Token Classification
Transformers
PyTorch
Japanese
deberta-v2
japanese
wikipedia
cc100
oscar
pos
dependency-parsing
Instructions to use KoichiYasuoka/deberta-large-japanese-juman-ud-goeswith with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use KoichiYasuoka/deberta-large-japanese-juman-ud-goeswith with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="KoichiYasuoka/deberta-large-japanese-juman-ud-goeswith")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("KoichiYasuoka/deberta-large-japanese-juman-ud-goeswith") model = AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/deberta-large-japanese-juman-ud-goeswith", device_map="auto") - Notebooks
- Google Colab
- Kaggle
deberta-large-japanese-juman-ud-goeswith
Model Description
This is a DeBERTa(V2) model pretrained on Japanese Wikipedia, CC-100, and OSCAR texts for POS-tagging and dependency-parsing (using goeswith for subwords), derived from deberta-v2-large-japanese.
How to Use
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/deberta-large-japanese-juman-ud-goeswith",trust_remote_code=True,aggregation_strategy="simple")
print(nlp("全学年にわたって小学校の国語の教科書に挿し絵が用いられている"))
fugashi is required.
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Model tree for KoichiYasuoka/deberta-large-japanese-juman-ud-goeswith
Base model
ku-nlp/deberta-v2-large-japanese