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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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- "masked-lm" |
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license: "cc-by-sa-4.0" |
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pipeline_tag: "fill-mask" |
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mask_token: "[MASK]" |
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widget: |
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- text: "日本に着いたら[MASK]を訪ねなさい。" |
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--- |
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# deberta-base-japanese-aozora |
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## Model Description |
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This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 36 hours 44 minutes for training. You can fine-tune `deberta-base-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-luw-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-aozora-ud-head), and so on. |
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## How to Use |
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```py |
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from transformers import AutoTokenizer,AutoModelForMaskedLM |
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tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-aozora") |
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model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-japanese-aozora") |
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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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