Data Preparation and Tokenization

If a model is the core component of an AI system, then data is the substrate from which it learns. The quality, quantity, and meticulous preparation of data are arguably more critical to the success of an LLM project than the specific model architecture. In this chapter, we will walk through the essential data preparation pipeline. We will cover the principles of sourcing and curating high-quality datasets, the necessary steps for cleaning and formatting that data, and the critical process of tokenization, which converts raw text into a numerical format that models can process. The entire process can be broadly classified into three phases: dataset curation and preprocessing, tokenization, and data handling with Hugging Face datasets.