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license: mit |
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## BERT-based Text Classification Model |
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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 |
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* Videogames & Shows |
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* Kids and fun |
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* Homestyle |
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* Travel |
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* Health |
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* Charity |
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* Electronics & Technology |
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* Sports |
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* Cultural & Music |
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* Education |
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* Convenience |
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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 |
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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 |
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Number of attention heads: 12 |
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Number of hidden layers: 12 |
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Max position embeddings: 512 |
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Type vocab size: 2 |
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Vocab size: 30522 |
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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. |
