The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Refine high-quality datasets and visual AI models

A Doctor for your data

The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

Interactively explore unstructured datasets from your dataframe.

Resources for Data Centric AI

A curated, but incomplete, list of data-centric AI resources.

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

Curated list of open source tooling for data-centric AI on unstructured data.

Lab assignments for Introduction to Data-Centric AI, MIT IAP 2024 👩🏽‍💻

[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark

Offical Repo for "Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale"

[NeurIPS 2023] This is the code for the paper `Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias`.

pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation

Introduction to Data-Centric AI, MIT IAP 2023 🤖

OpenDataVal: a Unified Benchmark for Data Valuation in Python (NeurIPS 2023)

Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning

nbsynthetic is simple and robust tabular synthetic data generation library for small and medium size datasets

[ECCV 2022] Official Implementation for Unsupervised Selective Labeling for More Effective Semi-Supervised Learning

A Data Centric NER annotation tool for your Named Entity Recognition projects