The "Duplicate Question Pairs NLP Project" focuses on using Natural Language Processing (NLP) techniques to determine whether two given questions are semantically similar or duplicates. By employing state-of-the-art machine learning models, such as Siamese networks or transformer-based architectures like BERT, the project aims to capture the semantic meaning of questions and compare them effectively.
The dataset used for training the model consists of pairs of questions labeled as duplicates or non-duplicates. Feature engineering plays a crucial role in extracting meaningful representations from the text, including word embeddings, sentence embeddings, or contextual embeddings.
During the training phase, the model learns to distinguish between duplicate and non-duplicate question pairs, optimizing its parameters based on various loss functions. Evaluation metrics like accuracy, precision, recall, and F1 score are used to assess the model's performance.
Here I want to make a simple recommender system to gauge the similarity between shows, users and to help me predict whether a user will enjoy a particular movie.