Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
cancer-genetics
oncology
gene-regulation
cancer-research
amino_acid
anatomical_system
cancer
cell
cellular_component
developing_anatomical_structure
gene_or_gene_product
immaterial_anatomical_entity
multi-tissue_structure
organ
organism
organism_subdivision
organism_substance
pathological_formation
simple_chemical
tissue
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1450c6a05ef768fc4b01489b843aceb98e293d985e922f1993b1ccd7a26609c2
- Size of remote file:
- 16.4 MB
- SHA256:
- 3d81efd0311d8d5900181604ebe2b6216b7f7f0dd1e209cf69a42bfbed21b97a
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