Token Classification
GLiNER
PyTorch
English
entity recognition
named-entity-recognition
zero-shot
zero-shot-ner
zero shot
biomedical-nlp
anatomical-entity-recognition
medical-terminology
anatomy
healthcare
body_part
organ
Instructions to use OpenMed/OpenMed-ZeroShot-NER-Anatomy-Base-220M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- GLiNER
How to use OpenMed/OpenMed-ZeroShot-NER-Anatomy-Base-220M with GLiNER:
from gliner import GLiNER model = GLiNER.from_pretrained("OpenMed/OpenMed-ZeroShot-NER-Anatomy-Base-220M") - Notebooks
- Google Colab
- Kaggle
feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-Anatomy-Base-220M
Browse files
README.md
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@@ -97,7 +97,7 @@ The Anatomy corpus is a specialized biomedical NER dataset designed for recogniz
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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-Anatomy-Base-220M"
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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 patient complained of pain in the left ventricle region."
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- **Input**: Biomedical text
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- **Output**: Named entity predictions
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For more information about GLiNER, visit the [GLiNER repository](https://github.com/urchade/gliner).
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## 📜 License
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Licensed under the Apache License 2.0. See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for details.
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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-Anatomy-Base-220M"
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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 patient complained of pain in the left ventricle region."
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- **Input**: Biomedical text
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- **Output**: Named entity predictions
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## 📜 License
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Licensed under the Apache License 2.0. See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for details.
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