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") - Notebooks
- Google Colab
- Kaggle
File size: 900 Bytes
eb01413 | 1 2 3 4 5 6 | Field | Response
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Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/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.
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