OpenMed-NER-DiseaseDetect-ModernMed-395M

What it does

This model detects disease entities in English clinical notes, research papers, and healthcare documents. It reports an F1 score of 0.878 on the BC5CDR benchmark test set, with 396 million parameters and a maximum sequence length of 8192 tokens.

Best for

• Disease mentions in clinical notes
• Disease extraction from research papers
• Disease entities in healthcare analytics

What it detects (1 labels)

• Disease

Quality

Disease Entity Recognition - Disease entities from the BC5CDR dataset

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-DiseaseDetect-ModernMed-395M", aggregation_strategy="simple")
print(ner("Patient prescribed 10 mg aspirin for hypertension."))

pip install --upgrade "openmed[onnx-runtime]"

from openmed import OnnxModel
model = OnnxModel.from_pretrained("OpenMed/OpenMed-NER-DiseaseDetect-ModernMed-395M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())