cleanlab
The standard package for data-centric AI, machine learning with label errors, and automatically finding and fixing dataset issues in Python.
Decision gist · record as of 2026-08-14
Yes. Cleanlab is well-maintained, permissively licensed, has low installation friction, and addresses a real problem—most ML projects spend effort optimizing models when data quality is the actual bottleneck. The library is backed by peer-reviewed research, works with any model, and offers concrete value even in a single iteration. Start with it if you suspect label errors or data quality issues are limiting your model's performance.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- You must have already trained a model and obtained its prediction probabilities and optionally feature embeddings for your dataset.
- Low friction installation with standard dependencies (numpy, scikit-learn, pandas, tqdm, termcolor).
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 is permissive, allowing commercial and derivative use with minimal restrictions. You must include a copy of the license and note any modifications to the code.
last release 2026-01-13 (213 days) · last repo commit 2026-01-13 · 11,627 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 95,735 downloads/mo, #13,251 on PyPI
Alternatives
Verify before relying
pip install cleanlab
import cleanlab
lab = cleanlab.Datalab(data=dataset, label="column_name_for_labels")
lab.find_issues(features=feature_embeddings, pred_probs=pred_probs)
lab.report()- Whether the package's performance scales well with very large datasets or whether memory/compute requirements become prohibitive at scale.
- How the confident learning algorithms perform when model predictions are severely miscalibrated or when the model class is very different from the training distribution.
What it is and what it does
Cleanlab is a data-centric machine learning library that uses your existing trained model to diagnose and fix dataset problems. Rather than focusing on model architecture improvements, it helps you identify label errors, outliers, duplicates, and other data quality issues by analyzing model predictions and feature embeddings. The core approach, called confident learning, estimates which labels are likely incorrect even when the model itself is imperfect.
You provide cleanlab with your dataset, model predictions, and optionally feature embeddings. It then reports which examples are problematic and why—whether due to annotation errors, data duplicates, or being out-of-distribution. The library works with any model (PyTorch, TensorFlow, scikit-learn, XGBoost, etc.) and any data type (text, images, audio, tabular). Common workflows involve iterating between cleaning data and retraining to progressively improve model performance without changing your modeling code.
Use it for
- Identify and correct mislabeled examples in a classification dataset before retraining to improve model accuracy.
- Detect duplicate or near-duplicate records in a dataset to reduce redundancy and improve training efficiency.
- Find outliers and out-of-distribution examples that may degrade model robustness on production data.
- Assess annotation quality in multi-annotator labeling tasks and identify which annotators are unreliable.
- Prioritize which unlabeled examples to annotate next to maximize model improvement with minimal labeling effort.
- Diagnose data issues in specialized tasks like token classification, image segmentation, or object detection.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Cleanlab is well-maintained, permissively licensed, has low installation friction, and addresses a real problem—most ML projects spend effort optimizing models when data quality is the actual bottleneck. The library is backed by peer-reviewed research, works with any model, and offers concrete value even in a single iteration. Start with it if you suspect label errors or data quality issues are limiting your model's performance.
Install
cleanlab on PyPI
Before you install
Low friction installation with standard dependencies (numpy, scikit-learn, pandas, tqdm, termcolor). Maintenance status is aging—last release was 213 days ago—but the repository remains active with 11627 stars and no archived status.
Requires Python 3.10 or later. You must have already trained a model and obtained its prediction probabilities and optionally feature embeddings for your dataset.
License in practice
Apache License 2.0 is permissive, allowing commercial and derivative use with minimal restrictions. You must include a copy of the license and note any modifications to the code.
Quickstart
pip install cleanlab
import cleanlab
lab = cleanlab.Datalab(data=dataset, label="column_name_for_labels")
lab.find_issues(features=feature_embeddings, pred_probs=pred_probs)
lab.report()Verify before relying
- Whether the package's performance scales well with very large datasets or whether memory/compute requirements become prohibitive at scale.
- How the confident learning algorithms perform when model predictions are severely miscalibrated or when the model class is very different from the training distribution.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 5 packagesnumpyscikit-learntqdmpandastermcolor |
| Maintenance | Aging 213 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 95,735 / month, #13,251 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Information TechnologyIntended Audience :: Science/ResearchNatural Language :: EnglishProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: cleanlab-2.9.0-py3-none-any.whl
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See also cleanlab-tlm · featuretools · missingno · fiftyone-brain · gibberish-detector · autogluon.features · argilla · interpret · autogluon · dtlpymetrics