Finds species names in English scientific text with zero-shot labels
What it does
This model detects species and organism names in English scientific and biomedical text, using user-supplied entity labels at inference time. It reports an F1 score of 0.709 on the Species-800 benchmark, with 166 million parameters and a GLiNER backbone.
Best for
- Species tagging in ecology studies
- Organism extraction from research papers
- Biocuration of taxonomic mentions
What it detects (1 labels)
Trained on the label below; you can pass any other label names at inference time.
- Species
Quality
| Benchmark | Species-800 · all models on this benchmark |
|---|---|
| F1 | 0.709 |
| Precision | 0.639 |
| Recall | 0.797 |
| Accuracy | 0.949 |
| Source | Reported by the authors (test_results.json in the repository) |
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-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-Organism-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.