Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Luca Foschini, Joan Giner-Miguelez, Pieter Gijsbers, Sujata Goswami, Nitisha Jain
Abstract
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that creates a shared representation across ML tools, frameworks, and platforms. Croissant makes datasets more discoverable, portable, and interoperable, thereby addressing significant challenges in ML data management. Croissant is already supported by several popular dataset repositories, spanning hundreds of thousands of datasets, enabling easy loading into the most commonly-used ML frameworks, regardless of where the data is stored. Our initial evaluation by human raters shows that Croissant metadata is readable, understandable, complete, yet concise.
BibTeX
@inproceedings{
akhtar2024croissant,
title={Croissant: A Metadata Format for {ML}-Ready Datasets},
author={Mubashara Akhtar and Omar Benjelloun and Costanza Conforti and Luca Foschini and Joan Giner-Miguelez and Pieter Gijsbers and Sujata Goswami and Nitisha Jain and Michalis Karamousadakis and Michael Kuchnik and Satyapriya Krishna and Sylvain Lesage and Quentin Lhoest and Pierre Marcenac and Manil Maskey and Peter Mattson and Luis Oala and Hamidah Oderinwale and Pierre Ruyssen and Tim Santos and Rajat Shinde and Elena Simperl and Arjun Suresh and Goeff Thomas and Slava Tykhonov and Joaquin Vanschoren and Susheel Varma and Jos van der Velde and Steffen Vogler and Carole-Jean Wu and Luyao Zhang},
booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2024},
url={https://openreview.net/forum?id=a0WAM6q6fV}
}