readme: ---
  language:
  - "ja"
  tags:
  - "japanese"
  - "token-classification"
  - "pos"
  - "dependency-parsing"
  datasets:
  - "universal_dependencies"
  license: "cc-by-sa-4.0"
  pipeline_tag: "token-classification"
  widget:
  - text: "国境の長いトンネルを抜けると雪国であった。"
  ---
  
  # roberta-large-japanese-luw-upos
  
  ## Model Description
  
  This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [roberta-large-japanese-aozora](https://huggingface.co/KoichiYasuoka/roberta-large-japanese-aozora). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
  
  ## How to Use
  
  ```py
  from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline
  tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-large-japanese-luw-upos")
  model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-large-japanese-luw-upos")
  pipeline=TokenClassificationPipeline(tokenizer=tokenizer,model=model,aggregation_strategy="simple")
  nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)]
  print(nlp("国境の長いトンネルを抜けると雪国であった。"))
  ```
  
  or
  
  ```py
  import esupar
  nlp=esupar.load("KoichiYasuoka/roberta-large-japanese-luw-upos")
  print(nlp("国境の長いトンネルを抜けると雪国であった。"))
  ```
  
  ## Reference
  
  安岡孝一: [Transformersと国語研長単位による日本語係り受け解析モデルの製作](http://id.nii.ac.jp/1001/00216223/), 情報処理学会研究報告, Vol.2022-CH-128, No.7 (2022年2月), pp.1-8.
  
  ## See Also
  
  [esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
  
  