diff --git a/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html b/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html
index 47b917162..debe5ff0e 100644
--- a/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html
+++ b/master/_modules/cleanlab/datalab/internal/issue_manager_factory.html
@@ -597,8 +597,20 @@
"data_valuation"
: DataValuationIssueManager,
"null": NullIssueManager,
},
-
"regression": {"label": RegressionLabelIssueManager},
-
"multilabel": {"label": MultilabelIssueManager},
+
"regression": {
+
"label": RegressionLabelIssueManager,
+
"outlier": OutlierIssueManager,
+
"near_duplicate": NearDuplicateIssueManager,
+
"non_iid": NonIIDIssueManager,
+
"null": NullIssueManager,
+
},
+
"multilabel": {
+
"label": MultilabelIssueManager,
+
"outlier": OutlierIssueManager,
+
"near_duplicate": NearDuplicateIssueManager,
+
"non_iid": NonIIDIssueManager,
+
"null": NullIssueManager,
+
},
}
"""Registry of issue managers that can be constructed from a string
and used in the Datalab class.
@@ -741,8 +753,20 @@
Source code for cleanlab.datalab.internal.issue_manager_factory
"non_iid",
"class_imbalance",
],
- "regression": ["label"],
- "multilabel": ["label"],
+ "regression": [
+ "null",
+ "label",
+ "outlier",
+ "near_duplicate",
+ "non_iid",
+ ],
+ "multilabel": [
+ "null",
+ "label",
+ "outlier",
+ "near_duplicate",
+ "non_iid",
+ ],
}
if task not in default_issue_types_dict:
task = "classification"
diff --git a/master/_sources/tutorials/audio.ipynb b/master/_sources/tutorials/audio.ipynb
index b9108c63a..0347ebe64 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 82e264190..bc4ecad4f 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 7093706d7..e939b997d 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\n",
" cmd = ' '.join([dep for dep in dependencies if dep != \"cleanlab\"])\n",
" %pip install $cmd\n",
"else:\n",
diff --git a/master/_sources/tutorials/datalab/tabular.ipynb b/master/_sources/tutorials/datalab/tabular.ipynb
index e881d022e..25369df12 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 d206dd99d..ab30df3c0 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 acd57bf48..845881f51 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 935593655..73fb0146e 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 1b012620e..2170b7fb8 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 1bfb268aa..96773272f 100644
--- a/master/_sources/tutorials/multilabel_classification.ipynb
+++ b/master/_sources/tutorials/multilabel_classification.ipynb
@@ -73,7 +73,7 @@
"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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 e5a868fb3..236ad0508 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 c3884c797..a8b57cfe7 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 8c851e7f1..49754aa89 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 6597dcf0b..526d0895d 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 a93fd7cd5..3aa099444 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 42bd5152a..2ed48cdfe 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 ef1b06281..c3725526f 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@2b6ad95c32cfaf3029361941cca8c4eaf2ac541e\n",
+ " %pip install git+https://github.com/cleanlab/cleanlab.git@55409591737a9cc39ab0da67e9cf10ceac579900\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 03c1e6e8c..6f8922b04 100644
--- a/master/searchindex.js
+++ b/master/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["cleanlab/benchmarking/index", "cleanlab/benchmarking/noise_generation", "cleanlab/classification", "cleanlab/count", "cleanlab/datalab/datalab", "cleanlab/datalab/guide/custom_issue_manager", "cleanlab/datalab/guide/generating_cluster_ids", "cleanlab/datalab/guide/index", "cleanlab/datalab/guide/issue_type_description", "cleanlab/datalab/index", "cleanlab/datalab/internal/data", "cleanlab/datalab/internal/data_issues", "cleanlab/datalab/internal/factory", "cleanlab/datalab/internal/index", "cleanlab/datalab/internal/issue_finder", "cleanlab/datalab/internal/issue_manager/_notices/not_registered", "cleanlab/datalab/internal/issue_manager/data_valuation", "cleanlab/datalab/internal/issue_manager/duplicate", "cleanlab/datalab/internal/issue_manager/imbalance", "cleanlab/datalab/internal/issue_manager/index", "cleanlab/datalab/internal/issue_manager/issue_manager", "cleanlab/datalab/internal/issue_manager/label", "cleanlab/datalab/internal/issue_manager/noniid", "cleanlab/datalab/internal/issue_manager/null", "cleanlab/datalab/internal/issue_manager/outlier", "cleanlab/datalab/internal/issue_manager/regression/index", "cleanlab/datalab/internal/issue_manager/regression