The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and documentation, while providing a lightweight front-end that behaves similarly for small datasets as for internet-scale corpora. The design of the library incorporates a distributed, community-driven approach to adding datasets and documenting usage. After a year of development, the library now includes more than 650 unique datasets, has more than 250 contributors, and has helped support a variety of novel cross-dataset research projects and shared tasks. The library is available at https://github.com/huggingface/datasets.
Datasets: A Community Library for Natural Language Processing
The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks.
- Year
- 2021
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- EMNLP (ACL) 2021 11
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- 32
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- arxiv.org/abs/2109.02846ARXIV-DEFAULT
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32Julien ChaumondLewis TunstallThomas WolfAlexander M. RushVictor SanhStas BekmanYacine JerniteQuentin LhoestAlbert Villanova del MoralAbhishek ThakurPatrick von PlatenSuraj PatilMariama DrameJulien PluJoe DavisonMario ŠaškoGunjan ChhablaniBhavitvya MalikSimon BrandeisTeven Le ScaoCanwen XuNicolas PatryAngelina McMillan-MajorPhilipp SchmidSylvain GuggerClément DelangueThéo MatussièreLysandre DebutPierric CistacThibault GoehringerVictor MustarFrançois Lagunas