WanJuanSiLu: A High-Quality Open-Source Webtext Dataset for Low-Resource Languages

• URL: http://arxiv.org/abs/2501.14506v1
• Date: Fri, 24 Jan 2025 14:06:29 GMT
• Title: WanJuanSiLu: A High-Quality Open-Source Webtext Dataset for Low-Resource Languages
• Authors: Jia Yu, Fei Yuan, Rui Min, Jing Yu, Pei Chu, Jiayang Li, Wei Li, Ruijie Zhang, Zhenxiang Li, Zhifei Ren, Dong Zheng, Wenjian Zhang, Yan Teng, Lingyu Meng, ZhenJiang Jin, Jiantao Qiu, ShaSha Wang, Zhongying Tu, Dahua Lin, Yu Wang, Yu Qiao, Yanfeng Wang, Conghui He,
• Abstract summary: The paper introduces the open-source dataset WanJuanSiLu, designed to provide high-quality training corpora for low-resource languages. We have developed a systematic data processing framework tailored for low-resource languages.
• Score: 62.1053122134059
• License:
• Abstract: 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

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