OpenMed-ZeroShot-NER-Oncology-Small-166M

What it does

This model detects 16 cancer-related entity types, such as cancer, gene or gene product, and cellular component, in English biomedical literature and clinical text. Users supply entity labels at inference time, and the model reports an F1 score of 0.703 on the BioNLP 2013 Cancer Genetics test set.

Best for

• Cancer pathway curation from literature
• Driver gene analysis in tumor biology
• Precision oncology literature mining

What it detects (16 labels)

Trained on the label below; you can pass any other label names at inference time.

• 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

Quality

Cancer Genetics - Cancer-related genetic entities

Use it

pip install gliner

from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Oncology-Small-166M")
print(model.predict_entities("Patient with type 2 diabetes and hypertension.", ["disease"]))

pip install "openmed[mlx]"

from openmed import analyze_text
from openmed.core.config import OpenMedConfig
result = analyze_text("Patient started imatinib in March.", model_name="OpenMed/OpenMed-ZeroShot-NER-Oncology-Small-166M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)