/label", "cleanlab/datalab/internal/issue_manager/underperforming_group", "cleanlab/datalab/internal/report", "cleanlab/datalab/optional_dependencies", "cleanlab/dataset", "cleanlab/experimental/cifar_cnn", "cleanlab/experimental/coteaching", "cleanlab/experimental/index", "cleanlab/experimental/label_issues_batched", "cleanlab/experimental/mnist_pytorch", "cleanlab/filter", "cleanlab/internal/index", "cleanlab/internal/label_quality_utils", "cleanlab/internal/latent_algebra", "cleanlab/internal/multiannotator_utils", "cleanlab/internal/multilabel_scorer", "cleanlab/internal/multilabel_utils", "cleanlab/internal/outlier", "cleanlab/internal/token_classification_utils", "cleanlab/internal/util", "cleanlab/internal/validation", 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Use cleanlab to find label issues": [[74, "5.-Use-cleanlab-to-find-label-issues"], [78, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[75, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[75, "Install-and-import-required-dependencies"]], "Create and load the data": [[75, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[75, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[75, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[75, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[75, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[75, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[75, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[76, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[76, "1.-Install-and-import-required-dependencies"], [82, "1.-Install-and-import-required-dependencies"], [85, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[76, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[76, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[76, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[76, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[76, "Get-additional-information"]], "Near duplicate issues": [[76, "Near-duplicate-issues"], [82, "Near-duplicate-issues"]], "Datalab Tutorials": [[77, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[78, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "1. Install required dependencies": [[78, "1.-Install-required-dependencies"], [79, "1.-Install-required-dependencies"], [90, "1.-Install-required-dependencies"], [92, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"]], "2. Load and process the data": [[78, "2.-Load-and-process-the-data"], [90, "2.-Load-and-process-the-data"], [92, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[78, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [92, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Construct K nearest neighbours graph": [[78, "4.-Construct-K-nearest-neighbours-graph"]], "Label issues": [[78, "Label-issues"], [79, "Label-issues"], [82, "Label-issues"]], "Outlier issues": [[78, "Outlier-issues"], [79, "Outlier-issues"], [82, "Outlier-issues"]], "Near-duplicate issues": [[78, "Near-duplicate-issues"], [79, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[79, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "2. Load and format the text dataset": [[79, "2.-Load-and-format-the-text-dataset"], [93, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[79, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[79, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[79, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[80, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[80, "Install-dependencies-and-import-them"], [83, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[80, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[80, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[81, "FAQ"]], "What data can cleanlab detect issues in?": [[81, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[81, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[81, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[81, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[81, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[81, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[81, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[81, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[81, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[81, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[81, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[81, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[81, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[81, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[82, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[82, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[82, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[82, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[82, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[82, "7.