Gliner Biomed Bi Base V1.0

🚀 GLiNER-BioMed

GLiNER-BioMed is a specialized suite of efficient open biomedical NER models. It addresses the limitations of traditional NER models and the high - cost issue of Large Language Models in resource - constrained scenarios. It achieves state - of - the - art zero - shot and few - shot performance in biomedical entity recognition tasks.

🚀 Quick Start

📦 Installation

Install the official GLiNER library with pip:

pip install gliner -U

💻 Usage Examples

Basic 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-bi-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

Advanced Usage

If you have a large amount of entities and want to pre - embed them, please, refer to the following code snippet:

labels = ["your entities"]
texts = ["your texts"]

entity_embeddings = model.encode_labels(labels, batch_size = 8)

outputs = model.batch_predict_with_embeds(texts, entity_embeddings, labels)

✨ Features

• Flexible Entity Recognition: GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using bidirectional transformer encoders (BERT - like), providing an alternative to traditional NER models limited to predefined entities.
• Specialized in Biomed: GLiNER - biomed, developed in collaboration with [DS4DH](https://www.unige.ch/medecine/radio/en/research - groups/1035teodoro) from the University of Geneva, introduces a specialized suite of efficient open biomedical NER models.
• State - of - the - art Performance: It 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.

📚 Documentation

The model was presented in the paper GLiNER - biomed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition.

The code is available at [https://github.com/ds4dh/GLiNER - biomed](https://github.com/ds4dh/GLiNER - biomed).

Benchmarking

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

🔗 Community

Join Our Discord

Connect with our community on Discord for news, support, and discussion about our models. Join Discord.

📄 License

This project is 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.'}
}