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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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- "wikipedia" |
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- "dependency-parsing" |
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base_model: tohoku-nlp/bert-base-japanese-v2 |
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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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# bert-base-japanese-unidic-luw-upos |
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## Model Description |
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This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-base-japanese-v2](https://huggingface.co/tohoku-nlp/bert-base-japanese-v2). Every long-unit-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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import torch |
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from transformers import AutoTokenizer,AutoModelForTokenClassification |
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tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-japanese-unidic-luw-upos") |
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model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-japanese-unidic-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/bert-base-japanese-unidic-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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[fugashi](https://pypi.org/project/fugashi) and [unidic-lite](https://pypi.org/project/unidic-lite) are required. |
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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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