Token Classification
GLiNER
PyTorch
English
nvidia
PII
PHI
GLiNER
information extraction
entity recognition
privacy
Instructions to use HeshamHaroon/fintuned_PII with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use HeshamHaroon/fintuned_PII with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("HeshamHaroon/fintuned_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
Download spm.model from HeshamHaroon/fintuned_PII: direct link, hf CLI and curl.
- Browser
- Download file 2.46 MB
-
https://huggingface.co/HeshamHaroon/fintuned_PII/resolve/main/spm.model
- Command line
-
hf download hf://HeshamHaroon/fintuned_PII/spm.model
-
curl -L -o spm.model https://huggingface.co/HeshamHaroon/fintuned_PII/resolve/main/spm.model
2.46 MB
- Xet hash:
- 6bd079d8585fbcdbf47cb3a534f888b5aaa32b8994301f5c47573e6cf825d7da
- Size of remote file:
- 2.46 MB
- SHA256:
- c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
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