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 added_tokens.json from nvidia/gliner-PII: direct link, hf CLI and curl.
- Browser
- Download file 86 Bytes
-
https://huggingface.co/nvidia/gliner-PII/resolve/main/added_tokens.json
- Command line
-
hf download hf://nvidia/gliner-PII/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/nvidia/gliner-PII/resolve/main/added_tokens.json
86 Bytes
| { | |
| "<<ENT>>": 128002, | |
| "<<SEP>>": 128003, | |
| "[FLERT]": 128001, | |
| "[MASK]": 128000 | |
| } | |