-Use-cleanlab-to-find-issues"]], "View report": [[82, "View-report"]], "View most likely examples with label errors": [[82, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[82, "View-most-severe-outliers"]], "View sets of near duplicate images": [[82, "View-sets-of-near-duplicate-images"]], "Dark images": [[82, "Dark-images"]], "View top examples of dark images": [[82, "View-top-examples-of-dark-images"]], "Low information images": [[82, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[83, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[83, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[83, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[83, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[83, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[83, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[83, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[83, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[83, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[83, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[83, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[83, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[83, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[83, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[83, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[83, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[83, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[83, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[83, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[83, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[83, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[83, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[84, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[85, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[85, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[85, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[85, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[85, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[85, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[85, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[85, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[85, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[86, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[86, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[86, "2.-Format-data,-labels,-and-model-predictions"], [87, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[86, "3.-Use-cleanlab-to-find-label-issues"], [87, "3.-Use-cleanlab-to-find-label-issues"], [91, "3.-Use-cleanlab-to-find-label-issues"], [94, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[86, "Label-quality-scores"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[86, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Finding Label Errors in Object Detection Datasets": [[87, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[87, "1.-Install-required-dependencies-and-download-data"], [91, "1.-Install-required-dependencies-and-download-data"], [94, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[87, "Get-label-quality-scores"], [91, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[87, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[87, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[87, "Other-uses-of-visualize"]], "Exploratory data analysys": [[87, "Exploratory-data-analysys"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[88, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[88, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[88, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[88, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[88, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[88, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[89, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[89, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[89, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[90, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[90, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[90, "4.-Train-a-more-robust-model-from-noisy-labels"], [93, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[90, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[91, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[91, "2.