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anthonyyazdaniml/
gliner-biomed-bi-large-v1.0-medication-regimen-ner

Name: gliner-biomed-bi-large-v1.0-medication-regimen-ner
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GLiNER-BioMed for medication regimen NER

This model is a fine-tuned version of GLiNER-BioMed-bi-large. This model is designed to extract details about drug administration, dosage, frequency, adverse drug events, and related clinical information, based on the N2C2 2018 Track 2 dataset.

One can find more details about the base GLiNER-BioMed models in the paper GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition. The GLiNER-BioMed code is available at https://github.com/ds4dh/GLiNER-biomed.

Model IDs

Bi-encoder version (this model): anthonyyazdaniml/gliner-biomed-bi-large-v1.0-medication-regimen-ner
Uni-encoder version (alternative): anthonyyazdaniml/gliner-biomed-large-v1.0-medication-regimen-ner

Intended use & capabilities

Recognized entity types:

Drug
Drug dosage
Drug strength
Drug form
Drug administration route
Frequency of drug administration
Treatment duration
Reason for drug prescription
Adverse drug event

How to use

First, ensure the gliner library is installed and up-to-date:

pip install gliner -U

Then, you can load and use the model in your Python scripts:

python

from gliner import GLiNER

model = GLiNER.from_pretrained("anthonyyazdaniml/gliner-biomed-bi-large-v1.0-medication-regimen-ner")

text = """
The patient was prescribed Lisinopril 10 mg tablets to treat her hypertension. 
She was instructed to take one tablet orally once daily for 14 days. 
Shortly after starting the medication, she reported experiencing dizziness.
"""

labels = [
  'Drug', 'Drug administration route',
  'Reason for drug prescription', 'Adverse drug event',
  'Drug dosage', 'Drug strength', 'Frequency of drug administration',
  'Treatment duration', 'Drug form'
]

entities = model.predict_entities(text, labels, threshold=0.5)

for entity in entities:
    print(entity["text"], "=>", entity["label"])

Expected output:

Lisinopril => Drug
10 mg => Drug strength
tablets => Drug form
hypertension => Reason for drug prescription
one => Drug dosage
tablet => Drug form
orally => Drug administration route
once daily => Frequency of drug administration
for 14 days => Treatment duration
dizziness => Adverse drug event

Citation

bibtex

@misc{yazdani2025glinerbiomedsuiteefficientmodels,
      title={GLiNER-biomed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition},
      author={Anthony Yazdani and Ihor Stepanov and Douglas Teodoro},
      year={2025},
      eprint={2504.00676},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.00676},
}