OpenMed-NER-PharmaDetect-BigMed-560M

What it does

This model identifies chemical or drug entities in English clinical texts and biomedical literature. It reports an F1 score of 0.953 on the BC5CDR chemicals test set, with a precision of 0.945 and recall of 0.961.

Best for

• Extracting drug mentions from clinical notes
• Mining chemical compounds in research papers
• Identifying medications in patient records

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