_norm": 83, "totensor": 83, "root": 83, "animal_class": 83, "non_animal_class": 83, "animal_idx": 83, "isin": 83, "test_idx": 83, "toronto": 83, "edu": 83, "kriz": 83, "5000": 83, "plot_imag": 83, "visualize_outli": 83, "txt_class": 83, "img": [83, 85], "npimg": 83, "show_label": 83, "data_subset": 83, "resnet50": 83, "corpu": 83, "2048": 83, "embed_imag": 83, "create_model": 83, "rwightman": 83, "v0": 83, "rsb": 83, "resnet50_a1_0": 83, "14fe96d1": 83, "pth": 83, "checkpoint": 83, "strang": 83, "odd": 83, "train_ood_features_scor": 83, "top_train_ood_features_idx": 83, "fun": 83, "negat": 83, "homogen": 83, "bottom_train_ood_features_idx": 83, "test_ood_features_scor": 83, "top_ood_features_idx": 83, "inevit": 83, "trade": 83, "5th": 83, "percentil": 83, "fifth_percentil": 83, "plt_rang": 83, "hist": 83, "train_outlier_scor": 83, "ylabel": 83, "axvlin": 83, "test_outlier_scor": 83, "ood_features_indic": 83, "revisit": 83, "unusu": 83, "return_invers": 83, "train_feature_embeddings_sc": 83, "test_feature_embeddings_sc": 83, "train_pred_label": 83, "9702": 83, "train_ood_predictions_scor": 83, "test_ood_predictions_scor": 83, "mainli": [83, 89], "lost": 83, "unsuit": 84, "ok": [84, 89], "convention": 84, "aforement": 84, "hypothet": 84, "contrast": 84, "tradit": 84, "disjoint": 84, "out_of_sample_pred_probs_for_a": 84, "out_of_sample_pred_probs_for_b": 84, "out_of_sample_pred_probs_for_c": 84, "out_of_sample_pred_prob": 84, "price": 85, "incom": 85, "ag": 85, "histgradientboostingregressor": 85, "r2_score": 85, "student_grades_r": 85, "final_scor": 85, "true_final_scor": 85, "homework": 85, "3d": 85, "hue": 85, "mpl_toolkit": 85, "mplot3d": 85, "axes3d": 85, "errors_idx": 85, "add_subplot": 85, "z": 85, "colorbar": 85, "errors_mask": 85, "feature_column": 85, "predicted_column": 85, "x_train_raw": 85, "x_test_raw": 85, "categorical_featur": [85, 87], "randomforestregressor": 85, "629763": 85, "521450": 85, "954607": 85, "547234": 85, "338296": 85, "754531": 85, "619090": 85, "312295": 85, "806626": 85, "784048": 85, "identified_issu": [85, 88], "659": 85, "367": 85, "560": 85, "318": 85, "688": 85, "657": 85, "view_datapoint": 85, "concat": 85, "consum": [85, 88], "baseline_model": [85, 88], "preds_og": 85, "r2_og": 85, "838": 85, "robustli": [85, 87, 88], "acceler": [85, 88], "found_label_issu": 85, "preds_cl": 85, "r2_cl": 85, "925": 85, "effort": [85, 87, 88], "favorit": 85, "64404888e": 85, "06755306e": 85, "05302732e": 85, "66635743e": 85, "53166364e": 85, "synthia": 86, "semantic_segment": 86, "imagesegment": 86, "given_mask": 86, "predicted_mask": 86, "set_printopt": [86, 89], "sky": 86, "sidewalk": 86, "veget": 86, "terrain": 86, "rider": 86, "pred_probs_filepath": 86, "1088": 86, "1920": 86, "label_filepath": 86, "synthia_class": 86, "maunal": 86, "100000": 86, "244800": 86, "system": 86, "leftmost": 86, "area": 86, "middl": [86, 89], "infact": 86, "rightmost": 86, "discrep": 86, "4997436": 86, "171961": 86, "37it": 86, "3263230": 86, "783379": 86, "275110": 86, "255792": 86, "78225": 86, "55990": 86, "54315": 86, "33591": 86, "24645": 86, "21054": 86, "15045": 86, "14171": 86, "13832": 86, "13498": 86, "11490": 86, "9149": 86, "8769": 86, "6999": 86, "6031": 86, "5011": 86, "mistakenli": 86, "class_issu": 86, "aim": [86, 89], "domin": 86, "extratreesclassifi": 87, "extratre": 87, "labelencod": [87, 88], "labels_raw": 87, "interg": [87, 88], "tress": 87, "827": 87, "637": 87, "cheat": 87, "0pt": 87, "233": 87, "labels_train": 87, "labels_test": 87, "acc_og": [87, 88], "783068783068783": 87, "acc_cl": [87, 88], "8095238095238095": 87, "earlier": [88, 89], "raw_label": 88, "raw_train_text": 88, "raw_test_text": 88, "raw_train_label": 88, "raw_test_label": 88, "encond": 88, "train_text": 88, "test_text": 88, "858050": 88, "545854": 88, "826194": 88, "965814": 88, "791923": 88, "646": 88, "390": 88, "628": 88, "702": 88, "863": 88, "135": 88, "735": 88, "print_as_df": 88, "inverse_transform": 88, "fight": 88, "bunch": 89, "conll": 89, "2003": 89, "love": 89, "n_i": 89, "optional_list_of_ordered_class_nam": 89, "deepai": 89, "conll2003": 89, "rm": 89, "tokenclassif": 89, "2023": 89, "2400": 89, "52e0": 89, "1a00": 89, "1029": 89, "connect": 89, "443": 89, "await": 89, "982975": 89, "960k": 89, "959": 89, "94k": 89, "kb": 89, "mb": 89, "directori": 89, "inflat": 89, "182": 89, "17045998": 89, "16m": 89, "octet": 89, "26m": 89, "1mb": 89, "bert": 89, "read_npz": 89, "filepath": 89, "corrsespond": 89, "iob2": 89, "given_ent": 89, "entity_map": 89, "readfil": 89, "sep": 89, "startswith": 89, "docstart": 89, "isalpha": 89, "isupp": 89, "indices_to_preview": 89, "nsentenc": 89, "eu": 89, "reject": 89, "boycott": 89, "british": 89, "lamb": 89, "00030412": 89, "00023826": 89, "99936208": 89, "00007009": 89, "00002545": 89, "99998795": 89, "00000401": 89, "00000218": 89, "00000455": 89, "00000131": 89, "00000749": 89, "99996115": 89, "00001371": 89, "0000087": 89, "00000895": 89, "99998936": 89, "00000382": 89, "00000178": 89, "00000366": 89, "00000137": 89, "99999101": 89, "00000266": 89, "00000174": 89, "0000035": 89, "00000109": 89, "99998768": 89, "00000482": 89, "00000202": 89, "00000438": 89, "0000011": 89, "00000465": 89, "99996392": 89, "00001105": 89, "0000116": 89, "00000878": 89, "99998671": 89, "00000364": 89, "00000213": 89, "00000472": 89, "00000281": 89, "99999073": 89, "00000211": 89, "00000159": 89, "00000442": 89, "00000115": 89, "peter": 89, "blackburn": 89, "00000358": 89, "00000529": 89, "99995623": 89, "000022": 89, "0000129": 89, "0000024": 89, "00001812": 89, "99994141": 89, "00001645": 89, "00002162": 89, "brussel": 89, "1996": 89, "00001172": 89, "00000821": 89, "00004661": 89, "0000618": 89, "99987167": 89, "99999061": 89, "00000201": 89, "00000195": 89, "00000408": 89, "00000135": 89, "2254": 89, "2907": 89, "19392": 89, "9962": 89, "8904": 89, "19303": 89, "12918": 89, "9256": 89, "11855": 89, "18392": 89, "20426": 89, "19402": 89, "14744": 89, "19371": 89, "4645": 89, "10331": 89, "9430": 89, "6143": 89, "18367": 89, "12914": 89, "todai": 89, "weather": 89, "march": 89, "scalfaro": 89, "northern": 89, "himself": 89, "said": 89, "germani": 89, "nastja": 89, "rysich": 89, "north": 89, "spla": 89, "fought": 89, "khartoum": 89, "govern": 89, "south": 89, "1983": 89, "autonomi": 89, "animist": 89, "region": 89, "moslem": 89, "arabis": 89, "mayor": 89, "antonio": 89, "gonzalez": 89, "garcia": 89, "revolutionari": 89, "parti": 89, "wednesdai": 89, "troop": 89, "raid": 89, "farm": 89, "stole": 89, "rape": 89, "women": 89, "spring": 89, "chg": 89, "hrw": 89, "12pct": 89, "princ": 89, "photo": 89, "moment": 89, "spokeswoman": 89, "rainier": 89, "told": 89, "reuter": 89, "danila": 89, "carib": 89, "w224": 89, "equip": 89, "radiomet": 89, "earn": 89, "19996": 89, "london": 89, "denom": 89, "sale": 89, "uk": 89, "jp": 89, "fr": 89, "maccabi": 89, "hapoel": 89, "haifa": 89, "tel": 89, "aviv": 89, "hospit": 89, "rever": 89, "roman": 89, "cathol": 89, "nun": 89, "admit": 89, "calcutta": 89, "week": 89, "ago": 89, "fever": 89, "vomit": 89, "allianc": 89, "embattl": 89, "kabul": 89, "salang": 89, "highwai": 89, "mondai": 89, "tuesdai": 89, "suprem": 89, "council": 89, "led": 89, "jumbish": 89, "milli": 89, "movement": 89, "warlord": 89, "abdul": 89, "rashid": 89, "dostum": 89, "dollar": 89, "exchang": 89, "3570": 89, "12049": 89, "born": 89, "1937": 89, "provinc": 89, "anhui": 89, "dai": 89, "came": 89, "shanghai": 89, "citi": 89, "prolif": 89, "author": 89, "teacher": 89, "chines": 89, "16764": 89, "1990": 89, "historian": 89, "alan": 89, "john": 89, "percival": 89, "taylor": 89, "di": 89, "20446": 89, "pace": 89, "bowler": 89, "ian": 89, "harvei": 89, "claim": 89, "victoria": 89, "15514": 89, "cotti": 89, "osc": 89, "foreign": 89, "minist": 89, "7525": 89, "sultan": 89, "specter": 89, "met": 89, "crown": 89, "abdullah": 89, "defenc": 89, "aviat": 89, "jeddah": 89, "saudi": 89, "agenc": 89, "2288": 89, "hi": 89, "customari": 89, "outfit": 89, "champion": 89, "damp": 89, "scalp": 89, "canada": 89, "reign": 89, "olymp": 89, "donovan": 89, "bailei": 89, "1992": 89, "linford": 89, "christi": 89, "britain": 89, "1984": 89, "1988": 89, "carl": 89, "lewi": 89, "ambigi": 89, "punctuat": 89, "chicago": 89, "digest": 89, "philadelphia": 89, "usda": 89, "york": 89, "token_issu": 89, "471": 89, "kean": 89, "year": 89, "contract": 89, "manchest": 89, "19072": 89, "societi": 89, "million": 89, "bite": 89, "deliv": 89, "19910": 89, "father": 89, "clarenc": 89, "woolmer": 89, "renam": 89, "uttar": 89, "pradesh": 89, "india": 89, "ranji": 89, "trophi": 89, "nation": 89, "championship": 89, "captain": 89, "1949": 89, "15658": 89, "19879": 89, "iii": 89, "brian": 89, "shimer": 89, "randi": 89, "jone": 89, "19104": 89}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [8, 0, 0, "-", "datalab"], [25, 0, 0, "-", "dataset"], [28, 0, 0, "-", "experimental"], [31, 0, 0, "-", "filter"], [32, 0, 0, "-", "internal"], [43, 0, 0, "-", "models"], [45, 0, 0, "-", "multiannotator"], [48, 0, 0, "-", "multilabel_classification"], [51, 0, 0, "-", "object_detection"], [54, 0, 0, "-", "outlier"], [55, 0, 0, "-", "rank"], [56, 0, 0, "-", "regression"], [60, 0, 0, "-", "segmentation"], [64, 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"], [12, 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": [[9, 0, 0, "-", "data"], [10, 0, 0, "-", "data_issues"], [13, 0, 0, "-", "issue_finder"], [11, 0, 0, "-", "issue_manager_factory"], [23, 0, 0, "-", "report"]], "cleanlab.datalab.internal.data": [[9, 2, 1, "", "Data"], [9, 5, 1, "", "DataFormatError"], [9, 5, 1, "", "DatasetDictError"], [9, 5, 1, "", "DatasetLoadError"], [9, 2, 1, "", "Label"]], "cleanlab.datalab.internal.data.Data": [[9, 4, 1, "", "class_names"], [9, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[9, 6, 1, "", "args"], [9, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[9, 6, 1, "", "args"], [9, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[9, 6, 1, "", "args"], [9, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[9, 4, 1, "", "class_names"], [9, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[10, 2, 1, "", "DataIssues"], [10, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[10, 3, 1, "", "collect_issues_from_imagelab"], [10, 3, 1, "", "collect_issues_from_issue_manager"], [10, 3, 1, "", "collect_statistics"], [10, 3, 1, "", "get_info"], [10, 3, 1, "", "get_issue_summary"], [10, 3, 1, "", "get_issues"], [10, 6, 1, "", "info"], [10, 6, 1, "", "issue_summary"], [10, 6, 1, "", "issues"], [10, 3, 1, "", "set_health_score"], [10, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[13, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[13, 3, 1, "", "find_issues"], [13, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[15, 0, 0, "-", "duplicate"], [16, 0, 0, "-", "imbalance"], [18, 0, 0, "-", "issue_manager"], [19, 0, 0, "-", "label"], [20, 0, 0, "-", "noniid"], [21, 0, 0, "-", "null"], [22, 0, 0, "-", "outlier"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[15, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[15, 3, 1, "", "collect_info"], [15, 6, 1, "", "description"], [15, 3, 1, "", "find_issues"], [15, 6, 1, "", "info"], [15, 6, 1, "", "issue_name"], [15, 6, 1, "", "issue_score_key"], [15, 6, 1, "", "issues"], [15, 3, 1, "", "make_summary"], [15, 6, 1, "", "near_duplicate_sets"], [15, 3, 1, "", "report"], [15, 6, 1, "", "summary"], [15, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[16, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[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.issue_manager": [[18, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[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.label": [[19, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 3, 1, "", "get_health_summary"], [19, 6, 1, "", "health_summary_parameters"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[20, 2, 1, "", "NonIIDIssueManager"], [20, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[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.null": [[21, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [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.outlier": [[22, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[22, 6, 1, "", "DEFAULT_THRESHOLDS"], [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, 6, 1, "", "ood"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[11, 7, 1, "", "REGISTRY"], [11, 1, 1, "", "list_default_issue_types"], [11, 1, 1, "", "list_possible_issue_types"], [11, 1, 1, "", "register"]], "cleanlab.datalab.internal.report": [[23, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[23, 3, 1, "", "get_report"], [23, 3, 1, "", "report"]], "cleanlab.dataset": [[25, 1, 1, "", "find_overlapping_classes"], [25, 1, 1, "", "health_summary"], [25, 1, 1, "", "overall_label_health_score"], [25, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[26, 0, 0, "-", "cifar_cnn"], [27, 0, 0, "-", "coteaching"], [29, 0, 0, "-", "label_issues_batched"], [30, 0, 0, "-", "mnist_pytorch"]], "cleanlab.experimental.cifar_cnn": [[26, 2, 1, "", "CNN"], [26, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[26, 6, 1, "", "T_destination"], [26, 3, 1, "", "__call__"], [26, 3, 1, "", "add_module"], [26, 3, 1, "", "apply"], [26, 3, 1, "", "bfloat16"], [26, 3, 1, "", "buffers"], [26, 3, 1, "", "children"], [26, 3, 1, "", "cpu"], [26, 3, 1, "", "cuda"], [26, 3, 1, "", "double"], [26, 6, 1, "", "dump_patches"], [26, 3, 1, "", "eval"], [26, 3, 1, "", "extra_repr"], [26, 3, 1, "", "float"], [26, 3, 1, "id0", "forward"], [26, 3, 1, "", "get_buffer"], [26, 3, 1, "", "get_extra_state"], [26, 3, 1, "", "get_parameter"], [26, 3, 1, "", "get_submodule"], [26, 3, 1, "", "half"], [26, 3, 1, "", "ipu"], [26, 3, 1, "", "load_state_dict"], [26, 3, 1, "", "modules"], [26, 3, 1, "", "named_buffers"], [26, 3, 1, "", "named_children"], [26, 3, 1, "", "named_modules"], [26, 3, 1, "", "named_parameters"], [26, 3, 1, "", "parameters"], [26, 3, 1, "", "register_backward_hook"], [26, 3, 1, "", "register_buffer"], [26, 3, 1, "", "register_forward_hook"], [26, 3, 1, "", "register_forward_pre_hook"], [26, 3, 1, "", "register_full_backward_hook"], [26, 3, 1, "", "register_load_state_dict_post_hook"], [26, 3, 1, "", "register_module"], [26, 3, 1, "", "register_parameter"], [26, 3, 1, "", "requires_grad_"], [26, 3, 1, "", "set_extra_state"], [26, 3, 1, "", "share_memory"], [26, 3, 1, "", "state_dict"], [26, 3, 1, "", "to"], [26, 3, 1, "", "to_empty"], [26, 3, 1, "", "train"], [26, 6, 1, "", "training"], [26, 3, 1, "", "type"], [26, 3, 1, "", "xpu"], [26, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[27, 1, 1, "", "adjust_learning_rate"], [27, 1, 1, "", "evaluate"], [27, 1, 1, "", "forget_rate_scheduler"], [27, 1, 1, "", "initialize_lr_scheduler"], [27, 1, 1, "", "loss_coteaching"], [27, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[29, 2, 1, "", "LabelInspector"], [29, 7, 1, "", "adj_confident_thresholds_shared"], [29, 1, 1, "", "find_label_issues_batched"], [29, 7, 1, "", "labels_shared"], [29, 7, 1, "", "pred_probs_shared"], [29, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[29, 3, 1, "", "get_confident_thresholds"], [29, 3, 1, "", "get_label_issues"], [29, 3, 1, "", "get_num_issues"], [29, 3, 1, "", "get_quality_scores"], [29, 3, 1, "", "score_label_quality"], [29, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[30, 2, 1, "", "CNN"], [30, 2, 1, "", "SimpleNet"], [30, 1, 1, "", "get_mnist_dataset"], [30, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[30, 3, 1, "", "__init_subclass__"], [30, 6, 1, "", "batch_size"], [30, 6, 1, "", "dataset"], [30, 6, 1, "", "epochs"], [30, 3, 1, "id0", "fit"], [30, 3, 1, "", "get_metadata_routing"], [30, 3, 1, "", "get_params"], [30, 6, 1, "", "loader"], [30, 6, 1, "", "log_interval"], [30, 6, 1, "", "lr"], [30, 6, 1, "", "momentum"], [30, 6, 1, "", "no_cuda"], [30, 3, 1, "id1", "predict"], [30, 3, 1, "id4", "predict_proba"], [30, 6, 1, "", "seed"], [30, 3, 1, "", "set_fit_request"], [30, 3, 1, "", "set_params"], [30, 3, 1, "", "set_predict_proba_request"], [30, 3, 1, "", "set_predict_request"], [30, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[30, 6, 1, "", "T_destination"], [30, 3, 1, "", "__call__"], [30, 3, 1, "", "add_module"], [30, 3, 1, "", "apply"], [30, 3, 1, "", "bfloat16"], [30, 3, 1, "", "buffers"], [30, 3, 1, "", "children"], [30, 3, 1, "", "cpu"], [30, 3, 1, "", "cuda"], [30, 3, 1, "", "double"], [30, 6, 1, "", "dump_patches"], [30, 3, 1, "", "eval"], [30, 3, 1, "", "extra_repr"], [30, 3, 1, "", "float"], [30, 3, 1, "", "forward"], [30, 3, 1, "", "get_buffer"], [30, 3, 1, "", "get_extra_state"], [30, 3, 1, "", "get_parameter"], [30, 3, 1, "", "get_submodule"], [30, 3, 1, "", "half"], [30, 3, 1, "", "ipu"], [30, 3, 1, "", "load_state_dict"], [30, 3, 1, "", "modules"], [30, 3, 1, "", "named_buffers"], [30, 3, 1, "", "named_children"], [30, 3, 1, "", "named_modules"], [30, 3, 1, "", "named_parameters"], [30, 3, 1, "", "parameters"], [30, 3, 1, "", "register_backward_hook"], [30, 3, 1, "", "register_buffer"], [30, 3, 1, "", "register_forward_hook"], [30, 3, 1, "", "register_forward_pre_hook"], [30, 3, 1, "", "register_full_backward_hook"], [30, 3, 1, "", "register_load_state_dict_post_hook"], [30, 3, 1, "", "register_module"], [30, 3, 1, "", "register_parameter"], [30, 3, 1, "", "requires_grad_"], [30, 3, 1, "", "set_extra_state"], [30, 3, 1, "", "share_memory"], [30, 3, 1, "", "state_dict"], [30, 3, 1, "", "to"], [30, 3, 1, "", "to_empty"], [30, 3, 1, "", "train"], [30, 6, 1, "", "training"], [30, 3, 1, "", "type"], [30, 3, 1, "", "xpu"], [30, 3, 1, "", "zero_grad"]], "cleanlab.filter": [[31, 1, 1, "", "find_label_issues"], [31, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [31, 1, 1, "", "find_predicted_neq_given"], [31, 7, 1, "", "pred_probs_by_class"], [31, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[33, 0, 0, "-", "label_quality_utils"], [34, 0, 0, "-", "latent_algebra"], [35, 0, 0, "-", "multiannotator_utils"], [36, 0, 0, "-", "multilabel_scorer"], [37, 0, 0, "-", "multilabel_utils"], [38, 0, 0, "-", "outlier"], [39, 0, 0, "-", "token_classification_utils"], [40, 0, 0, "-", "util"], [41, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[33, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[34, 1, 1, "", "compute_inv_noise_matrix"], [34, 1, 1, "", "compute_noise_matrix_from_inverse"], [34, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [34, 1, 1, "", "compute_py"], [34, 1, 1, "", "compute_py_inv_noise_matrix"], [34, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[35, 1, 1, "", "assert_valid_inputs_multiannotator"], [35, 1, 1, "", "assert_valid_pred_probs"], [35, 1, 1, "", "check_consensus_label_classes"], [35, 1, 1, "", "compute_soft_cross_entropy"], [35, 1, 1, "", "find_best_temp_scaler"], [35, 1, 1, "", "format_multiannotator_labels"], [35, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[36, 2, 1, "", "Aggregator"], [36, 2, 1, "", "ClassLabelScorer"], [36, 2, 1, "", "MultilabelScorer"], [36, 1, 1, "", "exponential_moving_average"], [36, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [36, 1, 1, "", "get_label_quality_scores"], [36, 1, 1, "", "multilabel_py"], [36, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[36, 3, 1, "", "__call__"], [36, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[36, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [36, 6, 1, "", "NORMALIZED_MARGIN"], [36, 6, 1, "", "SELF_CONFIDENCE"], [36, 3, 1, "", "__call__"], [36, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[36, 3, 1, "", "__call__"], [36, 3, 1, "", "aggregate"], [36, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[37, 1, 1, "", "get_onehot_num_classes"], [37, 1, 1, "", "int2onehot"], [37, 1, 1, "", "onehot2int"], [37, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[38, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[39, 1, 1, "", "color_sentence"], [39, 1, 1, "", "filter_sentence"], [39, 1, 1, "", "get_sentence"], [39, 1, 1, "", "mapping"], [39, 1, 1, "", "merge_probs"], [39, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[40, 1, 1, "", "append_extra_datapoint"], [40, 1, 1, "", "clip_noise_rates"], [40, 1, 1, "", "clip_values"], [40, 1, 1, "", "compress_int_array"], [40, 1, 1, "", "confusion_matrix"], [40, 1, 1, "", "csr_vstack"], [40, 1, 1, "", "estimate_pu_f1"], [40, 1, 1, "", "extract_indices_tf"], [40, 1, 1, "", "force_two_dimensions"], [40, 1, 1, "", "format_labels"], [40, 1, 1, "", "get_missing_classes"], [40, 1, 1, "", "get_num_classes"], [40, 1, 1, "", "get_unique_classes"], [40, 1, 1, "", "is_tensorflow_dataset"], [40, 1, 1, "", "is_torch_dataset"], [40, 1, 1, "", "num_unique_classes"], [40, 1, 1, "", "print_inverse_noise_matrix"], [40, 1, 1, "", "print_joint_matrix"], [40, 1, 1, "", "print_noise_matrix"], [40, 1, 1, "", "print_square_matrix"], [40, 1, 1, "", "remove_noise_from_class"], [40, 1, 1, "", "round_preserving_row_totals"], [40, 1, 1, "", "round_preserving_sum"], [40, 1, 1, "", "smart_display_dataframe"], [40, 1, 1, "", "subset_X_y"], [40, 1, 1, "", "subset_data"], [40, 1, 1, "", "subset_labels"], [40, 1, 1, "", "train_val_split"], [40, 1, 1, "", "unshuffle_tensorflow_dataset"], [40, 1, 1, "", "value_counts"], [40, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[41, 1, 1, "", "assert_indexing_works"], [41, 1, 1, "", "assert_nonempty_input"], [41, 1, 1, "", "assert_valid_class_labels"], [41, 1, 1, "", "assert_valid_inputs"], [41, 1, 1, "", "labels_to_array"]], "cleanlab.models": [[44, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[44, 2, 1, "", "KerasWrapperModel"], [44, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[44, 3, 1, "", "fit"], [44, 3, 1, "", "get_params"], [44, 3, 1, "", "predict"], [44, 3, 1, "", "predict_proba"], [44, 3, 1, "", "set_params"], [44, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[44, 3, 1, "", "fit"], [44, 3, 1, "", "get_params"], [44, 3, 1, "", "predict"], [44, 3, 1, "", "predict_proba"], [44, 3, 1, "", "set_params"], [44, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[45, 1, 1, "", "convert_long_to_wide_dataset"], [45, 1, 1, "", "get_active_learning_scores"], [45, 1, 1, "", "get_active_learning_scores_ensemble"], [45, 1, 1, "", "get_label_quality_multiannotator"], [45, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [45, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[46, 0, 0, "-", "dataset"], [47, 0, 0, "-", "filter"], [49, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[46, 1, 1, "", "common_multilabel_issues"], [46, 1, 1, "", "multilabel_health_summary"], [46, 1, 1, "", "overall_multilabel_health_score"], [46, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[47, 1, 1, "", "find_label_issues"], [47, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[49, 1, 1, "", "get_label_quality_scores"], [49, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[50, 0, 0, "-", "filter"], [52, 0, 0, "-", "rank"], [53, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[50, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[52, 1, 1, "", "compute_badloc_box_scores"], [52, 1, 1, "", "compute_overlooked_box_scores"], [52, 1, 1, "", "compute_swap_box_scores"], [52, 1, 1, "", "get_label_quality_scores"], [52, 1, 1, "", "issues_from_scores"], [52, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[53, 1, 1, "", "bounding_box_size_distribution"], [53, 1, 1, "", "class_label_distribution"], [53, 1, 1, "", "get_sorted_bbox_count_idxs"], [53, 1, 1, "", "object_counts_per_image"], [53, 1, 1, "", "plot_class_distribution"], [53, 1, 1, "", "plot_class_size_distributions"], [53, 1, 1, "", "visualize"]], "cleanlab.outlier": [[54, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[54, 3, 1, "", "fit"], [54, 3, 1, "", "fit_score"], [54, 3, 1, "", "score"]], "cleanlab.rank": [[55, 1, 1, "", "find_top_issues"], [55, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [55, 1, 1, "", "get_label_quality_ensemble_scores"], [55, 1, 1, "", "get_label_quality_scores"], [55, 1, 1, "", "get_normalized_margin_for_each_label"], [55, 1, 1, "", "get_self_confidence_for_each_label"], [55, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[57, 0, 0, "-", "learn"], [58, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[57, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[57, 3, 1, "", "__init_subclass__"], [57, 3, 1, "", "find_label_issues"], [57, 3, 1, "", "fit"], [57, 3, 1, "", "get_aleatoric_uncertainty"], [57, 3, 1, "", "get_epistemic_uncertainty"], [57, 3, 1, "", "get_label_issues"], [57, 3, 1, "", "get_metadata_routing"], [57, 3, 1, "", "get_params"], [57, 3, 1, "", "predict"], [57, 3, 1, "", "save_space"], [57, 3, 1, "", "score"], [57, 3, 1, "", "set_fit_request"], [57, 3, 1, "", "set_params"], [57, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[58, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[59, 0, 0, "-", "filter"], [61, 0, 0, "-", "rank"], [62, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[59, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[61, 1, 1, "", "get_label_quality_scores"], [61, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[62, 1, 1, "", "common_label_issues"], [62, 1, 1, "", "display_issues"], [62, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[63, 0, 0, "-", "filter"], [65, 0, 0, "-", "rank"], [66, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[63, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[65, 1, 1, "", "get_label_quality_scores"], [65, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[66, 1, 1, "", "common_label_issues"], [66, 1, 1, "", "display_issues"], [66, 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, 69, 73, 74, 76, 77, 78, 81, 87, 88, 89], "count": [3, 78], "datalab": [4, 5, 6, 7, 8, 70, 71, 72, 73, 74, 78], "creat": [5, 70, 71, 78, 80], "your": [5, 67, 70, 71, 74, 76, 78], "own": 5, "issu": [5, 6, 7, 17, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 81, 82, 86, 87, 89], "manag": [5, 17], "prerequisit": 5, "implement": 5, "issuemanag": [5, 70], "basic": 5, "check": 5, "intermedi": 5, "advanc": [5, 70], "us": [5, 69, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "guid": [6, 8], "type": [6, 7, 78], "custom": [6, 70], "can": [7, 71, 75, 76, 78, 80], "detect": [7, 71, 73, 74, 76, 78, 82, 83], "estim": [7, 78, 80], "each": 7, "label": [7, 19, 67, 69, 71, 73, 74, 76, 77, 78, 80, 81, 82, 85, 86, 87, 88, 89], "outlier": [7, 22, 38, 54, 73, 74, 77, 83], "Near": [7, 71, 73, 74, 77], "duplic": [7, 15, 71, 73, 74, 77], "non": 7, "iid": 7, "class": [7, 68, 78, 86], "imbal": [7, 16], "imag": [7, 77, 83], "specif": [7, 86], "option": 7, "paramet": [7, 78], "get": [8, 70, 71, 80, 81, 82, 86, 89], "start": [8, 75], "api": 8, "refer": 8, "data": [9, 67, 69, 70, 71, 73, 75, 76, 78, 80, 81, 82, 83, 85, 86, 87, 89], "data_issu": 10, "factori": 11, "intern": [12, 32], "issue_find": 13, "issue_manag": [17, 18], "regist": 17, "unregist": 17, "noniid": 20, "null": 21, "report": [23, 77], "dataset": [25, 46, 67, 71, 74, 75, 76, 77, 78, 81, 82, 83, 85, 86, 88, 89], "cifar_cnn": 26, "coteach": 27, "experiment": 28, "label_issues_batch": 29, "mnist_pytorch": 30, "filter": [31, 47, 50, 59, 63, 78], "label_quality_util": 33, "latent_algebra": 34, "multiannotator_util": 35, "multilabel_scor": 36, "multilabel_util": 37, "token_classification_util": 39, "util": 40, "valid": [41, 77, 84], "fasttext": 42, "model": [43, 67, 69, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 87, 88], "kera": 44, "multiannot": [45, 80], "multilabel_classif": 48, "rank": [49, 52, 55, 58, 61, 65, 78], "object_detect": 51, "summari": [53, 62, 66], "regress": [56, 57, 58, 76, 85], "learn": [57, 71, 76, 78, 87], "segment": [60, 86], "token_classif": [64, 89], "cleanlab": [67, 69, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "open": [67, 76], "sourc": [67, 76], "document": 67, "quickstart": 67, "1": [67, 68, 69, 70, 71, 73, 74, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "instal": [67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "2": [67, 68, 69, 70, 71, 73, 74, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "find": [67, 69, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "common": [67, 68, 89], "3": [67, 69, 70, 71, 73, 74, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "handl": [67, 76], "error": [67, 76, 77, 78, 80, 81, 82, 85, 86, 88, 89], "train": [67, 69, 76, 83, 85, 87, 88], "robust": [67, 78, 85, 87, 88], "noisi": [67, 78, 85, 87, 88], "4": [67, 69, 70, 71, 73, 74, 77, 78, 80, 82, 83, 85, 87, 88], "curat": [67, 75], "fix": [67, 76], "level": [67, 75, 78, 89], "5": [67, 69, 71, 73, 77, 78, 80, 85, 87], "improv": [67, 80], "via": [67, 78, 80], "mani": [67, 78], "other": [67, 80, 82, 85], "techniqu": 67, "contribut": 67, "easi": 67, "mode": 67, "how": [68, 76, 78, 80, 81, 89], "migrat": 68, "version": 68, "0": 68, "from": [68, 70, 71, 78, 85, 87, 88], "pre": [68, 69, 83], "function": [68, 70], "name": 68, "chang": 68, "modul": [68, 78], "new": 68, "remov": 68, "argument": [68, 70], "variabl": 68, "audio": 69, "speechbrain": 69, "depend": [69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "import": [69, 70, 71, 75, 77, 78, 80], "them": [69, 75, 78], "load": [69, 70, 71, 73, 74, 85, 87, 88], "featur": [69, 77, 83], "fit": 69, "linear": 69, "comput": [69, 73, 74, 77, 80, 84, 87], "out": [69, 70, 71, 73, 74, 77, 80, 84, 87], "sampl": [69, 70, 71, 73, 74, 77, 80, 84, 87], "predict": [69, 70, 71, 73, 74, 77, 80, 81, 82, 84, 87], "probabl": [69, 70, 71, 73, 74, 77, 80, 84, 87], "workflow": [70, 78], "audit": [70, 71], "requir": [70, 71, 73, 74, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "classifi": [70, 71], "instanti": 70, "object": [70, 82], "increment": 70, "search": 70, "specifi": 70, "nondefault": 70, "save": 70, "ad": 70, "A": 71, "unifi": 71, "all": [71, 78], "kind": [71, 82], "skip": [71, 75, 78, 80], "detail": [71, 75, 78, 80], "more": [71, 78, 85, 87, 88], "about": 71, "addit": 71, "inform": [71, 77], "tutori": [72, 75, 79], "tabular": [73, 87], "numer": 73, "categor": 73, "column": 73, "process": [73, 83, 85, 87], "select": [73, 87], "construct": 73, "k": [73, 77, 84], "nearest": 73, "neighbour": 73, "graph": 73, "text": [74, 88, 89], "format": [74, 76, 81, 82, 88], "defin": [74, 77, 85, 88], "fetch": [75, 77], "evalu": 75, "health": [75, 78], "8": [75, 78], "popular": 75, "faq": 76, "what": [76, 78, 84], "do": [76, 78], "i": [76, 78, 84], "infer": 76, "correct": 76, "exampl": [76, 77, 78, 83], "ha": 76, "flag": 76, "should": 76, "v": 76, "test": [76, 78, 83], "big": 76, "limit": 76, "memori": 76, "why": 76, "isn": 76, "t": 76, "cleanlearn": [76, 78], "work": [76, 78, 80, 89], "me": 76, "differ": [76, 82], "clean": [76, 78], "final": 76, "hyperparamet": 76, "tune": 76, "onli": 76, "one": [76, 78, 81, 86], "doe": [76, 80, 89], "take": 76, "so": 76, "long": 76, "ml": [76, 78], "run": 76, "identifi": [76, 82], "licens": 76, "under": 76, "an": 76, "answer": 76, "question": 76, "pytorch": [77, 83], "normal": 77, "fashion": 77, "mnist": 77, "prepar": 77, "fold": [77, 84], "cross": [77, 84], "embed": [77, 83], "7": [77, 78], "view": 77, "most": [77, 89], "like": 77, "sever": 77, "set": [77, 78], "dark": 77, "top": [77, 86], "low": 77, "The": 78, "centric": 78, "ai": 78, "machin": 78, "find_label_issu": 78, "line": 78, "code": 78, "visual": [78, 82, 83, 86], "twenti": 78, "lowest": 78, "qualiti": [78, 80, 81, 82, 86, 89], "see": 78, "now": 78, "let": 78, "": 78, "happen": 78, "we": 78, "merg": 78, "seafoam": 78, "green": 78, "yellow": 78, "too": 78, "you": 78, "re": 78, "6": 78, "One": 78, "score": [78, 80, 81, 82, 86, 89], "rule": 78, "overal": [78, 86], "accur": 78, "thi": 78, "directli": 78, "fulli": 78, "character": 78, "nois": 78, "matrix": [78, 81], "joint": 78, "prior": 78, "true": 78, "distribut": 78, "flip": 78, "rate": 78, "ani": 78, "again": 78, "support": 78, "lot": 78, "method": 78, "filter_bi": 78, "automat": 78, "everi": 78, "uniqu": 78, "num_label_issu": 78, "threshold": 78, "found": 78, "Not": 78, "sure": 78, "when": 78, "ensembl": 78, "multipl": [78, 80], "predictor": 78, "consensu": 80, "annot": 80, "initi": 80, "major": 80, "vote": 80, "better": 80, "statist": 80, "compar": 80, "inspect": 80, "potenti": [80, 85, 88], "retrain": 80, "further": 80, "multi": 81, "given": 81, "hot": 81, "binari": 81, "download": [82, 86, 89], "objectlab": 82, "timm": 83, "cifar10": 83, "some": 83, "pred_prob": [83, 86, 89], "wai": 85, "semant": 86, "which": 86, "ar": 86, "commonli": 86, "mislabel": [86, 89], "focus": 86, "scikit": 87, "token": 89, "word": 89, "sentenc": 89, "contain": 89, "particular": 89}, "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/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "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/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/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/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", "Datalab guides", "Datalab Issue Types", "datalab", "data", "data_issues", "factory", "internal", "issue_finder", "<no title>", "duplicate", "imbalance", "issue_manager", "issue_manager", "label", "noniid", "null", "outlier", "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, 68, 70, 71, 78, 80, 81], "helper": [1, 13, 29, 33, 35, 36, 37, 38, 39, 40, 52, 75, 77, 89], "method": [1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35, 36, 39, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 70, 71, 73, 74, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "ar": [1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 16, 17, 18, 19, 20, 25, 26, 28, 29, 30, 31, 32, 34, 35, 36, 37, 39, 40, 41, 43, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 88, 89], "us": [1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 43, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 67, 68, 70, 75, 79, 84], "benchmark": [1, 26, 67, 68, 70, 71, 78, 80, 81], "cleanlab": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 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, 43, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 68, 70, 71, 75, 79, 84], "": [1, 2, 3, 7, 25, 26, 30, 33, 36, 38, 40, 45, 46, 50, 52, 53, 54, 55, 57, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "core": [1, 4, 29, 31, 59, 61, 86], "algorithm": [1, 2, 7, 27, 40, 45, 54, 63, 65, 67, 76, 78, 80, 89], "These": [1, 2, 3, 7, 17, 28, 31, 32, 43, 45, 46, 49, 54, 58, 62, 63, 65, 66, 69, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "introduc": [1, 69, 76, 78], "synthet": [1, 80, 81, 86], "nois": [1, 2, 3, 25, 31, 34, 40, 46, 70, 71, 75, 80], "label": [1, 2, 3, 4, 5, 6, 9, 13, 17, 18, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 40, 41, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 70, 75, 79, 83, 84], "classif": [1, 3, 4, 7, 11, 23, 25, 29, 31, 34, 36, 37, 40, 45, 46, 47, 48, 49, 54, 55, 63, 64, 65, 66, 67, 68, 70, 71, 79, 80, 83, 84, 85, 86], "dataset": [1, 2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 16, 18, 20, 21, 22, 29, 30, 31, 34, 36, 40, 44, 45, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 69, 70, 72, 73, 79, 80, 84, 87], "specif": [1, 3, 4, 6, 11, 13, 23, 28, 43, 47, 50, 53, 62, 66, 71, 73, 74, 77, 78, 89], "thi": [1, 2, 3, 4, 5, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 25, 26, 27, 28, 29, 30, 31, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "modul": [1, 3, 10, 11, 12, 13, 17, 23, 25, 26, 27, 28, 29, 30, 31, 40, 43, 45, 54, 55, 67, 76, 77, 81], "provid": [1, 2, 3, 4, 5, 7, 11, 13, 19, 25, 26, 27, 29, 30, 31, 34, 40, 44, 45, 46, 47, 52, 53, 54, 55, 57, 59, 61, 62, 65, 66, 67, 69, 70, 71, 74, 76, 77, 78, 80, 83, 84, 85, 86, 87, 88, 89], "gener": [1, 2, 3, 5, 7, 19, 23, 25, 36, 40, 41, 54, 55, 57, 62, 69, 70, 71, 75, 76, 77, 78, 80, 81, 83, 84, 85, 86, 88, 89], "valid": [1, 2, 3, 4, 7, 9, 25, 31, 32, 34, 35, 36, 40, 45, 47, 50, 53, 55, 57, 58, 66, 68, 69, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89], "matric": [1, 3, 34, 76], "which": [1, 2, 3, 4, 7, 9, 10, 11, 13, 18, 20, 23, 25, 26, 30, 31, 34, 36, 39, 40, 45, 46, 47, 50, 52, 53, 54, 55, 57, 58, 61, 62, 63, 65, 67, 68, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 88, 89], "learn": [1, 2, 3, 4, 7, 11, 13, 18, 23, 27, 28, 29, 30, 31, 33, 35, 40, 43, 45, 47, 54, 56, 58, 61, 65, 67, 69, 70, 73, 74, 75, 79, 80, 85, 88], "i": [1, 2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "possibl": [1, 2, 3, 7, 25, 26, 30, 31, 33, 34, 36, 47, 48, 49, 50, 52, 53, 54, 55, 57, 63, 65, 66, 71, 76, 78, 80, 81, 82, 85, 86, 89], "noisi": [1, 2, 3, 25, 27, 30, 31, 34, 40, 46, 47, 49, 55, 57, 58, 59, 61, 62, 68, 70, 71, 73, 74, 76, 79, 80], "given": [1, 2, 3, 7, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 39, 40, 45, 46, 47, 50, 52, 53, 54, 55, 57, 58, 62, 63, 65, 66, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 86, 87, 88, 89], "matrix": [1, 2, 3, 4, 7, 13, 25, 31, 33, 34, 37, 40, 41, 47, 52, 54, 55, 73, 83], "trace": [1, 70, 71, 78, 80, 81], "valu": [1, 2, 3, 4, 7, 9, 10, 13, 18, 20, 21, 25, 26, 27, 29, 30, 31, 33, 34, 36, 40, 45, 46, 47, 49, 50, 52, 54, 55, 57, 58, 59, 61, 62, 63, 66, 69, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 86, 88, 89], "more": [1, 2, 3, 4, 5, 7, 10, 13, 20, 25, 26, 29, 30, 33, 36, 40, 45, 46, 47, 48, 49, 50, 52, 53, 55, 57, 58, 61, 62, 63, 65, 67, 69, 70, 73, 74, 75, 76, 77, 80, 81, 82, 83, 86, 89], "function": [1, 2, 3, 4, 5, 10, 11, 13, 19, 20, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 69, 71, 75, 76, 78, 80, 81, 82, 86, 87, 88, 89], "noise_matrix_is_valid": 1, "noise_matrix": [1, 2, 3, 7, 34, 40, 70, 71, 78, 80, 81], "py": [1, 3, 23, 26, 27, 31, 34, 36, 45, 70, 71, 78, 80, 81], "verbos": [1, 2, 4, 5, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 29, 31, 45, 46, 47, 52, 54, 55, 57, 59, 61, 62, 66, 70, 78, 80], "fals": [1, 2, 3, 4, 5, 9, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 35, 39, 40, 41, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 63, 69, 70, 71, 73, 74, 76, 77, 78, 80, 82, 83, 85, 86, 88], "sourc": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66], "prior": [1, 2, 3, 25, 31, 34, 36], "repres": [1, 2, 3, 4, 5, 7, 9, 13, 20, 25, 29, 31, 34, 37, 40, 45, 46, 47, 50, 52, 53, 54, 55, 57, 59, 61, 62, 66, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 87, 88, 89], "p": [1, 2, 3, 7, 25, 31, 33, 34, 40, 45, 53, 54, 55, 59, 71, 73, 74, 77, 78, 80, 89], "true_label": [1, 2, 3, 25, 34, 40, 78, 80], "k": [1, 2, 3, 4, 7, 9, 13, 15, 19, 20, 22, 25, 29, 31, 33, 34, 35, 36, 37, 38, 39, 40, 45, 46, 47, 48, 49, 50, 53, 54, 55, 57, 59, 61, 62, 63, 65, 66, 69, 70, 71, 76, 78, 80, 81, 82, 83, 86, 87, 89], "check": [1, 2, 4, 6, 7, 9, 13, 26, 29, 30, 35, 41, 44, 50, 53, 57, 67, 69, 70, 71, 76, 77, 78, 80, 81, 85, 87, 88], "learnabl": 1, "mean": [1, 2, 5, 9, 10, 18, 20, 27, 30, 34, 36, 52, 57, 71, 74, 76, 78, 80, 81, 83, 85, 88], "achiev": [1, 2, 26, 27, 30, 57, 80, 89], "better": [1, 4, 31, 45, 47, 55, 57, 58, 67, 69, 71, 73, 74, 76, 78, 81, 82, 83, 88, 89], "than": [1, 2, 3, 5, 7, 20, 22, 25, 31, 40, 44, 45, 50, 52, 54, 55, 57, 61, 65, 69, 76, 77, 78, 80, 81, 82, 83, 84, 86, 87, 89], "random": [1, 2, 3, 5, 7, 29, 36, 45, 55, 57, 69, 70, 71, 73, 76, 77, 78, 80, 81, 83, 87], "perform": [1, 2, 5, 7, 20, 22, 26, 30, 36, 57, 67, 70, 76, 78, 80, 81, 84, 85, 87, 88], "averag": [1, 3, 18, 22, 25, 26, 30, 36, 38, 45, 46, 54, 55, 76, 80, 83], "amount": [1, 3, 77], "paramet": [1, 2, 3, 4, 6, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 69, 71, 74, 77, 87, 88], "np": [1, 2, 3, 4, 5, 13, 25, 27, 29, 31, 33, 34, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 62, 63, 65, 66, 69, 70, 71, 73, 75, 76, 77, 78, 80, 81, 83, 85, 86, 87, 88, 89], "ndarrai": [1, 2, 3, 4, 13, 19, 20, 25, 27, 29, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 65, 89], "an": [1, 2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 38, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "arrai": [1, 2, 3, 4, 5, 9, 13, 20, 25, 27, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 69, 70, 71, 74, 76, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "shape": [1, 2, 3, 4, 13, 25, 27, 29, 31, 33, 34, 35, 36, 38, 39, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 69, 75, 76, 78, 81, 82, 83, 86, 89], "condit": [1, 2, 3, 34, 39, 40, 55, 77, 78, 89], "probabl": [1, 2, 3, 4, 7, 13, 19, 22, 25, 29, 30, 31, 33, 34, 36, 37, 39, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 63, 65, 66, 67, 68, 75, 76, 78, 79, 81, 82, 83, 86, 89], "k_": [1, 2, 3, 34, 40], "k_y": [1, 2, 3, 34, 40], "contain": [1, 2, 3, 4, 9, 10, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 38, 39, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 61, 62, 63, 65, 66, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88], "fraction": [1, 2, 3, 7, 16, 27, 34, 40, 45, 57, 73, 76], "exampl": [1, 2, 3, 4, 5, 7, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 36, 37, 38, 39, 40, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 80, 81, 82, 84, 85, 86, 87, 88, 89], "everi": [1, 2, 3, 4, 13, 26, 30, 31, 34, 39, 40, 47, 55, 57, 58, 69, 70, 71, 73, 74, 76, 77, 80, 82, 84, 86, 87, 89], "class": [1, 2, 3, 4, 5, 6, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 87, 88, 89], "other": [1, 2, 3, 4, 7, 13, 18, 25, 26, 28, 29, 30, 31, 34, 37, 40, 41, 43, 45, 46, 49, 54, 55, 57, 62, 69, 70, 71, 73, 74, 76, 77, 78, 81, 83, 86, 89], "assum": [1, 2, 3, 9, 31, 34, 38, 39, 40, 55, 59, 62, 76, 83, 86, 89], "column": [1, 2, 3, 4, 7, 9, 10, 25, 29, 31, 34, 36, 37, 39, 40, 45, 46, 47, 49, 50, 53, 54, 55, 57, 62, 63, 65, 66, 69, 70, 71, 74, 75, 76, 77, 80, 82, 85, 86, 87, 88, 89], "sum": [1, 2, 3, 20, 25, 34, 36, 40, 46, 47, 49, 52, 57, 70, 71, 76, 77, 78, 80, 81, 86, 89], "1": [1, 2, 3, 4, 5, 7, 9, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 75, 76, 84], "each": [1, 2, 3, 4, 5, 6, 10, 11, 13, 16, 18, 19, 20, 23, 25, 26, 27, 29, 30, 31, 33, 34, 36, 37, 38, 40, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "true": [1, 2, 3, 4, 5, 7, 9, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 34, 36, 39, 40, 41, 44, 45, 46, 47, 50, 52, 53, 54, 55, 57, 59, 61, 62, 66, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "return": [1, 2, 3, 4, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 88, 89], "type": [1, 2, 3, 4, 5, 8, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 68, 69, 70, 71, 73, 74, 77, 81, 82, 86, 87, 89], "bool": [1, 2, 3, 4, 9, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 36, 39, 40, 45, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 66], "is_valid": 1, "whether": [1, 3, 4, 7, 9, 10, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 30, 31, 40, 45, 46, 47, 49, 50, 66, 69, 71, 73, 74, 75, 77, 78, 85, 88, 89], "generate_noisy_label": [1, 70, 71, 78, 80, 81], "from": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 18, 19, 23, 24, 25, 26, 27, 29, 30, 31, 34, 36, 37, 38, 39, 40, 45, 47, 49, 52, 53, 54, 55, 57, 58, 63, 65, 66, 67, 69, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 86, 89], "perfect": [1, 2, 25, 57, 78, 82], "exactli": [1, 3, 7, 25, 26, 30, 31, 48, 54, 70, 71, 73, 74, 77, 78], "yield": [1, 26, 30], "between": [1, 4, 7, 12, 13, 17, 18, 20, 25, 26, 27, 28, 29, 30, 31, 32, 33, 35, 38, 43, 45, 46, 49, 52, 54, 55, 57, 58, 61, 65, 66, 68, 69, 70, 71, 73, 74, 77, 78, 80, 81, 82, 83, 85, 86, 88, 89], "below": [1, 3, 7, 25, 26, 29, 30, 31, 33, 36, 45, 46, 47, 52, 53, 61, 65, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "we": [1, 2, 3, 4, 5, 7, 10, 18, 26, 29, 30, 31, 36, 40, 41, 45, 52, 55, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "loop": [1, 3, 34, 40, 77], "implement": [1, 2, 3, 4, 6, 11, 18, 26, 27, 29, 30, 34, 40, 57, 67, 69, 70, 73, 83, 84, 87], "what": [1, 4, 6, 7, 13, 23, 25, 27, 29, 31, 45, 46, 50, 52, 69, 70, 71, 73, 74, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "doe": [1, 2, 3, 7, 29, 30, 31, 36, 41, 52, 57, 59, 61, 65, 69, 70, 71, 73, 74, 77, 81, 85, 86, 88], "do": [1, 2, 4, 7, 25, 29, 30, 40, 41, 54, 55, 59, 69, 70, 71, 73, 74, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "fast": 1, "explain": [1, 7], "python": [1, 2, 30, 44, 57, 70, 71, 75, 83], "pseudocod": [1, 84], "happen": [1, 7, 31, 47, 80, 86], "n": [1, 2, 3, 4, 5, 25, 26, 29, 30, 31, 33, 34, 35, 36, 38, 39, 40, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 65, 69, 74, 75, 76, 77, 80, 81, 85, 86, 87, 88, 89], "without": [1, 2, 4, 7, 9, 11, 16, 26, 30, 49, 57, 67, 69, 74, 78, 82, 83, 88], "ani": [1, 2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 29, 30, 31, 33, 35, 39, 40, 44, 45, 47, 49, 50, 52, 53, 55, 57, 59, 61, 62, 67, 69, 70, 71, 73, 74, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88], "distinct": [1, 40, 89], "natur": [1, 7, 80, 83], "number": [1, 2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 34, 35, 36, 37, 38, 39, 40, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 65, 66, 68, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 86, 89], "0": [1, 2, 3, 4, 5, 7, 9, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "count_joint": 1, "len": [1, 2, 3, 5, 25, 29, 34, 39, 40, 41, 54, 55, 57, 70, 71, 74, 75, 76, 77, 78, 80, 81, 83, 85, 87, 88, 89], "y": [1, 2, 3, 4, 30, 34, 36, 40, 41, 44, 53, 57, 58, 69, 70, 71, 73, 76, 78, 80, 81, 83, 85, 88], "round": [1, 29, 31, 40, 57, 76, 85], "astyp": [1, 80], "int": [1, 2, 3, 4, 5, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 36, 37, 38, 39, 40, 46, 47, 49, 53, 54, 55, 57, 59, 61, 62, 63, 66, 69, 70, 77, 83], "rang": [1, 3, 5, 9, 34, 36, 38, 40, 57, 58, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 89], "idx_flip": 1, "where": [1, 2, 3, 4, 5, 7, 9, 10, 13, 18, 25, 29, 31, 34, 35, 36, 37, 38, 39, 40, 41, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 69, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 88, 89], "pragma": 1, "cover": [1, 3, 68, 75], "choic": [1, 31, 77, 81, 83], "replac": [1, 39, 44, 55, 70, 71, 74, 75, 77, 80, 83, 87, 88], "generate_noise_matrix_from_trac": [1, 70, 71, 78, 80, 81], "max_trace_prob": 1, "min_trace_prob": 1, "1e": [1, 3, 55, 69, 70, 71], "05": [1, 20, 39, 57, 63, 65, 75, 76, 78, 82], "max_noise_r": 1, "99999": 1, "min_noise_r": 1, "valid_noise_matrix": [1, 70, 71, 78, 80, 81], "none": [1, 2, 3, 4, 5, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 39, 40, 41, 44, 45, 46, 47, 48, 49, 52, 53, 54, 55, 57, 59, 61, 62, 65, 66, 70, 71, 76, 77, 78, 80, 81, 86], "frac_zero_noise_r": 1, "seed": [1, 2, 3, 7, 20, 30, 36, 57, 69, 70, 71, 73, 75, 78, 80, 81, 87], "max_it": [1, 69, 74, 83, 88], "10000": [1, 29, 75, 76], "x": [1, 2, 3, 4, 7, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 30, 31, 33, 34, 36, 39, 40, 41, 44, 45, 47, 53, 54, 55, 57, 59, 69, 70, 71, 73, 75, 76, 77, 78, 80, 81, 83, 85, 87, 88], "diagon": [1, 3, 4, 13, 31, 34, 40], "equal": [1, 3, 7, 9, 47, 52, 62, 84], "creat": [1, 2, 6, 13, 26, 29, 30, 31, 40, 57, 67, 69, 73, 74, 76, 77, 86, 88, 89], "impli": [1, 25, 46], "float": [1, 2, 7, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 33, 35, 36, 39, 40, 45, 46, 47, 49, 52, 53, 57, 61, 65, 69, 70, 71, 78, 80, 81], "entri": [1, 3, 4, 13, 25, 26, 30, 31, 33, 37, 40, 45, 46, 47, 50, 73, 74, 78, 81, 82, 87, 88], "maximum": [1, 7, 54, 62, 66, 86], "minimum": [1, 7, 16, 31, 33, 47, 52, 65], "noise_r": 1, "non": [1, 2, 3, 4, 6, 13, 20, 26, 30, 31, 52, 57, 70, 76, 78, 80, 82, 83], "default": [1, 2, 3, 4, 7, 11, 13, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 36, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 70, 76, 77, 86], "If": [1, 2, 3, 4, 7, 9, 10, 13, 20, 22, 25, 26, 29, 30, 31, 33, 34, 36, 39, 40, 44, 45, 46, 47, 50, 52, 53, 54, 57, 58, 59, 61, 62, 65, 66, 67, 68, 69, 70, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "have": [1, 2, 3, 4, 7, 13, 17, 20, 25, 26, 28, 29, 30, 31, 34, 36, 40, 44, 45, 46, 47, 50, 52, 53, 54, 55, 57, 58, 62, 66, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "all": [1, 2, 3, 4, 5, 7, 10, 11, 13, 18, 23, 25, 26, 29, 30, 31, 34, 36, 37, 39, 40, 44, 45, 46, 47, 48, 49, 52, 53, 54, 55, 57, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "necessari": [1, 2, 3, 5, 7, 9, 39, 70], "In": [1, 2, 3, 7, 25, 26, 29, 30, 45, 46, 48, 69, 70, 71, 73, 74, 75, 76, 77, 78, 81, 82, 83, 84, 85, 86, 87, 88, 89], "particular": [1, 4, 7, 10, 11, 13, 15, 16, 18, 20, 21, 22, 26, 30, 40, 45, 49, 53, 57, 62, 66, 67, 69, 71, 74, 76, 80, 81, 83, 85, 87, 88], "satisfi": [1, 3, 25], "requir": [1, 2, 4, 5, 6, 7, 8, 9, 24, 26, 27, 28, 29, 30, 31, 34, 40, 43, 44, 47, 54, 55, 57, 59, 67, 68, 69, 75, 76, 78, 84], "argument": [1, 2, 3, 4, 7, 13, 19, 26, 29, 30, 31, 36, 41, 44, 45, 46, 47, 49, 52, 53, 54, 55, 