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: 1,451 Bytes
eb01413 | 1 2 3 4 5 6 7 8 9 10 | 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/ |