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---
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'}]
``` |