57, 61, 62, 63, 65, 71, 74, 75, 76, 77, 82, 85, 88, 89], "when": [1, 2, 3, 4, 7, 9, 11, 19, 20, 26, 30, 31, 34, 36, 40, 44, 47, 49, 50, 52, 54, 55, 57, 58, 70, 71, 73, 74, 76, 77, 80, 84, 85, 86, 87, 88, 89], "The": [1, 2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 33, 34, 35, 36, 37, 38, 40, 44, 45, 46, 47, 50, 52, 53, 54, 55, 57, 59, 62, 63, 65, 67, 69, 70, 71, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "rate": [1, 2, 3, 7, 27, 40, 69, 89], "set": [1, 2, 3, 4, 6, 7, 9, 10, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 35, 36, 40, 44, 45, 47, 50, 52, 53, 54, 55, 57, 59, 61, 62, 70, 71, 73, 74, 76, 80, 81, 83, 84, 85, 86, 87, 88, 89], "note": [1, 2, 3, 5, 7, 26, 29, 30, 31, 36, 40, 45, 50, 52, 53, 54, 55, 57, 58, 62, 68, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "you": [1, 2, 3, 4, 5, 7, 11, 13, 25, 26, 28, 29, 30, 31, 36, 43, 44, 45, 47, 50, 52, 53, 54, 55, 57, 58, 59, 62, 63, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "high": [1, 2, 13, 29, 31, 40, 52, 55, 57, 70, 71, 75, 77, 78, 82, 85, 86, 87, 88, 89], "mai": [1, 2, 3, 4, 7, 10, 17, 18, 25, 26, 28, 29, 30, 31, 34, 36, 40, 45, 46, 50, 52, 53, 54, 55, 57, 59, 62, 66, 68, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 84, 85, 86, 88, 89], "imposs": [1, 7, 78], "also": [1, 2, 3, 4, 5, 7, 18, 25, 26, 29, 30, 31, 39, 44, 45, 54, 57, 62, 65, 66, 67, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89], "low": [1, 7, 40, 45, 67, 70, 71, 74, 78, 82, 86], "zero": [1, 3, 4, 13, 26, 30, 33, 40, 41, 70, 77, 81, 82, 83], "forc": [1, 2, 3, 4, 30, 70, 89], "instead": [1, 2, 3, 7, 10, 13, 23, 25, 26, 29, 30, 31, 34, 40, 44, 45, 47, 49, 54, 55, 57, 58, 61, 63, 65, 68, 69, 73, 76, 77, 78, 81, 82, 83, 85, 86, 87, 88, 89], "onli": [1, 2, 3, 4, 7, 13, 19, 20, 25, 26, 29, 30, 31, 33, 34, 39, 40, 44, 45, 54, 55, 57, 59, 61, 65, 66, 67, 69, 70, 71, 74, 77, 80, 81, 82, 83, 84, 85, 86, 88, 89], "guarante": [1, 3, 4, 12, 17, 26, 28, 30, 32, 34, 43, 68], "produc": [1, 2, 4, 7, 13, 36, 45, 55, 57, 59, 61, 67, 69, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 86, 87, 88, 89], "higher": [1, 4, 7, 25, 31, 33, 34, 36, 45, 46, 57, 71, 74, 76, 82], "opposit": [1, 89], "occur": [1, 3, 7, 25, 39, 52, 70, 71, 76, 77, 83], "small": [1, 3, 7, 25, 29, 36, 40, 46, 53, 74, 75, 77, 81, 83, 88], "numpi": [1, 3, 4, 5, 7, 9, 29, 30, 36, 38, 39, 41, 44, 49, 52, 57, 58, 63, 65, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "max": [1, 31, 54, 55, 77, 83], "tri": [1, 26, 30, 84], "befor": [1, 2, 3, 26, 30, 40, 54, 57, 62, 74, 76, 78, 80, 83, 85, 87, 88], "option": [1, 2, 3, 4, 5, 6, 9, 10, 13, 19, 20, 25, 26, 29, 30, 31, 34, 36, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 62, 65, 66, 67, 69, 70, 71, 73, 76, 77, 78, 85, 86, 87], "left": [1, 2, 31, 33, 38, 40, 47, 50, 53, 70, 71, 81, 82, 83, 86], "stochast": 1, "exceed": 1, "generate_n_rand_probabilities_that_sum_to_m": 1, "m": [1, 26, 30, 35, 36, 45, 50, 52, 53, 54, 70, 71, 75, 80, 81, 82, 89], "max_prob": 1, "min_prob": 1, "dirichlet": 1, "ones": [1, 26, 30, 44, 76, 78, 86], "length": [1, 4, 9, 20, 25, 27, 31, 40, 47, 50, 54, 55, 57, 59, 62, 66, 69, 81, 83, 86, 87, 89], "must": [1, 2, 3, 4, 13, 25, 26, 27, 28, 30, 31, 34, 36, 37, 40, 43, 44, 45, 46, 47, 54, 55, 57, 59, 61, 62, 63, 65, 66, 69, 80, 84, 86, 89], "randomly_distribute_n_balls_into_k_bin": 1, "max_balls_per_bin": 1, "min_balls_per_bin": 1, "uniformli": 1, "integ": [1, 2, 3, 7, 9, 25, 29, 31, 37, 40, 41, 45, 47, 53, 59, 61, 62, 63, 65, 66, 69, 76, 80, 81, 82, 86, 87, 88, 89], "ball": [1, 75], "bin": [1, 3, 47, 70, 71, 83], "ensur": [1, 2, 7, 26, 30, 40, 41, 52, 55, 57, 69, 70, 71, 74, 77, 78, 83, 84, 85, 87, 88], "most": [1, 3, 4, 5, 7, 13, 25, 29, 31, 36, 44, 45, 46, 47, 50, 52, 53, 54, 55, 58, 61, 65, 66, 67, 68, 69, 70, 71, 73, 74, 76, 78, 80, 81, 82, 83, 85, 86, 87, 88], "least": [1, 7, 25, 29, 45, 46, 52, 55, 65, 76, 77, 80, 83, 86], "int_arrai": [1, 40], "can": [2, 3, 4, 5, 6, 10, 11, 13, 23, 25, 26, 27, 28, 29, 30, 31, 35, 36, 37, 40, 41, 43, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 57, 58, 59, 62, 63, 66, 67, 68, 69, 70, 73, 74, 77, 81, 82, 83, 84, 85, 86, 87, 88, 89], "model": [2, 3, 4, 7, 13, 25, 26, 27, 28, 29, 30, 31, 33, 34, 35, 39, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 68, 70, 71, 75, 79, 84, 86, 89], "For": [2, 3, 4, 5, 6, 7, 8, 13, 18, 24, 25, 26, 29, 30, 31, 34, 36, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 61, 63, 65, 66, 67, 69, 71, 73, 75, 76, 77, 78, 80, 81, 82, 83, 84, 86, 87, 88, 89], "regular": [2, 3, 29, 44], "multi": [2, 3, 7, 25, 26, 29, 30, 31, 35, 36, 37, 40, 41, 46, 47, 48, 49, 54, 55, 67, 76, 78, 79], "task": [2, 4, 9, 11, 13, 23, 25, 29, 34, 36, 37, 38, 40, 45, 47, 55, 57, 67, 69, 74, 75, 76, 78, 81, 83, 86, 88, 89], "cleanlearn": [2, 3, 7, 19, 26, 40, 44, 57, 58, 67, 68, 85, 87, 88], "wrap": [2, 26, 30, 44, 54, 57, 67, 70, 71, 73, 74, 78, 85, 87, 88], "instanc": [2, 3, 4, 5, 7, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 30, 36, 44, 53, 54, 57, 62, 69, 70, 71, 73, 74, 77, 78, 87], "sklearn": [2, 3, 4, 7, 25, 30, 36, 40, 44, 54, 57, 58, 67, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 83, 84, 85, 87, 88], "classifi": [2, 3, 30, 36, 40, 45, 48, 54, 55, 67, 68, 69, 73, 74, 76, 80, 81, 83, 84, 86, 87, 88, 89], "adher": [2, 30, 57], "estim": [2, 3, 4, 6, 10, 18, 25, 29, 30, 31, 34, 40, 45, 46, 47, 52, 54, 57, 59, 61, 65, 67, 68, 69, 70, 71, 73, 74, 76, 77, 79, 81, 82, 83, 84, 85, 86, 89], "api": [2, 3, 11, 44, 54, 57, 68, 76, 85], "defin": [2, 3, 4, 5, 7, 11, 18, 25, 26, 27, 29, 30, 31, 55, 57, 59, 70, 71, 73, 80, 83, 89], "four": [2, 7, 75, 78, 89], "clf": [2, 3, 4, 36, 57, 67, 73, 76, 78, 81, 87], "fit": [2, 3, 4, 7, 30, 44, 54, 57, 67, 70, 71, 73, 74, 76, 77, 78, 80, 81, 83, 84, 85, 87, 88, 89], "sample_weight": [2, 30, 57, 78], "predict_proba": [2, 4, 25, 30, 36, 44, 69, 70, 71, 73, 74, 76, 78, 80, 81, 83, 87], "predict": [2, 3, 4, 7, 13, 18, 19, 22, 25, 29, 30, 31, 33, 34, 36, 37, 39, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 68, 75, 76, 78, 79, 83, 85, 86, 88, 89], "score": [2, 3, 4, 5, 7, 10, 15, 16, 18, 19, 20, 21, 22, 25, 29, 31, 33, 36, 38, 45, 46, 47, 49, 50, 52, 54, 55, 57, 58, 61, 63, 65, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 83, 85, 87, 88], "data": [2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 27, 28, 29, 30, 31, 36, 37, 40, 43, 44, 45, 46, 47, 48, 52, 54, 55, 56, 57, 62, 63, 64, 65, 66, 68, 72, 74, 77, 79, 84, 88], "e": [2, 3, 4, 7, 9, 13, 18, 25, 26, 29, 30, 31, 34, 36, 37, 40, 41, 45, 46, 47, 48, 54, 55, 57, 59, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 88], "featur": [2, 3, 4, 7, 13, 15, 19, 20, 21, 22, 36, 40, 54, 57, 67, 70, 71, 73, 74, 78, 80, 85, 87], "element": [2, 3, 4, 25, 31, 33, 40, 45, 47, 55, 62, 63, 65, 69, 74, 76, 88, 89], "first": [2, 7, 14, 20, 21, 25, 29, 36, 40, 45, 46, 50, 53, 55, 57, 69, 70, 73, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "index": [2, 7, 20, 25, 31, 38, 39, 40, 41, 46, 55, 57, 62, 65, 66, 69, 70, 71, 73, 75, 77, 78, 80, 82, 83, 85, 86, 88, 89], "should": [2, 3, 4, 5, 7, 11, 13, 18, 20, 25, 26, 29, 30, 31, 33, 34, 36, 39, 40, 44, 45, 46, 49, 50, 52, 53, 54, 55, 57, 58, 62, 63, 65, 66, 69, 70, 71, 73, 74, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "correspond": [2, 3, 4, 7, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 29, 30, 31, 33, 34, 36, 39, 40, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 59, 62, 63, 65, 66, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "differ": [2, 4, 5, 7, 10, 12, 17, 20, 25, 26, 28, 29, 30, 31, 32, 36, 40, 41, 43, 45, 50, 52, 54, 57, 69, 70, 71, 73, 74, 77, 78, 80, 83, 84, 87], "sampl": [2, 3, 4, 7, 13, 16, 31, 33, 36, 47, 50, 53, 55, 57, 58, 67, 68, 75, 76, 78, 79, 81, 82, 85, 86, 88, 89], "size": [2, 7, 26, 29, 30, 31, 36, 47, 52, 53, 57, 59, 61, 73, 76, 77, 78, 80, 81, 84, 86, 88], "here": [2, 4, 5, 7, 11, 29, 31, 34, 44, 45, 46, 47, 49, 50, 53, 54, 65, 67, 68, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "re": [2, 4, 26, 30, 39, 45, 57, 67, 69, 70, 73, 74, 76, 85, 86, 87, 88, 89], "weight": [2, 7, 26, 27, 30, 36, 45, 52, 55, 57, 69, 70, 71, 74, 83, 88], "loss": [2, 27, 44, 55, 57, 77], "while": [2, 3, 7, 26, 29, 30, 35, 36, 40, 50, 53, 57, 67, 76, 77, 78, 80, 85], "train": [2, 3, 4, 7, 13, 26, 27, 30, 36, 40, 44, 45, 50, 53, 54, 57, 58, 68, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 84, 86, 89], "support": [2, 3, 4, 9, 29, 36, 40, 41, 54, 55, 65, 67, 68, 69, 70, 71, 76, 77], "your": [2, 3, 4, 6, 7, 13, 25, 26, 28, 29, 30, 31, 36, 40, 43, 44, 45, 46, 47, 49, 54, 55, 57, 58, 59, 61, 62, 68, 69, 73, 75, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "recommend": [2, 4, 7, 10, 13, 29, 31, 45, 70, 71, 76, 77, 84, 85], "furthermor": 2, "correctli": [2, 3, 7, 25, 26, 30, 31, 34, 41, 46, 47, 52, 57, 59, 74, 76, 81, 82, 85, 86, 88], "clonabl": [2, 57], "via": [2, 4, 7, 10, 13, 18, 25, 27, 29, 30, 36, 40, 45, 50, 53, 54, 55, 57, 58, 61, 65, 69, 70, 71, 73, 74, 75, 76, 77, 81, 82, 83, 84, 85, 86, 87, 88, 89], "base": [2, 3, 4, 5, 7, 9, 10, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 26, 29, 30, 31, 34, 35, 36, 38, 39, 40, 41, 44, 45, 46, 47, 49, 52, 54, 55, 57, 58, 61, 63, 65, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 89], "clone": [2, 57, 81], "intern": [2, 3, 5, 7, 8, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 29, 33, 34, 35, 36, 37, 38, 39, 40, 41, 49, 53, 57, 63, 68, 70, 76, 78, 80, 81, 83, 89], "multipl": [2, 3, 4, 9, 10, 25, 31, 39, 45, 46, 47, 49, 52, 53, 57, 67, 70, 71, 76, 77, 79, 81, 82, 85], "g": [2, 3, 4, 7, 9, 18, 25, 26, 30, 31, 37, 40, 47, 48, 54, 55, 57, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 88], "manual": [2, 57, 69, 76, 83, 84, 85, 87, 88, 89], "pytorch": [2, 26, 27, 30, 57, 67, 69, 76, 79, 81, 86], "call": [2, 3, 4, 7, 10, 11, 15, 16, 18, 19, 20, 21, 22, 26, 30, 36, 40, 44, 54, 57, 69, 70, 71, 74, 76, 78, 83, 84, 86, 88, 89], "__init__": [2, 27, 57, 77], "independ": [2, 3, 7, 46, 57, 84, 89], "compat": [2, 26, 29, 30, 44, 57, 58, 61, 65, 67, 76, 84, 85, 87, 88], "neural": [2, 27, 44, 54, 57, 69, 76, 77, 81, 83], "network": [2, 26, 27, 30, 44, 54, 57, 69, 74, 76, 77, 81, 83, 88], "typic": [2, 26, 30, 54, 57, 69, 71, 73, 74, 77, 83, 84, 87, 88], "initi": [2, 3, 10, 26, 30, 45, 57, 74, 76, 87], "insid": [2, 30, 57, 76, 78], "There": [2, 3, 67, 78, 80, 81], "two": [2, 3, 7, 20, 25, 26, 29, 30, 37, 40, 50, 52, 53, 68, 70, 71, 73, 74, 76, 77, 78, 81, 85, 86, 88, 89], "new": [2, 5, 11, 18, 26, 29, 30, 35, 39, 40, 45, 57, 69, 70, 74, 75, 76, 83, 84, 88, 89], "notion": 2, "confid": [2, 3, 7, 18, 25, 29, 31, 34, 36, 40, 45, 46, 47, 50, 52, 53, 54, 55, 57, 61, 65, 67, 78, 80, 81, 82, 84, 86, 87, 89], "packag": [2, 4, 5, 6, 7, 8, 12, 24, 28, 31, 32, 40, 43, 50, 53, 57, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "prune": [2, 3, 31, 47, 57, 68, 82], "everyth": [2, 78], "els": [2, 70, 75, 76, 77, 80, 81], "mathemat": [2, 3, 7, 34], "keep": [2, 10, 11, 40, 67, 70, 75, 76, 86], "belong": [2, 3, 7, 25, 31, 33, 34, 46, 47, 48, 49, 54, 55, 59, 63, 65, 66, 77, 78, 81, 83, 86, 89], "2": [2, 3, 4, 5, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 40, 44, 46, 47, 49, 54, 55, 57, 58, 62, 63, 65, 66, 75, 76, 84], "error": [2, 3, 4, 7, 26, 30, 31, 33, 34, 38, 40, 46, 47, 49, 50, 52, 53, 55, 57, 59, 61, 62, 65, 68, 69, 70, 71, 73, 74, 75, 79, 87], "erron": [2, 3, 25, 31, 34, 40, 46, 47, 55, 57, 58, 59, 83, 85], "import": [2, 3, 4, 5, 9, 10, 11, 15, 16, 18, 19, 20, 21, 22, 23, 25, 29, 36, 38, 39, 45, 49, 52, 57, 58, 63, 65, 66, 67, 73, 74, 76, 81, 82, 83, 85, 86, 87, 88, 89], "linear_model": [2, 4, 25, 40, 57, 67, 69, 70, 71, 74, 76, 78, 80, 83, 88], "logisticregress": [2, 3, 4, 25, 40, 67, 69, 70, 71, 74, 76, 78, 80, 83, 88], "logreg": 2, "cl": [2, 11, 57, 67, 76, 78, 85, 87, 88], "pass": [2, 3, 4, 7, 9, 10, 11, 13, 19, 23, 26, 29, 30, 31, 35, 36, 40, 44, 45, 47, 54, 55, 57, 63, 67, 69, 70, 71, 74, 75, 76, 78, 80, 82, 83, 85, 88], "x_train": [2, 70, 71, 78, 80, 81, 85, 87], "labels_maybe_with_error": 2, "had": [2, 3, 57, 82], "issu": [2, 3, 4, 8, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 31, 43, 46, 47, 48, 49, 50, 51, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 68, 72, 79, 80, 84, 85, 88], "pred": [2, 31, 40, 84, 85, 87, 88], "x_test": [2, 70, 71, 78, 81, 85, 87], "might": [2, 45, 57, 62, 70, 71, 76, 77, 87, 88], "case": [2, 3, 10, 25, 36, 45, 57, 69, 70, 71, 73, 75, 77, 78, 83, 85, 87, 88, 89], "standard": [2, 3, 4, 25, 31, 44, 46, 47, 49, 55, 57, 67, 70, 71, 73, 75, 78, 87], "adapt": [2, 26, 28, 40, 43, 57, 83], "skorch": [2, 57, 67, 76], "kera": [2, 43, 57, 67, 76], "scikera": [2, 44, 57, 76], "open": [2, 29, 75, 82, 89], "doesn": [2, 57, 67], "t": [2, 3, 7, 14, 21, 26, 27, 29, 30, 31, 36, 38, 39, 49, 54, 55, 57, 63, 65, 66, 67, 70, 71, 75, 77, 78, 81, 82, 89], "alreadi": [2, 4, 13, 26, 29, 30, 34, 44, 45, 57, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 87, 88], "exist": [2, 4, 7, 9, 26, 29, 30, 39, 44, 50, 52, 54, 57, 67, 68, 70, 71, 74, 80, 81, 88, 89], "made": [2, 4, 13, 57, 74, 77, 80, 82, 84, 85, 87, 88], "easi": [2, 34, 57, 70, 71, 75, 76, 78, 81], "inherit": [2, 5, 27, 57], "baseestim": [2, 30, 57], "yourmodel": [2, 57], "def": [2, 5, 11, 26, 30, 44, 57, 69, 70, 71, 75, 76, 77, 78, 80, 81, 83, 85, 88, 89], "self": [2, 3, 4, 5, 9, 10, 11, 13, 26, 27, 29, 30, 31, 36, 54, 55, 57, 70, 75, 77, 81, 86, 87, 89], "refer": [2, 7, 26, 30, 46, 47, 49, 50, 52, 53, 57, 61, 62, 70, 71, 73, 74, 76, 77, 78, 84, 85], "origin": [2, 4, 7, 30, 31, 39, 40, 44, 46, 47, 50, 53, 54, 57, 58, 61, 63, 65, 70, 73, 74, 76, 77, 78, 82, 83, 85, 87, 88, 89], "total": [2, 3, 25, 29, 40, 46, 66, 76, 77, 86], "state": [2, 3, 4, 26, 27, 30, 35, 57, 78, 81, 82, 89], "art": [2, 27, 78, 81], "northcutt": [2, 3, 25, 54, 55], "et": [2, 3, 25, 27, 54, 55], "al": [2, 3, 25, 27, 54, 55], "2021": [2, 3, 25, 54, 55], "weak": 2, "supervis": [2, 7, 70, 71, 76, 80], "find": [2, 4, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 28, 29, 30, 31, 35, 39, 40, 43, 50, 53, 54, 55, 57, 59, 63, 65, 68, 70, 79, 84], "uncertainti": [2, 7, 33, 54, 57, 76, 83, 85], "It": [2, 3, 4, 5, 7, 9, 10, 13, 18, 23, 26, 30, 31, 34, 36, 38, 45, 52, 53, 57, 67, 70, 71, 76, 77, 78, 81, 84], "work": [2, 3, 4, 5, 7, 9, 25, 26, 29, 30, 31, 34, 39, 40, 41, 44, 45, 55, 57, 67, 68, 70, 71, 75, 83, 85, 88], "includ": [2, 3, 4, 5, 7, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 28, 29, 30, 39, 40, 43, 45, 46, 49, 50, 54, 55, 57, 61, 62, 63, 65, 67, 68, 70, 71, 73, 74, 76, 77, 78, 81, 82, 83, 89], "deep": [2, 28, 30, 43, 44, 57, 74], "see": [2, 3, 4, 10, 25, 26, 29, 30, 31, 36, 40, 44, 46, 47, 49, 50, 53, 54, 55, 57, 63, 65, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "subfield": 2, "theori": [2, 78], "machin": [2, 4, 11, 13, 23, 28, 43, 57, 70, 71, 75, 80], "across": [2, 3, 4, 5, 7, 10, 13, 18, 25, 29, 36, 46, 53, 54, 70, 71, 73, 74, 75, 76, 77, 78, 82, 84], "varieti": [2, 87, 88], "like": [2, 3, 4, 5, 7, 11, 13, 23, 25, 26, 29, 30, 31, 34, 40, 44, 45, 46, 49, 50, 52, 55, 57, 58, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 76, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "pu": [2, 40], "input": [2, 3, 4, 7, 13, 20, 25, 26, 29, 30, 34, 36, 39, 40, 41, 44, 53, 57, 67, 68, 71, 74, 75, 76, 77, 78, 80, 81, 82, 85, 86, 88, 89], "discret": [2, 31, 34, 40, 54, 55, 59, 61, 62], "vector": [2, 3, 4, 7, 13, 31, 34, 36, 37, 40, 54, 55, 67, 69, 70, 71, 73, 74, 77, 78, 81, 82, 83, 86, 88, 89], "would": [2, 3, 4, 26, 29, 30, 31, 40, 47, 57, 67, 70, 76, 77, 78, 83, 85, 88, 89], "obtain": [2, 4, 7, 13, 31, 45, 47, 50, 53, 55, 58, 69, 71, 74, 76, 80, 82, 84, 86, 89], "been": [2, 25, 31, 34, 39, 40, 45, 46, 50, 52, 54, 55, 57, 69, 70, 73, 76, 78, 80, 81, 82, 83, 86, 89], "dure": [2, 13, 54, 57, 69, 73, 74, 76, 78, 81, 84, 85, 87, 88, 89], "denot": [2, 3, 34, 36, 40, 47, 54, 55, 65], "tild": 2, "paper": [2, 7, 45, 54, 63, 65, 75, 78, 80, 83, 85, 89], "cv_n_fold": [2, 3, 57, 88], "5": [2, 3, 4, 15, 16, 18, 19, 20, 21, 22, 23, 25, 30, 31, 33, 35, 36, 40, 45, 46, 49, 50, 53, 57, 58, 65, 70, 74, 75, 76, 81, 82, 83, 84, 86, 88, 89], "converge_latent_estim": [2, 3], "pulearn": [2, 40], "find_label_issues_kwarg": [2, 7, 57, 68, 76, 78], "label_quality_scores_kwarg": [2, 7], "low_memori": [2, 47, 63, 76], "clean": [2, 52, 55, 57, 58, 67, 70, 71, 75, 85, 87, 88], "even": [2, 3, 25, 29, 33, 34, 40, 57, 69, 76, 78, 80, 81, 82], "messi": [2, 57, 78], "ridden": [2, 57], "autom": [2, 57, 67, 71, 75, 76], "robust": [2, 34, 57, 71, 76], "prone": [2, 57], "out": [2, 3, 4, 7, 13, 22, 26, 30, 31, 36, 44, 47, 48, 50, 53, 54, 55, 57, 58, 66, 67, 68, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88, 89], "current": [2, 3, 7, 10, 11, 18, 26, 30, 31, 36, 45, 52, 57, 70, 71, 76, 80], "intend": [2, 10, 11, 12, 13, 23, 32, 45, 61, 65, 69, 70, 71, 74, 78], "A": [2, 3, 4, 5, 7, 9, 10, 11, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 27, 30, 31, 34, 35, 36, 37, 39, 40, 44, 45, 46, 49, 52, 53, 54, 55, 57, 59, 61, 62, 66, 68, 69, 70, 73, 74, 75, 76, 77, 78, 80, 82, 84, 87, 88, 89], "follow": [2, 3, 7, 11, 25, 26, 29, 30, 36, 38, 45, 46, 50, 52, 53, 54, 57, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "experiment": [2, 26, 27, 29, 30, 47, 68, 76], "wrapper": [2, 4, 44, 69, 85, 87, 88], "around": [2, 4, 52, 70, 71, 82, 83, 89], "fasttext": [2, 43], "store": [2, 4, 7, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 30, 54, 57, 73, 74, 75, 86, 87, 88, 89], "along": [2, 36, 47, 65, 70, 71, 76, 77, 83], "dimens": [2, 38, 40, 59, 62, 76, 77, 83, 86], "select": [2, 6, 20, 45, 55, 77, 80, 83], "split": [2, 3, 4, 7, 9, 29, 36, 39, 40, 57, 69, 70, 71, 73, 74, 75, 77, 78, 81, 84, 87, 89], "cross": [2, 3, 7, 25, 31, 34, 35, 36, 47, 50, 53, 55, 57, 58, 68, 69, 70, 71, 73, 74, 75, 76, 78, 79, 81, 82, 85, 86, 87, 88, 89], "fold": [2, 3, 25, 31, 34, 57, 69, 73, 75, 76, 82, 86, 87], "By": [2, 4, 25, 46, 47, 57, 70, 86], "need": [2, 3, 7, 25, 26, 29, 30, 31, 46, 47, 49, 54, 57, 67, 69, 70, 71, 74, 76, 78, 80, 81, 82, 86, 88], "holdout": [2, 3, 57], "comput": [2, 3, 4, 5, 7, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 33, 34, 35, 36, 38, 40, 45, 46, 47, 49, 52, 53, 54, 55, 57, 58, 59, 61, 67, 68, 70, 71, 75, 76, 78, 79, 81, 82, 83, 85, 86, 88], "them": [2, 3, 4, 5, 6, 7, 8, 9, 24, 26, 28, 29, 30, 31, 43, 45, 54, 57, 68, 70, 71, 73, 74, 76, 77, 80, 81, 83, 85, 86, 87, 88, 89], "numer": [2, 3, 4, 7, 10, 18, 36, 52, 54, 57, 62, 67, 68, 69, 70, 71, 72, 74, 77, 78, 80, 83, 85, 87, 88], "consist": [2, 3, 26, 30, 40, 45, 86, 89], "latent": [2, 3, 34], "thei": [2, 3, 4, 12, 17, 20, 26, 27, 28, 30, 31, 32, 40, 44, 47, 52, 55, 57, 58, 61, 65, 67, 69, 70, 71, 73, 74, 76, 77, 78, 80, 83, 85, 88, 89], "relat": [2, 3, 10, 15, 16, 20, 21, 22, 34, 40, 46, 57, 71], "close": [2, 3, 7, 29, 34, 54, 69, 70, 71, 73, 74, 76, 77, 78, 82], "form": [2, 3, 7, 26, 27, 30, 34, 39, 40, 55, 57, 76], "equival": [2, 3, 26, 30, 34, 54, 83], "iter": [2, 3, 25, 26, 30, 31, 40, 46, 47, 57, 80, 86], "enforc": [2, 26, 30, 40], "perfectli": [2, 25, 46, 78], "certain": [2, 3, 4, 13, 26, 30, 44, 57, 70, 71, 75, 83], "dict": [2, 3, 4, 7, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 29, 30, 31, 35, 36, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 65, 70, 71, 76, 77, 89], "keyword": [2, 3, 4, 7, 13, 19, 26, 29, 30, 31, 33, 36, 39, 44, 45, 47, 54, 55, 57, 63, 65, 70], "filter": [2, 3, 7, 29, 39, 46, 48, 49, 51, 60, 61, 62, 64, 65, 66, 67, 68, 69, 74, 75, 76, 77, 81, 82, 85, 86, 87, 88, 89], "find_label_issu": [2, 3, 7, 29, 31, 46, 47, 49, 50, 52, 57, 59, 61, 62, 63, 65, 66, 67, 68, 76, 81, 82, 85, 86, 87, 88, 89], "particularli": [2, 67, 80, 83], "filter_bi": [2, 3, 29, 31, 47, 68, 76], "frac_nois": [2, 31, 47, 63, 76], "min_examples_per_class": [2, 31, 47, 76, 78], "impact": [2, 7, 70, 71, 77], "ml": [2, 4, 7, 57, 67, 70, 71, 73, 74, 77, 80, 87, 88], "accuraci": [2, 27, 55, 69, 76, 77, 78, 80, 83, 85, 86, 87, 88], "n_job": [2, 29, 31, 47, 59, 61, 63, 76, 83, 86], "disabl": [2, 26, 30, 31, 83], "process": [2, 3, 5, 10, 13, 29, 31, 39, 45, 47, 59, 61, 63, 69, 70, 80, 84, 88], "caus": [2, 31, 36, 70, 71], "rank": [2, 3, 25, 29, 31, 36, 46, 47, 48, 50, 51, 53, 54, 56, 60, 62, 63, 64, 66, 67, 68, 70, 71, 75, 76, 81, 82, 83, 85, 86, 87, 88, 89], "get_label_quality_scor": [2, 29, 31, 36, 45, 47, 49, 50, 52, 55, 58, 61, 63, 65, 68, 78, 81, 82, 85, 86, 89], "adjust_pred_prob": [2, 7, 49, 54, 55, 78], "control": [2, 4, 6, 7, 13, 29, 31, 38, 45, 53, 54, 57, 63, 65, 70, 71, 75, 76], "how": [2, 3, 4, 7, 10, 11, 13, 18, 25, 26, 27, 29, 30, 34, 40, 45, 46, 49, 50, 52, 54, 55, 57, 61, 65, 67, 70, 71, 73, 74, 75, 77, 82, 83, 84, 85, 86, 87, 88], "much": [2, 7, 25, 29, 31, 57, 76, 78, 80, 83], "output": [2, 3, 4, 7, 13, 26, 27, 30, 34, 40, 44, 45, 46, 50, 52, 53, 54, 57, 61, 62, 65, 66, 67, 68, 69, 70, 74, 75, 76, 77, 82, 83, 84, 85, 88], "print": [2, 4, 5, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 40, 45, 46, 47, 52, 54, 55, 57, 59, 61, 62, 66, 68, 69, 71, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "suppress": [2, 29, 45, 52, 54, 55, 57, 59, 61, 62, 86, 89], "statement": [2, 29, 45, 52, 54, 55, 57, 59, 61, 62], "big": [2, 29, 47, 53, 57, 78], "limit": [2, 4, 13, 29, 47, 82, 86, 89], "memori": [2, 26, 29, 30, 47, 53, 59, 61, 70, 86], "label_issues_batch": [2, 28, 47, 76], "find_label_issues_batch": [2, 29, 47, 76], "pred_prob": [2, 3, 4, 7, 13, 19, 20, 22, 25, 29, 31, 33, 34, 35, 36, 37, 40, 41, 45, 46, 47, 49, 50, 53, 54, 55, 59, 61, 62, 63, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 87, 88], "threshold": [2, 3, 5, 7, 15, 16, 18, 22, 29, 52, 53, 54, 55, 61, 65, 70, 82, 83, 86, 89], "inverse_noise_matrix": [2, 3, 7, 34, 40, 68, 78], "label_issu": [2, 29, 31, 47, 50, 57, 59, 68, 69, 74, 76, 77, 78, 85, 87, 88], "clf_kwarg": [2, 3, 7, 57], "clf_final_kwarg": [2, 57], "validation_func": [2, 3, 7], "correct": [2, 7, 25, 29, 31, 33, 45, 46, 47, 49, 50, 52, 53, 55, 57, 58, 61, 65, 67, 69, 73, 74, 77, 78, 80, 82, 84, 85], "result": [2, 3, 7, 10, 13, 15, 16, 18, 19, 20, 21, 22, 26, 29, 30, 31, 33, 40, 47, 49, 50, 53, 55, 57, 58, 59, 61, 65, 69, 70, 71, 73, 74, 76, 77, 78, 80, 85, 86, 87, 88, 89], "identifi": [2, 3, 4, 5, 7, 9, 13, 23, 25, 29, 31, 47, 50, 55, 57, 58, 59, 62, 63, 65, 66, 67, 69, 70, 71, 73, 74, 75, 77, 78, 81, 83, 85, 86, 87, 88, 89], "final": [2, 7, 57, 73, 82, 84, 85, 87], "remain": [2, 57, 68, 77, 85, 87, 88, 89], "datasetlik": [2, 40, 57], "beyond": [2, 4, 5, 6, 8, 24, 67, 86], "pd": [2, 3, 4, 5, 10, 15, 16, 18, 19, 20, 21, 22, 25, 35, 44, 45, 46, 57, 65, 69, 70, 71, 73, 74, 76, 78, 80, 85, 87, 88, 89], "datafram": [2, 3, 4, 5, 9, 10, 15, 16, 18, 19, 20, 21, 22, 25, 29, 35, 40, 41, 44, 45, 46, 57, 62, 66, 68, 69, 70, 71, 73, 74, 76, 77, 78, 80, 85, 86, 88, 89], "scipi": [2, 10, 40], "spars": [2, 4, 7, 10, 13, 40, 41, 73], "csr_matrix": [2, 4, 10, 13], "torch": [2, 26, 27, 30, 69, 74, 75, 77, 83, 88], "util": [2, 4, 13, 23, 26, 27, 30, 32, 45, 57, 67, 68, 69, 70, 71, 76, 77, 78, 83], "tensorflow": [2, 40, 44, 67, 69, 76], "object": [2, 4, 9, 10, 13, 23, 26, 27, 29, 30, 36, 40, 41, 44, 47, 50, 51, 52, 53, 54, 57, 65, 67, 69, 71, 73, 77, 78, 79, 85, 88], "list": [2, 3, 4, 9, 11, 15, 16, 18, 19, 20, 21, 22, 25, 26, 27, 29, 30, 31, 37, 39, 40, 41, 44, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 61, 62, 63, 65, 66, 68, 69, 70, 71, 75, 77, 78, 81, 82, 85, 88, 89], "index_list": 2, "subset": [2, 3, 4, 13, 25, 29, 31, 40, 55, 62, 66, 69, 73, 74, 76, 77, 81, 82, 83, 84, 85, 87, 88, 89], "wa": [2, 3, 9, 11, 29, 40, 45, 46, 52, 54, 66, 69, 70, 71, 73, 74, 76, 78, 81, 82, 84, 86, 87, 88, 89], "abl": [2, 3, 7, 57, 69, 76, 78, 80, 81], "format": [2, 3, 4, 7, 9, 26, 29, 30, 31, 34, 35, 36, 37, 40, 41, 44, 45, 46, 47, 50, 53, 54, 55, 57, 59, 61, 62, 65, 66, 70, 71, 73, 75, 77, 80, 85, 86, 87, 89], "make": [2, 3, 26, 29, 30, 36, 44, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 87, 88], "sure": [2, 29, 31, 36, 69, 70, 71, 73, 74, 75, 77, 80, 81, 82, 83, 85, 87, 88], "shuffl": [2, 7, 40, 69, 77, 81, 83], "ha": [2, 3, 4, 7, 15, 16, 17, 18, 19, 20, 21, 22, 26, 30, 34, 36, 39, 40, 45, 50, 52, 57, 63, 65, 66, 67, 69, 70, 71, 73, 74, 78, 80, 81, 82, 83, 84, 85, 87, 88, 89], "batch": [2, 29, 40, 44, 45, 59, 61, 76, 77, 83], "order": [2, 7, 25, 26, 30, 31, 34, 35, 36, 38, 40, 45, 46, 47, 50, 53, 54, 55, 59, 62, 63, 65, 66, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 82, 85, 86, 88, 89], "destroi": [2, 40], "oper": [2, 26, 29, 30, 40, 44, 55, 67, 74, 83, 87, 88], "eg": [2, 7, 40, 50, 53, 70, 71, 76], "repeat": [2, 40, 45, 80, 83], "appli": [2, 26, 30, 31, 36, 37, 39, 40, 49, 54, 63, 69, 70, 71, 73, 77, 80, 81, 83, 84, 85, 86, 87, 88], "array_lik": [2, 3, 25, 31, 40, 47, 54, 58], "some": [2, 3, 4, 7, 11, 18, 25, 26, 28, 30, 31, 34, 39, 40, 43, 45, 46, 47, 49, 50, 53, 54, 55, 57, 59, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89], "seri": [2, 3, 29, 40, 41, 57, 65], "row": [2, 3, 4, 10, 13, 21, 25, 29, 31, 33, 34, 38, 40, 45, 46, 47, 49, 54, 55, 57, 62, 63, 65, 66, 69, 70, 73, 74, 75, 76, 77, 80, 81, 83, 87, 89], "rather": [2, 3, 20, 25, 40, 44, 45, 52, 61, 65, 80, 84, 86, 88, 89], "leav": [2, 31], "per": [2, 3, 10, 25, 29, 31, 36, 39, 45, 46, 47, 49, 52, 55, 58, 59, 61, 65, 71, 76, 82, 89], "determin": [2, 3, 7, 13, 18, 20, 25, 29, 31, 36, 40, 45, 47, 50, 52, 55, 61, 65, 70, 80, 83, 85], "cutoff": [2, 3, 83], "consid": [2, 3, 4, 7, 10, 13, 19, 20, 22, 25, 26, 30, 31, 40, 45, 52, 54, 55, 58, 61, 65, 69, 71, 73, 74, 76, 77, 78, 82, 83, 84, 85, 86, 87, 88], "section": [2, 3, 5, 7, 68, 73, 77], "3": [2, 3, 4, 5, 25, 26, 30, 31, 34, 35, 36, 37, 38, 39, 40, 44, 47, 54, 55, 57, 58, 63, 65, 75, 76, 84], "equat": [2, 3, 34], "advanc": [2, 3, 4, 6, 13, 52, 54, 65, 68, 71, 72, 78], "user": [2, 3, 4, 11, 13, 23, 26, 30, 31, 52, 54, 55, 57, 61, 65, 78], "specifi": [2, 3, 4, 7, 10, 11, 13, 23, 26, 29, 30, 31, 36, 39, 45, 46, 47, 50, 52, 54, 55, 57, 58, 66, 68, 69, 71, 74, 76, 77, 80, 82, 85, 88], "automat": [2, 3, 4, 20, 25, 67, 73, 74, 75, 76, 77, 80, 82, 85, 86, 87, 88, 89], "greater": [2, 3, 4, 6, 7, 22, 29, 38, 40, 52, 71, 75, 76, 89], "count": [2, 18, 20, 25, 29, 31, 34, 40, 46, 47, 53, 68, 76, 77], "observ": [2, 3, 34, 69, 70, 71, 80, 83, 85], "mislabel": [2, 7, 25, 29, 31, 34, 45, 46, 47, 50, 52, 55, 61, 63, 65, 67, 69, 73, 74, 76, 77, 78, 81, 82, 85, 87, 88], "one": [2, 3, 4, 7, 20, 25, 26, 29, 30, 31, 36, 40, 45, 46, 47, 49, 50, 52, 53, 54, 55, 57, 58, 59, 61, 62, 63, 65, 66, 67, 69, 70, 71, 73, 74, 77, 80, 83, 84, 85, 87, 88, 89], "get_label_issu": [2, 29, 57, 78, 85, 87, 88], "either": [2, 3, 5, 7, 26, 29, 30, 31, 45, 47, 52, 54, 55, 59, 61, 71, 81, 82], "boolean": [2, 5, 7, 18, 29, 31, 39, 45, 47, 50, 55, 57, 59, 61, 62, 67, 69, 71, 74, 76, 77, 82, 85, 86, 88], "label_issues_mask": [2, 31, 55, 57, 68], "indic": [2, 3, 4, 5, 7, 10, 18, 25, 29, 30, 31, 33, 36, 40, 44, 45, 46, 47, 49, 50, 52, 54, 55, 57, 58, 61, 63, 65, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "its": [2, 4, 6, 7, 13, 26, 29, 30, 31, 38, 39, 47, 50, 53, 54, 55, 57, 59, 63, 65, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 84, 85, 86, 88, 89], "return_indices_ranked_bi": [2, 29, 31, 47, 63, 68, 76, 78, 81, 87, 88], "significantli": [2, 77, 78, 80, 84], "reduc": [2, 29, 31, 40, 69, 76], "time": [2, 7, 26, 29, 30, 40, 45, 68, 70, 75, 76, 77, 78, 82, 83, 85, 86, 87, 88, 89], "take": [2, 4, 7, 13, 25, 26, 30, 35, 36, 40, 44, 55, 73, 77, 80, 87, 89], "run": [2, 4, 5, 6, 8, 11, 13, 20, 24, 26, 29, 30, 57, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 87, 88, 89], "skip": [2, 7, 26, 30, 57, 69, 76, 81, 89], "slow": [2, 3], "step": [2, 5, 20, 36, 76, 77, 78, 80, 84], "caution": [2, 4, 76], "previous": [2, 4, 10, 40, 54, 57, 68, 69, 70, 73, 74, 80, 84, 87], "assign": [2, 5, 15, 16, 18, 19, 20, 21, 22, 35, 36, 40, 57, 70, 73, 76, 77, 85, 86, 87, 89], "individu": [2, 10, 20, 26, 30, 45, 49, 52, 55, 57, 63, 65, 68, 71, 73, 76, 80, 81, 82, 87, 89], "still": [2, 29, 30, 40, 54, 76, 77, 83, 87], "extra": [2, 26, 30, 40, 44, 45, 46, 57, 74, 76, 77, 80, 83], "receiv": [2, 7, 26, 30, 46, 49, 50, 57, 59, 63, 71, 82], "overwritten": [2, 57], "callabl": [2, 3, 36, 39, 44, 49], "x_val": 2, "y_val": 2, "map": [2, 3, 9, 29, 30, 35, 39, 40, 53, 55, 57, 62, 69, 70, 71, 76, 77, 78, 81, 89], "appropri": [2, 7, 13, 47, 55, 70, 73, 81, 82], "earli": [2, 77], "stop": [2, 77], "x_valid": 2, "y_valid": 2, "could": [2, 18, 25, 40, 54, 70, 73, 77, 81, 85, 87, 89], "f": [2, 5, 69, 70, 73, 74, 75, 76, 77, 78, 80, 81, 83, 85, 87, 88], "ignor": [2, 26, 30, 39, 44, 57, 62, 66, 69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "allow": [2, 25, 26, 29, 30, 33, 40, 45, 53, 54, 57, 59, 61, 69, 76, 77, 84, 86, 88], "access": [2, 7, 10, 26, 30, 57, 71, 77, 81], "hyperparamet": [2, 49, 54, 77], "purpos": [2, 70, 71, 76, 81, 85], "want": [2, 4, 25, 29, 41, 45, 47, 57, 70, 74, 75, 77, 80, 82, 83, 84, 86, 88, 89], "explicitli": [2, 30, 57], "yourself": [2, 4, 29, 71], "altern": [2, 7, 36, 40, 44, 45, 55, 68, 69, 73, 74, 76, 77, 78, 80, 81, 83, 85, 88], "same": [2, 3, 4, 5, 7, 9, 11, 20, 26, 29, 30, 31, 40, 44, 45, 47, 54, 55, 57, 61, 62, 65, 66, 67, 70, 71, 73, 74, 76, 77, 82, 83, 84, 85, 86, 87, 88], "effect": [2, 7, 26, 30, 45, 54, 57, 73, 74, 76, 83], "offer": [2, 4, 69, 70, 71, 74, 76, 78, 81, 88], "after": [2, 3, 4, 10, 15, 16, 18, 19, 20, 21, 22, 26, 30, 40, 45, 57, 70, 74, 76, 77, 78, 80, 82, 83, 84, 85, 86, 88], "attribut": [2, 4, 5, 7, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 30, 36, 54, 57, 70, 87], "label_issues_df": [2, 57, 77], "similar": [2, 7, 25, 26, 30, 38, 40, 45, 49, 50, 52, 54, 57, 61, 65, 70, 71, 73, 74, 76, 77, 78, 82, 83, 86], "document": [2, 3, 4, 7, 11, 25, 26, 29, 30, 31, 36, 39, 44, 46, 47, 49, 52, 53, 54, 57, 61, 62, 63, 65, 68, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 87, 88, 89], "descript": [2, 4, 5, 7, 15, 16, 18, 19, 20, 21, 22, 23, 25, 40, 50, 57, 70, 71], "were": [2, 3, 4, 25, 30, 46, 52, 65, 69, 73, 76, 78, 80, 82, 84, 86, 87], "present": [2, 3, 4, 7, 9, 10, 16, 25, 40, 54, 62, 67, 73, 76, 77, 83], "actual": [2, 3, 4, 25, 45, 46, 55, 71, 76, 78, 89], "num_class": [2, 25, 29, 40, 44, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 83, 87, 88], "uniqu": [2, 40, 62, 70, 76, 81, 83], "given_label": [2, 4, 25, 34, 57, 62, 66, 69, 70, 71, 73, 74, 77, 78, 85, 86, 88, 89], "normal": [2, 3, 20, 31, 33, 36, 39, 40, 55, 76, 78, 83], "trick": [2, 76], "distribut": [2, 3, 4, 7, 13, 20, 22, 25, 30, 31, 35, 45, 53, 54, 55, 67, 70, 71, 73, 74, 77, 83], "account": [2, 25, 45, 49, 54, 55, 74, 76, 78, 80, 81, 83, 85, 88], "word": [2, 3, 39, 65, 66, 76], "remov": [2, 7, 25, 26, 30, 31, 57, 67, 74, 75, 76, 77, 83, 85, 87, 88], "so": [2, 3, 5, 7, 11, 20, 25, 26, 29, 30, 31, 40, 45, 46, 52, 55, 57, 61, 65, 69, 70, 71, 74, 77, 78, 83, 86], "proportion": [2, 7, 31], "just": [2, 3, 4, 7, 10, 25, 27, 29, 40, 44, 55, 57, 59, 67, 68, 69, 71, 73, 74, 76, 77, 78, 81, 82, 83, 84, 86, 87, 88], "procedur": 2, "get": [2, 3, 4, 10, 26, 27, 30, 31, 36, 39, 40, 45, 47, 49, 54, 55, 57, 58, 59, 67, 69, 74, 75, 76, 77, 78, 83, 84, 85, 87, 88], "detect": [2, 4, 5, 6, 10, 11, 13, 18, 22, 38, 48, 50, 51, 52, 53, 54, 55, 56, 57, 60, 64, 67, 70, 72, 77, 79, 81, 85, 86, 87, 88, 89], "arg": [2, 9, 18, 26, 27, 30, 36, 40, 55, 57], "kwarg": [2, 5, 7, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 30, 36, 44, 57, 59, 61, 63, 76], "test": [2, 7, 20, 30, 36, 44, 57, 67, 70, 71, 73, 74, 77, 84, 85, 87, 88, 89], "expect": [2, 3, 26, 30, 31, 36, 45, 54, 55, 57, 76, 78, 80, 81, 82, 85, 87, 88, 89], "class_predict": 2, "evalu": [2, 7, 26, 27, 29, 30, 57, 69, 70, 71, 76, 77, 78, 80, 84, 85, 86, 87, 88], "simpli": [2, 25, 55, 70, 71, 73, 74, 76, 78, 85, 86, 88, 89], "quantifi": [2, 4, 5, 7, 10, 31, 49, 54, 57, 67, 71, 73, 74, 77, 78, 82], "save_spac": [2, 7, 57], "potenti": [2, 7, 25, 31, 39, 47, 50, 55, 57, 59, 61, 68, 69, 70, 71, 73, 74, 75, 77, 78, 81, 82, 86, 87, 89], "cach": [2, 74, 83, 88], "panda": [2, 4, 5, 9, 15, 16, 18, 19, 20, 21, 22, 25, 40, 41, 44, 45, 46, 68, 69, 70, 71, 73, 74, 75, 78, 80, 85, 86, 87, 88], "unlik": [2, 31, 33, 36, 44, 46, 47, 49, 65, 70, 80, 81, 83, 85], "both": [2, 4, 7, 13, 20, 25, 26, 30, 31, 40, 45, 47, 55, 59, 61, 66, 67, 70, 76, 77, 78, 80, 89], "mask": [2, 29, 31, 39, 40, 47, 50, 55, 57, 59, 61, 62, 67, 75, 76, 80, 82, 86, 89], "prefer": [2, 55, 63], "plan": 2, "subsequ": [2, 3, 26, 30, 74, 76, 78, 82, 88], "invok": [2, 26, 30, 78, 84], "scratch": [2, 57], "To": [2, 4, 5, 6, 7, 8, 10, 13, 20, 24, 26, 29, 30, 31, 44, 45, 47, 49, 53, 54, 55, 57, 58, 59, 61, 67, 69, 70, 71, 73, 74, 76, 77, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "share": [2, 55, 57], "mostli": [2, 40, 52, 57], "longer": [2, 35, 39, 57, 68, 74, 76, 82, 88], "info": [2, 4, 5, 10, 15, 16, 18, 19, 20, 21, 22, 25, 46, 57, 65, 70, 71, 75, 76, 89], "about": [2, 3, 4, 5, 7, 10, 15, 16, 18, 19, 20, 21, 22, 23, 25, 27, 29, 33, 45, 46, 49, 57, 62, 65, 69, 70, 73, 74, 75, 76, 77, 78, 80, 83], "docstr": [2, 25, 26, 30, 40, 57, 75, 78], "unless": [2, 26, 30, 57, 76], "our": [2, 3, 7, 44, 45, 55, 57, 67, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "is_label_issu": [2, 57, 69, 70, 71, 73, 74, 77, 78, 85, 88], "entir": [2, 7, 20, 29, 31, 34, 46, 47, 52, 55, 57, 59, 61, 62, 67, 70, 71, 76, 82, 83, 84, 86, 89], "accur": [2, 3, 4, 7, 13, 25, 29, 31, 45, 46, 47, 50, 53, 55, 57, 58, 59, 61, 62, 68, 71, 73, 74, 76, 77, 80, 85], "label_qu": [2, 45, 57, 78, 80, 85, 88], "measur": [2, 25, 45, 46, 57, 67, 75, 76, 78, 80, 81, 86, 87, 89], "qualiti": [2, 3, 4, 5, 7, 10, 25, 29, 31, 33, 36, 45, 46, 47, 49, 50, 52, 55, 57, 58, 61, 63, 65, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 79, 85, 87, 88], "lower": [2, 4, 5, 7, 10, 22, 29, 36, 45, 46, 49, 52, 55, 57, 58, 61, 65, 69, 71, 73, 74, 77, 80, 81, 82, 83, 85, 86, 88, 89], "eas": 2, "comparison": [2, 26, 30, 78, 80, 85], "against": [2, 26, 30, 70, 73, 76, 80, 81], "predicted_label": [2, 4, 57, 62, 66, 69, 70, 71, 73, 74, 77, 78, 85, 86, 88], "ad": [2, 26, 30, 71, 80, 85], "precis": [2, 47, 50, 76, 78, 86, 89], "definit": [2, 5, 57, 73, 87], "accessor": [2, 57], "describ": [2, 7, 45, 54, 55, 57, 63, 65, 78, 80, 81, 82, 84, 89], "precomput": [2, 34, 57, 75], "clear": [2, 57, 74, 85, 88], "save": [2, 4, 13, 26, 29, 30, 53, 57, 76, 82, 86, 89], "space": [2, 7, 54, 57, 73, 75, 77], "place": [2, 26, 30, 40, 57, 80, 87], "larg": [2, 29, 57, 76, 83, 86, 89], "deploi": [2, 57, 76], "care": [2, 7, 26, 30, 57, 76, 78], "avail": [2, 4, 5, 9, 11, 23, 30, 57, 78, 80, 82, 85], "cannot": [2, 4, 9, 11, 40, 84, 89], "anymor": 2, "classmethod": [2, 15, 16, 18, 19, 20, 21, 22, 30, 36, 57], "__init_subclass__": [2, 30, 57], "set_": [2, 30, 57], "_request": [2, 30, 57], "pep": [2, 30, 57], "487": [2, 30, 57], "look": [2, 4, 5, 13, 26, 30, 40, 57, 62, 70, 71, 73, 76, 78, 80, 81, 82, 83, 86, 87, 89], "inform": [2, 4, 5, 7, 10, 13, 23, 26, 30, 40, 45, 46, 50, 53, 57, 62, 65, 66, 67, 69, 70, 73, 74, 78, 80, 83, 86, 89], "__metadata_request__": [2, 30, 57], "infer": [2, 30, 40, 57, 62, 66, 77, 80, 81, 85, 87, 88], "signatur": [2, 26, 30, 57], "accept": [2, 26, 30, 55, 57, 70, 71], "metadata": [2, 30, 57, 89], "through": [2, 4, 5, 30, 57, 69, 71, 74, 75, 80, 83, 85, 88], "develop": [2, 6, 30, 57, 76, 78, 89], "request": [2, 30, 57, 71, 74, 75, 81, 87, 88, 89], "those": [2, 3, 7, 29, 30, 31, 44, 45, 47, 57, 61, 65, 66, 67, 69, 76, 77, 82, 86], "http": [2, 4, 5, 6, 7, 8, 24, 26, 27, 29, 30, 33, 40, 54, 57, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "www": [2, 30, 57, 83], "org": [2, 26, 27, 30, 40, 54, 57, 76, 78, 89], "dev": [2, 30, 57], "0487": [2, 30, 57], "get_metadata_rout": [2, 30, 57], "rout": [2, 30, 57], "pleas": [2, 26, 30, 44, 57, 67, 69, 70, 71, 74, 75, 76, 77, 78, 80, 81, 83, 85, 88, 89], "guid": [2, 5, 30, 57, 68, 77], "mechan": [2, 26, 30, 57], "metadatarequest": [2, 30, 57], "encapsul": [2, 13, 30, 52, 57], "get_param": [2, 30, 44, 57], "subobject": [2, 30, 57], "param": [2, 7, 26, 30, 44, 54, 57], "name": [2, 4, 5, 7, 9, 10, 25, 26, 30, 35, 36, 40, 44, 45, 46, 53, 57, 62, 66, 69, 71, 74, 75, 77, 78, 81, 86, 88, 89], "set_fit_request": [2, 30, 57], "union": [2, 3, 4, 9, 29, 30, 36, 40, 41, 47, 53, 57, 61, 65], "str": [2, 3, 4, 9, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 29, 30, 31, 34, 36, 39, 40, 44, 45, 46, 50, 52, 53, 55, 57, 62, 66, 69, 70, 80, 81, 89], "unchang": [2, 26, 30, 57, 89], "relev": [2, 13, 20, 30, 57, 77], "enable_metadata_rout": [2, 30, 57], "set_config": [2, 30, 57], "meta": [2, 30, 57], "rais": [2, 4, 9, 10, 26, 30, 33, 36, 57, 76], "alia": [2, 26, 30, 57], "metadata_rout": [2, 30, 57], "retain": [2, 30, 40, 57], "chang": [2, 26, 29, 30, 33, 57, 65, 69, 70, 74, 76, 82, 83, 88, 89], "version": [2, 4, 5, 6, 7, 8, 12, 17, 24, 26, 28, 30, 32, 33, 40, 43, 44, 55, 57, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 87, 88, 89], "sub": [2, 30, 52, 57], "pipelin": [2, 30, 57], "otherwis": [2, 7, 25, 26, 29, 30, 31, 37, 39, 40, 47, 54, 57, 59, 61, 62, 66, 74, 76, 88], "updat": [2, 10, 26, 29, 30, 57, 68, 70, 77], "set_param": [2, 30, 44, 57], "simpl": [2, 26, 30, 31, 45, 55, 57, 70, 71, 73, 74, 77, 80, 83, 85, 87, 88], "well": [2, 3, 7, 26, 30, 33, 34, 45, 47, 55, 57, 62, 65, 66, 68, 70, 71, 73, 74, 76, 77, 78, 80, 82, 83], "nest": [2, 26, 30, 57, 63, 65, 66, 89], "latter": [2, 26, 30, 57, 83], "compon": [2, 30, 57], "__": [2, 30, 57], "set_score_request": [2, 57], "structur": [3, 54, 73, 87], "unobserv": 3, "less": [3, 4, 7, 29, 36, 45, 54, 55, 59, 61, 65, 73, 75, 76, 77, 78, 82, 89], "channel": [3, 69, 78], "character": 3, "flip": 3, "nm": 3, "invers": [3, 7, 25, 34, 40, 46, 71, 75, 88], "inv": 3, "confident_joint": [3, 18, 25, 31, 40, 46, 47, 68, 76, 78], "un": 3, "under": [3, 7, 26, 30, 46, 53, 54, 83], "joint": [3, 25, 31, 34, 40, 46, 47, 75], "num_label_issu": [3, 29, 31, 47, 62, 66, 68], "estimation_method": [3, 29], "off_diagon": 3, "multi_label": [3, 25, 31, 40, 41, 47, 81], "don": [3, 67, 71, 78, 82], "statis": 3, "compute_confident_joint": [3, 25, 31, 40, 47, 78], "off": [3, 31, 40, 52, 77, 78, 82, 83], "j": [3, 4, 13, 25, 26, 30, 31, 47, 50, 53, 54, 63, 65, 66, 70, 71, 78, 86, 89], "confident_learn": [3, 31, 47, 78], "off_diagonal_calibr": 3, "calibr": [3, 31, 40, 45, 80], "cj": [3, 34, 40], "axi": [3, 34, 36, 59, 62, 69, 70, 71, 76, 77, 78, 80, 81, 83, 85, 86], "bincount": [3, 70, 71, 78, 80, 81], "alwai": [3, 7, 26, 30, 40, 69, 78, 85, 87, 88], "estimate_issu": 3, "over": [3, 7, 26, 29, 30, 52, 53, 59, 61, 71, 73, 75, 76, 77, 78, 83, 85, 87], "As": [3, 5, 67, 70, 71, 74, 78, 85, 89], "add": [3, 4, 5, 10, 26, 30, 44, 53, 69, 70, 71, 74, 76, 77, 78, 81, 88], "approach": [3, 25, 29, 31, 73, 78, 81, 83, 85, 87], "custom": [3, 5, 8, 26, 29, 30, 36, 39, 55, 71, 74, 78, 88], "know": [3, 70, 71, 76, 78, 80], "cut": [3, 52, 67, 78], "off_diagonal_custom": 3, "tl": 3, "dr": 3, "sometim": [3, 83, 89], "underestim": 3, "few": [3, 53, 67, 76, 80, 81, 82, 83, 89], "4": [3, 4, 15, 16, 18, 19, 20, 21, 22, 35, 36, 39, 49, 50, 52, 53, 55, 58, 65, 75, 76, 81, 86, 89], "detail": [3, 11, 25, 26, 30, 36, 40, 44, 45, 46, 47, 49, 50, 52, 53, 54, 61, 62, 63, 67, 68, 69, 81, 83, 89], "num_issu": [3, 5, 29, 69, 70, 71, 73, 74, 77, 78], "calibrate_confident_joint": 3, "up": [3, 14, 20, 21, 31, 36, 45, 75, 76, 82, 85, 88, 89], "p_": [3, 25, 31], "pair": [3, 7, 25, 31, 78], "v": [3, 7, 29, 46, 47, 49, 55, 70, 71, 81, 83, 84], "rest": [3, 4, 5, 6, 7, 8, 24, 46, 47, 49, 57, 70, 71, 73, 74, 77, 78, 80, 85, 87, 88], "fashion": [3, 59, 87], "2x2": 3, "incorrectli": [3, 25, 46, 47, 50, 73, 89], "calibrated_cj": 3, "c": [3, 7, 39, 47, 55, 67, 69, 70, 71, 73, 74, 76, 78, 81, 83, 84, 85, 87], "whose": [3, 4, 7, 13, 22, 26, 30, 34, 39, 45, 49, 52, 58, 61, 65, 66, 69, 70, 71, 73, 74, 76, 77, 78, 81, 82, 83, 86, 89], "truli": [3, 83, 86], "estimate_joint": [3, 25, 78], "joint_estim": 3, "confident_joint_distribut": 3, "recal": [3, 47, 78, 82, 84, 86, 89], "return_indices_of_off_diagon": 3, "frequenc": [3, 20, 45, 46, 53, 62, 83], "done": [3, 7, 57, 70, 76, 78, 81, 83, 84], "overfit": [3, 7, 50, 53, 69, 70, 71, 73, 74, 77, 84, 87], "classifict": 3, "singl": [3, 4, 20, 25, 26, 30, 36, 37, 40, 45, 46, 52, 53, 54, 55, 65, 69, 70, 76, 78, 81, 82, 87], "baselin": [3, 26, 31, 83, 85, 88], "proxi": 3, "tupl": [3, 26, 30, 34, 35, 37, 39, 40, 45, 47, 53, 61, 63, 65, 66, 69, 89], "confident_joint_count": 3, "indices_off_diagon": 3, "simplif": 3, "effici": [3, 4, 29, 34, 45, 59, 61, 67, 76, 77, 86, 88], "practic": [3, 71, 77, 78, 83, 85, 87, 88], "complet": [3, 69, 70, 71, 73, 74, 77, 78, 82], "gist": 3, "cj_ish": 3, "guess": [3, 34, 78, 80], "8": [3, 4, 5, 35, 36, 37, 39, 49, 63, 65, 69, 70, 71, 73, 74, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "parallel": [3, 31, 63, 75], "again": [3, 44, 83, 87], "simplifi": [3, 11], "understand": [3, 6, 25, 46, 53, 71, 78, 85, 86, 89], "100": [3, 26, 30, 55, 70, 71, 73, 75, 76, 77, 78, 81, 86, 87, 88, 89], "optim": [3, 26, 27, 30, 44, 77, 80], "speed": [3, 31, 75, 76, 85, 88], "dtype": [3, 19, 20, 26, 30, 39, 40, 49, 65, 69, 82], "enumer": [3, 26, 30, 69, 70, 71, 77, 89], "s_label": 3, "confident_bin": 3, "6": [3, 4, 30, 36, 40, 65, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "num_confident_bin": 3, "argmax": [3, 31, 55, 59, 62, 69, 76, 78, 83, 86], "elif": 3, "estimate_lat": 3, "py_method": [3, 34], "cnt": [3, 34], "1d": [3, 29, 31, 36, 37, 40, 41, 49, 58, 69, 87], "eqn": [3, 34], "margin": [3, 31, 34, 36, 55], "marginal_p": [3, 34], "shorthand": [3, 10], "proport": [3, 7, 25, 46, 78, 84], "poorli": [3, 34, 87], "inv_noise_matrix": 3, "estimate_py_and_noise_matrices_from_prob": [3, 78], "variabl": [3, 5, 11, 40, 57, 58, 69, 70, 73, 78, 81, 85], "exact": [3, 34, 70, 71, 73, 77, 87], "within": [3, 4, 12, 26, 27, 30, 32, 47, 52, 61, 63, 65, 70, 71, 77, 82, 86], "percent": 3, "often": [3, 25, 34, 46, 76, 78, 84, 86], "estimate_confident_joint_and_cv_pred_proba": 3, "mani": [3, 7, 40, 41, 53, 69, 70, 73, 74, 76, 77, 82, 83, 88], "wai": [3, 4, 44, 67, 68, 69, 70, 71, 73, 74, 76, 78, 80, 81, 82, 84, 87, 88], "pro": 3, "con": 3, "pred_proba": [3, 84], "combin": [3, 25, 70, 75, 76, 77, 78, 84, 85], "becaus": [3, 34, 40, 52, 76, 78, 80, 82], "littl": [3, 29, 75, 82, 89], "uniform": [3, 55, 75, 76, 78], "20": [3, 5, 66, 69, 71, 75, 77, 78, 86, 89], "Such": [3, 77, 83], "bound": [3, 19, 26, 30, 50, 52, 53, 82], "reason": [3, 18, 26, 30], "comment": [3, 39, 89], "end": [3, 26, 30, 53], "file": [3, 4, 9, 28, 29, 43, 53, 69, 70, 73, 74, 75, 76, 82, 83, 86, 87, 89], "estimate_py_noise_matrices_and_cv_pred_proba": [3, 78], "handl": [3, 4, 5, 7, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 29, 30, 68, 70, 71, 78, 86, 87, 89], "five": [3, 50, 53, 78, 82], "estimate_cv_predicted_prob": [3, 78], "estimate_noise_matric": 3, "get_confident_threshold": [3, 29], "amongst": [3, 7], "confident_threshold": [3, 7, 18, 29, 54], "unifi": 4, "audit": [4, 6, 9, 10, 13, 69, 72, 73, 74, 77, 78, 82], "kind": [4, 5, 69, 70, 73, 74, 75, 77, 78], "addit": [4, 5, 6, 7, 8, 10, 23, 24, 26, 30, 36, 41, 45, 63, 69, 70, 73, 74, 77, 78, 80, 83, 84, 87, 88], "depend": [4, 5, 6, 7, 8, 9, 10, 24, 28, 31, 33, 40, 43, 47, 54, 57, 58, 67], "instal": [4, 5, 6, 7, 8, 24, 26, 28, 29, 30, 31, 43, 44, 59, 61], "pip": [4, 5, 6, 8, 24, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "development": [4, 5, 6, 8, 24], "git": [4, 5, 6, 8, 24, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 87, 88], "github": [4, 5, 6, 8, 24, 26, 27, 40, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 87, 88], "com": [4, 5, 6, 8, 24, 26, 27, 29, 33, 40, 54, 67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "egg": [4, 5, 6, 8, 24, 67, 75], "label_nam": [4, 5, 7, 9, 67, 69, 70, 71, 73, 74, 77, 78], "image_kei": [4, 77], "interfac": [4, 67, 76, 78], "librari": [4, 7, 30, 50, 53, 54, 67, 70, 74, 75, 76, 88], "goal": 4, "track": [4, 10, 11, 67, 70, 75, 76, 78], "intermedi": [4, 6, 71], "statist": [4, 7, 10, 18, 20, 25, 45, 46, 71, 73, 74, 77, 78], "convert": [4, 9, 26, 30, 37, 41, 45, 52, 61, 65, 68, 69, 74, 75, 77, 80, 81, 82, 88], "hug": [4, 9, 77], "face": [4, 9, 13, 75, 77, 81], "kei": [4, 5, 7, 9, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 30, 36, 45, 46, 52, 54, 70, 71, 76, 77, 78, 80, 82], "string": [4, 7, 9, 11, 15, 16, 18, 19, 20, 21, 22, 23, 25, 26, 30, 40, 45, 46, 58, 62, 65, 66, 73, 74, 76, 80, 81, 88, 89], "dictionari": [4, 5, 9, 10, 13, 15, 16, 18, 19, 20, 21, 22, 25, 26, 30, 35, 40, 45, 46, 49, 50, 52, 53, 70, 71, 73, 74, 78, 80, 81, 82], "path": [4, 9, 26, 29, 30, 53, 69, 70, 76, 82], "local": [4, 9, 26, 27, 30, 69, 70, 71, 75, 76, 77, 78, 80, 81, 83, 85, 89], "text": [4, 5, 7, 9, 15, 16, 18, 19, 20, 21, 22, 36, 54, 63, 65, 66, 67, 70, 71, 72, 75, 76, 78, 79, 80, 83], "txt": [4, 9, 89], "csv": [4, 9, 73, 74, 85, 87, 88], "json": [4, 9], "hub": [4, 9, 83], "regress": [4, 9, 11, 23, 70, 71, 74, 79, 80, 83, 88], "imag": [4, 6, 25, 30, 50, 52, 53, 54, 59, 61, 62, 67, 70, 71, 75, 76, 79, 80, 81, 82, 84, 86], "point": [4, 5, 20, 26, 30, 70, 71, 76, 78, 80], "field": [4, 7, 26, 30], "themselv": [4, 85, 87, 88], "cleanvis": [4, 7], "level": [4, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 39, 63, 65, 71, 77, 79, 86], "load_dataset": [4, 9, 77], "glue": 4, "sst2": 4, "properti": [4, 9, 10], "has_label": [4, 9], "class_nam": [4, 9, 25, 46, 53, 62, 66, 67, 75, 78, 82, 86, 89], "empti": [4, 9, 34, 45, 71, 81], "find_issu": [4, 5, 7, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 67, 69, 70, 71, 73, 74, 77, 78], "knn_graph": [4, 7, 13, 15, 20, 22, 73], "issue_typ": [4, 5, 7, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 69, 70, 71, 73, 74, 77, 78], "sort": [4, 13, 29, 31, 36, 38, 45, 47, 50, 52, 53, 55, 61, 63, 65, 69, 71, 73, 74, 76, 77, 78, 80, 81, 82, 85, 86, 87, 88, 89], "common": [4, 10, 13, 71, 72, 75, 76, 78, 81, 82, 86], "real": [4, 13, 67, 70, 71, 76, 78, 80, 81, 85, 86], "world": [4, 13, 67, 70, 71, 76, 78, 80, 85, 86], "interact": [4, 13, 76], "embed": [4, 7, 13, 54, 67, 69, 70, 71, 73, 74, 78, 88], "thereof": [4, 13], "insight": [4, 13, 80], "act": [4, 7, 52, 70], "issuefind": [4, 13, 23], "logic": [4, 11, 29, 31, 59, 61, 86], "best": [4, 13, 35, 45, 55, 70, 71, 73, 76, 80, 81, 83, 85, 87, 88, 89], "2d": [4, 13, 29, 36, 37, 39, 40, 45, 69, 81, 87], "num_exampl": [4, 13, 15, 16, 18, 19, 20, 21, 22, 23, 25, 46, 69, 70, 71, 73, 74, 77, 78], "represent": [4, 7, 13, 26, 30, 37, 47, 67, 69, 70, 71, 74, 76, 77, 78, 83, 88], "num_featur": [4, 13, 26, 30, 44], "distanc": [4, 7, 13, 20, 22, 38, 52, 54, 73, 83], "nearest": [4, 7, 13, 19, 20, 22, 38, 54, 71, 74, 83], "neighbor": [4, 7, 13, 19, 20, 22, 38, 54, 70, 71, 73, 74, 77, 83], "graph": [4, 7, 10, 13, 20], "squar": [4, 13, 40, 57, 75, 85], "csr": [4, 13], "evenli": [4, 13], "omit": [4, 13, 52, 53, 77, 82], "itself": [4, 13, 26, 30, 82], "duplic": [4, 6, 13, 17, 18, 26, 30, 67, 70, 78], "explicit": [4, 13], "precend": [4, 13], "construct": [4, 5, 7, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23, 26, 30, 36, 44], "neither": [4, 7, 11, 13, 82], "nor": [4, 7, 11, 13], "collect": [4, 7, 10, 13, 15, 16, 18, 19, 20, 21, 22, 45, 80, 89], "unspecifi": [4, 13, 31, 47], "interest": [4, 13, 18, 62, 66, 74, 78, 86, 87, 88, 89], "constructor": [4, 7, 13, 19], "issuemanag": [4, 6, 10, 11, 13, 15, 16, 18, 19, 20, 21, 22, 23], "respons": [4, 13, 18, 57, 58, 75, 85, 89], "random_st": [4, 69, 70, 71, 77, 78, 81, 83, 87], "lab": [4, 15, 16, 18, 19, 20, 21, 22, 29, 67, 69, 70, 71, 73, 74, 75, 77, 78, 81], "nearestneighbor": [4, 7, 54, 73, 83], "comprehens": [4, 67, 77], "nbr": 4, "n_neighbor": [4, 7, 54], "metric": [4, 7, 15, 20, 40, 44, 54, 69, 73, 74, 77, 78, 85, 87, 88], "euclidean": [4, 7, 52, 54, 73], "kneighbors_graph": [4, 73], "mode": [4, 26, 29, 30, 73, 83], "4x4": 4, "float64": [4, 20, 26, 30, 65], "compress": [4, 7, 40, 59, 61], "toarrai": 4, "NOT": [4, 29, 74], "23606798": 4, "41421356": 4, "configur": [4, 13, 36, 71], "suppos": [4, 50, 83, 85, 87, 88], "who": [4, 52, 73, 78, 87, 89], "manag": [4, 6, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22, 70], "clean_learning_kwarg": [4, 7, 19], "labelissuemanag": [4, 7, 19], "prune_method": [4, 68], "prune_by_noise_r": [4, 31, 47, 78], "report": [4, 5, 8, 12, 15, 16, 18, 19, 20, 21, 22, 25, 46, 66, 67, 69, 70, 71, 73, 74, 78, 89], "include_descript": [4, 15, 16, 18, 19, 20, 21, 22, 23], "show_summary_scor": [4, 23], "summari": [4, 5, 10, 15, 16, 18, 19, 20, 21, 22, 25, 44, 46, 51, 60, 61, 63, 64, 65, 68, 69, 70, 71, 73, 74, 75, 77, 78, 82, 86, 89], "show": [4, 20, 26, 30, 35, 40, 53, 62, 66, 71, 73, 74, 75, 76, 77, 78, 80, 83, 85, 86, 87, 89], "top": [4, 25, 29, 31, 40, 47, 50, 53, 55, 62, 66, 67, 69, 70, 71, 73, 