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---
language:
- "ja"
tags:
- "japanese"
- "token-classification"
- "pos"
base_model: tokyotech-llm/Llama-3-Swallow-8B-v0.1
datasets:
- "universal_dependencies"
license: "llama3"
pipeline_tag: "token-classification"
widget:
- text: "国境の長いトンネルを抜けると雪国であった。"
---
# Llama-3-Swallow-8B-upos
## Model Description
This is a LLaMA model for POS-tagging, derived from [Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1). Every short-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech) and [FEATS](https://universaldependencies.org/u/feat/).
## How to Use
```py
from transformers import pipeline
nlp=pipeline("upos","KoichiYasuoka/Llama-3-Swallow-8B-upos",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("国境の長いトンネルを抜けると雪国であった。"))
```
## Reference
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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