Token Classification
GLiNER
PyTorch
Japanese
NER
information extraction
relation extraction
summarization
sentiment extraction
question-answering
Instructions to use vumichien/ner-jp-gliner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use vumichien/ner-jp-gliner with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("vumichien/ner-jp-gliner") text = "Cristiano Ronaldo dos Santos Aveiro was born on 5 February 1985 in Funchal, Madeira, Portugal." labels = ["person", "date", "location"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) - Notebooks
- Google Colab
- Kaggle
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