74, 75, 76, 78, 82, 83, 85, 88, 89], "suffer": [4, 7, 10, 18, 47, 55, 66, 89], "onc": [4, 18, 25, 26, 30, 70, 76, 78, 81, 82, 87], "familiar": 4, "usag": [4, 29, 44], "found": [4, 5, 7, 10, 11, 15, 16, 18, 19, 20, 21, 22, 26, 30, 40, 67, 69, 70, 71, 73, 74, 76, 77, 83, 85, 87, 88, 89], "issue_summari": [4, 7, 10, 70], "overal": [4, 5, 7, 10, 15, 16, 18, 19, 20, 21, 22, 25, 36, 45, 46, 49, 52, 53, 57, 61, 62, 63, 65, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 82, 89], "sever": [4, 5, 7, 9, 10, 18, 26, 29, 30, 31, 49, 52, 54, 55, 61, 65, 67, 69, 70, 71, 73, 74, 75, 76, 78, 82, 83, 87, 88, 89], "dataissu": [4, 10, 13, 23], "outlier": [4, 6, 11, 17, 18, 32, 55, 67, 70, 71, 78, 79], "someth": [4, 5, 26, 30, 55], "123": [4, 70, 71], "456": [4, 69, 87, 88], "nearest_neighbor": 4, "7": [4, 36, 37, 44, 63, 65, 69, 70, 71, 73, 74, 75, 76, 80, 81, 82, 83, 85, 86, 87, 88, 89], "9": [4, 15, 16, 18, 19, 20, 21, 22, 36, 37, 49, 63, 65, 69, 70, 71, 73, 74, 75, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "distance_to_nearest_neighbor": [4, 70, 71, 73, 74, 77, 78], "789": 4, "get_issu": [4, 7, 10, 69, 71, 73, 74, 77], "issue_nam": [4, 5, 7, 10, 11, 15, 16, 18, 19, 20, 21, 22, 70, 71], "focu": [4, 10, 74, 86, 89], "full": [4, 7, 10, 29, 53, 77, 89], "summar": [4, 10, 15, 16, 18, 19, 20, 21, 22, 25, 46, 62, 66, 67, 86], "valueerror": [4, 9, 10, 33, 36, 76], "specific_issu": [4, 10], "exhibit": [4, 7, 10, 62, 71, 73, 74, 77, 78, 82], "lie": [4, 7, 38, 54, 55, 69, 70, 71, 73, 74, 77, 78, 88], "directli": [4, 11, 13, 23, 29, 44, 45, 71, 74, 81, 82, 85, 88], "compar": [4, 45, 54, 65, 70, 71, 73, 78], "get_issue_summari": [4, 10, 71], "get_info": [4, 10, 71], "yet": [4, 14, 17, 21, 75, 80], "list_possible_issue_typ": [4, 11], "regist": [4, 5, 11, 12, 14, 21, 26, 30, 70], "registri": [4, 11], "list_default_issue_typ": [4, 11], "folder": [4, 69, 70, 77], "load": [4, 9, 29, 53, 75, 76, 77, 78, 82, 83, 86, 89], "futur": [4, 7, 18, 26, 30, 45, 67, 70], "overwrit": [4, 70], "separ": [4, 25, 36, 49, 70, 71, 76, 77, 82, 84], "static": 4, "rememb": [4, 76, 78], "part": [4, 7, 26, 30, 31, 50, 52, 53, 69, 70, 75, 86, 89], "ident": [4, 7, 18, 40], "walk": 5, "alongsid": [5, 26, 30, 70, 76], "pre": [5, 7, 26, 30, 70, 71], "runtim": [5, 26, 29, 30, 57, 59, 61, 69, 76, 77], "issue_manager_factori": [5, 11, 70], "myissuemanag": [5, 11], "decor": [5, 11], "start": [5, 26, 27, 30, 67, 73, 81, 89], "ll": [5, 36, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 84, 85, 87, 88, 89], "thing": [5, 30, 78, 85, 88], "next": [5, 45, 67, 69, 73, 74, 80, 82, 85, 87, 88, 89], "dummi": 5, "randint": [5, 36, 70, 71], "mark": [5, 68, 82, 83, 85], "regard": [5, 71, 78], "rand": [5, 36, 70, 71], "is_": [5, 7, 70], "_issu": [5, 7, 70], "issue_score_kei": [5, 15, 16, 18, 19, 20, 21, 22, 70], "whole": [5, 20, 26, 30, 71], "make_summari": [5, 15, 16, 18, 19, 20, 21, 22, 70], "popul": 5, "verbosity_level": [5, 15, 16, 18, 19, 20, 21, 22], "std": 5, "raw_scor": 5, "bit": 5, "involv": [5, 29, 62, 66, 76, 81], "intermediate_arg": 5, "min": [5, 36, 52, 65, 70, 76, 83], "sin_filt": 5, "sin": 5, "arang": 5, "kernel": 5, "wip": 5, "progress": 5, "issue_manag": [5, 7, 8, 10, 12, 15, 16, 19, 20, 21, 22, 70], "instanti": [5, 13, 29, 44, 54, 69, 71, 73, 88], "477762": 5, "286455": 5, "term": [5, 7, 34, 40, 69, 70, 71, 73, 74, 77, 78], "4778": 5, "is_basic_issu": 5, "basic_scor": 5, "13": [5, 15, 22, 69, 70, 71, 73, 74, 75, 77, 78, 80, 82, 83, 85, 86, 87, 88, 89], "003042": 5, "058117": 5, "11": [5, 44, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 86, 87, 88, 89], "121908": 5, "15": [5, 38, 57, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 86, 87, 88, 89], "169312": 5, "17": [5, 69, 73, 74, 75, 77, 78, 80, 82, 83, 85, 86, 88, 89], "229044": 5, "2865": 5, "is_intermediate_issu": 5, "intermediate_scor": 5, "000000": [5, 70, 75, 78], "007059": 5, "009967": 5, "010995": 5, "087332": 5, "016296": 5, "03947": 5, "019459": 5, "794251": 5, "search": [6, 7, 16, 20, 21, 39, 57, 76, 84], "nondefault": 6, "Near": 6, "iid": [6, 20, 71, 73, 74, 77, 78], "imbal": [6, 17, 49, 54, 55], "togeth": [6, 7, 34, 70, 71, 73, 74, 77, 78, 85, 88, 89], "built": [6, 36], "own": [6, 26, 28, 30, 43, 49, 50, 53, 59, 63, 69, 71, 73, 74, 77, 80, 81, 85, 86, 87, 88, 89], "prerequisit": 6, "basic": [6, 30, 44, 83], "page": [7, 71, 76, 78], "variou": [7, 10, 28, 41, 43, 67, 70, 71, 73, 74, 75, 78, 80, 82, 87], "sai": [7, 26, 30, 81, 86], "why": [7, 74], "matter": [7, 25, 46], "three": [7, 25, 45, 46, 57, 62, 69, 70, 71, 73, 75, 78, 80, 84, 85, 86, 87, 89], "_score": 7, "flag": [7, 18, 20, 31, 36, 46, 47, 50, 57, 67, 69, 70, 71, 73, 74, 75, 77, 78, 82, 83, 85, 86, 88], "badli": [7, 52, 89], "code": [7, 26, 30, 34, 40, 44, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "issue_scor": 7, "outlier_scor": [7, 22, 70, 71, 73, 74, 77, 78, 83], "atyp": [7, 54, 70, 71, 73, 74, 77, 78, 83], "datapoint": [7, 31, 36, 40, 55, 58, 67, 69, 70, 71, 73, 74, 76, 84, 85, 87, 88], "is_issu": [7, 18], "is_outlier_issu": [7, 70, 71, 73, 74, 77, 78], "annot": [7, 25, 35, 45, 46, 47, 49, 50, 52, 53, 62, 65, 66, 67, 69, 70, 71, 73, 74, 76, 77, 78, 79, 82, 86], "transform": [7, 36, 38, 40, 54, 55, 71, 74, 77, 83, 87, 88, 89], "dissimilar": [7, 73, 74], "preced": 7, "cosin": [7, 54, 83], "incorrect": [7, 52, 55, 58, 69, 70, 71, 73, 74, 77, 78, 82, 85, 87], "due": [7, 29, 31, 55, 59, 61, 69, 70, 71, 73, 74, 77, 78], "appear": [7, 25, 35, 46, 47, 50, 58, 71, 73, 74, 77, 85, 86], "likelihood": [7, 29, 31, 47, 52, 54, 55, 59, 63], "now": [7, 29, 68, 69, 71, 80, 82, 83, 85, 87, 88, 89], "u": [7, 69, 70, 73, 76, 77, 78, 80, 81, 84, 85, 86, 87, 88, 89], "token": [7, 39, 61, 62, 63, 64, 65, 66, 76, 78, 79], "etc": [7, 18, 26, 30, 34, 44, 45, 63, 67, 70, 71, 73, 74, 76, 77, 78], "calcul": [7, 20, 29, 36, 45, 49, 50, 52, 54, 57, 61, 75, 77], "hamper": [7, 75, 77], "analyt": [7, 67, 80], "lead": [7, 52, 55, 77, 82], "draw": [7, 70, 71], "conclus": 7, "try": [7, 29, 31, 44, 45, 59, 61, 67, 71, 76, 78, 86], "veri": [7, 25, 46, 50, 52, 70, 71, 73, 74, 77, 78, 80, 83, 85, 88], "rare": [7, 31, 53, 70, 71, 73, 74, 76, 77, 78], "anomal": [7, 55, 70, 71, 73, 74, 77, 78], "articl": [7, 29, 76], "ai": [7, 67, 69, 70, 71, 73, 74, 75, 76, 77, 79, 80, 81, 83, 85, 87, 88, 89], "blog": 7, "unexpect": [7, 26, 30], "consequ": 7, "inspect": [7, 69, 71, 77, 78, 82, 85, 88], "neg": [7, 52, 70, 71, 75], "affect": [7, 26, 30, 59, 65, 74, 76], "extrem": [7, 70, 71, 73, 74, 76, 77, 78], "rel": [7, 25, 45, 46, 54, 70, 71, 73, 74, 77, 78, 83], "record": [7, 26, 30, 69, 73, 85], "abbrevi": 7, "misspel": 7, "typo": [7, 66], "resolut": 7, "video": [7, 75], "audio": [7, 70, 71, 76, 79], "minor": [7, 39], "variat": 7, "translat": 7, "d": [7, 38, 73, 74, 78, 81, 87, 89], "constant": [7, 57], "median": 7, "question": [7, 18, 67, 78], "nearli": [7, 18, 71, 73, 74, 77], "awar": [7, 68, 78], "presenc": [7, 78], "signific": [7, 71, 73, 74, 77, 78], "violat": [7, 71, 73, 74, 77, 78], "assumpt": [7, 71, 73, 74, 77, 78], "changepoint": [7, 71, 73, 74, 77, 78], "shift": [7, 71, 73, 74, 77, 78], "drift": [7, 71, 73, 74, 77, 78], "autocorrel": [7, 71, 73, 74, 77, 78], "almost": [7, 71, 73, 74, 77, 78], "adjac": [7, 71, 73, 74, 77, 78], "tend": [7, 25, 34, 71, 73, 74, 77, 78, 86, 89], "sequenti": [7, 26, 30, 44, 77], "gap": 7, "group": [7, 20, 75, 82, 89], "b": [7, 15, 16, 18, 19, 20, 21, 22, 25, 39, 40, 65, 73, 74, 75, 78, 84, 87, 89], "x1": [7, 50, 53, 82], "x2": [7, 50, 53, 82], "10th": 7, "100th": 7, "90": [7, 65, 73, 78, 84, 87], "similarli": [7, 26, 30, 70, 73, 76, 77, 82], "math": [7, 77], "behind": [7, 54, 78], "fundament": 7, "proper": [7, 40, 45, 50, 53, 74, 77, 80, 82, 87], "closer": [7, 52, 82], "scenario": [7, 55, 70, 71], "underli": [7, 54, 63, 65, 89], "stem": [7, 54, 83], "evolv": 7, "influenc": 7, "accordingli": 7, "emploi": [7, 81, 83], "partit": [7, 84], "ahead": 7, "good": [7, 26, 30, 44, 46, 52, 55, 59, 61, 62, 67, 77], "fix": [7, 45, 74, 78, 85, 88], "problem": [7, 29, 36, 62, 67, 70, 71, 74, 76, 77], "deploy": [7, 78, 85, 87, 88], "overlook": [7, 52, 82], "fact": 7, "thu": [7, 25, 30, 46, 69, 73, 74, 78, 84, 87, 89], "diagnos": [7, 71, 76], "rarest": 7, "q": [7, 82], "fall": [7, 52, 61, 65, 78, 83], "subpar": 7, "special": [7, 39], "techniqu": 7, "smote": 7, "asymmetr": [7, 25], "properli": [7, 29, 35, 40, 41, 59, 76, 81, 83, 85, 86], "too": [7, 31, 36, 54, 76, 77, 82], "dark": [7, 86], "bright": [7, 89], "blurri": [7, 77], "abnorm": [7, 53, 77], "exert": [7, 71], "possible_issue_typ": 7, "label_kwarg": 7, "outlier_kwarg": 7, "near_dupl": [7, 11, 15, 70, 71, 73, 74, 77, 78], "near_duplicate_kwarg": 7, "non_iid": [7, 11, 20, 71, 73, 74, 77, 78], "non_iid_kwarg": 7, "health_summary_paramet": [7, 19], "health_summari": [7, 19, 25, 67, 75], "health_summary_kwarg": 7, "tandem": [7, 75], "view": [7, 26, 30, 31, 61, 63, 65, 67, 69, 70, 71, 73, 74, 75, 78, 80, 81, 82, 83, 84, 85, 87, 88, 89], "sensit": 7, "ood_kwarg": 7, "outofdistribut": [7, 22, 54, 83], "outsid": 7, "knn": [7, 10, 20, 54, 73, 83], "outlierissuemanag": [7, 11, 22, 70], "nearduplicateissuemanag": [7, 11, 15], "noniidissuemanag": [7, 11, 20], "num_permut": [7, 20], "permut": [7, 20], "significance_threshold": [7, 20], "signic": 7, "noniid": [7, 17], "class_imbalance_kwarg": 7, "classimbalanceissuemanag": [7, 16], "data_issu": [8, 12, 13, 23, 70], "issue_find": [8, 12], "factori": [8, 12, 13], "datalab": [9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 67, 69, 77, 80, 87, 88], "except": [9, 44, 55, 70, 71, 77, 80], "dataformaterror": 9, "with_traceback": 9, "tb": 9, "__traceback__": 9, "datasetdicterror": 9, "datasetdict": 9, "usual": [9, 23, 77, 80, 85], "datasetloaderror": 9, "dataset_typ": 9, "fail": 9, "map_to_int": 9, "hold": 9, "is_avail": [9, 77], "serv": [10, 13, 80], "central": [10, 89], "repositori": 10, "strategi": [10, 36], "being": [10, 25, 26, 30, 31, 36, 39, 40, 55, 73, 78, 85, 86, 87], "_infostrategi": 10, "basi": 10, "collect_statist": 10, "reus": [10, 18], "avoid": [10, 26, 29, 30, 31, 38, 40, 47, 50, 53, 57, 59, 61, 70, 71, 76], "recomput": [10, 88], "weighted_knn_graph": 10, "issue_manager_that_computes_knn_graph": 10, "collect_issues_from_issue_manag": 10, "collect_issues_from_imagelab": 10, "imagelab": 10, "set_health_scor": 10, "health": [10, 19, 25, 46, 67], "get_data_statist": 10, "concret": 11, "subclass": [11, 26, 30, 54, 70], "my_issu": 11, "stabl": [12, 17, 28, 32, 40, 43, 54, 68], "unregist": 12, "instati": 13, "public": [13, 78, 82, 86, 89], "creation": [13, 30], "execut": [13, 26, 30, 70, 76, 82], "coordin": [13, 50, 52, 53, 82, 89], "behavior": [13, 25, 26, 30], "At": [13, 76], "associ": [13, 26, 30, 53, 80], "get_available_issue_typ": 13, "isn": [14, 21], "direct": [14, 21, 26, 30], "10": [15, 19, 20, 26, 27, 53, 54, 55, 66, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89], "_": [15, 18, 19, 20, 21, 36, 39, 40, 69, 70, 75, 77, 78, 81, 87], "classvar": [15, 16, 18, 19, 20, 21, 22], "short": [15, 16, 18, 19, 20, 21, 22, 39, 40], "item": [15, 16, 18, 19, 20, 21, 22, 40, 70, 71, 77, 78, 80, 81], "some_info_kei": [15, 16, 18, 19, 20, 21, 22], "additional_info_kei": [15, 16, 18, 19, 20, 21, 22], "near_duplicate_set": [15, 70, 71, 73, 74, 77, 78], "occurr": [15, 16, 18, 20, 21, 22, 39], "collect_info": [15, 16, 18, 19, 20, 21, 22], "near_duplicate_scor": [15, 70, 71, 73, 74, 77, 78], "info_to_omit": [15, 16, 18, 19, 20, 21, 22], "compos": [15, 16, 18, 19, 20, 21, 22, 26, 30, 74, 83, 88], "is_x_issu": [15, 16, 18, 19, 20, 21, 22], "x_score": [15, 16, 18, 19, 20, 21, 22], "val_a": [15, 16, 18, 19, 20, 21, 22], "val_b1": [15, 16, 18, 19, 20, 21, 22], "val_b2": [15, 16, 18, 19, 20, 21, 22], "report_str": [15, 16, 18, 19, 20, 21, 22, 23], "class_imbal": 16, "class_imbalance_scor": 16, "bleed": [17, 28], "edg": [17, 28, 52, 67, 78, 89], "sharp": [17, 28], "null": 17, "abc": 18, "believ": [18, 86], "priori": [18, 78], "global": 18, "anoth": [18, 25, 29, 39, 52, 55, 73, 74, 76, 78, 80, 83, 88], "abstract": 18, "applic": [19, 45, 78, 80, 81, 89], "typevar": [19, 26, 30, 52, 53], "_scalartype_co": 19, "covari": [19, 57, 85], "get_health_summari": 19, "summary_dict": 19, "label_scor": [19, 69, 70, 71, 73, 74, 77, 78], "simplified_kolmogorov_smirnov_test": 20, "neighbor_histogram": 20, "non_neighbor_histogram": 20, "kolmogorov": 20, "smirnov": 20, "largest": [20, 29, 36, 55, 59, 61, 86], "empir": [20, 35, 45], "cumul": 20, "ecdf": 20, "histogram": [20, 73, 85], "absolut": 20, "25": [20, 26, 36, 38, 75, 77, 78, 80, 81, 82, 89], "dimension": [20, 40, 69, 78, 83], "trial": 20, "non_iid_scor": [20, 71, 73, 74, 77, 78], "nullissuemanag": 21, "miss": [21, 26, 30, 40, 50, 52, 73, 76, 82, 85], "null_track": 21, "null_scor": 21, "default_threshold": 22, "37037": 22, "q3_avg_dist": 22, "iqr_avg_dist": 22, "median_outlier_scor": 22, "ood": [22, 54, 55, 70, 71, 74, 77, 78, 83], "exclud": [23, 62, 66, 70, 89], "get_report": 23, "overview": [25, 69, 71, 73, 74, 77, 80, 82, 83, 85, 87, 88, 89], "modifi": [25, 26, 29, 30, 40, 76, 78], "help": [25, 26, 30, 53, 67, 68, 69, 70, 73, 74, 75, 76, 80, 81, 85, 86, 87, 88, 89], "rank_classes_by_label_qu": [25, 71], "merg": [25, 39, 67, 75, 76, 89], "find_overlapping_class": [25, 76, 78], "ascend": [25, 38, 46, 77, 78], "problemat": [25, 46, 62, 66, 69, 82, 89], "unnorm": [25, 46, 78], "abov": [25, 26, 29, 30, 40, 45, 52, 55, 61, 65, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 84, 85, 86, 87, 88, 89], "model_select": [25, 36, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 83, 85, 87, 88], "cross_val_predict": [25, 30, 69, 70, 71, 73, 74, 78, 80, 84, 85, 87, 88], "get_data_labels_from_dataset": 25, "yourfavoritemodel": [25, 78], "cv": [25, 36, 69, 70, 71, 73, 78, 80, 87], "df": [25, 40, 66, 69], "overall_label_qu": [25, 46], "col": 25, "prob": [25, 39, 78, 84], "divid": [25, 46, 55], "label_nois": [25, 46], "human": [25, 75, 86, 89], "clearli": [25, 55, 77, 82, 86], "num": [25, 46, 75, 78], "overlap": [25, 67, 75, 76, 78], "ontolog": 25, "publish": [25, 89], "therefor": [25, 55], "vehicl": [25, 75], "truck": [25, 75, 83, 86], "intuit": [25, 46], "car": [25, 75, 82, 86], "frequent": [25, 45, 73, 76, 85], "confus": [25, 26, 30, 31, 40, 88, 89], "characterist": 25, "l": [25, 26, 30, 50, 52, 53], "class1": 25, "class2": 25, "relationship": 25, "arbitrari": [25, 61, 65, 70, 83, 85], "match": [25, 26, 30, 31, 45, 46, 55, 70, 71, 75, 77, 82, 84, 86], "dog": [25, 40, 46, 48, 62, 75, 76, 83, 84, 89], "cat": [25, 40, 46, 48, 75, 76, 83, 84], "captur": [25, 69, 82, 83, 86], "co": [25, 26, 27], "noisy_label": [25, 70, 71, 81], "overlapping_class": 25, "descend": [25, 26, 30, 36, 46, 53], "overall_label_health_scor": [25, 46, 78], "suggest": [25, 45, 46, 52, 74, 76, 77, 85, 88], "half": [25, 26, 30, 46, 75, 89], "health_scor": [25, 46], "classes_by_label_qu": [25, 71], "cnn": [26, 30, 77], "cifar": [26, 27, 75, 83], "teach": [26, 27], "bhanml": 26, "blob": 26, "master": [26, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 87, 88], "call_bn": 26, "bn": 26, "input_channel": 26, "n_output": 26, "dropout_r": 26, "top_bn": 26, "architectur": [26, 30], "shown": [26, 53, 70, 76, 80, 83, 84, 86, 89], "forward": [26, 27, 30, 77, 80], "overridden": [26, 30], "although": [26, 30, 54, 73, 87], "recip": [26, 30], "afterward": [26, 30], "sinc": [26, 30, 33, 41, 46, 61, 65, 76, 80, 81, 82, 84, 89], "former": [26, 30], "hook": [26, 30, 75], "silent": [26, 29, 30], "t_destin": [26, 30], "__call__": [26, 30, 36], "add_modul": [26, 30], "child": [26, 30], "fn": [26, 30], "recurs": [26, 30, 36], "submodul": [26, 30], "children": [26, 30, 89], "nn": [26, 27, 30, 77], "init": [26, 30, 78], "doc": [26, 30, 69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "no_grad": [26, 30, 77, 83], "init_weight": [26, 30], "linear": [26, 30, 74, 77, 88], "fill_": [26, 30], "net": [26, 30, 69, 75, 77], "in_featur": [26, 30], "out_featur": [26, 30], "bia": [26, 30, 77], "tensor": [26, 27, 30, 69, 77, 83], "requires_grad": [26, 30], "bfloat16": [26, 30], "cast": [26, 30, 69], "buffer": [26, 30], "datatyp": [26, 30], "member": [26, 30, 70], "xdoctest": [26, 30], "undefin": [26, 30], "var": [26, 30], "buf": [26, 30], "20l": [26, 30], "1l": [26, 30], "5l": [26, 30], "immedi": [26, 30, 83], "cpu": [26, 30, 31, 69, 77], "move": [26, 30, 36, 68, 75], "cuda": [26, 30, 69, 77], "devic": [26, 30, 69, 77], "gpu": [26, 30, 69, 74, 88], "live": [26, 30], "copi": [26, 30, 57, 69, 70, 71, 73, 76, 81, 84, 85, 87], "doubl": [26, 30], "dump_patch": [26, 30], "eval": [26, 30, 77, 81, 83], "dropout": [26, 30], "batchnorm": [26, 30], "grad": [26, 30], "extra_repr": [26, 30], "line": [26, 30, 67, 70, 75, 80, 83, 89], "get_buff": [26, 30], "target": [26, 27, 30, 57, 58, 83, 85], "throw": [26, 30], "get_submodul": [26, 30], "explan": [26, 30], "fulli": [26, 30, 44, 76], "qualifi": [26, 30], "referenc": [26, 30], "attributeerror": [26, 30], "invalid": [26, 30], "resolv": [26, 30, 89], "get_extra_st": [26, 30], "state_dict": [26, 30], "set_extra_st": [26, 30], "build": [26, 30, 77, 86], "pickleabl": [26, 30], "serial": [26, 30], "backward": [26, 30, 77], "break": [26, 30, 77], "pickl": [26, 30, 82], "get_paramet": [26, 30], "let": [26, 30, 54, 55, 69, 71, 73, 74, 76, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "net_b": [26, 30], "net_c": [26, 30], "conv": [26, 30], "conv2d": [26, 30, 77], "16": [26, 30, 36, 61, 69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 86, 88, 89], "33": [26, 30, 75, 82], "kernel_s": [26, 30], "stride": [26, 30], "200": [26, 30, 55, 75, 82, 89], "diagram": [26, 30, 84], "degre": [26, 30, 85], "queri": [26, 30, 71, 77], "named_modul": [26, 30], "o": [26, 30, 38, 39, 69, 70, 71, 75, 76, 78, 81, 82, 89], "transit": [26, 30], "ipu": [26, 30], "load_state_dict": [26, 30], "strict": [26, 30, 36], "persist": [26, 30], "strictli": [26, 30], "namedtupl": [26, 30], "missing_kei": [26, 30], "unexpected_kei": [26, 30], "runtimeerror": [26, 30], "idx": [26, 30, 40, 41, 53, 70, 76, 77, 78, 80, 82, 83], "named_buff": [26, 30], "prefix": [26, 30, 69, 89], "prepend": [26, 30], "running_var": [26, 30], "named_children": [26, 30], "conv4": [26, 30], "conv5": [26, 30], "memo": [26, 30], "remove_dupl": [26, 30], "named_paramet": [26, 30], "register_backward_hook": [26, 30], "deprec": [26, 30, 33], "favor": [26, 30], "register_full_backward_hook": [26, 30], "removablehandl": [26, 30], "register_buff": [26, 30], "running_mean": [26, 30], "register_forward_hook": [26, 30], "posit": [26, 30, 40, 75, 83], "won": [26, 30, 70, 71, 76, 81], "inplac": [26, 30, 80], "register_forward_pre_hook": [26, 30], "gradient": [26, 30, 73, 77, 85], "respect": [26, 30, 53, 78], "grad_input": [26, 30], "grad_output": [26, 30], "technic": [26, 30], "caller": [26, 30], "register_load_state_dict_post_hook": [26, 30], "post": [26, 30], "incompatible_kei": [26, 30], "modif": [26, 30], "thrown": [26, 30], "clearn": [26, 30], "register_modul": [26, 30], "register_paramet": [26, 30], "requires_grad_": [26, 30], "autograd": [26, 30], "freez": [26, 30, 69, 74, 88], "finetun": [26, 30], "gan": [26, 30], "share_memori": [26, 30], "share_memory_": [26, 30], "destin": [26, 30], "keep_var": [26, 30], "shallow": [26, 30], "howev": [26, 30, 40, 69, 73, 74, 77, 80, 84, 86, 87, 88], "releas": [26, 30, 68, 76, 83], "design": [26, 30], "ordereddict": [26, 30], "detach": [26, 30, 77], "non_block": [26, 30], "memory_format": [26, 30], "channels_last": [26, 30], "Its": [26, 30, 36, 46, 52], "complex": [26, 30], "integr": [26, 30, 67], "asynchron": [26, 30], "host": [26, 30], "pin": [26, 30, 74, 75, 88], "desir": [26, 30, 39, 53], "4d": [26, 30], "ignore_w": [26, 30], "determinist": [26, 30, 69], "1913": [26, 30], "3420": [26, 30], "5113": [26, 30], "2325": [26, 30], "env": [26, 30], "torch_doctest_cuda1": [26, 30], "gpu1": [26, 30], "1914": [26, 30], "5112": [26, 30], "2324": [26, 30], "float16": [26, 30], "cdoubl": [26, 30], "3741": [26, 30], "2382": [26, 30], "5593": [26, 30], "4443": [26, 30], "complex128": [26, 30], "6122": [26, 30], "1150": [26, 30], "to_empti": [26, 30], "storag": [26, 30], "dst_type": [26, 30], "xpu": [26, 30], "zero_grad": [26, 30, 77], "set_to_non": [26, 30], "context": [26, 30, 82], "noisili": [27, 78], "han": 27, "2018": 27, "cifar_cnn": [27, 28], "loss_coteach": 27, "y_1": 27, "y_2": 27, "forget_r": 27, "class_weight": 27, "logit": [27, 44, 77], "decim": [27, 40], "quickli": [27, 69, 73, 74, 76, 77, 81, 83, 86, 87, 89], "forget": [27, 36, 89], "rate_schedul": 27, "epoch": [27, 30, 76, 77], "initialize_lr_schedul": 27, "lr": [27, 30], "001": [27, 55, 76], "250": [27, 70, 71, 78, 82], "epoch_decay_start": 27, "80": [27, 73, 81, 85, 87], "schedul": 27, "adjust": [27, 31, 49, 54, 55, 67, 78], "beta": 27, "adam": 27, "adjust_learning_r": 27, "alpha_plan": 27, "beta1_plan": 27, "forget_rate_schedul": 27, "num_gradu": 27, "expon": 27, "tell": [27, 74, 77, 78, 88], "train_load": [27, 30], "model1": [27, 78], "optimizer1": 27, "model2": [27, 78], "optimizer2": 27, "dataload": [27, 77, 83], "parser": 27, "parse_arg": 27, "num_iter_per_epoch": 27, "print_freq": 27, "topk": 27, "top1": 27, "top5": 27, "test_load": 27, "offici": [28, 43, 89], "wish": [28, 43, 83, 86, 89], "mnist_pytorch": 28, "coteach": [28, 68], "mini": [29, 59, 61, 76], "With": [29, 74, 78, 80, 85, 86, 88, 89], "approxim": [29, 54, 80], "low_self_confid": [29, 31, 47], "self_confid": [29, 31, 36, 47, 49, 55, 63, 65, 76, 78, 81, 87, 88], "conveni": [29, 69, 74, 88], "script": 29, "labelinspector": [29, 76], "adj_confident_thresholds_shar": 29, "labels_shar": 29, "pred_probs_shar": 29, "labels_fil": [29, 76], "pred_probs_fil": [29, 76], "batch_siz": [29, 30, 59, 61, 76, 77, 83, 86], "quality_score_kwarg": 29, "num_issue_kwarg": 29, "return_mask": 29, "variant": [29, 45, 86], "read": [29, 33, 71, 76, 78, 83, 89], "zarr": [29, 76], "memmap": [29, 86], "pythonspe": 29, "mmap": [29, 76], "hdf5": 29, "further": [29, 46, 47, 49, 52, 53, 61, 62, 69, 76], "yourfil": 29, "r": [29, 57, 70, 71, 85, 86], "npy": [29, 75, 76, 86], "mmap_mod": [29, 86], "tip": [29, 31, 44, 76], "save_arrai": 29, "your_arrai": 29, "disk": [29, 75, 76], "npz": [29, 89], "maxim": [29, 45, 59, 61, 86], "multiprocess": [29, 31, 47, 59, 61, 76, 77, 86], "linux": [29, 59, 61], "physic": [29, 31, 59, 61, 82, 86], "psutil": [29, 31, 59, 61, 86], "demonstr": [29, 70, 71, 74, 76, 77, 78, 80, 81, 82, 85, 86], "labels_arrai": [29, 41], "predprob": 29, "pred_probs_arrai": 29, "back": [29, 53, 70, 76, 82, 83], "store_result": 29, "becom": [29, 83], "verifi": [29, 76, 80, 83], "long": [29, 45, 54, 80], "enough": [29, 40, 76], "chunk": [29, 84], "ram": [29, 75], "faster": [29, 54, 57, 59, 61, 76, 78], "end_index": 29, "labels_batch": 29, "pred_probs_batch": 29, "update_confident_threshold": 29, "batch_result": 29, "score_label_qu": 29, "indices_of_examples_with_issu": [29, 76], "shortcut": 29, "encount": [29, 31, 59], "1000": [29, 69, 74, 76, 83], "aggreg": [29, 36, 45, 49, 52, 55, 65, 76, 78, 80], "get_num_issu": 29, "fetch": [29, 69, 71], "seen": [29, 76, 83, 89], "far": [29, 45], "get_quality_scor": 29, "label_quality_scor": [29, 49, 52, 55, 58, 78, 82, 85], "method1": 29, "method2": 29, "normalized_margin": [29, 31, 36, 47, 49, 55, 63, 65], "low_normalized_margin": [29, 31, 47], "issue_indic": [29, 52, 77], "update_num_issu": 29, "split_arr": 29, "arr": [29, 76], "chunksiz": 29, "convnet": 30, "bespok": [30, 44], "get_mnist_dataset": 30, "loader": [30, 77], "download": [30, 69, 76, 83], "mnist": [30, 67, 69, 75], "get_sklearn_digits_dataset": 30, "handwritten": 30, "digit": [30, 69, 75], "last": [30, 36, 50, 53, 70, 71, 76, 80, 89], "sklearn_digits_test_s": 30, "hard": [30, 75, 83], "simplenet": 30, "64": [30, 73, 77, 78, 82, 87], "log_interv": 30, "50": [30, 76, 78, 80, 82, 83], "01": [30, 55, 57, 69, 78, 81, 82, 85], "momentum": 30, "no_cuda": 30, "test_batch_s": [30, 77], "templat": 30, "enabl": 30, "flexibli": 30, "among": [30, 45, 78], "test_set": 30, "Be": 30, "overrid": 30, "train_idx": [30, 40, 83], "train_label": [30, 83, 88], "scikit": [30, 40, 54, 67, 69, 70, 71, 73, 74, 76, 79, 85, 88], "set_predict_proba_request": 30, "set_predict_request": 30, "encourag": [31, 47, 55, 58], "multilabel_classif": [31, 46, 47, 49, 55, 76, 81], "pred_probs_by_class": 31, "prune_count_matrix_col": 31, "rank_by_kwarg": [31, 47, 55, 78], "num_to_remove_per_class": [31, 47], "bad": [31, 47, 52, 55, 74, 76, 88], "seem": [31, 78, 81], "fewer": [31, 40, 82], "aren": 31, "confidence_weighted_entropi": [31, 36, 47, 49, 55, 63, 65], "label_issues_idx": [31, 55], "entropi": [31, 33, 35, 36, 54, 55], "prune_by_class": [31, 47, 78], "predicted_neq_given": [31, 47, 78], "prune_counts_matrix": 31, "smallest": [31, 55], "unus": 31, "number_of_mislabeled_examples_in_class_k": 31, "delet": [31, 67, 76, 88], "thread": [31, 47], "window": [31, 75], "shorter": [31, 50], "find_predicted_neq_given": 31, "find_label_issues_using_argmax_confusion_matrix": 31, "latent_algebra": [32, 68], "label_quality_util": 32, "multilabel_util": [32, 81], "multilabel_scor": [32, 49], "token_classification_util": [32, 89], "get_normalized_entropi": 33, "min_allowed_prob": 33, "wikipedia": 33, "activ": [33, 35, 45, 67, 80], "towardsdatasci": 33, "cheatsheet": 33, "ec57bc067c0b": 33, "clip": [33, 40, 69], "behav": 33, "unnecessari": [33, 76], "slightli": [33, 87, 88], "interv": [33, 36, 83], "herein": 34, "inexact": 34, "cours": 34, "propag": 34, "throughout": [34, 40, 57, 69, 80, 86, 89], "compute_ps_py_inv_noise_matrix": 34, "compute_py_inv_noise_matrix": 34, "compute_inv_noise_matrix": 34, "easili": [34, 68, 69, 71, 73, 74, 78, 80, 81, 83, 84, 85, 86, 87, 88], "increas": [34, 52, 54, 55, 69, 70, 76, 80, 81, 89], "dot": [34, 65, 76], "compute_noise_matrix_from_invers": 34, "compute_pi": 34, "true_labels_class_count": 34, "compute_pyx": 34, "pyx": 34, "multiannot": 35, "assert_valid_inputs_multiannot": 35, "labels_multiannot": [35, 45], "ensembl": [35, 36, 45, 55, 73, 76, 81, 83, 85, 87], "allow_single_label": 35, "annotator_id": 35, "assert_valid_pred_prob": 35, "pred_probs_unlabel": [35, 45], "format_multiannotator_label": [35, 45, 80], "lexicograph": [35, 40], "formatted_label": [35, 40], "old": [35, 40, 68, 75], "th": [35, 39, 40, 45, 47, 50, 52, 53, 54, 63, 65, 66, 74, 81, 82, 89], "check_consensus_label_class": 35, "consensus_label": [35, 45, 80], "consensus_method": [35, 45], "consensu": [35, 45, 67, 79, 89], "establish": [35, 85, 88], "compute_soft_cross_entropi": 35, "soft": [35, 75], "find_best_temp_scal": 35, "coarse_search_rang": [35, 57, 76], "fine_search_s": [35, 57, 76], "temperatur": [35, 36, 52, 61, 65], "scale": [35, 38, 75, 76, 83, 86, 87], "factor": [35, 36, 59, 61], "minim": [35, 52, 83], "temp_scale_pred_prob": 35, "temp": 35, "sharpen": [35, 75], "smoothen": 35, "classlabelscor": 36, "enum": 36, "get_self_confidence_for_each_label": [36, 55], "get_normalized_margin_for_each_label": [36, 55], "get_confidence_weighted_entropy_for_each_label": [36, 55], "75": [36, 70, 71, 75, 80, 81, 82, 85, 89], "from_str": 36, "scorer": 36, "exponential_moving_averag": [36, 49], "alpha": [36, 49, 52, 70, 71, 78, 81, 85], "exponenti": 36, "ema": 36, "s_1": 36, "s_k": 36, "ema_k": 36, "accord": [36, 47, 73, 74, 78, 89], "formula": [36, 38], "_t": 36, "cdot": 36, "s_t": 36, "qquad": 36, "leq": 36, "_1": 36, "give": [36, 55, 78, 80, 86], "recent": [36, 89], "success": 36, "previou": [36, 77, 82], "discount": 36, "s_ema": 36, "175": [36, 78, 82], "softmin": [36, 49, 52, 61, 65], "underflow": 36, "nan": [36, 45, 73, 80, 85, 87], "possible_method": 36, "aggregated_scor": 36, "multilabelscor": 36, "base_scor": 36, "base_scorer_kwarg": 36, "aggregator_kwarg": [36, 49], "n_sampl": 36, "n_label": 36, "binari": [36, 40, 47, 49, 78, 89], "worst": [36, 80], "class_label_quality_scor": 36, "get_class_label_quality_scor": 36, "42": [36, 75, 82, 89], "452": 36, "new_scor": 36, "575": 36, "get_label_quality_scores_per_class": [36, 49], "ml_scorer": 36, "multilabel_pi": 36, "binar": [36, 37], "second": [36, 38, 40, 53, 55, 70, 76, 78, 89], "get_cross_validated_multilabel_pred_prob": 36, "reformat": [36, 69], "wider": 36, "splitter": 36, "kfold": [36, 77], "multiclass": [36, 40, 45, 81], "onevsrestclassifi": [36, 81], "randomforestclassifi": [36, 78, 81], "n_split": [36, 77, 81], "stack_compl": 37, "pred_prob_slic": 37, "extend": [37, 77, 83, 89], "get_onehot_num_class": 37, "onehot": 37, "encod": [37, 53, 59, 62, 73, 74, 76, 85, 86, 87, 88], "multilabel": [37, 81], "int2onehot": [37, 81], "hot": [37, 47, 53, 59, 62, 73, 75, 76, 85, 86, 87], "onehot2int": [37, 81], "onehot_matrix": 37, "transform_distances_to_scor": 38, "exp": [38, 54, 55, 70], "dt": 38, "right": [38, 50, 53, 74, 81, 82, 83, 88], "num_neighbor": 38, "slice": 38, "ood_features_scor": [38, 54, 83], "95122942": 38, "83945702": 38, "token_classif": [39, 63, 65, 66, 76], "get_sent": [39, 89], "sentenc": [39, 63, 65, 66, 74, 88], "readabl": 39, "filter_sent": [39, 89], "lambda": [39, 69, 70, 80], "long_sent": 39, "headlin": 39, "process_token": 39, "charact": [39, 40], "s1": 39, "s2": 39, "processed_token": 39, "rule": [39, 75], "alecnlcb": 39, "entiti": [39, 67, 76, 89], "mapped_ent": 39, "unique_ident": 39, "loc": [39, 70, 71, 77, 89], "merge_prob": 39, "probs_merg": 39, "55": [39, 75, 82, 85], "0125": [39, 65], "0375": 39, "075": 39, "025": 39, "color_sent": 39, "color": [39, 62, 70, 71, 73, 78, 81, 83, 85, 86], "red": [39, 53, 70, 71, 75, 78, 81, 82, 83, 86], "colored_sent": 39, "termcolor": 39, "31msentenc": 39, "0m": 39, "ancillari": 40, "remove_noise_from_class": 40, "class_without_nois": 40, "any_other_class": 40, "choos": [40, 55, 73, 76, 78, 85, 87], "tradition": 40, "clip_noise_r": 40, "clip_valu": 40, "new_sum": 40, "preserv": 40, "value_count": [40, 76], "fill": 40, "wherea": [40, 47, 84], "come": [40, 70, 71, 76, 86], "major": [40, 45, 68, 77, 83], "versu": [40, 78], "value_counts_fill_missing_class": 40, "get_missing_class": 40, "round_preserving_sum": 40, "obviou": 40, "cgdeboer": 40, "iteround": 40, "round_preserving_row_tot": 40, "reach": 40, "estimate_pu_f1": 40, "prob_s_eq_1": 40, "claesen": 40, "f1": [40, 74, 78], "confusion_matrix": 40, "BE": 40, "print_square_matrix": 40, "left_nam": 40, "top_nam": 40, "titl": [40, 70, 71, 78, 81, 83], "short_titl": 40, "round_plac": 40, "pretti": [40, 78], "print_noise_matrix": [40, 78], "print_inverse_noise_matrix": 40, "print_joint_matrix": [40, 78], "joint_matrix": 40, "compress_int_arrai": 40, "num_possible_valu": 40, "train_val_split": 40, "holdout_idx": 40, "subset_x_i": 40, "extract": [40, 54, 69, 74, 80, 83, 86, 88], "subset_label": 40, "subset_data": 40, "extract_indices_tf": 40, "allow_shuffl": 40, "turn": [40, 67, 82], "unshuffle_tensorflow_dataset": 40, "shuffledataset": 40, "histori": 40, "pre_x": 40, "buffer_s": 40, "is_torch_dataset": 40, "is_tensorflow_dataset": 40, "csr_vstack": 40, "csr_matric": 40, "append": [40, 69, 75, 77, 78, 80, 81, 83, 89], "bottom": [40, 50, 53, 82], "vstack": [40, 75, 76, 77, 78, 80, 81], "append_extra_datapoint": 40, "to_data": 40, "from_data": 40, "taken": 40, "One": [40, 54, 76], "get_num_class": 40, "label_matrix": 40, "canon": 40, "num_unique_class": 40, "get_unique_class": 40, "format_label": 40, "smart_display_datafram": 40, "displai": [40, 53, 62, 66, 69, 74, 78, 88, 89], "jupyt": [40, 69, 70, 71, 75, 76, 77, 78, 80, 81, 83, 85, 89], "notebook": [40, 45, 69, 71, 75, 76, 78, 80, 81, 82, 86, 89], "consol": 40, "force_two_dimens": 40, "html": [40, 54, 73, 76, 78], "assert_valid_input": 41, "allow_missing_class": 41, "allow_one_class": 41, "assert_valid_class_label": 41, "assert_nonempty_input": 41, "assert_indexing_work": 41, "length_x": 41, "labels_to_arrai": 41, "labellik": 41, "keraswrappermodel": [44, 67], "keraswrappersequenti": 44, "tf": [44, 69], "legaci": 44, "lack": 44, "keraswrapp": 44, "huggingface_keras_imdb": 44, "unit": [44, 89], "model_kwarg": [44, 57], "compile_kwarg": 44, "sparsecategoricalcrossentropi": 44, "layer": [44, 69, 74, 83, 88], "dens": 44, "my_keras_model": 44, "from_logit": 44, "compil": 44, "declar": 44, "apply_softmax": 44, "analysi": 45, "analyz": [45, 67, 78, 80, 81], "get_label_quality_multiannot": [45, 80], "vote": 45, "crowdsourc": [45, 67, 80], "dawid": [45, 80], "skene": [45, 80], "analog": [45, 75, 80], "chosen": [45, 55, 80], "crowdlab": [45, 80], "unlabel": [45, 80, 83, 86], "decid": [45, 74, 75, 80, 85, 88, 89], "get_active_learning_scor": [45, 80], "activelab": [45, 80], "priorit": [45, 52, 82, 86, 89], "showcas": 45, "main": 45, "best_qual": 45, "quality_method": 45, "calibrate_prob": 45, "return_detailed_qu": 45, "return_annotator_stat": 45, "return_weight": 45, "label_quality_score_kwarg": 45, "necessarili": [45, 74, 78], "did": [45, 46, 69, 73, 78, 80, 85, 87, 88], "id": [45, 70, 77, 80], "majority_vot": 45, "ti": 45, "broken": [45, 53, 75], "highest": [45, 53, 70, 77, 84], "0th": 45, "consensus_quality_scor": [45, 80], "annotator_agr": [45, 80], "reman": 45, "1st": 45, "2nd": [45, 59], "3rd": 45, "consensus_label_suffix": 45, "consensus_quality_score_suffix": 45, "suffix": 45, "emsembl": 45, "weigh": [45, 75], "agreement": [45, 80], "agre": 45, "prevent": 45, "overconfid": [45, 84], "wrong": [45, 50, 52, 68, 70, 71, 74, 76, 78, 82, 88], "detailed_label_qu": [45, 80], "annotator_stat": [45, 80], "model_weight": 45, "annotator_weight": 45, "warn": [45, 70], "labels_info": 45, "num_annot": [45, 80], "deriv": [45, 80], "quality_annotator_1": 45, "quality_annotator_2": 45, "quality_annotator_m": 45, "lowest": [45, 53, 71, 77, 80, 81, 82, 86], "annotator_qu": [45, 80], "num_examples_label": [45, 80], "agreement_with_consensu": [45, 80], "worst_class": [45, 80], "trustworthi": [45, 80, 85], "get_label_quality_multiannotator_ensembl": 45, "func": 45, "weigtht": 45, "budget": 45, "retrain": [45, 85, 88], "active_learning_scor": 45, "improv": [45, 71, 75, 76, 77, 78, 85, 86, 87, 88], "active_learning_scores_unlabel": 45, "get_active_learning_scores_ensembl": 45, "henc": [45, 69, 70, 80], "get_majority_vote_label": [45, 80], "event": 45, "lastli": [45, 73], "convert_long_to_wide_dataset": 45, "labels_multiannotator_long": 45, "wide": [45, 69, 87, 88], "suitabl": [45, 73, 87], "labels_multiannotator_wid": 45, "common_multilabel_issu": 46, "mutual": [46, 81], "exclus": [46, 81], "vice": 46, "versa": 46, "rank_classes_by_multilabel_qu": 46, "overall_multilabel_health_scor": 46, "multilabel_health_summari": 46, "classes_by_multilabel_qu": 46, "inner": [47, 61], "find_multilabel_issues_per_class": 47, "per_class_label_issu": 47, "label_issues_list": 47, "labels_list": 47, "pred_probs_list": [47, 55, 77, 78], "anim": [48, 83], "rat": 48, "predat": 48, "pet": 48, "reptil": 48, "manner": [49, 80, 85, 87, 88], "box": [50, 52, 53, 75, 82], "object_detect": [50, 52, 53, 82], "return_indices_ranked_by_scor": [50, 82], "overlapping_label_check": [50, 52], "suboptim": [50, 52], "locat": [50, 52, 82, 86, 89], "bbox": [50, 53, 82], "image_nam": [50, 53], "y1": [50, 53, 82], "y2": [50, 53, 82], "later": [50, 53, 54, 88, 89], "mmdetect": [50, 53, 82], "corner": [50, 53, 82], "swap": [50, 52, 62, 66], "penal": [50, 52], "concern": [50, 52, 67, 71], "aggregation_weight": 52, "imperfect": [52, 76], "chose": [52, 80, 82], "imperfectli": [52, 82], "dirti": [52, 55, 58, 85], "subtyp": 52, "badloc": 52, "nonneg": 52, "issues_from_scor": [52, 61, 62, 65, 66, 82, 86, 89], "compute_overlooked_box_scor": 52, "high_probability_threshold": 52, "auxiliary_input": [52, 53], "vari": [52, 71], "iou": 52, "heavili": 52, "auxiliarytypesdict": 52, "pred_label": [52, 88], "pred_label_prob": 52, "pred_bbox": 52, "lab_label": 52, "lab_bbox": 52, "similarity_matrix": 52, "min_possible_similar": 52, "scores_overlook": 52, "compute_badloc_box_scor": 52, "low_probability_threshold": 52, "scores_badloc": 52, "compute_swap_box_scor": 52, "accident": [52, 73, 74, 88], "scores_swap": 52, "pool_box_scores_per_imag": 52, "box_scor": 52, "image_scor": [52, 61, 86], "object_counts_per_imag": 53, "discov": [53, 71, 89], "auxiliari": [53, 83, 86], "_get_valid_inputs_for_compute_scor": 53, "object_count": 53, "bounding_box_size_distribut": 53, "down": 53, "bbox_siz": 53, "class_label_distribut": 53, "class_distribut": 53, "get_sorted_bbox_count_idx": 53, "plot": [53, 70, 71, 78, 81, 83, 85, 86], "sorted_idx": [53, 83], "plot_class_size_distribut": 53, "class_to_show": 53, "hidden": [53, 83], "max_class_to_show": 53, "plot_class_distribut": 53, "visual": [53, 70, 71, 77, 85, 87, 89], "prediction_threshold": 53, "overlai": [53, 82], "figsiz": [53, 70, 71, 77, 78, 81, 83], "save_path": [53, 82], "blue": [53, 75, 78, 82], "overlaid": 53, "side": [53, 75, 82], "figur": [53, 78, 81, 83, 85], "extens": [53, 78, 80], "png": [53, 82], "pdf": [53, 54], "ep": 53, "svg": 53, "matplotlib": [53, 70, 71, 77, 78, 81, 82, 83, 85], "Of": 54, "li": 54, "smaller": [54, 81, 82], "find_top_issu": [54, 55, 83], "reli": [54, 69, 70, 71, 74, 82, 83, 88], "dist_metr": 54, "dim": [54, 77, 86], "subtract": [54, 55], "renorm": [54, 55, 76], "least_confid": 54, "sum_": 54, "log": [54, 55, 68], "softmax": [54, 61, 65, 77], "literatur": 54, "gen": 54, "liu": 54, "lochman": 54, "zach": 54, "openaccess": 54, "thecvf": 54, "content": [54, 69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "cvpr2023": 54, "liu_gen_pushing_the_limits_of_softmax": 54, "based_out": 54, "distribution_detection_cvpr_2023_pap": 54, "fit_scor": [54, 83], "ood_predictions_scor": 54, "categor": [54, 70, 71, 72, 85, 87], "pretrain": [54, 69, 74, 83, 88], "adjust_confident_threshold": 54, "probabilist": [54, 69, 70, 71, 73, 74, 83, 84, 87], "order_label_issu": [55, 68], "whichev": [55, 84], "argsort": [55, 74, 77, 78, 83, 85, 88], "max_": 55, "get_label_quality_ensemble_scor": [55, 76, 78], "weight_ensemble_members_bi": 55, "custom_weight": 55, "log_loss_search_t_valu": 55, "0001": [55, 75], "scheme": 55, "log_loss_search": 55, "log_loss": [55, 74], "1e0": 55, "1e1": 55, "1e2": 55, "2e2": 55, "quality_scor": [55, 83], "forth": 55, "top_issue_indic": 55, "rank_bi": [55, 68], "weird": [55, 66], "minu": 55, "prob_label": 55, "max_prob_not_label": 55, "idea": 55, "AND": [55, 74], "corrupt": [57, 85], "linearregress": [57, 76, 85], "y_with_nois": 57, "n_boot": [57, 76], "include_aleatoric_uncertainti": [57, 76], "sole": [57, 70, 80, 83, 87], "larger": [57, 59, 61, 75, 76, 77], "bootstrap": [57, 76, 85], "resampl": [57, 69, 76], "epistem": [57, 76, 83, 85], "aleator": [57, 76, 85], "model_final_kwarg": 57, "coars": 57, "thorough": [57, 76], "fine": [57, 69, 74, 83, 88], "grain": 57, "grid": 57, "get_epistemic_uncertainti": 57, "varianc": [57, 78], "epistemic_uncertainti": 57, "get_aleatoric_uncertainti": 57, "residu": [57, 58, 76], "deviat": [57, 85], "ie": 57, "aleatoric_uncertainti": 57, "outr": 58, "contin": 58, "raw": [58, 67, 68, 71, 75, 77, 80, 82, 83], "aka": [58, 69, 78, 89], "00323821": 58, "33692597": 58, "00191686": 58, "semant": [59, 61, 62, 79], "segment": [59, 61, 62, 79], "pixel": [59, 61, 62, 83, 86], "h": [59, 61, 62, 86], "height": [59, 61, 62, 86], "w": [59, 61, 62, 86], "width": [59, 61, 62, 86], "labels_one_hot": [59, 62, 86], "stream": [59, 83, 89], "downsampl": [59, 61, 86], "shrink": [59, 61], "divis": [59, 61, 70], "segmant": [61, 62], "num_pixel_issu": [61, 86], "product": [61, 76, 77], "pixel_scor": [61, 86], "display_issu": [61, 62, 63, 65, 66, 86, 89], "highlight": [62, 66, 70, 71, 73, 86], "enter": 62, "legend": [62, 70, 71, 81, 82, 85, 86], "colormap": 62, "background": 62, "person": [62, 76, 82, 86, 89], "common_label_issu": [62, 66, 86, 89], "ambigu": [62, 66, 69, 74, 75, 78, 88, 89], "systemat": [62, 66, 80], "misunderstood": [62, 66], "issues_df": [62, 77], "filter_by_class": [62, 86], "class_index": 62, "issues_subset": [62, 66], "95": [63, 65, 73, 75, 78, 85], "token_score_method": 65, "sentence_score_method": 65, "sentence_score_kwarg": 65, "compris": [65, 66], "token_scor": [65, 89], "converg": 65, "toward": 65, "_softmin_sentence_scor": 65, "sentence_scor": [65, 89], "token_info": 65, "70": [65, 73, 85], "02": [65, 70, 71, 78, 82, 85], "03": [65, 75, 78, 82, 89], "04": [65, 82, 85], "08": [65, 78, 82, 89], "commonli": [66, 68, 70, 71, 81, 89], "filter_by_token": [66, 89], "But": [66, 78, 89], "restrict": [66, 76], "reliabl": [67, 69, 76, 80, 86, 87], "thousand": 67, "imagenet": [67, 75], "popular": [67, 80, 82], "centric": [67, 79], "capabl": 67, "minut": [67, 69, 73, 74, 75, 80, 81, 82, 85, 86, 87, 88, 89], "conda": 67, "feature_embed": [67, 83], "Then": [67, 76, 77, 85, 87, 88], "your_dataset": [67, 69, 70, 71, 73, 74, 77], "column_name_of_label": [67, 69, 70, 71, 73, 74, 77], "plagu": [67, 71], "untrain": 67, "\u30c4": 67, "label_issues_info": [67, 71], "sklearn_compatible_model": 67, "framework": [67, 81, 82], "complianc": 67, "tag": [67, 81, 89], "sequenc": 67, "recognit": [67, 69, 76, 89], "train_data": [67, 83, 85, 87, 88], "gotten": 67, "test_data": [67, 78, 81, 83, 85, 87, 88], "deal": [67, 71], "tutori": [67, 69, 70, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "faq": [67, 79], "feel": [67, 69, 71, 76], "free": [67, 69, 71, 76, 78], "ask": [67, 76], "slack": [67, 76], "project": [67, 85], "welcom": 67, "commun": [67, 76], "guidelin": [67, 82], "piec": 67, "studio": [67, 71, 76], "platform": [67, 76], "tabular": [67, 70, 71, 72, 76, 79, 80], "automl": [67, 76], "foundat": 67, "smart": [67, 76], "edit": [67, 76], "easier": [67, 78], "unreli": [67, 69, 73, 74, 87], "older": 68, "outlin": 68, "substitut": 68, "v2": [68, 73, 87], "get_noise_indic": 68, "psx": 68, "sorted_index_method": 68, "order_label_error": 68, "label_errors_bool": 68, "latent_estim": 68, "num_label_error": 68, "learningwithnoisylabel": 68, "neatli": 68, "organ": [68, 73, 75, 87, 89], "reorgan": 68, "baseline_method": 68, "incorpor": [68, 78], "research": [68, 78], "polyplex": 68, "terminologi": 68, "label_error": 68, "quickstart": [69, 70, 71, 73, 74, 75, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "spoken": 69, "500": [69, 83, 89], "english": [69, 75], "pronunci": 69, "wav": 69, "huggingfac": [69, 70, 71, 77], "voxceleb": 69, "speech": [69, 89], "your_pred_prob": [69, 70, 71, 73, 74], "tensorflow_io": 69, "26": [69, 70, 75, 77, 78, 80, 82], "huggingface_hub": 69, "12": [69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 82, 83, 85, 86, 87, 88, 89], "branch": [69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 87, 88], "reproduc": [69, 73, 78, 80], "command": 69, "wget": [69, 82, 86, 89], "navig": 69, "link": [69, 75, 82], "browser": 69, "jakobovski": 69, "archiv": [69, 89], "v1": 69, "tar": [69, 83], "gz": [69, 83], "mkdir": [69, 89], "spoken_digit": 69, "xf": 69, "6_nicolas_32": 69, "data_path": 69, "listdir": 69, "nondeterminist": 69, "file_nam": 69, "endswith": 69, "file_path": 69, "join": [69, 76, 77], "39": [69, 70, 74, 75, 76, 77, 82, 85, 86, 88, 89], "7_george_26": 69, "0_nicolas_24": 69, "0_nicolas_6": 69, "listen": 69, "display_exampl": 69, "click": [69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "expand": [69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "pulldown": [69, 70, 71, 75, 77, 78, 80, 81, 83, 85, 89], "colab": [69, 70, 71, 75, 76, 77, 78, 80, 81, 83, 85, 89], "tfio": 69, "pathlib": 69, "ipython": 69, "load_wav_16k_mono": 69, "filenam": 69, "khz": 69, "file_cont": 69, "io": [69, 75], "read_fil": 69, "sample_r": 69, "decode_wav": 69, "desired_channel": 69, "squeez": 69, "int64": [69, 80], "rate_in": 69, "rate_out": 69, "16000": 69, "wav_file_nam": 69, "audio_r": 69, "wav_file_exampl": 69, "plai": [69, 75, 76], "button": 69, "wav_file_name_exampl": 69, "7_jackson_43": 69, "hear": 69, "extractor": 69, "encoderclassifi": 69, "spkrec": 69, "xvect": 69, "feature_extractor": 69, "from_hparam": 69, "run_opt": 69, "uncom": 69, "wav_audio_file_path": 69, "head": [69, 71, 73, 74, 75, 77, 78, 80, 85, 87, 88], "torchaudio": 69, "extract_audio_embed": 69, "emb": [69, 77], "signal": 69, "encode_batch": 69, "embeddings_list": [69, 77], "embeddings_arrai": 69, "512": [69, 77], "14": [69, 70, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "196315": 69, "3194594": 69, "478977": 69, "2890828": 69, "8170278": 69, "892647": 69, "24": [69, 75, 78, 80, 82], "898054": 69, "256194": 69, "559642": 69, "559715": 69, "620667": 69, "285246": 69, "21": [69, 70, 71, 75, 78, 82, 89], "709623": 69, "5033712": 69, "913803": 69, "8198366": 69, "1831512": 69, "208761": 69, "08426": 69, "3210406": 69, "005453": 69, "2161605": 69, "478239": 69, "682179": 69, "0538025": 69, "242471": 69, "0914207": 69, "7833488": 69, "039538": 69, "23": [69, 75, 77, 78, 82], "56918": 69, "19": [69, 74, 75, 77, 78, 83, 85, 86, 88], "761095": 69, "1258287": 69, "753235": 69, "3508894": 69, "598273": 69, "237122": 69, "2500": 69, "leverag": [69, 74, 76, 78, 80, 88], "tune": [69, 74, 75, 83, 88], "computation": [69, 74, 88], "intens": [69, 74, 88], "held": [69, 73, 74, 75, 82, 83, 84, 87], "straightforward": [69, 73, 87], "benefit": [69, 84, 86, 87], "tol": 69, "num_crossval_fold": [69, 73, 80, 87], "decreas": [69, 76], "never": [69, 78, 81, 83, 84], "accuracy_scor": [69, 74, 78, 87, 88], "cv_accuraci": 69, "9772": 69, "probabilit": [69, 88], "9980": 69, "176": [69, 75, 78, 81], "006488": 69, "2318": 69, "008269": 69, "986": 69, "010354": 69, "469": 69, "013459": 69, "516": 69, "013478": 69, "investig": 69, "100541": 69, "998729": 69, "998768": 69, "980980": 69, "998217": 69, "18": [69, 74, 75, 78, 82, 83, 85, 86, 88], "identified_label_issu": [69, 74], "lowest_quality_label": [69, 74, 78, 85, 88], "sort_valu": [69, 71, 73, 74, 77, 78, 80], "1946": 69, "1871": 69, "1955": 69, "2132": 69, "worth": [69, 78], "iloc": [69, 73, 74, 85, 87, 88], "6_yweweler_35": 69, "6_yweweler_36": 69, "6_yweweler_14": 69, "6_theo_27": 69, "4_george_31": 69, "6_nicolas_8": 69, "sound": 69, "quit": [69, 83], "22": [69, 70, 75, 77, 78, 81, 82, 89], "blindli": [69, 76, 85, 87, 88], "trust": [69, 76, 78, 80, 84, 85, 87, 88], "address": [70, 71, 74, 76, 88], "underneath": 70, "hood": 70, "alert": 70, "introduct": 70, "mayb": [70, 71, 74], "examin": [70, 71, 73, 87], "your_feature_matrix": [70, 71], "toi": [70, 71, 75, 77, 78, 80], "train_test_split": [70, 71, 83, 87, 88], "inf": [70, 71], "mid": [70, 71], "bins_map": [70, 71], "create_data": [70, 71], "y_bin": [70, 71], "y_i": [70, 71], "y_bin_idx": [70, 71], "y_train": [70, 71, 78, 85], "y_test": [70, 71, 78, 85], "y_train_idx": [70, 71], "y_test_idx": [70, 71], "test_siz": [70, 71, 87, 88], "slide": [70, 71, 75], "decis": [70, 71, 87], "boundari": [70, 71], "frame": [70, 71], "x_out": [70, 71], "tini": [70, 71], "concaten": [70, 71, 84], "y_out": [70, 71], "y_out_bin": [70, 71], "y_out_bin_idx": [70, 71], "exact_duplicate_idx": [70, 71], "x_duplic": [70, 71], "y_duplic": [70, 71], "y_duplicate_idx": [70, 71], "noisy_labels_idx": [70, 71, 81], "scatter": [70, 71, 78, 81, 85], "black": [70, 71, 75, 85], "cyan": [70, 71], "pyplot": [70, 71, 77, 78, 81, 83, 85], "plt": [70, 71, 77, 78, 81, 83, 85], "plot_data": [70, 71, 78, 81, 85], "fig": [70, 71, 75, 77, 83, 85], "ax": [70, 71, 77, 83, 85], "subplot": [70, 71, 77, 83], "set_titl": [70, 71, 77, 83], "set_xlabel": [70, 71], "x_1": [70, 71], "fontsiz": [70, 71, 77, 78, 81], "set_ylabel": [70, 71], "x_2": [70, 71], "set_xlim": [70, 71], "set_ylim": [70, 71], "linestyl": [70, 71], "circl": [70, 71, 78, 81], "misclassifi": [70, 71], "zip": [70, 71, 77, 82, 89], "label_err": [70, 71], "180": [70, 71, 82], "marker": [70, 71], "facecolor": [70, 71], "edgecolor": [70, 71], "linewidth": [70, 71, 83], "dup": [70, 71], "first_legend": [70, 71], "align": [70, 71], "title_fontproperti": [70, 71], "semibold": [70, 71], "second_legend": [70, 71], "45": [70, 71, 75, 78, 82], "gca": [70, 71], "add_artist": [70, 71], "tight_layout": [70, 71], "ideal": [70, 71], "logist": [70, 71, 74, 80, 83, 88], "remaind": 70, "modal": [70, 71, 76, 80], "regardless": [70, 71], "132": [70, 71, 78, 82], "9318": [70, 71], "77": [70, 71, 73, 82, 87], "006939": [70, 71], "007830": [70, 71], "40": [70, 71, 74, 75, 77], "014826": [70, 71], "107": [70, 71, 78, 81], "021220": [70, 71], "120": [70, 71, 87], "026403": [70, 71], "notic": [70, 78, 80, 82], "5221": [70, 71], "126": [70, 71, 78, 82], "046465": [70, 71], "130": [70, 71], "068695": [70, 71], "129": [70, 71], "127": [70, 71], "076251": [70, 71], "128": [70, 71, 77], "083941": [70, 71], "2465": [70, 71], "is_near_duplicate_issu": [70, 71, 73, 74, 77, 78], "131": [70, 71, 86], "000000e": [70, 71], "00": [70, 71, 73, 75, 77, 86, 87], "463180e": [70, 71], "07": [70, 71, 78, 82], "51": [70, 71, 73, 75, 78, 82], "857172e": [70, 