Token Classification
GLiNER
PyTorch
ONNX
English
nvidia
PII
PHI
GLiNER
information extraction
entity recognition
privacy
Instructions to use Rizwan313/gliner-PII with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use Rizwan313/gliner-PII with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("Rizwan313/gliner-PII") - Notebooks
- Google Colab
- Kaggle
| Field | Response |
|---|---|
| Generatable or reverse engineerable personal data? | No |
| Personal data used to create this model? | No |
| How often is dataset reviewed? | The dataset was reviewed during its creation, model training, evaluation, and before release. |
| Is there provenance for all datasets used in training? | Yes |
| Does data labeling (annotation, metadata) comply with privacy laws? | Yes. Labels were automatically injected during the synthetic data generation process, so no real personal data was ever viewed or handled. |
| Is data compliant with data subject requests for data correction or removal, if such a request was made? | Not Applicable. |
| Applicable Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/ |