Token Classification
GLiNER
PyTorch
English
entity recognition
NER
named entity recognition
zero shot
zero-shot
Instructions to use numind/NuNER_Zero-4k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use numind/NuNER_Zero-4k with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("numind/NuNER_Zero-4k") 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 numind/NuNER_Zero-4k: direct link, hf CLI and curl.
- Browser
- Download file 1.8 GB
-
https://huggingface.co/numind/NuNER_Zero-4k/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://numind/NuNER_Zero-4k/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/numind/NuNER_Zero-4k/resolve/main/pytorch_model.bin
1.8 GB
- Xet hash:
- 2fbb18cd72b9b9a2cbcba4097eacca799aafe5968b748a23fd4de621f2483fad
- Size of remote file:
- 1.8 GB
- SHA256:
- c66bb9161e1fec21b831a73659cc4ddd6347b25f1fee1cba9d98db6bea988ab9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.