GLiNER-BioMed introduces a specialized suite of efficient open biomedical Named Entity Recognition (NER) models based on the GLiNER framework. GLiNER-BioMed leverages synthetic annotations distilled from large generative biomedical language models to achieve state-of-the-art zero-shot and few-shot performance in biomedical entity recognition tasks.
We publicly released pre-trained GLiNER-BioMed models at multiple scales and variants (uni-encoder and bi-encoder). You can directly access and use these models from Hugging Face Hub:
| Model type | Uni-encoder model | Bi-encoder model |
|---|---|---|
| Small | gliner-biomed-small-v1.0 | gliner-biomed-bi-small-v1.0 |
| Base | gliner-biomed-base-v1.0 | gliner-biomed-bi-base-v1.0 |
| Large | gliner-biomed-large-v1.0 | gliner-biomed-bi-large-v1.0 |
To support reproducibility and further research, we also release the corpora used in pre- and post-training stages:
We also release the synthetic annotation model used to generate large-scale biomedical entity annotations:
Install the official GLiNER library with pip:
pip install glinerAfter installing the GLiNER library, you can easily load a GLiNER-BioMed model and perform named entity recognition:
from gliner import GLiNER
model = GLiNER.from_pretrained("Ihor/gliner-biomed-large-v1.0")
text = """
The patient, a 45-year-old male, was diagnosed with type 2 diabetes mellitus and hypertension.
He was prescribed Metformin 500mg twice daily and Lisinopril 10mg once daily.
A recent lab test showed elevated HbA1c levels at 8.2%.
"""
labels = ["Disease", "Drug", "Drug dosage", "Drug frequency", "Lab test", "Lab test value", "Demographic information"]
entities = model.predict_entities(text, labels, threshold=0.5)
for entity in entities:
print(entity["text"], "=>", entity["label"])Expected output:
45-year-old male => Demographic information
type 2 diabetes mellitus => Disease
hypertension => Disease
Metformin => Drug
500mg => Drug dosage
twice daily => Drug frequency
Lisinopril => Drug
10mg => Drug dosage
once daily => Drug frequency
HbA1c levels => Lab test
8.2% => Lab test value
For more detailed documentation and usage examples, visit the official GLiNER repository.
If you use GLiNER-BioMed models or resources in your research, please cite our work:
@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},
}