FineWeb-HQ

Dataset Summary

FineWeb-HQ is a high-quality, model-filtered pretraining dataset derived as a subset of FineWeb. FineWeb-HQ was created by selecting the top 10% of FineWeb documents based on a deep learning classifier trained to identify structured and knowledge-rich samples. This classifier uses XLM-RoBERTa embeddings to score documents.

To validate our approach, we pretrained 1B-parameter LLM models with a Llama-like architecture across multiple languages and scripts. The results showed improvements on standard English benchmarks, with our dataset outperforming its English counterparts DCLM and FineWeb-Edu. For its multilingual version, FineWeb2-HQ, evaluations on CMMLU (Chinese), MMLU (German), and MMLU (French) demonstrated that it matches FineWeb2's performance while trained on 6x fewer tokens and surpasses it when fully trained.

Dataset	Ours	DCLM	FW-Edu	FW
Average Rank	1.8333	2.3889	2.4444	3.3333
ARC (Challenge)	0.3550	0.3530	0.3850	0.3010
ARC (Easy)	0.6670	0.6470	0.6970	0.5880
CommonsenseQA	0.3870	0.4100	0.3770	0.3850
HellaSwag	0.6040	0.5960	0.5700	0.5930
MMLU	0.3400	0.3160	0.3470	0.3030
OpenBookQA	0.3860	0.3840	0.4180	0.3560
PIQA	0.7510	0.7510	0.7410	0.7620
WinoGrande	0.5720	0.5610	0.5660	0.5550
TriviaQA	0.0820	0.1240	0.0320	0.0370

For more details, see our paper Enhancing Multilingual LLM Pretraining with Model-Based Data Selection.

Key features

• High-quality selection: Top 10% of FineWeb documents by quality
• Multilingual version: FineWeb2-HQ
• Model-based filtering: Uses an XLM-RoBERTa embedding-based classifier to score documents
• Enhanced benchmark performance: Surpasses FineWeb benchmark performance and competitive to DCLM and FineWeb-Edu
• Fully open: Emphasis on transparency

Dataset structure

Data fields

Each data entry includes the original FineWeb data fields with the addition of:

• quality_score: quality score obtained by the quality classifier
• embeddings: array of float arrays containing 768-dimensional XLM-RoBERTa embeddings for every 512 token chunk of the tokenized text

Licensing information

Like FineWeb, this dataset is released under Open Data Commons Attribution License (ODC-By) v1.0 license and is subject to CommonCrawl's Terms of Use.

Dataset origin

Being a subset of FineWeb (v1.3.0), this data covers websites over the 2013-2024 time period.

FineWeb is sourced from the internet at large, it is very likely that some personable identifiable information (PII) will be present, even if the FineWeb processing has already anonymized email addresses and public IP addresses. If you find your own PII and would like it removed, please fill out the FineWeb PII removal/opt out form.

Considerations for Using the Data

Before using this dataset for training models, we recommend performing additional filtering for sensitive content such as PII or harmful content. For the aspects of social impact, discussion of biases, and known limitations, we also refer to the FineWeb documentation.

Citation information

This work has been Accepted to the Benchmarks and Datasets Track at the Thirty-Ninth Conference on Neural Information Processing Systems (NeurIPS 2025).

Until the final proceedings are published, please use the following temporary citation (which links to the public preprint):

@article
{messmer2025multilingdatacomp,
title={Enhancing Multilingual LLM Pretraining with Model-Based Data Selection},
author={Bettina Messmer and Vinko Sabolčec and Martin Jaggi},
journal={arXiv},
year={2025},
url={https://arxiv.org/abs/2502.10361},
}