OpenMed-NER-ProteinDetect-PubMed-335M

What it does

This model detects five protein-related entity types—Protein, Protein complex, Protein (enumeration), Protein family or group, and Protein variant—in English clinical and research text. It reports an F1 score of 0.935 on the FSU-PRGE benchmark and runs on PyTorch, ONNX, and Apple Silicon.

Best for

• Mining protein mentions from research papers
• Extracting protein complexes from clinical notes
• Annotating protein variants in biomedical text

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