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Tokenization is the process of breaking text or data streams into meaningful units (tokens) such as words, subwords, or symbols. It enables downstream analysis, indexing, and model input preparation across search, NLP and data pipelines. Choice of tokenization affects vocabulary size, performance and handling of languages.
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Tokenization splits text into processing units such as words, subwords or symbols.
Tokenization grew out of linguistic and lexical text processing, which needed to split character sequences into manageable units for analysis and search. Unicode standards formalized text-boundary rules; modern language models added word and subword methods for their input representation.
A tokenizer reads a character sequence and produces an ordered sequence of tokens, usually with an ID mapping. Rules decide how whitespace, punctuation, Unicode graphemes or subwords are handled. The choice affects vocabulary, context length, search quality, runtime and support for different languages.
Text becomes units that a search, analytics or language-processing system can handle.
Segmentation rules determine which character sequences belong together and how they are represented.
Tokenizers are chosen for language, data format, model vocabulary and search or runtime requirements.
Tokenization directly shapes how search systems and language models find, store and process text, making it an architectural decision.
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