This project aims at classifiying newspaper texts into four distinct categories: World News, Sports News, Business News, and Science/Technology News. We employ the AG News dataset for our study, consisting of 30,000 training and 1,900 test samples per class. Our model architecture combines Embedding Bag and a Linear Layer to achieve accurate text classification. Furthermore, we employ LIME (Local Interpretable Model-agnostic Explanations) for text data, which enhances the explainability of our classification results.
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rajib216/Text-Classification-and-Explanation-using-LIME
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