The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Summary
Training large language models requires huge text collections. Most such corpora are never published, and multilingual ones are especially scarce and poorly documented. This paper describes how the BigScience workshop built ROOTS (Responsible Open-science Open-collaboration Text Sources). BigScience was a year-long open collaboration with over a thousand participants, and ROOTS is the 1.6TB dataset used to train the 176-billion-parameter BLOOM model.
The corpus covers 46 natural and 13 programming languages and merges 498 component datasets. About 62% of its bytes come from sources that community members proposed and documented. These include the BigScience Catalogue, regional hackathons, the Masader repository of Arabic resources, and code from GitHub and StackExchange. The remaining 38% is OSCAR web text, filtered with help from native speakers, deduplicated and cleaned of personal information.
The authors also analyse the result, for example by using tokenizer statistics to spot unusual components. They release a large subset of the data under gated access tied to an ethical charter, along with their processing tools. This makes ROOTS a documented, reusable resource for multilingual modelling.
Key points
- ROOTS totals 1.6TB across 59 languages (46 natural, 13 programming) and was the training data for BLOOM (176B parameters).
- The composition is roughly 62% community-curated sources and 38% filtered OSCAR web crawl. Hackathons for the Catalogue, organised together with communities including Machine Learning Tokyo, Masakhane and LatinX in AI, yielded 252 sources.
- Coverage is highly skewed by region: Eurasian languages account for about 1321.89 GB, Indonesian for 18GB, and the whole Africa macroarea for only 0.4GB.
- For code, about 32% of files turned out to be near-duplicates. Syntax checks on 500K samples failed for only about 1% of Python and 2% of PHP files.
- Tokens-per-byte analysis compared the BLOOM, GPT-NeoX 20B and T5 tokenizers. It showed that domains such as s2orc and global_voices are well represented in ROOTS but out of domain for C4. It also flagged distinctive components such as project_gutenberg, wiktionary and habibi.
Cited by
- Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models
- The Falcon Series of Open Language Models
- Challenges and Applications of Large Language Models
- Large Language Models Struggle to Learn Long-Tail Knowledge
- OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
- What matters when building vision-language models?
- DataComp-LM: In search of the next generation of training sets for language models
- CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages
Cite
@misc{laurenccon2023bigscience,
title = {{The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset}},
author = {Laurenccon, Hugo and Saulnier, Lucile and Wang, Thomas and Akiki, Christopher and Moral, Albert Villanova del and Scao, Teven Le and Werra, Leandro Von and Mou, Chenghao and Ponferrada, Eduardo Gonzalez and Nguyen, Huu and Frohberg, Jorg and vSavsko, Mario and Lhoest, Quentin and McMillan-Major, Angelina and Dupont, Gerard and Biderman, Stella and Rogers, Anna and allal, Loubna Ben and Toni, Francesco De and Pistilli, Giada and Nguyen, Olivier and Nikpoor, Somaieh and Masoud, Maraim and Colombo, Pierre and Rosa, Javier de la and Villegas, Paulo and Thrush, Tristan and Longpre, Shayne and Nagel, Sebastian and Weber, Leon and Munoz, Manuel and Zhu, Jian and Strien, Daniel Van and Alyafeai, Zaid and Almubarak, Khalid and Vu, Minh Chien and Gonzalez-Dios, Itziar and Soroa, Aitor and Lo, Kyle and Dey, Manan and Suarez, Pedro Ortiz and Gokaslan, Aaron and Bose, Shamik and Adelani, David and Phan, Long and Tran, Hieu and Yu, Ian and Pai, Suhas and Chim, Jenny and Lepercq, Violette and Ilic, Suzana and Mitchell, Margaret and Luccioni, Sasha Alexandra and Jernite, Yacine},
year = {2023},
doi = {10.48550/arxiv.2303.03915},
eprint = {2303.03915},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2303.03915}
}References
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