71], "859087e": [70, 71], "30": [70, 75, 76, 77, 81, 86, 89], "3293": 70, "025076": 70, "026534": 70, "050766": 70, "051025": 70, "home": [70, 74, 75, 83, 88], "runner": [70, 74, 83, 88], "297": [70, 82], "userwarn": 70, "327": [70, 82], "306": 70, "34": [70, 75, 78, 80, 82, 83, 89], "54": [70, 75, 78, 82], "039117": 70, "53": [70, 73, 75, 81, 82, 87], "044594": 70, "105": 70, "105121": 70, "133588": [70, 71], "43": [70, 75, 78, 82, 88], "168035": 70, "125": 70, "090878": 70, "37": [70, 75], "169462": 70, "109": [70, 75, 82], "194566": 70, "35": [70, 75, 80, 81, 82, 89], "196302": 70, "206314": 70, "average_ood_scor": 70, "32933380816554325": 70, "52": [70, 75, 82, 89], "085049e": 70, "087324e": 70, "89": [70, 73, 82, 85], "92": [70, 78, 82, 87], "574261e": 70, "583757e": 70, "91": [70, 82, 88], "314215e": 70, "341292e": 70, "specfi": 70, "new_lab": 70, "scoring_funct": 70, "div": 70, "rem": 70, "inv_scal": 70, "49": [70, 75, 78, 82], "superstitionissuemanag": 70, "unlucki": 70, "superstit": 70, "to_seri": 70, "issues_mask": 70, "summary_scor": 70, "32": [70, 75, 80, 82], "9242": 70, "is_superstition_issu": 70, "superstition_scor": 70, "047581": 70, "090635": 70, "129591": 70, "65": [70, 82, 87], "164840": 70, "demo": [71, 73, 81, 87], "lurk": [71, 77, 78], "thoroughli": 71, "preprocess": [71, 73, 83, 85, 87, 88], "review": [71, 73, 74, 75, 76, 78, 82, 85, 86, 87, 88, 89], "8218": 71, "is_non_iid_issu": [71, 73, 74, 77, 78], "810274": 71, "826147": 71, "849587": 71, "855359": 71, "855485": 71, "821750488732925": 71, "auto": [71, 75, 76, 85, 87, 88], "conceptu": 71, "931818": 71, "522080": 71, "246459": 71, "821750": 71, "betweeen": 71, "864232": 71, "586131": 71, "235095": 71, "970324": 71, "825563": 71, "548979": 71, "221560": 71, "890575": 71, "533367": 71, "622256": 71, "199185": 71, "755724": 71, "499498": 71, "179601": 71, "948362": 71, "632385": 71, "292800": 71, "878267": 71, "examples_w_issu": [71, 76], "inde": [71, 74], "miscellan": [71, 89], "206897": 71, "041667": 71, "793103": 71, "071429": 71, "103448": 71, "928571": 71, "053333": 71, "101266": 71, "946667": 71, "portion": 71, "huge": [71, 78], "worri": 71, "critic": 71, "highli": [71, 77], "sql": [73, 87], "databas": [73, 87], "excel": [73, 87], "parquet": [73, 87], "student": [73, 85, 87, 89], "grade": [73, 85, 87], "900": [73, 85, 87], "exam": [73, 85, 87], "letter": [73, 87, 89], "hundr": [73, 87], "histgradientboostingclassifi": 73, "reflect": [73, 74, 80, 82, 83, 85, 87, 88], "standardscal": [73, 83, 87], "possibli": [73, 87], "grades_data": [73, 87], "read_csv": [73, 74, 85, 87, 88], "stud_id": [73, 87], "exam_1": [73, 85, 87], "exam_2": [73, 85, 87], "exam_3": [73, 85, 87], "letter_grad": [73, 87], "f48f73": [73, 87], "0bd4e7": [73, 87], "81": [73, 74, 82, 85, 87, 89], "great": [73, 75, 87], "particip": [73, 87], "cb9d7a": [73, 87], "61": [73, 77, 78, 82, 87], "94": [73, 75, 78, 82, 85, 87], "78": [73, 75, 78, 82, 85, 87], "9acca4": [73, 87], "48": [73, 75, 78, 82, 87], "x_raw": [73, 87], "cat_featur": 73, "x_encod": [73, 87], "get_dummi": [73, 85, 87], "drop_first": [73, 87], "numeric_featur": [73, 87], "scaler": [73, 83, 87], "x_process": [73, 87], "fit_transform": [73, 87], "bring": [73, 74, 77, 80, 85, 87, 88], "byod": [73, 74, 77, 80, 85, 87, 88], "boost": [73, 76, 80, 85], "xgboost": [73, 76, 85], "think": [73, 76, 81, 86, 89], "carefulli": [73, 74, 77, 87], "nonzero": 73, "suspici": [73, 87], "tabl": [73, 75, 80, 87], "358": 73, "294": [73, 82], "46": [73, 75, 78, 82], "941": 73, "7109": 73, "000005": [73, 74, 77], "886": 73, "000059": 73, "709": 73, "000104": 73, "723": 73, "000169": 73, "689": 73, "000181": 73, "7154": 73, "012085": 73, "061510": 73, "115512": 73, "124391": 73, "214163": 73, "2169": 73, "690": 73, "246": [73, 82], "185": [73, 75, 82, 89], "582": 73, "691": 73, "168": [73, 78], "915": 73, "187": [73, 75], "27": [73, 75, 78, 82, 86, 89], "0014": [73, 75], "595": 73, "702427": 73, "147": [73, 78, 82], "711186": 73, "157": [73, 78], "721394": 73, "771": 73, "731979": 73, "898": 73, "740335": 73, "0014153602099278074": 73, "issue_result": 73, "000842": 73, "555944": 73, "004374": 73, "sorted_issu": 73, "73": [73, 75, 81, 82, 85], "86": [73, 77, 78, 82, 85, 87], "deserv": 73, "outlier_result": 73, "sorted_outli": 73, "56": [73, 75, 85], "96": [73, 75, 78, 81, 82, 85], "lt": [73, 74, 75, 77, 80, 86], "style": [73, 86], "font": 73, "18px": 73, "ff00ff": 73, "bac": 73, "unintend": [73, 74], "mistak": [73, 74, 77, 87, 88], "duplicate_result": 73, "58": [73, 75, 77, 78, 82, 87, 89], "perhap": [73, 78, 80], "twice": 73, "67": [73, 75, 82, 85], "wari": [73, 74, 76], "intent": [74, 88], "servic": [74, 88], "onlin": [74, 88], "bank": [74, 75, 88], "banking77": [74, 88], "oo": [74, 88], "000": [74, 75, 77, 88, 89], "categori": [74, 77, 88], "scope": [74, 88], "dive": 74, "your_featur": 74, "sentence_transform": [74, 88], "sentencetransform": [74, 88], "payment": [74, 88], "cancel_transf": [74, 88], "transfer": [74, 88], "fund": [74, 88], "cancel": [74, 88], "transact": [74, 88], "my": [74, 88], "revert": [74, 88], "morn": [74, 88], "realis": [74, 88], "yesterdai": [74, 88], "rent": [74, 88], "realli": [74, 80, 86, 88], "tomorrow": [74, 88], "raw_text": [74, 88], "lost_or_stolen_phon": [74, 88], "beneficiary_not_allow": [74, 88], "change_pin": [74, 88], "apple_pay_or_google_pai": [74, 88], "card_payment_fee_charg": [74, 88], "card_about_to_expir": [74, 88], "visa_or_mastercard": [74, 88], "supported_cards_and_curr": [74, 88], "getting_spare_card": [74, 88], "utter": [74, 88], "continu": [74, 77, 80, 85, 87, 88, 89], "suit": [74, 75, 88], "electra": [74, 88], "discrimin": [74, 88], "googl": [74, 88], "text_embed": 74, "No": [74, 76, 88], "google_electra": [74, 88], "pool": [74, 76, 83, 88], "400": [74, 88], "data_dict": [74, 78, 80], "84": [74, 82], "41": [74, 75, 82, 85], "38": [74, 75, 82], "9720": 74, "981": 74, "974": 74, "000150": 74, "982": [74, 75], "000218": 74, "971": 74, "000512": 74, "980": [74, 75], "000947": 74, "9122": 74, "994": 74, "676322": 74, "999": [74, 89], "693868": 74, "697240": 74, "433": 74, "700874": 74, "989": 74, "713590": 74, "0656": 74, "160": 74, "006237": 74, "148": 74, "546": 74, "006485": 74, "514": 74, "481": 74, "008165": 74, "0000": [74, 75, 78], "313": [74, 82], "564102": 74, "572258": 74, "28": [74, 75, 77, 78, 80, 89], "574915": 74, "31": [74, 75, 78, 80, 82], "575507": 74, "575874": 74, "791961": 74, "258508": 74, "699010": 74, "183136": 74, "771112": 74, "to_numpi": [74, 85, 88], "data_with_suggested_label": 74, "suggested_label": 74, "charg": [74, 88], "cash": [74, 88], "holidai": [74, 88], "sent": [74, 88, 89], "card": [74, 75, 88], "mine": [74, 88], "expir": [74, 88], "me": [74, 88], "withdraw": 74, "monei": 74, "whoever": [74, 88], "outlier_issu": [74, 77], "lowest_quality_outli": 74, "OR": 74, "636c65616e6c616220697320617765736f6d6521": 74, "phone": [74, 75], "gone": 74, "gt": [74, 80, 89], "samp": 74, "br": 74, "press": [74, 89], "nonsens": 74, "sens": 74, "detriment": 74, "duplicate_issu": 74, "fee": 74, "pai": 74, "go": [74, 75, 78], "shortlist": [74, 85, 88], "curat": [74, 79], "mnist_test_set": 75, "imagenet_val_set": 75, "tench": 75, "goldfish": 75, "white": [75, 89], "shark": 75, "tiger": 75, "hammerhead": 75, "electr": 75, "rai": 75, "stingrai": 75, "cock": 75, "hen": 75, "ostrich": 75, "brambl": 75, "goldfinch": 75, "hous": 75, "finch": 75, "junco": 75, "indigo": 75, "bunt": 75, "american": [75, 89], "robin": 75, "bulbul": 75, "jai": 75, "magpi": 75, "chickade": 75, "dipper": 75, "kite": 75, "bald": 75, "eagl": 75, "vultur": 75, "grei": 75, "owl": 75, "fire": 75, "salamand": 75, "smooth": 75, "newt": 75, "spot": [75, 82], "axolotl": 75, "bullfrog": 75, "tree": 75, "frog": [75, 83], "tail": 75, "loggerhead": 75, "sea": 75, "turtl": 75, "leatherback": 75, "mud": 75, "terrapin": 75, "band": 75, "gecko": 75, "green": [75, 89], "iguana": 75, "carolina": 75, "anol": 75, "desert": 75, "grassland": 75, "whiptail": 75, "lizard": 75, "agama": 75, "frill": 75, "neck": 75, "allig": 75, "gila": 75, "monster": 75, "european": 75, "chameleon": 75, "komodo": 75, "dragon": 75, "nile": 75, "crocodil": 75, "triceratop": 75, "worm": 75, "snake": 75, "ring": 75, "eastern": 75, "hog": 75, "nose": 75, "kingsnak": 75, "garter": 75, "water": 75, "vine": 75, "night": 75, "boa": 75, "constrictor": 75, "african": 75, "rock": 75, "indian": 75, "cobra": 75, "mamba": 75, "saharan": 75, "horn": 75, "viper": 75, "diamondback": 75, "rattlesnak": 75, "sidewind": 75, "trilobit": 75, "harvestman": 75, "scorpion": 75, "yellow": 75, "garden": 75, "spider": 75, "barn": 75, "southern": 75, "widow": 75, "tarantula": 75, "wolf": 75, "tick": 75, "centiped": 75, "grous": 75, "ptarmigan": 75, "ruf": 75, "prairi": 75, "peacock": 75, "quail": 75, "partridg": 75, "parrot": 75, "macaw": 75, "sulphur": 75, "crest": 75, "cockatoo": 75, "lorikeet": 75, "coucal": 75, "bee": 75, "eater": 75, "hornbil": 75, "hummingbird": 75, "jacamar": 75, "toucan": 75, "duck": [75, 88], "breast": 75, "mergans": 75, "goos": 75, "swan": 75, "tusker": 75, "echidna": 75, "platypu": 75, "wallabi": 75, "koala": 75, "wombat": 75, "jellyfish": 75, "anemon": 75, "brain": 75, "coral": 75, "flatworm": 75, "nematod": 75, "conch": 75, "snail": 75, "slug": 75, "chiton": 75, "chamber": 75, "nautilu": 75, "dung": 75, "crab": 75, "fiddler": 75, "king": 75, "lobster": 75, "spini": 75, "crayfish": 75, "hermit": 75, "isopod": 75, "stork": 75, "spoonbil": 75, "flamingo": 75, "heron": 75, "egret": 75, "bittern": 75, "crane": 75, "bird": [75, 83], "limpkin": 75, "gallinul": 75, "coot": 75, "bustard": 75, "ruddi": 75, "turnston": 75, "dunlin": 75, "redshank": 75, "dowitch": 75, "oystercatch": 75, "pelican": 75, "penguin": 75, "albatross": 75, "whale": 75, "killer": 75, "dugong": 75, "lion": 75, "chihuahua": 75, "japanes": 75, "chin": 75, "maltes": 75, "pekinges": 75, "shih": 75, "tzu": 75, "charl": 75, "spaniel": 75, "papillon": 75, "terrier": 75, "rhodesian": 75, "ridgeback": 75, "afghan": [75, 89], "hound": 75, "basset": 75, "beagl": 75, "bloodhound": 75, "bluetick": 75, "coonhound": 75, "tan": 75, "walker": 75, "foxhound": 75, "redbon": 75, "borzoi": 75, "irish": 75, "wolfhound": 75, "italian": 75, "greyhound": 75, "whippet": 75, "ibizan": 75, "norwegian": 75, "elkhound": 75, "otterhound": 75, "saluki": 75, "scottish": 75, "deerhound": 75, "weimaran": 75, "staffordshir": 75, "bull": 75, "bedlington": 75, "border": 75, "kerri": 75, "norfolk": 75, "norwich": 75, "yorkshir": 75, "wire": 75, "fox": 75, "lakeland": 75, "sealyham": 75, "airedal": 75, "cairn": 75, "australian": 75, "dandi": 75, "dinmont": 75, "boston": 75, "miniatur": 75, "schnauzer": 75, "giant": 75, "tibetan": 75, "silki": 75, "coat": [75, 77], "wheaten": 75, "west": 75, "highland": 75, "lhasa": 75, "apso": 75, "flat": 75, "retriev": 75, "curli": 75, "golden": 75, "labrador": 75, "chesapeak": 75, "bai": 75, "german": [75, 89], "shorthair": 75, "pointer": 75, "vizsla": 75, "setter": 75, "gordon": 75, "brittani": 75, "clumber": 75, "springer": 75, "welsh": 75, "cocker": 75, "sussex": 75, "kuvasz": 75, "schipperk": 75, "groenendael": 75, "malinoi": 75, "briard": 75, "kelpi": 75, "komondor": 75, "sheepdog": 75, "shetland": 75, "colli": 75, "bouvier": 75, "de": 75, "flandr": 75, "rottweil": 75, "shepherd": 75, "dobermann": 75, "pinscher": 75, "swiss": [75, 89], "mountain": 75, "bernes": 75, "appenzel": 75, "sennenhund": 75, "entlebuch": 75, "boxer": 75, "bullmastiff": 75, "mastiff": 75, "french": 75, "bulldog": 75, "dane": 75, "st": 75, "bernard": 75, "huski": 75, "alaskan": 75, "malamut": 75, "siberian": 75, "dalmatian": 75, "affenpinsch": 75, "basenji": 75, "pug": 75, "leonberg": 75, "newfoundland": 75, "pyrenean": 75, "samoi": 75, "pomeranian": 75, "chow": 75, "keeshond": 75, "griffon": 75, "bruxelloi": 75, "pembrok": 75, "corgi": 75, "cardigan": 75, "poodl": 75, "mexican": 75, "hairless": 75, "tundra": 75, "coyot": 75, "dingo": 75, "dhole": 75, "wild": 75, "hyena": 75, "kit": 75, "arctic": 75, "tabbi": 75, "persian": 75, "siames": 75, "egyptian": 75, "mau": 75, "cougar": 75, "lynx": 75, "leopard": 75, "snow": 75, "jaguar": 75, "cheetah": 75, "brown": [75, 86], "bear": 75, "polar": 75, "sloth": 75, "mongoos": 75, "meerkat": 75, "beetl": 75, "ladybug": 75, "ground": [75, 78, 80, 85], "longhorn": 75, "leaf": 75, "rhinocero": 75, "weevil": 75, "fly": 75, "ant": 75, "grasshopp": 75, "cricket": 75, "stick": 75, "insect": 75, "cockroach": 75, "manti": 75, "cicada": 75, "leafhopp": 75, "lacew": 75, "dragonfli": 75, "damselfli": 75, "admir": 75, "ringlet": 75, "monarch": 75, "butterfli": 75, "gossam": 75, "wing": 75, "starfish": 75, "urchin": 75, "cucumb": 75, "cottontail": 75, "rabbit": 75, "hare": 75, "angora": 75, "hamster": 75, "porcupin": 75, "squirrel": 75, "marmot": 75, "beaver": 75, "guinea": 75, "pig": 75, "sorrel": 75, "zebra": 75, "boar": 75, "warthog": 75, "hippopotamu": 75, "ox": 75, "buffalo": 75, "bison": 75, "bighorn": 75, "sheep": 75, "alpin": 75, "ibex": 75, "hartebeest": 75, "impala": 75, "gazel": 75, "dromedari": 75, "llama": 75, "weasel": 75, "mink": 75, "polecat": 75, "foot": 75, "ferret": 75, "otter": 75, "skunk": 75, "badger": 75, "armadillo": 75, "toed": 75, "orangutan": 75, "gorilla": 75, "chimpanze": 75, "gibbon": 75, "siamang": 75, "guenon": 75, "pata": 75, "monkei": 75, "baboon": 75, "macaqu": 75, "langur": 75, "colobu": 75, "probosci": 75, "marmoset": 75, "capuchin": 75, "howler": 75, "titi": 75, "geoffroi": 75, "lemur": 75, "indri": 75, "asian": 75, "eleph": 75, "bush": 75, "snoek": 75, "eel": 75, "coho": 75, "salmon": 75, "beauti": 75, "clownfish": 75, "sturgeon": 75, "garfish": 75, "lionfish": 75, "pufferfish": 75, "abacu": 75, "abaya": 75, "academ": 75, "gown": 75, "accordion": 75, "acoust": 75, "guitar": 75, "aircraft": 75, "carrier": 75, "airlin": 75, "airship": 75, "altar": 75, "ambul": 75, "amphibi": 75, "clock": [75, 89], "apiari": 75, "apron": 75, "wast": 75, "assault": 75, "rifl": 75, "backpack": 75, "bakeri": 75, "balanc": 75, "beam": 75, "balloon": 75, "ballpoint": 75, "pen": 75, "aid": 75, "banjo": 75, "balust": 75, "barbel": 75, "barber": 75, "chair": [75, 82], "barbershop": 75, "baromet": 75, "barrel": 75, "wheelbarrow": 75, "basebal": 75, "basketbal": 75, "bassinet": 75, "bassoon": 75, "swim": 75, "cap": 75, "bath": 75, "towel": 75, "bathtub": 75, "station": 75, "wagon": 75, "lighthous": 75, "beaker": 75, "militari": 75, "beer": 75, "bottl": 75, "glass": 75, "bell": 75, "cot": 75, "bib": 75, "bicycl": [75, 86], "bikini": 75, "binder": 75, "binocular": 75, "birdhous": 75, "boathous": 75, "bobsleigh": 75, "bolo": 75, "tie": 75, "poke": 75, "bonnet": 75, "bookcas": 75, "bookstor": 75, "bow": 75, "brass": 75, "bra": 75, "breakwat": 75, "breastplat": 75, "broom": 75, "bucket": 75, "buckl": 75, "bulletproof": 75, "vest": 75, "butcher": 75, "shop": 75, "taxicab": 75, "cauldron": 75, "candl": 75, "cannon": 75, "cano": 75, "mirror": [75, 82], "carousel": 75, "tool": [75, 78, 80], "carton": 75, "wheel": 75, "teller": 75, "cassett": 75, "player": 75, "castl": 75, "catamaran": 75, "cd": 75, "cello": 75, "mobil": [75, 89], "chain": 75, "fenc": [75, 86], "mail": 75, "chainsaw": 75, "chest": 75, "chiffoni": 75, "chime": 75, "china": 75, "cabinet": 75, "christma": 75, "stock": 75, "church": 75, "movi": 75, "theater": 75, "cleaver": 75, "cliff": 75, "dwell": 75, "cloak": 75, "clog": 75, "cocktail": 75, "shaker": 75, "coffe": 75, "mug": 75, "coffeemak": 75, "coil": 75, "lock": 75, "keyboard": 75, "confectioneri": 75, "ship": [75, 83], "corkscrew": 75, "cornet": 75, "cowboi": 75, "boot": 75, "hat": 75, "cradl": 75, "crash": 75, "helmet": 75, "crate": 75, "infant": 75, "bed": 75, "crock": 75, "pot": 75, "croquet": 75, "crutch": 75, "cuirass": 75, "dam": 75, "desk": 75, "desktop": 75, "rotari": 75, "dial": 75, "telephon": 75, "diaper": 75, "watch": 75, "dine": 75, "dishcloth": 75, "dishwash": 75, "disc": 75, "brake": 75, "dock": 75, "sled": 75, "dome": 75, "doormat": 75, "drill": 75, "rig": 75, "drum": 75, "drumstick": 75, "dumbbel": 75, "dutch": 75, "oven": 75, "fan": 75, "locomot": 75, "entertain": 75, "center": 75, "envelop": 75, "espresso": 75, "powder": 75, "feather": 75, "fireboat": 75, "engin": [75, 86], "screen": 75, "sheet": 75, "flagpol": 75, "flute": 75, "footbal": 75, "forklift": 75, "fountain": 75, "poster": 75, "freight": 75, "fry": 75, "pan": 75, "fur": 75, "garbag": 75, "ga": 75, "pump": 75, "goblet": 75, "kart": 75, "golf": 75, "cart": 75, "gondola": 75, "gong": 75, "grand": 75, "piano": 75, "greenhous": 75, "grill": 75, "groceri": 75, "guillotin": 75, "barrett": 75, "hair": 75, "sprai": 75, "hammer": 75, "dryer": 75, "hand": [75, 78], "handkerchief": 75, "drive": 75, "harmonica": 75, "harp": 75, "harvest": 75, "hatchet": 75, "holster": 75, "honeycomb": 75, "hoop": 75, "skirt": 75, "horizont": 75, "bar": 75, "hors": [75, 83, 88], "drawn": 75, "hourglass": 75, "ipod": 75, "cloth": 75, "iron": 75, "jack": 75, "lantern": 75, "jean": 75, "jeep": 75, "shirt": [75, 77], "jigsaw": 75, "puzzl": 75, "pull": 75, "rickshaw": 75, "joystick": 75, "kimono": 75, "knee": 75, "pad": 75, "knot": 75, "ladl": 75, "lampshad": 75, "laptop": 75, "lawn": 75, "mower": 75, "knife": 75, "lifeboat": 75, "lighter": 75, "limousin": 75, "ocean": 75, "liner": 75, "lipstick": 75, "slip": 75, "shoe": 75, "lotion": 75, "speaker": 75, "loup": 75, "sawmil": 75, "magnet": 75, "compass": 75, "bag": [75, 77, 83, 84], "mailbox": 75, "tight": 75, "tank": 75, "manhol": 75, "maraca": 75, "marimba": 75, "maypol": 75, "maze": 75, "cup": [75, 82], "medicin": 75, "megalith": 75, "microphon": 75, "microwav": 75, "milk": 75, "minibu": 75, "miniskirt": 75, "minivan": 75, "missil": 75, "mitten": 75, "mix": 75, "bowl": 75, "modem": 75, "monasteri": 75, "monitor": 75, "mope": 75, "mortar": 75, "mosqu": 75, "mosquito": 75, "scooter": 75, "bike": 75, "tent": 75, "mous": [75, 76], "mousetrap": 75, "van": 75, "muzzl": 75, "nail": 75, "brace": 75, "necklac": 75, "nippl": 75, "obelisk": 75, "obo": 75, "ocarina": 75, "odomet": 75, "oil": 75, "oscilloscop": 75, "overskirt": 75, "bullock": 75, "oxygen": 75, "packet": 75, "paddl": 75, "padlock": 75, "paintbrush": 75, "pajama": 75, "palac": [75, 89], "parachut": 75, "park": 75, "bench": 75, "meter": 75, "passeng": 75, "patio": 75, "payphon": 75, "pedest": 75, "pencil": 75, "perfum": 75, "petri": 75, "dish": 75, "photocopi": 75, "plectrum": 75, "pickelhaub": 75, "picket": 75, "pickup": 75, "pier": 75, "piggi": 75, "pill": 75, "pillow": 75, "ping": 75, "pong": 75, "pinwheel": 75, "pirat": 75, "pitcher": 75, "plane": 75, "planetarium": 75, "plastic": 75, "plate": 75, "rack": 75, "plow": 75, "plunger": 75, "polaroid": 75, "camera": 75, "pole": [75, 86], "polic": 75, "poncho": 75, "billiard": 75, "soda": 75, "potter": 75, "power": [75, 78, 89], "prayer": 75, "rug": 75, "printer": 75, "prison": 75, "projectil": 75, "projector": 75, "hockei": 75, "puck": 75, "punch": 75, "purs": 75, "quill": 75, "quilt": 75, "race": 75, "racket": 75, "radiat": 75, "radio": 75, "telescop": 75, "rain": 75, "recreat": 75, "reel": 75, "reflex": 75, "refriger": 75, "remot": 75, "restaur": 75, "revolv": 75, "rotisseri": 75, "eras": 75, "rugbi": 75, "ruler": 75, "safe": 75, "safeti": 75, "salt": 75, "sandal": [75, 77], "sarong": 75, "saxophon": 75, "scabbard": 75, "school": 75, "bu": [75, 86], "schooner": 75, "scoreboard": 75, "crt": 75, "screw": 75, "screwdriv": 75, "seat": 75, "belt": 75, "sew": 75, "shield": 75, "shoji": 75, "basket": 75, "shovel": 75, "shower": 75, "curtain": 75, "ski": 75, "sleep": 75, "door": 75, "slot": 75, "snorkel": 75, "snowmobil": 75, "snowplow": 75, "soap": 75, "dispens": 75, "soccer": [75, 89], "sock": 75, "solar": 75, "thermal": 75, "collector": 75, "sombrero": 75, "soup": 75, "heater": 75, "shuttl": 75, "spatula": 75, "motorboat": 75, "web": 75, "spindl": 75, "sport": [75, 89], "spotlight": 75, "stage": 75, "steam": 75, "arch": 75, "bridg": 75, "steel": 75, "stethoscop": 75, "scarf": 75, "stone": 75, "wall": [75, 86], "stopwatch": 75, "stove": 75, "strainer": 75, "tram": 75, "stretcher": 75, "couch": 75, "stupa": 75, "submarin": 75, "sundial": 75, "sunglass": 75, "sunscreen": 75, "suspens": 75, "mop": 75, "sweatshirt": 75, "swimsuit": 75, "swing": 75, "switch": 75, "syring": 75, "lamp": 75, "tape": 75, "teapot": 75, "teddi": 75, "televis": [75, 89], "tenni": 75, "thatch": 75, "roof": 75, "front": 75, "thimbl": 75, "thresh": 75, "throne": 75, "tile": 75, "toaster": 75, "tobacco": 75, "toilet": 75, "totem": 75, "tow": 75, "tractor": 75, "semi": 75, "trailer": 75, "trai": 75, "trench": 75, "tricycl": 75, "trimaran": 75, "tripod": 75, "triumphal": 75, "trolleybu": 75, "trombon": 75, "tub": 75, "turnstil": 75, "typewrit": 75, "umbrella": 75, "unicycl": 75, "upright": 75, "vacuum": 75, "cleaner": 75, "vase": 75, "vault": 75, "velvet": 75, "vend": 75, "vestment": 75, "viaduct": 75, "violin": 75, "volleybal": 75, "waffl": 75, "wallet": 75, "wardrob": 75, "sink": 75, "wash": 75, "jug": 75, "tower": 75, "whiskei": 75, "whistl": 75, "wig": 75, "shade": [75, 86], "windsor": 75, "wine": 75, "wok": 75, "wooden": 75, "spoon": 75, "wool": 75, "rail": 75, "shipwreck": 75, "yawl": 75, "yurt": 75, "websit": 75, "comic": 75, "book": 75, "crossword": 75, "traffic": [75, 82, 86], "sign": [75, 86, 89], "light": [75, 77, 82, 86], "dust": 75, "jacket": [75, 82], "menu": 75, "guacamol": 75, "consomm": 75, "trifl": 75, "ic": 75, "cream": 75, "pop": 75, "baguett": 75, "bagel": 75, "pretzel": 75, "cheeseburg": 75, "mash": 75, "potato": 75, "cabbag": 75, "broccoli": 75, "cauliflow": 75, "zucchini": 75, "spaghetti": 75, "squash": 75, "acorn": 75, "butternut": 75, "artichok": 75, "pepper": 75, "cardoon": 75, "mushroom": 75, "granni": 75, "smith": 75, "strawberri": 75, "orang": 75, "lemon": 75, "pineappl": 75, "banana": 75, "jackfruit": 75, "custard": 75, "appl": 75, "pomegran": 75, "hai": 75, "carbonara": 75, "chocol": 75, "syrup": 75, "dough": 75, "meatloaf": 75, "pizza": 75, "pie": 75, "burrito": 75, "eggnog": 75, "alp": 75, "bubbl": 75, "reef": 75, "geyser": 75, "lakeshor": 75, "promontori": 75, "shoal": 75, "seashor": 75, "vallei": 75, "volcano": 75, "bridegroom": 75, "scuba": 75, "diver": 75, "rapese": 75, "daisi": 75, "ladi": 75, "slipper": 75, "corn": 75, "rose": 75, "hip": 75, "chestnut": 75, "fungu": 75, "agar": 75, "gyromitra": 75, "stinkhorn": 75, "earth": 75, "star": 75, "wood": 75, "bolet": 75, "ear": 75, "cifar10_test_set": 75, "airplan": [75, 83], "automobil": [75, 83], "deer": [75, 83], "cifar100_test_set": 75, "aquarium_fish": 75, "babi": 75, "boi": 75, "camel": 75, "caterpillar": 75, "cattl": [75, 89], "cloud": 75, "dinosaur": 75, "dolphin": 75, "flatfish": 75, "forest": 75, "girl": 75, "kangaroo": 75, "lawn_mow": 75, "man": 75, "maple_tre": 75, "motorcycl": [75, 86], "oak_tre": 75, "orchid": 75, "palm_tre": 75, "pear": 75, "pickup_truck": 75, "pine_tre": 75, "plain": 75, "poppi": 75, "possum": 75, "raccoon": 75, "road": [75, 86], "rocket": 75, "seal": 75, "shrew": 75, "skyscrap": 75, "streetcar": 75, "sunflow": 75, "sweet_pepp": 75, "trout": 75, "tulip": 75, "willow_tre": 75, "woman": [75, 82], "caltech256": 75, "ak47": 75, "bat": 75, "glove": 75, "birdbath": 75, "blimp": 75, "bonsai": 75, "boom": 75, "breadmak": 75, "buddha": 75, "bulldoz": 75, "cactu": 75, "cake": 75, "tire": 75, "cartman": 75, "cereal": 75, "chandeli": 75, "chess": 75, "board": 75, "chimp": 75, "chopstick": 75, "coffin": 75, "coin": 75, "comet": 75, "cormor": 75, "globe": 75, "diamond": 75, "dice": 75, "doorknob": 75, "drink": 75, "straw": 75, "dumb": 75, "eiffel": 75, "elk": 75, "ewer": 75, "eyeglass": 75, "fern": 75, "fighter": 75, "jet": [75, 85], "extinguish": 75, "hydrant": 75, "firework": 75, "flashlight": 75, "floppi": 75, "fri": 75, "frisbe": 75, "galaxi": 75, "giraff": 75, "goat": 75, "gate": 75, "grape": 75, "pick": 75, "hamburg": 75, "hammock": 75, "harpsichord": 75, "hawksbil": 75, "helicopt": 75, "hibiscu": 75, "homer": 75, "simpson": 75, "horsesho": 75, "air": 75, "skeleton": 75, "ibi": 75, "cone": 75, "iri": 75, "jesu": 75, "christ": 75, "joi": 75, "kayak": 75, "ketch": 75, "ladder": 75, "lath": 75, "licens": 75, "lightbulb": 75, "lightn": 75, "mandolin": 75, "mar": 75, "mattress": 75, "megaphon": 75, "menorah": 75, "microscop": 75, "minaret": 75, "minotaur": 75, "motorbik": 75, "mussel": 75, "neckti": 75, "octopu": 75, "palm": 75, "pilot": 75, "paperclip": 75, "shredder": 75, "pci": 75, "peopl": [75, 82], "pez": 75, "picnic": 75, "pram": 75, "prai": 75, "pyramid": 75, "rainbow": 75, "roulett": 75, "saddl": 75, "saturn": 75, "segwai": 75, "propel": 75, "sextant": 75, "music": 75, "skateboard": 75, "smokestack": 75, "sneaker": 75, "boat": 75, "stain": 75, "steer": 75, "stirrup": 75, "superman": 75, "sushi": 75, "armi": [75, 89], "sword": 75, "tambourin": 75, "teepe": 75, "court": 75, "theodolit": 75, "tomato": 75, "tombston": 75, "tour": 75, "pisa": 75, "treadmil": 75, "fork": 75, "tweezer": 75, "unicorn": 75, "vcr": 75, "waterfal": 75, "watermelon": 75, "weld": 75, "windmil": 75, "xylophon": 75, "yarmulk": 75, "yo": 75, "toad": 75, "twenty_news_test_set": 75, "alt": 75, "atheism": 75, "comp": 75, "graphic": [75, 86], "misc": [75, 89], "sy": 75, "ibm": 75, "pc": 75, "hardwar": 75, "mac": 75, "forsal": 75, "rec": 75, "sci": 75, "crypt": 75, "electron": 75, "med": 75, "soc": 75, "religion": 75, "christian": [75, 89], "talk": [75, 89], "polit": 75, "gun": 75, "mideast": 75, "amazon": 75, "neutral": 75, "imdb_test_set": 75, "all_class": 75, "20news_test_set": 75, "_load_classes_predprobs_label": 75, "dataset_nam": 75, "labelerror": 75, "url_bas": 75, "5392f6c71473055060be3044becdde1cbc18284d": 75, "url_label": 75, "original_test_label": 75, "_original_label": 75, "url_prob": 75, "cross_validated_predicted_prob": 75, "_pyx": 75, "num_part": 75, "datatset": 75, "bytesio": 75, "allow_pickl": 75, "pred_probs_part": 75, "url": 75, "_of_": 75, "nload": 75, "imdb": 75, "ve": [75, 78, 80, 82], "interpret": [75, 76, 78], "capit": 75, "29780": 75, "256": [75, 76, 82], "29": [75, 77, 80, 81, 82, 86, 89], "780": 75, "medic": [75, 89], "doctor": 75, "254": [75, 82], "359223": 75, "333333": 75, "640777": 75, "184": [75, 78], "258427": 75, "341176": 75, "263158": 75, "658824": 75, "337349": 75, "246575": 75, "662651": 75, "248": 75, "330000": 75, "355769": 75, "670000": 75, "251": [75, 82], "167": [75, 78, 82], "252": 75, "112": 75, "253": [75, 82], "022989": 75, "255": [75, 77], "049505": 75, "190": [75, 78, 82], "66": 75, "002216": 75, "000974": 75, "59": [75, 82, 89], "88": [75, 77, 78, 81, 82, 85], "000873": 75, "000739": 75, "79": [75, 82, 87], "32635": 75, "32636": 75, "47": [75, 82], "32637": 75, "32638": 75, "32639": 75, "32640": 75, "051": 75, "93": [75, 82, 85, 87], "002242": 75, "997758": 75, "002088": 75, "001045": 75, "997912": 75, "002053": 75, "997947": 75, "001980": 75, "000991": 75, "998020": 75, "001946": 75, "002915": 75, "998054": 75, "001938": 75, "002904": 75, "998062": 75, "001020": 75, "998980": 75, "001018": 75, "002035": 75, "998982": 75, "999009": 75, "0003": 75, "0002": 75, "36": [75, 89], "44": [75, 81, 82], "71": [75, 78, 82], "071": 75, "067269": 75, "929": 75, "046": 75, "058243": 75, "954": 75, "035": 75, "032096": 75, "965": 75, "031": 75, "012232": 75, "969": 75, "022": 75, "025896": 75, "978": 75, "020": [75, 78], "013092": 75, "018": 75, "013065": 75, "016": 75, "030542": 75, "984": 75, "013": 75, "020833": 75, "987": 75, "012": 75, "010020": 75, "988": 75, "0073": 75, "0020": 75, "0016": 75, "0015": 75, "0013": 75, "0012": 75, "0010": 75, "0008": 75, "0007": 75, "0006": 75, "0005": 75, "0004": 75, "244": [75, 82], "98": [75, 76, 85], "452381": 75, "459770": 75, "72": [75, 78, 81, 85], "523364": 75, "460784": 75, "446602": 75, "57": [75, 78], "68": [75, 78, 82, 87], "103774": 75, "030612": 75, "97": [75, 76, 78, 82, 85, 87, 89], "110092": 75, "049020": 75, "99": [75, 78, 87], "0034": 75, "0032": 75, "0026": 75, "0025": 75, "4945": 75, "4946": 75, "4947": 75, "4948": 75, "4949": 75, "4950": 75, "846": 75, "82": [75, 78, 82], "7532": 75, "532": 75, "034483": 75, "009646": 75, "965517": 75, "030457": 75, "020513": 75, "969543": 75, "028061": 75, "035443": 75, "971939": 75, "025316": 75, "005168": 75, "974684": 75, "049751": 75, "979487": 75, "019920": 75, "042802": 75, "980080": 75, "017677": 75, "005115": 75, "982323": 75, "012987": 75, "005236": 75, "987013": 75, "012723": 75, "025126": 75, "987277": 75, "010989": 75, "008264": 75, "989011": 75, "010283": 75, "027778": 75, "989717": 75, "009677": 75, "990323": 75, "007614": 75, "010127": 75, "992386": 75, "005051": 75, "994949": 75, "005025": 75, "994975": 75, "005013": 75, "994987": 75, "001859": 75, "001328": 75, "000929": 75, "000664": 75, "186": [75, 78], "188": [75, 78, 81], "189": [75, 78], "snippet": 76, "nlp": [76, 89], "mind": [76, 78], "number_of_class": 76, "total_number_of_data_point": 76, "drop": [76, 80, 85, 88], "feed": 76, "alphabet": 76, "labels_proper_format": 76, "your_classifi": 76, "issues_datafram": 76, "class_predicted_for_flagged_exampl": 76, "class_predicted_for_all_exampl": 76, "grant": 76, "datataset": 76, "fair": [76, 78], "game": 76, "speedup": [76, 83], "flexibl": 76, "tempfil": 76, "mkdtemp": 76, "sped": 76, "anywai": 76, "pred_probs_merg": 76, "merge_rare_class": 76, "count_threshold": 76, "class_mapping_orig2new": 76, "heath_summari": 76, "num_examples_per_class": 76, "rare_class": 76, "num_classes_merg": 76, "other_class": 76, "labels_merg": 76, "new_c": 76, "merged_prob": 76, "keepdim": 76, "hstack": [76, 77, 78, 80], "new_class": 76, "original_class": 76, "num_check": 76, "ones_array_ref": 76, "isclos": 76, "though": [76, 78, 89], "successfulli": 76, "meaning": [76, 83], "virtuou": [76, 80], "cycl": [76, 80], "jointli": 76, "junk": 76, "clutter": 76, "unknown": 76, "caltech": 76, "intersect": 76, "combined_boolean_mask": 76, "mask1": 76, "mask2": 76, "gradientboostingclassifi": [76, 78], "true_error": [76, 78, 81], "101": [76, 82], "102": [76, 81, 82], "104": [76, 78, 82], "model_to_find_error": 76, "model_to_return": 76, "cl0": 76, "randomizedsearchcv": 76, "expens": 76, "param_distribut": 76, "learning_r": [76, 78], "max_depth": [76, 78], "magnitud": 76, "coeffici": [76, 85], "optin": 76, "environ": [76, 78], "rerun": [76, 78], "cell": [76, 78], "On": [76, 78, 82], "unabl": [76, 78], "render": [76, 78], "nbviewer": [76, 78], "cleanlearningcleanlearn": [76, 78], "linearregressionlinearregress": 76, "assist": 76, "streamlin": 76, "ux": 76, "agpl": 76, "compani": 76, "commerci": 76, "alter": 76, "email": 76, "discuss": 76, "anywher": 76, "60": [77, 78], "excess": 77, "torchvis": [77, 83], "tensordataset": 77, "stratifiedkfold": [77, 81], "tqdm": 77, "fashion_mnist": 77, "num_row": 77, "60000": 77, "pil": 77, "transformed_dataset": 77, "with_format": 77, "unsqueez": 77, "num_proc": 77, "cpu_count": 77, "torch_dataset": 77, "quick": [77, 81], "super": 77, "relu": 77, "batchnorm2d": 77, "maxpool2d": 77, "lazylinear": 77, "flatten": 77, "get_test_accuraci": 77, "testload": [77, 83], "energi": 77, "trainload": [77, 83], "n_epoch": 77, "patienc": 77, "criterion": 77, "crossentropyloss": 77, "adamw": 77, "best_test_accuraci": 77, "start_epoch": 77, "running_loss": 77, "best_epoch": 77, "end_epoch": 77, "3f": [77, 85], "acc": [77, 78], "time_taken": 77, "compute_embed": 77, "compute_pred_prob": 77, "train_batch_s": 77, "num_work": 77, "worker": [77, 89], "train_id_list": 77, "test_id_list": 77, "train_id": 77, "test_id": 77, "embeddings_model": 77, "ntrain": 77, "trainset": 77, "testset": 77, "pin_memori": 77, "fold_embed": 77, "fold_pred_prob": 77, "finish": 77, "483": 77, "835": 77, "542": 77, "331": 77, "310": 77, "524": 77, "83it": 77, "62": [77, 78, 82, 85], "99it": 77, "492": 77, "87": [77, 82, 85, 88], "085": 77, "852": 77, "330": [77, 82], "290": [77, 82], "727": 77, "78it": 77, "476": 77, "305": [77, 85], "868": 77, "328": [77, 82], "335": 77, "710": 77, "52it": 77, "19it": 77, "reorder": 77, "vision": 77, "low_inform": 77, "odd_aspect_ratio": 77, "odd_siz": 77, "grayscal": 77, "exce": 77, "max_preval": 77, "7620": 77, "3692": 77, "3521": 77, "225": [77, 81], "166": [77, 89], "9661": 77, "40378": 77, "687452": 77, "54473": 77, "705050": 77, "29412": 77, "715470": 77, "25316": 77, "716273": 77, "52247": 77, "725283": 77, "9581": 77, "19228": 77, "dress": 77, "54078": 77, "000010": 77, "pullov": 77, "32657": 77, "21282": 77, "000011": 77, "11262": 77, "000014": 77, "0268": 77, "30659": 77, "000015": 77, "30968": 77, "258": 77, "000017": 77, "9762": 77, "54565": 77, "47139": 77, "000026": 77, "7834": 77, "42819": 77, "629362": 77, "51431": 77, "654330": 77, "55548": 77, "658364": 77, "51191": 77, "668572": 77, "50081": 77, "669703": 77, "7834321613629787": 77, "110901": 77, "974390": 77, "998733": 77, "937117": 77, "998755": 77, "53564": 77, "5473": 77, "trouser": 77, "plot_label_issue_exampl": 77, "ncol": [77, 83], "nrow": [77, 83], "ceil": 77, "axes_list": 77, "label_issue_indic": 77, "gl": 77, "sl": 77, "fontdict": 77, "imshow": [77, 83], "cmap": [77, 85], "grai": 77, "subplots_adjust": 77, "hspace": 77, "outsiz": 77, "outlier_issues_df": 77, "depict": [77, 81, 82, 83, 84, 86], "plot_outlier_issues_exampl": 77, "n_comparison_imag": 77, "sample_from_class": 77, "number_of_sampl": 77, "non_outlier_indic": 77, "isnul": 77, "non_outlier_indices_excluding_curr": 77, "sampled_indic": 77, "label_scores_of_sampl": 77, "top_score_indic": 77, "top_label_indic": 77, "sampled_imag": 77, "get_image_given_label_and_sampl": 77, "image_from_dataset": 77, "corresponding_label": 77, "comparison_imag": 77, "images_to_plot": 77, "idlist": 77, "iterrow": 77, "especi": [77, 85, 87, 88], "near_duplicate_issu": 77, "closest": 77, "counterpart": 77, "near_duplicate_issues_df": 77, "plot_near_duplicate_issue_exampl": 77, "seen_id_pair": 77, "get_image_and_given_label_and_predicted_label": 77, "duplicate_imag": 77, "nd_set": 77, "challeng": 77, "dark_issu": 77, "reveal": [77, 86], "dark_scor": 77, "dark_issues_df": 77, "is_dark_issu": 77, "34848": 77, "203922": 77, "50270": 77, "204588": 77, "3936": 77, "213098": 77, "733": 77, "217686": 77, "8094": 77, "230118": 77, "plot_image_issue_exampl": 77, "difficult": 77, "disproportion": 77, "lowinfo_issu": 77, "low_information_scor": 77, "lowinfo_issues_df": 77, "is_low_information_issu": 77, "53050": 77, "067975": 77, "40875": 77, "089929": 77, "9594": 77, "092601": 77, "34825": 77, "107744": 77, "37530": 77, "108516": 77, "lot": 77, "depth": 78, "survei": [78, 89], "focus": [78, 80], "scienc": 78, "multivariate_norm": [78, 80, 81], "make_data": [78, 80], "cov": [78, 80, 81], "avg_trac": [78, 81], "test_label": [78, 81, 83, 88], "py_tru": 78, "noise_matrix_tru": 78, "noise_marix": 78, "s_test": 78, "noisy_test_label": 78, "purpl": 78, "val": 78, "namespac": 78, "exec": 78, "markerfacecolor": [78, 81], "markeredgecolor": [78, 81, 85], "markers": [78, 81, 85], "markeredgewidth": [78, 81, 85], "realist": 78, "7560": 78, "638483e": 78, "897052e": 78, "548986e": 78, "924634e": 78, "374580e": 78, "4643": 78, "050286": 78, "065420": 78, "249": [78, 82, 89], "109420": 78, "111687": 78, "115403": 78, "3312": 78, "007136": 78, "119": [78, 82], "033725": 78, "103": [78, 82], "033738": 78, "238": [78, 82], "037825": 78, "236": [78, 82], "037843": 78, "222": 78, "614915": 78, "122": [78, 82], "624422": 78, "625965": 78, "626079": 78, "118": 78, "627675": 78, "695174": 78, "323529": 78, "522929": 78, "013722": 78, "675606": 78, "646438": 78, "anyth": 78, "enhanc": [78, 80, 82], "magic": 78, "83": [78, 82, 85, 87, 89], "liter": 78, "identif": 78, "x27": 78, "logisticregressionlogisticregress": 78, "ever": 78, "truth": [78, 80, 85], "092": 78, "040": 78, "024": 78, "004": 78, "surpris": 78, "arxiv": 78, "ab": 78, "1705": 78, "01936": 78, "ton": 78, "yourfavoritemodel1": 78, "merged_label": 78, "merged_test_label": 78, "newli": [78, 80], "yourfavoritemodel2": 78, "yourfavoritemodel3": 78, "cl3": 78, "takeawai": 78, "That": [78, 81], "randomli": 78, "my_test_pred_prob": 78, "my_test_pr": 78, "issues_test": 78, "corrected_test_label": 78, "pretend": 78, "cl_test_pr": 78, "69": [78, 85], "fairli": 78, "label_acc": 78, "percentag": 78, "offset": 78, "nquestion": 78, "overestim": 78, "answer": 78, "experienc": 78, "06": [78, 82, 89], "76": [78, 81, 82, 85, 87], "knowledg": 78, "quantiti": [78, 85], "prioiri": 78, "known": 78, "versatil": 78, "label_issues_indic": 78, "213": [78, 82], "212": [78, 87], "218": [78, 82], "152": 78, "197": [78, 82], "196": [78, 82], "170": 78, "214": 78, "164": [78, 81], "198": [78, 82], "191": [78, 82], "63": [78, 82], "121": [78, 88, 89], "117": [78, 85], "206": [78, 82], "115": [78, 82], "193": 78, "194": 78, "201": [78, 82], "174": 78, "163": 78, "150": [78, 80, 82, 89], "169": [78, 89], "151": [78, 82], "precision_scor": 78, "recall_scor": 78, "f1_score": 78, "true_label_issu": 78, "filter_by_list": 78, "718750": [78, 80], "807018": 78, "912": 78, "733333": 78, "800000": 78, "721311": 78, "792793": 78, "908": 78, "676923": 78, "765217": 78, "892": 78, "567901": 78, "702290": 78, "844": 78, "gaug": 78, "label_issues_count": 78, "155": [78, 82], "156": 78, "172": [78, 81], "easiest": 78, "modular": 78, "penalti": 78, "l2": 78, "model3": 78, "n_estim": 78, "cv_pred_probs_1": 78, "cv_pred_probs_2": 78, "cv_pred_probs_3": 78, "label_quality_scores_best": 78, "cv_pred_probs_ensembl": 78, "label_quality_scores_bett": 78, "superior": [78, 84], "workflow": [79, 85], "speechbrain": 79, "timm": 79, "glad": 80, "multiannotator_label": 80, "300": [80, 89], "noisier": 80, "111": [80, 85], "local_data": [80, 81], "true_labels_train": [80, 81], "noise_matrix_bett": 80, "noise_matrix_wors": 80, "transpos": [80, 83], "dropna": 80, "zfill": 80, "row_na_check": 80, "notna": 80, "reset_index": 80, "a0001": 80, "a0002": 80, "a0003": 80, "a0004": 80, "a0005": 80, "a0006": 80, "a0007": 80, "a0008": 80, "a0009": 80, "a0010": 80, "a0041": 80, "a0042": 80, "a0043": 80, "a0044": 80, "a0045": 80, "a0046": 80, "a0047": 80, "a0048": 80, "a0049": 80, "a0050": 80, "na": 80, "60856743": 80, "41693214": 80, "40908785": 80, "87147629": 80, "64941785": 80, "10774851": 80, "0524466": 80, "71853246": 80, "37169848": 80, "66031048": 80, "multiannotator_util": 80, "crude": 80, "straight": 80, "majority_vote_label": 80, "736157": 80, "757738": 80, "782255": 80, "715585": 80, "824273": 80, "quality_annotator_a0001": 80, "quality_annotator_a0002": 80, "quality_annotator_a0003": 80, "quality_annotator_a0004": 80, "quality_annotator_a0005": 80, "quality_annotator_a0006": 80, "quality_annotator_a0007": 80, "quality_annotator_a0008": 80, "quality_annotator_a0009": 80, "quality_annotator_a0010": 80, "quality_annotator_a0041": 80, "quality_annotator_a0042": 80, "quality_annotator_a0043": 80, "quality_annotator_a0044": 80, "quality_annotator_a0045": 80, "quality_annotator_a0046": 80, "quality_annotator_a0047": 80, "quality_annotator_a0048": 80, "quality_annotator_a0049": 80, "quality_annotator_a0050": 80, "070551": 80, "216064": 80, "119178": 80, "alongisd": 80, "244982": 80, "208333": 80, "295978": 80, "294118": 80, "324194": 80, "310345": 80, "355315": 80, "346154": 80, "439728": 80, "480000": 80, "a0031": 80, "523205": 80, "580645": 80, "a0034": 80, "535313": 80, "607143": 80, "a0021": 80, "607002": 80, "a0015": 80, "609527": 80, "678571": 80, "a0011": 80, "621101": 80, "692308": 80, "wors": 80, "improved_consensus_label": 80, "majority_vote_accuraci": 80, "cleanlab_label_accuraci": 80, "8581081081081081": 80, "9797297297297297": 80, "besid": 80, "sorted_consensus_quality_scor": 80, "worst_qual": 80, "better_qu": 80, "worst_quality_accuraci": 80, "better_quality_accuraci": 80, "9893238434163701": 80, "improved_pred_prob": 80, "treat": [80, 81, 85, 89], "analzi": 80, "copyright": 81, "advertis": 81, "violenc": 81, "nsfw": 81, "ranked_label_issu": [81, 87, 88], "multioutput": 81, "multioutputclassifi": 81, "celeba": 81, "make_multilabel_data": 81, "boxes_coordin": 81, "box_multilabel": 81, "make_multi": 81, "bx1": 81, "by1": 81, "bx2": 81, "by2": 81, "label_list": 81, "ur": 81, "upper": 81, "inidx": 81, "logical_and": 81, "tolist": 81, "inv_d": 81, "labels_idx": 81, "true_labels_test": 81, "dict_unique_label": 81, "get_color_arrai": 81, "dcolor": 81, "aa4400": 81, "55227f": 81, "55a100": 81, "00ff00": 81, "007f7f": 81, "386b55": 81, "0000ff": 81, "simplic": 81, "advis": 81, "y_onehot": 81, "single_class_label": 81, "stratifi": [81, 84], "kf": 81, "train_index": 81, "test_index": 81, "clf_cv": 81, "x_train_cv": 81, "x_test_cv": 81, "y_train_cv": 81, "y_test_cv": 81, "y_pred_cv": 81, "saw": 81, "num_to_displai": 81, "09": [81, 82], "275": 81, "267": 81, "171": 81, "234": 81, "165": 81, "227": [81, 82], "262": [81, 82], "263": [81, 82], "266": [81, 82], "139": 81, "143": [81, 82], "216": [81, 82, 89], "265": 81, "159": [81, 82], "despit": [81, 89], "suspect": 81, "888": 81, "8224": 81, "9632": 81, "968": 81, "6512": 81, "0444": 81, "774": 81, "labels_binary_format": 81, "labels_list_format": 81, "surround": 82, "scene": 82, "coco": 82, "everydai": 82, "has_label_issu": 82, "insal": 82, "nc": [82, 86, 89], "s3": [82, 86, 89], "amazonaw": [82, 86, 89], "objectdetectionbenchmark": 82, "tutorial_obj": 82, "pkl": 82, "example_imag": 82, "unzip": [82, 89], "begin": 82, "detectron2": 82, "image_path": 82, "rb": 82, "image_to_visu": 82, "seg_map": 82, "334": 82, "float32": 82, "bboxes_ignor": 82, "286": 82, "285": 82, "224": 82, "231": 82, "293": 82, "235": 82, "289": [82, 85], "282": 82, "74": [82, 85, 87], "281": 82, "271": 82, "280": 82, "277": 82, "279": 82, "287": 82, "299": 82, "276": 82, "307": 82, "321": 82, "326": 82, "333": 82, "261": 82, "319": 82, "257": 82, "295": 82, "283": 82, "243": 82, "303": 82, "316": 82, "247": 82, "323": 82, "226": 82, "228": 82, "232": 82, "219": 82, "239": 82, "240": 82, "209": [82, 89], "242": 82, "202": 82, "230": 82, "215": 82, "220": 82, "229": 82, "85": [82, 85], "217": [82, 89], "237": 82, "207": 82, "204": 82, "205": 82, "223": 82, "153": 82, "149": 82, "140": 82, "124": 82, "268": 82, "273": 82, "108": 82, "284": 82, "110": 82, "136": 82, "145": 82, "173": 82, "317": 82, "192": 82, "329": 82, "332": 82, "324": 82, "203": 82, "320": 82, "314": 82, "199": 82, "291": 82, "000000481413": 82, "jpg": 82, "42398": 82, "44503": 82, "337": [82, 88], "29968": 82, "336": 82, "21005": 82, "9978472": 82, "forgot": 82, "drew": 82, "label_issue_idx": 82, "num_examples_to_show": 82, "113": [82, 85], "candid": 82, "97489622": 82, "70610878": 82, "98764951": 82, "88899237": 82, "99085805": 82, "issue_idx": 82, "95569726e": 82, "03354841e": 82, "57510169e": 82, "58447666e": 82, "39755858e": 82, "suppli": 82, "issue_to_visu": 82, "000000009483": 82, "95569726168054e": 82, "addition": [82, 86], "visibl": 82, "missmatch": 82, "likelei": 82, "agnost": 82, "vaidat": 82, "inconsist": 82, "000000395701": 82, "033548411774308e": 82, "armchair": 82, "tv": 82, "000000154004": 82, "38300759625496356": 82, "foreground": 82, "000000448410": 82, "0008575101690203273": 82, "crowd": 82, "alon": 82, "explor": [82, 83], "resembl": [82, 83], "contribut": 82, "000000499768": 82, "9748962231208227": 82, "000000521141": 82, "8889923658893665": 82, "000000143931": 82, "9876495074395956": 82, "train_feature_embed": 83, "ood_train_feature_scor": 83, "test_feature_embed": 83, "ood_test_feature_scor": 83, "ood_train_predictions_scor": 83, "train_pred_prob": 83, "ood_test_predictions_scor": 83, "test_pred_prob": 83, "pylab": 83, "rcparam": 83, "baggingclassifi": 83, "therebi": 83, "rescal": 83, "transform_norm": 83, "totensor": 83, "root": 83, "animal_class": 83, "non_animal_class": 83, "animal_idx": 83, "isin": 83, "test_idx": 83, "toronto": 83, "edu": 83, "kriz": 83, "5000": 83, "plot_imag": 83, "visualize_outli": 83, "txt_class": 83, "img": [83, 85], "npimg": 83, "show_label": 83, "data_subset": 83, "resnet50": 83, "corpu": 83, "2048": 83, "embed_imag": 83, "create_model": 83, "rwightman": 83, "v0": 83, "rsb": 83, "resnet50_a1_0": 83, "14fe96d1": 83, "pth": 83, "checkpoint": 83, "strang": 83, "odd": 83, "train_ood_features_scor": 83, "top_train_ood_features_idx": 83, "fun": 83, "negat": 83, "homogen": 83, "bottom_train_ood_features_idx": 83, "test_ood_features_scor": 83, "top_ood_features_idx": 83, "inevit": 83, "trade": 83, "5th": 83, "percentil": 83, "fifth_percentil": 83, "plt_rang": 83, "hist": 83, "train_outlier_scor": 83, "ylabel": 83, "axvlin": 83, "test_outlier_scor": 83, "ood_features_indic": 83, "revisit": 83, "unusu": 83, "return_invers": 83, "train_feature_embeddings_sc": 83, "test_feature_embeddings_sc": 83, "train_pred_label": 83, "9702": 83, "train_ood_predictions_scor": 83, "test_ood_predictions_scor": 83, "mainli": [83, 89], "lost": 83, "unsuit": 84, "ok": [84, 89], "convention": 84, "aforement": 84, "hypothet": 84, "contrast": 84, "tradit": 84, "disjoint": 84, "out_of_sample_pred_probs_for_a": 84, "out_of_sample_pred_probs_for_b": 84, "out_of_sample_pred_probs_for_c": 84, "out_of_sample_pred_prob": 84, "price": 85, "incom": 85, "ag": 85, "histgradientboostingregressor": 85, "r2_score": 85, "student_grades_r": 85, "final_scor": 85, "true_final_scor": 85, "homework": 85, "3d": 85, "hue": 85, "mpl_toolkit": 85, "mplot3d": 85, "axes3d": 85, "errors_idx": 85, "add_subplot": 85, "z": 85, "colorbar": 85, "errors_mask": 85, "feature_column": 85, "predicted_column": 85, "x_train_raw": 85, "x_test_raw": 85, "categorical_featur": [85, 87], "randomforestregressor": 85, "629763": 85, "521450": 85, "954607": 85, "547234": 85, "338296": 85, "754531": 85, "619090": 85, "312295": 85, "806626": 85, "784048": 85, "identified_issu": [85, 88], "659": 85, "367": 85, "560": 85, "318": 85, "688": 85, "657": 85, "view_datapoint": 85, "concat": 85, "consum": [85, 88], "baseline_model": [85, 88], "preds_og": 85, "r2_og": 85, "838": 85, "robustli": [85, 87, 88], "acceler": [85, 88], "found_label_issu": 85, "preds_cl": 85, "r2_cl": 85, "925": 85, "effort": [85, 87, 88], "favorit": 85, "64404888e": 85, "06755306e": 85, "05302732e": 85, "66635743e": 85, "53166364e": 85, "synthia": 86, "semantic_segment": 86, "imagesegment": 86, "given_mask": 86, "predicted_mask": 86, "set_printopt": [86, 89], "sky": 86, "sidewalk": 86, "veget": 86, "terrain": 86, "rider": 86, "pred_probs_filepath": 86, "1088": 86, "1920": 86, "label_filepath": 86, "synthia_class": 86, "maunal": 86, "100000": 86, "244800": 86, "system": 86, "leftmost": 86, "area": 86, "middl": [86, 89], "infact": 86, "rightmost": 86, "discrep": 86, "4997436": 86, "179461": 86, "57it": 86, "3263230": 86, "783379": 86, "275110": 86, "255792": 86, "78225": 86, "55990": 86, "54315": 86, "33591": 86, "24645": 86, "21054": 86, "15045": 86, "14171": 86, "13832": 86, "13498": 86, "11490": 86, "9149": 86, "8769": 86, "6999": 86, "6031": 86, "5011": 86, "mistakenli": 86, "class_issu": 86, "aim": [86, 89], "domin": 86, "extratreesclassifi": 87, "extratre": 87, "labelencod": [87, 88], "labels_raw": 87, "interg": [87, 88], "tress": 87, "827": 87, "637": 87, "cheat": 87, "0pt": 87, "233": 87, "labels_train": 87, "labels_test": 87, "acc_og": [87, 88], "783068783068783": 87, "acc_cl": [87, 88], "8095238095238095": 87, "earlier": [88, 89], "raw_label": 88, "raw_train_text": 88, "raw_test_text": 88, "raw_train_label": 88, "raw_test_label": 88, "encond": 88, "train_text": 88, "test_text": 88, "858050": 88, "545854": 88, "826194": 88, "965814": 88, "791923": 88, "646": 88, "390": 88, "628": 88, "702": 88, "863": 88, "135": 88, "735": 88, "print_as_df": 88, "inverse_transform": 88, "fight": 88, "bunch": 89, "conll": 89, "2003": 89, "love": 89, "n_i": 89, "optional_list_of_ordered_class_nam": 89, "deepai": 89, "conll2003": 89, "rm": 89, "tokenclassif": 89, "2023": 89, "2400": 89, "52e0": 89, "1a01": 89, "connect": 89, "443": 89, "await": 89, "982975": 89, "960k": 89, "959": 89, "94k": 89, "kb": 89, "mb": 89, "directori": 89, "inflat": 89, "195": 89, "144": 89, "17045998": 89, "16m": 89, "octet": 89, "26m": 89, "6mb": 89, "bert": 89, "read_npz": 89, "filepath": 89, "corrsespond": 89, "iob2": 89, "given_ent": 89, "entity_map": 89, "readfil": 89, "sep": 89, "startswith": 89, "docstart": 89, "isalpha": 89, "isupp": 89, "indices_to_preview": 89, "nsentenc": 89, "eu": 89, "reject": 89, "boycott": 89, "british": 89, "lamb": 89, "00030412": 89, "00023826": 89, "99936208": 89, "00007009": 89, "00002545": 89, "99998795": 89, "00000401": 89, "00000218": 89, "00000455": 89, "00000131": 89, "00000749": 89, "99996115": 89, "00001371": 89, "0000087": 89, "00000895": 89, "99998936": 89, "00000382": 89, "00000178": 