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 pytorch_model.bin from HeshamHaroon/fintuned_PII: direct link, hf CLI and curl.
- Browser
- Download file 1.78 GB
-
https://huggingface.co/HeshamHaroon/fintuned_PII/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://HeshamHaroon/fintuned_PII/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/HeshamHaroon/fintuned_PII/resolve/main/pytorch_model.bin
1.78 GB
- Xet hash:
- 2a09b50e98afed6b1f92e724ea9b8cfe90eafc028f7ed3155f26f3435eed6bf9
- Size of remote file:
- 1.78 GB
- SHA256:
- a4dfd0dcbd718acc86dca65fa99f1097d2796c8d7a681e1bc42f40f946c03802
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