Finds species and organism names in English biomedical text
What it does
This model detects species and organism names, including scientific and common names and abbreviations, in English biomedical text. It is a 209M-parameter GLiNER model that reports an F1 of 0.832 on the Linnaeus benchmark, with users supplying entity labels at inference time.
Best for
- Biodiversity literature mining
- Metagenomics report annotation
- Taxonomy-aware curation tasks
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 | 0.832 |
| Precision | 0.770 |
| Recall | 0.906 |
| Accuracy | 0.984 |
| 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-Multi-209M")
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-Multi-209M", 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.