89, "00000366": 89, "00000137": 89, "99999101": 89, "00000266": 89, "00000174": 89, "0000035": 89, "00000109": 89, "99998768": 89, "00000482": 89, "00000202": 89, "00000438": 89, "0000011": 89, "00000465": 89, "99996392": 89, "00001105": 89, "0000116": 89, "00000878": 89, "99998671": 89, "00000364": 89, "00000213": 89, "00000472": 89, "00000281": 89, "99999073": 89, "00000211": 89, "00000159": 89, "00000442": 89, "00000115": 89, "peter": 89, "blackburn": 89, "00000358": 89, "00000529": 89, "99995623": 89, "000022": 89, "0000129": 89, "0000024": 89, "00001812": 89, "99994141": 89, "00001645": 89, "00002162": 89, "brussel": 89, "1996": 89, "00001172": 89, "00000821": 89, "00004661": 89, "0000618": 89, "99987167": 89, "99999061": 89, "00000201": 89, "00000195": 89, "00000408": 89, "00000135": 89, "2254": 89, "2907": 89, "19392": 89, "9962": 89, "8904": 89, "19303": 89, "12918": 89, "9256": 89, "11855": 89, "18392": 89, "20426": 89, "19402": 89, "14744": 89, "19371": 89, "4645": 89, "10331": 89, "9430": 89, "6143": 89, "18367": 89, "12914": 89, "todai": 89, "weather": 89, "march": 89, "scalfaro": 89, "northern": 89, "himself": 89, "said": 89, "germani": 89, "nastja": 89, "rysich": 89, "north": 89, "spla": 89, "fought": 89, "khartoum": 89, "govern": 89, "south": 89, "1983": 89, "autonomi": 89, "animist": 89, "region": 89, "moslem": 89, "arabis": 89, "mayor": 89, "antonio": 89, "gonzalez": 89, "garcia": 89, "revolutionari": 89, "parti": 89, "wednesdai": 89, "troop": 89, "raid": 89, "farm": 89, "stole": 89, "rape": 89, "women": 89, "spring": 89, "chg": 89, "hrw": 89, "12pct": 89, "princ": 89, "photo": 89, "moment": 89, "spokeswoman": 89, "rainier": 89, "told": 89, "reuter": 89, "danila": 89, "carib": 89, "w224": 89, "equip": 89, "radiomet": 89, "earn": 89, "19996": 89, "london": 89, "denom": 89, "sale": 89, "uk": 89, "jp": 89, "fr": 89, "maccabi": 89, "hapoel": 89, "haifa": 89, "tel": 89, "aviv": 89, "hospit": 89, "rever": 89, "roman": 89, "cathol": 89, "nun": 89, "admit": 89, "calcutta": 89, "week": 89, "ago": 89, "fever": 89, "vomit": 89, "allianc": 89, "embattl": 89, "kabul": 89, "salang": 89, "highwai": 89, "mondai": 89, "tuesdai": 89, "suprem": 89, "council": 89, "led": 89, "jumbish": 89, "milli": 89, "movement": 89, "warlord": 89, "abdul": 89, "rashid": 89, "dostum": 89, "dollar": 89, "exchang": 89, "3570": 89, "12049": 89, "born": 89, "1937": 89, "provinc": 89, "anhui": 89, "dai": 89, "came": 89, "shanghai": 89, "citi": 89, "prolif": 89, "author": 89, "teacher": 89, "chines": 89, "16764": 89, "1990": 89, "historian": 89, "alan": 89, "john": 89, "percival": 89, "taylor": 89, "di": 89, "20446": 89, "pace": 89, "bowler": 89, "ian": 89, "harvei": 89, "claim": 89, "victoria": 89, "15514": 89, "cotti": 89, "osc": 89, "foreign": 89, "minist": 89, "7525": 89, "sultan": 89, "specter": 89, "met": 89, "crown": 89, "abdullah": 89, "defenc": 89, "aviat": 89, "jeddah": 89, "saudi": 89, "agenc": 89, "2288": 89, "hi": 89, "customari": 89, "outfit": 89, "champion": 89, "damp": 89, "scalp": 89, "canada": 89, "reign": 89, "olymp": 89, "donovan": 89, "bailei": 89, "1992": 89, "linford": 89, "christi": 89, "britain": 89, "1984": 89, "1988": 89, "carl": 89, "lewi": 89, "ambigi": 89, "punctuat": 89, "chicago": 89, "digest": 89, "philadelphia": 89, "usda": 89, "york": 89, "token_issu": 89, "471": 89, "kean": 89, "year": 89, "contract": 89, "manchest": 89, "19072": 89, "societi": 89, "million": 89, "bite": 89, "deliv": 89, "19910": 89, "father": 89, "clarenc": 89, "woolmer": 89, "renam": 89, "uttar": 89, "pradesh": 89, "india": 89, "ranji": 89, "trophi": 89, "nation": 89, "championship": 89, "captain": 89, "1949": 89, "15658": 89, "19879": 89, "iii": 89, "brian": 89, "shimer": 89, "randi": 89, "jone": 89, "19104": 89}, "objects": {"cleanlab": [[0, 0, 0, "-", "benchmarking"], [2, 0, 0, "-", "classification"], [3, 0, 0, "-", "count"], [8, 0, 0, "-", "datalab"], [25, 0, 0, "-", "dataset"], [28, 0, 0, "-", "experimental"], [31, 0, 0, "-", "filter"], [32, 0, 0, "-", "internal"], [43, 0, 0, "-", "models"], [45, 0, 0, "-", "multiannotator"], [48, 0, 0, "-", "multilabel_classification"], [51, 0, 0, "-", "object_detection"], [54, 0, 0, "-", "outlier"], [55, 0, 0, "-", "rank"], [56, 0, 0, "-", "regression"], [60, 0, 0, "-", "segmentation"], [64, 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"], [12, 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": [[9, 0, 0, "-", "data"], [10, 0, 0, "-", "data_issues"], [13, 0, 0, "-", "issue_finder"], [11, 0, 0, "-", "issue_manager_factory"], [23, 0, 0, "-", "report"]], "cleanlab.datalab.internal.data": [[9, 2, 1, "", "Data"], [9, 5, 1, "", "DataFormatError"], [9, 5, 1, "", "DatasetDictError"], [9, 5, 1, "", "DatasetLoadError"], [9, 2, 1, "", "Label"]], "cleanlab.datalab.internal.data.Data": [[9, 4, 1, "", "class_names"], [9, 4, 1, "", "has_labels"]], "cleanlab.datalab.internal.data.DataFormatError": [[9, 6, 1, "", "args"], [9, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetDictError": [[9, 6, 1, "", "args"], [9, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.DatasetLoadError": [[9, 6, 1, "", "args"], [9, 3, 1, "", "with_traceback"]], "cleanlab.datalab.internal.data.Label": [[9, 4, 1, "", "class_names"], [9, 4, 1, "", "is_available"]], "cleanlab.datalab.internal.data_issues": [[10, 2, 1, "", "DataIssues"], [10, 1, 1, "", "get_data_statistics"]], "cleanlab.datalab.internal.data_issues.DataIssues": [[10, 3, 1, "", "collect_issues_from_imagelab"], [10, 3, 1, "", "collect_issues_from_issue_manager"], [10, 3, 1, "", "collect_statistics"], [10, 3, 1, "", "get_info"], [10, 3, 1, "", "get_issue_summary"], [10, 3, 1, "", "get_issues"], [10, 6, 1, "", "info"], [10, 6, 1, "", "issue_summary"], [10, 6, 1, "", "issues"], [10, 3, 1, "", "set_health_score"], [10, 4, 1, "", "statistics"]], "cleanlab.datalab.internal.issue_finder": [[13, 2, 1, "", "IssueFinder"]], "cleanlab.datalab.internal.issue_finder.IssueFinder": [[13, 3, 1, "", "find_issues"], [13, 3, 1, "", "get_available_issue_types"]], "cleanlab.datalab.internal.issue_manager": [[15, 0, 0, "-", "duplicate"], [16, 0, 0, "-", "imbalance"], [18, 0, 0, "-", "issue_manager"], [19, 0, 0, "-", "label"], [20, 0, 0, "-", "noniid"], [21, 0, 0, "-", "null"], [22, 0, 0, "-", "outlier"]], "cleanlab.datalab.internal.issue_manager.duplicate": [[15, 2, 1, "", "NearDuplicateIssueManager"]], "cleanlab.datalab.internal.issue_manager.duplicate.NearDuplicateIssueManager": [[15, 3, 1, "", "collect_info"], [15, 6, 1, "", "description"], [15, 3, 1, "", "find_issues"], [15, 6, 1, "", "info"], [15, 6, 1, "", "issue_name"], [15, 6, 1, "", "issue_score_key"], [15, 6, 1, "", "issues"], [15, 3, 1, "", "make_summary"], [15, 6, 1, "", "near_duplicate_sets"], [15, 3, 1, "", "report"], [15, 6, 1, "", "summary"], [15, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.imbalance": [[16, 2, 1, "", "ClassImbalanceIssueManager"]], "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager": [[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.issue_manager": [[18, 2, 1, "", "IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager": [[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.label": [[19, 2, 1, "", "LabelIssueManager"]], "cleanlab.datalab.internal.issue_manager.label.LabelIssueManager": [[19, 3, 1, "", "collect_info"], [19, 6, 1, "", "description"], [19, 3, 1, "", "find_issues"], [19, 3, 1, "", "get_health_summary"], [19, 6, 1, "", "health_summary_parameters"], [19, 6, 1, "", "info"], [19, 6, 1, "", "issue_name"], [19, 6, 1, "", "issue_score_key"], [19, 6, 1, "", "issues"], [19, 3, 1, "", "make_summary"], [19, 3, 1, "", "report"], [19, 6, 1, "", "summary"], [19, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager.noniid": [[20, 2, 1, "", "NonIIDIssueManager"], [20, 1, 1, "", "simplified_kolmogorov_smirnov_test"]], "cleanlab.datalab.internal.issue_manager.noniid.NonIIDIssueManager": [[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.null": [[21, 2, 1, "", "NullIssueManager"]], "cleanlab.datalab.internal.issue_manager.null.NullIssueManager": [[21, 3, 1, "", "collect_info"], [21, 6, 1, "", "description"], [21, 3, 1, "", "find_issues"], [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.outlier": [[22, 2, 1, "", "OutlierIssueManager"]], "cleanlab.datalab.internal.issue_manager.outlier.OutlierIssueManager": [[22, 6, 1, "", "DEFAULT_THRESHOLDS"], [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, 6, 1, "", "ood"], [22, 3, 1, "", "report"], [22, 6, 1, "", "summary"], [22, 6, 1, "", "verbosity_levels"]], "cleanlab.datalab.internal.issue_manager_factory": [[11, 7, 1, "", "REGISTRY"], [11, 1, 1, "", "list_default_issue_types"], [11, 1, 1, "", "list_possible_issue_types"], [11, 1, 1, "", "register"]], "cleanlab.datalab.internal.report": [[23, 2, 1, "", "Reporter"]], "cleanlab.datalab.internal.report.Reporter": [[23, 3, 1, "", "get_report"], [23, 3, 1, "", "report"]], "cleanlab.dataset": [[25, 1, 1, "", "find_overlapping_classes"], [25, 1, 1, "", "health_summary"], [25, 1, 1, "", "overall_label_health_score"], [25, 1, 1, "", "rank_classes_by_label_quality"]], "cleanlab.experimental": [[26, 0, 0, "-", "cifar_cnn"], [27, 0, 0, "-", "coteaching"], [29, 0, 0, "-", "label_issues_batched"], [30, 0, 0, "-", "mnist_pytorch"]], "cleanlab.experimental.cifar_cnn": [[26, 2, 1, "", "CNN"], [26, 1, 1, "", "call_bn"]], "cleanlab.experimental.cifar_cnn.CNN": [[26, 6, 1, "", "T_destination"], [26, 3, 1, "", "__call__"], [26, 3, 1, "", "add_module"], [26, 3, 1, "", "apply"], [26, 3, 1, "", "bfloat16"], [26, 3, 1, "", "buffers"], [26, 3, 1, "", "children"], [26, 3, 1, "", "cpu"], [26, 3, 1, "", "cuda"], [26, 3, 1, "", "double"], [26, 6, 1, "", "dump_patches"], [26, 3, 1, "", "eval"], [26, 3, 1, "", "extra_repr"], [26, 3, 1, "", "float"], [26, 3, 1, "id0", "forward"], [26, 3, 1, "", "get_buffer"], [26, 3, 1, "", "get_extra_state"], [26, 3, 1, "", "get_parameter"], [26, 3, 1, "", "get_submodule"], [26, 3, 1, "", "half"], [26, 3, 1, "", "ipu"], [26, 3, 1, "", "load_state_dict"], [26, 3, 1, "", "modules"], [26, 3, 1, "", "named_buffers"], [26, 3, 1, "", "named_children"], [26, 3, 1, "", "named_modules"], [26, 3, 1, "", "named_parameters"], [26, 3, 1, "", "parameters"], [26, 3, 1, "", "register_backward_hook"], [26, 3, 1, "", "register_buffer"], [26, 3, 1, "", "register_forward_hook"], [26, 3, 1, "", "register_forward_pre_hook"], [26, 3, 1, "", "register_full_backward_hook"], [26, 3, 1, "", "register_load_state_dict_post_hook"], [26, 3, 1, "", "register_module"], [26, 3, 1, "", "register_parameter"], [26, 3, 1, "", "requires_grad_"], [26, 3, 1, "", "set_extra_state"], [26, 3, 1, "", "share_memory"], [26, 3, 1, "", "state_dict"], [26, 3, 1, "", "to"], [26, 3, 1, "", "to_empty"], [26, 3, 1, "", "train"], [26, 6, 1, "", "training"], [26, 3, 1, "", "type"], [26, 3, 1, "", "xpu"], [26, 3, 1, "", "zero_grad"]], "cleanlab.experimental.coteaching": [[27, 1, 1, "", "adjust_learning_rate"], [27, 1, 1, "", "evaluate"], [27, 1, 1, "", "forget_rate_scheduler"], [27, 1, 1, "", "initialize_lr_scheduler"], [27, 1, 1, "", "loss_coteaching"], [27, 1, 1, "", "train"]], "cleanlab.experimental.label_issues_batched": [[29, 2, 1, "", "LabelInspector"], [29, 7, 1, "", "adj_confident_thresholds_shared"], [29, 1, 1, "", "find_label_issues_batched"], [29, 7, 1, "", "labels_shared"], [29, 7, 1, "", "pred_probs_shared"], [29, 1, 1, "", "split_arr"]], "cleanlab.experimental.label_issues_batched.LabelInspector": [[29, 3, 1, "", "get_confident_thresholds"], [29, 3, 1, "", "get_label_issues"], [29, 3, 1, "", "get_num_issues"], [29, 3, 1, "", "get_quality_scores"], [29, 3, 1, "", "score_label_quality"], [29, 3, 1, "", "update_confident_thresholds"]], "cleanlab.experimental.mnist_pytorch": [[30, 2, 1, "", "CNN"], [30, 2, 1, "", "SimpleNet"], [30, 1, 1, "", "get_mnist_dataset"], [30, 1, 1, "", "get_sklearn_digits_dataset"]], "cleanlab.experimental.mnist_pytorch.CNN": [[30, 3, 1, "", "__init_subclass__"], [30, 6, 1, "", "batch_size"], [30, 6, 1, "", "dataset"], [30, 6, 1, "", "epochs"], [30, 3, 1, "id0", "fit"], [30, 3, 1, "", "get_metadata_routing"], [30, 3, 1, "", "get_params"], [30, 6, 1, "", "loader"], [30, 6, 1, "", "log_interval"], [30, 6, 1, "", "lr"], [30, 6, 1, "", "momentum"], [30, 6, 1, "", "no_cuda"], [30, 3, 1, "id1", "predict"], [30, 3, 1, "id4", "predict_proba"], [30, 6, 1, "", "seed"], [30, 3, 1, "", "set_fit_request"], [30, 3, 1, "", "set_params"], [30, 3, 1, "", "set_predict_proba_request"], [30, 3, 1, "", "set_predict_request"], [30, 6, 1, "", "test_batch_size"]], "cleanlab.experimental.mnist_pytorch.SimpleNet": [[30, 6, 1, "", "T_destination"], [30, 3, 1, "", "__call__"], [30, 3, 1, "", "add_module"], [30, 3, 1, "", "apply"], [30, 3, 1, "", "bfloat16"], [30, 3, 1, "", "buffers"], [30, 3, 1, "", "children"], [30, 3, 1, "", "cpu"], [30, 3, 1, "", "cuda"], [30, 3, 1, "", "double"], [30, 6, 1, "", "dump_patches"], [30, 3, 1, "", "eval"], [30, 3, 1, "", "extra_repr"], [30, 3, 1, "", "float"], [30, 3, 1, "", "forward"], [30, 3, 1, "", "get_buffer"], [30, 3, 1, "", "get_extra_state"], [30, 3, 1, "", "get_parameter"], [30, 3, 1, "", "get_submodule"], [30, 3, 1, "", "half"], [30, 3, 1, "", "ipu"], [30, 3, 1, "", "load_state_dict"], [30, 3, 1, "", "modules"], [30, 3, 1, "", "named_buffers"], [30, 3, 1, "", "named_children"], [30, 3, 1, "", "named_modules"], [30, 3, 1, "", "named_parameters"], [30, 3, 1, "", "parameters"], [30, 3, 1, "", "register_backward_hook"], [30, 3, 1, "", "register_buffer"], [30, 3, 1, "", "register_forward_hook"], [30, 3, 1, "", "register_forward_pre_hook"], [30, 3, 1, "", "register_full_backward_hook"], [30, 3, 1, "", "register_load_state_dict_post_hook"], [30, 3, 1, "", "register_module"], [30, 3, 1, "", "register_parameter"], [30, 3, 1, "", "requires_grad_"], [30, 3, 1, "", "set_extra_state"], [30, 3, 1, "", "share_memory"], [30, 3, 1, "", "state_dict"], [30, 3, 1, "", "to"], [30, 3, 1, "", "to_empty"], [30, 3, 1, "", "train"], [30, 6, 1, "", "training"], [30, 3, 1, "", "type"], [30, 3, 1, "", "xpu"], [30, 3, 1, "", "zero_grad"]], "cleanlab.filter": [[31, 1, 1, "", "find_label_issues"], [31, 1, 1, "", "find_label_issues_using_argmax_confusion_matrix"], [31, 1, 1, "", "find_predicted_neq_given"], [31, 7, 1, "", "pred_probs_by_class"], [31, 7, 1, "", "prune_count_matrix_cols"]], "cleanlab.internal": [[33, 0, 0, "-", "label_quality_utils"], [34, 0, 0, "-", "latent_algebra"], [35, 0, 0, "-", "multiannotator_utils"], [36, 0, 0, "-", "multilabel_scorer"], [37, 0, 0, "-", "multilabel_utils"], [38, 0, 0, "-", "outlier"], [39, 0, 0, "-", "token_classification_utils"], [40, 0, 0, "-", "util"], [41, 0, 0, "-", "validation"]], "cleanlab.internal.label_quality_utils": [[33, 1, 1, "", "get_normalized_entropy"]], "cleanlab.internal.latent_algebra": [[34, 1, 1, "", "compute_inv_noise_matrix"], [34, 1, 1, "", "compute_noise_matrix_from_inverse"], [34, 1, 1, "", "compute_ps_py_inv_noise_matrix"], [34, 1, 1, "", "compute_py"], [34, 1, 1, "", "compute_py_inv_noise_matrix"], [34, 1, 1, "", "compute_pyx"]], "cleanlab.internal.multiannotator_utils": [[35, 1, 1, "", "assert_valid_inputs_multiannotator"], [35, 1, 1, "", "assert_valid_pred_probs"], [35, 1, 1, "", "check_consensus_label_classes"], [35, 1, 1, "", "compute_soft_cross_entropy"], [35, 1, 1, "", "find_best_temp_scaler"], [35, 1, 1, "", "format_multiannotator_labels"], [35, 1, 1, "", "temp_scale_pred_probs"]], "cleanlab.internal.multilabel_scorer": [[36, 2, 1, "", "Aggregator"], [36, 2, 1, "", "ClassLabelScorer"], [36, 2, 1, "", "MultilabelScorer"], [36, 1, 1, "", "exponential_moving_average"], [36, 1, 1, "", "get_cross_validated_multilabel_pred_probs"], [36, 1, 1, "", "get_label_quality_scores"], [36, 1, 1, "", "multilabel_py"], [36, 1, 1, "", "softmin"]], "cleanlab.internal.multilabel_scorer.Aggregator": [[36, 3, 1, "", "__call__"], [36, 6, 1, "", "possible_methods"]], "cleanlab.internal.multilabel_scorer.ClassLabelScorer": [[36, 6, 1, "", "CONFIDENCE_WEIGHTED_ENTROPY"], [36, 6, 1, "", "NORMALIZED_MARGIN"], [36, 6, 1, "", "SELF_CONFIDENCE"], [36, 3, 1, "", "__call__"], [36, 3, 1, "", "from_str"]], "cleanlab.internal.multilabel_scorer.MultilabelScorer": [[36, 3, 1, "", "__call__"], [36, 3, 1, "", "aggregate"], [36, 3, 1, "", "get_class_label_quality_scores"]], "cleanlab.internal.multilabel_utils": [[37, 1, 1, "", "get_onehot_num_classes"], [37, 1, 1, "", "int2onehot"], [37, 1, 1, "", "onehot2int"], [37, 1, 1, "", "stack_complement"]], "cleanlab.internal.outlier": [[38, 1, 1, "", "transform_distances_to_scores"]], "cleanlab.internal.token_classification_utils": [[39, 1, 1, "", "color_sentence"], [39, 1, 1, "", "filter_sentence"], [39, 1, 1, "", "get_sentence"], [39, 1, 1, "", "mapping"], [39, 1, 1, "", "merge_probs"], [39, 1, 1, "", "process_token"]], "cleanlab.internal.util": [[40, 1, 1, "", "append_extra_datapoint"], [40, 1, 1, "", "clip_noise_rates"], [40, 1, 1, "", "clip_values"], [40, 1, 1, "", "compress_int_array"], [40, 1, 1, "", "confusion_matrix"], [40, 1, 1, "", "csr_vstack"], [40, 1, 1, "", "estimate_pu_f1"], [40, 1, 1, "", "extract_indices_tf"], [40, 1, 1, "", "force_two_dimensions"], [40, 1, 1, "", "format_labels"], [40, 1, 1, "", "get_missing_classes"], [40, 1, 1, "", "get_num_classes"], [40, 1, 1, "", "get_unique_classes"], [40, 1, 1, "", "is_tensorflow_dataset"], [40, 1, 1, "", "is_torch_dataset"], [40, 1, 1, "", "num_unique_classes"], [40, 1, 1, "", "print_inverse_noise_matrix"], [40, 1, 1, "", "print_joint_matrix"], [40, 1, 1, "", "print_noise_matrix"], [40, 1, 1, "", "print_square_matrix"], [40, 1, 1, "", "remove_noise_from_class"], [40, 1, 1, "", "round_preserving_row_totals"], [40, 1, 1, "", "round_preserving_sum"], [40, 1, 1, "", "smart_display_dataframe"], [40, 1, 1, "", "subset_X_y"], [40, 1, 1, "", "subset_data"], [40, 1, 1, "", "subset_labels"], [40, 1, 1, "", "train_val_split"], [40, 1, 1, "", "unshuffle_tensorflow_dataset"], [40, 1, 1, "", "value_counts"], [40, 1, 1, "", "value_counts_fill_missing_classes"]], "cleanlab.internal.validation": [[41, 1, 1, "", "assert_indexing_works"], [41, 1, 1, "", "assert_nonempty_input"], [41, 1, 1, "", "assert_valid_class_labels"], [41, 1, 1, "", "assert_valid_inputs"], [41, 1, 1, "", "labels_to_array"]], "cleanlab.models": [[44, 0, 0, "-", "keras"]], "cleanlab.models.keras": [[44, 2, 1, "", "KerasWrapperModel"], [44, 2, 1, "", "KerasWrapperSequential"]], "cleanlab.models.keras.KerasWrapperModel": [[44, 3, 1, "", "fit"], [44, 3, 1, "", "get_params"], [44, 3, 1, "", "predict"], [44, 3, 1, "", "predict_proba"], [44, 3, 1, "", "set_params"], [44, 3, 1, "", "summary"]], "cleanlab.models.keras.KerasWrapperSequential": [[44, 3, 1, "", "fit"], [44, 3, 1, "", "get_params"], [44, 3, 1, "", "predict"], [44, 3, 1, "", "predict_proba"], [44, 3, 1, "", "set_params"], [44, 3, 1, "", "summary"]], "cleanlab.multiannotator": [[45, 1, 1, "", "convert_long_to_wide_dataset"], [45, 1, 1, "", "get_active_learning_scores"], [45, 1, 1, "", "get_active_learning_scores_ensemble"], [45, 1, 1, "", "get_label_quality_multiannotator"], [45, 1, 1, "", "get_label_quality_multiannotator_ensemble"], [45, 1, 1, "", "get_majority_vote_label"]], "cleanlab.multilabel_classification": [[46, 0, 0, "-", "dataset"], [47, 0, 0, "-", "filter"], [49, 0, 0, "-", "rank"]], "cleanlab.multilabel_classification.dataset": [[46, 1, 1, "", "common_multilabel_issues"], [46, 1, 1, "", "multilabel_health_summary"], [46, 1, 1, "", "overall_multilabel_health_score"], [46, 1, 1, "", "rank_classes_by_multilabel_quality"]], "cleanlab.multilabel_classification.filter": [[47, 1, 1, "", "find_label_issues"], [47, 1, 1, "", "find_multilabel_issues_per_class"]], "cleanlab.multilabel_classification.rank": [[49, 1, 1, "", "get_label_quality_scores"], [49, 1, 1, "", "get_label_quality_scores_per_class"]], "cleanlab.object_detection": [[50, 0, 0, "-", "filter"], [52, 0, 0, "-", "rank"], [53, 0, 0, "-", "summary"]], "cleanlab.object_detection.filter": [[50, 1, 1, "", "find_label_issues"]], "cleanlab.object_detection.rank": [[52, 1, 1, "", "compute_badloc_box_scores"], [52, 1, 1, "", "compute_overlooked_box_scores"], [52, 1, 1, "", "compute_swap_box_scores"], [52, 1, 1, "", "get_label_quality_scores"], [52, 1, 1, "", "issues_from_scores"], [52, 1, 1, "", "pool_box_scores_per_image"]], "cleanlab.object_detection.summary": [[53, 1, 1, "", "bounding_box_size_distribution"], [53, 1, 1, "", "class_label_distribution"], [53, 1, 1, "", "get_sorted_bbox_count_idxs"], [53, 1, 1, "", "object_counts_per_image"], [53, 1, 1, "", "plot_class_distribution"], [53, 1, 1, "", "plot_class_size_distributions"], [53, 1, 1, "", "visualize"]], "cleanlab.outlier": [[54, 2, 1, "", "OutOfDistribution"]], "cleanlab.outlier.OutOfDistribution": [[54, 3, 1, "", "fit"], [54, 3, 1, "", "fit_score"], [54, 3, 1, "", "score"]], "cleanlab.rank": [[55, 1, 1, "", "find_top_issues"], [55, 1, 1, "", "get_confidence_weighted_entropy_for_each_label"], [55, 1, 1, "", "get_label_quality_ensemble_scores"], [55, 1, 1, "", "get_label_quality_scores"], [55, 1, 1, "", "get_normalized_margin_for_each_label"], [55, 1, 1, "", "get_self_confidence_for_each_label"], [55, 1, 1, "", "order_label_issues"]], "cleanlab.regression": [[57, 0, 0, "-", "learn"], [58, 0, 0, "-", "rank"]], "cleanlab.regression.learn": [[57, 2, 1, "", "CleanLearning"]], "cleanlab.regression.learn.CleanLearning": [[57, 3, 1, "", "__init_subclass__"], [57, 3, 1, "", "find_label_issues"], [57, 3, 1, "", "fit"], [57, 3, 1, "", "get_aleatoric_uncertainty"], [57, 3, 1, "", "get_epistemic_uncertainty"], [57, 3, 1, "", "get_label_issues"], [57, 3, 1, "", "get_metadata_routing"], [57, 3, 1, "", "get_params"], [57, 3, 1, "", "predict"], [57, 3, 1, "", "save_space"], [57, 3, 1, "", "score"], [57, 3, 1, "", "set_fit_request"], [57, 3, 1, "", "set_params"], [57, 3, 1, "", "set_score_request"]], "cleanlab.regression.rank": [[58, 1, 1, "", "get_label_quality_scores"]], "cleanlab.segmentation": [[59, 0, 0, "-", "filter"], [61, 0, 0, "-", "rank"], [62, 0, 0, "-", "summary"]], "cleanlab.segmentation.filter": [[59, 1, 1, "", "find_label_issues"]], "cleanlab.segmentation.rank": [[61, 1, 1, "", "get_label_quality_scores"], [61, 1, 1, "", "issues_from_scores"]], "cleanlab.segmentation.summary": [[62, 1, 1, "", "common_label_issues"], [62, 1, 1, "", "display_issues"], [62, 1, 1, "", "filter_by_class"]], "cleanlab.token_classification": [[63, 0, 0, "-", "filter"], [65, 0, 0, "-", "rank"], [66, 0, 0, "-", "summary"]], "cleanlab.token_classification.filter": [[63, 1, 1, "", "find_label_issues"]], "cleanlab.token_classification.rank": [[65, 1, 1, "", "get_label_quality_scores"], [65, 1, 1, "", "issues_from_scores"]], "cleanlab.token_classification.summary": [[66, 1, 1, "", "common_label_issues"], [66, 1, 1, "", "display_issues"], [66, 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, 69, 73, 74, 76, 77, 78, 81, 87, 88, 89], "count": [3, 78], "datalab": [4, 5, 6, 7, 8, 70, 71, 72, 73, 74, 78], "creat": [5, 70, 71, 78, 80], "your": [5, 67, 70, 71, 74, 76, 78], "own": 5, "issu": [5, 6, 7, 17, 67, 69, 70, 71, 73, 74, 75, 76, 77, 78, 81, 82, 86, 87, 89], "manag": [5, 17], "prerequisit": 5, "implement": 5, "issuemanag": [5, 70], "basic": 5, "check": 5, "intermedi": 5, "advanc": [5, 70], "us": [5, 69, 71, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "guid": [6, 8], "type": [6, 7, 78], "custom": [6, 70], "can": [7, 71, 75, 76, 78, 80], "detect": [7, 71, 73, 74, 76, 78, 82, 83], "estim": [7, 78, 80], "each": 7, "label": [7, 19, 67, 69, 71, 73, 74, 76, 77, 78, 80, 81, 82, 85, 86, 87, 88, 89], "outlier": [7, 22, 38, 54, 73, 74, 77, 83], "Near": [7, 71, 73, 74, 77], "duplic": [7, 15, 71, 73, 74, 77], "non": 7, "iid": 7, "class": [7, 68, 78, 86], "imbal": [7, 16], "imag": [7, 77, 83], "specif": [7, 86], "option": 7, "paramet": [7, 78], "get": [8, 70, 71, 80, 81, 82, 86, 89], "start": [8, 75], "api": 8, "refer": 8, "data": [9, 67, 69, 70, 71, 73, 75, 76, 78, 80, 81, 82, 83, 85, 86, 87, 89], "data_issu": 10, "factori": 11, "intern": [12, 32], "issue_find": 13, "issue_manag": [17, 18], "regist": 17, "unregist": 17, "noniid": 20, "null": 21, "report": [23, 77], "dataset": [25, 46, 67, 71, 74, 75, 76, 77, 78, 81, 82, 83, 85, 86, 88, 89], "cifar_cnn": 26, "coteach": 27, "experiment": 28, "label_issues_batch": 29, "mnist_pytorch": 30, "filter": [31, 47, 50, 59, 63, 78], "label_quality_util": 33, "latent_algebra": 34, "multiannotator_util": 35, "multilabel_scor": 36, "multilabel_util": 37, "token_classification_util": 39, "util": 40, "valid": [41, 77, 84], "fasttext": 42, "model": [43, 67, 69, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 87, 88], "kera": 44, "multiannot": [45, 80], "multilabel_classif": 48, "rank": [49, 52, 55, 58, 61, 65, 78], "object_detect": 51, "summari": [53, 62, 66], "regress": [56, 57, 58, 76, 85], "learn": [57, 71, 76, 78, 87], "segment": [60, 86], "token_classif": [64, 89], "cleanlab": [67, 69, 73, 74, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "open": [67, 76], "sourc": [67, 76], "document": 67, "quickstart": 67, "1": [67, 68, 69, 70, 71, 73, 74, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "instal": [67, 69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "2": [67, 68, 69, 70, 71, 73, 74, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "find": [67, 69, 71, 73, 74, 75, 76, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "common": [67, 68, 89], "3": [67, 69, 70, 71, 73, 74, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "handl": [67, 76], "error": [67, 76, 77, 78, 80, 81, 82, 85, 86, 88, 89], "train": [67, 69, 76, 83, 85, 87, 88], "robust": [67, 78, 85, 87, 88], "noisi": [67, 78, 85, 87, 88], "4": [67, 69, 70, 71, 73, 74, 77, 78, 80, 82, 83, 85, 87, 88], "curat": [67, 75], "fix": [67, 76], "level": [67, 75, 78, 89], "5": [67, 69, 71, 73, 77, 78, 80, 85, 87], "improv": [67, 80], "via": [67, 78, 80], "mani": [67, 78], "other": [67, 80, 82, 85], "techniqu": 67, "contribut": 67, "easi": 67, "mode": 67, "how": [68, 76, 78, 80, 81, 89], "migrat": 68, "version": 68, "0": 68, "from": [68, 70, 71, 78, 85, 87, 88], "pre": [68, 69, 83], "function": [68, 70], "name": 68, "chang": 68, "modul": [68, 78], "new": 68, "remov": 68, "argument": [68, 70], "variabl": 68, "audio": 69, "speechbrain": 69, "depend": [69, 70, 71, 73, 74, 75, 77, 78, 80, 81, 82, 83, 85, 86, 87, 88, 89], "import": [69, 70, 71, 75, 77, 78, 80], "them": [69, 75, 78], "load": [69, 70, 71, 73, 74, 85, 87, 88], "featur": [69, 77, 83], "fit": 69, "linear": 69, "comput": [69, 73, 74, 77, 80, 84, 87], "out": [69, 70, 71, 73, 74, 77, 80, 84, 87], "sampl": [69, 70, 71, 73, 74, 77, 80, 84, 87], "predict": [69, 70, 71, 73, 74, 77, 80, 81, 82, 84, 87], "probabl": [69, 70, 71, 73, 74, 77, 80, 84, 87], "workflow": [70, 78], "audit": [70, 71], "requir": [70, 71, 73, 74, 77, 80, 81, 82, 83, 85, 86, 87, 88, 89], "classifi": [70, 71], "instanti": 70, "object": [70, 82], "increment": 70, "search": 70, "specifi": 70, "nondefault": 70, "save": 70, "ad": 70, "A": 71, "unifi": 71, "all": [71, 78], "kind": [71, 82], "skip": [71, 75, 78, 80], "detail": [71, 75, 78, 80], "more": [71, 78, 85, 87, 88], "about": 71, "addit": 71, "inform": [71, 77], "tutori": [72, 75, 79], "tabular": [73, 87], "numer": 73, "categor": 73, "column": 73, "process": [73, 83, 85, 87], "select": [73, 87], "construct": 73, "k": [73, 77, 84], "nearest": 73, "neighbour": 73, "graph": 73, "text": [74, 88, 89], "format": [74, 76, 81, 82, 88], "defin": [74, 77, 85, 88], "fetch": [75, 77], "evalu": 75, "health": [75, 78], "8": [75, 78], "popular": 75, "faq": 76, "what": [76, 78, 84], "do": [76, 78], "i": [76, 78, 84], "infer": 76, "correct": 76, "exampl": [76, 77, 78, 83], "ha": 76, "flag": 76, "should": 76, "v": 76, "test": [76, 78, 83], "big": 76, "limit": 76, "memori": 76, "why": 76, "isn": 76, "t": 76, "cleanlearn": [76, 78], "work": [76, 78, 80, 89], "me": 76, "differ": [76, 82], "clean": [76, 78], "final": 76, "hyperparamet": 76, "tune": 76, "onli": 76, "one": [76, 78, 81, 86], "doe": [76, 80, 89], "take": 76, "so": 76, "long": 76, "ml": [76, 78], "run": 76, "identifi": [76, 82], "licens": 76, "under": 76, "an": 76, "answer": 76, "question": 76, "pytorch": [77, 83], "normal": 77, "fashion": 77, "mnist": 77, "prepar": 77, "fold": [77, 84], "cross": [77, 84], "embed": [77, 83], "7": [77, 78], "view": 77, "most": [77, 89], "like": 77, "sever": 77, "set": [77, 78], "dark": 77, "top": [77, 86], "low": 77, "The": 78, "centric": 78, "ai": 78, "machin": 78, "find_label_issu": 78, "line": 78, "code": 78, "visual": [78, 82, 83, 86], "twenti": 78, "lowest": 78, "qualiti": [78, 80, 81, 82, 86, 89], "see": 78, "now": 78, "let": 78, "": 78, "happen": 78, "we": 78, "merg": 78, "seafoam": 78, "green": 78, "yellow": 78, "too": 78, "you": 78, "re": 78, "6": 78, "One": 78, "score": [78, 80, 81, 82, 86, 89], "rule": 78, "overal": [78, 86], "accur": 78, "thi": 78, "directli": 78, "fulli": 78, "character": 78, "nois": 78, "matrix": [78, 81], "joint": 78, "prior": 78, "true": 78, "distribut": 78, "flip": 78, "rate": 78, "ani": 78, "again": 78, "support": 78, "lot": 78, "method": 78, "filter_bi": 78, "automat": 78, "everi": 78, "uniqu": 78, "num_label_issu": 78, "threshold": 78, "found": 78, "Not": 78, "sure": 78, "when": 78, "ensembl": 78, "multipl": [78, 80], "predictor": 78, "consensu": 80, "annot": 80, "initi": 80, "major": 80, "vote": 80, "better": 80, "statist": 80, "compar": 80, "inspect": 80, "potenti": [80, 85, 88], "retrain": 80, "further": 80, "multi": 81, "given": 81, "hot": 81, "binari": 81, "download": [82, 86, 89], "objectlab": 82, "timm": 83, "cifar10": 83, "some": 83, "pred_prob": [83, 86, 89], "wai": 85, "semant": 86, "which": 86, "ar": 86, "commonli": 86, "mislabel": [86, 89], "focus": 86, "scikit": 87, "token": 89, "word": 89, "sentenc": 89, "contain": 89, "particular": 89}, "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 f2f340c30..12758e6e6 100644 --- a/master/tutorials/audio.html +++ b/master/tutorials/audio.html @@ -1483,7 +1483,7 @@
-/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:297: UserWarning: Overwriting columns ['outlier_score', 'is_outlier_issue'] in self.issues with columns from issue manager OutlierIssueManager.
+/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:297: UserWarning: Overwriting columns ['is_outlier_issue', 'outlier_score'] in self.issues with columns from issue manager OutlierIssueManager.
warnings.warn(
/home/runner/work/cleanlab/cleanlab/cleanlab/datalab/internal/data_issues.py:327: UserWarning: Overwriting row in self.issue_summary with row from issue manager OutlierIssueManager.
warnings.warn(
@@ -1432,7 +1432,7 @@ Functionality 3: Save and load Datalab objects
Datalab
makes it very easy to check your datasets for all sorts of issues that are important to deal with for training robust models. The inputs it uses to detect issues can come from any model you have trained (the better your model, the more accurate the issue detection will be).
To learn more, check out this examples notebook and the advanced Datalab tutorial.
+To learn more, check out this example notebook (demonstrates Datalab applied to a real dataset) and the advanced Datalab tutorial (demonstrates configuration and customization options to exert greater control).
diff --git a/master/tutorials/datalab/datalab_quickstart.ipynb b/master/tutorials/datalab/datalab_quickstart.ipynb index 428589054..a4d04fb3d 100644 --- a/master/tutorials/datalab/datalab_quickstart.ipynb +++ b/master/tutorials/datalab/datalab_quickstart.ipynb @@ -78,10 +78,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:33.469542Z", - "iopub.status.busy": "2023-11-20T20:32:33.469351Z", - "iopub.status.idle": "2023-11-20T20:32:34.500487Z", - "shell.execute_reply": "2023-11-20T20:32:34.499885Z" + "iopub.execute_input": "2023-11-21T08:09:16.277922Z", + "iopub.status.busy": "2023-11-21T08:09:16.277714Z", + "iopub.status.idle": "2023-11-21T08:09:17.330413Z", + "shell.execute_reply": "2023-11-21T08:09:17.329797Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:32:34.503345Z", - "iopub.status.busy": "2023-11-20T20:32:34.502902Z", - "iopub.status.idle": "2023-11-20T20:32:34.505963Z", - "shell.execute_reply": "2023-11-20T20:32:34.505392Z" + "iopub.execute_input": "2023-11-21T08:09:17.333390Z", + "iopub.status.busy": "2023-11-21T08:09:17.332969Z", + "iopub.status.idle": "2023-11-21T08:09:17.336371Z", + "shell.execute_reply": "2023-11-21T08:09:17.335841Z" } }, "outputs": [], @@ -250,10 +250,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:34.508592Z", - "iopub.status.busy": "2023-11-20T20:32:34.508203Z", - "iopub.status.idle": "2023-11-20T20:32:34.517536Z", - "shell.execute_reply": "2023-11-20T20:32:34.517033Z" + "iopub.execute_input": "2023-11-21T08:09:17.338978Z", + "iopub.status.busy": "2023-11-21T08:09:17.338557Z", + "iopub.status.idle": "2023-11-21T08:09:17.348157Z", + "shell.execute_reply": "2023-11-21T08:09:17.347659Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:34.519840Z", - "iopub.status.busy": "2023-11-20T20:32:34.519477Z", - "iopub.status.idle": "2023-11-20T20:32:34.523906Z", - "shell.execute_reply": "2023-11-20T20:32:34.523422Z" + "iopub.execute_input": "2023-11-21T08:09:17.350302Z", + "iopub.status.busy": "2023-11-21T08:09:17.350108Z", + "iopub.status.idle": "2023-11-21T08:09:17.354550Z", + "shell.execute_reply": "2023-11-21T08:09:17.354079Z" } }, "outputs": [], @@ -443,10 +443,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:34.526233Z", - "iopub.status.busy": "2023-11-20T20:32:34.526031Z", - "iopub.status.idle": "2023-11-20T20:32:34.800096Z", - "shell.execute_reply": "2023-11-20T20:32:34.799485Z" + "iopub.execute_input": "2023-11-21T08:09:17.356909Z", + "iopub.status.busy": "2023-11-21T08:09:17.356713Z", + "iopub.status.idle": "2023-11-21T08:09:17.628106Z", + "shell.execute_reply": "2023-11-21T08:09:17.627493Z" }, "nbsphinx": "hidden" }, @@ -515,10 +515,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:34.802853Z", - "iopub.status.busy": "2023-11-20T20:32:34.802647Z", - "iopub.status.idle": "2023-11-20T20:32:35.170934Z", - "shell.execute_reply": "2023-11-20T20:32:35.170290Z" + "iopub.execute_input": "2023-11-21T08:09:17.630816Z", + "iopub.status.busy": "2023-11-21T08:09:17.630614Z", + "iopub.status.idle": "2023-11-21T08:09:17.999037Z", + "shell.execute_reply": "2023-11-21T08:09:17.998384Z" } }, "outputs": [ @@ -554,10 +554,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:35.173355Z", - "iopub.status.busy": "2023-11-20T20:32:35.173147Z", - "iopub.status.idle": "2023-11-20T20:32:35.176045Z", - "shell.execute_reply": "2023-11-20T20:32:35.175500Z" + "iopub.execute_input": "2023-11-21T08:09:18.001717Z", + "iopub.status.busy": "2023-11-21T08:09:18.001244Z", + "iopub.status.idle": "2023-11-21T08:09:18.004303Z", + "shell.execute_reply": "2023-11-21T08:09:18.003786Z" } }, "outputs": [], @@ -596,10 +596,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:35.178459Z", - "iopub.status.busy": "2023-11-20T20:32:35.178258Z", - "iopub.status.idle": "2023-11-20T20:32:35.202236Z", - "shell.execute_reply": "2023-11-20T20:32:35.201743Z" + "iopub.execute_input": "2023-11-21T08:09:18.006766Z", + "iopub.status.busy": "2023-11-21T08:09:18.006403Z", + "iopub.status.idle": "2023-11-21T08:09:18.030659Z", + "shell.execute_reply": "2023-11-21T08:09:18.030036Z" } }, "outputs": [], @@ -632,10 +632,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:35.204586Z", - "iopub.status.busy": "2023-11-20T20:32:35.204251Z", - "iopub.status.idle": "2023-11-20T20:32:36.458769Z", - "shell.execute_reply": "2023-11-20T20:32:36.458040Z" + "iopub.execute_input": "2023-11-21T08:09:18.033219Z", + "iopub.status.busy": "2023-11-21T08:09:18.032822Z", + "iopub.status.idle": "2023-11-21T08:09:19.343733Z", + "shell.execute_reply": "2023-11-21T08:09:19.342983Z" } }, "outputs": [ @@ -677,10 +677,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.461652Z", - "iopub.status.busy": "2023-11-20T20:32:36.461156Z", - "iopub.status.idle": "2023-11-20T20:32:36.478467Z", - "shell.execute_reply": "2023-11-20T20:32:36.477930Z" + "iopub.execute_input": "2023-11-21T08:09:19.347630Z", + "iopub.status.busy": "2023-11-21T08:09:19.346235Z", + "iopub.status.idle": "2023-11-21T08:09:19.364152Z", + "shell.execute_reply": "2023-11-21T08:09:19.363645Z" } }, "outputs": [ @@ -814,10 +814,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.481003Z", - "iopub.status.busy": "2023-11-20T20:32:36.480646Z", - "iopub.status.idle": "2023-11-20T20:32:36.487233Z", - "shell.execute_reply": "2023-11-20T20:32:36.486595Z" + "iopub.execute_input": "2023-11-21T08:09:19.366740Z", + "iopub.status.busy": "2023-11-21T08:09:19.366373Z", + "iopub.status.idle": "2023-11-21T08:09:19.373109Z", + "shell.execute_reply": "2023-11-21T08:09:19.372483Z" } }, "outputs": [ @@ -907,10 +907,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.489757Z", - "iopub.status.busy": "2023-11-20T20:32:36.489404Z", - "iopub.status.idle": "2023-11-20T20:32:36.495590Z", - "shell.execute_reply": "2023-11-20T20:32:36.494997Z" + "iopub.execute_input": "2023-11-21T08:09:19.375578Z", + "iopub.status.busy": "2023-11-21T08:09:19.375069Z", + "iopub.status.idle": "2023-11-21T08:09:19.381331Z", + "shell.execute_reply": "2023-11-21T08:09:19.380721Z" } }, "outputs": [ @@ -977,10 +977,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.498095Z", - "iopub.status.busy": "2023-11-20T20:32:36.497725Z", - "iopub.status.idle": "2023-11-20T20:32:36.506412Z", - "shell.execute_reply": "2023-11-20T20:32:36.505889Z" + "iopub.execute_input": "2023-11-21T08:09:19.383700Z", + "iopub.status.busy": "2023-11-21T08:09:19.383268Z", + "iopub.status.idle": "2023-11-21T08:09:19.391810Z", + "shell.execute_reply": "2023-11-21T08:09:19.391208Z" } }, "outputs": [ @@ -1122,10 +1122,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.508696Z", - "iopub.status.busy": "2023-11-20T20:32:36.508333Z", - "iopub.status.idle": "2023-11-20T20:32:36.517506Z", - "shell.execute_reply": "2023-11-20T20:32:36.516962Z" + "iopub.execute_input": "2023-11-21T08:09:19.394285Z", + "iopub.status.busy": "2023-11-21T08:09:19.393921Z", + "iopub.status.idle": "2023-11-21T08:09:19.403180Z", + "shell.execute_reply": "2023-11-21T08:09:19.402579Z" } }, "outputs": [ @@ -1241,10 +1241,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.519928Z", - "iopub.status.busy": "2023-11-20T20:32:36.519565Z", - "iopub.status.idle": "2023-11-20T20:32:36.527049Z", - "shell.execute_reply": "2023-11-20T20:32:36.526537Z" + "iopub.execute_input": "2023-11-21T08:09:19.405691Z", + "iopub.status.busy": "2023-11-21T08:09:19.405303Z", + "iopub.status.idle": "2023-11-21T08:09:19.412717Z", + "shell.execute_reply": "2023-11-21T08:09:19.412127Z" }, "scrolled": true }, @@ -1357,10 +1357,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:36.529506Z", - "iopub.status.busy": "2023-11-20T20:32:36.529146Z", - "iopub.status.idle": "2023-11-20T20:32:36.539031Z", - "shell.execute_reply": "2023-11-20T20:32:36.538507Z" + "iopub.execute_input": "2023-11-21T08:09:19.415152Z", + "iopub.status.busy": "2023-11-21T08:09:19.414796Z", + "iopub.status.idle": "2023-11-21T08:09:19.424574Z", + "shell.execute_reply": "2023-11-21T08:09:19.423984Z" } }, "outputs": [ @@ -1453,7 +1453,7 @@ "source": [ "`Datalab` makes it very easy to check your datasets for all sorts of issues that are important to deal with for training robust models. The inputs it uses to detect issues can come from *any* model you have trained (the better your model, the more accurate the issue detection will be).\n", "\n", - "To learn more, check out this [examples notebook](https://github.com/cleanlab/examples/blob/master/datalab_image_classification/datalab.ipynb) and the [advanced Datalab tutorial](datalab_advanced.html)." + "To learn more, check out this [example notebook](https://github.com/cleanlab/examples/blob/master/datalab_image_classification/datalab.ipynb) (demonstrates Datalab applied to a real dataset) and the [advanced Datalab tutorial](datalab_advanced.html) (demonstrates configuration and customization options to exert greater control)." ] } ], diff --git a/master/tutorials/datalab/tabular.ipynb b/master/tutorials/datalab/tabular.ipynb index c640f534b..1bfc63eb7 100644 --- a/master/tutorials/datalab/tabular.ipynb +++ b/master/tutorials/datalab/tabular.ipynb @@ -74,10 +74,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:41.789698Z", - "iopub.status.busy": "2023-11-20T20:32:41.789256Z", - "iopub.status.idle": "2023-11-20T20:32:42.774357Z", - "shell.execute_reply": "2023-11-20T20:32:42.773747Z" + "iopub.execute_input": "2023-11-21T08:09:24.352475Z", + "iopub.status.busy": "2023-11-21T08:09:24.352291Z", + "iopub.status.idle": "2023-11-21T08:09:25.349712Z", + "shell.execute_reply": "2023-11-21T08:09:25.349078Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:32:42.777399Z", - "iopub.status.busy": "2023-11-20T20:32:42.776926Z", - "iopub.status.idle": "2023-11-20T20:32:42.796657Z", - "shell.execute_reply": "2023-11-20T20:32:42.796058Z" + "iopub.execute_input": "2023-11-21T08:09:25.352735Z", + "iopub.status.busy": "2023-11-21T08:09:25.352254Z", + "iopub.status.idle": "2023-11-21T08:09:25.372356Z", + "shell.execute_reply": "2023-11-21T08:09:25.371799Z" } }, "outputs": [], @@ -155,10 +155,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:42.799491Z", - "iopub.status.busy": "2023-11-20T20:32:42.799011Z", - "iopub.status.idle": "2023-11-20T20:32:42.941253Z", - "shell.execute_reply": "2023-11-20T20:32:42.940652Z" + "iopub.execute_input": "2023-11-21T08:09:25.375034Z", + "iopub.status.busy": "2023-11-21T08:09:25.374665Z", + "iopub.status.idle": "2023-11-21T08:09:25.648497Z", + "shell.execute_reply": "2023-11-21T08:09:25.647858Z" } }, "outputs": [ @@ -265,10 +265,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:42.943695Z", - "iopub.status.busy": "2023-11-20T20:32:42.943215Z", - "iopub.status.idle": "2023-11-20T20:32:42.946835Z", - "shell.execute_reply": "2023-11-20T20:32:42.946242Z" + "iopub.execute_input": "2023-11-21T08:09:25.650926Z", + "iopub.status.busy": "2023-11-21T08:09:25.650733Z", + "iopub.status.idle": "2023-11-21T08:09:25.654384Z", + "shell.execute_reply": "2023-11-21T08:09:25.653798Z" } }, "outputs": [], @@ -289,10 +289,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:42.949139Z", - "iopub.status.busy": "2023-11-20T20:32:42.948787Z", - "iopub.status.idle": "2023-11-20T20:32:42.956625Z", - "shell.execute_reply": "2023-11-20T20:32:42.956039Z" + "iopub.execute_input": "2023-11-21T08:09:25.656761Z", + "iopub.status.busy": "2023-11-21T08:09:25.656298Z", + "iopub.status.idle": "2023-11-21T08:09:25.664389Z", + "shell.execute_reply": "2023-11-21T08:09:25.663763Z" } }, "outputs": [], @@ -337,10 +337,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:42.959374Z", - "iopub.status.busy": "2023-11-20T20:32:42.958908Z", - "iopub.status.idle": "2023-11-20T20:32:42.961731Z", - "shell.execute_reply": "2023-11-20T20:32:42.961128Z" + "iopub.execute_input": "2023-11-21T08:09:25.667081Z", + "iopub.status.busy": "2023-11-21T08:09:25.666642Z", + "iopub.status.idle": "2023-11-21T08:09:25.669479Z", + "shell.execute_reply": "2023-11-21T08:09:25.668877Z" } }, "outputs": [], @@ -362,10 +362,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:42.964166Z", - "iopub.status.busy": "2023-11-20T20:32:42.963732Z", - "iopub.status.idle": "2023-11-20T20:32:46.557261Z", - "shell.execute_reply": "2023-11-20T20:32:46.556562Z" + "iopub.execute_input": "2023-11-21T08:09:25.671825Z", + "iopub.status.busy": "2023-11-21T08:09:25.671430Z", + "iopub.status.idle": "2023-11-21T08:09:29.278265Z", + "shell.execute_reply": "2023-11-21T08:09:29.277553Z" } }, "outputs": [], @@ -401,10 +401,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:46.560778Z", - "iopub.status.busy": "2023-11-20T20:32:46.560146Z", - "iopub.status.idle": "2023-11-20T20:32:46.569973Z", - "shell.execute_reply": "2023-11-20T20:32:46.569461Z" + "iopub.execute_input": "2023-11-21T08:09:29.281842Z", + "iopub.status.busy": "2023-11-21T08:09:29.281270Z", + "iopub.status.idle": "2023-11-21T08:09:29.291026Z", + "shell.execute_reply": "2023-11-21T08:09:29.290418Z" } }, "outputs": [], @@ -436,10 +436,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:46.572428Z", - "iopub.status.busy": "2023-11-20T20:32:46.572088Z", - "iopub.status.idle": "2023-11-20T20:32:47.854978Z", - "shell.execute_reply": "2023-11-20T20:32:47.854244Z" + "iopub.execute_input": "2023-11-21T08:09:29.293775Z", + "iopub.status.busy": "2023-11-21T08:09:29.293265Z", + "iopub.status.idle": "2023-11-21T08:09:30.615685Z", + "shell.execute_reply": "2023-11-21T08:09:30.614966Z" } }, "outputs": [ @@ -474,10 +474,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.858619Z", - "iopub.status.busy": "2023-11-20T20:32:47.857936Z", - "iopub.status.idle": "2023-11-20T20:32:47.879874Z", - "shell.execute_reply": "2023-11-20T20:32:47.879283Z" + "iopub.execute_input": "2023-11-21T08:09:30.620079Z", + "iopub.status.busy": "2023-11-21T08:09:30.618561Z", + "iopub.status.idle": "2023-11-21T08:09:30.642679Z", + "shell.execute_reply": "2023-11-21T08:09:30.642095Z" }, "scrolled": true }, @@ -602,10 +602,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.882808Z", - "iopub.status.busy": "2023-11-20T20:32:47.882365Z", - "iopub.status.idle": "2023-11-20T20:32:47.892330Z", - "shell.execute_reply": "2023-11-20T20:32:47.891743Z" + "iopub.execute_input": "2023-11-21T08:09:30.646969Z", + "iopub.status.busy": "2023-11-21T08:09:30.645857Z", + "iopub.status.idle": "2023-11-21T08:09:30.658265Z", + "shell.execute_reply": "2023-11-21T08:09:30.657693Z" } }, "outputs": [ @@ -709,10 +709,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.896116Z", - "iopub.status.busy": "2023-11-20T20:32:47.895000Z", - "iopub.status.idle": "2023-11-20T20:32:47.909362Z", - "shell.execute_reply": "2023-11-20T20:32:47.908782Z" + "iopub.execute_input": "2023-11-21T08:09:30.662481Z", + "iopub.status.busy": "2023-11-21T08:09:30.661346Z", + "iopub.status.idle": "2023-11-21T08:09:30.675664Z", + "shell.execute_reply": "2023-11-21T08:09:30.675085Z" } }, "outputs": [ @@ -841,10 +841,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.913675Z", - "iopub.status.busy": "2023-11-20T20:32:47.912553Z", - "iopub.status.idle": "2023-11-20T20:32:47.925127Z", - "shell.execute_reply": "2023-11-20T20:32:47.924531Z" + "iopub.execute_input": "2023-11-21T08:09:30.679946Z", + "iopub.status.busy": "2023-11-21T08:09:30.678830Z", + "iopub.status.idle": "2023-11-21T08:09:30.691299Z", + "shell.execute_reply": "2023-11-21T08:09:30.690732Z" } }, "outputs": [ @@ -958,10 +958,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.929494Z", - "iopub.status.busy": "2023-11-20T20:32:47.928365Z", - "iopub.status.idle": "2023-11-20T20:32:47.942464Z", - "shell.execute_reply": "2023-11-20T20:32:47.941879Z" + "iopub.execute_input": "2023-11-21T08:09:30.695552Z", + "iopub.status.busy": "2023-11-21T08:09:30.694443Z", + "iopub.status.idle": "2023-11-21T08:09:30.708130Z", + "shell.execute_reply": "2023-11-21T08:09:30.707674Z" } }, "outputs": [ @@ -1072,10 +1072,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.946160Z", - "iopub.status.busy": "2023-11-20T20:32:47.945557Z", - "iopub.status.idle": "2023-11-20T20:32:47.952712Z", - "shell.execute_reply": "2023-11-20T20:32:47.952176Z" + "iopub.execute_input": "2023-11-21T08:09:30.711020Z", + "iopub.status.busy": "2023-11-21T08:09:30.710537Z", + "iopub.status.idle": "2023-11-21T08:09:30.717380Z", + "shell.execute_reply": "2023-11-21T08:09:30.716812Z" } }, "outputs": [ @@ -1159,10 +1159,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.955087Z", - "iopub.status.busy": "2023-11-20T20:32:47.954876Z", - "iopub.status.idle": "2023-11-20T20:32:47.961968Z", - "shell.execute_reply": "2023-11-20T20:32:47.961177Z" + "iopub.execute_input": "2023-11-21T08:09:30.719851Z", + "iopub.status.busy": "2023-11-21T08:09:30.719376Z", + "iopub.status.idle": "2023-11-21T08:09:30.726272Z", + "shell.execute_reply": "2023-11-21T08:09:30.725649Z" } }, "outputs": [ @@ -1246,10 +1246,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:47.964435Z", - "iopub.status.busy": "2023-11-20T20:32:47.964067Z", - "iopub.status.idle": "2023-11-20T20:32:47.971090Z", - "shell.execute_reply": "2023-11-20T20:32:47.970455Z" + "iopub.execute_input": "2023-11-21T08:09:30.728567Z", + "iopub.status.busy": "2023-11-21T08:09:30.728237Z", + "iopub.status.idle": "2023-11-21T08:09:30.735274Z", + "shell.execute_reply": "2023-11-21T08:09:30.734633Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/datalab/text.html b/master/tutorials/datalab/text.html index 48a43d8c4..c3a0a382c 100644 --- a/master/tutorials/datalab/text.html +++ b/master/tutorials/datalab/text.html @@ -938,7 +938,7 @@
This dataset has 10 classes.
