Rbt3

🚀 重新训练的3层RoBERTa-wwm-ext模型

本项目提供了一个重新训练的3层RoBERTa-wwm-ext模型，旨在进一步加速中文自然语言处理任务，为相关领域的研究和应用提供有力支持。

✨ 主要特性

中文全词掩码预训练BERT

为了进一步加速中文自然语言处理，我们提供了采用全词掩码的中文预训练BERT模型。

Pre-Training with Whole Word Masking for Chinese BERT
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu

项目开发基础

本仓库基于以下项目开发：https://github.com/google-research/bert

相关项目推荐

你可能还对以下项目感兴趣：

• 中文BERT系列: https://github.com/ymcui/Chinese-BERT-wwm
• 中文MacBERT: https://github.com/ymcui/MacBERT
• 中文ELECTRA: https://github.com/ymcui/Chinese-ELECTRA
• 中文XLNet: https://github.com/ymcui/Chinese-XLNet
• 知识蒸馏工具包 - TextBrewer: https://github.com/airaria/TextBrewer

更多资源

由HFL提供的更多资源：https://github.com/ymcui/HFL-Anthology

📄 许可证

本项目采用Apache-2.0许可证。

📚 详细文档

引用说明

如果您发现本技术报告或资源有用，请在您的论文中引用以下技术报告：

• 主要引用: https://arxiv.org/abs/2004.13922

@inproceedings{cui-etal-2020-revisiting,
    title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing",
    author = "Cui, Yiming  and
      Che, Wanxiang  and
      Liu, Ting  and
      Qin, Bing  and
      Wang, Shijin  and
      Hu, Guoping",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58",
    pages = "657--668",
}

• 次要引用: https://arxiv.org/abs/1906.08101

@article{chinese-bert-wwm,
  title={Pre-Training with Whole Word Masking for Chinese BERT},
  author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping},
  journal={arXiv preprint arXiv:1906.08101},
  year={2019}
 }