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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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base_model: tokyotech-llm/Swallow-7b-plus-hf |
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datasets: |
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- "universal_dependencies" |
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license: "llama2" |
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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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# Swallow-7b-plus-upos |
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## Model Description |
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This is a LLaMA model for POS-tagging, derived from [Swallow-7b-plus-hf](https://huggingface.co/tokyotech-llm/Swallow-7b-plus-hf). Every short-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 pipeline |
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nlp=pipeline("upos","KoichiYasuoka/Swallow-7b-plus-upos",trust_remote_code=True,aggregation_strategy="simple") |
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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: [GPT系モデルの系列ラベリングによる品詞付与](http://hdl.handle.net/2433/288964), 東洋学へのコンピュータ利用, 第38回研究セミナー (2024年7月26日), pp.3-10. |
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