OpenMed-ZeroShot-NER-Pathology-Large-459M

What it does

This model performs zero-shot named-entity recognition for disease mentions in English clinical and research text, with users supplying entity labels at inference time. It reports an F1 score of 0.898 on the NCBI Disease benchmark and is based on the GLiNER large architecture with 459M parameters.

Best for

• Disease mentions from research abstracts
• Entity extraction from clinical notes
• Custom disease labels in biomedical text

What it detects (1 labels)

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

• Disease

Quality

Disease Entity Recognition - Disease entities from the NCBI dataset

Use it

pip install gliner

from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Pathology-Large-459M")
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-Pathology-Large-459M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)