chinese-roberta-wwm-ext.zip

hfl/chinese-roberta-wwm-ext · Hugging Face https://huggingface.co/hfl/chinese-roberta-wwm-ext 网页Chinese BERT with Whole Word Masking. For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. Pre … hfl / chinese-roberta-wwm-ext Copied like 114 Fill-Mask PyTorch TensorFlow JAX Transformers Chinese bert AutoTrain Compatible arxiv: 1906.08101 arxiv: 2004.13922 License: apache-2.0 Model card Files and versions Please use 'Bert' related functions to load this model! Chinese BERT with Whole Word Masking For further accelerating Chinese natural language processing, we provide Chinese pre-trained BERT with Whole Word Masking. Pre-Training with Whole Word Masking for Chinese BERT Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu This repository is developed based on：https://github.com/google-research/bert You may also interested in, Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm Chinese MacBERT: https://github.com/ymcui/MacBERT Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA Chinese XLNet: https://github.com/ymcui/Chinese-XLNet Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer More resources by HFL: https://github.com/ymcui/HFL-Anthology Citation If you find the technical report or resource is useful, please cite the following technical report in your paper. Primary: 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", } Secondary: 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} }hfl/chinese-roberta-wwm-ext · Hugging Face https://huggingface.co/hfl/chinese-roberta-wwm-ext ### 模型简介 带全词掩码（Whole Word Masking）的中文BERT。为进一步加速中文自然语言处理研究与应用，我们推出了基于全词掩码预训练的中文BERT模型。预训练…… --- `hfl/chinese-roberta-wwm-ext` 已复制，获赞 114 任务类型：填充掩码（Fill-Mask） 支持框架：PyTorch、TensorFlow、JAX 适配库：Transformers 语言：中文 模型类型：bert 支持AutoTrain 关联arXiv论文编号：1906.08101、2004.13922 许可证：Apache-2.0 包含内容：模型卡片、文件与版本库 请使用BERT相关函数加载此模型！ --- # 模型详情 带全词掩码的中文BERT 为进一步加速中文自然语言处理，我们提供了基于全词掩码预训练的中文BERT模型。 ## 论文信息 《Pre-Training with Whole Word Masking for Chinese BERT》 作者：崔一鸣、车万翔、刘挺、秦兵、杨子清、王石金、胡国平 本仓库基于谷歌研究团队的BERT实现开发：https://github.com/google-research/bert 您可能还对以下资源感兴趣： - 中文BERT系列模型：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 - HFL发布的更多资源合集：https://github.com/ymcui/HFL-Anthology --- ## 引用方式 若您认为本技术报告或相关资源对研究有所帮助，请在论文中引用以下文献： ### 主要引用 论文链接：https://arxiv.org/abs/2004.13922 bibtex @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 论文链接：https://arxiv.org/abs/1906.08101 bibtex @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} }

搜集汇总

背景与挑战

背景概述

该数据集是一个基于全词掩码技术的中文预训练BERT模型，专为加速中文自然语言处理任务而设计。模型源自Google Research的BERT实现，并提供了相关的学术论文引用和资源链接。