cleanlab/cleanlab

Health v2 · maintenance only

Adoption not part of the score

11636 stars · 917 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Cleanlab is a Python library for data-centric AI that automatically detects issues in ML datasets, such as label errors, outliers, duplicates, and other data quality problems, using your existing model outputs. It works with text, image, audio, and tabular data and supports robust training, multi-annotator consensus, and active learning.

Use cases

• find mislabeled examples in my training dataset
• detect outliers and duplicates in a machine learning dataset
• clean noisy labels before training a classifier
• estimate which annotators produce low-quality labels
• decide which data points to label next with active learning
• audit dataset quality for image, text, audio, or tabular data
• train robust models on real-world messy data

When to choose

• you have a supervised ML dataset and suspect label errors or other data issues
• you want to leverage existing model predictions or embeddings to find dataset problems
• you need multi-annotator consensus or annotator-quality scoring
• you want a well-established, actively maintained open-source data quality library

When to avoid

• you need LLM output monitoring, guardrails, or hallucination detection - that is Cleanlab's separate commercial TLM platform, not this library
• you need general-purpose data wrangling or ETL rather than ML-specific dataset issue detection
• your workflow is outside Python

Facets

library · maturity stable

machine-learning data-science analytics nlp image-processing audio-processing data-science machine-learning analytics python cross-platform data-quality noisy-labels label-errors outlier-detection out-of-distribution-detection data-centric-ai active-learning data-cleaning data-validation weak-supervision multi-annotator confident-learning data-engineering

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