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--- | |
language: | |
- "ja" | |
tags: | |
- "japanese" | |
- "token-classification" | |
- "pos" | |
- "wikipedia" | |
- "dependency-parsing" | |
datasets: | |
- "universal_dependencies" | |
license: "cc-by-sa-4.0" | |
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。" | |
--- | |
# bert-large-japanese-luw-upos | |
## Model Description | |
This is a BERT model pre-trained on Japanese Wikipedia texts for POS-tagging and dependency-parsing, derived from [bert-large-japanese-char-extended](https://huggingface.co/KoichiYasuoka/bert-large-japanese-char-extended). Every long-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 | |
import torch | |
from transformers import AutoTokenizer,AutoModelForTokenClassification | |
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-large-japanese-luw-upos") | |
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-large-japanese-luw-upos") | |
s="国境の長いトンネルを抜けると雪国であった。" | |
p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]] | |
print(list(zip(s,p))) | |
``` | |
or | |
```py | |
import esupar | |
nlp=esupar.load("KoichiYasuoka/bert-large-japanese-luw-upos") | |
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: [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. | |
## See Also | |
[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa models | |