
# deberta-large-japanese-aozora

## Model Description

This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 127 hours 8 minutes for training. You can fine-tune `deberta-large-japanese-aozora` for downstream tasks, such as [POS-tagging](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-luw-upos), [dependency-parsing](https://huggingface.co/KoichiYasuoka/deberta-large-japanese-aozora-ud-head), and so on.

## How to Use

```py
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-aozora")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-large-japanese-aozora")
```

## 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.


{"language": ["ja"], "license": "cc-by-sa-4.0", "tags": ["japanese", "masked-lm"], "pipeline_tag": "fill-mask", "mask_token": "[MASK]", "widget": [{"text": "\u65e5\u672c\u306b\u7740\u3044\u305f\u3089[MASK]\u3092\u8a2a\u306d\u306a\u3055\u3044\u3002"}]}
transformers pytorch deberta-v2 fill-mask japanese masked-lm ja license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 


# deberta-large-japanese-aozora

## Model Description

This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 127 hours 8 minutes for training. You can fine-tune 'deberta-large-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.

## How to Use



## 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.


# deberta-large-japanese-aozora ## Model Description

This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 127 hours 8 minutes for training. You can fine-tune 'deberta-large-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on. ## How to Use ## 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.
TAGS
#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # deberta-large-japanese-aozora ## Model Description

This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 127 hours 8 minutes for training. You can fine-tune 'deberta-large-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on. ## How to Use ## 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.
passage: TAGS
#transformers #pytorch #deberta-v2 #fill-mask #japanese #masked-lm #ja #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# deberta-large-japanese-aozora## Model Description

This is a DeBERTa(V2) model pre-trained on 青空文庫 texts. NVIDIA A100-SXM4-40GB took 127 hours 8 minutes for training. You can fine-tune 'deberta-large-japanese-aozora' for downstream tasks, such as POS-tagging, dependency-parsing, and so on.## How to Use## 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.