Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
disease-entity-recognition
medical-diagnosis
ncbi
pathology
disease
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Pathology-Medium-209M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Pathology-Medium-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Pathology-Medium-209M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-Pathology-Medium-209M
3884fb9 verified - Xet hash:
- d33df660a9c92ab2261a5176c377067105addd97ef3ddb5115844a187e45a5b3
- Size of remote file:
- 781 MB
- SHA256:
- c7cd338b48e76a2b7772a1b43c2be8ca16b0bbd72394e8a013a08a3d54519697
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.