Detects disease names in English clinical and research text
Extract biomedical entitiesDiseases and conditionsEnglishMedium · 396Mapache-2.0
What it does
This model identifies disease entities in English clinical texts, research papers, and healthcare documents. It is built on ModernBERT-large with 396M parameters and reports an F1 score of 0.865 on the NCBI Disease benchmark.
Best for
- Extracting diseases from medical records
- Mining disease mentions in scientific literature
- Annotating disease entities in patient notes
What it detects (1 labels)
- Disease
Quality
| Benchmark | NCBI Disease · all models on this benchmark |
|---|---|
| F1 | 0.865 |
| Precision | 0.859 |
| Recall | 0.871 |
| Accuracy | 0.966 |
| Source | Reported by the authors (test_results.json in the repository) |
Disease Entity Recognition - Disease entities from the NCBI dataset
Use it
pip install transformers torch
from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-PathologyDetect-ModernMed-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-PathologyDetect-ModernMed-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.