OpenMed-NER-ChemicalDetect-SnowMed-568M

What it does

This model detects the entity type 'Chemical or drug' in English biomedical literature, including clinical texts and research papers. It reports an F1 score of 0.949 on the BC4CHEMD benchmark, with 567 million parameters and a maximum sequence length of 8194 tokens.

Best for

• Drug interaction detection in clinical notes
• Chemical entity extraction from research papers
• Adverse event monitoring in patient records

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