Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
leukemia
hematology
cancer
clinical-medicine
disease
gene
protein
treatment
Instructions to use OpenMed/OpenMed-ZeroShot-NER-BloodCancer-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-BloodCancer-Medium-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Medium-209M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-BloodCancer-Medium-209M
f7bffc7 verified - Xet hash:
- 26971a84286f95d27f712ef6f63735d9c965a84de51ff275cfeaf22bad2f10e2
- Size of remote file:
- 781 MB
- SHA256:
- d2f1c6f2efd9f32f96aa1175b0cc3677d31061f6c08ff1b9a4bd9a9c2b5858a3
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