Gliner Biomed Base V1.0

🚀 GLiNER-BioMed

GLiNER-BioMed is a specialized suite of efficient open biomedical Named Entity Recognition (NER) models. It can identify any entity type using bidirectional transformer encoders, offering a practical alternative to traditional NER models and Large Language Models (LLMs). This model was developed in collaboration with DS4DH from the University of Geneva, leveraging synthetic annotations from large generative biomedical language models to achieve excellent performance in biomedical entity recognition tasks.

🚀 Quick Start

Installation

Install the official GLiNER library with pip:

pip install gliner -U

Usage

After 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-base-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

✨ Features

• Flexible Entity Recognition: Capable of identifying any entity type, providing a practical alternative to traditional NER models limited to predefined entities.
• Efficient Performance: 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.

📦 Installation

Install the official GLiNER library with pip:

pip install gliner -U

💻 Usage Examples

Basic Usage

from gliner import GLiNER

model = GLiNER.from_pretrained("Ihor/gliner-biomed-base-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"])

📚 Documentation

Benchmarking

We examined our models on 8 complex real - world datasets and compared them with other GLiNER models.

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📄 License

This project is licensed under the Apache - 2.0 license.

Citation

This work

If you use GLiNER - biomed models in your work, please cite:

@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},
}

Previous work

@misc{zaratiana2023gliner,
      title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer},
      author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois},
      year={2023},
      eprint={2311.08526},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{stepanov2024gliner,
      title={GLiNER multi - task: Generalist Lightweight Model for Various Information Extraction Tasks},
      author={Ihor Stepanov and Mykhailo Shtopko},
      year={2024},
      eprint={2406.12925},
      archivePrefix={arXiv},
      primaryClass={id='cs.LG' full_name='Machine Learning' is_active=True alt_name=None in_archive='cs' is_general=False description='Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.'}
}