The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
This work addresses the need for open, well-documented datasets to advance LLM development, though it is incremental as it builds on existing data curation methods.
The authors tackled the problem of limited public knowledge on high-quality pretraining datasets for large language models by introducing FineWeb, a 15-trillion token dataset from Common Crawl that improves LLM performance, and FineWeb-Edu, a 1.3-trillion token subset that boosts performance on benchmarks like MMLU and ARC.
The performance of a large language model (LLM) depends heavily on the quality and size of its pretraining dataset. However, the pretraining datasets for state-of-the-art open LLMs like Llama 3 and Mixtral are not publicly available and very little is known about how they were created. In this work, we introduce FineWeb, a 15-trillion token dataset derived from 96 Common Crawl snapshots that produces better-performing LLMs than other open pretraining datasets. To advance the understanding of how best to curate high-quality pretraining datasets, we carefully document and ablate all of the design choices used in FineWeb, including in-depth investigations of deduplication and filtering strategies. In addition, we introduce FineWeb-Edu, a 1.3-trillion token collection of educational text filtered from FineWeb. LLMs pretrained on FineWeb-Edu exhibit dramatically better performance on knowledge- and reasoning-intensive benchmarks like MMLU and ARC. Along with our datasets, we publicly release our data curation codebase and all of the models trained during our ablation experiments.