-Classes: {'change_pin', 'beneficiary_not_allowed', 'getting_spare_card', 'cancel_transfer', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_payment_fee_charged'}
+Classes: {'lost_or_stolen_phone', 'beneficiary_not_allowed', 'change_pin', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'cancel_transfer', 'card_about_to_expire', 'visa_or_mastercard', 'supported_cards_and_currencies', 'getting_spare_card'}
Let’s view the i-th example in the dataset:
@@ -985,43 +985,43 @@As demonstrated above, cleanlab can automatically shortlist the most likely issues in your dataset to help you better curate your dataset for subsequent modeling. With this shortlist, you can decide whether to fix these label issues or remove nonsensical or duplicated examples from your dataset to obtain a higher-quality dataset for training your next ML model. cleanlab’s issue detection can be run with outputs from any type of model you initially trained.
diff --git a/master/tutorials/datalab/text.ipynb b/master/tutorials/datalab/text.ipynb index 9450c9a0e..518f19aa7 100644 --- a/master/tutorials/datalab/text.ipynb +++ b/master/tutorials/datalab/text.ipynb @@ -75,10 +75,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:52.575977Z", - "iopub.status.busy": "2023-11-20T20:32:52.575528Z", - "iopub.status.idle": "2023-11-20T20:32:54.937269Z", - "shell.execute_reply": "2023-11-20T20:32:54.936714Z" + "iopub.execute_input": "2023-11-21T08:09:35.328863Z", + "iopub.status.busy": "2023-11-21T08:09:35.328235Z", + "iopub.status.idle": "2023-11-21T08:09:37.683797Z", + "shell.execute_reply": "2023-11-21T08:09:37.683197Z" }, "nbsphinx": "hidden" }, @@ -93,7 +93,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d54db05155764ee79fb8bed826d68136", + "model_id": "84efccf1fdb54b2fbd5ffafd72a0d2d4", "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:32:54.940299Z", - "iopub.status.busy": "2023-11-20T20:32:54.939859Z", - "iopub.status.idle": "2023-11-20T20:32:54.943455Z", - "shell.execute_reply": "2023-11-20T20:32:54.942825Z" + "iopub.execute_input": "2023-11-21T08:09:37.686963Z", + "iopub.status.busy": "2023-11-21T08:09:37.686395Z", + "iopub.status.idle": "2023-11-21T08:09:37.689918Z", + "shell.execute_reply": "2023-11-21T08:09:37.689297Z" } }, "outputs": [], @@ -167,10 +167,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:54.945857Z", - "iopub.status.busy": "2023-11-20T20:32:54.945488Z", - "iopub.status.idle": "2023-11-20T20:32:54.948798Z", - "shell.execute_reply": "2023-11-20T20:32:54.948205Z" + "iopub.execute_input": "2023-11-21T08:09:37.692242Z", + "iopub.status.busy": "2023-11-21T08:09:37.692040Z", + "iopub.status.idle": "2023-11-21T08:09:37.695169Z", + "shell.execute_reply": "2023-11-21T08:09:37.694641Z" }, "nbsphinx": "hidden" }, @@ -200,10 +200,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:54.951369Z", - "iopub.status.busy": "2023-11-20T20:32:54.951016Z", - "iopub.status.idle": "2023-11-20T20:32:55.014131Z", - "shell.execute_reply": "2023-11-20T20:32:55.013517Z" + "iopub.execute_input": "2023-11-21T08:09:37.697722Z", + "iopub.status.busy": "2023-11-21T08:09:37.697197Z", + "iopub.status.idle": "2023-11-21T08:09:37.880336Z", + "shell.execute_reply": "2023-11-21T08:09:37.879693Z" } }, "outputs": [ @@ -293,10 +293,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:55.016618Z", - "iopub.status.busy": "2023-11-20T20:32:55.016109Z", - "iopub.status.idle": "2023-11-20T20:32:55.020522Z", - "shell.execute_reply": "2023-11-20T20:32:55.019994Z" + "iopub.execute_input": "2023-11-21T08:09:37.882668Z", + "iopub.status.busy": "2023-11-21T08:09:37.882468Z", + "iopub.status.idle": "2023-11-21T08:09:37.886833Z", + "shell.execute_reply": "2023-11-21T08:09:37.886311Z" } }, "outputs": [ @@ -305,7 +305,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'change_pin', 'beneficiary_not_allowed', 'getting_spare_card', 'cancel_transfer', 'supported_cards_and_currencies', 'apple_pay_or_google_pay', 'card_about_to_expire', 'visa_or_mastercard', 'lost_or_stolen_phone', 'card_payment_fee_charged'}\n" + "Classes: {'lost_or_stolen_phone', 'beneficiary_not_allowed', 'change_pin', 'apple_pay_or_google_pay', 'card_payment_fee_charged', 'cancel_transfer', 'card_about_to_expire', 'visa_or_mastercard', 'supported_cards_and_currencies', 'getting_spare_card'}\n" ] } ], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:55.022841Z", - "iopub.status.busy": "2023-11-20T20:32:55.022499Z", - "iopub.status.idle": "2023-11-20T20:32:55.026100Z", - "shell.execute_reply": "2023-11-20T20:32:55.025481Z" + "iopub.execute_input": "2023-11-21T08:09:37.889162Z", + "iopub.status.busy": "2023-11-21T08:09:37.888966Z", + "iopub.status.idle": "2023-11-21T08:09:37.892867Z", + "shell.execute_reply": "2023-11-21T08:09:37.892357Z" } }, "outputs": [ @@ -387,17 +387,17 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:32:55.028777Z", - "iopub.status.busy": "2023-11-20T20:32:55.028279Z", - "iopub.status.idle": "2023-11-20T20:33:04.039924Z", - "shell.execute_reply": "2023-11-20T20:33:04.039210Z" + "iopub.execute_input": "2023-11-21T08:09:37.895400Z", + "iopub.status.busy": "2023-11-21T08:09:37.895052Z", + "iopub.status.idle": "2023-11-21T08:09:48.146709Z", + "shell.execute_reply": "2023-11-21T08:09:48.145986Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f3a80e73a7b443beb304232769a2adf5", + "model_id": "bdbfd3c9e01e4c66a7d2920c798024d3", "version_major": 2, "version_minor": 0 }, @@ -411,7 +411,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f1eac02d89ea410296effca01dd5fdf1", + "model_id": "ef9b066900fb4ced8f7b4f486b6b61a6", "version_major": 2, "version_minor": 0 }, @@ -425,7 +425,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2f6960c8c542429984f644a4477a1bbd", + "model_id": "e90c4b7b38ac41bf97dab284611fe567", "version_major": 2, "version_minor": 0 }, @@ -439,7 +439,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "24defa2b39da45b489630e31a68d7a34", + "model_id": "1dbde4ffa31c4efb85eddbafeda00e66", "version_major": 2, "version_minor": 0 }, @@ -453,7 +453,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0d646f3871ca44fba85ea234b692e0de", + "model_id": "f69acc9daea44e8f8636482936ec63ea", "version_major": 2, "version_minor": 0 }, @@ -467,7 +467,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "48b3ebdabbf748608b1f8b746f57cf16", + "model_id": "3d07442b240545d6bd98314771e24331", "version_major": 2, "version_minor": 0 }, @@ -481,7 +481,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5fe16afe08654d5fbbb7a8efb3b9006a", + "model_id": "2111d77e8324438fa80daa16cba4c64c", "version_major": 2, "version_minor": 0 }, @@ -535,10 +535,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:04.043353Z", - "iopub.status.busy": "2023-11-20T20:33:04.042928Z", - "iopub.status.idle": "2023-11-20T20:33:05.288496Z", - "shell.execute_reply": "2023-11-20T20:33:05.287832Z" + "iopub.execute_input": "2023-11-21T08:09:48.150018Z", + "iopub.status.busy": "2023-11-21T08:09:48.149787Z", + "iopub.status.idle": "2023-11-21T08:09:49.317159Z", + "shell.execute_reply": "2023-11-21T08:09:49.316481Z" }, "scrolled": true }, @@ -570,10 +570,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:05.292133Z", - "iopub.status.busy": "2023-11-20T20:33:05.291530Z", - "iopub.status.idle": "2023-11-20T20:33:05.295016Z", - "shell.execute_reply": "2023-11-20T20:33:05.294440Z" + "iopub.execute_input": "2023-11-21T08:09:49.320806Z", + "iopub.status.busy": "2023-11-21T08:09:49.320342Z", + "iopub.status.idle": "2023-11-21T08:09:49.323504Z", + "shell.execute_reply": "2023-11-21T08:09:49.322949Z" } }, "outputs": [], @@ -593,10 +593,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:05.297976Z", - "iopub.status.busy": "2023-11-20T20:33:05.297541Z", - "iopub.status.idle": "2023-11-20T20:33:06.602112Z", - "shell.execute_reply": "2023-11-20T20:33:06.601301Z" + "iopub.execute_input": "2023-11-21T08:09:49.326388Z", + "iopub.status.busy": "2023-11-21T08:09:49.325962Z", + "iopub.status.idle": "2023-11-21T08:09:50.636016Z", + "shell.execute_reply": "2023-11-21T08:09:50.635252Z" }, "scrolled": true }, @@ -638,10 +638,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.605661Z", - "iopub.status.busy": "2023-11-20T20:33:06.604934Z", - "iopub.status.idle": "2023-11-20T20:33:06.628259Z", - "shell.execute_reply": "2023-11-20T20:33:06.627633Z" + "iopub.execute_input": "2023-11-21T08:09:50.641239Z", + "iopub.status.busy": "2023-11-21T08:09:50.639659Z", + "iopub.status.idle": "2023-11-21T08:09:50.664791Z", + "shell.execute_reply": "2023-11-21T08:09:50.664192Z" }, "scrolled": true }, @@ -766,10 +766,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.635537Z", - "iopub.status.busy": "2023-11-20T20:33:06.635066Z", - "iopub.status.idle": "2023-11-20T20:33:06.646046Z", - "shell.execute_reply": "2023-11-20T20:33:06.645375Z" + "iopub.execute_input": "2023-11-21T08:09:50.669150Z", + "iopub.status.busy": "2023-11-21T08:09:50.667876Z", + "iopub.status.idle": "2023-11-21T08:09:50.681035Z", + "shell.execute_reply": "2023-11-21T08:09:50.680452Z" }, "scrolled": true }, @@ -879,10 +879,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.649442Z", - "iopub.status.busy": "2023-11-20T20:33:06.649024Z", - "iopub.status.idle": "2023-11-20T20:33:06.654657Z", - "shell.execute_reply": "2023-11-20T20:33:06.654046Z" + "iopub.execute_input": "2023-11-21T08:09:50.685353Z", + "iopub.status.busy": "2023-11-21T08:09:50.684227Z", + "iopub.status.idle": "2023-11-21T08:09:50.691781Z", + "shell.execute_reply": "2023-11-21T08:09:50.691207Z" } }, "outputs": [ @@ -920,10 +920,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.657989Z", - "iopub.status.busy": "2023-11-20T20:33:06.657517Z", - "iopub.status.idle": "2023-11-20T20:33:06.665476Z", - "shell.execute_reply": "2023-11-20T20:33:06.664975Z" + "iopub.execute_input": "2023-11-21T08:09:50.695017Z", + "iopub.status.busy": "2023-11-21T08:09:50.694817Z", + "iopub.status.idle": "2023-11-21T08:09:50.702157Z", + "shell.execute_reply": "2023-11-21T08:09:50.701550Z" } }, "outputs": [ @@ -1040,10 +1040,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.668159Z", - "iopub.status.busy": "2023-11-20T20:33:06.667766Z", - "iopub.status.idle": "2023-11-20T20:33:06.674700Z", - "shell.execute_reply": "2023-11-20T20:33:06.674211Z" + "iopub.execute_input": "2023-11-21T08:09:50.704614Z", + "iopub.status.busy": "2023-11-21T08:09:50.704255Z", + "iopub.status.idle": "2023-11-21T08:09:50.711038Z", + "shell.execute_reply": "2023-11-21T08:09:50.710520Z" } }, "outputs": [ @@ -1126,10 +1126,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.677235Z", - "iopub.status.busy": "2023-11-20T20:33:06.676846Z", - "iopub.status.idle": "2023-11-20T20:33:06.683494Z", - "shell.execute_reply": "2023-11-20T20:33:06.682950Z" + "iopub.execute_input": "2023-11-21T08:09:50.713287Z", + "iopub.status.busy": "2023-11-21T08:09:50.712928Z", + "iopub.status.idle": "2023-11-21T08:09:50.720090Z", + "shell.execute_reply": "2023-11-21T08:09:50.719464Z" } }, "outputs": [ @@ -1237,10 +1237,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.685859Z", - "iopub.status.busy": "2023-11-20T20:33:06.685572Z", - "iopub.status.idle": "2023-11-20T20:33:06.695914Z", - "shell.execute_reply": "2023-11-20T20:33:06.695269Z" + "iopub.execute_input": "2023-11-21T08:09:50.722513Z", + "iopub.status.busy": "2023-11-21T08:09:50.722149Z", + "iopub.status.idle": "2023-11-21T08:09:50.731346Z", + "shell.execute_reply": "2023-11-21T08:09:50.730840Z" } }, "outputs": [ @@ -1351,10 +1351,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.698397Z", - "iopub.status.busy": "2023-11-20T20:33:06.698189Z", - "iopub.status.idle": "2023-11-20T20:33:06.704394Z", - "shell.execute_reply": "2023-11-20T20:33:06.703751Z" + "iopub.execute_input": "2023-11-21T08:09:50.733786Z", + "iopub.status.busy": "2023-11-21T08:09:50.733415Z", + "iopub.status.idle": "2023-11-21T08:09:50.739208Z", + "shell.execute_reply": "2023-11-21T08:09:50.738614Z" } }, "outputs": [ @@ -1422,10 +1422,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.706901Z", - "iopub.status.busy": "2023-11-20T20:33:06.706413Z", - "iopub.status.idle": "2023-11-20T20:33:06.712217Z", - "shell.execute_reply": "2023-11-20T20:33:06.711618Z" + "iopub.execute_input": "2023-11-21T08:09:50.741542Z", + "iopub.status.busy": "2023-11-21T08:09:50.741177Z", + "iopub.status.idle": "2023-11-21T08:09:50.746859Z", + "shell.execute_reply": "2023-11-21T08:09:50.746262Z" } }, "outputs": [ @@ -1503,10 +1503,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:06.714730Z", - "iopub.status.busy": "2023-11-20T20:33:06.714357Z", - "iopub.status.idle": "2023-11-20T20:33:06.719989Z", - "shell.execute_reply": "2023-11-20T20:33:06.719450Z" + "iopub.execute_input": "2023-11-21T08:09:50.749290Z", + "iopub.status.busy": "2023-11-21T08:09:50.748944Z", + "iopub.status.idle": "2023-11-21T08:09:50.754186Z", + "shell.execute_reply": "2023-11-21T08:09:50.753648Z" }, "nbsphinx": "hidden" }, @@ -1556,7 +1556,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "039540c4098d4867bd077a2ce39d8e39": { + "0a163ebe579b47869711b0e6f4e62c87": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1608,29 +1608,59 @@ "width": null } }, - "0d646f3871ca44fba85ea234b692e0de": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "0a1779c499014bc68a84cfc642ce1725": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_2a0716bed248445ca1e8c04bccbaf146", - "IPY_MODEL_6f84d648d19445758024085e38f78d88", - "IPY_MODEL_44114d530986407794206b2157b4c514" - ], - "layout": "IPY_MODEL_668f3b52d76340ecbf73e0d153d01388" + "_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 } }, - "0e67b54bd6804b27b18acd1fd029fcd3": { + "0b1ff99343e14f8386920438d324ef62": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -1646,30 +1676,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_ab98e93b1bbc4325bb17e181debcb6e4", - "max": 1.0, + "layout": "IPY_MODEL_591c9dd8aaac4ac69b56816710cc9aa2", + "max": 231508.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_a053aa210df54945b8918f4bae67cd52", - "value": 0.0 - } - }, - "1175a3f3a7794d018ca694be5ffa6003": { - "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_3a33834b8968499cac761ee7650c7498", + "value": 231508.0 } }, - "12ae094ce5d04f0a8b2439e257e3165b": { + "0fea425ed46b43829f77b5de1a61c2b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1721,23 +1736,28 @@ "width": null } }, - "2005f8d011dc4f3ea9a0a944e5cbdbb3": { + "107075362c9d42b480b6c546b52e7735": { "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_0fea425ed46b43829f77b5de1a61c2b0", + "placeholder": "", + "style": "IPY_MODEL_3e817664dd7a4b439a33fe50f30624c6", + "value": " 466k/466k [00:00<00:00, 3.60MB/s]" } }, - "2066ea65fcab495b98d0ec633c205c06": { + "112c6d5b0cb34133ac7cbec5d2389adb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1752,22 +1772,31 @@ "description_width": "" } }, - "20bfb16a2a934759827d6f8b51c241fd": { + "172c78277ca14877b1a3da40b9138f52": { "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_cadc44c76fb44c07a30f82ff23e67e89", + "max": 665.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_dc5807a6220944eebc1c543ddb7c73e8", + "value": 665.0 } }, - "211e1a6b61264e52806cfdf399bd4bb6": { + "18595bd57aff40a9a0108eafcb2ad410": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1819,71 +1848,23 @@ "width": null } }, - "21fd4b0c85a242a78e4c8a0fe5191728": { - "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_4353172fa52c4daa86930ad97e72fc83", - "placeholder": "", - "style": "IPY_MODEL_2066ea65fcab495b98d0ec633c205c06", - "value": "config.json: 100%" - } - }, - "24defa2b39da45b489630e31a68d7a34": { - "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_a65959fce8f0476bbd4578941ebe290f", - "IPY_MODEL_9523201e089c4b1484304c2389d890a1", - "IPY_MODEL_3e54a9e06b3d4257a475a4f68cc0486c" - ], - "layout": "IPY_MODEL_e756314819e141ed88bcd0bc1c4bce8a" - } - }, - "250f73e3763746ccbae18521d684039d": { + "19aecc8940484ac5a737db0f0af3a58d": { "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_c41ad12ee15e45b0bed1ebd67822eb1f", - "placeholder": "", - "style": "IPY_MODEL_20bfb16a2a934759827d6f8b51c241fd", - "value": "" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "26324b4b25c04ea7818bba469a441452": { + "1b702e942afe47ebacb4d2c7211c47cd": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1935,43 +1916,53 @@ "width": null } }, - "284816cb41ea47f194b9ab0973ee79f6": { + "1b893a8a45c143eb8779f891294f0f1f": { "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_bedca6f1a4d041c69a5315ea83880c71", + "max": 2211.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ee1d278a26754d46a3d364c3b88cc77e", + "value": 2211.0 } }, - "29fa280d9abf40aaa5e6496643f73d8a": { + "1dbde4ffa31c4efb85eddbafeda00e66": { "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_fc492bb933e1425ca731e0b9e16e3c25", - "placeholder": "", - "style": "IPY_MODEL_8227fe1ff0464d6a8c247f5d9d6f5d56", - "value": " 29.0/29.0 [00:00<00:00, 3.78kB/s]" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3fabc6db74244b9c9ac3c796dba34826", + "IPY_MODEL_4ec31c7de3eb406482338591622b33ad", + "IPY_MODEL_a55cc233450248318737b37fa4c14461" + ], + "layout": "IPY_MODEL_1b702e942afe47ebacb4d2c7211c47cd" } }, - "2a0716bed248445ca1e8c04bccbaf146": { + "1e592846b61749b9a36d221e430c1b90": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1986,37 +1977,34 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_47346d3705ec42b8869269ec44935be4", + "layout": "IPY_MODEL_e1cbae967deb4f79b112b62a3b327652", "placeholder": "", - "style": "IPY_MODEL_33db3079f4134514a013b7c5842aac85", - "value": "tokenizer.json: 100%" + "style": "IPY_MODEL_67163f62148c4b41a5a499a1a3d66288", + "value": " 232k/232k [00:00<00:00, 1.75MB/s]" } }, - "2e04d555cb3d419680fe93aa2c4d77fd": { + "207a23a480a54deb924c5c17c968cc98": { "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_ccf33f86a3404b2e981f990bab45814a", - "max": 665.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6706a6b6dece48c7b645ddae5d18f15a", - "value": 665.0 + "layout": "IPY_MODEL_c85ead9cc74541aba00a004e422e4e82", + "placeholder": "", + "style": "IPY_MODEL_ca9ac305cc49437495789a0dda9a227b", + "value": " 2.21k/2.21k [00:00<00:00, 292kB/s]" } }, - "2f6960c8c542429984f644a4477a1bbd": { + "2111d77e8324438fa80daa16cba4c64c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -2031,60 +2019,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_21fd4b0c85a242a78e4c8a0fe5191728", - "IPY_MODEL_2e04d555cb3d419680fe93aa2c4d77fd", - "IPY_MODEL_d4bc8433e74843428c969e4d10ed6f70" + "IPY_MODEL_49ce368efd6c41a68e1b59f615be2c5c", + "IPY_MODEL_0b1ff99343e14f8386920438d324ef62", + "IPY_MODEL_1e592846b61749b9a36d221e430c1b90" ], - "layout": "IPY_MODEL_9736b54786404e6d87505bf512eabc59" - } - }, - "31f68703509a4908be64b113f1722c9a": { - "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": "" - } - }, - "33264e12ba984b149e0836334b6b3690": { - "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": "" - } - }, - "33db3079f4134514a013b7c5842aac85": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "DescriptionStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "description_width": "" + "layout": "IPY_MODEL_7436e4f9268b4fd19d5d944b06fc348d" } }, - "3482d0d18bc445d1b8e392deca24c9c6": { + "2c54172478ff492d83b81da0d976e710": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2136,7 +2078,23 @@ "width": null } }, - "37687a58523a4f48bf2665a92fdaed16": { + "2d9ce6f633934cc8a757a868256a51b1": { + "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": "" + } + }, + "32b53331dede4e649b77ac43b00f78f9": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2188,7 +2146,7 @@ "width": null } }, - "37e5007d690b41a29aa34b75f470b548": { + "3662a0fdf755483ea3df820923e4a269": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2203,128 +2161,66 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_26324b4b25c04ea7818bba469a441452", + "layout": "IPY_MODEL_0a1779c499014bc68a84cfc642ce1725", "placeholder": "", - "style": "IPY_MODEL_284816cb41ea47f194b9ab0973ee79f6", - "value": " 2.21k/2.21k [00:00<00:00, 300kB/s]" + "style": "IPY_MODEL_c7970c7de6db433cafa82db7c4ba55c2", + "value": "tokenizer_config.json: 100%" } }, - "3e54a9e06b3d4257a475a4f68cc0486c": { + "3a33834b8968499cac761ee7650c7498": { "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", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_3482d0d18bc445d1b8e392deca24c9c6", - "placeholder": "", - "style": "IPY_MODEL_6be4c1d8e0af45c8a8f9ab1ca1dd7a1e", - "value": " 54.2M/54.2M [00:00<00:00, 203MB/s]" - } - }, - "4353172fa52c4daa86930ad97e72fc83": { - "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_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": "" } }, - "44114d530986407794206b2157b4c514": { + "3d07442b240545d6bd98314771e24331": { "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_37687a58523a4f48bf2665a92fdaed16", - "placeholder": "", - "style": "IPY_MODEL_31f68703509a4908be64b113f1722c9a", - "value": " 466k/466k [00:00<00:00, 15.4MB/s]" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3662a0fdf755483ea3df820923e4a269", + "IPY_MODEL_ad57643441a9400cb2a24bf8a4bffbf4", + "IPY_MODEL_c4ff8f77501d44afa0533b89ce66ced6" + ], + "layout": "IPY_MODEL_0a163ebe579b47869711b0e6f4e62c87" } }, - "44bbcc32b7ee46ddb11e73e585b804b0": { + "3e817664dd7a4b439a33fe50f30624c6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_039540c4098d4867bd077a2ce39d8e39", - "placeholder": "", - "style": "IPY_MODEL_b19018911894446ab6ac0320c0c580b7", - "value": " 0/0 [00:00<?, ?it/s]" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "47346d3705ec42b8869269ec44935be4": { + "3f803816262543999985de624aa862a8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2376,29 +2272,43 @@ "width": null } }, - "48b3ebdabbf748608b1f8b746f57cf16": { + "3fabc6db74244b9c9ac3c796dba34826": { "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_a21dcc04456d4c34a486f1bfd4e02863", - "IPY_MODEL_9fd02b530c504169a3325c314f6f9356", - "IPY_MODEL_29fa280d9abf40aaa5e6496643f73d8a" - ], - "layout": "IPY_MODEL_211e1a6b61264e52806cfdf399bd4bb6" + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_63121665fa8c4bd586d1adeafc28d768", + "placeholder": "", + "style": "IPY_MODEL_b55b8af54d12442eb30f5c13b43373d9", + "value": "pytorch_model.bin: 100%" + } + }, + "41c756dd41814d2c853bafb048e267b4": { + "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": "" } }, - "49a5150a59814722906cb8d36334e5ff": { + "44df00352a2443e89267d2862e274554": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2450,7 +2360,43 @@ "width": null } }, - "4c3d271520b44bfd92d342d615467f64": { + "48e63068978f4b71843894353be3f288": { + "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": "" + } + }, + "49ce368efd6c41a68e1b59f615be2c5c": { + "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_18595bd57aff40a9a0108eafcb2ad410", + "placeholder": "", + "style": "IPY_MODEL_9aad58654a4441d5b85e2f5ff161ee7a", + "value": "vocab.txt: 100%" + } + }, + "4afdaee1b7be4a4eb13b8379fa88af2b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2502,7 +2448,31 @@ "width": null } }, - "568e9cf1380c4df3b6e2cf760ad2e7d4": { + "4ec31c7de3eb406482338591622b33ad": { + "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_5b409a661584439bb06e50825f285ab2", + "max": 54245363.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_f66a6f6f614b4a599f9ac0cd065c14ee", + "value": 54245363.0 + } + }, + "52dd4522849c4df38e47a768855f2f49": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -2517,13 +2487,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_67f21d4199244b31b947b3591d496230", + "layout": "IPY_MODEL_c12db3c77d8a4470bdf6259bfddde092", "placeholder": "", - "style": "IPY_MODEL_7b41eff5d2d7462493f0cfabd6e27ff4", - "value": " 232k/232k [00:00<00:00, 26.8MB/s]" + "style": "IPY_MODEL_78abf288375b4f029096d9c6f45c5a4f", + "value": " 391/391 [00:00<00:00, 46.9kB/s]" } }, - "5b6bf2edb533466eac4844a61d7ef1b1": { + "591c9dd8aaac4ac69b56816710cc9aa2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2575,45 +2545,7 @@ "width": null } }, - "5fe16afe08654d5fbbb7a8efb3b9006a": { - "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_b6bbde45fc1c4d37ad3934b454d1ca6d", - "IPY_MODEL_6ced4f75fee643e3b56ed72a093932dd", - "IPY_MODEL_568e9cf1380c4df3b6e2cf760ad2e7d4" - ], - "layout": "IPY_MODEL_ae2b4daf42254d41bdc0f45124112b43" - } - }, - "65ad633e62c74b8882469e75fbe3bfcf": { - "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": "" - } - }, - "668f3b52d76340ecbf73e0d153d01388": { + "5a00d1eb7d7d4ef2b1bb67dab959baaa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2665,23 +2597,22 @@ "width": null } }, - "6706a6b6dece48c7b645ddae5d18f15a": { + "5ae37c06cebd4198bfa640e1ccd9a494": { "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": "" } }, - "67f21d4199244b31b947b3591d496230": { + "5b409a661584439bb06e50825f285ab2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2733,46 +2664,28 @@ "width": null } }, - "6be4c1d8e0af45c8a8f9ab1ca1dd7a1e": { - "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": "" - } - }, - "6ced4f75fee643e3b56ed72a093932dd": { + "5f170dd530e0495ab21ece306876e5e0": { "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_5b6bf2edb533466eac4844a61d7ef1b1", - "max": 231508.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_33264e12ba984b149e0836334b6b3690", - "value": 231508.0 + "layout": "IPY_MODEL_2c54172478ff492d83b81da0d976e710", + "placeholder": "", + "style": "IPY_MODEL_112c6d5b0cb34133ac7cbec5d2389adb", + "value": ".gitattributes: 100%" } }, - "6f84d648d19445758024085e38f78d88": { + "6009a1260cae419f9469970afa1525c0": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -2788,15 +2701,67 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_99f7fb1de49a4b8c805d00e9d33c5266", - "max": 466062.0, + "layout": "IPY_MODEL_cec07c03b1064d5989cc246ef655e392", + "max": 1.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_65ad633e62c74b8882469e75fbe3bfcf", - "value": 466062.0 + "style": "IPY_MODEL_2d9ce6f633934cc8a757a868256a51b1", + "value": 0.0 + } + }, + "63121665fa8c4bd586d1adeafc28d768": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "782f2b13354544f785bd75906c4245e8": { + "650ae1fa0c8f479090946a949743aef0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2848,23 +2813,43 @@ "width": null } }, - "7a797f6c6b4d4d38b79f19a57ac7bfde": { + "67163f62148c4b41a5a499a1a3d66288": { "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": "" } }, - "7b41eff5d2d7462493f0cfabd6e27ff4": { + "67e14d729c4241608896687e5ab9fe5c": { + "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_4afdaee1b7be4a4eb13b8379fa88af2b", + "placeholder": "", + "style": "IPY_MODEL_41c756dd41814d2c853bafb048e267b4", + "value": "README.md: 100%" + } + }, + "68aa460054cb4cb493b54c8fe18f613b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -2879,7 +2864,7 @@ "description_width": "" } }, - "7c206cb2d08b4c0492b50e812220f447": { + "6f771a745ddc4cd6a522467e6d87b708": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2931,7 +2916,7 @@ "width": null } }, - "8221b786332842b5a52de89a56e4b56e": { + "7161bb5e334345629fb192401c57bb2c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -2983,22 +2968,7 @@ "width": null } }, - "8227fe1ff0464d6a8c247f5d9d6f5d56": { - "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": "" - } - }, - "877f9b36021745209a8675e0ad738984": { + "7308bd016a67460bb70bf0955ad2ab79": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3013,31 +2983,7 @@ "description_width": "" } }, - "8c7de965c57b47de9969c023bf51cd0b": { - "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_782f2b13354544f785bd75906c4245e8", - "max": 2211.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_96e4c7ea35c94547b45868edc53831d0", - "value": 2211.0 - } - }, - "918222bc77134442ae21b7c05cec6d17": { + "7436e4f9268b4fd19d5d944b06fc348d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3084,52 +3030,27 @@ "overflow_y": null, "padding": null, "right": null, - "top": null, - "visibility": null, - "width": null - } - }, - "9523201e089c4b1484304c2389d890a1": { - "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_918222bc77134442ae21b7c05cec6d17", - "max": 54245363.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c4162e61ec4747eda3a2bb153a31bd01", - "value": 54245363.0 + "top": null, + "visibility": null, + "width": null } }, - "96e4c7ea35c94547b45868edc53831d0": { + "78abf288375b4f029096d9c6f45c5a4f": { "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": "" } }, - "9736b54786404e6d87505bf512eabc59": { + "793eed7abd274693b6f5b1acb8764c9f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3181,7 +3102,7 @@ "width": null } }, - "99f7fb1de49a4b8c805d00e9d33c5266": { + "822b64cf88564502820ccaf61631a1b0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3233,7 +3154,7 @@ "width": null } }, - "9d76360f5e3345d284d810048ec3cb2a": { + "8240cd8b020047e3840806ef452c2d87": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3248,95 +3169,135 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_7c206cb2d08b4c0492b50e812220f447", + "layout": "IPY_MODEL_3f803816262543999985de624aa862a8", "placeholder": "", - "style": "IPY_MODEL_c46fda5f1d6e40469032558367ab0c9e", - "value": "README.md: 100%" + "style": "IPY_MODEL_7308bd016a67460bb70bf0955ad2ab79", + "value": "config.json: 100%" } }, - "9fd02b530c504169a3325c314f6f9356": { + "82b2dcac34f1409fbec55f00318c8c04": { "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": "" + } + }, + "84efccf1fdb54b2fbd5ffafd72a0d2d4": { + "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", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_a508496db5f747cab996226d174cf171", + "IPY_MODEL_6009a1260cae419f9469970afa1525c0", + "IPY_MODEL_d1274f2fc9604c7ab40d6376139fcdd4" + ], + "layout": "IPY_MODEL_32b53331dede4e649b77ac43b00f78f9" + } + }, + "8eb23f63a9f34e9ba69cb036a2b13b80": { + "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_12ae094ce5d04f0a8b2439e257e3165b", - "max": 29.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2005f8d011dc4f3ea9a0a944e5cbdbb3", - "value": 29.0 + "layout": "IPY_MODEL_a18d771a7bed461e8986e7575ed905cc", + "placeholder": "", + "style": "IPY_MODEL_e0376bb191cf4aeda10cbf66f620e9ef", + "value": " 665/665 [00:00<00:00, 87.6kB/s]" } }, - "a053aa210df54945b8918f4bae67cd52": { + "9aad58654a4441d5b85e2f5ff161ee7a": { "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": "" } }, - "a21dcc04456d4c34a486f1bfd4e02863": { + "9dc42a0110134970b5df8a2c89f8b77c": { "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_bf12b56d42454630837fda5aae752489", - "placeholder": "", - "style": "IPY_MODEL_877f9b36021745209a8675e0ad738984", - "value": "tokenizer_config.json: 100%" + "layout": "IPY_MODEL_e97995e049b44615b50176300e51d371", + "max": 466062.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_19aecc8940484ac5a737db0f0af3a58d", + "value": 466062.0 } }, - "a65959fce8f0476bbd4578941ebe290f": { + "a0bd40cc15de406e8e312d63cf9a0102": { "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_d1de1b1d4d13403bb0c3db4f0c3ba68b", - "placeholder": "", - "style": "IPY_MODEL_be51cc71dc354041ba49e1ca1c6917c3", - "value": "pytorch_model.bin: 100%" + "layout": "IPY_MODEL_7161bb5e334345629fb192401c57bb2c", + "max": 391.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_82b2dcac34f1409fbec55f00318c8c04", + "value": 391.0 } }, - "ab98e93b1bbc4325bb17e181debcb6e4": { + "a18d771a7bed461e8986e7575ed905cc": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3385,10 +3346,76 @@ "right": null, "top": null, "visibility": null, - "width": "20px" + "width": null + } + }, + "a508496db5f747cab996226d174cf171": { + "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_aeb258677428489fba795a7bac6375ab", + "placeholder": "", + "style": "IPY_MODEL_48e63068978f4b71843894353be3f288", + "value": "" + } + }, + "a55cc233450248318737b37fa4c14461": { + "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_eff47d174257487d802fef33fde361d7", + "placeholder": "", + "style": "IPY_MODEL_68aa460054cb4cb493b54c8fe18f613b", + "value": " 54.2M/54.2M [00:00<00:00, 212MB/s]" + } + }, + "ad57643441a9400cb2a24bf8a4bffbf4": { + "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_6f771a745ddc4cd6a522467e6d87b708", + "max": 29.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_ffbc5d11132b4d3f953e99c99e0cefc1", + "value": 29.0 } }, - "ae2b4daf42254d41bdc0f45124112b43": { + "aeb258677428489fba795a7bac6375ab": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3440,7 +3467,44 @@ "width": null } }, - "ae6d140b37134cccbe0ce004c0f895d8": { + "b55b8af54d12442eb30f5c13b43373d9": { + "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": "" + } + }, + "bdbfd3c9e01e4c66a7d2920c798024d3": { + "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_5f170dd530e0495ab21ece306876e5e0", + "IPY_MODEL_a0bd40cc15de406e8e312d63cf9a0102", + "IPY_MODEL_52dd4522849c4df38e47a768855f2f49" + ], + "layout": "IPY_MODEL_5a00d1eb7d7d4ef2b1bb67dab959baaa" + } + }, + "bedca6f1a4d041c69a5315ea83880c71": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3492,94 +3556,7 @@ "width": null } }, - "b19018911894446ab6ac0320c0c580b7": { - "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": "" - } - }, - "b6bbde45fc1c4d37ad3934b454d1ca6d": { - "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_4c3d271520b44bfd92d342d615467f64", - "placeholder": "", - "style": "IPY_MODEL_b6fe8cd016e84742b84d2a8492ed4e64", - "value": "vocab.txt: 100%" - } - }, - "b6fe8cd016e84742b84d2a8492ed4e64": { - "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": "" - } - }, - "ba70d09674694e6895ea5e47b5f2b309": { - "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_49a5150a59814722906cb8d36334e5ff", - "placeholder": "", - "style": "IPY_MODEL_ccead207e816488daf46b779a82278d6", - "value": " 391/391 [00:00<00:00, 51.7kB/s]" - } - }, - "be51cc71dc354041ba49e1ca1c6917c3": { - "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": "" - } - }, - "bf12b56d42454630837fda5aae752489": { + "c12db3c77d8a4470bdf6259bfddde092": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3631,7 +3608,28 @@ "width": null } }, - "c1a1796d01f44b4fb7d6e14772a0ae72": { + "c4ff8f77501d44afa0533b89ce66ced6": { + "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_c69b45f0b9974436b4613e30124cfb6b", + "placeholder": "", + "style": "IPY_MODEL_f4f2a8b14d2c45ff868dc735d6356e64", + "value": " 29.0/29.0 [00:00<00:00, 3.77kB/s]" + } + }, + "c69b45f0b9974436b4613e30124cfb6b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3683,23 +3681,22 @@ "width": null } }, - "c4162e61ec4747eda3a2bb153a31bd01": { + "c7970c7de6db433cafa82db7c4ba55c2": { "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": "" } }, - "c41ad12ee15e45b0bed1ebd67822eb1f": { + "c85ead9cc74541aba00a004e422e4e82": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3751,22 +3748,7 @@ "width": null } }, - "c46fda5f1d6e40469032558367ab0c9e": { - "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": "" - } - }, - "c5828a960da94f30a1b66491e789d6f5": { + "c905e4d4faba44e88810db1f13d1208f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3781,13 +3763,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_8221b786332842b5a52de89a56e4b56e", + "layout": "IPY_MODEL_650ae1fa0c8f479090946a949743aef0", "placeholder": "", - "style": "IPY_MODEL_1175a3f3a7794d018ca694be5ffa6003", - "value": ".gitattributes: 100%" + "style": "IPY_MODEL_f0ef357162ad4e09b50aafb279c26266", + "value": "tokenizer.json: 100%" } }, - "ccead207e816488daf46b779a82278d6": { + "ca9ac305cc49437495789a0dda9a227b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3802,7 +3784,7 @@ "description_width": "" } }, - "ccf33f86a3404b2e981f990bab45814a": { + "cadc44c76fb44c07a30f82ff23e67e89": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3854,7 +3836,7 @@ "width": null } }, - "d1de1b1d4d13403bb0c3db4f0c3ba68b": { + "cec07c03b1064d5989cc246ef655e392": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3903,10 +3885,10 @@ "right": null, "top": null, "visibility": null, - "width": null + "width": "20px" } }, - "d4bc8433e74843428c969e4d10ed6f70": { + "d1274f2fc9604c7ab40d6376139fcdd4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3921,59 +3903,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_f0f810fd6c4c47909c9ab85960dfc0e9", + "layout": "IPY_MODEL_d3ccfa2190244a05bacac0d4b411e713", "placeholder": "", - "style": "IPY_MODEL_f2d6b9d677ff4ecaafb373fc3bd97a5b", - "value": " 665/665 [00:00<00:00, 89.3kB/s]" - } - }, - "d54db05155764ee79fb8bed826d68136": { - "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_250f73e3763746ccbae18521d684039d", - "IPY_MODEL_0e67b54bd6804b27b18acd1fd029fcd3", - "IPY_MODEL_44bbcc32b7ee46ddb11e73e585b804b0" - ], - "layout": "IPY_MODEL_ae6d140b37134cccbe0ce004c0f895d8" - } - }, - "dcd5d8646ff048b580a8b3e3ea17d670": { - "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_df916fbe4ae84ceb943c03c23ffb85cf", - "max": 391.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_7a797f6c6b4d4d38b79f19a57ac7bfde", - "value": 391.0 + "style": "IPY_MODEL_5ae37c06cebd4198bfa640e1ccd9a494", + "value": " 0/0 [00:00<?, ?it/s]" } }, - "df916fbe4ae84ceb943c03c23ffb85cf": { + "d3ccfa2190244a05bacac0d4b411e713": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4025,7 +3961,38 @@ "width": null } }, - "e756314819e141ed88bcd0bc1c4bce8a": { + "dc5807a6220944eebc1c543ddb7c73e8": { + "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": "" + } + }, + "e0376bb191cf4aeda10cbf66f620e9ef": { + "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": "" + } + }, + "e1cbae967deb4f79b112b62a3b327652": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4077,7 +4044,29 @@ "width": null } }, - "f0f810fd6c4c47909c9ab85960dfc0e9": { + "e90c4b7b38ac41bf97dab284611fe567": { + "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_8240cd8b020047e3840806ef452c2d87", + "IPY_MODEL_172c78277ca14877b1a3da40b9138f52", + "IPY_MODEL_8eb23f63a9f34e9ba69cb036a2b13b80" + ], + "layout": "IPY_MODEL_822b64cf88564502820ccaf61631a1b0" + } + }, + "e97995e049b44615b50176300e51d371": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4129,44 +4118,23 @@ "width": null } }, - "f1eac02d89ea410296effca01dd5fdf1": { - "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_9d76360f5e3345d284d810048ec3cb2a", - "IPY_MODEL_8c7de965c57b47de9969c023bf51cd0b", - "IPY_MODEL_37e5007d690b41a29aa34b75f470b548" - ], - "layout": "IPY_MODEL_c1a1796d01f44b4fb7d6e14772a0ae72" - } - }, - "f2d6b9d677ff4ecaafb373fc3bd97a5b": { + "ee1d278a26754d46a3d364c3b88cc77e": { "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": "" } }, - "f3a80e73a7b443beb304232769a2adf5": { + "ef9b066900fb4ced8f7b4f486b6b61a6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -4181,14 +4149,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_c5828a960da94f30a1b66491e789d6f5", - "IPY_MODEL_dcd5d8646ff048b580a8b3e3ea17d670", - "IPY_MODEL_ba70d09674694e6895ea5e47b5f2b309" + "IPY_MODEL_67e14d729c4241608896687e5ab9fe5c", + "IPY_MODEL_1b893a8a45c143eb8779f891294f0f1f", + "IPY_MODEL_207a23a480a54deb924c5c17c968cc98" ], - "layout": "IPY_MODEL_f5540318e4314e51ac6554d736f409fc" + "layout": "IPY_MODEL_793eed7abd274693b6f5b1acb8764c9f" } }, - "f5540318e4314e51ac6554d736f409fc": { + "eff47d174257487d802fef33fde361d7": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4240,56 +4208,88 @@ "width": null } }, - "fc492bb933e1425ca731e0b9e16e3c25": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "f0ef357162ad4e09b50aafb279c26266": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" + } + }, + "f4f2a8b14d2c45ff868dc735d6356e64": { + "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": "" + } + }, + "f66a6f6f614b4a599f9ac0cd065c14ee": { + "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": "" + } + }, + "f69acc9daea44e8f8636482936ec63ea": { + "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_c905e4d4faba44e88810db1f13d1208f", + "IPY_MODEL_9dc42a0110134970b5df8a2c89f8b77c", + "IPY_MODEL_107075362c9d42b480b6c546b52e7735" + ], + "layout": "IPY_MODEL_44df00352a2443e89267d2862e274554" + } + }, + "ffbc5d11132b4d3f953e99c99e0cefc1": { + "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": "" } } }, diff --git a/master/tutorials/dataset_health.ipynb b/master/tutorials/dataset_health.ipynb index 720b155d1..4714f5689 100644 --- a/master/tutorials/dataset_health.ipynb +++ b/master/tutorials/dataset_health.ipynb @@ -68,10 +68,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:11.763787Z", - "iopub.status.busy": "2023-11-20T20:33:11.763596Z", - "iopub.status.idle": "2023-11-20T20:33:12.758487Z", - "shell.execute_reply": "2023-11-20T20:33:12.757869Z" + "iopub.execute_input": "2023-11-21T08:09:55.619155Z", + "iopub.status.busy": "2023-11-21T08:09:55.618966Z", + "iopub.status.idle": "2023-11-21T08:09:56.625511Z", + "shell.execute_reply": "2023-11-21T08:09:56.624881Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:33:12.761409Z", - "iopub.status.busy": "2023-11-20T20:33:12.760895Z", - "iopub.status.idle": "2023-11-20T20:33:12.764033Z", - "shell.execute_reply": "2023-11-20T20:33:12.763513Z" + "iopub.execute_input": "2023-11-21T08:09:56.628711Z", + "iopub.status.busy": "2023-11-21T08:09:56.628008Z", + "iopub.status.idle": "2023-11-21T08:09:56.631272Z", + "shell.execute_reply": "2023-11-21T08:09:56.630652Z" }, "id": "_UvI80l42iyi" }, @@ -201,10 +201,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:12.766234Z", - "iopub.status.busy": "2023-11-20T20:33:12.766036Z", - "iopub.status.idle": "2023-11-20T20:33:12.778617Z", - "shell.execute_reply": "2023-11-20T20:33:12.778106Z" + "iopub.execute_input": "2023-11-21T08:09:56.633902Z", + "iopub.status.busy": "2023-11-21T08:09:56.633713Z", + "iopub.status.idle": "2023-11-21T08:09:56.646331Z", + "shell.execute_reply": "2023-11-21T08:09:56.645704Z" }, "nbsphinx": "hidden" }, @@ -283,10 +283,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:12.780967Z", - "iopub.status.busy": "2023-11-20T20:33:12.780764Z", - "iopub.status.idle": "2023-11-20T20:33:16.096993Z", - "shell.execute_reply": "2023-11-20T20:33:16.096414Z" + "iopub.execute_input": "2023-11-21T08:09:56.648794Z", + "iopub.status.busy": "2023-11-21T08:09:56.648313Z", + "iopub.status.idle": "2023-11-21T08:10:02.849654Z", + "shell.execute_reply": "2023-11-21T08:10:02.848960Z" }, "id": "dhTHOg8Pyv5G" }, @@ -2182,7 +2182,13 @@ "\n", "\n", "🎯 Cifar100_test_set 🎯\n", - "\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "\n", "Loaded the 'cifar100_test_set' dataset with predicted probabilities of shape (10000, 100)\n", "\n", @@ -2557,13 +2563,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", " * Overall, about 18% (1,846 of the 10,000) labels in your dataset have potential issues.\n", " ** The overall label health score for this dataset is: 0.82.\n", "\n", diff --git a/master/tutorials/faq.html b/master/tutorials/faq.html index a551cff9a..9610d54c7 100644 --- a/master/tutorials/faq.html +++ b/master/tutorials/faq.html @@ -932,13 +932,13 @@If your question is not addressed anywhere, please open a new Github issue. Our developers may also provide personalized assistance in our Slack Community.
diff --git a/master/tutorials/faq.ipynb b/master/tutorials/faq.ipynb index 679b77e78..c2c5b4103 100644 --- a/master/tutorials/faq.ipynb +++ b/master/tutorials/faq.ipynb @@ -18,10 +18,10 @@ "id": "2a4efdde", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:20.441289Z", - "iopub.status.busy": "2023-11-20T20:33:20.441108Z", - "iopub.status.idle": "2023-11-20T20:33:21.426585Z", - "shell.execute_reply": "2023-11-20T20:33:21.425981Z" + "iopub.execute_input": "2023-11-21T08:10:07.143495Z", + "iopub.status.busy": "2023-11-21T08:10:07.143049Z", + "iopub.status.idle": "2023-11-21T08:10:08.148389Z", + "shell.execute_reply": "2023-11-21T08:10:08.147707Z" }, "nbsphinx": "hidden" }, @@ -97,10 +97,10 @@ "id": "239d5ee7", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:21.429619Z", - "iopub.status.busy": "2023-11-20T20:33:21.429307Z", - "iopub.status.idle": "2023-11-20T20:33:21.432764Z", - "shell.execute_reply": "2023-11-20T20:33:21.432211Z" + "iopub.execute_input": "2023-11-21T08:10:08.151701Z", + "iopub.status.busy": "2023-11-21T08:10:08.151099Z", + "iopub.status.idle": "2023-11-21T08:10:08.154788Z", + "shell.execute_reply": "2023-11-21T08:10:08.154185Z" } }, "outputs": [], @@ -136,10 +136,10 @@ "id": "28b324aa", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:21.435463Z", - "iopub.status.busy": "2023-11-20T20:33:21.435025Z", - "iopub.status.idle": "2023-11-20T20:33:23.373800Z", - "shell.execute_reply": "2023-11-20T20:33:23.373111Z" + "iopub.execute_input": "2023-11-21T08:10:08.157279Z", + "iopub.status.busy": "2023-11-21T08:10:08.156798Z", + "iopub.status.idle": "2023-11-21T08:10:10.125375Z", + "shell.execute_reply": "2023-11-21T08:10:10.124702Z" } }, "outputs": [], @@ -162,10 +162,10 @@ "id": "28b324ab", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.377241Z", - "iopub.status.busy": "2023-11-20T20:33:23.376481Z", - "iopub.status.idle": "2023-11-20T20:33:23.411041Z", - "shell.execute_reply": "2023-11-20T20:33:23.410375Z" + "iopub.execute_input": "2023-11-21T08:10:10.128981Z", + "iopub.status.busy": "2023-11-21T08:10:10.128189Z", + "iopub.status.idle": "2023-11-21T08:10:10.165781Z", + "shell.execute_reply": "2023-11-21T08:10:10.164972Z" } }, "outputs": [], @@ -188,10 +188,10 @@ "id": "90c10e18", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.414145Z", - "iopub.status.busy": "2023-11-20T20:33:23.413679Z", - "iopub.status.idle": "2023-11-20T20:33:23.446512Z", - "shell.execute_reply": "2023-11-20T20:33:23.445860Z" + "iopub.execute_input": "2023-11-21T08:10:10.169002Z", + "iopub.status.busy": "2023-11-21T08:10:10.168526Z", + "iopub.status.idle": "2023-11-21T08:10:10.203492Z", + "shell.execute_reply": "2023-11-21T08:10:10.202835Z" } }, "outputs": [], @@ -213,10 +213,10 @@ "id": "88839519", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.449387Z", - "iopub.status.busy": "2023-11-20T20:33:23.449047Z", - "iopub.status.idle": "2023-11-20T20:33:23.452403Z", - "shell.execute_reply": "2023-11-20T20:33:23.451883Z" + "iopub.execute_input": "2023-11-21T08:10:10.206744Z", + "iopub.status.busy": "2023-11-21T08:10:10.206265Z", + "iopub.status.idle": "2023-11-21T08:10:10.209333Z", + "shell.execute_reply": "2023-11-21T08:10:10.208830Z" } }, "outputs": [], @@ -238,10 +238,10 @@ "id": "558490c2", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.454792Z", - "iopub.status.busy": "2023-11-20T20:33:23.454434Z", - "iopub.status.idle": "2023-11-20T20:33:23.457158Z", - "shell.execute_reply": "2023-11-20T20:33:23.456650Z" + "iopub.execute_input": "2023-11-21T08:10:10.211796Z", + "iopub.status.busy": "2023-11-21T08:10:10.211366Z", + "iopub.status.idle": "2023-11-21T08:10:10.214088Z", + "shell.execute_reply": "2023-11-21T08:10:10.213532Z" } }, "outputs": [], @@ -298,10 +298,10 @@ "id": "41714b51", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.459780Z", - "iopub.status.busy": "2023-11-20T20:33:23.459437Z", - "iopub.status.idle": "2023-11-20T20:33:23.486542Z", - "shell.execute_reply": "2023-11-20T20:33:23.485851Z" + "iopub.execute_input": "2023-11-21T08:10:10.216658Z", + "iopub.status.busy": "2023-11-21T08:10:10.216204Z", + "iopub.status.idle": "2023-11-21T08:10:10.246683Z", + "shell.execute_reply": "2023-11-21T08:10:10.246035Z" } }, "outputs": [ @@ -315,7 +315,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a0a3e1d0ea46429196251824fcdfc628", + "model_id": "77fe53bc0d0b46b291880f01745c2a75", "version_major": 2, "version_minor": 0 }, @@ -329,7 +329,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5efb438f2c814efbad68e107b18a4c31", + "model_id": "4718bab615de48138738b46d1327472b", "version_major": 2, "version_minor": 0 }, @@ -387,10 +387,10 @@ "id": "20476c70", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.494080Z", - "iopub.status.busy": "2023-11-20T20:33:23.493648Z", - "iopub.status.idle": "2023-11-20T20:33:23.500432Z", - "shell.execute_reply": "2023-11-20T20:33:23.499828Z" + "iopub.execute_input": "2023-11-21T08:10:10.251546Z", + "iopub.status.busy": "2023-11-21T08:10:10.251115Z", + "iopub.status.idle": "2023-11-21T08:10:10.258250Z", + "shell.execute_reply": "2023-11-21T08:10:10.257748Z" }, "nbsphinx": "hidden" }, @@ -421,10 +421,10 @@ "id": "6983cdad", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.502794Z", - "iopub.status.busy": "2023-11-20T20:33:23.502429Z", - "iopub.status.idle": "2023-11-20T20:33:23.506341Z", - "shell.execute_reply": "2023-11-20T20:33:23.505802Z" + "iopub.execute_input": "2023-11-21T08:10:10.260706Z", + "iopub.status.busy": "2023-11-21T08:10:10.260350Z", + "iopub.status.idle": "2023-11-21T08:10:10.264061Z", + "shell.execute_reply": "2023-11-21T08:10:10.263511Z" }, "nbsphinx": "hidden" }, @@ -447,10 +447,10 @@ "id": "9092b8a0", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.508579Z", - "iopub.status.busy": "2023-11-20T20:33:23.508220Z", - "iopub.status.idle": "2023-11-20T20:33:23.515124Z", - "shell.execute_reply": "2023-11-20T20:33:23.514598Z" + "iopub.execute_input": "2023-11-21T08:10:10.266428Z", + "iopub.status.busy": "2023-11-21T08:10:10.266067Z", + "iopub.status.idle": "2023-11-21T08:10:10.273026Z", + "shell.execute_reply": "2023-11-21T08:10:10.272490Z" } }, "outputs": [], @@ -500,10 +500,10 @@ "id": "b0a01109", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.517405Z", - "iopub.status.busy": "2023-11-20T20:33:23.517051Z", - "iopub.status.idle": "2023-11-20T20:33:23.552227Z", - "shell.execute_reply": "2023-11-20T20:33:23.551583Z" + "iopub.execute_input": "2023-11-21T08:10:10.275392Z", + "iopub.status.busy": "2023-11-21T08:10:10.275034Z", + "iopub.status.idle": "2023-11-21T08:10:10.312381Z", + "shell.execute_reply": "2023-11-21T08:10:10.311696Z" } }, "outputs": [], @@ -520,10 +520,10 @@ "id": "8b1da032", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.555173Z", - "iopub.status.busy": "2023-11-20T20:33:23.554767Z", - "iopub.status.idle": "2023-11-20T20:33:23.596520Z", - "shell.execute_reply": "2023-11-20T20:33:23.595732Z" + "iopub.execute_input": "2023-11-21T08:10:10.315533Z", + "iopub.status.busy": "2023-11-21T08:10:10.315050Z", + "iopub.status.idle": "2023-11-21T08:10:10.354208Z", + "shell.execute_reply": "2023-11-21T08:10:10.353462Z" }, "nbsphinx": "hidden" }, @@ -602,10 +602,10 @@ "id": "4c9e9030", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.599908Z", - "iopub.status.busy": "2023-11-20T20:33:23.599446Z", - "iopub.status.idle": "2023-11-20T20:33:23.717964Z", - "shell.execute_reply": "2023-11-20T20:33:23.717259Z" + "iopub.execute_input": "2023-11-21T08:10:10.357490Z", + "iopub.status.busy": "2023-11-21T08:10:10.357065Z", + "iopub.status.idle": "2023-11-21T08:10:10.477828Z", + "shell.execute_reply": "2023-11-21T08:10:10.477069Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "8751619e", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:23.720938Z", - "iopub.status.busy": "2023-11-20T20:33:23.720536Z", - "iopub.status.idle": "2023-11-20T20:33:26.236639Z", - "shell.execute_reply": "2023-11-20T20:33:26.235895Z" + "iopub.execute_input": "2023-11-21T08:10:10.480835Z", + "iopub.status.busy": "2023-11-21T08:10:10.480282Z", + "iopub.status.idle": "2023-11-21T08:10:12.978160Z", + "shell.execute_reply": "2023-11-21T08:10:12.977318Z" } }, "outputs": [ @@ -761,10 +761,10 @@ "id": "623df36d", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:26.239543Z", - "iopub.status.busy": "2023-11-20T20:33:26.239315Z", - "iopub.status.idle": "2023-11-20T20:33:26.298278Z", - "shell.execute_reply": "2023-11-20T20:33:26.297666Z" + "iopub.execute_input": "2023-11-21T08:10:12.980830Z", + "iopub.status.busy": "2023-11-21T08:10:12.980618Z", + "iopub.status.idle": "2023-11-21T08:10:13.041260Z", + "shell.execute_reply": "2023-11-21T08:10:13.040673Z" } }, "outputs": [ @@ -874,28 +874,7 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "05f57ef8b7df4d99a6bd44a3c6f74434": { - "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_5f1f34b848404f10b9e408027a29f006", - "placeholder": "", - "style": "IPY_MODEL_81c8b8a85d1645b9ab152f5597e2196f", - "value": "number of examples processed for checking labels: " - } - }, - "087593326d75413093b4109a553f5bba": { + "10dfea9c77df45c58a469d8cb2783a8e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -947,59 +926,31 @@ "width": null } }, - "1716cf2233d34e9da84989eb71da9590": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "130d80a19c3944dfacb6954cc345922c": { + "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", + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", "_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": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_67817907cf194da2a49ad4d49a9db689", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_be306dd80bba4e7a8027f12651829f58", + "value": 50.0 } }, - "1f8f580207554b25aa44709417d6970a": { + "33e51882eef14b66bff8fd4a70ca3d39": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1014,39 +965,50 @@ "description_width": "" } }, - "2677b8c5242b40ee91205ef5e600d8f1": { + "4718bab615de48138738b46d1327472b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "model_name": "HBoxModel", "state": { + "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_name": "HBoxModel", "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_6f701f3aa1914e999e0744e4bf17676e", + "IPY_MODEL_9bde0eac9d1c40b9aa2e84baf8aa576e", + "IPY_MODEL_4fce070c575d4a4a995ceb811ef4e1bd" + ], + "layout": "IPY_MODEL_b6b25cecbefb48bab68a91da8bbc59a3" } }, - "3ce6160c4c1b4e69ad84de4a2be76168": { + "4fce070c575d4a4a995ceb811ef4e1bd": { "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_d94baa0ceb6e447b9b4fd7a5faf316a0", + "placeholder": "", + "style": "IPY_MODEL_f0a9cb1779d4435594b55e4260328c86", + "value": " 10000/? [00:00<00:00, 825244.27it/s]" } }, - "3e359595dbb8450c989b68a2993f3bbb": { + "5fb27529a3714977aa04b9c5d6caf60d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1098,52 +1060,7 @@ "width": null } }, - "471a993f6a0f48dd8163ec66b01f9d13": { - "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_c57e986bf9814d16871ee13de739ac0f", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_2677b8c5242b40ee91205ef5e600d8f1", - "value": 50.0 - } - }, - "48ccc93c3c2d45f997b350ee87662d8c": { - "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_1716cf2233d34e9da84989eb71da9590", - "placeholder": "", - "style": "IPY_MODEL_b12e2f9c57484a99a9602157f9b23c82", - "value": " 10000/? [00:00<00:00, 1144296.39it/s]" - } - }, - "4ee2cd8aeba64e92a91c9db6f5c167b6": { + "6274f34e4a3441879d59801cbfae2053": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1195,44 +1112,7 @@ "width": null } }, - "5c79815a4acf43e58f4f86c490f5aba9": { - "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": "" - } - }, - "5efb438f2c814efbad68e107b18a4c31": { - "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_05f57ef8b7df4d99a6bd44a3c6f74434", - "IPY_MODEL_471a993f6a0f48dd8163ec66b01f9d13", - "IPY_MODEL_48ccc93c3c2d45f997b350ee87662d8c" - ], - "layout": "IPY_MODEL_3e359595dbb8450c989b68a2993f3bbb" - } - }, - "5f1f34b848404f10b9e408027a29f006": { + "67817907cf194da2a49ad4d49a9db689": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1284,31 +1164,7 @@ "width": null } }, - "6972e04d168e4d6794070d6f948dad5f": { - "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_4ee2cd8aeba64e92a91c9db6f5c167b6", - "max": 50.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_3ce6160c4c1b4e69ad84de4a2be76168", - "value": 50.0 - } - }, - "6ad2936986e84ad2815e64333504dd68": { + "6f701f3aa1914e999e0744e4bf17676e": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1323,13 +1179,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_087593326d75413093b4109a553f5bba", + "layout": "IPY_MODEL_10dfea9c77df45c58a469d8cb2783a8e", "placeholder": "", - "style": "IPY_MODEL_1f8f580207554b25aa44709417d6970a", - "value": " 10000/? [00:00<00:00, 1055436.34it/s]" + "style": "IPY_MODEL_b9bf1bf2f3fe4b68a180cf1559954b8d", + "value": "number of examples processed for checking labels: " } }, - "81c8b8a85d1645b9ab152f5597e2196f": { + "74c8fb7c3e134ce29d4889062d4861e7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1344,7 +1200,7 @@ "description_width": "" } }, - "a0a3e1d0ea46429196251824fcdfc628": { + "77fe53bc0d0b46b291880f01745c2a75": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -1359,14 +1215,38 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_aa01de61964a4d56bce43ea1cc96f29b", - "IPY_MODEL_6972e04d168e4d6794070d6f948dad5f", - "IPY_MODEL_6ad2936986e84ad2815e64333504dd68" + "IPY_MODEL_d9dcc597385445e9b83c2de0b466927d", + "IPY_MODEL_130d80a19c3944dfacb6954cc345922c", + "IPY_MODEL_9c734373e916465a91d4e8bb14587fb7" ], - "layout": "IPY_MODEL_ab1dfa12b84e48d393fb9ccc3cfa51fc" + "layout": "IPY_MODEL_bf324747cb6b4aa69d2b9eee390ab9d2" } }, - "aa01de61964a4d56bce43ea1cc96f29b": { + "9bde0eac9d1c40b9aa2e84baf8aa576e": { + "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_5fb27529a3714977aa04b9c5d6caf60d", + "max": 50.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_bc96821286754fc68345d02fae37d734", + "value": 50.0 + } + }, + "9c734373e916465a91d4e8bb14587fb7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -1381,13 +1261,65 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_e02109ca2ce34d27827d115de0afbdc2", + "layout": "IPY_MODEL_6274f34e4a3441879d59801cbfae2053", "placeholder": "", - "style": "IPY_MODEL_5c79815a4acf43e58f4f86c490f5aba9", - "value": "number of examples processed for estimating thresholds: " + "style": "IPY_MODEL_33e51882eef14b66bff8fd4a70ca3d39", + "value": " 10000/? [00:00<00:00, 1066167.77it/s]" + } + }, + "a1da34c996e84702a838a11b9fc42a3a": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "ab1dfa12b84e48d393fb9ccc3cfa51fc": { + "b6b25cecbefb48bab68a91da8bbc59a3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1439,7 +1371,7 @@ "width": null } }, - "b12e2f9c57484a99a9602157f9b23c82": { + "b9bf1bf2f3fe4b68a180cf1559954b8d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -1454,7 +1386,39 @@ "description_width": "" } }, - "c57e986bf9814d16871ee13de739ac0f": { + "bc96821286754fc68345d02fae37d734": { + "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": "" + } + }, + "be306dd80bba4e7a8027f12651829f58": { + "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": "" + } + }, + "bf324747cb6b4aa69d2b9eee390ab9d2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1506,7 +1470,7 @@ "width": null } }, - "e02109ca2ce34d27827d115de0afbdc2": { + "d94baa0ceb6e447b9b4fd7a5faf316a0": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -1557,6 +1521,42 @@ "visibility": null, "width": null } + }, + "d9dcc597385445e9b83c2de0b466927d": { + "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_a1da34c996e84702a838a11b9fc42a3a", + "placeholder": "", + "style": "IPY_MODEL_74c8fb7c3e134ce29d4889062d4861e7", + "value": "number of examples processed for estimating thresholds: " + } + }, + "f0a9cb1779d4435594b55e4260328c86": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } } }, "version_major": 2, diff --git a/master/tutorials/image.html b/master/tutorials/image.html index f463aa5fc..c178f40f9 100644 --- a/master/tutorials/image.html +++ b/master/tutorials/image.html @@ -874,67 +874,67 @@Convert the transformed dataset to a torch dataset. Torch datasets are more efficient with dataloading in practice.
Training on fold: 1 ... -epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 5.139 -epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.609 +epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.542 +epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.524 Computing feature embeddings ...
-100%|██████████| 40/40 [00:00<00:00, 61.51it/s]
+100%|██████████| 40/40 [00:00<00:00, 58.83it/s]
-100%|██████████| 40/40 [00:00<00:00, 63.84it/s]
+100%|██████████| 40/40 [00:00<00:00, 62.99it/s]
-epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.692
-epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 5.033
+epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.852
+epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.727
Computing feature embeddings ...
-100%|██████████| 40/40 [00:00<00:00, 60.32it/s]
+100%|██████████| 40/40 [00:00<00:00, 60.78it/s]
-100%|██████████| 40/40 [00:00<00:00, 63.03it/s]
+100%|██████████| 40/40 [00:00<00:00, 60.83it/s]
-epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.994
-epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.830
+epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.868
+epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.710
Computing feature embeddings ...
-100%|██████████| 40/40 [00:00<00:00, 60.07it/s]
+100%|██████████| 40/40 [00:00<00:00, 61.52it/s]
-100%|██████████| 40/40 [00:00<00:00, 64.05it/s]
+100%|██████████| 40/40 [00:00<00:00, 61.19it/s]
Finished Training
+
+
Reorder rows of the dataset based on row order in features
and pred_probs
. Carefully ensure your ordering of the dataset matches these objects!