-Get-data,-labels,-and-pred_probs"], [94, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[91, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[91, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[91, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[92, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[92, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[92, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[93, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[93, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[94, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[94, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[94, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[94, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[94, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.datalab.datalab"], [9, 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cleanlab.datalab.internal.issue_manager.data_valuation)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info 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attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.verbosity_levels"]], "issuemanager (class in cleanlab.datalab.internal.issue_manager.issue_manager)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager"]], "cleanlab.datalab.internal.issue_manager.issue_manager": [[20, "module-cleanlab.datalab.internal.issue_manager.issue_manager"]], "collect_info() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], 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Improve your data via many other techniques": [[72, "improve-your-data-via-many-other-techniques"]], "Contributing": [[72, "contributing"]], "Easy Mode": [[72, "easy-mode"], [78, "Easy-Mode"], [79, "Easy-Mode"], [82, "Easy-Mode"]], "How to migrate to versions >= 2.0.0 from pre 1.0.1": [[73, "how-to-migrate-to-versions-2-0-0-from-pre-1-0-1"]], "Function and class name changes": [[73, "function-and-class-name-changes"]], "Module name changes": [[73, "module-name-changes"]], "New modules": [[73, "new-modules"]], "Removed modules": [[73, "removed-modules"]], "Common argument and variable name changes": [[73, "common-argument-and-variable-name-changes"]], "Audio Classification with SpeechBrain and Cleanlab": [[74, "Audio-Classification-with-SpeechBrain-and-Cleanlab"]], "1. Install dependencies and import them": [[74, "1.-Install-dependencies-and-import-them"]], "2. Load the data": [[74, "2.-Load-the-data"]], "3. Use pre-trained SpeechBrain model to featurize audio": [[74, "3.-Use-pre-trained-SpeechBrain-model-to-featurize-audio"]], "4. Fit linear model and compute out-of-sample predicted probabilities": [[74, "4.-Fit-linear-model-and-compute-out-of-sample-predicted-probabilities"]], "5. Use cleanlab to find label issues": [[74, "5.-Use-cleanlab-to-find-label-issues"], [78, "5.-Use-cleanlab-to-find-label-issues"]], "Datalab: Advanced workflows to audit your data": [[75, "Datalab:-Advanced-workflows-to-audit-your-data"]], "Install and import required dependencies": [[75, "Install-and-import-required-dependencies"]], "Create and load the data": [[75, "Create-and-load-the-data"]], "Get out-of-sample predicted probabilities from a classifier": [[75, "Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "Instantiate Datalab object": [[75, "Instantiate-Datalab-object"]], "Functionality 1: Incremental issue search": [[75, "Functionality-1:-Incremental-issue-search"]], "Functionality 2: Specifying nondefault arguments": [[75, "Functionality-2:-Specifying-nondefault-arguments"]], "Functionality 3: Save and load Datalab objects": [[75, "Functionality-3:-Save-and-load-Datalab-objects"]], "Functionality 4: Adding a custom IssueManager": [[75, "Functionality-4:-Adding-a-custom-IssueManager"]], "Datalab: A unified audit to detect all kinds of issues in data and labels": [[76, "Datalab:-A-unified-audit-to-detect-all-kinds-of-issues-in-data-and-labels"]], "1. Install and import required dependencies": [[76, "1.-Install-and-import-required-dependencies"], [82, "1.-Install-and-import-required-dependencies"], [85, "1.-Install-and-import-required-dependencies"]], "2. Create and load the data (can skip these details)": [[76, "2.-Create-and-load-the-data-(can-skip-these-details)"]], "3. Get out-of-sample predicted probabilities from a classifier": [[76, "3.-Get-out-of-sample-predicted-probabilities-from-a-classifier"]], "4. Use Datalab to find issues in the dataset": [[76, "4.-Use-Datalab-to-find-issues-in-the-dataset"]], "5. Learn more about the issues in your dataset": [[76, "5.