OpenMed-ZeroShot-NER-Organism-Multi-209M

What it does

This model performs zero-shot named-entity recognition, identifying species names in English scientific and biomedical text, with the user supplying entity labels at inference time. It reports an F1 score of 0.692 on the Species-800 benchmark, and has 209 million parameters.

Best for

• Species mentions in ecology papers
• Organism tagging in biocuration
• Custom entity detection in research

What it detects (1 labels)

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

• Species

Quality

Species Entity Recognition - Species names from the Species-800 dataset

Use it

pip install gliner

from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Organism-Multi-209M")
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-Organism-Multi-209M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)