cleanlab/cleanlab-studio-tutorials

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cleanlab/cleanlab-studio-tutorials

cleanlab

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cleanlab

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

Jupyter Notebook

multiannotator-benchmarks

Benchmarking algorithms for assessing quality of data labeled by multiple annotators

Jupyter Notebook

cleanlab-studio

Client interface for all things Cleanlab Studio

cleanvision-examples

Notebooks demonstrating example applications of the cleanvision library

Jupyter Notebook

vizzy

Cleanlab Vizzy: illustrating the core ideas behind the Cleanlab algorithm

culture

Our company culture

cleanlab-tools

Miscellaneous code made available for purposes of education, reproducibility, and transparency

Jupyter Notebook

label-error-detection-benchmarks

Jupyter Notebook

ood-detection-benchmarks

Evaluation of algorithms to detect out-of-distribution data

Jupyter Notebook

aws-marketplace

Documentation and Example Notebooks for using AWS Marketplace solutions from Cleanlab

Jupyter Notebook

regression-label-error-benchmark

Benchmark algorithms to detect erroneous label values in regression datasets

token-label-error-benchmarks

Benchmarking methods for label error detection in token classification tasks

Jupyter Notebook

multilabel-error-detection-benchmarks

Benchmarking label error detection algorithms for multi-label classification

Jupyter Notebook