The FineWeb Datasets: Decanting the Web for the Finest Text Data at
  Scale
Authors: name: Guilherme Penedo, name: Hynek Kydlíček, name: Loubna Ben allal, name: Anton Lozhkov, name: Margaret Mitchell, name: Colin Raffel, name: Leandro Von Werra, name: Thomas Wolf
Organization: FineData

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.