This paper introduces the open-source dataset WanJuanSiLu, designed to provide high-quality training corpora for low-resource languages, thereby advancing the research and development of multilingual models. To achieve this, we have developed a systematic data processing framework tailored for low-resource languages. This framework encompasses key stages such as data extraction, corpus cleaning, content deduplication, security filtering, quality evaluation, and theme classification. Through the implementation of this framework, we have significantly improved both the quality and security of the dataset, while maintaining its linguistic diversity. As of now, data for all five languages have been fully open-sourced. The dataset can be accessed at https://opendatalab.com/applyMultilingualCorpus, and GitHub repository is available at https://github.com/opendatalab/WanJuan3.0
@article{arxiv.2501.14506,
title = {WanJuanSiLu: A High-Quality Open-Source Webtext Dataset for Low-Resource Languages},
author = {Jia Yu and Fei Yuan and Rui Min and Jing Yu and Pei Chu and Jiayang Li and Wei Li and Ruijie Zhang and Zhenxiang Li and Zhifei Ren and Dong Zheng and Wenjian Zhang and Yan Teng and Lingyu Meng and ZhenJiang Jin and Jiantao Qiu and ShaSha Wang and Zhongying Tu and Dahua Lin and Yu Wang and Yu Qiao and Yanfeng Wang and Conghui He},
journal= {arXiv preprint arXiv:2501.14506},
year = {2025}
}