OpenMed-NER-DiseaseDetect-SuperClinical-434M

What it does

This model detects disease entities in English clinical notes and biomedical literature. It reports an F1 score of 0.912 on the BC5CDR disease benchmark, with 434M parameters and a maximum sequence length of 512 tokens.

Best for

• Disease mentions in medical records
• Disease entities in research papers
• Disease extraction for literature mining

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-SuperClinical-434M", 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-SuperClinical-434M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())

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-NER-DiseaseDetect-SuperClinical-434M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)