File size: 1,135 Bytes
56b9b51 2fcc4e8 67957af bc67de7 56b9b51 de67423 56b9b51 2fcc4e8 56b9b51 2fcc4e8 817e6d0 347d3ed 817e6d0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
---
language:
- "zh"
tags:
- "chinese"
- "token-classification"
- "pos"
- "wikipedia"
- "dependency-parsing"
base_model: hfl/chinese-roberta-wwm-ext
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "token-classification"
---
# chinese-roberta-base-upos
## Model Description
This is a BERT model pre-trained on Chinese Wikipedia texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/chinese-roberta-base-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/chinese-roberta-base-upos")
```
or
```py
import esupar
nlp=esupar.load("KoichiYasuoka/chinese-roberta-base-upos")
```
## See Also
[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
|