Data Preparation for NLP Seq2Seq Models

Building effective natural language processing (NLP) models requires more than just choosing the right architecture; it demands meticulously prepared training data. This course empowers you to transform raw text into high-quality datasets, ready to train powerful sequence-to-sequence (Seq2Seq) models for various NLP tasks. You will learn the critical steps to ensure your data is clean, consistent, and optimally structured for model consumption. What you'll learn: * Understand the core components and data requirements of sequence-to-sequence models. * Learn fundamental text preprocessing techniques, including tokenization, normalization, and handling special characters. * Apply methods for creating input, target, and final sequence pairs from raw text data. * Practice building robust data pipelines for efficient data loading and batching. * Explore strategies for managing vocabulary, handling out-of-vocabulary tokens, and implementing subword tokenization. * Identify and mitigate common data quality issues and potential biases in NLP datasets. The course begins with foundational concepts of NLP data, progresses through various preprocessing steps, and culminates in practical exercises for structuring datasets suitable for training. This course is designed for beginners with an interest in natural language processing and machine learning, with no prior experience in data preparation for Seq2Seq models required. Start your journey to building high-performing NLP models by mastering the art of data preparation.