OpenMed-NER-SpeciesDetect-ElectraMed-335M

What it does

This model detects species and organism names in English clinical text and biomedical literature. It reports an F1 score of 0.88 on the Linnaeus benchmark, with 334 million parameters and a maximum sequence length of 512 tokens.

Best for

• Species mentions in research papers
• Organism names in clinical notes
• Entity extraction for biomedical knowledge graphs

What it detects (1 labels)

• Species

Quality

Species Entity Recognition - Species and organism names

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-SpeciesDetect-ElectraMed-335M", 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-SpeciesDetect-ElectraMed-335M-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-SpeciesDetect-ElectraMed-335M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)