Repository under construction
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.
This repository is currently under development. Complete resources, including data, training pipelines, and further documentation, will soon be available here.
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 |
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},
}
Updates, full documentation, and complete pipelines will soon be available.
🌟 Stay tuned!