Token Classification
GLiNER
PyTorch
English
NER
GLiNER
information-extraction
entity-recognition
biomed
biological-entities
disease
chemical
gene
variant
species
cell-line
biored
Instructions to use anthonyyazdaniml/gliner-biomed-large-v1.0-disease-chemical-gene-variant-species-cellline-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use anthonyyazdaniml/gliner-biomed-large-v1.0-disease-chemical-gene-variant-species-cellline-ner with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("anthonyyazdaniml/gliner-biomed-large-v1.0-disease-chemical-gene-variant-species-cellline-ner") - Notebooks
- Google Colab
- Kaggle
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# GLiNER-BioMed for diseases/phenotypes, chemicals, genes/gene products, sequence variants, organisms, and cell lines NER
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This model is a fine-tuned version of
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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](https://huggingface.co/papers/2504.00676). The GLiNER-BioMed code is available at [https://github.com/ds4dh/GLiNER-biomed](https://github.com/ds4dh/GLiNER-biomed).
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# GLiNER-BioMed for diseases/phenotypes, chemicals, genes/gene products, sequence variants, organisms, and cell lines NER
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This model is a fine-tuned version of [GLiNER-BioMed-large](https://huggingface.co/Ihor/gliner-biomed-large-v1.0). 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.
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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](https://huggingface.co/papers/2504.00676). The GLiNER-BioMed code is available at [https://github.com/ds4dh/GLiNER-biomed](https://github.com/ds4dh/GLiNER-biomed).
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