Practitioners should consider using already-optimized codebases, especially in the pre-training phase, to ensure effective use of computational resources, capital, power, and effort. Existing open-source codebases targeted at foundation model pretraining can be significantly more accessible to new practitioners and help contribute to efficient training strategies.
An English, cleaned version of Common Crawl’s web crawl corpus (https://commoncrawl.org ).
A pertaining dataset of 16T tokens, covering 167 languages, cleaned, deduplicated, and refined. Combines mC4 into 2020, with OSCAR project data up to 2023.
A large pool of 13B image-text pairs from CommonCrawl and a curated 1B subset
A pretraining dataset of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials.
A manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages.
The fully multilingual, cleaned version of Common Crawl’s web crawl corpus (https://commoncrawl.org ).
The Open License Corpus is a 228B token corpus of permissively-licensed, primarily English text data for pretraining.
A dataset containing the majority of the high-quality, mathematical text from the internet. It is filtered and extracted from over 200B HTML files on Common Crawl down to a set of 6.3 million documents containing a total of 14.7B tokens.
The Open Super-large Crawled Aggregated coRpus provides web-based multilingual datasets across 166 languages.
A collection of ~40M creative open-access academic papers, cleaned, filtered, and formatted for pre-training of language models, originally derived from the Semantic Scholar Open Research Corpus (S2ORC).
An open-source, English dataset with ∼256GB of legal and administrative data, covering court opinions, contracts, administrative rules, and legislative records.
A pretraining dataset of 30 trillion filtered and deduplicated tokens (100+ trillions raw) from 84 CommonCrawl dumps covering 5 languages, along with 40+ pre-computed data quality annotations that can be used for further filtering and weighting.
A massive multilingual pretraining corpus from BigScience, comprised of 1.6TB of text spanning 59 languages. It is a mix of OSCAR (https://oscar-project.org/ ) and the datasets found in the BigScience Catalogue (https://huggingface.co/spaces/bigscience/SourcingCatalog) .
An 825GB English pretraining corpus that mixes portions of common crawl with 22 smaller, high-quality datasets combined together.
The Proof-Pile-2 is a 55 billion token dataset of mathematical and scientific documents.
An English-only, web-only, deduplicated pretraining dataset of five trillion tokens.
The Stack is a 6TB, permissively-licensed pretraining dataset from active GitHub repositories covering 358 programming languages.
A manually audited multilingual pre-training corpus (document-level dataset) for 16 African languages and four high-resource languages widely spoken in Africa (English, French, Arabic and Portuguese)
A dataset format for high-performance streaming of data. Especially useful for modalities other than language that are more I/O intensive for training’, such as images, video, or audio.
A large-scale multilingual legal dataset and superset of the Pile of Law, suited for pretraining language models. It spans over 24 languages and five legal text types.
Nearly three million unique newspaper and periodical editions (70B words) from the French National Library.
An open-source replication of the WebText dataset from OpenAI, that was used to train GPT-2.
Croissant is an open-source metadata format developed by MLCommons to standardise the description of machine learning (ML) datasets, enhancing their discoverability, portability, and interoperability across various tools and platforms. It builds upon the schema.org vocabulary, extending it to encapsulate ML-specific attributes, including dataset structure, resources, and semantics. Croissant is particularly relevant in scenarios requiring consistent dataset documentation to facilitate seamless integration into ML workflows.
