Cleanlab

cleanlab

Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

cleanvision

Automatically find issues in image datasets and practice data-centric computer vision.

label-errors

🛠️ Corrected Test Sets for ImageNet, MNIST, CIFAR, Caltech-256, QuickDraw, IMDB, Amazon Reviews, 20News, and AudioSet

examples

Notebooks demonstrating example applications of the cleanlab library

multiannotator-benchmarks

Benchmarking algorithms for assessing quality of data labeled by multiple annotators

cleanlab-studio

Client interface to Cleanlab Studio and the Trustworthy Language Model

cleanvision-examples

Notebooks demonstrating example applications of the cleanvision library

vizzy

Cleanlab Vizzy: illustrating the core ideas behind the Cleanlab algorithm

cleanlab-tools

Cookbooks showcasing various applications of Cleanlab products

culture

Our company culture

cleanlab-tlm

Python client library for Cleanlab Trustworthy Language Model

label-error-detection-benchmarks

cleanlab-codex

Python client library to integrate Cleanlab Codex into RAG applications

ood-detection-benchmarks

Evaluation of algorithms to detect out-of-distribution data

aws-marketplace

Documentation and Example Notebooks for using AWS Marketplace solutions from Cleanlab

regression-label-error-benchmark

Benchmark algorithms to detect erroneous label values in regression datasets

cleanlab-studio-tutorials

Automated repo - do not touch

token-label-error-benchmarks

Benchmarking methods for label error detection in token classification tasks

multilabel-error-detection-benchmarks

Benchmarking label error detection algorithms for multi-label classification

cleanlab-docs

nblint-action

.github

assets

Binary assets (e.g. README images) factored out into a separate repository

codex-python

Cleanlab Codex Python SDK

datasets

Repo where small data files can be downloaded

deploy-bot

merge-bot