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-Small-166M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Oncology-Small-166M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Oncology-Small-166M") - Notebooks
- Google Colab
- Kaggle
File size: 207 Bytes
7d4b5a1 | 1 2 3 4 5 6 7 | {
"eval_loss": 1272.9971923828125,
"seqeval_accuracy": 0.9106171486713889,
"seqeval_f1": 0.7030391797876235,
"seqeval_precision": 0.707755824240637,
"seqeval_recall": 0.6983849847228285
} |