Chinese Fineweb Edu Dataset V2.1 [[中文]](#chinese) [[English]](#english)
81
500 commits
1 linked in READMEs
updated Jan 28, 2026
[OpenCSG Community] [👾github] [wechat] [Twitter]
The Chinese Fineweb Edu Dataset V2.1 is an enhanced version of the V2 dataset, designed specifically for natural language processing (NLP) tasks in the education sector. This version introduces two new data sources, map-cc and opencsg-cc, and retains data with scores ranging from 2 to 3. The dataset entries are organized into different folders based on their scores, allowing for flexible selection of data according to time and computational power requirements during training.
score: 4-5
score: 3-4
score: 2-3
We warmly invite developers and researchers interested in this field to follow and engage with the community, working together to advance the technology. Stay tuned for the open-source release of the dataset!
Usage of the Chinese Fineweb Edu dataset requires adherence to the OpenCSG Community License. The Chinese Fineweb Edu dataset supports commercial use. If you plan to use the OpenCSG model or its derivatives for commercial purposes, you must comply with the terms and conditions outlined in the OpenCSG Community License as well as the Apache 2.0 License. For commercial use, please send an email to [email protected] and obtain permission.
[OpenCSG 社区] [👾github] [微信] [推特]
**Chinese Fineweb Edu Dataset V2.1** 是 V2 数据集的增强版本,专为教育领域的自然语言处理(NLP)任务设计和优化。此版本引入了两个新的数据源 **map-cc** 和 **opencsg-cc**,并保留了评分为 2 到 3 的数据。数据条目根据评分存储在不同的文件夹中,用户可以根据时间和计算资源的需求灵活选择训练数据。score: 4-5
score: 3-4
score: 2-3
我们诚邀对这一领域感兴趣的开发者和研究者关注和联系社区,共同推动技术的进步。敬请期待数据集的开源发布!
使用 Chinese Fineweb Edu V2数据集需要遵循 OpenCSG 社区许可证。Chinese Fineweb Edu V2数据集支持商业用途。如果您计划将 OpenCSG 模型或其衍生产品用于商业目的,您必须遵守 OpenCSG 社区许可证以及 Apache 2.0 许可证中的条款和条件。如用于商业用途,需发送邮件至 [email protected],并获得许可。
@misc{yu2025opencsgchinesecorpusseries,
title={OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for LLM Training},
author={Yijiong Yu and Ziyun Dai and Zekun Wang and Wei Wang and Ran Chen and Ji Pei},
year={2025},
eprint={2501.08197},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.08197},
}
Chinese Fineweb Edu Dataset V2.1 [[中文]](#chinese) [[English]](#english)
81
500 commits
1 linked in READMEs
updated Jan 28, 2026
[OpenCSG Community] [👾github] [wechat] [Twitter]
The Chinese Fineweb Edu Dataset V2.1 is an enhanced version of the V2 dataset, designed specifically for natural language processing (NLP) tasks in the education sector. This version introduces two new data sources, map-cc and opencsg-cc, and retains data with scores ranging from 2 to 3. The dataset entries are organized into different folders based on their scores, allowing for flexible selection of data according to time and computational power requirements during training.
score: 4-5
score: 3-4
score: 2-3
We warmly invite developers and researchers interested in this field to follow and engage with the community, working together to advance the technology. Stay tuned for the open-source release of the dataset!
Usage of the Chinese Fineweb Edu dataset requires adherence to the OpenCSG Community License. The Chinese Fineweb Edu dataset supports commercial use. If you plan to use the OpenCSG model or its derivatives for commercial purposes, you must comply with the terms and conditions outlined in the OpenCSG Community License as well as the Apache 2.0 License. For commercial use, please send an email to [email protected] and obtain permission.
[OpenCSG 社区] [👾github] [微信] [推特]
**Chinese Fineweb Edu Dataset V2.1** 是 V2 数据集的增强版本,专为教育领域的自然语言处理(NLP)任务设计和优化。此版本引入了两个新的数据源 **map-cc** 和 **opencsg-cc**,并保留了评分为 2 到 3 的数据。数据条目根据评分存储在不同的文件夹中,用户可以根据时间和计算资源的需求灵活选择训练数据。score: 4-5
score: 3-4
score: 2-3
我们诚邀对这一领域感兴趣的开发者和研究者关注和联系社区,共同推动技术的进步。敬请期待数据集的开源发布!
使用 Chinese Fineweb Edu V2数据集需要遵循 OpenCSG 社区许可证。Chinese Fineweb Edu V2数据集支持商业用途。如果您计划将 OpenCSG 模型或其衍生产品用于商业目的,您必须遵守 OpenCSG 社区许可证以及 Apache 2.0 许可证中的条款和条件。如用于商业用途,需发送邮件至 [email protected],并获得许可。
@misc{yu2025opencsgchinesecorpusseries,
title={OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for LLM Training},
author={Yijiong Yu and Ziyun Dai and Zekun Wang and Wei Wang and Ran Chen and Ji Pei},
year={2025},
eprint={2501.08197},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.08197},
}