👉 Token engineering is a crucial process in natural language processing (NLP) that involves transforming raw text data into structured, machine-readable formats called tokens. These tokens can be words, subwords, characters, or even punctuation marks, depending on the specific requirements and the chosen model architecture. The primary goal of token engineering is to optimize the input representation for machine learning models, making it easier for them to process and understand the nuances of human language. By carefully selecting and manipulating tokens, engineers can enhance model performance, reduce computational complexity, and improve the efficiency of tasks such as text classification, translation, and sentiment analysis. This process often includes techniques like tokenization, embedding, and subword tokenization to ensure that the model can effectively capture linguistic patterns and context.
