---
title: WanJuan-Arabic
canonical_url: "https://www.modelscope.cn/datasets/OpenDataLab/WanJuan-Arabic"
md_url: "https://www.modelscope.cn/datasets/OpenDataLab/WanJuan-Arabic.md"
repository: OpenDataLab/WanJuan-Arabic
chinese_name: "WanJuan-Arabic（万卷丝路-阿拉伯语）"
last_updated: 2025-11-26
license: cc-by-4.0
storage_size: "104 GB"
downloads: 522
stars: 1
---

# WanJuan-Arabic

> WanJuan-Arabic - OpenDataLab 在 ModelScope 开源的数据集。“万卷·丝路”阿拉伯语语料库，体积超过220GB，包含7个大类和34个小类，覆盖历史、政治、文化、房产、购物、天气、餐饮、百科、专业知识等多个当地特色内容。丰富的主题分类不仅方便了研究人员根据具体需求检索数据，也确保了该语料能够适应不同研究领域多种需求。

OpenDataLab/WanJuan-Arabic 是 ModelScope 魔搭社区上的数据集，存储大小 104 GB，采用 cc-by-4.0 许可。

- **Repository**: OpenDataLab/WanJuan-Arabic
- **License**: cc-by-4.0
- **Storage size**: 104 GB
- **Downloads**: 522
- **Stars**: 1
- **Last updated**: 2025-11-26

Source: https://www.modelscope.cn/datasets/OpenDataLab/WanJuan-Arabic

---

## 💡 Introduction
WanJuan-Arabic（万卷丝路-阿拉伯语） corpus, with a volume exceeding 220GB, comprises 7 major categories and 34 subcategories. It covers a wide range of local-specific content, including history, politics, culture, real estate, shopping, weather, dining, encyclopedias, and professional knowledge. The rich thematic classification not only facilitates researchers in retrieving data according to specific needs but also ensures that the corpus can adapt to diverse research requirements across various fields.

## 💻 Download
You can Download WanJuan-Arabic（万卷丝路-阿拉伯语） from OpenDataLab：
[https://opendatalab.com/OpenDataLab/WanJuan-Arabic/tree/main](https://opendatalab.com/OpenDataLab/WanJuan-Arabic/tree/main)

## 📌 License
WanJuan-Arabic（万卷丝路-阿拉伯语） corpus is licensed under the CC BY 4.0 license. You are free to share and adapt the dataset, provided you comply with the following conditions:

Attribution: You must give appropriate credit to the author, provide a link to the license, and indicate if changes were made to the original dataset. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

No Additional Restrictions: You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. For the full text of the license, please visit the CC BY 4.0 license.

## 📍 Special Notes
Please note that certain subsets of this dataset may be subject to other licensing agreements. Before using specific subsets, be sure to carefully review the relevant agreements to ensure compliant usage. For more detailed licensing information, please refer to the documentation or metadata associated with the specific subsets.

As a non-profit organization, OpenDataLab advocates for a harmonious and friendly open-source exchange environment. If you find any content within the open dataset that infringes upon your legal rights, please email us at OpenDataLab@pjlab.org.cn. In your email, please provide a detailed description of the infringement and relevant proof of ownership. We will initiate an investigation and take necessary actions (such as removing the relevant data) within 3 business days. However, you must ensure the authenticity of your complaint; otherwise, any adverse consequences resulting from the actions taken will be your sole responsibility.

## 📋 Citation
```
@misc{yu2025wanjuansiluhighqualityopensourcewebtext,
      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},
      year={2025},
      eprint={2501.14506},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.14506}, 
}
```
```
@misc{he2024opendatalabempoweringgeneralartificial,
      title={OpenDataLab: Empowering General Artificial Intelligence with Open Datasets}, 
      author={Conghui He and Wei Li and Zhenjiang Jin and Chao Xu and Bin Wang and Dahua Lin},
      year={2024},
      eprint={2407.13773},
      archivePrefix={arXiv},
      primaryClass={cs.DL},
      url={https://arxiv.org/abs/2407.13773}, 
}
```