Finds species and organism names in English biomedical text
Zero-shot NER (your own labels)Species and organismsEnglishMedium · 209Mapache-2.0
What it does
This model performs zero-shot named-entity recognition, identifying species and organism mentions in English biomedical text. It is based on the GLiNER medium architecture with 209M parameters and reports an F1 score of 0.975 on the Linnaeus benchmark.
Best for
- Biodiversity literature mining
- Metagenomics report entity extraction
- Taxonomy-aware curation of scientific articles
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 | 0.975 |
| Precision | 0.968 |
| Recall | 0.983 |
| Accuracy | 0.998 |
| 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-Medium-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-Medium-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.