Finds species and organism names in English biomedical text
Zero-shot NER (your own labels)Species and organismsEnglishSmall · 166Mapache-2.0
What it does
This model performs zero-shot named-entity recognition, identifying species and organism mentions in English biomedical text, including scientific and common names. It is based on GLiNER and has 166M parameters, with a reported F1 of 0.785 on the Linnaeus benchmark.
Best for
- Biodiversity literature mining
- Metagenomics report entity extraction
- Taxonomy-aware curation of scientific papers
What it detects (1 labels)
Trained on the label below; you can pass any other label names at inference time.
- Species
Quality
| Benchmark | Linnaeus · all models on this benchmark |
|---|---|
| F1 | 0.785 |
| Precision | 0.700 |
| Recall | 0.893 |
| Accuracy | 0.979 |
| Source | Reported by the authors (test_results.json in the repository) |
Species Entity Recognition - Species and organism names
Use it
pip install gliner
from gliner import GLiNER
model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Species-Small-166M")
print(model.predict_entities("Patient with type 2 diabetes and hypertension.", ["disease"]))Apple Silicon (MLX) build
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-Species-Small-166M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
print(e.label, e.text)All model information shown here is public and belongs to its authors: use and download it freely under each model’s license. The selection, organization, search design and presentation of this catalog are © 2026 Clinical Corpus. Please link to these pages rather than copy them.