Datasets: A Community Library for Natural Language Processing
Abstrak
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.
Topik & Kata Kunci
Penulis (32)
Quentin Lhoest
Albert Villanova del Moral
Yacine Jernite
Abhishek Thakur
Patrick von Platen
Suraj Patil
Julien Chaumond
Mariama Drame
Julien Plu
Lewis Tunstall
Joe Davison
Mario Šaško
Gunjan Chhablani
Bhavitvya Malik
Simon Brandeis
Teven Le Scao
Victor Sanh
Canwen Xu
Nicolas Patry
Angelina McMillan-Major
Philipp Schmid
Sylvain Gugger
Clément Delangue
Théo Matussière
Lysandre Debut
Stas Bekman
Pierric Cistac
Thibault Goehringer
Victor Mustar
François Lagunas
Alexander M. Rush
Thomas Wolf
Akses Cepat
- Tahun Terbit
- 2021
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