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anthonyyazdaniml/
gliner-biomed-bi-large-v1.0-disease-chemical-gene-variant-species-cellline-ner

Name: gliner-biomed-bi-large-v1.0-disease-chemical-gene-variant-species-cellline-ner
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GLiNER-BioMed for diseases/phenotypes, chemicals, genes/gene products, sequence variants, organisms, and cell lines NER

This model is a fine-tuned version of GLiNER-BioMed-bi-large. This model is designed to extract details about diseases/phenotypes, chemicals, genes/gene products, sequence variants, organisms, and cell lines, based on the BioRED dataset.

One can find more details about the base GLiNER-BioMed models in the paper GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition. The GLiNER-BioMed code is available at https://github.com/ds4dh/GLiNER-biomed.

Model IDs

Bi-encoder version (this model): anthonyyazdaniml/gliner-biomed-bi-large-v1.0-disease-chemical-gene-variant-species-cellline-ner
Uni-encoder version (alternative): anthonyyazdaniml/gliner-biomed-large-v1.0-disease-chemical-gene-variant-species-cellline-ner

Intended use & capabilities

Recognized entity types:

Disease or phenotype
Chemical entity
Gene or gene product
Sequence variant
Organism
Cell line

How to use

First, ensure the gliner library is installed and up-to-date:

pip install gliner -U

Then, you can load and use the model in your Python scripts:

python

from gliner import GLiNER

model = GLiNER.from_pretrained("anthonyyazdaniml/gliner-biomed-bi-large-v1.0-disease-chemical-gene-variant-species-cellline-ner")

text = """
Mutations in the EGFR gene, such as L858R, are commonly associated with non-small cell lung cancer.
Gefitinib is an approved treatment for this condition.
The A549 cell line, derived from Homo sapiens, is frequently used to study its molecular pathways.
"""

labels = [
  'Disease or phenotype', 'Chemical entity', 'Gene or gene product',
  'Sequence variant', 'Organism', 'Cell line'
]

entities = model.predict_entities(text, labels, threshold=0.5)

for entity in entities:
    print(entity["text"], "=>", entity["label"])

Expected output:

EGFR => Gene or gene product
L858R => Sequence variant
non-small cell lung cancer => Disease or phenotype
Gefitinib => Chemical entity
A549 => Cell line
Homo sapiens => Organism

Citation

bibtex

@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},
}