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ckiplab/bert-base-han-chinese-pos
bert-base-han-chinese-pos is a token classification model from ckiplab. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as gpl-3.0.
This model provides part-of-speech (POS) tagging for the ancient Chinese language. Our training dataset covers four eras of the Chinese language.
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From the Hugging Face model README
This model provides part-of-speech (POS) tagging for the ancient Chinese language. Our training dataset covers four eras of the Chinese language.
The copyright of the datasets belongs to the Institute of Linguistics, Academia Sinica.
Using our model in your script
from transformers import (
AutoTokenizer,
AutoModel,
)
tokenizer = AutoTokenizer.from_pretrained("ckiplab/bert-base-han-chinese-pos")
model = AutoModel.from_pretrained("ckiplab/bert-base-han-chinese-pos")
Using our model for inference
>>> from transformers import pipeline
>>> classifier = pipeline("token-classification", model="ckiplab/bert-base-han-chinese-pos")
>>> classifier("帝堯曰放勳")
[{'entity': 'NB1',
'score': 0.99410427,
'index': 1,
'word': '帝',
'start': 0,
'end': 1},
{'entity': 'NB1',
'score': 0.98874336,
'index': 2,
'word': '堯',
'start': 1,
'end': 2},
{'entity': 'VG',
'score': 0.97059363,
'index': 3,
'word': '曰',
'start': 2,
'end': 3},
{'entity': 'NB1',
'score': 0.9864504,
'index': 4,
'word': '放',
'start': 3,
'end': 4},
{'entity': 'NB1',
'score': 0.9543974,
'index': 5,
'word': '勳',
'start': 4,
'end': 5}]