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feat: Upload fine-tuned medical NER model OpenMed-ZeroShot-NER-Genomic-Tiny-60M

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  1. README.md +2 -4
README.md CHANGED
@@ -96,7 +96,7 @@ The Gellus corpus is a biomedical NER dataset specifically designed for gene rec
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  ### Installation
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  ```bash
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- pip install gliner==0.2.21
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  ```
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  ### Usage
@@ -109,7 +109,7 @@ from transformers import pipeline
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  model_name = "OpenMed/OpenMed-ZeroShot-NER-Genomic-Tiny-60M"
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  from gliner import GLiNER
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- model = GLiNER.from_pretrained("OpenMed-ZeroShot-NER-Genomic-Tiny-60M")
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  # Example usage with default entity types
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  text = "The BRCA2 gene is associated with hereditary breast cancer."
@@ -162,8 +162,6 @@ This model is particularly useful for:
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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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-
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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-Genomic-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 BRCA2 gene is associated with hereditary breast cancer."
 
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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.