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-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-Oncology-Large-459M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Oncology-Large-459M") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 495e3377781f3c097e8eba256d8d8f19fa416ffca9aecae051c808fcbbdecae5
- Size of remote file:
- 1.78 GB
- SHA256:
- f27b3d7e3a3f46078130990172038b9cf3039c7a0faa4bd0d568324828cc7a37
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.