Your labels are lying. Your model can prove it.

What it does

Cleanlab diagnoses data quality problems in machine learning datasets by using your existing model’s predictions and embeddings. It flags label errors, outliers, duplicates, and other issues across text, image, audio, and tabular data, then helps train models that cope with noisy labels. The library also scores consensus among multiple annotators and suggests which data points to re-label next.

The interesting bit

The core trick is “confident learning”: the library estimates label noise without requiring a perfect model, and the README claims provable guarantees for doing so even when your classifier is mediocre. Rather than swapping architectures, you iterate on the dataset itself—sometimes gaining performance without changing a line of modeling code.

Key highlights

• Framework-agnostic across PyTorch, TensorFlow, HuggingFace, OpenAI, XGBoost, scikit-learn, and others
• Covers tasks beyond vanilla classification: multi-label, token classification, regression, object detection, image segmentation, and out-of-distribution detection
• Datalab interface automatically detects and reports dataset issues across text, image, audio, and tabular modalities
• Backed by peer-reviewed research (JAIR, ICML, NeurIPS, ICLR) with published confident-learning theory
• Supports multi-annotator consensus scoring and active-learning recommendations

Verdict

Data scientists sitting on messy real-world datasets should look here before tuning hyperparameters. If your data is already pristine and manually verified, you will not find much use for it.