OpenMed-NER-PathologyDetect-BioMed-109M

What it does

This model identifies disease entities in English clinical notes, research papers, and healthcare documents. It reports an F1 score of 0.887 on the NCBI Disease benchmark, with 109 million parameters and a maximum sequence length of 512 tokens.

Best for

• Disease mentions in clinical notes
• Disease extraction from research papers
• Disease annotation in healthcare documents

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-BioMed-109M", 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-BioMed-109M-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-BioMed-109M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)