CORE MACHINE LEARNING
SYSTEMS RESEARCH
Croissant: A Metadata Format for ML-Ready Datasets
December 12, 2024
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.
AUTHORS
Written by
Mubashara Akhtar
Omar Benjelloun
Costanza Conforti
Luca Foschini
Pieter Gijsbers
Joan Giner-Miguelez
Sujata Goswami
Nitisha Jain
Michalis Karamousadakis
Satyapriya Krishna
Sylvain Lesage
Quentin Lhoest
Pierre Marcenac
Manil Maskey
Peter Mattson
Luis Oala
Hamidah Oderinwale
Pierre Ruyssen
Tim Santos
Rajat Shinde
Elena Simperl
Arjun Suresh
Goeffry Thomas
Slava Tykhonov
Joaquin Vanschoren
Susheel Varma
Jos van der Velde
Steffen Vogler
Luyao Zhang
Michael Kuchnik
Publisher
NeurIPS
Related Publications
May 12, 2026
HUMAN & MACHINE INTELLIGENCE
RESEARCH
NeuralSet: A High-Performing Python Package for Neuro-AI
Corentin Bel, Linnea Evanson, Julien Gadonneix, Andrea Santos Revilla, Mingfang (Lucy) Zhang, Julie Bonnaire, Charlotte Caucheteux, Alexandre Défossez, Théo Desbordes, Pablo Diego-Simón, Shubh Khanna, Juliette Millet, Pierre Orhan, Saarang Panchavati, Antoine Ratouchniak, Alexis Thual, Hubert Jacob Banville, Jarod Levy, Jean Remi King, Josephine Raugel, Jérémy Rapin, Katelyn Begany, Marlene Careil, Simon Dahan, Sophia Houhamdi, Stéphane d'Ascoli, Teon Brooks, Yohann Benchetrit
May 12, 2026
November 18, 2025
RESEARCH
CORE MACHINE LEARNING
Souper-Model: How Simple Arithmetic Unlocks State-of-the-Art LLM Performance
Roberta Raileanu, * Equal authorship, Alexis Audran-Reiss, Amar Budhiraja *, Anton Protopopov, Bhavul Gauri, Despoina Magka, Gaurav Chaurasia, Michael Slater, Shalini Maiti *, Tatiana Shavrina, Yoram Bachrach
November 18, 2025
November 11, 2025
COMPUTER VISION
SYSTEMS RESEARCH
CATransformers: Carbon Aware Transformers Through Joint Model-Hardware Optimization
Irene Wang, Mostafa Elhouishi, Divya Mahajan, Bilge Acun, Carole-Jean Wu, Daniel Jiang, Ekin Sumbul, Newsha Ardalani, Samuel Hsia
November 11, 2025
October 13, 2025
REINFORCEMENT LEARNING
RESEARCH
SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models
Paria Rashidinejad, Cai Zhou, Tommi Jaakkola, DiJia Su, Bo Liu, Feiyu Chen, Chenyu Wang, Shannon Zejiang Shen, Sid Wang, Siyan Zhao, Song Jiang, Yuandong Tian
October 13, 2025
Help Us Pioneer The Future of AI
We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.
Our approach
Latest news
Foundational models
Meta © 2026