Instructions to use 0xhikae/ja-ner-ja with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use 0xhikae/ja-ner-ja with spaCy:
!pip install https://huggingface.co/0xhikae/ja-ner-ja/resolve/main/ja-ner-ja-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("ja-ner-ja") # Importing as module. import ja-ner-ja nlp = ja-ner-ja.load() - Notebooks
- Google Colab
- Kaggle
ja-ner-ja
Japanese Named Entity Recognition model built with spaCy.
Labels
| Label | Precision | Recall | F1 |
|---|---|---|---|
| PERSON | 0.950 | 0.949 | 0.949 |
| DATE_OF_BIRTH | 0.872 | 0.872 | 0.872 |
| ADDRESS | 0.904 | 0.967 | 0.934 |
| ORGANIZATION | 0.929 | 0.828 | 0.876 |
| BANK_ACCOUNT | 0.947 | 0.857 | 0.900 |
Architecture
- Pipeline: tok2vec → ner
- spaCy version: >=3.8.13,<3.9.0
Install
pip install https://huggingface.co/0xhikae/ja-ner-ja/resolve/main/ja_ner_ja-0.2.0-py3-none-any.whl
Usage
import spacy
nlp = spacy.load("ja_ner_ja")
doc = nlp("田中太郎は東京都渋谷区に住んでいます。")
for ent in doc.ents:
print(ent.text, ent.label_)
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Evaluation results
- F1self-reported0.922
- Precisionself-reported0.931
- Recallself-reported0.914