OpenMed-NER-GenomicDetect-SuperClinical-434M

What it does

This model performs named-entity recognition to detect cell line names in English clinical texts and research papers. It is a 434M-parameter DeBERTa-v3 model that reports an F1 score of 0.992 on the Gelius benchmark.

Best for

• Cell line extraction from research papers
• Cell line mentions in clinical notes
• Cell line identification in biomedical literature

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-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-GenomicDetect-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-GenomicDetect-SuperClinical-434M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)