Finds species and organism names in English biomedical text
Zero-shot NER (your own labels)Species and organismsEnglishMedium · 220Mapache-2.0
What it does
This model performs zero-shot named-entity recognition, detecting species and organism mentions in English biomedical literature and clinical text. It reports an F1 of 0.939 on the Linnaeus benchmark, with 220M parameters and a GLiNER backbone.
Best for
- Biodiversity literature mining
- Metagenomics reporting
- Taxonomy-aware curation
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 | 0.939 |
| Precision | 0.913 |
| Recall | 0.966 |
| Accuracy | 0.994 |
| 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-Base-220M")
print(model.predict_entities("Patient with type 2 diabetes and hypertension.", ["disease"]))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.