Here we can see a lot of low information images belong to the Sandal class.
diff --git a/master/tutorials/image.ipynb b/master/tutorials/image.ipynb index b1e4ff362..08c7524f7 100644 --- a/master/tutorials/image.ipynb +++ b/master/tutorials/image.ipynb @@ -71,10 +71,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:31.629173Z", - "iopub.status.busy": "2023-11-20T20:33:31.628719Z", - "iopub.status.idle": "2023-11-20T20:33:33.764885Z", - "shell.execute_reply": "2023-11-20T20:33:33.764262Z" + "iopub.execute_input": "2023-11-21T08:10:18.258133Z", + "iopub.status.busy": "2023-11-21T08:10:18.257949Z", + "iopub.status.idle": "2023-11-21T08:10:20.366284Z", + "shell.execute_reply": "2023-11-21T08:10:20.365585Z" }, "nbsphinx": "hidden" }, @@ -112,10 +112,10 @@ "execution_count": 2, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:33.767803Z", - "iopub.status.busy": "2023-11-20T20:33:33.767385Z", - "iopub.status.idle": "2023-11-20T20:33:33.771144Z", - "shell.execute_reply": "2023-11-20T20:33:33.770649Z" + "iopub.execute_input": "2023-11-21T08:10:20.369424Z", + "iopub.status.busy": "2023-11-21T08:10:20.369024Z", + "iopub.status.idle": "2023-11-21T08:10:20.372928Z", + "shell.execute_reply": "2023-11-21T08:10:20.372374Z" } }, "outputs": [], @@ -152,17 +152,17 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:33.773478Z", - "iopub.status.busy": "2023-11-20T20:33:33.773117Z", - "iopub.status.idle": "2023-11-20T20:33:46.006704Z", - "shell.execute_reply": "2023-11-20T20:33:46.006150Z" + "iopub.execute_input": "2023-11-21T08:10:20.375365Z", + "iopub.status.busy": "2023-11-21T08:10:20.375012Z", + "iopub.status.idle": "2023-11-21T08:10:36.724947Z", + "shell.execute_reply": "2023-11-21T08:10:36.724285Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "48028e2d8e864d12bcf33caebd44f169", + "model_id": "17abac331e704de9a7ebacb0a7b2d5bb", "version_major": 2, "version_minor": 0 }, @@ -176,7 +176,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "88232f2a0333433daf181c32d047baa5", + "model_id": "6e6755b4623a4f0a905b741ee7cb5453", "version_major": 2, "version_minor": 0 }, @@ -190,7 +190,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b20ae6b1e0ec42618e40f7da092950a5", + "model_id": "aeaeeb5988cb4638aadb1b0840bf3745", "version_major": 2, "version_minor": 0 }, @@ -204,7 +204,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "524c2b01440a4c4b8419c369e0bf7393", + "model_id": "36ec644042e74e14874a773565a61470", "version_major": 2, "version_minor": 0 }, @@ -218,7 +218,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b43869cf841c45c69f9946692ef05b2a", + "model_id": "f5e3565367434275816c74477904c0b2", "version_major": 2, "version_minor": 0 }, @@ -232,7 +232,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "551aaea6faa348c6b6974eb09fcbc1c5", + "model_id": "238ab964fff64bba8f57438714294a3e", "version_major": 2, "version_minor": 0 }, @@ -246,7 +246,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ce8ea6b5cf584edcbdd7c21a26970e86", + "model_id": "7329880eab5847d298a65b5166a87566", "version_major": 2, "version_minor": 0 }, @@ -260,7 +260,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "25525fef3faa41bfa3d7fb01644654df", + "model_id": "b938a28698dd446c98758c5ae37fbbee", "version_major": 2, "version_minor": 0 }, @@ -274,7 +274,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d3e80e28ba1b48afba73110f15d0fb40", + "model_id": "2a2efa39f0394a3ba2646b507175ce6a", "version_major": 2, "version_minor": 0 }, @@ -288,7 +288,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e7fbe13ae9d4837bc4327ad2e0b8c44", + "model_id": "00d5fb535f6e4fe69370f09780bb731a", "version_major": 2, "version_minor": 0 }, @@ -302,7 +302,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f54927ed92ee4370819b6a462532901d", + "model_id": "0f05792de3bd42fc8948e91877389b10", "version_major": 2, "version_minor": 0 }, @@ -344,10 +344,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:46.009274Z", - "iopub.status.busy": "2023-11-20T20:33:46.009052Z", - "iopub.status.idle": "2023-11-20T20:33:46.013441Z", - "shell.execute_reply": "2023-11-20T20:33:46.012774Z" + "iopub.execute_input": "2023-11-21T08:10:36.727565Z", + "iopub.status.busy": "2023-11-21T08:10:36.727213Z", + "iopub.status.idle": "2023-11-21T08:10:36.731171Z", + "shell.execute_reply": "2023-11-21T08:10:36.730649Z" } }, "outputs": [ @@ -372,17 +372,17 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:46.016172Z", - "iopub.status.busy": "2023-11-20T20:33:46.015780Z", - "iopub.status.idle": "2023-11-20T20:33:56.992939Z", - "shell.execute_reply": "2023-11-20T20:33:56.992342Z" + "iopub.execute_input": "2023-11-21T08:10:36.733369Z", + "iopub.status.busy": "2023-11-21T08:10:36.733167Z", + "iopub.status.idle": "2023-11-21T08:10:47.327744Z", + "shell.execute_reply": "2023-11-21T08:10:47.327138Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "de9a8b3d0e42412eb75d8abc7d35ee38", + "model_id": "840bc43fd3d340428a779ceef8112f22", "version_major": 2, "version_minor": 0 }, @@ -420,10 +420,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:33:56.995688Z", - "iopub.status.busy": "2023-11-20T20:33:56.995263Z", - "iopub.status.idle": "2023-11-20T20:34:18.070931Z", - "shell.execute_reply": "2023-11-20T20:34:18.070202Z" + "iopub.execute_input": "2023-11-21T08:10:47.330620Z", + "iopub.status.busy": "2023-11-21T08:10:47.330286Z", + "iopub.status.idle": "2023-11-21T08:11:09.239929Z", + "shell.execute_reply": "2023-11-21T08:11:09.239191Z" } }, "outputs": [], @@ -456,10 +456,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:18.074102Z", - "iopub.status.busy": "2023-11-20T20:34:18.073666Z", - "iopub.status.idle": "2023-11-20T20:34:18.079589Z", - "shell.execute_reply": "2023-11-20T20:34:18.079082Z" + "iopub.execute_input": "2023-11-21T08:11:09.243446Z", + "iopub.status.busy": "2023-11-21T08:11:09.242919Z", + "iopub.status.idle": "2023-11-21T08:11:09.248274Z", + "shell.execute_reply": "2023-11-21T08:11:09.247640Z" } }, "outputs": [], @@ -497,10 +497,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:18.082138Z", - "iopub.status.busy": "2023-11-20T20:34:18.081605Z", - "iopub.status.idle": "2023-11-20T20:34:18.086103Z", - "shell.execute_reply": "2023-11-20T20:34:18.085599Z" + "iopub.execute_input": "2023-11-21T08:11:09.250718Z", + "iopub.status.busy": "2023-11-21T08:11:09.250348Z", + "iopub.status.idle": "2023-11-21T08:11:09.254490Z", + "shell.execute_reply": "2023-11-21T08:11:09.253930Z" }, "nbsphinx": "hidden" }, @@ -637,10 +637,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:18.088377Z", - "iopub.status.busy": "2023-11-20T20:34:18.088180Z", - "iopub.status.idle": "2023-11-20T20:34:18.097740Z", - "shell.execute_reply": "2023-11-20T20:34:18.097240Z" + "iopub.execute_input": "2023-11-21T08:11:09.256878Z", + "iopub.status.busy": "2023-11-21T08:11:09.256559Z", + "iopub.status.idle": "2023-11-21T08:11:09.266476Z", + "shell.execute_reply": "2023-11-21T08:11:09.265868Z" }, "nbsphinx": "hidden" }, @@ -765,10 +765,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:18.100298Z", - "iopub.status.busy": "2023-11-20T20:34:18.099861Z", - "iopub.status.idle": "2023-11-20T20:34:18.127203Z", - "shell.execute_reply": "2023-11-20T20:34:18.126716Z" + "iopub.execute_input": "2023-11-21T08:11:09.268829Z", + "iopub.status.busy": "2023-11-21T08:11:09.268471Z", + "iopub.status.idle": "2023-11-21T08:11:09.303768Z", + "shell.execute_reply": "2023-11-21T08:11:09.303199Z" } }, "outputs": [], @@ -805,10 +805,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:18.129759Z", - "iopub.status.busy": "2023-11-20T20:34:18.129316Z", - "iopub.status.idle": "2023-11-20T20:34:51.336048Z", - "shell.execute_reply": "2023-11-20T20:34:51.335316Z" + "iopub.execute_input": "2023-11-21T08:11:09.306400Z", + "iopub.status.busy": "2023-11-21T08:11:09.306095Z", + "iopub.status.idle": "2023-11-21T08:11:41.507278Z", + "shell.execute_reply": "2023-11-21T08:11:41.506420Z" } }, "outputs": [ @@ -824,14 +824,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 5.139\n" + "epoch: 1 loss: 0.483 test acc: 86.835 time_taken: 4.542\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.609\n", + "epoch: 2 loss: 0.331 test acc: 88.310 time_taken: 4.524\n", "Computing feature embeddings ...\n" ] }, @@ -848,7 +848,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:04, 8.47it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 16.46it/s]" ] }, { @@ -856,7 +856,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 44.69it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 48.22it/s]" ] }, { @@ -864,7 +864,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 55.85it/s]" + " 42%|████▎ | 17/40 [00:00<00:00, 54.86it/s]" ] }, { @@ -872,7 +872,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 62.81it/s]" + " 60%|██████ | 24/40 [00:00<00:00, 58.05it/s]" ] }, { @@ -880,7 +880,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 67.44it/s]" + " 78%|███████▊ | 31/40 [00:00<00:00, 59.76it/s]" ] }, { @@ -888,7 +888,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 61.51it/s]" + "100%|██████████| 40/40 [00:00<00:00, 58.83it/s]" ] }, { @@ -918,7 +918,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:04, 9.18it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 18.80it/s]" ] }, { @@ -926,7 +926,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 48.34it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 50.33it/s]" ] }, { @@ -934,7 +934,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 58.97it/s]" + " 42%|████▎ | 17/40 [00:00<00:00, 58.48it/s]" ] }, { @@ -942,7 +942,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 65.22it/s]" + " 62%|██████▎ | 25/40 [00:00<00:00, 64.34it/s]" ] }, { @@ -950,7 +950,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 69.42it/s]" + " 80%|████████ | 32/40 [00:00<00:00, 66.01it/s]" ] }, { @@ -958,7 +958,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 63.84it/s]" + "100%|██████████| 40/40 [00:00<00:00, 62.99it/s]" ] }, { @@ -980,14 +980,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.692\n" + "epoch: 1 loss: 0.492 test acc: 87.085 time_taken: 4.852\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 5.033\n", + "epoch: 2 loss: 0.330 test acc: 88.290 time_taken: 4.727\n", "Computing feature embeddings ...\n" ] }, @@ -1004,7 +1004,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 2/40 [00:00<00:01, 19.25it/s]" + " 2%|▎ | 1/40 [00:00<00:04, 9.13it/s]" ] }, { @@ -1012,7 +1012,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 10/40 [00:00<00:00, 50.59it/s]" + " 20%|██ | 8/40 [00:00<00:00, 42.65it/s]" ] }, { @@ -1020,7 +1020,7 @@ "output_type": "stream", "text": [ "\r", - " 45%|████▌ | 18/40 [00:00<00:00, 59.95it/s]" + " 40%|████ | 16/40 [00:00<00:00, 57.04it/s]" ] }, { @@ -1028,7 +1028,7 @@ "output_type": "stream", "text": [ "\r", - " 65%|██████▌ | 26/40 [00:00<00:00, 64.42it/s]" + " 60%|██████ | 24/40 [00:00<00:00, 63.24it/s]" ] }, { @@ -1036,7 +1036,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 34/40 [00:00<00:00, 67.05it/s]" + " 78%|███████▊ | 31/40 [00:00<00:00, 65.36it/s]" ] }, { @@ -1044,7 +1044,15 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 60.32it/s]" + "100%|██████████| 40/40 [00:00<00:00, 72.89it/s]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\r", + "100%|██████████| 40/40 [00:00<00:00, 60.78it/s]" ] }, { @@ -1074,7 +1082,7 @@ "output_type": "stream", "text": [ "\r", - " 5%|▌ | 2/40 [00:00<00:02, 18.26it/s]" + " 2%|▎ | 1/40 [00:00<00:04, 7.96it/s]" ] }, { @@ -1082,7 +1090,7 @@ "output_type": "stream", "text": [ "\r", - " 25%|██▌ | 10/40 [00:00<00:00, 49.74it/s]" + " 22%|██▎ | 9/40 [00:00<00:00, 43.54it/s]" ] }, { @@ -1090,7 +1098,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 58.44it/s]" + " 42%|████▎ | 17/40 [00:00<00:00, 56.73it/s]" ] }, { @@ -1098,7 +1106,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 63.85it/s]" + " 62%|██████▎ | 25/40 [00:00<00:00, 62.96it/s]" ] }, { @@ -1106,7 +1114,7 @@ "output_type": "stream", "text": [ "\r", - " 82%|████████▎ | 33/40 [00:00<00:00, 67.64it/s]" + " 82%|████████▎ | 33/40 [00:00<00:00, 67.62it/s]" ] }, { @@ -1114,7 +1122,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 63.03it/s]" + "100%|██████████| 40/40 [00:00<00:00, 60.83it/s]" ] }, { @@ -1136,14 +1144,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.994\n" + "epoch: 1 loss: 0.476 test acc: 86.305 time_taken: 4.868\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.830\n", + "epoch: 2 loss: 0.328 test acc: 86.335 time_taken: 4.710\n", "Computing feature embeddings ...\n" ] }, @@ -1160,7 +1168,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:04, 8.02it/s]" + " 5%|▌ | 2/40 [00:00<00:01, 19.05it/s]" ] }, { @@ -1168,7 +1176,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 44.23it/s]" + " 25%|██▌ | 10/40 [00:00<00:00, 51.29it/s]" ] }, { @@ -1176,7 +1184,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 56.79it/s]" + " 45%|████▌ | 18/40 [00:00<00:00, 61.74it/s]" ] }, { @@ -1184,7 +1192,7 @@ "output_type": "stream", "text": [ "\r", - " 60%|██████ | 24/40 [00:00<00:00, 61.36it/s]" + " 65%|██████▌ | 26/40 [00:00<00:00, 65.73it/s]" ] }, { @@ -1192,7 +1200,7 @@ "output_type": "stream", "text": [ "\r", - " 80%|████████ | 32/40 [00:00<00:00, 65.62it/s]" + " 85%|████████▌ | 34/40 [00:00<00:00, 69.56it/s]" ] }, { @@ -1200,7 +1208,7 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 60.07it/s]" + "100%|██████████| 40/40 [00:00<00:00, 61.52it/s]" ] }, { @@ -1230,7 +1238,7 @@ "output_type": "stream", "text": [ "\r", - " 2%|▎ | 1/40 [00:00<00:04, 8.48it/s]" + " 5%|▌ | 2/40 [00:00<00:02, 18.34it/s]" ] }, { @@ -1238,7 +1246,7 @@ "output_type": "stream", "text": [ "\r", - " 22%|██▎ | 9/40 [00:00<00:00, 46.26it/s]" + " 22%|██▎ | 9/40 [00:00<00:00, 45.04it/s]" ] }, { @@ -1246,7 +1254,7 @@ "output_type": "stream", "text": [ "\r", - " 42%|████▎ | 17/40 [00:00<00:00, 59.87it/s]" + " 42%|████▎ | 17/40 [00:00<00:00, 57.87it/s]" ] }, { @@ -1254,7 +1262,7 @@ "output_type": "stream", "text": [ "\r", - " 62%|██████▎ | 25/40 [00:00<00:00, 66.53it/s]" + " 62%|██████▎ | 25/40 [00:00<00:00, 64.27it/s]" ] }, { @@ -1262,7 +1270,7 @@ "output_type": "stream", "text": [ "\r", - " 85%|████████▌ | 34/40 [00:00<00:00, 72.24it/s]" + " 82%|████████▎ | 33/40 [00:00<00:00, 68.45it/s]" ] }, { @@ -1270,21 +1278,21 @@ "output_type": "stream", "text": [ "\r", - "100%|██████████| 40/40 [00:00<00:00, 64.05it/s]" + "100%|██████████| 40/40 [00:00<00:00, 61.19it/s]" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "\n" + "Finished Training\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Finished Training\n" + "\n" ] } ], @@ -1347,10 +1355,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:51.338922Z", - "iopub.status.busy": "2023-11-20T20:34:51.338483Z", - "iopub.status.idle": "2023-11-20T20:34:51.352766Z", - "shell.execute_reply": "2023-11-20T20:34:51.352234Z" + "iopub.execute_input": "2023-11-21T08:11:41.510269Z", + "iopub.status.busy": "2023-11-21T08:11:41.509984Z", + "iopub.status.idle": "2023-11-21T08:11:41.524878Z", + "shell.execute_reply": "2023-11-21T08:11:41.524297Z" } }, "outputs": [], @@ -1375,10 +1383,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:51.355205Z", - "iopub.status.busy": "2023-11-20T20:34:51.354733Z", - "iopub.status.idle": "2023-11-20T20:34:51.787258Z", - "shell.execute_reply": "2023-11-20T20:34:51.786555Z" + "iopub.execute_input": "2023-11-21T08:11:41.528191Z", + "iopub.status.busy": "2023-11-21T08:11:41.527593Z", + "iopub.status.idle": "2023-11-21T08:11:41.987616Z", + "shell.execute_reply": "2023-11-21T08:11:41.986890Z" } }, "outputs": [], @@ -1398,10 +1406,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:34:51.790267Z", - "iopub.status.busy": "2023-11-20T20:34:51.790011Z", - "iopub.status.idle": "2023-11-20T20:38:12.567008Z", - "shell.execute_reply": "2023-11-20T20:38:12.566283Z" + "iopub.execute_input": "2023-11-21T08:11:41.990825Z", + "iopub.status.busy": "2023-11-21T08:11:41.990293Z", + "iopub.status.idle": "2023-11-21T08:15:03.947350Z", + "shell.execute_reply": "2023-11-21T08:15:03.946586Z" } }, "outputs": [ @@ -1438,7 +1446,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0f51acd0294d4c5787f936f144a867e6", + "model_id": "383b11e2af1e4e5db72eaf0ad28d4f90", "version_major": 2, "version_minor": 0 }, @@ -1477,10 +1485,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:12.569843Z", - "iopub.status.busy": "2023-11-20T20:38:12.569281Z", - "iopub.status.idle": "2023-11-20T20:38:13.042273Z", - "shell.execute_reply": "2023-11-20T20:38:13.041616Z" + "iopub.execute_input": "2023-11-21T08:15:03.950477Z", + "iopub.status.busy": "2023-11-21T08:15:03.949762Z", + "iopub.status.idle": "2023-11-21T08:15:04.427867Z", + "shell.execute_reply": "2023-11-21T08:15:04.427187Z" } }, "outputs": [ @@ -1652,10 +1660,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.045809Z", - "iopub.status.busy": "2023-11-20T20:38:13.045204Z", - "iopub.status.idle": "2023-11-20T20:38:13.108468Z", - "shell.execute_reply": "2023-11-20T20:38:13.107909Z" + "iopub.execute_input": "2023-11-21T08:15:04.431207Z", + "iopub.status.busy": "2023-11-21T08:15:04.430759Z", + "iopub.status.idle": "2023-11-21T08:15:04.495822Z", + "shell.execute_reply": "2023-11-21T08:15:04.495180Z" } }, "outputs": [ @@ -1759,10 +1767,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.111229Z", - "iopub.status.busy": "2023-11-20T20:38:13.110714Z", - "iopub.status.idle": "2023-11-20T20:38:13.119875Z", - "shell.execute_reply": "2023-11-20T20:38:13.119369Z" + "iopub.execute_input": "2023-11-21T08:15:04.498580Z", + "iopub.status.busy": "2023-11-21T08:15:04.498099Z", + "iopub.status.idle": "2023-11-21T08:15:04.507308Z", + "shell.execute_reply": "2023-11-21T08:15:04.506702Z" } }, "outputs": [ @@ -1892,10 +1900,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.122319Z", - "iopub.status.busy": "2023-11-20T20:38:13.121811Z", - "iopub.status.idle": "2023-11-20T20:38:13.126716Z", - "shell.execute_reply": "2023-11-20T20:38:13.126100Z" + "iopub.execute_input": "2023-11-21T08:15:04.509744Z", + "iopub.status.busy": "2023-11-21T08:15:04.509374Z", + "iopub.status.idle": "2023-11-21T08:15:04.514121Z", + "shell.execute_reply": "2023-11-21T08:15:04.513592Z" }, "nbsphinx": "hidden" }, @@ -1941,10 +1949,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.129019Z", - "iopub.status.busy": "2023-11-20T20:38:13.128682Z", - "iopub.status.idle": "2023-11-20T20:38:13.803701Z", - "shell.execute_reply": "2023-11-20T20:38:13.803024Z" + "iopub.execute_input": "2023-11-21T08:15:04.516530Z", + "iopub.status.busy": "2023-11-21T08:15:04.516175Z", + "iopub.status.idle": "2023-11-21T08:15:05.189600Z", + "shell.execute_reply": "2023-11-21T08:15:05.188909Z" } }, "outputs": [ @@ -1979,10 +1987,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.806179Z", - "iopub.status.busy": "2023-11-20T20:38:13.805928Z", - "iopub.status.idle": "2023-11-20T20:38:13.814630Z", - "shell.execute_reply": "2023-11-20T20:38:13.814012Z" + "iopub.execute_input": "2023-11-21T08:15:05.192320Z", + "iopub.status.busy": "2023-11-21T08:15:05.191958Z", + "iopub.status.idle": "2023-11-21T08:15:05.200786Z", + "shell.execute_reply": "2023-11-21T08:15:05.200302Z" } }, "outputs": [ @@ -2149,10 +2157,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.817092Z", - "iopub.status.busy": "2023-11-20T20:38:13.816668Z", - "iopub.status.idle": "2023-11-20T20:38:13.824337Z", - "shell.execute_reply": "2023-11-20T20:38:13.823853Z" + "iopub.execute_input": "2023-11-21T08:15:05.203307Z", + "iopub.status.busy": "2023-11-21T08:15:05.202947Z", + "iopub.status.idle": "2023-11-21T08:15:05.210602Z", + "shell.execute_reply": "2023-11-21T08:15:05.210107Z" }, "nbsphinx": "hidden" }, @@ -2228,10 +2236,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:13.826642Z", - "iopub.status.busy": "2023-11-20T20:38:13.826217Z", - "iopub.status.idle": "2023-11-20T20:38:14.286779Z", - "shell.execute_reply": "2023-11-20T20:38:14.286121Z" + "iopub.execute_input": "2023-11-21T08:15:05.212912Z", + "iopub.status.busy": "2023-11-21T08:15:05.212544Z", + "iopub.status.idle": "2023-11-21T08:15:05.673620Z", + "shell.execute_reply": "2023-11-21T08:15:05.672940Z" } }, "outputs": [ @@ -2268,10 +2276,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.289664Z", - "iopub.status.busy": "2023-11-20T20:38:14.289163Z", - "iopub.status.idle": "2023-11-20T20:38:14.305949Z", - "shell.execute_reply": "2023-11-20T20:38:14.305417Z" + "iopub.execute_input": "2023-11-21T08:15:05.676360Z", + "iopub.status.busy": "2023-11-21T08:15:05.675969Z", + "iopub.status.idle": "2023-11-21T08:15:05.691810Z", + "shell.execute_reply": "2023-11-21T08:15:05.691269Z" } }, "outputs": [ @@ -2428,10 +2436,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.308506Z", - "iopub.status.busy": "2023-11-20T20:38:14.308086Z", - "iopub.status.idle": "2023-11-20T20:38:14.314237Z", - "shell.execute_reply": "2023-11-20T20:38:14.313629Z" + "iopub.execute_input": "2023-11-21T08:15:05.694566Z", + "iopub.status.busy": "2023-11-21T08:15:05.694143Z", + "iopub.status.idle": "2023-11-21T08:15:05.700241Z", + "shell.execute_reply": "2023-11-21T08:15:05.699627Z" }, "nbsphinx": "hidden" }, @@ -2476,10 +2484,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.316507Z", - "iopub.status.busy": "2023-11-20T20:38:14.316142Z", - "iopub.status.idle": "2023-11-20T20:38:14.758875Z", - "shell.execute_reply": "2023-11-20T20:38:14.758194Z" + "iopub.execute_input": "2023-11-21T08:15:05.702534Z", + "iopub.status.busy": "2023-11-21T08:15:05.702182Z", + "iopub.status.idle": "2023-11-21T08:15:06.094537Z", + "shell.execute_reply": "2023-11-21T08:15:06.093700Z" } }, "outputs": [ @@ -2554,10 +2562,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.761675Z", - "iopub.status.busy": "2023-11-20T20:38:14.761433Z", - "iopub.status.idle": "2023-11-20T20:38:14.771424Z", - "shell.execute_reply": "2023-11-20T20:38:14.770770Z" + "iopub.execute_input": "2023-11-21T08:15:06.097459Z", + "iopub.status.busy": "2023-11-21T08:15:06.097244Z", + "iopub.status.idle": "2023-11-21T08:15:06.108363Z", + "shell.execute_reply": "2023-11-21T08:15:06.107909Z" } }, "outputs": [ @@ -2685,10 +2693,10 @@ "execution_count": 27, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.774306Z", - "iopub.status.busy": "2023-11-20T20:38:14.773957Z", - "iopub.status.idle": "2023-11-20T20:38:14.780518Z", - "shell.execute_reply": "2023-11-20T20:38:14.779868Z" + "iopub.execute_input": "2023-11-21T08:15:06.111763Z", + "iopub.status.busy": "2023-11-21T08:15:06.110901Z", + "iopub.status.idle": "2023-11-21T08:15:06.116651Z", + "shell.execute_reply": "2023-11-21T08:15:06.116210Z" }, "nbsphinx": "hidden" }, @@ -2725,10 +2733,10 @@ "execution_count": 28, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.783274Z", - "iopub.status.busy": "2023-11-20T20:38:14.783039Z", - "iopub.status.idle": "2023-11-20T20:38:14.982193Z", - "shell.execute_reply": "2023-11-20T20:38:14.981516Z" + "iopub.execute_input": "2023-11-21T08:15:06.119199Z", + "iopub.status.busy": "2023-11-21T08:15:06.118957Z", + "iopub.status.idle": "2023-11-21T08:15:06.288857Z", + "shell.execute_reply": "2023-11-21T08:15:06.288048Z" } }, "outputs": [ @@ -2770,10 +2778,10 @@ "execution_count": 29, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.984816Z", - "iopub.status.busy": "2023-11-20T20:38:14.984441Z", - "iopub.status.idle": "2023-11-20T20:38:14.992903Z", - "shell.execute_reply": "2023-11-20T20:38:14.992290Z" + "iopub.execute_input": "2023-11-21T08:15:06.291781Z", + "iopub.status.busy": "2023-11-21T08:15:06.291565Z", + "iopub.status.idle": "2023-11-21T08:15:06.299967Z", + "shell.execute_reply": "2023-11-21T08:15:06.299328Z" } }, "outputs": [ @@ -2859,10 +2867,10 @@ "execution_count": 30, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:14.995574Z", - "iopub.status.busy": "2023-11-20T20:38:14.995074Z", - "iopub.status.idle": "2023-11-20T20:38:15.184538Z", - "shell.execute_reply": "2023-11-20T20:38:15.184019Z" + "iopub.execute_input": "2023-11-21T08:15:06.302392Z", + "iopub.status.busy": "2023-11-21T08:15:06.301954Z", + "iopub.status.idle": "2023-11-21T08:15:06.469345Z", + "shell.execute_reply": "2023-11-21T08:15:06.468821Z" } }, "outputs": [ @@ -2893,10 +2901,10 @@ "execution_count": 31, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:15.187015Z", - "iopub.status.busy": "2023-11-20T20:38:15.186672Z", - "iopub.status.idle": "2023-11-20T20:38:15.191266Z", - "shell.execute_reply": "2023-11-20T20:38:15.190670Z" + "iopub.execute_input": "2023-11-21T08:15:06.472313Z", + "iopub.status.busy": "2023-11-21T08:15:06.471690Z", + "iopub.status.idle": "2023-11-21T08:15:06.476832Z", + "shell.execute_reply": "2023-11-21T08:15:06.476400Z" }, "nbsphinx": "hidden" }, @@ -2933,139 +2941,29 @@ "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { - "01614767a83948fa8e86ee4304c4d4ba": { - "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_c779fc2871664020998850299a7ddd3e", - "max": 10000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_44928fac3f7147dfb1dc6df1d280ccaa", - "value": 10000.0 - } - }, - "01641a2ad627457ca1997504835d5ea6": { - "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": "" - } - }, - "01be6e03a73e439bb56bb60d38339e89": { - "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_693298b1d2794b6fa3754cb8d30e3ddd", - "placeholder": "", - "style": "IPY_MODEL_05589418ec204505b69108ab1e3e3fcf", - "value": " 5.15k/5.15k [00:00<00:00, 638kB/s]" - } - }, - "03ff247338ad48baa1c669032b7da351": { - "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": "" - } - }, - "05589418ec204505b69108ab1e3e3fcf": { - "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": "" - } - }, - "07113d7cf4e846f8870c58c42cfaa24f": { - "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_84a17f651ff94283bbd80920a2108119", - "placeholder": "", - "style": "IPY_MODEL_adc8913a3f014e889e245e96addb29a5", - "value": " 3.13k/3.13k [00:00<00:00, 417kB/s]" - } - }, - "0774373388fa46558225957d6f8ef740": { + "00d5fb535f6e4fe69370f09780bb731a": { "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_f967c00757204de787c61238ce385d32", - "placeholder": "", - "style": "IPY_MODEL_84fc08030cf640bdb8903d2b88b629f0", - "value": " 8.85k/8.85k [00:00<00:00, 1.07MB/s]" + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_62aba93e6d1443ef944cba728bfb075f", + "IPY_MODEL_5a8329ec53324bf09bd8446efc66e09a", + "IPY_MODEL_907c6bc8381f4c0398777b883338efbe" + ], + "layout": "IPY_MODEL_3c3ab5f2163d44718a15427d139e2b96" } }, - "0837969dc9a4456ba48fee4d28c99201": { + "05bf72f17f66450db11ab0343e2f4a92": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3117,7 +3015,37 @@ "width": null } }, - "0b5c12ff7e5f4272b05312a7c947f42d": { + "0706c7ac0f014b07b29b0f00f4fe6563": { + "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": "" + } + }, + "0b3226e253954bffa9d4152c174f78cb": { + "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": "" + } + }, + "0d9e50cc326048f89d4ba45f99df9574": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3169,7 +3097,29 @@ "width": null } }, - "0c654c2ed5254dd2a06412cc243827cb": { + "0f05792de3bd42fc8948e91877389b10": { + "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_d639341d172948d9877d1c6c3602eb27", + "IPY_MODEL_ae0ec41b943d4815b0364787ed43a132", + "IPY_MODEL_4ce58997facf45a68f3272c8e25476a1" + ], + "layout": "IPY_MODEL_601fd080e58b47cfbf5afa2073934a46" + } + }, + "10a144afd941436686e5eb0abdfb647b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3221,22 +3171,7 @@ "width": null } }, - "0d0ec3c5c87044a88631bbc17caddd4d": { - "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": "" - } - }, - "0dbf24fb54d442b18a2afb64da344cf5": { + "135d7ed31fa04b328a6a522a17a75f9e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3288,7 +3223,7 @@ "width": null } }, - "0e9fd9b7e2c04cb3b27ae6a5b39c6643": { + "178301fe093c465fad01b1c698b40db4": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3340,7 +3275,7 @@ "width": null } }, - "0f51acd0294d4c5787f936f144a867e6": { + "17abac331e704de9a7ebacb0a7b2d5bb": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -3355,56 +3290,53 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_21ef8862f77e43b3b903eed5bc3dd00a", - "IPY_MODEL_db5f8379f5f645209fdd1c01702db84c", - "IPY_MODEL_10dcd97c0eaa41f68e57b9d9b8ab10ec" + "IPY_MODEL_3ff1eb63a7d343fca37329c3fd49b84c", + "IPY_MODEL_4e880784e0de4a1a87c4919ac54382b5", + "IPY_MODEL_316572d7b0df4dcea2874066929f5fc6" ], - "layout": "IPY_MODEL_323b616232d34d2c981fa30aff426319" + "layout": "IPY_MODEL_8572c70172c64f9bb9f6916497c70410" } }, - "10889a4c08de4efe8696ecf09185e3f8": { + "17f0aabf25cd4ababf0d602d3a3aa3f6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_39f41133e33244798f1fd5308f8338a5", - "placeholder": "", - "style": "IPY_MODEL_0d0ec3c5c87044a88631bbc17caddd4d", - "value": "Downloading data: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "10dcd97c0eaa41f68e57b9d9b8ab10ec": { + "18b47621656348409b5d33b3d5da0cd9": { "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_0837969dc9a4456ba48fee4d28c99201", - "placeholder": "", - "style": "IPY_MODEL_f4aff97d336f4e5c96f81cf80fdee564", - "value": " 60000/60000 [00:28<00:00, 2127.44it/s]" + "layout": "IPY_MODEL_e652e5327e1d47638b302b6cd670be79", + "max": 8845.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_a5e0c89448bf4656a32cd7784f9bf7fc", + "value": 8845.0 } }, - "12c62f98b1614d299c05d2a04c586766": { + "1ac6d55a17dd4dddb69df64307db835b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3419,23 +3351,22 @@ "description_width": "" } }, - "149959a2af424f009c160ab889b46a60": { + "1f4c1fb893ac40a9a5ab56ed2d344467": { "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": "" } }, - "18e830b21c694e79a6883824bea40014": { + "200b37b64d154fa8a2732dddbd48ce7c": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3450,7 +3381,7 @@ "description_width": "" } }, - "1b1f2df01673466e8ad13f05d189e913": { + "2067cc0813434dc89eb9a86fe60e326c": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3502,7 +3433,50 @@ "width": null } }, - "1d9b3f85beac49a5a57fd9e17e5bd9a5": { + "238ab964fff64bba8f57438714294a3e": { + "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_2fecc5d665444b07bf33bfe7da19f6ab", + "IPY_MODEL_9e0dea7fc84441be9c0246e93cc8be8c", + "IPY_MODEL_f759cd46ed8b4426a1cb436ee60f0928" + ], + "layout": "IPY_MODEL_e6001b3466b548dba925b562602b9082" + } + }, + "23e2cbf074c441369576d3ff1bf3d379": { + "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_05bf72f17f66450db11ab0343e2f4a92", + "placeholder": "", + "style": "IPY_MODEL_36c34f6c5a0743339e98b7996941a077", + "value": "Downloading metadata: 100%" + } + }, + "25e0f6166ffb4ca89ed4d9d4e68d425e": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3554,11 +3528,57 @@ "width": null } }, - "201226d4835447c4b62158c0339cb597": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { + "2a2efa39f0394a3ba2646b507175ce6a": { + "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_37aed048505443e39625a1cc738de353", + "IPY_MODEL_49579578b8d945ebbe20752e30903d75", + "IPY_MODEL_a9c13dc210a549879c84f5cc6f3525db" + ], + "layout": "IPY_MODEL_6fdf9247d4294ded907d918a8770e898" + } + }, + "2b7c918a38494cbcb981f5fbb885adc1": { + "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_682a1d7a400e4bc09bb9f9c90d12ceaa", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_e409101d664e415db7273d1d977d0a2d", + "value": 60000.0 + } + }, + "2cdb1028439d4fff829be37812d60a8e": { + "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", @@ -3606,7 +3626,52 @@ "width": null } }, - "202954e727c944819895beb546171b0d": { + "2fecc5d665444b07bf33bfe7da19f6ab": { + "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_5ff95972494c48f68ec493214e223d05", + "placeholder": "", + "style": "IPY_MODEL_dd62db19398244a29b8193eca299e34c", + "value": "Downloading data: 100%" + } + }, + "3022c14082c9454da81cd52274b97751": { + "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_c99e527f010141b7bf2c727d6523d516", + "max": 4.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_77834d748c6340bc910f8c05d5d78931", + "value": 4.0 + } + }, + "304d0ae7f4fb4e7299927d7c24d4425a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3658,22 +3723,7 @@ "width": null } }, - "211130be421244f6b3714ce1807bde58": { - "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": "" - } - }, - "21ef8862f77e43b3b903eed5bc3dd00a": { + "316572d7b0df4dcea2874066929f5fc6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3688,59 +3738,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_0b5c12ff7e5f4272b05312a7c947f42d", + "layout": "IPY_MODEL_e71917cbe6ec459bbbcb3b8bca2fc195", "placeholder": "", - "style": "IPY_MODEL_2b87b33c19694669a346dd38e62d3a87", - "value": "100%" - } - }, - "2390619468d34af396b893ce54a6b26c": { - "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_758b14a88c104520b726e1f0b9d30ab5", - "max": 4.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5146f2e01fed451ab61b4dbf5f09393f", - "value": 4.0 - } - }, - "25525fef3faa41bfa3d7fb01644654df": { - "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_f6ad2e1179124d2ab72069d848575efa", - "IPY_MODEL_d905b794fbff44a496ea697372fa71f4", - "IPY_MODEL_01be6e03a73e439bb56bb60d38339e89" - ], - "layout": "IPY_MODEL_a26e1015d1e84175802529f355278b86" + "style": "IPY_MODEL_0706c7ac0f014b07b29b0f00f4fe6563", + "value": " 4.83k/4.83k [00:00<00:00, 561kB/s]" } }, - "258cc8e320a04b3dbebacd49719336d4": { + "31b820fbb02b43ecb0e283d960cb3bc7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -3755,13 +3759,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_2a8e5d6aef3340678ded6e574ab89670", + "layout": "IPY_MODEL_f412500c28c644b8a1e441588b43428d", "placeholder": "", - "style": "IPY_MODEL_211130be421244f6b3714ce1807bde58", - "value": " 10000/10000 [00:01<00:00, 7430.74 examples/s]" + "style": "IPY_MODEL_1f4c1fb893ac40a9a5ab56ed2d344467", + "value": " 26.4M/26.4M [00:00<00:00, 102MB/s]" } }, - "269ac21b2a074052b587d17101e024ed": { + "33c33ea69bcb4d399d0a06381305beca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3776,7 +3780,7 @@ "description_width": "" } }, - "26c420a5563b49839677e1197d3e99f1": { + "343b6ab90ce54c2ca7cf316d5f2f16de": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3828,7 +3832,22 @@ "width": null } }, - "2932c05c5dfd49ff816cfd4becc97c29": { + "355fe27e55b640bc8e02dbfd47196146": { + "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": "" + } + }, + "36c34f6c5a0743339e98b7996941a077": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -3843,7 +3862,93 @@ "description_width": "" } }, - "2a8e5d6aef3340678ded6e574ab89670": { + "36ec644042e74e14874a773565a61470": { + "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_96cea967e91946809f69e283650f3cfa", + "IPY_MODEL_ab5fa6577cfc4343acdcb9a8ab0e727c", + "IPY_MODEL_b96ae38d17ac4e8aa05c91d02b41299c" + ], + "layout": "IPY_MODEL_a8c4e4f9bd9448faa8fccb0e764ae544" + } + }, + "36f9f77c6285402d812cdb63ca806652": { + "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_f7fbd9dd7b464b40924c26865d5e9681", + "placeholder": "", + "style": "IPY_MODEL_c29858bcd50d47429d581dde84247d4b", + "value": "Downloading readme: 100%" + } + }, + "37aed048505443e39625a1cc738de353": { + "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_bf8ec1f5fa20443899c855bfe1c12eb3", + "placeholder": "", + "style": "IPY_MODEL_355fe27e55b640bc8e02dbfd47196146", + "value": "Extracting data files: 100%" + } + }, + "383b11e2af1e4e5db72eaf0ad28d4f90": { + "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_8ab720dfbd6a462fb08368b3d9839d7d", + "IPY_MODEL_dffa766b9fcd46038c8d200644bf8b7c", + "IPY_MODEL_cb251949a9cd4ac08a2cc55bc7a674cd" + ], + "layout": "IPY_MODEL_6b4023a1ea4f4deda95d9bf97f8c7cd8" + } + }, + "3bf38eef814041e4b752768880cf7579": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3895,7 +4000,7 @@ "width": null } }, - "2ab495a687184b419a060dc882882111": { + "3c3ab5f2163d44718a15427d139e2b96": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -3947,22 +4052,7 @@ "width": null } }, - "2b87b33c19694669a346dd38e62d3a87": { - "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": "" - } - }, - "2cadd1d327464866afa5782070311337": { + "3c9a30da8dab4b158596efeb727c2f35": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4014,7 +4104,7 @@ "width": null } }, - "323b616232d34d2c981fa30aff426319": { + "3e10f9d6e8fb4d7cbd2f12501209ddd2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4066,83 +4156,70 @@ "width": null } }, - "32608dbbeef04240877642ecc80be120": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "3f4636f46f4843de9d7fa780de83b077": { + "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_343b6ab90ce54c2ca7cf316d5f2f16de", + "placeholder": "", + "style": "IPY_MODEL_b4ede8281fbd4c0c8892e794a3ce4b21", + "value": "Downloading data: 100%" } }, - "32e66cc00d5c4aee80424466c15f077c": { + "3ff1eb63a7d343fca37329c3fd49b84c": { "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_0c654c2ed5254dd2a06412cc243827cb", - "max": 4.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_9004cb5dad534950ae97a4b4509d248d", - "value": 4.0 + "layout": "IPY_MODEL_25e0f6166ffb4ca89ed4d9d4e68d425e", + "placeholder": "", + "style": "IPY_MODEL_d1678c4a444448f78cf6faf9e88de31c", + "value": "Downloading builder script: 100%" + } + }, + "418ada9017a240f2af05c0e27592ba7b": { + "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_58aafbb38ce44e65bd1e1688b81e5cb3", + "placeholder": "", + "style": "IPY_MODEL_33c33ea69bcb4d399d0a06381305beca", + "value": " 3.13k/3.13k [00:00<00:00, 417kB/s]" } }, - "33a829d4a6124bbaa8266503d677d6ac": { + "41e95f2819d04888807bd8eab71799b7": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -4158,15 +4235,73 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_9fd213954a9741cab513ca0a5d8a10e9", - "max": 4422102.0, + "layout": "IPY_MODEL_fe2d322a45aa49fa960d5cb543a823ae", + "max": 3126.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_e6ed001c028f4d7c81f00a0930b18ba7", - "value": 4422102.0 + "style": "IPY_MODEL_afb6f3242d3a40cda4ed8edc06b38b8a", + "value": 3126.0 } }, - "3728a4de8022495a827f3655079dcd46": { + "44b74f9e842a4b9e8369a69109792a23": { + "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_7a38fe3433cc40b79c739437d901f9ec", + "placeholder": "", + "style": "IPY_MODEL_5c83a3995a5c42a084cdd0764336918f", + "value": "Map (num_proc=4): 100%" + } + }, + "47944801fdbc4336b1f1042d884d88ab": { + "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": "" + } + }, + "4847c86d46074c699d0ac99c342e0cec": { + "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_7140bb2211794e1ca9f8c688a96d8d52", + "placeholder": "", + "style": "IPY_MODEL_8161c47587dd4325bf5320cdb3e3307d", + "value": " 8.85k/8.85k [00:00<00:00, 1.12MB/s]" + } + }, + "49579578b8d945ebbe20752e30903d75": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -4182,15 +4317,52 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_fd57481a82b14bee9778af0988c28884", + "layout": "IPY_MODEL_9a57f0a473114b8682bf41684295ba93", "max": 4.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_e3bda5c528804abca61f24b1f7926116", + "style": "IPY_MODEL_605cde6aa66b4a218b4f7c7c4ed203b0", "value": 4.0 } }, - "39f41133e33244798f1fd5308f8338a5": { + "4a1038ba5ca34ac7aa5ce69c8232162a": { + "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": "" + } + }, + "4ae53e1419d24fffbcb9d4fdd38f865c": { + "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_f90a38a1a205402da89c93919685a73d", + "IPY_MODEL_3022c14082c9454da81cd52274b97751", + "IPY_MODEL_fb8fe6ad653f459f92e86d630912eb12" + ], + "layout": "IPY_MODEL_6afbf50701544bbbac9e9504800ad4da" + } + }, + "4c47fc6d8139452aab032090d30e3e0a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4242,7 +4414,28 @@ "width": null } }, - "3c53d6e6289d484a8fb160cc0b438f37": { + "4ce58997facf45a68f3272c8e25476a1": { + "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_178301fe093c465fad01b1c698b40db4", + "placeholder": "", + "style": "IPY_MODEL_0b3226e253954bffa9d4152c174f78cb", + "value": " 10000/10000 [00:01<00:00, 7408.20 examples/s]" + } + }, + "4e6af7162845419ba6f1d7fd006379bf": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4294,7 +4487,31 @@ "width": null } }, - "3ddfc9c0bbc945a1a7e8d1652c660bc4": { + "4e880784e0de4a1a87c4919ac54382b5": { + "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_3c9a30da8dab4b158596efeb727c2f35", + "max": 4833.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_6f6be57a9fef422fad4ddb9d9b40d293", + "value": 4833.0 + } + }, + "4ebc0c652f3f4de59738117def507e2b": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4346,65 +4563,22 @@ "width": null } }, - "44227b492d524436ad162cff2208b767": { - "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_2cadd1d327464866afa5782070311337", - "placeholder": "", - "style": "IPY_MODEL_47be2a974001421895885c6bf602e7b2", - "value": "Downloading data: 100%" - } - }, - "44928fac3f7147dfb1dc6df1d280ccaa": { + "4edfe39b139c4989a38f9747e8b10610": { "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": "" } }, - "453f3dc04e4d431fa0ea8abb408674d9": { - "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_0e9fd9b7e2c04cb3b27ae6a5b39c6643", - "placeholder": "", - "style": "IPY_MODEL_502b15340dd84accba6e1ac814a769f9", - "value": " 4.83k/4.83k [00:00<00:00, 421kB/s]" - } - }, - "47b3d8daf0f7497693fd67eacb621870": { + "4fbf87f19aea459c84c330ad8902ac6a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4456,7 +4630,7 @@ "width": null } }, - "47be2a974001421895885c6bf602e7b2": { + "51cee4accf5c486181474bab1553406a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4471,149 +4645,74 @@ "description_width": "" } }, - "48028e2d8e864d12bcf33caebd44f169": { - "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_b0302d36fa39486a9f28512d4ab15e25", - "IPY_MODEL_b0749091ca6f4d28981083f8fce9bddc", - "IPY_MODEL_453f3dc04e4d431fa0ea8abb408674d9" - ], - "layout": "IPY_MODEL_32608dbbeef04240877642ecc80be120" - } - }, - "4ac5d5f7897343b39185fc9ec9cb96d6": { - "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_b335dc67ea8d4747b6641fffb470c472", - "max": 8845.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_5b5e20425ad04af49557176e01a5fe60", - "value": 8845.0 - } - }, - "4d4d971616cf4f37b26a87cb61ccce1a": { - "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_e804ed8ce44641cba3198712d0d56201", - "placeholder": "", - "style": "IPY_MODEL_03ff247338ad48baa1c669032b7da351", - "value": " 4.42M/4.42M [00:00<00:00, 74.9MB/s]" - } - }, - "502b15340dd84accba6e1ac814a769f9": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "54fac451fc6143cb9755bc0306cbf2ea": { + "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 } }, - "5146f2e01fed451ab61b4dbf5f09393f": { + "569feec053884b3dba76ed37d94408ab": { "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": "" } }, - "524c2b01440a4c4b8419c369e0bf7393": { - "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_89682759652f47629df772e755e56104", - "IPY_MODEL_3728a4de8022495a827f3655079dcd46", - "IPY_MODEL_e0a88a82ced749ba9d7cf4a76817105d" - ], - "layout": "IPY_MODEL_47b3d8daf0f7497693fd67eacb621870" - } - }, - "551aaea6faa348c6b6974eb09fcbc1c5": { - "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_44227b492d524436ad162cff2208b767", - "IPY_MODEL_7e3b9aa5f9d047b3ba8dd4e5c4d37987", - "IPY_MODEL_6852c1a863dc410eabef5430d3fc6264" - ], - "layout": "IPY_MODEL_e8e2782be53e40caa2354669e657b533" - } - }, - "58e5fe0e48ee41eaa6d07db906fda880": { + "56de4a4423784dd5ac7bbb5cd0ee4026": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -4628,7 +4727,7 @@ "description_width": "" } }, - "5a593af6f357451788fb2065bd7ddb3a": { + "5707ae6f19fd449caabccffd73d63fc6": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4680,23 +4779,31 @@ "width": null } }, - "5b5e20425ad04af49557176e01a5fe60": { + "581c0319c71f4ef898a4b8987964c3da": { "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/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": "ProgressView", + "bar_style": "success", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_304d0ae7f4fb4e7299927d7c24d4425a", + "max": 4422102.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_67f112b3346247bc9be2ae6c5b40aaed", + "value": 4422102.0 } }, - "5d672c887e9f476c8529e6a2e66e1d71": { + "58aafbb38ce44e65bd1e1688b81e5cb3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4748,65 +4855,46 @@ "width": null } }, - "5d967541646841d8b5dd1496807fc7ad": { + "5a8329ec53324bf09bd8446efc66e09a": { "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_e7a502585e244930a84158ac3e03c208", - "placeholder": "", - "style": "IPY_MODEL_58e5fe0e48ee41eaa6d07db906fda880", - "value": " 26.4M/26.4M [00:00<00:00, 112MB/s]" - } - }, - "6004d4cbf844405abb4f4b90b2b46cd4": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", - "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "layout": "IPY_MODEL_54fac451fc6143cb9755bc0306cbf2ea", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_47944801fdbc4336b1f1042d884d88ab", + "value": 60000.0 } }, - "6852c1a863dc410eabef5430d3fc6264": { + "5c83a3995a5c42a084cdd0764336918f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_7a3de7072363429db143ce63167fc8ac", - "placeholder": "", - "style": "IPY_MODEL_9c0a25d03fe84904a5272422c280ddcd", - "value": " 29.5k/29.5k [00:00<00:00, 3.22MB/s]" + "_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": "" } }, - "693298b1d2794b6fa3754cb8d30e3ddd": { + "5df54a79704945ffb870cae91223f3dd": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4858,69 +4946,59 @@ "width": null } }, - "6aef84bbda1349a09ead1b964cf97707": { - "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": "" - } - }, - "6bb33f94379547448de2a4fb81857dde": { - "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": "" - } - }, - "6cd7ba9f007b42f1a799e610cb223c6a": { - "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": "" - } - }, - "72d6a12c9dce40deaf1f072febb2b139": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "ProgressStyleModel", + "5ff95972494c48f68ec493214e223d05": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", "state": { - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "ProgressStyleModel", + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "StyleView", - "bar_color": null, - "description_width": "" + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null } }, - "758b14a88c104520b726e1f0b9d30ab5": { + "601fd080e58b47cfbf5afa2073934a46": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -4972,43 +5050,39 @@ "width": null } }, - "792c8badbbcf455a8cb318e8dd8c5476": { + "605cde6aa66b4a218b4f7c7c4ed203b0": { "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_1d9b3f85beac49a5a57fd9e17e5bd9a5", - "placeholder": "", - "style": "IPY_MODEL_79656e33f9cd45bfa0b1b6ce55f178b6", - "value": "Extracting data files: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "79656e33f9cd45bfa0b1b6ce55f178b6": { + "606e2ec41f774e10b3216e9985e8c3ee": { "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": "" } }, - "7a03d60be7ea4fe798f89923738a0141": { + "60f1f1a34cd743a99f862fb9bd8275c2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5060,7 +5134,7 @@ "width": null } }, - "7a3de7072363429db143ce63167fc8ac": { + "61216e82298d4011a94865a1a2e8511f": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5112,22 +5186,28 @@ "width": null } }, - "7d0d02c45d3f4315b639e21b13471e94": { + "62aba93e6d1443ef944cba728bfb075f": { "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_135d7ed31fa04b328a6a522a17a75f9e", + "placeholder": "", + "style": "IPY_MODEL_1ac6d55a17dd4dddb69df64307db835b", + "value": "Generating train split: 100%" } }, - "7e3b9aa5f9d047b3ba8dd4e5c4d37987": { + "64786a1aacd042ef91cbf08e381a87a9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", @@ -5143,37 +5223,31 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_26c420a5563b49839677e1197d3e99f1", - "max": 29515.0, + "layout": "IPY_MODEL_2cdb1028439d4fff829be37812d60a8e", + "max": 5148.0, "min": 0.0, "orientation": "horizontal", - "style": "IPY_MODEL_88a2264c8acf4d51857377b85f5e2e5a", - "value": 29515.0 + "style": "IPY_MODEL_c646352ad02146689668d52d23584062", + "value": 5148.0 } }, - "7e7fbe13ae9d4837bc4327ad2e0b8c44": { + "67f112b3346247bc9be2ae6c5b40aaed": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HBoxModel", + "model_name": "ProgressStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HBoxModel", + "_model_name": "ProgressStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HBoxView", - "box_style": "", - "children": [ - "IPY_MODEL_c4dec09a365b435db5f8a35a8e9caac7", - "IPY_MODEL_f26058b640c243cb9ff437136ea514b0", - "IPY_MODEL_e8d3dbe311c94456b19ca4f524b8d671" - ], - "layout": "IPY_MODEL_3c53d6e6289d484a8fb160cc0b438f37" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "84a17f651ff94283bbd80920a2108119": { + "682a1d7a400e4bc09bb9f9c90d12ceaa": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5225,96 +5299,59 @@ "width": null } }, - "84fc08030cf640bdb8903d2b88b629f0": { - "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": "" - } - }, - "88232f2a0333433daf181c32d047baa5": { - "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_cf807421f8ba46cf83291a5fc1c986c4", - "IPY_MODEL_a7d831278d6c4302a0b905439cc613f1", - "IPY_MODEL_07113d7cf4e846f8870c58c42cfaa24f" - ], - "layout": "IPY_MODEL_a30b8ec39a4c492cbd38d703138bd328" - } - }, - "88a2264c8acf4d51857377b85f5e2e5a": { - "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": "" - } - }, - "89682759652f47629df772e755e56104": { - "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_ab52c57c736a4d01b48719366a91c755", - "placeholder": "", - "style": "IPY_MODEL_89f8121e955241cc9eeb9c582d55dd54", - "value": "Downloading data files: 100%" - } - }, - "89f8121e955241cc9eeb9c582d55dd54": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "69ce5f9ce1b94195a0f33220a60fa670": { + "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 } }, - "8fdcb4c6596c43d6b6cd045f74a1e7c8": { + "6afbf50701544bbbac9e9504800ad4da": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5366,44 +5403,7 @@ "width": null } }, - "9004cb5dad534950ae97a4b4509d248d": { - "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": "" - } - }, - "909d804677f74aa680f28c70c86362f1": { - "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_dc509f7ef1cb42d8a16edd756c66cbcf", - "placeholder": "", - "style": "IPY_MODEL_93b33ae952124a7f945d1dafbf81809d", - "value": "Map (num_proc=4): 100%" - } - }, - "915530c2968f4c44baa11933c20920c9": { + "6b4023a1ea4f4deda95d9bf97f8c7cd8": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5455,22 +5455,7 @@ "width": null } }, - "91b1f9d101914e39af45b58d21128e0c": { - "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": "" - } - }, - "92e3972554604f2ab22d517a7c7f79c3": { + "6d336b84fee942e9aa7b71fc49c3e337": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5522,7 +5507,7 @@ "width": null } }, - "93b33ae952124a7f945d1dafbf81809d": { + "6e0569b029c84213b1cf767843c7dc04": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -5537,7 +5522,45 @@ "description_width": "" } }, - "93b922cf8c414b80bef502f26bebd232": { + "6e6755b4623a4f0a905b741ee7cb5453": { + "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_23e2cbf074c441369576d3ff1bf3d379", + "IPY_MODEL_41e95f2819d04888807bd8eab71799b7", + "IPY_MODEL_418ada9017a240f2af05c0e27592ba7b" + ], + "layout": "IPY_MODEL_81e9ef8b8429496f8d9a868246ab3abe" + } + }, + "6f6be57a9fef422fad4ddb9d9b40d293": { + "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": "" + } + }, + "6fdf9247d4294ded907d918a8770e898": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5589,23 +5612,7 @@ "width": null } }, - "94a89a5ef94645138137c1821fa0adc9": { - "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": "" - } - }, - "970002fca31842488c16c21f274526b8": { + "7140bb2211794e1ca9f8c688a96d8d52": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5657,37 +5664,66 @@ "width": null } }, - "990b476ad51140478ed290f8546081bf": { + "7329880eab5847d298a65b5166a87566": { "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_e3fd2ce3378240fcb497451cc53f0af2", + "IPY_MODEL_581c0319c71f4ef898a4b8987964c3da", + "IPY_MODEL_ca1b6d1298bd4bedb6d54ab041ed1568" + ], + "layout": "IPY_MODEL_86e0c44fe8bf445785d7c00123bbb74d" } }, - "9c0a25d03fe84904a5272422c280ddcd": { + "74360a7e484f420ba0cb857affbfcc21": { "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/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_6d336b84fee942e9aa7b71fc49c3e337", + "placeholder": "", + "style": "IPY_MODEL_56de4a4423784dd5ac7bbb5cd0ee4026", + "value": " 5.15k/5.15k [00:00<00:00, 604kB/s]" + } + }, + "747f7277cf55435fb4eeb18604789631": { + "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": "" } }, - "9fd213954a9741cab513ca0a5d8a10e9": { + "74f66b76509b4c27a3af13f7e48971ff": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5739,43 +5775,75 @@ "width": null } }, - "a1bd1b98aa8d4e439e7038a3386b0dd3": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "DescriptionStyleModel", + "757eb8f93d074944928213c74cdd5876": { + "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 } }, - "a24569cb00d7446fbf013c519735f2b4": { + "77834d748c6340bc910f8c05d5d78931": { "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_f4ea967b03694affb97c1c821d5fa82f", - "placeholder": "", - "style": "IPY_MODEL_6bb33f94379547448de2a4fb81857dde", - "value": "Generating test split: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "a26e1015d1e84175802529f355278b86": { + "7a38fe3433cc40b79c739437d901f9ec": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5827,29 +5895,22 @@ "width": null } }, - "a27cbf95e9124302ad3381f88bf96687": { + "8161c47587dd4325bf5320cdb3e3307d": { "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_ad3bec5e5d7448b3b47bf9fd566c10bd", - "IPY_MODEL_32e66cc00d5c4aee80424466c15f077c", - "IPY_MODEL_ded0d9490ad0464a9ea173deb658884d" - ], - "layout": "IPY_MODEL_202954e727c944819895beb546171b0d" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "a30b8ec39a4c492cbd38d703138bd328": { + "81e9ef8b8429496f8d9a868246ab3abe": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5901,31 +5962,29 @@ "width": null } }, - "a7d831278d6c4302a0b905439cc613f1": { + "840bc43fd3d340428a779ceef8112f22": { "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_93b922cf8c414b80bef502f26bebd232", - "max": 3126.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_d7edc2c692e446b2808dc209aafe6aac", - "value": 3126.0 + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_44b74f9e842a4b9e8369a69109792a23", + "IPY_MODEL_2b7c918a38494cbcb981f5fbb885adc1", + "IPY_MODEL_87dadaf567524fa2b5095ca8713cadd4" + ], + "layout": "IPY_MODEL_3e10f9d6e8fb4d7cbd2f12501209ddd2" } }, - "aa836377b0644584a8ee8fddb0770e86": { + "8572c70172c64f9bb9f6916497c70410": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -5977,28 +6036,22 @@ "width": null } }, - "ab3bcf26cabd4d628a3e6bbd88cbd8a8": { + "866bf7e31f8840feb77c866ad6be2355": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "HTMLModel", + "model_name": "DescriptionStyleModel", "state": { - "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", - "_model_name": "HTMLModel", + "_model_name": "DescriptionStyleModel", "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - "_view_name": "HTMLView", - "description": "", - "description_tooltip": null, - "layout": "IPY_MODEL_970002fca31842488c16c21f274526b8", - "placeholder": "", - "style": "IPY_MODEL_990b476ad51140478ed290f8546081bf", - "value": "Downloading readme: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "ab52c57c736a4d01b48719366a91c755": { + "86e0c44fe8bf445785d7c00123bbb74d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6050,7 +6103,7 @@ "width": null } }, - "ad3bec5e5d7448b3b47bf9fd566c10bd": { + "87dadaf567524fa2b5095ca8713cadd4": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -6065,28 +6118,50 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_d365c14db178402bb34b0be706cf6d05", + "layout": "IPY_MODEL_60f1f1a34cd743a99f862fb9bd8275c2", "placeholder": "", - "style": "IPY_MODEL_269ac21b2a074052b587d17101e024ed", - "value": "Computing checksums: 100%" + "style": "IPY_MODEL_ed01dc5ecdf5453eb5dbc7933f840eee", + "value": " 60000/60000 [00:10<00:00, 8360.10 examples/s]" } }, - "adc8913a3f014e889e245e96addb29a5": { + "8ab720dfbd6a462fb08368b3d9839d7d": { "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/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c74936d51f0740ed921a9700622a4a89", + "placeholder": "", + "style": "IPY_MODEL_866bf7e31f8840feb77c866ad6be2355", + "value": "100%" + } + }, + "8ba20bad13d342f1a72b0c8fd4402bb9": { + "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": "" } }, - "b0302d36fa39486a9f28512d4ab15e25": { + "907c6bc8381f4c0398777b883338efbe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -6101,59 +6176,49 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_aa836377b0644584a8ee8fddb0770e86", + "layout": "IPY_MODEL_0d9e50cc326048f89d4ba45f99df9574", "placeholder": "", - "style": "IPY_MODEL_12c62f98b1614d299c05d2a04c586766", - "value": "Downloading builder script: 100%" + "style": "IPY_MODEL_6e0569b029c84213b1cf767843c7dc04", + "value": " 60000/60000 [00:08<00:00, 7468.00 examples/s]" } }, - "b0749091ca6f4d28981083f8fce9bddc": { + "96cea967e91946809f69e283650f3cfa": { "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_2ab495a687184b419a060dc882882111", - "max": 4833.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_72d6a12c9dce40deaf1f072febb2b139", - "value": 4833.0 + "layout": "IPY_MODEL_9db392828b6542b8a1c55b8ee519a2e2", + "placeholder": "", + "style": "IPY_MODEL_17f0aabf25cd4ababf0d602d3a3aa3f6", + "value": "Downloading data files: 100%" } }, - "b20ae6b1e0ec42618e40f7da092950a5": { + "98f7bd253cee4c3fa731747eefe6d4fc": { "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_ab3bcf26cabd4d628a3e6bbd88cbd8a8", - "IPY_MODEL_4ac5d5f7897343b39185fc9ec9cb96d6", - "IPY_MODEL_0774373388fa46558225957d6f8ef740" - ], - "layout": "IPY_MODEL_de8eaca934674764a720c4df0e744316" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" } }, - "b335dc67ea8d4747b6641fffb470c472": { + "9a57f0a473114b8682bf41684295ba93": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6205,29 +6270,7 @@ "width": null } }, - "b43869cf841c45c69f9946692ef05b2a": { - "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_10889a4c08de4efe8696ecf09185e3f8", - "IPY_MODEL_f99629b380e4462a8550a52edaa77d72", - "IPY_MODEL_5d967541646841d8b5dd1496807fc7ad" - ], - "layout": "IPY_MODEL_eb679a35e2404cd4b4dbf14f669d61d8" - } - }, - "be703ed8d63b4d37aac4ba0d78f88a04": { + "9db392828b6542b8a1c55b8ee519a2e2": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6279,28 +6322,47 @@ "width": null } }, - "c4dec09a365b435db5f8a35a8e9caac7": { + "9e0dea7fc84441be9c0246e93cc8be8c": { "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_5a593af6f357451788fb2065bd7ddb3a", - "placeholder": "", - "style": "IPY_MODEL_ed3cefdbf7374abbb10dae18f7e8495f", - "value": "Generating train split: 100%" + "layout": "IPY_MODEL_757eb8f93d074944928213c74cdd5876", + "max": 29515.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_eb00096028b146cba10bd44c7165be0f", + "value": 29515.0 + } + }, + "a5e0c89448bf4656a32cd7784f9bf7fc": { + "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": "" } }, - "c779fc2871664020998850299a7ddd3e": { + "a8c4e4f9bd9448faa8fccb0e764ae544": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6352,23 +6414,91 @@ "width": null } }, - "c7e1a6ec7f054efab5206037087be6fb": { + "a9c13dc210a549879c84f5cc6f3525db": { "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/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_c692bc7715ba4f168b9786293efb4715", + "placeholder": "", + "style": "IPY_MODEL_200b37b64d154fa8a2732dddbd48ce7c", + "value": " 4/4 [00:00<00:00, 3.82it/s]" + } + }, + "ab5fa6577cfc4343acdcb9a8ab0e727c": { + "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_4e6af7162845419ba6f1d7fd006379bf", + "max": 4.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_baa811997184433b93e8f74b07145b46", + "value": 4.0 + } + }, + "ae0ec41b943d4815b0364787ed43a132": { + "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_5707ae6f19fd449caabccffd73d63fc6", + "max": 10000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_606e2ec41f774e10b3216e9985e8c3ee", + "value": 10000.0 + } + }, + "ae1e9e16acf9437eb4b8d0844ef00632": { + "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": "" } }, - "ce8ea6b5cf584edcbdd7c21a26970e86": { + "aeaeeb5988cb4638aadb1b0840bf3745": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -6383,139 +6513,60 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_fecc45267966445c932534eb2bb35ce4", - "IPY_MODEL_33a829d4a6124bbaa8266503d677d6ac", - "IPY_MODEL_4d4d971616cf4f37b26a87cb61ccce1a" + "IPY_MODEL_36f9f77c6285402d812cdb63ca806652", + "IPY_MODEL_18b47621656348409b5d33b3d5da0cd9", + "IPY_MODEL_4847c86d46074c699d0ac99c342e0cec" ], - "layout": "IPY_MODEL_eaa8f8640aef4221acfc59cbfff1289e" + "layout": "IPY_MODEL_69ce5f9ce1b94195a0f33220a60fa670" } }, - "cf807421f8ba46cf83291a5fc1c986c4": { + "afb6f3242d3a40cda4ed8edc06b38b8a": { "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_0dbf24fb54d442b18a2afb64da344cf5", - "placeholder": "", - "style": "IPY_MODEL_7d0d02c45d3f4315b639e21b13471e94", - "value": "Downloading metadata: 100%" + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" } }, - "d34b5b7ccc3143248409e3c3821854a6": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "b3c5fabf52194e3491a45effd0b8c9f0": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" } }, - "d365c14db178402bb34b0be706cf6d05": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", + "b4ede8281fbd4c0c8892e794a3ce4b21": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "_view_name": "StyleView", + "description_width": "" } }, - "d3e80e28ba1b48afba73110f15d0fb40": { + "b938a28698dd446c98758c5ae37fbbee": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HBoxModel", @@ -6530,14 +6581,75 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_792c8badbbcf455a8cb318e8dd8c5476", - "IPY_MODEL_2390619468d34af396b893ce54a6b26c", - "IPY_MODEL_f203f58903084ca5844e4dd7f95ef930" + "IPY_MODEL_fe2fa3e58ddc4b249fa7ea4d5663b952", + "IPY_MODEL_64786a1aacd042ef91cbf08e381a87a9", + "IPY_MODEL_74360a7e484f420ba0cb857affbfcc21" ], - "layout": "IPY_MODEL_fa4c777bec884573b66325b782d20cbe" + "layout": "IPY_MODEL_61216e82298d4011a94865a1a2e8511f" + } + }, + "b96ae38d17ac4e8aa05c91d02b41299c": { + "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_10a144afd941436686e5eb0abdfb647b", + "placeholder": "", + "style": "IPY_MODEL_f431a961370144c9b7e31682cd0263aa", + "value": " 4/4 [00:05<00:00, 1.24s/it]" + } + }, + "baa811997184433b93e8f74b07145b46": { + "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": "" + } + }, + "bc60cda6e0614f61b3d44bf44f48defa": { + "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_74f66b76509b4c27a3af13f7e48971ff", + "max": 26421880.