OpenMed-NER-AnatomyDetect-BioMed-335M

What it does

This model detects the single entity type Anatomy, covering anatomical structures and body parts, in English clinical notes, research papers, and healthcare documents. It reports an F1 score of 0.896 on the AnatEM benchmark, with 334M parameters and a maximum sequence length of 512 tokens.

Best for

• Extracting anatomy mentions from clinical notes
• Mining anatomical entities in research papers
• Building biomedical knowledge graphs

What it detects (1 labels)

• Anatomy

Quality

Anatomical Entity Recognition - Anatomical structures and body parts

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-AnatomyDetect-BioMed-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-AnatomyDetect-BioMed-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-AnatomyDetect-BioMed-335M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)