OpenMed-NER-GenomicDetect-ElectraMed-560M

What it does

This model identifies cell line entities in English clinical and research text. It reports an F1 score of 0.993 on the Gelus benchmark, with a precision of 1.0 and recall of 0.986, and is based on a 559M parameter XLM-RoBERTa architecture.

Best for

• Cell line mentions in medical records
• Cell line extraction from research papers
• Cell line identification in healthcare documents

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-ElectraMed-560M", 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-ElectraMed-560M-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-ElectraMed-560M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)