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README.md

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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 rich 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.