-Learn-more-about-the-issues-in-your-dataset"]], "Get additional information": [[76, "Get-additional-information"]], "Near duplicate issues": [[76, "Near-duplicate-issues"], [82, "Near-duplicate-issues"]], "Datalab Tutorials": [[77, "datalab-tutorials"]], "Detecting Issues in Tabular Data\u00a0(Numeric/Categorical columns) with Datalab": [[78, "Detecting-Issues-in-Tabular-Data\u00a0(Numeric/Categorical-columns)-with-Datalab"]], "1. Install required dependencies": [[78, "1.-Install-required-dependencies"], [79, "1.-Install-required-dependencies"], [90, "1.-Install-required-dependencies"], [92, "1.-Install-required-dependencies"], [93, "1.-Install-required-dependencies"]], "2. Load and process the data": [[78, "2.-Load-and-process-the-data"], [90, "2.-Load-and-process-the-data"], [92, "2.-Load-and-process-the-data"]], "3. Select a classification model and compute out-of-sample predicted probabilities": [[78, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"], [92, "3.-Select-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Construct K nearest neighbours graph": [[78, "4.-Construct-K-nearest-neighbours-graph"]], "Label issues": [[78, "Label-issues"], [79, "Label-issues"], [82, "Label-issues"]], "Outlier issues": [[78, "Outlier-issues"], [79, "Outlier-issues"], [82, "Outlier-issues"]], "Near-duplicate issues": [[78, "Near-duplicate-issues"], [79, "Near-duplicate-issues"]], "Detecting Issues in a Text Dataset with Datalab": [[79, "Detecting-Issues-in-a-Text-Dataset-with-Datalab"]], "2. Load and format the text dataset": [[79, "2.-Load-and-format-the-text-dataset"], [93, "2.-Load-and-format-the-text-dataset"]], "3. Define a classification model and compute out-of-sample predicted probabilities": [[79, "3.-Define-a-classification-model-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to find issues in your dataset": [[79, "4.-Use-cleanlab-to-find-issues-in-your-dataset"]], "Non-IID issues (data drift)": [[79, "Non-IID-issues-(data-drift)"]], "Find Dataset-level Issues for Dataset Curation": [[80, "Find-Dataset-level-Issues-for-Dataset-Curation"]], "Install dependencies and import them": [[80, "Install-dependencies-and-import-them"], [83, "Install-dependencies-and-import-them"]], "Fetch the data (can skip these details)": [[80, "Fetch-the-data-(can-skip-these-details)"]], "Start of tutorial: Evaluate the health of 8 popular datasets": [[80, "Start-of-tutorial:-Evaluate-the-health-of-8-popular-datasets"]], "FAQ": [[81, "FAQ"]], "What data can cleanlab detect issues in?": [[81, "What-data-can-cleanlab-detect-issues-in?"]], "How do I format classification labels for cleanlab?": [[81, "How-do-I-format-classification-labels-for-cleanlab?"]], "How do I infer the correct labels for examples cleanlab has flagged?": [[81, "How-do-I-infer-the-correct-labels-for-examples-cleanlab-has-flagged?"]], "How should I handle label errors in train vs. test data?": [[81, "How-should-I-handle-label-errors-in-train-vs.-test-data?"]], "How can I find label issues in big datasets with limited memory?": [[81, "How-can-I-find-label-issues-in-big-datasets-with-limited-memory?"]], "Why isn\u2019t CleanLearning working for me?": [[81, "Why-isn\u2019t-CleanLearning-working-for-me?"]], "How can I use different models for data cleaning vs. final training in CleanLearning?": [[81, "How-can-I-use-different-models-for-data-cleaning-vs.-final-training-in-CleanLearning?"]], "How do I hyperparameter tune only the final model trained (and not the one finding label issues) in CleanLearning?": [[81, "How-do-I-hyperparameter-tune-only-the-final-model-trained-(and-not-the-one-finding-label-issues)-in-CleanLearning?"]], "Why does regression.learn.CleanLearning take so long?": [[81, "Why-does-regression.learn.CleanLearning-take-so-long?"]], "How do I specify pre-computed data slices/clusters when detecting the Underperforming Group Issue?": [[81, "How-do-I-specify-pre-computed-data-slices/clusters-when-detecting-the-Underperforming-Group-Issue?"]], "How to handle near-duplicate data identified by cleanlab?": [[81, "How-to-handle-near-duplicate-data-identified-by-cleanlab?"]], "What ML models should I run cleanlab with? How do I fix the issues cleanlab has identified?": [[81, "What-ML-models-should-I-run-cleanlab-with?-How-do-I-fix-the-issues-cleanlab-has-identified?"]], "What license is cleanlab open-sourced under?": [[81, "What-license-is-cleanlab-open-sourced-under?"]], "Can\u2019t find an answer to your question?": [[81, "Can't-find-an-answer-to-your-question?"]], "Image Classification with PyTorch and Cleanlab": [[82, "Image-Classification-with-PyTorch-and-Cleanlab"]], "2. Fetch and normalize the Fashion-MNIST dataset": [[82, "2.-Fetch-and-normalize-the-Fashion-MNIST-dataset"]], "3. Define a classification model": [[82, "3.-Define-a-classification-model"]], "4. Prepare the dataset for K-fold cross-validation": [[82, "4.