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
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Download BIAS.md from HeshamHaroon/fintuned_PII: direct link, hf CLI and curl.
- Browser
- Download file 900 Bytes
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https://huggingface.co/HeshamHaroon/fintuned_PII/resolve/main/BIAS.md
- Command line
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hf download hf://HeshamHaroon/fintuned_PII/BIAS.md
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curl -L -o BIAS.md https://huggingface.co/HeshamHaroon/fintuned_PII/resolve/main/BIAS.md
900 Bytes
| Field | Response |
|---|---|
| Participation considerations from adversely impacted groups protected classes in model design and testing: | Not applicable for the training phase, as the model was trained exclusively on synthetic data. Stakeholder review with impacted groups is recommended during deployment to validate real-world performance. |
| Bias Metric (If Measured): | Strict F1 Score at a 0.3 threshold. Performance on evaluation datasets was: Argilla PII (0.70), AI4Privacy (0.64), and nvidia/Nemotron-PII (0.87). Demographic performance breakdowns are not available. |
| Measures taken to mitigate against unwanted bias: | Not applicable. |