chinese-roberta-wwm-ext-large
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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

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More resources by HFL: https://github.com/ymcui/HFL-Anthology

示例代码

from modelscope.models import Model
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

if __name__ == '__main__':
    task = Tasks.fill_mask
    sentence1 = '巴黎是[MASK]国的首都。'
    model_id = 'dienstag/chinese-roberta-wwm-ext-large'
    model = Model.from_pretrained(model_id)
    pipeline_ins = pipeline(task=Tasks.fill_mask,
                            model=model,
                            model_revision='v1.0.0')
    print(pipeline_ins(input=sentence1))

Citation

If you find the technical report or resource is useful, please cite the following technical report in your paper.

@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",
}
@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}
 }