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| license: mit |
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| ## BERT-based Text Classification Model |
| This model is a fine-tuned version of the bert-base-uncased model, specifically adapted for text classification across a diverse set of categories. The model has been trained on a dataset collected from multiple sources, including the News Category Dataset on Kaggle and various other websites. |
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| The model classifies text into one of the following 12 categories: |
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| * Food |
| * Videogames & Shows |
| * Kids and fun |
| * Homestyle |
| * Travel |
| * Health |
| * Charity |
| * Electronics & Technology |
| * Sports |
| * Cultural & Music |
| * Education |
| * Convenience |
| The model has demonstrated robust performance with an accuracy of 0.721459, F1 score of 0.659451, precision of 0.707620, and recall of 0.635155. |
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| ## Model Architecture |
| The model leverages the BertForSequenceClassification architecture, It has been fine-tuned on the aforementioned dataset, with the following key configuration parameters: |
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| Hidden size: 768 |
| Number of attention heads: 12 |
| Number of hidden layers: 12 |
| Max position embeddings: 512 |
| Type vocab size: 2 |
| Vocab size: 30522 |
| The model uses the GELU activation function in its hidden layers and applies dropout with a probability of 0.1 to the attention probabilities to prevent overfitting. |