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_747f7277cf55435fb4eeb18604789631", + "value": 26421880.0 } }, - "d402495ce6d9485dad1649ef6a5e6198": { + "bf8ec1f5fa20443899c855bfe1c12eb3": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6589,7 +6701,22 @@ "width": null } }, - "d4dd8310053d46eb88540bfec436fe60": { + "c29858bcd50d47429d581dde84247d4b": { + "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": "" + } + }, + "c5f29035128845fcbb2a4fcb9e041167": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6641,7 +6768,7 @@ "width": null } }, - "d7edc2c692e446b2808dc209aafe6aac": { + "c646352ad02146689668d52d23584062": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", @@ -6657,7 +6784,7 @@ "description_width": "" } }, - "d879a2e95cbc438e8493d7702aa3d529": { + "c692bc7715ba4f168b9786293efb4715": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6709,55 +6836,7 @@ "width": null } }, - "d905b794fbff44a496ea697372fa71f4": { - "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_5d672c887e9f476c8529e6a2e66e1d71", - "max": 5148.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6aef84bbda1349a09ead1b964cf97707", - "value": 5148.0 - } - }, - "db5f8379f5f645209fdd1c01702db84c": { - "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_1b1f2df01673466e8ad13f05d189e913", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_6004d4cbf844405abb4f4b90b2b46cd4", - "value": 60000.0 - } - }, - "dc509f7ef1cb42d8a16edd756c66cbcf": { + "c74936d51f0740ed921a9700622a4a89": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6809,22 +6888,7 @@ "width": null } }, - "dd7fd75fcdcd4369833b938332c3023e": { - "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": "" - } - }, - "de8eaca934674764a720c4df0e744316": { + "c99e527f010141b7bf2c727d6523d516": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -6876,29 +6940,7 @@ "width": null } }, - "de9a8b3d0e42412eb75d8abc7d35ee38": { - "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_909d804677f74aa680f28c70c86362f1", - "IPY_MODEL_f8fff61f71d24c38b2f558913fbc8e35", - "IPY_MODEL_f2c39cf53e60411c942c69aebac75140" - ], - "layout": "IPY_MODEL_8fdcb4c6596c43d6b6cd045f74a1e7c8" - } - }, - "ded0d9490ad0464a9ea173deb658884d": { + "ca1b6d1298bd4bedb6d54ab041ed1568": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -6913,13 +6955,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_3ddfc9c0bbc945a1a7e8d1652c660bc4", + "layout": "IPY_MODEL_4c47fc6d8139452aab032090d30e3e0a", "placeholder": "", - "style": "IPY_MODEL_01641a2ad627457ca1997504835d5ea6", - "value": " 4/4 [00:00<00:00, 733.49it/s]" + "style": "IPY_MODEL_98f7bd253cee4c3fa731747eefe6d4fc", + "value": " 4.42M/4.42M [00:00<00:00, 55.1MB/s]" } }, - "e0a88a82ced749ba9d7cf4a76817105d": { + "cb251949a9cd4ac08a2cc55bc7a674cd": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -6934,97 +6976,88 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_d4dd8310053d46eb88540bfec436fe60", + "layout": "IPY_MODEL_c5f29035128845fcbb2a4fcb9e041167", "placeholder": "", - "style": "IPY_MODEL_2932c05c5dfd49ff816cfd4becc97c29", - "value": " 4/4 [00:01<00:00, 2.23it/s]" + "style": "IPY_MODEL_4edfe39b139c4989a38f9747e8b10610", + "value": " 60000/60000 [00:28<00:00, 2141.56it/s]" } }, - "e3bda5c528804abca61f24b1f7926116": { + "d1678c4a444448f78cf6faf9e88de31c": { "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": "" } }, - "e6ed001c028f4d7c81f00a0930b18ba7": { + "d639341d172948d9877d1c6c3602eb27": { "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_4ebc0c652f3f4de59738117def507e2b", + "placeholder": "", + "style": "IPY_MODEL_4a1038ba5ca34ac7aa5ce69c8232162a", + "value": "Generating test split: 100%" } }, - "e7a502585e244930a84158ac3e03c208": { - "model_module": "@jupyter-widgets/base", - "model_module_version": "1.2.0", - "model_name": "LayoutModel", - "state": { - "_model_module": "@jupyter-widgets/base", - "_model_module_version": "1.2.0", - "_model_name": "LayoutModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/base", - "_view_module_version": "1.2.0", - "_view_name": "LayoutView", - "align_content": null, - "align_items": null, - "align_self": null, - "border": null, - "bottom": null, - "display": null, - "flex": null, - "flex_flow": null, - "grid_area": null, - "grid_auto_columns": null, - "grid_auto_flow": null, - "grid_auto_rows": null, - "grid_column": null, - "grid_gap": null, - "grid_row": null, - "grid_template_areas": null, - "grid_template_columns": null, - "grid_template_rows": null, - "height": null, - "justify_content": null, - "justify_items": null, - "left": null, - "margin": null, - "max_height": null, - "max_width": null, - "min_height": null, - "min_width": null, - "object_fit": null, - "object_position": null, - "order": null, - "overflow": null, - "overflow_x": null, - "overflow_y": null, - "padding": null, - "right": null, - "top": null, - "visibility": null, - "width": null + "dd62db19398244a29b8193eca299e34c": { + "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": "" + } + }, + "dffa766b9fcd46038c8d200644bf8b7c": { + "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_2067cc0813434dc89eb9a86fe60e326c", + "max": 60000.0, + "min": 0.0, + "orientation": "horizontal", + "style": "IPY_MODEL_8ba20bad13d342f1a72b0c8fd4402bb9", + "value": 60000.0 } }, - "e804ed8ce44641cba3198712d0d56201": { + "e1e630f8a8af4b73bc6555384939dd3a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7076,7 +7109,22 @@ "width": null } }, - "e8d3dbe311c94456b19ca4f524b8d671": { + "e3fc21908c4c4246862f128f67b934ca": { + "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": "" + } + }, + "e3fd2ce3378240fcb497451cc53f0af2": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -7091,13 +7139,29 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_d34b5b7ccc3143248409e3c3821854a6", + "layout": "IPY_MODEL_fabd302ba8a5447aa734842dc751cdfe", "placeholder": "", - "style": "IPY_MODEL_a1bd1b98aa8d4e439e7038a3386b0dd3", - "value": " 60000/60000 [00:08<00:00, 7387.28 examples/s]" + "style": "IPY_MODEL_569feec053884b3dba76ed37d94408ab", + "value": "Downloading data: 100%" + } + }, + "e409101d664e415db7273d1d977d0a2d": { + "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": "" } }, - "e8e2782be53e40caa2354669e657b533": { + "e6001b3466b548dba925b562602b9082": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7149,7 +7213,7 @@ "width": null } }, - "eaa8f8640aef4221acfc59cbfff1289e": { + "e652e5327e1d47638b302b6cd670be79": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7201,7 +7265,7 @@ "width": null } }, - "eb679a35e2404cd4b4dbf14f669d61d8": { + "e71917cbe6ec459bbbcb3b8bca2fc195": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7253,88 +7317,23 @@ "width": null } }, - "ed3cefdbf7374abbb10dae18f7e8495f": { + "eb00096028b146cba10bd44c7165be0f": { "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": "" } }, - "f203f58903084ca5844e4dd7f95ef930": { - "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_d879a2e95cbc438e8493d7702aa3d529", - "placeholder": "", - "style": "IPY_MODEL_91b1f9d101914e39af45b58d21128e0c", - "value": " 4/4 [00:00<00:00, 3.87it/s]" - } - }, - "f26058b640c243cb9ff437136ea514b0": { - "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_7a03d60be7ea4fe798f89923738a0141", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_94a89a5ef94645138137c1821fa0adc9", - "value": 60000.0 - } - }, - "f2c39cf53e60411c942c69aebac75140": { - "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_d402495ce6d9485dad1649ef6a5e6198", - "placeholder": "", - "style": "IPY_MODEL_6cd7ba9f007b42f1a799e610cb223c6a", - "value": " 60000/60000 [00:10<00:00, 7986.28 examples/s]" - } - }, - "f4aff97d336f4e5c96f81cf80fdee564": { + "ed01dc5ecdf5453eb5dbc7933f840eee": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", @@ -7349,7 +7348,7 @@ "description_width": "" } }, - "f4ea967b03694affb97c1c821d5fa82f": { + "f1d37505db474911a8f7429decf11b2a": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7401,74 +7400,7 @@ "width": null } }, - "f54927ed92ee4370819b6a462532901d": { - "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_a24569cb00d7446fbf013c519735f2b4", - "IPY_MODEL_01614767a83948fa8e86ee4304c4d4ba", - "IPY_MODEL_258cc8e320a04b3dbebacd49719336d4" - ], - "layout": "IPY_MODEL_915530c2968f4c44baa11933c20920c9" - } - }, - "f6ad2e1179124d2ab72069d848575efa": { - "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_92e3972554604f2ab22d517a7c7f79c3", - "placeholder": "", - "style": "IPY_MODEL_18e830b21c694e79a6883824bea40014", - "value": "Downloading data: 100%" - } - }, - "f8fff61f71d24c38b2f558913fbc8e35": { - "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_be703ed8d63b4d37aac4ba0d78f88a04", - "max": 60000.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_c7e1a6ec7f054efab5206037087be6fb", - "value": 60000.0 - } - }, - "f967c00757204de787c61238ce385d32": { + "f412500c28c644b8a1e441588b43428d": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7520,31 +7452,65 @@ "width": null } }, - "f99629b380e4462a8550a52edaa77d72": { + "f431a961370144c9b7e31682cd0263aa": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "f5e3565367434275816c74477904c0b2": { + "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", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_3f4636f46f4843de9d7fa780de83b077", + "IPY_MODEL_bc60cda6e0614f61b3d44bf44f48defa", + "IPY_MODEL_31b820fbb02b43ecb0e283d960cb3bc7" + ], + "layout": "IPY_MODEL_e1e630f8a8af4b73bc6555384939dd3a" + } + }, + "f759cd46ed8b4426a1cb436ee60f0928": { + "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_fa19246e2a844f19858891c7bf54f4d5", - "max": 26421880.0, - "min": 0.0, - "orientation": "horizontal", - "style": "IPY_MODEL_149959a2af424f009c160ab889b46a60", - "value": 26421880.0 + "layout": "IPY_MODEL_3bf38eef814041e4b752768880cf7579", + "placeholder": "", + "style": "IPY_MODEL_ae1e9e16acf9437eb4b8d0844ef00632", + "value": " 29.5k/29.5k [00:00<00:00, 3.46MB/s]" } }, - "fa19246e2a844f19858891c7bf54f4d5": { + "f7fbd9dd7b464b40924c26865d5e9681": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7596,7 +7562,28 @@ "width": null } }, - "fa4c777bec884573b66325b782d20cbe": { + "f90a38a1a205402da89c93919685a73d": { + "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_4fbf87f19aea459c84c330ad8902ac6a", + "placeholder": "", + "style": "IPY_MODEL_51cee4accf5c486181474bab1553406a", + "value": "Computing checksums: 100%" + } + }, + "fabd302ba8a5447aa734842dc751cdfe": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7648,7 +7635,28 @@ "width": null } }, - "fd57481a82b14bee9778af0988c28884": { + "fb8fe6ad653f459f92e86d630912eb12": { + "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_5df54a79704945ffb870cae91223f3dd", + "placeholder": "", + "style": "IPY_MODEL_e3fc21908c4c4246862f128f67b934ca", + "value": " 4/4 [00:00<00:00, 737.40it/s]" + } + }, + "fe2d322a45aa49fa960d5cb543a823ae": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", @@ -7700,7 +7708,7 @@ "width": null } }, - "fecc45267966445c932534eb2bb35ce4": { + "fe2fa3e58ddc4b249fa7ea4d5663b952": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "HTMLModel", @@ -7715,9 +7723,9 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_201226d4835447c4b62158c0339cb597", + "layout": "IPY_MODEL_f1d37505db474911a8f7429decf11b2a", "placeholder": "", - "style": "IPY_MODEL_dd7fd75fcdcd4369833b938332c3023e", + "style": "IPY_MODEL_b3c5fabf52194e3491a45effd0b8c9f0", "value": "Downloading data: 100%" } } diff --git a/master/tutorials/indepth_overview.ipynb b/master/tutorials/indepth_overview.ipynb index d8353ce43..27113b1e6 100644 --- a/master/tutorials/indepth_overview.ipynb +++ b/master/tutorials/indepth_overview.ipynb @@ -53,10 +53,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:20.150459Z", - "iopub.status.busy": "2023-11-20T20:38:20.150273Z", - "iopub.status.idle": "2023-11-20T20:38:21.186823Z", - "shell.execute_reply": "2023-11-20T20:38:21.186228Z" + "iopub.execute_input": "2023-11-21T08:15:11.719976Z", + "iopub.status.busy": "2023-11-21T08:15:11.719778Z", + "iopub.status.idle": "2023-11-21T08:15:12.834495Z", + "shell.execute_reply": "2023-11-21T08:15:12.833860Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:38:21.189772Z", - "iopub.status.busy": "2023-11-20T20:38:21.189238Z", - "iopub.status.idle": "2023-11-20T20:38:21.449576Z", - "shell.execute_reply": "2023-11-20T20:38:21.448981Z" + "iopub.execute_input": "2023-11-21T08:15:12.837917Z", + "iopub.status.busy": "2023-11-21T08:15:12.837269Z", + "iopub.status.idle": "2023-11-21T08:15:13.124645Z", + "shell.execute_reply": "2023-11-21T08:15:13.124000Z" }, "id": "avXlHJcXjruP" }, @@ -234,10 +234,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:21.452723Z", - "iopub.status.busy": "2023-11-20T20:38:21.452279Z", - "iopub.status.idle": "2023-11-20T20:38:21.464191Z", - "shell.execute_reply": "2023-11-20T20:38:21.463707Z" + "iopub.execute_input": "2023-11-21T08:15:13.127838Z", + "iopub.status.busy": "2023-11-21T08:15:13.127399Z", + "iopub.status.idle": "2023-11-21T08:15:13.139862Z", + "shell.execute_reply": "2023-11-21T08:15:13.139315Z" }, "nbsphinx": "hidden" }, @@ -340,10 +340,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:21.466510Z", - "iopub.status.busy": "2023-11-20T20:38:21.466143Z", - "iopub.status.idle": "2023-11-20T20:38:21.665081Z", - "shell.execute_reply": "2023-11-20T20:38:21.664393Z" + "iopub.execute_input": "2023-11-21T08:15:13.142524Z", + "iopub.status.busy": "2023-11-21T08:15:13.142088Z", + "iopub.status.idle": "2023-11-21T08:15:13.377180Z", + "shell.execute_reply": "2023-11-21T08:15:13.376435Z" } }, "outputs": [ @@ -393,10 +393,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:21.667868Z", - "iopub.status.busy": "2023-11-20T20:38:21.667489Z", - "iopub.status.idle": "2023-11-20T20:38:21.693950Z", - "shell.execute_reply": "2023-11-20T20:38:21.693453Z" + "iopub.execute_input": "2023-11-21T08:15:13.380165Z", + "iopub.status.busy": "2023-11-21T08:15:13.379678Z", + "iopub.status.idle": "2023-11-21T08:15:13.406667Z", + "shell.execute_reply": "2023-11-21T08:15:13.406122Z" } }, "outputs": [], @@ -427,10 +427,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:21.696417Z", - "iopub.status.busy": "2023-11-20T20:38:21.696018Z", - "iopub.status.idle": "2023-11-20T20:38:22.956921Z", - "shell.execute_reply": "2023-11-20T20:38:22.956297Z" + "iopub.execute_input": "2023-11-21T08:15:13.409323Z", + "iopub.status.busy": "2023-11-21T08:15:13.408955Z", + "iopub.status.idle": "2023-11-21T08:15:14.790664Z", + "shell.execute_reply": "2023-11-21T08:15:14.789923Z" } }, "outputs": [ @@ -471,10 +471,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:22.959749Z", - "iopub.status.busy": "2023-11-20T20:38:22.959209Z", - "iopub.status.idle": "2023-11-20T20:38:22.975650Z", - "shell.execute_reply": "2023-11-20T20:38:22.975113Z" + "iopub.execute_input": "2023-11-21T08:15:14.793579Z", + "iopub.status.busy": "2023-11-21T08:15:14.793155Z", + "iopub.status.idle": "2023-11-21T08:15:14.810437Z", + "shell.execute_reply": "2023-11-21T08:15:14.809795Z" }, "scrolled": true }, @@ -599,10 +599,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:22.978112Z", - "iopub.status.busy": "2023-11-20T20:38:22.977722Z", - "iopub.status.idle": "2023-11-20T20:38:23.838592Z", - "shell.execute_reply": "2023-11-20T20:38:23.837879Z" + "iopub.execute_input": "2023-11-21T08:15:14.812980Z", + "iopub.status.busy": "2023-11-21T08:15:14.812528Z", + "iopub.status.idle": "2023-11-21T08:15:15.733701Z", + "shell.execute_reply": "2023-11-21T08:15:15.732755Z" }, "id": "AaHC5MRKjruT" }, @@ -721,10 +721,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:23.841571Z", - "iopub.status.busy": "2023-11-20T20:38:23.841034Z", - "iopub.status.idle": "2023-11-20T20:38:23.855078Z", - "shell.execute_reply": "2023-11-20T20:38:23.854563Z" + "iopub.execute_input": "2023-11-21T08:15:15.736588Z", + "iopub.status.busy": "2023-11-21T08:15:15.736305Z", + "iopub.status.idle": "2023-11-21T08:15:15.752119Z", + "shell.execute_reply": "2023-11-21T08:15:15.751494Z" }, "id": "Wy27rvyhjruU" }, @@ -773,10 +773,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:23.857342Z", - "iopub.status.busy": "2023-11-20T20:38:23.857144Z", - "iopub.status.idle": "2023-11-20T20:38:23.937856Z", - "shell.execute_reply": "2023-11-20T20:38:23.937144Z" + "iopub.execute_input": "2023-11-21T08:15:15.755040Z", + "iopub.status.busy": "2023-11-21T08:15:15.754590Z", + "iopub.status.idle": "2023-11-21T08:15:15.848572Z", + "shell.execute_reply": "2023-11-21T08:15:15.847827Z" }, "id": "Db8YHnyVjruU" }, @@ -883,10 +883,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:23.940411Z", - "iopub.status.busy": "2023-11-20T20:38:23.940143Z", - "iopub.status.idle": "2023-11-20T20:38:24.143549Z", - "shell.execute_reply": "2023-11-20T20:38:24.142879Z" + "iopub.execute_input": "2023-11-21T08:15:15.851408Z", + "iopub.status.busy": "2023-11-21T08:15:15.851106Z", + "iopub.status.idle": "2023-11-21T08:15:16.056551Z", + "shell.execute_reply": "2023-11-21T08:15:16.055832Z" }, "id": "iJqAHuS2jruV" }, @@ -923,10 +923,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.146126Z", - "iopub.status.busy": "2023-11-20T20:38:24.145916Z", - "iopub.status.idle": "2023-11-20T20:38:24.163337Z", - "shell.execute_reply": "2023-11-20T20:38:24.162741Z" + "iopub.execute_input": "2023-11-21T08:15:16.059090Z", + "iopub.status.busy": "2023-11-21T08:15:16.058867Z", + "iopub.status.idle": "2023-11-21T08:15:16.077082Z", + "shell.execute_reply": "2023-11-21T08:15:16.076518Z" }, "id": "PcPTZ_JJG3Cx" }, @@ -988,10 +988,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.165767Z", - "iopub.status.busy": "2023-11-20T20:38:24.165399Z", - "iopub.status.idle": "2023-11-20T20:38:24.175238Z", - "shell.execute_reply": "2023-11-20T20:38:24.174690Z" + "iopub.execute_input": "2023-11-21T08:15:16.079742Z", + "iopub.status.busy": "2023-11-21T08:15:16.079348Z", + "iopub.status.idle": "2023-11-21T08:15:16.090092Z", + "shell.execute_reply": "2023-11-21T08:15:16.089457Z" }, "id": "0lonvOYvjruV" }, @@ -1138,10 +1138,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.177445Z", - "iopub.status.busy": "2023-11-20T20:38:24.177244Z", - "iopub.status.idle": "2023-11-20T20:38:24.269534Z", - "shell.execute_reply": "2023-11-20T20:38:24.268810Z" + "iopub.execute_input": "2023-11-21T08:15:16.092732Z", + "iopub.status.busy": "2023-11-21T08:15:16.092272Z", + "iopub.status.idle": "2023-11-21T08:15:16.196365Z", + "shell.execute_reply": "2023-11-21T08:15:16.195626Z" }, "id": "MfqTCa3kjruV" }, @@ -1222,10 +1222,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.272709Z", - "iopub.status.busy": "2023-11-20T20:38:24.271997Z", - "iopub.status.idle": "2023-11-20T20:38:24.405467Z", - "shell.execute_reply": "2023-11-20T20:38:24.404768Z" + "iopub.execute_input": "2023-11-21T08:15:16.199332Z", + "iopub.status.busy": "2023-11-21T08:15:16.199047Z", + "iopub.status.idle": "2023-11-21T08:15:16.362845Z", + "shell.execute_reply": "2023-11-21T08:15:16.362037Z" }, "id": "9ZtWAYXqMAPL" }, @@ -1285,10 +1285,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.408652Z", - "iopub.status.busy": "2023-11-20T20:38:24.408153Z", - "iopub.status.idle": "2023-11-20T20:38:24.412192Z", - "shell.execute_reply": "2023-11-20T20:38:24.411658Z" + "iopub.execute_input": "2023-11-21T08:15:16.365931Z", + "iopub.status.busy": "2023-11-21T08:15:16.365427Z", + "iopub.status.idle": "2023-11-21T08:15:16.370349Z", + "shell.execute_reply": "2023-11-21T08:15:16.369688Z" }, "id": "0rXP3ZPWjruW" }, @@ -1326,10 +1326,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.414783Z", - "iopub.status.busy": "2023-11-20T20:38:24.414291Z", - "iopub.status.idle": "2023-11-20T20:38:24.418847Z", - "shell.execute_reply": "2023-11-20T20:38:24.418307Z" + "iopub.execute_input": "2023-11-21T08:15:16.372940Z", + "iopub.status.busy": "2023-11-21T08:15:16.372566Z", + "iopub.status.idle": "2023-11-21T08:15:16.377656Z", + "shell.execute_reply": "2023-11-21T08:15:16.377086Z" }, "id": "-iRPe8KXjruW" }, @@ -1384,10 +1384,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.421186Z", - "iopub.status.busy": "2023-11-20T20:38:24.420846Z", - "iopub.status.idle": "2023-11-20T20:38:24.459580Z", - "shell.execute_reply": "2023-11-20T20:38:24.459063Z" + "iopub.execute_input": "2023-11-21T08:15:16.380165Z", + "iopub.status.busy": "2023-11-21T08:15:16.379759Z", + "iopub.status.idle": "2023-11-21T08:15:16.420635Z", + "shell.execute_reply": "2023-11-21T08:15:16.419986Z" }, "id": "ZpipUliyjruW" }, @@ -1438,10 +1438,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.461924Z", - "iopub.status.busy": "2023-11-20T20:38:24.461625Z", - "iopub.status.idle": "2023-11-20T20:38:24.506928Z", - "shell.execute_reply": "2023-11-20T20:38:24.506291Z" + "iopub.execute_input": "2023-11-21T08:15:16.423373Z", + "iopub.status.busy": "2023-11-21T08:15:16.422988Z", + "iopub.status.idle": "2023-11-21T08:15:16.470618Z", + "shell.execute_reply": "2023-11-21T08:15:16.469941Z" }, "id": "SLq-3q4xjruX" }, @@ -1510,10 +1510,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.509339Z", - "iopub.status.busy": "2023-11-20T20:38:24.508971Z", - "iopub.status.idle": "2023-11-20T20:38:24.613358Z", - "shell.execute_reply": "2023-11-20T20:38:24.612701Z" + "iopub.execute_input": "2023-11-21T08:15:16.473194Z", + "iopub.status.busy": "2023-11-21T08:15:16.472993Z", + "iopub.status.idle": "2023-11-21T08:15:16.581508Z", + "shell.execute_reply": "2023-11-21T08:15:16.580685Z" }, "id": "g5LHhhuqFbXK" }, @@ -1545,10 +1545,10 @@ "execution_count": 21, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.616337Z", - "iopub.status.busy": "2023-11-20T20:38:24.615939Z", - "iopub.status.idle": "2023-11-20T20:38:24.715217Z", - "shell.execute_reply": "2023-11-20T20:38:24.714499Z" + "iopub.execute_input": "2023-11-21T08:15:16.584891Z", + "iopub.status.busy": "2023-11-21T08:15:16.584471Z", + "iopub.status.idle": "2023-11-21T08:15:16.707437Z", + "shell.execute_reply": "2023-11-21T08:15:16.706682Z" }, "id": "p7w8F8ezBcet" }, @@ -1605,10 +1605,10 @@ "execution_count": 22, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.718090Z", - "iopub.status.busy": "2023-11-20T20:38:24.717615Z", - "iopub.status.idle": "2023-11-20T20:38:24.920898Z", - "shell.execute_reply": "2023-11-20T20:38:24.920382Z" + "iopub.execute_input": "2023-11-21T08:15:16.710848Z", + "iopub.status.busy": "2023-11-21T08:15:16.710210Z", + "iopub.status.idle": "2023-11-21T08:15:16.915769Z", + "shell.execute_reply": "2023-11-21T08:15:16.915049Z" }, "id": "WETRL74tE_sU" }, @@ -1643,10 +1643,10 @@ "execution_count": 23, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:24.923493Z", - "iopub.status.busy": "2023-11-20T20:38:24.923242Z", - "iopub.status.idle": "2023-11-20T20:38:25.122566Z", - "shell.execute_reply": "2023-11-20T20:38:25.121841Z" + "iopub.execute_input": "2023-11-21T08:15:16.918636Z", + "iopub.status.busy": "2023-11-21T08:15:16.918166Z", + "iopub.status.idle": "2023-11-21T08:15:17.183079Z", + "shell.execute_reply": "2023-11-21T08:15:17.182318Z" }, "id": "kCfdx2gOLmXS" }, @@ -1808,10 +1808,10 @@ "execution_count": 24, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:25.125525Z", - "iopub.status.busy": "2023-11-20T20:38:25.125035Z", - "iopub.status.idle": "2023-11-20T20:38:25.131807Z", - "shell.execute_reply": "2023-11-20T20:38:25.131299Z" + "iopub.execute_input": "2023-11-21T08:15:17.186072Z", + "iopub.status.busy": "2023-11-21T08:15:17.185779Z", + "iopub.status.idle": "2023-11-21T08:15:17.192835Z", + "shell.execute_reply": "2023-11-21T08:15:17.192204Z" }, "id": "-uogYRWFYnuu" }, @@ -1865,10 +1865,10 @@ "execution_count": 25, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:25.134200Z", - "iopub.status.busy": "2023-11-20T20:38:25.133787Z", - "iopub.status.idle": "2023-11-20T20:38:25.340624Z", - "shell.execute_reply": "2023-11-20T20:38:25.339968Z" + "iopub.execute_input": "2023-11-21T08:15:17.195417Z", + "iopub.status.busy": "2023-11-21T08:15:17.194913Z", + "iopub.status.idle": "2023-11-21T08:15:17.410311Z", + "shell.execute_reply": "2023-11-21T08:15:17.409666Z" }, "id": "pG-ljrmcYp9Q" }, @@ -1915,10 +1915,10 @@ "execution_count": 26, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:25.343473Z", - "iopub.status.busy": "2023-11-20T20:38:25.342949Z", - "iopub.status.idle": "2023-11-20T20:38:26.431732Z", - "shell.execute_reply": "2023-11-20T20:38:26.431022Z" + "iopub.execute_input": "2023-11-21T08:15:17.412922Z", + "iopub.status.busy": "2023-11-21T08:15:17.412708Z", + "iopub.status.idle": "2023-11-21T08:15:18.508447Z", + "shell.execute_reply": "2023-11-21T08:15:18.507802Z" }, "id": "wL3ngCnuLEWd" }, diff --git a/master/tutorials/multiannotator.ipynb b/master/tutorials/multiannotator.ipynb index 44a7f7fca..f1504888c 100644 --- a/master/tutorials/multiannotator.ipynb +++ b/master/tutorials/multiannotator.ipynb @@ -89,10 +89,10 @@ "id": "a3ddc95f", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:31.640100Z", - "iopub.status.busy": "2023-11-20T20:38:31.639920Z", - "iopub.status.idle": "2023-11-20T20:38:32.631874Z", - "shell.execute_reply": "2023-11-20T20:38:32.631286Z" + "iopub.execute_input": "2023-11-21T08:15:24.378820Z", + "iopub.status.busy": "2023-11-21T08:15:24.378616Z", + "iopub.status.idle": "2023-11-21T08:15:25.432429Z", + "shell.execute_reply": "2023-11-21T08:15:25.431696Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:38:32.635082Z", - "iopub.status.busy": "2023-11-20T20:38:32.634595Z", - "iopub.status.idle": "2023-11-20T20:38:32.637765Z", - "shell.execute_reply": "2023-11-20T20:38:32.637212Z" + "iopub.execute_input": "2023-11-21T08:15:25.435719Z", + "iopub.status.busy": "2023-11-21T08:15:25.435333Z", + "iopub.status.idle": "2023-11-21T08:15:25.438874Z", + "shell.execute_reply": "2023-11-21T08:15:25.438345Z" } }, "outputs": [], @@ -264,10 +264,10 @@ "id": "c37c0a69", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.640266Z", - "iopub.status.busy": "2023-11-20T20:38:32.639901Z", - "iopub.status.idle": "2023-11-20T20:38:32.648191Z", - "shell.execute_reply": "2023-11-20T20:38:32.647689Z" + "iopub.execute_input": "2023-11-21T08:15:25.441327Z", + "iopub.status.busy": "2023-11-21T08:15:25.440964Z", + "iopub.status.idle": "2023-11-21T08:15:25.449290Z", + "shell.execute_reply": "2023-11-21T08:15:25.448715Z" }, "nbsphinx": "hidden" }, @@ -351,10 +351,10 @@ "id": "99f69523", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.650579Z", - "iopub.status.busy": "2023-11-20T20:38:32.650227Z", - "iopub.status.idle": "2023-11-20T20:38:32.698591Z", - "shell.execute_reply": "2023-11-20T20:38:32.698044Z" + "iopub.execute_input": "2023-11-21T08:15:25.451621Z", + "iopub.status.busy": "2023-11-21T08:15:25.451279Z", + "iopub.status.idle": "2023-11-21T08:15:25.500928Z", + "shell.execute_reply": "2023-11-21T08:15:25.500221Z" } }, "outputs": [], @@ -380,10 +380,10 @@ "id": "8f241c16", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.701083Z", - "iopub.status.busy": "2023-11-20T20:38:32.700687Z", - "iopub.status.idle": "2023-11-20T20:38:32.719687Z", - "shell.execute_reply": "2023-11-20T20:38:32.719195Z" + "iopub.execute_input": "2023-11-21T08:15:25.504188Z", + "iopub.status.busy": "2023-11-21T08:15:25.503666Z", + "iopub.status.idle": "2023-11-21T08:15:25.523895Z", + "shell.execute_reply": "2023-11-21T08:15:25.523338Z" } }, "outputs": [ @@ -598,10 +598,10 @@ "id": "4f0819ba", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.721941Z", - "iopub.status.busy": "2023-11-20T20:38:32.721718Z", - "iopub.status.idle": "2023-11-20T20:38:32.725737Z", - "shell.execute_reply": "2023-11-20T20:38:32.725161Z" + "iopub.execute_input": "2023-11-21T08:15:25.526417Z", + "iopub.status.busy": "2023-11-21T08:15:25.526200Z", + "iopub.status.idle": "2023-11-21T08:15:25.530488Z", + "shell.execute_reply": "2023-11-21T08:15:25.529870Z" } }, "outputs": [ @@ -672,10 +672,10 @@ "id": "d009f347", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.728016Z", - "iopub.status.busy": "2023-11-20T20:38:32.727822Z", - "iopub.status.idle": "2023-11-20T20:38:32.754978Z", - "shell.execute_reply": "2023-11-20T20:38:32.754493Z" + "iopub.execute_input": "2023-11-21T08:15:25.532779Z", + "iopub.status.busy": "2023-11-21T08:15:25.532584Z", + "iopub.status.idle": "2023-11-21T08:15:25.561936Z", + "shell.execute_reply": "2023-11-21T08:15:25.561258Z" } }, "outputs": [], @@ -699,10 +699,10 @@ "id": "cbd1e415", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.757402Z", - "iopub.status.busy": "2023-11-20T20:38:32.756969Z", - "iopub.status.idle": "2023-11-20T20:38:32.784302Z", - "shell.execute_reply": "2023-11-20T20:38:32.783820Z" + "iopub.execute_input": "2023-11-21T08:15:25.564837Z", + "iopub.status.busy": "2023-11-21T08:15:25.564361Z", + "iopub.status.idle": "2023-11-21T08:15:25.592631Z", + "shell.execute_reply": "2023-11-21T08:15:25.591897Z" } }, "outputs": [], @@ -739,10 +739,10 @@ "id": "6ca92617", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:32.786534Z", - "iopub.status.busy": "2023-11-20T20:38:32.786336Z", - "iopub.status.idle": "2023-11-20T20:38:34.060313Z", - "shell.execute_reply": "2023-11-20T20:38:34.059698Z" + "iopub.execute_input": "2023-11-21T08:15:25.596085Z", + "iopub.status.busy": "2023-11-21T08:15:25.595635Z", + "iopub.status.idle": "2023-11-21T08:15:26.972849Z", + "shell.execute_reply": "2023-11-21T08:15:26.972131Z" } }, "outputs": [], @@ -772,10 +772,10 @@ "id": "bf945113", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.063384Z", - "iopub.status.busy": "2023-11-20T20:38:34.063009Z", - "iopub.status.idle": "2023-11-20T20:38:34.070262Z", - "shell.execute_reply": "2023-11-20T20:38:34.069650Z" + "iopub.execute_input": "2023-11-21T08:15:26.976578Z", + "iopub.status.busy": "2023-11-21T08:15:26.975887Z", + "iopub.status.idle": "2023-11-21T08:15:26.984119Z", + "shell.execute_reply": "2023-11-21T08:15:26.983468Z" }, "scrolled": true }, @@ -886,10 +886,10 @@ "id": "14251ee0", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.072942Z", - "iopub.status.busy": "2023-11-20T20:38:34.072742Z", - "iopub.status.idle": "2023-11-20T20:38:34.086541Z", - "shell.execute_reply": "2023-11-20T20:38:34.086024Z" + "iopub.execute_input": "2023-11-21T08:15:26.986683Z", + "iopub.status.busy": "2023-11-21T08:15:26.986466Z", + "iopub.status.idle": "2023-11-21T08:15:27.001920Z", + "shell.execute_reply": "2023-11-21T08:15:27.001225Z" } }, "outputs": [ @@ -1139,10 +1139,10 @@ "id": "efe16638", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.089094Z", - "iopub.status.busy": "2023-11-20T20:38:34.088637Z", - "iopub.status.idle": "2023-11-20T20:38:34.095575Z", - "shell.execute_reply": "2023-11-20T20:38:34.095074Z" + "iopub.execute_input": "2023-11-21T08:15:27.004786Z", + "iopub.status.busy": "2023-11-21T08:15:27.004326Z", + "iopub.status.idle": "2023-11-21T08:15:27.012030Z", + "shell.execute_reply": "2023-11-21T08:15:27.011408Z" }, "scrolled": true }, @@ -1316,10 +1316,10 @@ "id": "abd0fb0b", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.097774Z", - "iopub.status.busy": "2023-11-20T20:38:34.097586Z", - "iopub.status.idle": "2023-11-20T20:38:34.100490Z", - "shell.execute_reply": "2023-11-20T20:38:34.099989Z" + "iopub.execute_input": "2023-11-21T08:15:27.014805Z", + "iopub.status.busy": "2023-11-21T08:15:27.014405Z", + "iopub.status.idle": "2023-11-21T08:15:27.017405Z", + "shell.execute_reply": "2023-11-21T08:15:27.016841Z" } }, "outputs": [], @@ -1341,10 +1341,10 @@ "id": "cdf061df", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.102966Z", - "iopub.status.busy": "2023-11-20T20:38:34.102538Z", - "iopub.status.idle": "2023-11-20T20:38:34.106883Z", - "shell.execute_reply": "2023-11-20T20:38:34.106357Z" + "iopub.execute_input": "2023-11-21T08:15:27.019942Z", + "iopub.status.busy": "2023-11-21T08:15:27.019577Z", + "iopub.status.idle": "2023-11-21T08:15:27.023670Z", + "shell.execute_reply": "2023-11-21T08:15:27.023047Z" }, "scrolled": true }, @@ -1396,10 +1396,10 @@ "id": "08949890", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.109241Z", - "iopub.status.busy": "2023-11-20T20:38:34.109044Z", - "iopub.status.idle": "2023-11-20T20:38:34.111767Z", - "shell.execute_reply": "2023-11-20T20:38:34.111249Z" + "iopub.execute_input": "2023-11-21T08:15:27.026330Z", + "iopub.status.busy": "2023-11-21T08:15:27.025956Z", + "iopub.status.idle": "2023-11-21T08:15:27.028856Z", + "shell.execute_reply": "2023-11-21T08:15:27.028309Z" } }, "outputs": [], @@ -1423,10 +1423,10 @@ "id": "6948b073", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.114042Z", - "iopub.status.busy": "2023-11-20T20:38:34.113845Z", - "iopub.status.idle": "2023-11-20T20:38:34.118272Z", - "shell.execute_reply": "2023-11-20T20:38:34.117638Z" + "iopub.execute_input": "2023-11-21T08:15:27.031319Z", + "iopub.status.busy": "2023-11-21T08:15:27.030934Z", + "iopub.status.idle": "2023-11-21T08:15:27.035820Z", + "shell.execute_reply": "2023-11-21T08:15:27.035111Z" } }, "outputs": [ @@ -1481,10 +1481,10 @@ "id": "6f8e6914", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.120666Z", - "iopub.status.busy": "2023-11-20T20:38:34.120431Z", - "iopub.status.idle": "2023-11-20T20:38:34.153210Z", - "shell.execute_reply": "2023-11-20T20:38:34.152719Z" + "iopub.execute_input": "2023-11-21T08:15:27.038466Z", + "iopub.status.busy": "2023-11-21T08:15:27.037996Z", + "iopub.status.idle": "2023-11-21T08:15:27.073000Z", + "shell.execute_reply": "2023-11-21T08:15:27.072281Z" } }, "outputs": [], @@ -1527,10 +1527,10 @@ "id": "b806d2ea", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:34.155415Z", - "iopub.status.busy": "2023-11-20T20:38:34.155217Z", - "iopub.status.idle": "2023-11-20T20:38:34.160137Z", - "shell.execute_reply": "2023-11-20T20:38:34.159603Z" + "iopub.execute_input": "2023-11-21T08:15:27.075947Z", + "iopub.status.busy": "2023-11-21T08:15:27.075739Z", + "iopub.status.idle": "2023-11-21T08:15:27.081029Z", + "shell.execute_reply": "2023-11-21T08:15:27.080493Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/multilabel_classification.ipynb b/master/tutorials/multilabel_classification.ipynb index b4beb5f24..5d3f11c93 100644 --- a/master/tutorials/multilabel_classification.ipynb +++ b/master/tutorials/multilabel_classification.ipynb @@ -63,10 +63,10 @@ "id": "7383d024-8273-4039-bccd-aab3020d331f", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:39.787603Z", - "iopub.status.busy": "2023-11-20T20:38:39.787410Z", - "iopub.status.idle": "2023-11-20T20:38:40.825707Z", - "shell.execute_reply": "2023-11-20T20:38:40.825106Z" + "iopub.execute_input": "2023-11-21T08:15:32.578754Z", + "iopub.status.busy": "2023-11-21T08:15:32.578565Z", + "iopub.status.idle": "2023-11-21T08:15:33.654723Z", + "shell.execute_reply": "2023-11-21T08:15:33.654142Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:38:40.828834Z", - "iopub.status.busy": "2023-11-20T20:38:40.828178Z", - "iopub.status.idle": "2023-11-20T20:38:41.109068Z", - "shell.execute_reply": "2023-11-20T20:38:41.108470Z" + "iopub.execute_input": "2023-11-21T08:15:33.657555Z", + "iopub.status.busy": "2023-11-21T08:15:33.657242Z", + "iopub.status.idle": "2023-11-21T08:15:33.956445Z", + "shell.execute_reply": "2023-11-21T08:15:33.955866Z" } }, "outputs": [], @@ -269,10 +269,10 @@ "id": "e8ff5c2f-bd52-44aa-b307-b2b634147c68", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:41.112088Z", - "iopub.status.busy": "2023-11-20T20:38:41.111861Z", - "iopub.status.idle": "2023-11-20T20:38:41.125669Z", - "shell.execute_reply": "2023-11-20T20:38:41.125099Z" + "iopub.execute_input": "2023-11-21T08:15:33.959430Z", + "iopub.status.busy": "2023-11-21T08:15:33.959201Z", + "iopub.status.idle": "2023-11-21T08:15:33.973318Z", + "shell.execute_reply": "2023-11-21T08:15:33.972674Z" }, "nbsphinx": "hidden" }, @@ -408,10 +408,10 @@ "id": "dac65d3b-51e8-4682-b829-beab610b56d6", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:41.127975Z", - "iopub.status.busy": "2023-11-20T20:38:41.127767Z", - "iopub.status.idle": "2023-11-20T20:38:43.805848Z", - "shell.execute_reply": "2023-11-20T20:38:43.805147Z" + "iopub.execute_input": "2023-11-21T08:15:33.975907Z", + "iopub.status.busy": "2023-11-21T08:15:33.975514Z", + "iopub.status.idle": "2023-11-21T08:15:36.603383Z", + "shell.execute_reply": "2023-11-21T08:15:36.602721Z" } }, "outputs": [ @@ -453,10 +453,10 @@ "id": "b5fa99a9-2583-4cd0-9d40-015f698cdb23", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:43.808737Z", - "iopub.status.busy": "2023-11-20T20:38:43.808274Z", - "iopub.status.idle": "2023-11-20T20:38:45.357918Z", - "shell.execute_reply": "2023-11-20T20:38:45.357272Z" + "iopub.execute_input": "2023-11-21T08:15:36.606154Z", + "iopub.status.busy": "2023-11-21T08:15:36.605745Z", + "iopub.status.idle": "2023-11-21T08:15:38.142526Z", + "shell.execute_reply": "2023-11-21T08:15:38.141907Z" } }, "outputs": [], @@ -498,10 +498,10 @@ "id": "ac1a60df", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:45.360730Z", - "iopub.status.busy": "2023-11-20T20:38:45.360316Z", - "iopub.status.idle": "2023-11-20T20:38:45.380519Z", - "shell.execute_reply": "2023-11-20T20:38:45.380000Z" + "iopub.execute_input": "2023-11-21T08:15:38.145534Z", + "iopub.status.busy": "2023-11-21T08:15:38.145096Z", + "iopub.status.idle": "2023-11-21T08:15:38.164240Z", + "shell.execute_reply": "2023-11-21T08:15:38.163696Z" } }, "outputs": [ @@ -543,10 +543,10 @@ "id": "d09115b6-ad44-474f-9c8a-85a459586439", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:45.382837Z", - "iopub.status.busy": "2023-11-20T20:38:45.382454Z", - "iopub.status.idle": "2023-11-20T20:38:46.654412Z", - "shell.execute_reply": "2023-11-20T20:38:46.653634Z" + "iopub.execute_input": "2023-11-21T08:15:38.166941Z", + "iopub.status.busy": "2023-11-21T08:15:38.166419Z", + "iopub.status.idle": "2023-11-21T08:15:39.571311Z", + "shell.execute_reply": "2023-11-21T08:15:39.570521Z" } }, "outputs": [ @@ -584,10 +584,10 @@ "id": "fffa88f6-84d7-45fe-8214-0e22079a06d1", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:46.657833Z", - "iopub.status.busy": "2023-11-20T20:38:46.657031Z", - "iopub.status.idle": "2023-11-20T20:38:49.459236Z", - "shell.execute_reply": "2023-11-20T20:38:49.458579Z" + "iopub.execute_input": "2023-11-21T08:15:39.574558Z", + "iopub.status.busy": "2023-11-21T08:15:39.573924Z", + "iopub.status.idle": "2023-11-21T08:15:42.383577Z", + "shell.execute_reply": "2023-11-21T08:15:42.382897Z" } }, "outputs": [ @@ -622,10 +622,10 @@ "id": "c1198575", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:49.461861Z", - "iopub.status.busy": "2023-11-20T20:38:49.461457Z", - "iopub.status.idle": "2023-11-20T20:38:49.466204Z", - "shell.execute_reply": "2023-11-20T20:38:49.465690Z" + "iopub.execute_input": "2023-11-21T08:15:42.386103Z", + "iopub.status.busy": "2023-11-21T08:15:42.385897Z", + "iopub.status.idle": "2023-11-21T08:15:42.390826Z", + "shell.execute_reply": "2023-11-21T08:15:42.390322Z" } }, "outputs": [ @@ -662,10 +662,10 @@ "id": "49161b19-7625-4fb7-add9-607d91a7eca1", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:49.468685Z", - "iopub.status.busy": "2023-11-20T20:38:49.468288Z", - "iopub.status.idle": "2023-11-20T20:38:49.472340Z", - "shell.execute_reply": "2023-11-20T20:38:49.471796Z" + "iopub.execute_input": "2023-11-21T08:15:42.392998Z", + "iopub.status.busy": "2023-11-21T08:15:42.392802Z", + "iopub.status.idle": "2023-11-21T08:15:42.397827Z", + "shell.execute_reply": "2023-11-21T08:15:42.397297Z" } }, "outputs": [], @@ -688,10 +688,10 @@ "id": "d1a2c008", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:49.474467Z", - "iopub.status.busy": "2023-11-20T20:38:49.474268Z", - "iopub.status.idle": "2023-11-20T20:38:49.477495Z", - "shell.execute_reply": "2023-11-20T20:38:49.476953Z" + "iopub.execute_input": "2023-11-21T08:15:42.400263Z", + "iopub.status.busy": "2023-11-21T08:15:42.399900Z", + "iopub.status.idle": "2023-11-21T08:15:42.403128Z", + "shell.execute_reply": "2023-11-21T08:15:42.402588Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/object_detection.ipynb b/master/tutorials/object_detection.ipynb index 9b93f7c73..b95d60dd2 100644 --- a/master/tutorials/object_detection.ipynb +++ b/master/tutorials/object_detection.ipynb @@ -70,10 +70,10 @@ "id": "0ba0dc70", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:54.444917Z", - "iopub.status.busy": "2023-11-20T20:38:54.444469Z", - "iopub.status.idle": "2023-11-20T20:38:55.485336Z", - "shell.execute_reply": "2023-11-20T20:38:55.484706Z" + "iopub.execute_input": "2023-11-21T08:15:47.401029Z", + "iopub.status.busy": "2023-11-21T08:15:47.400584Z", + "iopub.status.idle": "2023-11-21T08:15:48.489258Z", + "shell.execute_reply": "2023-11-21T08:15:48.488641Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:38:55.488330Z", - "iopub.status.busy": "2023-11-20T20:38:55.487876Z", - "iopub.status.idle": "2023-11-20T20:38:56.762123Z", - "shell.execute_reply": "2023-11-20T20:38:56.761235Z" + "iopub.execute_input": "2023-11-21T08:15:48.492205Z", + "iopub.status.busy": "2023-11-21T08:15:48.491715Z", + "iopub.status.idle": "2023-11-21T08:15:51.011067Z", + "shell.execute_reply": "2023-11-21T08:15:51.010298Z" } }, "outputs": [], @@ -130,10 +130,10 @@ "id": "df8be4c6", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:56.765024Z", - "iopub.status.busy": "2023-11-20T20:38:56.764810Z", - "iopub.status.idle": "2023-11-20T20:38:56.768061Z", - "shell.execute_reply": "2023-11-20T20:38:56.767513Z" + "iopub.execute_input": "2023-11-21T08:15:51.014160Z", + "iopub.status.busy": "2023-11-21T08:15:51.013682Z", + "iopub.status.idle": "2023-11-21T08:15:51.017086Z", + "shell.execute_reply": "2023-11-21T08:15:51.016578Z" } }, "outputs": [], @@ -165,10 +165,10 @@ "id": "2e9ffd6f", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:56.770246Z", - "iopub.status.busy": "2023-11-20T20:38:56.770052Z", - "iopub.status.idle": "2023-11-20T20:38:56.776059Z", - "shell.execute_reply": "2023-11-20T20:38:56.775588Z" + "iopub.execute_input": "2023-11-21T08:15:51.019420Z", + "iopub.status.busy": "2023-11-21T08:15:51.019039Z", + "iopub.status.idle": "2023-11-21T08:15:51.025076Z", + "shell.execute_reply": "2023-11-21T08:15:51.024608Z" } }, "outputs": [], @@ -194,10 +194,10 @@ "id": "56705562", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:56.778573Z", - "iopub.status.busy": "2023-11-20T20:38:56.778194Z", - "iopub.status.idle": "2023-11-20T20:38:57.383984Z", - "shell.execute_reply": "2023-11-20T20:38:57.383326Z" + "iopub.execute_input": "2023-11-21T08:15:51.027345Z", + "iopub.status.busy": "2023-11-21T08:15:51.027007Z", + "iopub.status.idle": "2023-11-21T08:15:51.652011Z", + "shell.execute_reply": "2023-11-21T08:15:51.651278Z" }, "scrolled": true }, @@ -237,10 +237,10 @@ "id": "b08144d7", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:57.386941Z", - "iopub.status.busy": "2023-11-20T20:38:57.386529Z", - "iopub.status.idle": "2023-11-20T20:38:57.392644Z", - "shell.execute_reply": "2023-11-20T20:38:57.392107Z" + "iopub.execute_input": "2023-11-21T08:15:51.655362Z", + "iopub.status.busy": "2023-11-21T08:15:51.654925Z", + "iopub.status.idle": "2023-11-21T08:15:51.661068Z", + "shell.execute_reply": "2023-11-21T08:15:51.660496Z" } }, "outputs": [ @@ -492,10 +492,10 @@ "id": "3d70bec6", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:57.395127Z", - "iopub.status.busy": "2023-11-20T20:38:57.394751Z", - "iopub.status.idle": "2023-11-20T20:38:57.398794Z", - "shell.execute_reply": "2023-11-20T20:38:57.398304Z" + "iopub.execute_input": "2023-11-21T08:15:51.663268Z", + "iopub.status.busy": "2023-11-21T08:15:51.663066Z", + "iopub.status.idle": "2023-11-21T08:15:51.667807Z", + "shell.execute_reply": "2023-11-21T08:15:51.667250Z" } }, "outputs": [ @@ -552,10 +552,10 @@ "id": "4caa635d", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:57.400954Z", - "iopub.status.busy": "2023-11-20T20:38:57.400753Z", - "iopub.status.idle": "2023-11-20T20:38:57.954146Z", - "shell.execute_reply": "2023-11-20T20:38:57.953514Z" + "iopub.execute_input": "2023-11-21T08:15:51.670229Z", + "iopub.status.busy": "2023-11-21T08:15:51.670002Z", + "iopub.status.idle": "2023-11-21T08:15:52.293210Z", + "shell.execute_reply": "2023-11-21T08:15:52.292463Z" } }, "outputs": [ @@ -611,10 +611,10 @@ "id": "a9b4c590", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:57.956735Z", - "iopub.status.busy": "2023-11-20T20:38:57.956514Z", - "iopub.status.idle": "2023-11-20T20:38:58.056910Z", - "shell.execute_reply": "2023-11-20T20:38:58.056378Z" + "iopub.execute_input": "2023-11-21T08:15:52.296313Z", + "iopub.status.busy": "2023-11-21T08:15:52.295886Z", + "iopub.status.idle": "2023-11-21T08:15:52.396285Z", + "shell.execute_reply": "2023-11-21T08:15:52.395704Z" } }, "outputs": [ @@ -655,10 +655,10 @@ "id": "ffd9ebcc", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:58.059275Z", - "iopub.status.busy": "2023-11-20T20:38:58.059070Z", - "iopub.status.idle": "2023-11-20T20:38:58.063560Z", - "shell.execute_reply": "2023-11-20T20:38:58.063053Z" + "iopub.execute_input": "2023-11-21T08:15:52.398792Z", + "iopub.status.busy": "2023-11-21T08:15:52.398476Z", + "iopub.status.idle": "2023-11-21T08:15:52.403250Z", + "shell.execute_reply": "2023-11-21T08:15:52.402727Z" } }, "outputs": [ @@ -695,10 +695,10 @@ "id": "4dd46d67", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:58.066036Z", - "iopub.status.busy": "2023-11-20T20:38:58.065634Z", - "iopub.status.idle": "2023-11-20T20:38:58.439535Z", - "shell.execute_reply": "2023-11-20T20:38:58.438845Z" + "iopub.execute_input": "2023-11-21T08:15:52.405758Z", + "iopub.status.busy": "2023-11-21T08:15:52.405299Z", + "iopub.status.idle": "2023-11-21T08:15:52.779773Z", + "shell.execute_reply": "2023-11-21T08:15:52.779062Z" } }, "outputs": [ @@ -757,10 +757,10 @@ "id": "ceec2394", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:58.442297Z", - "iopub.status.busy": "2023-11-20T20:38:58.442065Z", - "iopub.status.idle": "2023-11-20T20:38:58.778074Z", - "shell.execute_reply": "2023-11-20T20:38:58.777460Z" + "iopub.execute_input": "2023-11-21T08:15:52.783064Z", + "iopub.status.busy": "2023-11-21T08:15:52.782575Z", + "iopub.status.idle": "2023-11-21T08:15:53.120065Z", + "shell.execute_reply": "2023-11-21T08:15:53.119356Z" } }, "outputs": [ @@ -807,10 +807,10 @@ "id": "94f82b0d", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:58.780900Z", - "iopub.status.busy": "2023-11-20T20:38:58.780681Z", - "iopub.status.idle": "2023-11-20T20:38:59.130514Z", - "shell.execute_reply": "2023-11-20T20:38:59.129867Z" + "iopub.execute_input": "2023-11-21T08:15:53.122843Z", + "iopub.status.busy": "2023-11-21T08:15:53.122445Z", + "iopub.status.idle": "2023-11-21T08:15:53.476855Z", + "shell.execute_reply": "2023-11-21T08:15:53.476168Z" } }, "outputs": [ @@ -857,10 +857,10 @@ "id": "1ea18c5d", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:59.133788Z", - "iopub.status.busy": "2023-11-20T20:38:59.133566Z", - "iopub.status.idle": "2023-11-20T20:38:59.594058Z", - "shell.execute_reply": "2023-11-20T20:38:59.593376Z" + "iopub.execute_input": "2023-11-21T08:15:53.480499Z", + "iopub.status.busy": "2023-11-21T08:15:53.480093Z", + "iopub.status.idle": "2023-11-21T08:15:53.916301Z", + "shell.execute_reply": "2023-11-21T08:15:53.915516Z" } }, "outputs": [ @@ -920,10 +920,10 @@ "id": "7e770d23", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:38:59.598715Z", - "iopub.status.busy": "2023-11-20T20:38:59.598088Z", - "iopub.status.idle": "2023-11-20T20:39:00.045563Z", - "shell.execute_reply": "2023-11-20T20:39:00.044889Z" + "iopub.execute_input": "2023-11-21T08:15:53.920866Z", + "iopub.status.busy": "2023-11-21T08:15:53.920421Z", + "iopub.status.idle": "2023-11-21T08:15:54.377542Z", + "shell.execute_reply": "2023-11-21T08:15:54.376830Z" } }, "outputs": [ @@ -966,10 +966,10 @@ "id": "57e84a27", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:00.048935Z", - "iopub.status.busy": "2023-11-20T20:39:00.048424Z", - "iopub.status.idle": "2023-11-20T20:39:00.247419Z", - "shell.execute_reply": "2023-11-20T20:39:00.246733Z" + "iopub.execute_input": "2023-11-21T08:15:54.380958Z", + "iopub.status.busy": "2023-11-21T08:15:54.380730Z", + "iopub.status.idle": "2023-11-21T08:15:54.610045Z", + "shell.execute_reply": "2023-11-21T08:15:54.609290Z" } }, "outputs": [ @@ -1012,10 +1012,10 @@ "id": "0302818a", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:00.250058Z", - "iopub.status.busy": "2023-11-20T20:39:00.249849Z", - "iopub.status.idle": "2023-11-20T20:39:00.430193Z", - "shell.execute_reply": "2023-11-20T20:39:00.429502Z" + "iopub.execute_input": "2023-11-21T08:15:54.612981Z", + "iopub.status.busy": "2023-11-21T08:15:54.612669Z", + "iopub.status.idle": "2023-11-21T08:15:54.794417Z", + "shell.execute_reply": "2023-11-21T08:15:54.793786Z" } }, "outputs": [ @@ -1050,10 +1050,10 @@ "id": "8ce74938", "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:39:00.432787Z", - "iopub.status.busy": "2023-11-20T20:39:00.432341Z", - "iopub.status.idle": "2023-11-20T20:39:00.436217Z", - "shell.execute_reply": "2023-11-20T20:39:00.435594Z" + "iopub.execute_input": "2023-11-21T08:15:54.797675Z", + "iopub.status.busy": "2023-11-21T08:15:54.797104Z", + "iopub.status.idle": "2023-11-21T08:15:54.801103Z", + "shell.execute_reply": "2023-11-21T08:15:54.800490Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/outliers.html b/master/tutorials/outliers.html index c2dea5805..69458bf53 100644 --- a/master/tutorials/outliers.html +++ b/master/tutorials/outliers.html @@ -926,7 +926,7 @@
-100%|██████████| 4997436/4997436 [00:29<00:00, 171961.37it/s]
+100%|██████████| 4997436/4997436 [00:27<00:00, 179461.57it/s]
Beyond scoring the overall label quality of each image, the above method produces a (0 to 1) quality score for each pixel. We can apply a thresholding function to these scores in order to extract the same style True
or False
mask as find_label_issues()
.
This dataset has 10 classes.
-Classes: {'card_about_to_expire', 'apple_pay_or_google_pay', 'getting_spare_card', 'change_pin', 'beneficiary_not_allowed', 'visa_or_mastercard', 'supported_cards_and_currencies', 'cancel_transfer', 'lost_or_stolen_phone', 'card_payment_fee_charged'}
+Classes: {'supported_cards_and_currencies', 'getting_spare_card', 'card_payment_fee_charged', 'card_about_to_expire', 'change_pin', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'lost_or_stolen_phone', 'cancel_transfer'}
Let’s print the first example in the train set.
diff --git a/master/tutorials/text.ipynb b/master/tutorials/text.ipynb index 52353e359..ac69e5f6b 100644 --- a/master/tutorials/text.ipynb +++ b/master/tutorials/text.ipynb @@ -114,10 +114,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:23.187464Z", - "iopub.status.busy": "2023-11-20T20:43:23.187274Z", - "iopub.status.idle": "2023-11-20T20:43:25.201961Z", - "shell.execute_reply": "2023-11-20T20:43:25.201245Z" + "iopub.execute_input": "2023-11-21T08:20:39.063885Z", + "iopub.status.busy": "2023-11-21T08:20:39.063687Z", + "iopub.status.idle": "2023-11-21T08:20:41.191252Z", + "shell.execute_reply": "2023-11-21T08:20:41.190618Z" }, "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@a6d131932745f88ab2c107abb8c4ae5fce815c1b\n", + " %pip install git+https://github.com/cleanlab/cleanlab.git@3afe8fe4807c3ba720a2b7c881c9857802b9e7fb\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": "2023-11-20T20:43:25.204847Z", - "iopub.status.busy": "2023-11-20T20:43:25.204532Z", - "iopub.status.idle": "2023-11-20T20:43:25.208039Z", - "shell.execute_reply": "2023-11-20T20:43:25.207506Z" + "iopub.execute_input": "2023-11-21T08:20:41.194339Z", + "iopub.status.busy": "2023-11-21T08:20:41.193729Z", + "iopub.status.idle": "2023-11-21T08:20:41.197335Z", + "shell.execute_reply": "2023-11-21T08:20:41.196796Z" } }, "outputs": [], @@ -184,10 +184,10 @@ "execution_count": 3, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.210217Z", - "iopub.status.busy": "2023-11-20T20:43:25.210013Z", - "iopub.status.idle": "2023-11-20T20:43:25.213166Z", - "shell.execute_reply": "2023-11-20T20:43:25.212675Z" + "iopub.execute_input": "2023-11-21T08:20:41.199795Z", + "iopub.status.busy": "2023-11-21T08:20:41.199409Z", + "iopub.status.idle": "2023-11-21T08:20:41.202679Z", + "shell.execute_reply": "2023-11-21T08:20:41.202149Z" }, "nbsphinx": "hidden" }, @@ -218,10 +218,10 @@ "execution_count": 4, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.215621Z", - "iopub.status.busy": "2023-11-20T20:43:25.215261Z", - "iopub.status.idle": "2023-11-20T20:43:25.261440Z", - "shell.execute_reply": "2023-11-20T20:43:25.260939Z" + "iopub.execute_input": "2023-11-21T08:20:41.205116Z", + "iopub.status.busy": "2023-11-21T08:20:41.204764Z", + "iopub.status.idle": "2023-11-21T08:20:41.352639Z", + "shell.execute_reply": "2023-11-21T08:20:41.352015Z" } }, "outputs": [ @@ -311,10 +311,10 @@ "execution_count": 5, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.263750Z", - "iopub.status.busy": "2023-11-20T20:43:25.263386Z", - "iopub.status.idle": "2023-11-20T20:43:25.267013Z", - "shell.execute_reply": "2023-11-20T20:43:25.266504Z" + "iopub.execute_input": "2023-11-21T08:20:41.355219Z", + "iopub.status.busy": "2023-11-21T08:20:41.354814Z", + "iopub.status.idle": "2023-11-21T08:20:41.358579Z", + "shell.execute_reply": "2023-11-21T08:20:41.358003Z" } }, "outputs": [], @@ -329,10 +329,10 @@ "execution_count": 6, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.269464Z", - "iopub.status.busy": "2023-11-20T20:43:25.268982Z", - "iopub.status.idle": "2023-11-20T20:43:25.273116Z", - "shell.execute_reply": "2023-11-20T20:43:25.272512Z" + "iopub.execute_input": "2023-11-21T08:20:41.360711Z", + "iopub.status.busy": "2023-11-21T08:20:41.360525Z", + "iopub.status.idle": "2023-11-21T08:20:41.364213Z", + "shell.execute_reply": "2023-11-21T08:20:41.363607Z" } }, "outputs": [ @@ -341,7 +341,7 @@ "output_type": "stream", "text": [ "This dataset has 10 classes.\n", - "Classes: {'card_about_to_expire', 'apple_pay_or_google_pay', 'getting_spare_card', 'change_pin', 'beneficiary_not_allowed', 'visa_or_mastercard', 'supported_cards_and_currencies', 'cancel_transfer', 'lost_or_stolen_phone', 'card_payment_fee_charged'}\n" + "Classes: {'supported_cards_and_currencies', 'getting_spare_card', 'card_payment_fee_charged', 'card_about_to_expire', 'change_pin', 'beneficiary_not_allowed', 'apple_pay_or_google_pay', 'visa_or_mastercard', 'lost_or_stolen_phone', 'cancel_transfer'}\n" ] } ], @@ -364,10 +364,10 @@ "execution_count": 7, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.275646Z", - "iopub.status.busy": "2023-11-20T20:43:25.275283Z", - "iopub.status.idle": "2023-11-20T20:43:25.278664Z", - "shell.execute_reply": "2023-11-20T20:43:25.278028Z" + "iopub.execute_input": "2023-11-21T08:20:41.366690Z", + "iopub.status.busy": "2023-11-21T08:20:41.366325Z", + "iopub.status.idle": "2023-11-21T08:20:41.369774Z", + "shell.execute_reply": "2023-11-21T08:20:41.369111Z" } }, "outputs": [ @@ -408,10 +408,10 @@ "execution_count": 8, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.281350Z", - "iopub.status.busy": "2023-11-20T20:43:25.280847Z", - "iopub.status.idle": "2023-11-20T20:43:25.284399Z", - "shell.execute_reply": "2023-11-20T20:43:25.283842Z" + "iopub.execute_input": "2023-11-21T08:20:41.372187Z", + "iopub.status.busy": "2023-11-21T08:20:41.371828Z", + "iopub.status.idle": "2023-11-21T08:20:41.375202Z", + "shell.execute_reply": "2023-11-21T08:20:41.374661Z" } }, "outputs": [], @@ -452,10 +452,10 @@ "execution_count": 9, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:25.286755Z", - "iopub.status.busy": "2023-11-20T20:43:25.286549Z", - "iopub.status.idle": "2023-11-20T20:43:33.803067Z", - "shell.execute_reply": "2023-11-20T20:43:33.802442Z" + "iopub.execute_input": "2023-11-21T08:20:41.377614Z", + "iopub.status.busy": "2023-11-21T08:20:41.377254Z", + "iopub.status.idle": "2023-11-21T08:20:50.551012Z", + "shell.execute_reply": "2023-11-21T08:20:50.550364Z" } }, "outputs": [ @@ -502,10 +502,10 @@ "execution_count": 10, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:33.806235Z", - "iopub.status.busy": "2023-11-20T20:43:33.805786Z", - "iopub.status.idle": "2023-11-20T20:43:33.809004Z", - "shell.execute_reply": "2023-11-20T20:43:33.808489Z" + "iopub.execute_input": "2023-11-21T08:20:50.554295Z", + "iopub.status.busy": "2023-11-21T08:20:50.553853Z", + "iopub.status.idle": "2023-11-21T08:20:50.557026Z", + "shell.execute_reply": "2023-11-21T08:20:50.556496Z" } }, "outputs": [], @@ -527,10 +527,10 @@ "execution_count": 11, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:33.811254Z", - "iopub.status.busy": "2023-11-20T20:43:33.810957Z", - "iopub.status.idle": "2023-11-20T20:43:33.813903Z", - "shell.execute_reply": "2023-11-20T20:43:33.813243Z" + "iopub.execute_input": "2023-11-21T08:20:50.559369Z", + "iopub.status.busy": "2023-11-21T08:20:50.559002Z", + "iopub.status.idle": "2023-11-21T08:20:50.561910Z", + "shell.execute_reply": "2023-11-21T08:20:50.561284Z" } }, "outputs": [], @@ -545,10 +545,10 @@ "execution_count": 12, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:33.816216Z", - "iopub.status.busy": "2023-11-20T20:43:33.815868Z", - "iopub.status.idle": "2023-11-20T20:43:36.007450Z", - "shell.execute_reply": "2023-11-20T20:43:36.006723Z" + "iopub.execute_input": "2023-11-21T08:20:50.564416Z", + "iopub.status.busy": "2023-11-21T08:20:50.563970Z", + "iopub.status.idle": "2023-11-21T08:20:52.818503Z", + "shell.execute_reply": "2023-11-21T08:20:52.817716Z" }, "scrolled": true }, @@ -571,10 +571,10 @@ "execution_count": 13, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.010959Z", - "iopub.status.busy": "2023-11-20T20:43:36.010206Z", - "iopub.status.idle": "2023-11-20T20:43:36.018250Z", - "shell.execute_reply": "2023-11-20T20:43:36.017605Z" + "iopub.execute_input": "2023-11-21T08:20:52.822285Z", + "iopub.status.busy": "2023-11-21T08:20:52.821480Z", + "iopub.status.idle": "2023-11-21T08:20:52.829951Z", + "shell.execute_reply": "2023-11-21T08:20:52.829244Z" } }, "outputs": [ @@ -675,10 +675,10 @@ "execution_count": 14, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.020799Z", - "iopub.status.busy": "2023-11-20T20:43:36.020366Z", - "iopub.status.idle": "2023-11-20T20:43:36.024683Z", - "shell.execute_reply": "2023-11-20T20:43:36.024190Z" + "iopub.execute_input": "2023-11-21T08:20:52.832248Z", + "iopub.status.busy": "2023-11-21T08:20:52.831944Z", + "iopub.status.idle": "2023-11-21T08:20:52.836232Z", + "shell.execute_reply": "2023-11-21T08:20:52.835707Z" } }, "outputs": [], @@ -692,10 +692,10 @@ "execution_count": 15, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.027211Z", - "iopub.status.busy": "2023-11-20T20:43:36.026782Z", - "iopub.status.idle": "2023-11-20T20:43:36.030395Z", - "shell.execute_reply": "2023-11-20T20:43:36.029770Z" + "iopub.execute_input": "2023-11-21T08:20:52.838696Z", + "iopub.status.busy": "2023-11-21T08:20:52.838309Z", + "iopub.status.idle": "2023-11-21T08:20:52.841937Z", + "shell.execute_reply": "2023-11-21T08:20:52.841296Z" } }, "outputs": [ @@ -730,10 +730,10 @@ "execution_count": 16, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.032806Z", - "iopub.status.busy": "2023-11-20T20:43:36.032373Z", - "iopub.status.idle": "2023-11-20T20:43:36.035704Z", - "shell.execute_reply": "2023-11-20T20:43:36.035083Z" + "iopub.execute_input": "2023-11-21T08:20:52.844498Z", + "iopub.status.busy": "2023-11-21T08:20:52.844007Z", + "iopub.status.idle": "2023-11-21T08:20:52.847322Z", + "shell.execute_reply": "2023-11-21T08:20:52.846720Z" } }, "outputs": [], @@ -753,10 +753,10 @@ "execution_count": 17, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.038093Z", - "iopub.status.busy": "2023-11-20T20:43:36.037728Z", - "iopub.status.idle": "2023-11-20T20:43:36.044815Z", - "shell.execute_reply": "2023-11-20T20:43:36.044199Z" + "iopub.execute_input": "2023-11-21T08:20:52.849824Z", + "iopub.status.busy": "2023-11-21T08:20:52.849464Z", + "iopub.status.idle": "2023-11-21T08:20:52.856744Z", + "shell.execute_reply": "2023-11-21T08:20:52.856146Z" } }, "outputs": [ @@ -881,10 +881,10 @@ "execution_count": 18, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.047272Z", - "iopub.status.busy": "2023-11-20T20:43:36.046938Z", - "iopub.status.idle": "2023-11-20T20:43:36.287961Z", - "shell.execute_reply": "2023-11-20T20:43:36.287384Z" + "iopub.execute_input": "2023-11-21T08:20:52.859409Z", + "iopub.status.busy": "2023-11-21T08:20:52.858913Z", + "iopub.status.idle": "2023-11-21T08:20:53.106683Z", + "shell.execute_reply": "2023-11-21T08:20:53.105724Z" }, "scrolled": true }, @@ -923,10 +923,10 @@ "execution_count": 19, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.290865Z", - "iopub.status.busy": "2023-11-20T20:43:36.290435Z", - "iopub.status.idle": "2023-11-20T20:43:36.565315Z", - "shell.execute_reply": "2023-11-20T20:43:36.564731Z" + "iopub.execute_input": "2023-11-21T08:20:53.110056Z", + "iopub.status.busy": "2023-11-21T08:20:53.109593Z", + "iopub.status.idle": "2023-11-21T08:20:53.410426Z", + "shell.execute_reply": "2023-11-21T08:20:53.409728Z" }, "scrolled": true }, @@ -959,10 +959,10 @@ "execution_count": 20, "metadata": { "execution": { - "iopub.execute_input": "2023-11-20T20:43:36.568258Z", - "iopub.status.busy": "2023-11-20T20:43:36.567827Z", - "iopub.status.idle": "2023-11-20T20:43:36.571859Z", - "shell.execute_reply": "2023-11-20T20:43:36.571281Z" + "iopub.execute_input": "2023-11-21T08:20:53.414751Z", + "iopub.status.busy": "2023-11-21T08:20:53.413542Z", + "iopub.status.idle": "2023-11-21T08:20:53.419270Z", + "shell.execute_reply": "2023-11-21T08:20:53.418671Z" }, "nbsphinx": "hidden" }, diff --git a/master/tutorials/token_classification.html b/master/tutorials/token_classification.html index 726c69c70..97eb900fc 100644 --- a/master/tutorials/token_classification.html +++ b/master/tutorials/token_classification.html @@ -857,16 +857,16 @@
---2023-11-20 20:43:41-- https://data.deepai.org/conll2003.zip
-Resolving data.deepai.org (data.deepai.org)... 185.93.1.247, 2400:52e0:1a00::1029:1
-Connecting to data.deepai.org (data.deepai.org)|185.93.1.247|:443... connected.
+--2023-11-21 08:20:58-- https://data.deepai.org/conll2003.zip
+Resolving data.deepai.org (data.deepai.org)... 169.150.249.166, 2400:52e0:1a01::999:1
+Connecting to data.deepai.org (data.deepai.org)|169.150.249.166|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 982975 (960K) [application/zip]
Saving to: ‘conll2003.zip’
conll2003.zip 100%[===================>] 959.94K --.-KB/s in 0.1s
-2023-11-20 20:43:41 (6.45 MB/s) - ‘conll2003.zip’ saved [982975/982975]
+2023-11-21 08:20:58 (6.35 MB/s) - ‘conll2003.zip’ saved [982975/982975]
mkdir: cannot create directory ‘data’: File exists
Archive: conll2003.zip
@@ -874,16 +874,16 @@ 1. Install required dependencies and download data