cleanlab

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

Cleanlab automatically detects and helps fix data quality issues—mislabeled examples, outliers, duplicates, and annotation errors—in datasets for machine learning, working with any model and any data modality.

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

pip install cleanlab

uv add cleanlab

poetry add cleanlab

Installing cleanlab

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.

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()

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.

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

Evidence: cleanlab-2.9.0-py3-none-any.whl

Keywords: machine_learning, data_cleaning, confident_learning, classification, weak_supervision, learning_with_noisy_labels, unsupervised_learning, datacentric_ai, datacentric

Tags

More Software Development packages