This project focuses on developing an automated system for generating responses to customer queries using advanced natural language processing (NLP) techniques. By leveraging machine learning models, this system aims to streamline customer support interactions by providing timely and relevant responses.
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Dataset Exploration and Preprocessing: Analyze and preprocess customer support data to extract meaningful information for model training.
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Model Training: Implement a sequence-to-sequence (seq2seq) or transformer-based model to learn the mapping between customer queries and appropriate responses.
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Evaluation: Assess the model's performance using metrics such as BLEU score to evaluate the quality of generated responses.
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Deployment: Provide a demo interface where users can input queries and receive automated responses, showcasing the practical application of the model.