-Prepare-the-dataset-for-K-fold-cross-validation"]], "5. Compute out-of-sample predicted probabilities and feature embeddings": [[82, "5.-Compute-out-of-sample-predicted-probabilities-and-feature-embeddings"]], "7. Use cleanlab to find issues": [[82, "7.-Use-cleanlab-to-find-issues"]], "View report": [[82, "View-report"]], "View most likely examples with label errors": [[82, "View-most-likely-examples-with-label-errors"]], "View most severe outliers": [[82, "View-most-severe-outliers"]], "View sets of near duplicate images": [[82, "View-sets-of-near-duplicate-images"]], "Dark images": [[82, "Dark-images"]], "View top examples of dark images": [[82, "View-top-examples-of-dark-images"]], "Low information images": [[82, "Low-information-images"]], "The Workflows of Data-centric AI for Classification with Noisy Labels": [[83, "The-Workflows-of-Data-centric-AI-for-Classification-with-Noisy-Labels"]], "Create the data (can skip these details)": [[83, "Create-the-data-(can-skip-these-details)"]], "Workflow 1: Use Datalab to detect many types of issues": [[83, "Workflow-1:-Use-Datalab-to-detect-many-types-of-issues"]], "Workflow 2: Use CleanLearning for more robust Machine Learning": [[83, "Workflow-2:-Use-CleanLearning-for-more-robust-Machine-Learning"]], "Clean Learning = Machine Learning with cleaned data": [[83, "Clean-Learning-=-Machine-Learning-with-cleaned-data"]], "Workflow 3: Use CleanLearning to find_label_issues in one line of code": [[83, "Workflow-3:-Use-CleanLearning-to-find_label_issues-in-one-line-of-code"]], "Visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[83, "Visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 4: Use cleanlab to find dataset-level and class-level issues": [[83, "Workflow-4:-Use-cleanlab-to-find-dataset-level-and-class-level-issues"]], "Now, let\u2019s see what happens if we merge classes \u201cseafoam green\u201d and \u201cyellow\u201d": [[83, "Now,-let's-see-what-happens-if-we-merge-classes-%22seafoam-green%22-and-%22yellow%22"]], "Workflow 5: Clean your test set too if you\u2019re doing ML with noisy labels!": [[83, "Workflow-5:-Clean-your-test-set-too-if-you're-doing-ML-with-noisy-labels!"]], "Workflow 6: One score to rule them all \u2013 use cleanlab\u2019s overall dataset health score": [[83, "Workflow-6:-One-score-to-rule-them-all----use-cleanlab's-overall-dataset-health-score"]], "How accurate is this dataset health score?": [[83, "How-accurate-is-this-dataset-health-score?"]], "Workflow(s) 7: Use count, rank, filter modules directly": [[83, "Workflow(s)-7:-Use-count,-rank,-filter-modules-directly"]], "Workflow 7.1 (count): Fully characterize label noise (noise matrix, joint, prior of true labels, \u2026)": [[83, "Workflow-7.1-(count):-Fully-characterize-label-noise-(noise-matrix,-joint,-prior-of-true-labels,-...)"]], "Use cleanlab to estimate and visualize the joint distribution of label noise and noise matrix of label flipping rates:": [[83, "Use-cleanlab-to-estimate-and-visualize-the-joint-distribution-of-label-noise-and-noise-matrix-of-label-flipping-rates:"]], "Workflow 7.2 (filter): Find label issues for any dataset and any model in one line of code": [[83, "Workflow-7.2-(filter):-Find-label-issues-for-any-dataset-and-any-model-in-one-line-of-code"]], "Again, we can visualize the twenty examples with lowest label quality to see if Cleanlab works.": [[83, "Again,-we-can-visualize-the-twenty-examples-with-lowest-label-quality-to-see-if-Cleanlab-works."]], "Workflow 7.2 supports lots of methods to find_label_issues() via the filter_by parameter.": [[83, "Workflow-7.2-supports-lots-of-methods-to-find_label_issues()-via-the-filter_by-parameter."]], "Workflow 7.3 (rank): Automatically rank every example by a unique label quality score. Find errors using cleanlab.count.num_label_issues as a threshold.": [[83, "Workflow-7.3-(rank):-Automatically-rank-every-example-by-a-unique-label-quality-score.-Find-errors-using-cleanlab.count.num_label_issues-as-a-threshold."]], "Again, we can visualize the label issues found to see if Cleanlab works.": [[83, "Again,-we-can-visualize-the-label-issues-found-to-see-if-Cleanlab-works."]], "Not sure when to use Workflow 7.2 or 7.3 to find label issues?": [[83, "Not-sure-when-to-use-Workflow-7.2-or-7.3-to-find-label-issues?"]], "Workflow 8: Ensembling label quality scores from multiple predictors": [[83, "Workflow-8:-Ensembling-label-quality-scores-from-multiple-predictors"]], "Tutorials": [[84, "tutorials"]], "Estimate Consensus and Annotator Quality for Data Labeled by Multiple Annotators": [[85, "Estimate-Consensus-and-Annotator-Quality-for-Data-Labeled-by-Multiple-Annotators"]], "2. Create the data (can skip these details)": [[85, "2.