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") 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 SAFETY&SECURITY.md from nvidia/gliner-PII: direct link, hf CLI and curl.
- Browser
- Download file 845 Bytes
-
https://huggingface.co/nvidia/gliner-PII/resolve/main/SAFETY%26SECURITY.md
- Command line
-
hf download 'hf://nvidia/gliner-PII/SAFETY&SECURITY.md'
-
curl -L -o 'SAFETY&SECURITY.md' https://huggingface.co/nvidia/gliner-PII/resolve/main/SAFETY%26SECURITY.md
845 Bytes
| Field | Response |
|---|---|
| Model Application Field(s): | Healthcare, Finance, Legal, Enterprise Data Governance, Data Privacy Compliance, Cybersecurity, Content Moderation |
| Describe the life critical impact (if present). | Not Applicable. |
| Use Case Restrictions: | Abide by NVIDIA Open Model License Agreement |
| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |