Token Classification
GLiNER
PyTorch
English
NER
GLiNER
information extraction
encoder
entity recognition
biomed
Instructions to use Ihor/gliner-biomed-small-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use Ihor/gliner-biomed-small-v1.0 with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("Ihor/gliner-biomed-small-v1.0") - Notebooks
- Google Colab
- Kaggle
Update links and bibtex
Browse files
README.md
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# GLiNER-BioMed
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The model was presented in the paper [GLiNER-BioMed:
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The code is available at [https://github.com/ds4dh/GLiNER-biomed](https://github.com/ds4dh/GLiNER-biomed).
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**GLiNER** is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
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**GLiNER-biomed**, developed in collaboration with [DS4DH](https://
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### How to Use
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Install the official GLiNER library with pip:
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If you use GLiNER-biomed models in your work, please cite:
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```bibtex
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}
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```
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# GLiNER-BioMed
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The model was presented in the paper [GLiNER-BioMed: a suite of efficient models for open biomedical named entity recognition](https://doi.org/10.1093/bioinformatics/btag322).
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The code is available at [https://github.com/ds4dh/GLiNER-biomed](https://github.com/ds4dh/GLiNER-biomed).
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**GLiNER** is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoders (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios.
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**GLiNER-biomed**, developed in collaboration with [DS4DH](https://ds4dh.unige.ch/) from the University of Geneva, introduces a specialized suite of efficient open biomedical 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.
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### How to Use
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Install the official GLiNER library with pip:
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If you use GLiNER-biomed models in your work, please cite:
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```bibtex
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@article{yazdani2026gliner,
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author = {Yazdani, Anthony and Stepanov, Ihor and Teodoro, Douglas},
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title = {{GLiNER-BioMed}: a suite of efficient models for open biomedical named entity recognition},
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journal = {Bioinformatics},
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volume = {42},
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number = {6},
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pages = {btag322},
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year = {2026},
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month = jun,
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issn = {1367-4811},
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doi = {10.1093/bioinformatics/btag322},
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url = {https://doi.org/10.1093/bioinformatics/btag322}
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}
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```
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