Instructions to use bijinpanicker/NuNER_Zero-span-ONNX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use bijinpanicker/NuNER_Zero-span-ONNX with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("bijinpanicker/NuNER_Zero-span-ONNX") - Notebooks
- Google Colab
- Kaggle
NuNER Zeroβspan (8-bit ONNX)
This is an 8-bit quantized ONNX version of numind/NuNER_Zero-span, a zero-shot named entity recognition (NER) model based on the GLiNER architecture.
π§ Features
- π§ Zero-shot span-based NER
- π¦ Quantized to 8-bit for faster, smaller inference
- π¬ Input: text + list of labels
- πͺ Output: text spans per label (max span length = 12 tokens)
- π Format: ONNX
π License
MIT License β same as the original model.
π Acknowledgements
Model tree for bijinpanicker/NuNER_Zero-span-ONNX
Base model
numind/NuNER_Zero-span