OpenMed-NER-ChemicalDetect-ModernClinical-395M

What it does

This model detects chemical compounds and substances in English biomedical literature, including clinical texts and research papers. It reports an F1 score of 0.937 on the BC4CHEMD benchmark and supports sequences up to 8192 tokens.

Best for

• Extracting drug names from clinical notes
• Mining chemical mentions in research papers
• Building biomedical knowledge graphs

What it detects (1 labels)

• Chemical or drug

Quality

Chemical Entity Recognition - Identifies chemical compounds and substances in biomedical literature

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-ChemicalDetect-ModernClinical-395M", 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-ChemicalDetect-ModernClinical-395M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())