
# deberta-base-japanese-wikipedia-luw-upos

## Model Description

This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from [deberta-base-japanese-wikipedia](https://huggingface.co/KoichiYasuoka/deberta-base-japanese-wikipedia). 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/deberta-base-japanese-wikipedia-luw-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia-luw-upos")
s="国境の長いトンネルを抜けると雪国であった。"
t=tokenizer.tokenize(s)
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(t,p)))
```

or

```py
import esupar
nlp=esupar.load("KoichiYasuoka/deberta-base-japanese-wikipedia-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: [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.

## See Also

[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models


{"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "wikipedia", "token-classification", "pos", "dependency-parsing"], "datasets": ["universal_dependencies"], "pipeline_tag": "token-classification", "widget": [{"text": "\u56fd\u5883\u306e\u9577\u3044\u30c8\u30f3\u30cd\u30eb\u3092\u629c\u3051\u308b\u3068\u96ea\u56fd\u3067\u3042\u3063\u305f\u3002"}]}
transformers pytorch deberta-v2 token-classification japanese wikipedia pos dependency-parsing ja dataset:universal_dependencies license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #token-classification #japanese #wikipedia #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 


# deberta-base-japanese-wikipedia-luw-upos

## Model Description

This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-base-japanese-wikipedia. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.

## How to Use



or



## Reference

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, 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.

## See Also

esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models


# deberta-base-japanese-wikipedia-luw-upos ## Model Description

This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-base-japanese-wikipedia. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS. ## How to Use



or ## Reference

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, 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. ## See Also

esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
TAGS
#transformers #pytorch #deberta-v2 #token-classification #japanese #wikipedia #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # deberta-base-japanese-wikipedia-luw-upos ## Model Description

This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-base-japanese-wikipedia. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS. ## How to Use



or ## Reference

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, 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. ## See Also

esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
passage: TAGS
#transformers #pytorch #deberta-v2 #token-classification #japanese #wikipedia #pos #dependency-parsing #ja #dataset-universal_dependencies #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# deberta-base-japanese-wikipedia-luw-upos## Model Description

This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts for POS-tagging and dependency-parsing, derived from deberta-base-japanese-wikipedia. Every long-unit-word is tagged by UPOS (Universal Part-Of-Speech) and FEATS.## How to Use



or## Reference

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, 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.## See Also

esupar: Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models