The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset

Hugo Laurençon, Lucile Saulnier, Thomas Wang, Christopher Akiki, Albert Villanova del Moral, Teven Le Scao, Leandro Von Werra, Chenghao Mou, Eduardo González Ponferrada, Huu Nguyen, Jörg Frohberg, Mario Šaško, Quentin Lhoest, Angelina McMillan-Major, Gérard Dupont, Stella Biderman, Anna Rogers, Loubna Ben allal, Francesco De Toni, Giada Pistilli et al. (34 additional authors not shown)

Keywords: BigScience, Dataset, Multilingual, Language Modeling

TL;DR: 1.6TB multilingual dataset created collaboratively within BigScience to train language models

Abstract: As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a values-driven undertaking, putting issues of ethics, harm, and governance in the foreground. This paper documents the data creation and curation efforts undertaken by BigScience to assemble the Responsible Open-science Open-collaboration Text Sources (ROOTS) corpus, a 1.6TB dataset spanning 59 languages that was used to train the 176-billion-parameter BigScience Large Open-science Open-access Multilingual (BLOOM) language model. We further release a large initial subset of the corpus and analyses thereof, and hope to empower large-scale monolingual and multilingual modeling projects with both the data and the processing tools, as well as stimulate research around this large multilingual corpus.

URL: https://hf.co/bigscience-data

Dataset Url:

Data: https://hf.co/bigscience-data
Tooling: https://github.com/bigscience-workshop/data-preparation

License:

- Each constituent subset of the dataset will be released under the license that applies to it. (See individual dataset page for specific license information: https://hf.co/bigscience-data)
- Tooling code released under Apache 2.0

Author Statement: Yes

Contribution Process Agreement: Yes

In Person Attendance: Yes

Community Implementations: [ 6 code implementations](https://www.catalyzex.com/paper/arxiv:2303.03915/code)