OpenMed-NER-GenomicDetect-MultiMed-568M

What it does

This model detects cell line entities in English clinical notes, research papers, and healthcare documents. It reports an F1 score of 0.997 on the Gelius benchmark, with 567M parameters and a maximum sequence length of 8194 tokens.

Best for

• Cell line mentions in medical records
• Cell line extraction from research papers
• Cell line annotation for knowledge graphs

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-MultiMed-568M", 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-MultiMed-568M-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-MultiMed-568M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)