OpenMed-NER-DiseaseDetect-MultiMed-335M

What it does

This model identifies disease entities in English clinical texts, research papers, and healthcare documents. It reports an F1 score of 0.902 on the BC5CDR disease benchmark, with 334 million parameters and a maximum sequence length of 512 tokens.

Best for

• Extracting diseases from medical records
• Mining disease mentions in scientific literature
• Building biomedical knowledge graphs

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