OpenMed-NER-ProteinDetect-SuperClinical-434M

What it does

This model identifies five types of protein-related entities, including proteins, protein complexes, and protein variants, in English biomedical literature and clinical documents. It reports an F1 score of 0.941 on the FSU-PRGE benchmark, with 434M parameters and a 512-token input limit.

Best for

• Protein mentions in research papers
• Protein complexes in clinical notes
• Protein variants in drug discovery

What it detects (5 labels)

• Protein
• Protein complex
• Protein (enumeration)
• Protein family or group
• Protein variant

Quality

Biomedical Entity Recognition - Various biomedical entities

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-ProteinDetect-SuperClinical-434M", 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-ProteinDetect-SuperClinical-434M-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-ProteinDetect-SuperClinical-434M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)