Cleanlab
Data-centric AI — automatically find label errors and data issues in any dataset.
About
Cleanlab's open-source library implements confident learning — published in JAIR with theoretical guarantees — to find mislabeled examples, outliers, and bad annotators in a single line of code. It works with any classifier (PyTorch, sklearn, XGBoost, OpenAI fine-tunes) and any data modality, CleanLearning trains noise-robust models on messy data, and Datalab audits entire datasets for dozens of issue types. Its findings on label errors in popular benchmarks are cataloged at labelerrors.com.
The honest picture: the estimates need a reasonably trained model as a starting point — garbage predictions in means noisy estimates out. Large datasets need real compute for the cross-validation under the hood, and the collaboration and AutoML conveniences live in the commercial Cleanlab Studio rather than the open-source core.
What it can do
- Confident-learning label error detection
- Datalab whole-dataset auditing
- Noise-robust CleanLearning training
- Outlier and annotator issue detection
- Any model, any data modality
Setup
pip install cleanlab. One-line API wraps your existing classifier — no pipeline rewrite.
Prerequisites
Python. A labeled dataset and a baseline trained model.
Use cases
Works with
Trust score — 7/10
Scored 7/10 (metrics tier, metrics-v1). Evidence: Verified via vendor site/docs/GitHub and reputable coverage, wave 51; Trending score 72/100 from researcher assessment.
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.