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README.md
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- **Primary Use Case**: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the Polish language.
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- **Applications**: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing.
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- **Supported Entity Types**:
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- `TEST`: Diagnostic procedures and laboratory tests.
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- `TREATMENT`: Medications, therapies, and other medical interventions.
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- **Loss Function**: Focal Loss to handle class imbalance
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- **Frameworks**: PyTorch, Hugging Face Transformers, SimpleTransformers
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## How to Use
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You can easily use this model with the Hugging Face `transformers` library. Here's an example of how to load and use the model for inference:
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- **Primary Use Case**: This model is designed to extract medical entities such as symptoms, diagnostic tests, and treatments from clinical text in the Polish language.
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- **Applications**: Suitable for healthcare professionals, clinical data analysis, and research into medical text processing.
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- **Supported Entity Types**:
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- `
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PROBLEM`: Diseases, symptoms, and medical conditions.
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- `TEST`: Diagnostic procedures and laboratory tests.
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- `TREATMENT`: Medications, therapies, and other medical interventions.
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- **Loss Function**: Focal Loss to handle class imbalance
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- **Frameworks**: PyTorch, Hugging Face Transformers, SimpleTransformers
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## Evaluation metrics
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- eval_loss = 0.3968946770636102
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- f1_score = 0.7556232119891866
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- precision = 0.7552069671056083
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- recall = 0.7560399159663865
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## How to Use
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You can easily use this model with the Hugging Face `transformers` library. Here's an example of how to load and use the model for inference:
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