OpenMed-NER-PathologyDetect-ModernClinical-395M

What it does

This model identifies disease entities in English clinical notes and biomedical literature. It reports an F1 score of 0.884 on the NCBI Disease benchmark, with 396 million parameters and support for sequences up to 8192 tokens.

Best for

• Disease mentions in clinical notes
• Disease extraction from research papers
• Disease annotation in biomedical literature

What it detects (1 labels)

• Disease

Quality

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-ModernClinical-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-PathologyDetect-ModernClinical-395M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())