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 pytorch_model.bin from nvidia/gliner-PII: direct link, hf CLI and curl.
- Browser
- Download file 1.78 GB
-
https://huggingface.co/nvidia/gliner-PII/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nvidia/gliner-PII/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nvidia/gliner-PII/resolve/main/pytorch_model.bin
1.78 GB
- Xet hash:
- 1681abccfc32f7e54ce44f234abaf5332a492705ab2c0303bfb7eeb75dfb2de8
- Size of remote file:
- 1.78 GB
- SHA256:
- a4dfd0dcbd718acc86dca65fa99f1097d2796c8d7a681e1bc42f40f946c03802
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.