Token Classification
GLiNER
PyTorch
English
nvidia
PII
PHI
GLiNER
information extraction
entity recognition
privacy
Instructions to use nvidia/gliner-PII with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use nvidia/gliner-PII with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("nvidia/gliner-PII") 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
Adding `safetensors` variant of this model
#8
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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oid sha256:c2b0d20643937e191d156fd754b39db238a451b8756489ed2c54a65e78ef97d6
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size 1781913592
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