Tokenization is the process of splitting text into smaller units such as words, subwords, or characters. These tokens are then converted into embeddings and fed into the Transformer. In the article, tokenization is a prerequisite for self-attention to work, as attention operates on discrete tokens. Effective tokenization balances vocabulary size and sequence length. It affects model performance and generalization. Different models use different tokenization schemes. Tokenization decisions influence the way self-attention interprets input.