Instructions to use KoichiYasuoka/deberta-base-japanese-wikipedia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoichiYasuoka/deberta-base-japanese-wikipedia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="KoichiYasuoka/deberta-base-japanese-wikipedia")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia") model = AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia") - Notebooks
- Google Colab
- Kaggle
deberta-base-japanese-wikipedia
Model Description
This is a DeBERTa(V2) model pre-trained on Japanese Wikipedia and 青空文庫 texts. NVIDIA A100-SXM4-40GB took 109 hours 27 minutes for training. You can fine-tune deberta-base-japanese-wikipedia for downstream tasks, such as POS-tagging, dependency-parsing, and so on.
How to Use
from transformers import AutoTokenizer,AutoModelForMaskedLM
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia")
model=AutoModelForMaskedLM.from_pretrained("KoichiYasuoka/deberta-base-japanese-wikipedia")
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.
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