OpenMed-NER-GenomicDetect-SuperMedical-355M

What it does

This model detects cell line names in English clinical texts and research papers. It reports an F1 score of 0.997 on the Gelius benchmark, with 354 million parameters and a maximum sequence length of 514 tokens.

Best for

• Cell line extraction from research papers
• Entity recognition in clinical records
• Biomedical literature mining

What it detects (1 labels)

• Cell line

Quality

Gene Entity Recognition - Gene-related entities

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-GenomicDetect-SuperMedical-355M", 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-GenomicDetect-SuperMedical-355M-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-GenomicDetect-SuperMedical-355M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)