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-Large-459M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Large-459M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-BloodCancer-Large-459M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-BloodCancer-Large-459M
eb8b9aa verified - Xet hash:
- 7372384b13e92bb0af2fe7242b668e7934c624fab16ff784288f5d32b6623eb9
- Size of remote file:
- 1.78 GB
- SHA256:
- f1623deabc3e385ca486105fadd76a1d835d3501f2a8e75d16d21c7f9655809d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.