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@@ -33,16 +33,15 @@ This model is intended for educational and demonstrational purposes only. It tak
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  - **Data Bias:** The model's knowledge is limited to the `gretelai/symptom_to_diagnosis` dataset. It cannot predict any condition outside of its 22-class training data and may perform poorly on symptom descriptions that are stylistically different from the training set.
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  - **Correlation, Not Causation:** The model learns statistical correlations between words and labels. It has no true understanding of biology or medicine.
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- ## How to Use
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-
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- To use this model, you must load the feature extractor (`Bio_ClinicalBERT`), the LightGBM classifier, and the label encoder.
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-
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  ## Training Data
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  This model was trained on the gretelai/symptom_to_diagnosis dataset, which contains ~1000 symptom descriptions across 22 balanced classes.
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  ## Evaluation
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  The model achieves a Macro F1-score of 0.834 and an Accuracy of 0.835 on the test set.
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  ```python
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  import torch
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  import joblib
 
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  - **Data Bias:** The model's knowledge is limited to the `gretelai/symptom_to_diagnosis` dataset. It cannot predict any condition outside of its 22-class training data and may perform poorly on symptom descriptions that are stylistically different from the training set.
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  - **Correlation, Not Causation:** The model learns statistical correlations between words and labels. It has no true understanding of biology or medicine.
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  ## Training Data
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  This model was trained on the gretelai/symptom_to_diagnosis dataset, which contains ~1000 symptom descriptions across 22 balanced classes.
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  ## Evaluation
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  The model achieves a Macro F1-score of 0.834 and an Accuracy of 0.835 on the test set.
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+ ## How to Use
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+ To use this model, you must load the feature extractor (`Bio_ClinicalBERT`), the LightGBM classifier, and the label encoder.
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+
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  ```python
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  import torch
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  import joblib