Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
gene-recognition
protein-recognition
genomics
molecular-biology
gene
protein
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Genome-Tiny-60M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Genome-Tiny-60M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Genome-Tiny-60M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-Genome-Tiny-60M
Browse files
README.md
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### Installation
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```bash
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pip install gliner
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```
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### Usage
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model_name = "OpenMed/OpenMed-ZeroShot-NER-Genome-Tiny-60M"
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from gliner import GLiNER
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model = GLiNER.from_pretrained(
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# Example usage with default entity types
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text = "The EGFR gene mutation was identified in lung cancer patients."
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### Installation
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```bash
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pip install -q "gliner[tokenizers]"
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```
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### Usage
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model_name = "OpenMed/OpenMed-ZeroShot-NER-Genome-Tiny-60M"
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from gliner import GLiNER
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model = GLiNER.from_pretrained(model_name)
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# Example usage with default entity types
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text = "The EGFR gene mutation was identified in lung cancer patients."
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