-Create-the-data-(can-skip-these-details)"]], "3. Get initial consensus labels via majority vote and compute out-of-sample predicted probabilities": [[85, "3.-Get-initial-consensus-labels-via-majority-vote-and-compute-out-of-sample-predicted-probabilities"]], "4. Use cleanlab to get better consensus labels and other statistics": [[85, "4.-Use-cleanlab-to-get-better-consensus-labels-and-other-statistics"]], "Comparing improved consensus labels": [[85, "Comparing-improved-consensus-labels"]], "Inspecting consensus quality scores to find potential consensus label errors": [[85, "Inspecting-consensus-quality-scores-to-find-potential-consensus-label-errors"]], "5. Retrain model using improved consensus labels": [[85, "5.-Retrain-model-using-improved-consensus-labels"]], "Further improvements": [[85, "Further-improvements"]], "How does cleanlab.multiannotator work?": [[85, "How-does-cleanlab.multiannotator-work?"]], "Find Label Errors in Multi-Label Classification Datasets": [[86, "Find-Label-Errors-in-Multi-Label-Classification-Datasets"]], "1. Install required dependencies and get dataset": [[86, "1.-Install-required-dependencies-and-get-dataset"]], "2. Format data, labels, and model predictions": [[86, "2.-Format-data,-labels,-and-model-predictions"], [87, "2.-Format-data,-labels,-and-model-predictions"]], "3. Use cleanlab to find label issues": [[86, "3.-Use-cleanlab-to-find-label-issues"], [87, "3.-Use-cleanlab-to-find-label-issues"], [91, "3.-Use-cleanlab-to-find-label-issues"], [94, "3.-Use-cleanlab-to-find-label-issues"]], "Label quality scores": [[86, "Label-quality-scores"]], "How to format labels given as a one-hot (multi-hot) binary matrix?": [[86, "How-to-format-labels-given-as-a-one-hot-(multi-hot)-binary-matrix?"]], "Finding Label Errors in Object Detection Datasets": [[87, "Finding-Label-Errors-in-Object-Detection-Datasets"]], "1. Install required dependencies and download data": [[87, "1.-Install-required-dependencies-and-download-data"], [91, "1.-Install-required-dependencies-and-download-data"], [94, "1.-Install-required-dependencies-and-download-data"]], "Get label quality scores": [[87, "Get-label-quality-scores"], [91, "Get-label-quality-scores"]], "4. Use ObjectLab to visualize label issues": [[87, "4.-Use-ObjectLab-to-visualize-label-issues"]], "Different kinds of label issues identified by ObjectLab": [[87, "Different-kinds-of-label-issues-identified-by-ObjectLab"]], "Other uses of visualize": [[87, "Other-uses-of-visualize"]], "Exploratory data analysys": [[87, "Exploratory-data-analysys"]], "Detect Outliers with Cleanlab and PyTorch Image Models (timm)": [[88, "Detect-Outliers-with-Cleanlab-and-PyTorch-Image-Models-(timm)"]], "1. Install the required dependencies": [[88, "1.-Install-the-required-dependencies"]], "2. Pre-process the Cifar10 dataset": [[88, "2.-Pre-process-the-Cifar10-dataset"]], "Visualize some of the training and test examples": [[88, "Visualize-some-of-the-training-and-test-examples"]], "3. Use cleanlab and feature embeddings to find outliers in the data": [[88, "3.-Use-cleanlab-and-feature-embeddings-to-find-outliers-in-the-data"]], "4. Use cleanlab and pred_probs to find outliers in the data": [[88, "4.-Use-cleanlab-and-pred_probs-to-find-outliers-in-the-data"]], "Computing Out-of-Sample Predicted Probabilities with Cross-Validation": [[89, "computing-out-of-sample-predicted-probabilities-with-cross-validation"]], "Out-of-sample predicted probabilities?": [[89, "out-of-sample-predicted-probabilities"]], "What is K-fold cross-validation?": [[89, "what-is-k-fold-cross-validation"]], "Find Noisy Labels in Regression Datasets": [[90, "Find-Noisy-Labels-in-Regression-Datasets"]], "3. Define a regression model and use cleanlab to find potential label errors": [[90, "3.-Define-a-regression-model-and-use-cleanlab-to-find-potential-label-errors"]], "4. Train a more robust model from noisy labels": [[90, "4.-Train-a-more-robust-model-from-noisy-labels"], [93, "4.-Train-a-more-robust-model-from-noisy-labels"]], "5. Other ways to find noisy labels in regression datasets": [[90, "5.-Other-ways-to-find-noisy-labels-in-regression-datasets"]], "Find Label Errors in Semantic Segmentation Datasets": [[91, "Find-Label-Errors-in-Semantic-Segmentation-Datasets"]], "2. Get data, labels, and pred_probs": [[91, "2.-Get-data,-labels,-and-pred_probs"], [94, "2.-Get-data,-labels,-and-pred_probs"]], "Visualize top label issues": [[91, "Visualize-top-label-issues"]], "Classes which are commonly mislabeled overall": [[91, "Classes-which-are-commonly-mislabeled-overall"]], "Focusing on one specific class": [[91, "Focusing-on-one-specific-class"]], "Classification with Tabular Data using Scikit-Learn and Cleanlab": [[92, "Classification-with-Tabular-Data-using-Scikit-Learn-and-Cleanlab"]], "4. Use cleanlab to find label issues": [[92, "4.