Detects gene and protein mentions in English biomedical text
Extract biomedical entitiesGenes, proteins and cellsEnglishMedium · 396Mapache-2.0
What it does
This model identifies mentions of genes and proteins in English clinical notes, research papers, and healthcare documents. It reports an F1 score of 0.867 on the BC2GM benchmark and is based on the ModernBERT architecture with 396M parameters.
Best for
- Gene and protein mention extraction
- Clinical text mining for entities
- Biomedical literature processing
What it detects (1 labels)
- Gene or protein
Quality
| Benchmark | BC2GM · all models on this benchmark |
|---|---|
| F1 | 0.867 |
| Precision | 0.866 |
| Recall | 0.867 |
| Accuracy | 0.962 |
| Source | Reported by the authors (test_results.json in the repository) |
Gene/Protein Entity Recognition - Gene and protein mentions
Use it
pip install transformers torch
from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomeDetect-ModernClinical-395M", aggregation_strategy="simple")
print(ner("Patient prescribed 10 mg aspirin for hypertension."))Android, browser, CPU (ONNX) build
pip install --upgrade "openmed[onnx-runtime]"
from openmed import OnnxModel
model = OnnxModel.from_pretrained("OpenMed/OpenMed-NER-GenomeDetect-ModernClinical-395M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
print(entity.to_dict())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.