Token Classification
GLiNER
PyTorch
GLiNER
NER
named entity recognition
PII
personally identifiable information
PHI
protected health information
de-identification
deidentification
biomedical NER
clinical NER
medical NLP
clinical NLP
information extraction
open-vocabulary NER
zero-shot NER
few-shot NER
multilingual
Instructions to use anthonyyazdaniml/GLiNER-BioClinX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use anthonyyazdaniml/GLiNER-BioClinX with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("anthonyyazdaniml/GLiNER-BioClinX") 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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