readme: ---
  language:
    - zh
  thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png
  tags:
    - pytorch
    - lm-head
    - bert
    - zh
  license: gpl-3.0
  ---
  
  # CKIP BERT Base Han Chinese
  
  Pretrained model on Ancient Chinese language using a masked language modeling (MLM) objective.
  
  ## Homepage
  * [ckiplab/han-transformers](https://github.com/ckiplab/han-transformers)
  
  ## Training Datasets
  The copyright of the datasets belongs to the Institute of Linguistics, Academia Sinica.
  * [中央研究院上古漢語標記語料庫](http://lingcorpus.iis.sinica.edu.tw/cgi-bin/kiwi/akiwi/kiwi.sh)
  * [中央研究院中古漢語語料庫](http://lingcorpus.iis.sinica.edu.tw/cgi-bin/kiwi/dkiwi/kiwi.sh)
  * [中央研究院近代漢語語料庫](http://lingcorpus.iis.sinica.edu.tw/cgi-bin/kiwi/pkiwi/kiwi.sh)
  * [中央研究院現代漢語語料庫](http://asbc.iis.sinica.edu.tw)
  
  ## Contributors
  * Chin-Tung Lin at [CKIP](https://ckip.iis.sinica.edu.tw)
  
  ## Usage
  
  * Using our model in your script
      ```python
      from transformers import (
        AutoTokenizer,
        AutoModel,
      )
  
      tokenizer = AutoTokenizer.from_pretrained("ckiplab/bert-base-han-chinese")
      model = AutoModel.from_pretrained("ckiplab/bert-base-han-chinese")
      ```
  
  * Using our model for inference
      ```python
      >>> from transformers import pipeline
      >>> unmasker = pipeline('fill-mask', model='ckiplab/bert-base-han-chinese')
      >>> unmasker("黎[MASK]於變時雍。")
  
      [{'sequence': '黎 民 於 變 時 雍 。',
      'score': 0.14885780215263367,
      'token': 3696,
      'token_str': '民'},
      {'sequence': '黎 庶 於 變 時 雍 。',
      'score': 0.0859643816947937,
      'token': 2433,
      'token_str': '庶'},
      {'sequence': '黎 氏 於 變 時 雍 。',
      'score': 0.027848130092024803,
      'token': 3694,
      'token_str': '氏'},
      {'sequence': '黎 人 於 變 時 雍 。',
      'score': 0.023678112775087357,
      'token': 782,
      'token_str': '人'},
      {'sequence': '黎 生 於 變 時 雍 。',
      'score': 0.018718384206295013,
      'token': 4495,
      'token_str': '生'}]
      ```