FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data
  Processing to Every Language
Authors: name: Guilherme Penedo; name: Hynek Kydlíček; name: Vinko Sabolčec; name: Bettina Messmer; name: Negar Foroutan; name: Amir Hossein Kargaran; name: Colin Raffel; name: Martin Jaggi; name: Leandro Von Werra; name: Thomas Wolf
Published: 2025-06-26 01:01:47

Abstract: Pre-training state-of-the-art large language models (LLMs) requires vast
amounts of clean and diverse text data. While the open development of large
high-quality English pre-training datasets has seen substantial recent
progress, training performant multilingual LLMs remains a challenge, in large
part due to the inherent difficulty of tailoring filtering and deduplication
pipelines to a large number of languages. In this work, we introduce a new
pre-training dataset curation pipeline based on FineWeb that can be
automatically adapted to support any language. We extensively ablate our
pipeline design choices on a set of nine diverse languages, guided by a set of
meaningful and informative evaluation tasks that were chosen through a novel
selection process based on measurable criteria. Ultimately, we show that our
pipeline can be used to create non-English corpora that produce more performant
models than prior datasets. We additionally introduce a straightforward and
principled approach to rebalance datasets that takes into consideration both
duplication count and quality, providing an additional performance uplift.
Finally, we scale our pipeline to over 1000 languages using almost 100 Common
Crawl snapshots to produce FineWeb2, a new 20 terabyte (5 billion document)
multilingual dataset which we release along with our pipeline, training, and
evaluation codebases.