OpenMed-NER-ChemicalDetect-ElectraMed-109M

What it does

This model detects chemical or drug entities in English biomedical literature, including clinical texts and research papers. It is a 109M-parameter BERT model fine-tuned on the BC4CHEMD dataset, reporting an F1 score of 0.937 on that benchmark's test set.

Best for

• Extracting drug names from clinical notes
• Mining chemical compounds in research papers
• Identifying substances in biomedical literature

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-ElectraMed-109M", 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-ElectraMed-109M-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-ChemicalDetect-ElectraMed-109M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)