OpenMed-NER-GenomeDetect-PubMed-335M

What it does

This model identifies mentions of genes or proteins in English clinical notes and research literature. It is a 334M-parameter BERT model fine-tuned on the BC2GM dataset, reporting an F1 score of 0.896 on that benchmark's test set.

Best for

• Gene mentions in medical records
• Protein names in research papers
• Entity extraction for literature mining

What it detects (1 labels)

• Gene or protein

Quality

Gene/Protein Entity Recognition - Gene and protein mentions

Use it

pip install transformers torch

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