Tokenization

Tokenization is the process of converting raw text into discrete units called tokens that neural networks can process numerically, serving as the fundamental preprocessing step for all language model operations. This technique breaks down text into manageable pieces such as words, subwords, characters, or byte-pairs, each assigned unique numerical identifiers within the model’s vocabulary. Tokenization algorithms like Byte-Pair Encoding (BPE), SentencePiece, and WordPiece optimize the balance between vocabulary size and representation efficiency, enabling models to handle diverse languages and out-of-vocabulary terms effectively. The process directly impacts model performance, computational efficiency, and memory usage, as token count determines input length and processing requirements. Advanced tokenization strategies incorporate context-aware splitting, multilingual support, and domain-specific vocabularies to enhance representation quality while maintaining computational tractability across diverse text processing applications.