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-XLarge-770M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Pathology-XLarge-770M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Pathology-XLarge-770M") 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
feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-Pathology-XLarge-770M
bdbe0d7 verified Download pytorch_model.bin from OpenMed/OpenMed-ZeroShot-NER-Pathology-XLarge-770M: direct link, hf CLI and curl.
- Browser
- Download file 2.43 GB
-
https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Pathology-XLarge-770M/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://OpenMed/OpenMed-ZeroShot-NER-Pathology-XLarge-770M/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/OpenMed/OpenMed-ZeroShot-NER-Pathology-XLarge-770M/resolve/main/pytorch_model.bin
2.43 GB
- Xet hash:
- 5c61656445789ed51d628c788b666ea945b2d2048ecd34b160d5d09f7d535eb2
- Size of remote file:
- 2.43 GB
- SHA256:
- c1cd7836fa4221c79bae2d1b18df4f1d0fd8fd7f5138c6d44e2eedebc01e1f7a
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