
# 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


{"language": ["zh"], "license": "apache-2.0", "tags": ["chinese", "token-classification", "pos", "wikipedia", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification"}
transformers pytorch bert token-classification chinese pos wikipedia dependency-parsing zh dataset:universal_dependencies license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# 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. Every word is tagged by UPOS (Universal Part-Of-Speech).

## How to Use



or



## See Also

esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models


# 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. Every word is tagged by UPOS (Universal Part-Of-Speech). ## How to Use



or ## See Also

esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
TAGS
#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # 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. Every word is tagged by UPOS (Universal Part-Of-Speech). ## How to Use



or ## See Also

esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
passage: TAGS
#transformers #pytorch #bert #token-classification #chinese #pos #wikipedia #dependency-parsing #zh #dataset-universal_dependencies #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# 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. Every word is tagged by UPOS (Universal Part-Of-Speech).## How to Use



or## See Also

esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models