<ul><li>This notebook presents a comprehensive approach to building a Transformer-based model for multiple-choice question answering.</li><li>The methodology includes four key stages: Data Preparation, Model Architecture, Training, and Inference.</li><li>An oversampling technique is integrated to mitigate biases caused by imbalanced answer distributions.</li><li>The model demonstrates successful generalization and accurate answers to unseen questions.</li></ul>

Transformer-Based Multiple Choice Question Answering: Implementation, Output Analysis, and Bias…

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