OpenMed-NER-PharmaDetect-SuperClinical-434M

What it does

This model detects chemical or drug entities in English clinical notes and research literature. It reports an F1 score of 0.961 on the BC5CDR chemicals benchmark, with 434 million parameters and a maximum sequence length of 512 tokens.

Best for

• Medication extraction from patient records
• Chemical entity mining in research papers
• Adverse event monitoring from clinical text

What it detects (1 labels)

• Chemical or drug

Quality

Chemical Entity Recognition - Chemical entities from the BC5CDR dataset

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-PharmaDetect-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-PharmaDetect-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-PharmaDetect-SuperClinical-434M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)