-Use-cleanlab-to-find-label-issues"]], "5. Train a more robust model from noisy labels": [[92, "5.-Train-a-more-robust-model-from-noisy-labels"]], "Text Classification with Noisy Labels": [[93, "Text-Classification-with-Noisy-Labels"]], "3. Define a classification model and use cleanlab to find potential label errors": [[93, "3.-Define-a-classification-model-and-use-cleanlab-to-find-potential-label-errors"]], "Find Label Errors in Token Classification (Text) Datasets": [[94, "Find-Label-Errors-in-Token-Classification-(Text)-Datasets"]], "Most common word-level token mislabels": [[94, "Most-common-word-level-token-mislabels"]], "Find sentences containing a particular mislabeled word": [[94, "Find-sentences-containing-a-particular-mislabeled-word"]], "Sentence label quality score": [[94, "Sentence-label-quality-score"]], "How does cleanlab.token_classification work?": [[94, "How-does-cleanlab.token_classification-work?"]]}, "indexentries": {"cleanlab.benchmarking": [[0, "module-cleanlab.benchmarking"]], "module": [[0, "module-cleanlab.benchmarking"], [1, "module-cleanlab.benchmarking.noise_generation"], [2, "module-cleanlab.classification"], [3, "module-cleanlab.count"], [4, "module-cleanlab.datalab.datalab"], [9, 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cleanlab.datalab.internal.issue_manager.data_valuation)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager"]], "cleanlab.datalab.internal.issue_manager.data_valuation": [[16, "module-cleanlab.datalab.internal.issue_manager.data_valuation"]], "collect_info() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.collect_info"]], "description (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager attribute)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.description"]], "find_issues() (cleanlab.datalab.internal.issue_manager.data_valuation.datavaluationissuemanager method)": [[16, "cleanlab.datalab.internal.issue_manager.data_valuation.DataValuationIssueManager.find_issues"]], "info 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attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.make_summary"]], "report() (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager class method)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.report"]], "summary (cleanlab.datalab.internal.issue_manager.imbalance.classimbalanceissuemanager attribute)": [[18, "cleanlab.datalab.internal.issue_manager.imbalance.ClassImbalanceIssueManager.summary"]], "verbosity_levels 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"cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.find_issues"]], "info (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.info"]], "issue_name (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_name"]], "issue_score_key (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issue_score_key"]], "issues (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager attribute)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.issues"]], "make_summary() (cleanlab.datalab.internal.issue_manager.issue_manager.issuemanager class method)": [[20, "cleanlab.datalab.internal.issue_manager.issue_manager.IssueManager.make_summary"]], 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module cleanlab.object_detection.summary)": [[58, "cleanlab.object_detection.summary.plot_class_distribution"]], "plot_class_size_distributions() (in module cleanlab.object_detection.summary)": [[58, "cleanlab.object_detection.summary.plot_class_size_distributions"]], "visualize() (in module cleanlab.object_detection.summary)": [[58, "cleanlab.object_detection.summary.visualize"]], "outofdistribution (class in cleanlab.outlier)": [[59, "cleanlab.outlier.OutOfDistribution"]], "cleanlab.outlier": [[59, "module-cleanlab.outlier"]], "fit() (cleanlab.outlier.outofdistribution method)": [[59, "cleanlab.outlier.OutOfDistribution.fit"]], "fit_score() (cleanlab.outlier.outofdistribution method)": [[59, "cleanlab.outlier.OutOfDistribution.fit_score"]], "score() (cleanlab.outlier.outofdistribution method)": [[59, "cleanlab.outlier.OutOfDistribution.score"]], "cleanlab.rank": [[60, "module-cleanlab.rank"]], "find_top_issues() (in module cleanlab.rank)": [[60, "cleanlab.rank.find_top_issues"]], 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diff --git a/master/tutorials/audio.html b/master/tutorials/audio.html
index 1d70ca23e..be4f89e6a 100644
--- a/master/tutorials/audio.html
+++ b/master/tutorials/audio.html
@@ -1274,7 +1274,7 @@
5. Use cleanlab to find label issues