OpenMed-ZeroShot-NER-Organism-Large-459M

What it does

This zero-shot model detects species and organism names in English scientific and clinical text, with users supplying entity labels at inference time. It reports an F1 score of 0.847 on the Species-800 benchmark, as stated by the author.

Best for

• Ecology studies organism tagging
• Biomedical literature entity extraction
• Custom species recognition without retraining

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-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-Organism-Large-459M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)