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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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- "wikipedia" |
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- "token-classification" |
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- "pos" |
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- "dependency-parsing" |
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base_model: KoichiYasuoka/deberta-large-japanese-wikipedia |
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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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# deberta-large-japanese-wikipedia-luw-upos |
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## Model Description |
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This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-large-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-wikipedia). 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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import torch |
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from transformers import AutoTokenizer,AutoModelForTokenClassification |
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tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-wikipedia-luw-upos") |
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model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-large-japanese-wikipedia-luw-upos") |
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s="国境の長いトンネルを抜けると雪国であった。" |
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t=tokenizer.tokenize(s) |
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p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]] |
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print(list(zip(t,p))) |
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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-large-japanese-wikipedia-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: [青空文庫DeBERTaモデルによる国語研長単位係り受け解析](http://hdl.handle.net/2433/275409), 東洋学へのコンピュータ利用, 第35回研究セミナー (2022年7月), pp.29-43. |
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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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