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--- |
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language: |
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- "zh" |
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tags: |
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- "chinese" |
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- "token-classification" |
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- "pos" |
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- "dependency-parsing" |
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base_model: IDEA-CCNL/Erlangshen-DeBERTa-v2-710M-Chinese |
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datasets: |
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- "universal_dependencies" |
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license: "apache-2.0" |
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pipeline_tag: "token-classification" |
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--- |
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# deberta-xlarge-chinese-erlangshen-upos |
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## Model Description |
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This is a DeBERTa(V2) model pre-trained on Chinese texts (both simplified and traditional) for POS-tagging and dependency-parsing, derived from [Erlangshen-DeBERTa-v2-710M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-DeBERTa-v2-710M-Chinese). Every word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech). |
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## How to Use |
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```py |
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from transformers import AutoTokenizer,AutoModelForTokenClassification |
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tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-xlarge-chinese-erlangshen-upos") |
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model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-xlarge-chinese-erlangshen-upos") |
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``` |
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or |
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```py |
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import esupar |
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nlp=esupar.load("KoichiYasuoka/deberta-xlarge-chinese-erlangshen-upos") |
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``` |
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## See Also |
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[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models |
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