Croissant Format Specification

TY - GEN

T1 - Croissant Format Specification

AU - Benjelloun, Omar

AU - Simperl, Elena

AU - Marcenac, Pierre

AU - Ruyssen, Pierre

AU - Conforti, Costanza

AU - Kuchnik, Michael

AU - van der Velde, Jos

AU - Oala, Luis

AU - Vogler, Steffen

AU - Akthar, Mubashara

AU - Jain, Nitisha

AU - Tykhonov, Vyacheslav

PY - 2024/3/1

Y1 - 2024/3/1

N2 - Datasets are the basis of machine learning (ML). However, a lack of standardization in the description and semantics of ML datasets has made it increasingly difficult for researchers and practitioners to explore, understand, and use all but a small fraction of popular datasets.The Croissant metadata format simplifies how data is used by ML models. It provides a vocabulary for dataset attributes, streamlining how data is loaded across ML frameworks such as PyTorch, TensorFlow or JAX. In doing so, Croissant enables the interchange of datasets between ML frameworks and beyond, tackling a variety of discoverability, portability, reproducibility, and responsible AI (RAI) challenges.

AB - Datasets are the basis of machine learning (ML). However, a lack of standardization in the description and semantics of ML datasets has made it increasingly difficult for researchers and practitioners to explore, understand, and use all but a small fraction of popular datasets.The Croissant metadata format simplifies how data is used by ML models. It provides a vocabulary for dataset attributes, streamlining how data is loaded across ML frameworks such as PyTorch, TensorFlow or JAX. In doing so, Croissant enables the interchange of datasets between ML frameworks and beyond, tackling a variety of discoverability, portability, reproducibility, and responsible AI (RAI) challenges.

M3 - Other contribution

ER -