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 | { | |
| "eval_loss": 57.82684326171875, | |
| "seqeval_accuracy": 0.9561068702290076, | |
| "seqeval_f1": 0.7755102040816326, | |
| "seqeval_precision": 0.6785714285714286, | |
| "seqeval_recall": 0.9047619047619048 | |
| } |