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

TL;DR

This paper presents WanJuanSiLu, a high-quality open-source webtext dataset for low-resource languages, developed through a systematic framework to enhance multilingual model training and research.

Contribution

The paper introduces a novel data processing framework tailored for low-resource languages, significantly improving dataset quality, security, and diversity.

Findings

Successfully open-sourced datasets for five low-resource languages.

Enhanced dataset quality and security through systematic processing.

Facilitated multilingual model development with high-quality corpora.

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