OpenMed-NER-PathologyDetect-ElectraMed-335M

What it does

This model detects disease entities in English clinical notes and biomedical literature. It reports an F1 score of 0.898 on the NCBI Disease benchmark and is based on the e5-large-v2 transformer with 334 million parameters.

Best for

• Disease mentions from clinical notes
• Disease extraction from research papers
• Disease entities for knowledge graphs

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-ElectraMed-335M", 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-ElectraMed-335M-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-PathologyDetect-ElectraMed-335M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)