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--- |
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language: |
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- "ja" |
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tags: |
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- "japanese" |
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- "token-classification" |
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- "pos" |
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- "dependency-parsing" |
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base_model: KoichiYasuoka/roberta-base-japanese-aozora-char |
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datasets: |
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- "universal_dependencies" |
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license: "cc-by-sa-4.0" |
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pipeline_tag: "token-classification" |
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widget: |
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- text: "国境の長いトンネルを抜けると雪国であった。" |
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--- |
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# roberta-base-japanese-char-luw-upos |
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## Model Description |
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This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [roberta-base-japanese-aozora-char](https://huggingface.co/KoichiYasuoka/roberta-base-japanese-aozora-char). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS](https://universaldependencies.org/u/feat/). |
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## How to Use |
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```py |
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from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline |
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tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-base-japanese-char-luw-upos") |
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model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-base-japanese-char-luw-upos") |
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pipeline=TokenClassificationPipeline(tokenizer=tokenizer,model=model,aggregation_strategy="simple") |
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nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)] |
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print(nlp("国境の長いトンネルを抜けると雪国であった。")) |
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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/roberta-base-japanese-char-luw-upos") |
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print(nlp("国境の長いトンネルを抜けると雪国であった。")) |
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``` |
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## Reference |
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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. |
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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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