Datasets:
license: apache-2.0
language:
- en
task_categories:
- text-generation
tags:
- llm
- pretraining
- web
- data-selection
size_categories:
- n>1T
FineWeb-Mask
📜 DATAMASK Paper | 💻 GitHub Repository | 📦 Fineweb-Mask Dataset
📚 Introduction
FineWeb-Mask is a 1.5 trillion token, high-efficiency pre-training dataset curated using the DATAMASK framework. Developed by the ByteDance Seed team, DATAMASK addresses the fundamental tension in large-scale data selection: the trade-off between high quality and high diversity.
By modeling data selection as a Mask Learning problem, we provide a derivative of the original FineWeb corpus. FineWeb-Mask is designed to eliminate semantic redundancy while preserving the highest quality samples, allowing models to achieve superior performance with significantly less data.
🎯 The Problem: The Quality-Diversity Trap
In large language model (LLM) pre-training, developers usually face two suboptimal choices:
- The Quality Trap: Filtering solely by quality scores leads to "diminishing returns." Samples become highly clustered, resulting in severe semantic redundancy.
- The Diversity Trap: Filtering solely for diversity often discards high-value quality samples, leading to worse performance than the original raw dataset.
- The Compute Bottleneck: Traditional diversity algorithms (like greedy selection) are computationally prohibitive for trillion-token datasets.
💡 Highlights: The DATAMASK Framework
DATAMASK breaks this deadlock through a "joint harvesting" strategy:
- Joint Optimization: Uses Policy Gradient algorithms to optimize both quality and diversity metrics within a unified framework.
- Extreme Acceleration: Through probability relaxation and specialized optimization techniques, DATAMASK reduces computation time by 98.9% compared to traditional greedy algorithms, making trillion-token selection feasible.
- The "Balancer": Includes a tunable parameter that allows developers to define the "Golden Ratio" between quality and diversity for their specific needs.
- Semantic De-redundancy: Visual analysis shows that FineWeb-Mask samples are distributed evenly across high-quality regions rather than being rigidly clustered.
📈 Evaluation Results
FineWeb-Mask demonstrates that 1+1 > 2. By selecting a subset that represents only ~10% of the original scale in specific experiments, we observed:
- Dense Models: A 3.2% average improvement across 12 benchmarks for 1.5B dense models.
- MoE Models: A 1.9% improvement for 7B Mixture-of-Experts (MoE) models.
- Length Bias Correction: While quality filters favor long text and diversity filters favor short text, DATAMASK finds a scientific middle ground.
| Model Size | Dataset | Avg. Score (12 Benchmarks) | Improvement |
|---|---|---|---|
| 1.5B Dense | FineWeb (Original) | Baseline | - |
| 1.5B Dense | FineWeb-Mask | +3.2% | 🚀 |
| 7B MoE | FineWeb (Original) | Baseline | - |
| 7B MoE | FineWeb-Mask | +1.9% | 🚀 |
❤️ Acknowledgements
FineWeb-Mask is built upon the incredible foundational work of the HuggingFace FineWeb team. We are grateful to the open-source community for providing the raw corpora that made this optimization possible.
🌟 Citation
If you find our dataset or the DATAMASK framework useful, please cite our work:
@misc{fan2025jointselectionlargescalepretraining,
title={Joint Selection for Large-Scale Pre-Training Data via Policy Gradient-based Mask Learning},
author={Ziqing Fan and Yuqiao Xian and Yan Sun and Li Shen},
year={2025},
eprint={2512.24265},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2512.24265},
}
💳 License
This dataset is released under the Apache 2.0 license. Users should also adhere to the original license terms of the FineWeb dataset and its constituent sources.
📧 Contact
- Ziqing Fan: zqfan_knight@sjtu.edu.cn
- Yuqiao Xian: ericxian1997@gmail.com
Would you like me to help you draft the "How to Use" section for loading this dataset via the Hugging Face datasets library?