---
title: chinese-roberta-wwm-ext
canonical_url: "https://www.modelscope.cn/models/dienstag/chinese-roberta-wwm-ext"
md_url: "https://www.modelscope.cn/models/dienstag/chinese-roberta-wwm-ext.md"
repository: dienstag/chinese-roberta-wwm-ext
last_updated: 2023-03-14
license: apache-2.0
pipeline_tag: fill-mask
tasks:
  - fill-mask
model_type:
  - bert
architectures:
  - BertForMaskedLM
library_name:
  - tensorflow
  - pytorch
frameworks:
  - pytorch
language:
  - zh
downloads: 11056
stars: 25
tags:
  - bert
---

# chinese-roberta-wwm-ext

> chinese-roberta-wwm-ext - dienstag 在 ModelScope 开源的模型。Please use 'Bert' related functions to load this model!

dienstag/chinese-roberta-wwm-ext 是 ModelScope 魔搭社区上的fill-mask模型，采用 apache-2.0 许可。

- **Repository**: dienstag/chinese-roberta-wwm-ext
- **License**: apache-2.0
- **Tasks**: fill-mask
- **Tags**: bert
- **Downloads**: 11056
- **Stars**: 25
- **Last updated**: 2023-03-14

Source: https://www.modelscope.cn/models/dienstag/chinese-roberta-wwm-ext

---

# 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](https://arxiv.org/abs/1906.08101)**  
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

## 示例代码
```python
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks


pipeline_ins = pipeline(
		'fill-mask',
		model='dienstag/chinese-roberta-wwm-ext',
        model_revision='v1.0.0'
)

print(pipeline_ins('巴黎是[MASK]国的首都。'))

```

## 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}
 }
```