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}
 }

Train1/chinese-roberta-wwm-ext

Please use 'Bert' related functions to load this model!

Chinese BERT with Whole Word Masking

示例代码

Citation

Train1/
chinese-roberta-wwm-ext