Что такое Token
В контексте ИИ и NLP, токен — это фундаментальная единица текста, такая как слово, часть слова или знак препинания, используемая для обработки и анализа.
Tokenization is the process of breaking down a text into these tokens. This is a crucial step in preparing text data for machine learning models, enabling the models to understand and process the text. The specific rules for tokenization can vary depending on the task and the model being used, with different tokenizers producing different results.
Tokenization methods range from simple whitespace splitting to more sophisticated techniques that consider subword units or character-level representations. The choice of tokenizer significantly impacts the performance of NLP models. For example, a word-based tokenizer might treat 'cat' and 'cats' as separate tokens, while a subword tokenizer might break 'cats' into 'cat' and 's'.
graph LR
Center["Что такое Token"]:::main
Pre_cryptography["cryptography"]:::pre --> Center
click Pre_cryptography "/terms/cryptography"
Rel_machine_learning["machine-learning"]:::related -.-> Center
click Rel_machine_learning "/terms/machine-learning"
Rel_token["token"]:::related -.-> Center
click Rel_token "/terms/token"
Rel_tokenizer["tokenizer"]:::related -.-> Center
click Rel_tokenizer "/terms/tokenizer"
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🧠 Проверка знаний
🧒 Простыми словами
A [token](/ru/terms/token) is like a single Lego brick that makes up a sentence. AI breaks sentences into these bricks (words, parts of words, or punctuation) so it can understand and build new sentences.
🤓 Expert Deep Dive
Tokenization is a critical preprocessing step in NLP pipelines. Subword tokenization algorithms like BPE, WordPiece, and SentencePiece have become dominant because they balance vocabulary size with the ability to represent rare words and morphology. BPE iteratively merges frequent pairs of characters or bytes, while WordPiece uses a likelihood-based approach. SentencePiece treats text as a sequence of Unicode characters and learns subword units directly, making it language-agnostic. The choice of tokenizer impacts downstream tasks: a word-level tokenizer struggles with OOV words, while character-level tokenizers result in very long sequences. Subword tokenizers offer a compromise, allowing models to handle morphology (e.g., 'running' -> 'run', '##ing') and unknown words by composing them from known subwords. The mapping from tokens to numerical IDs and then to dense vector embeddings (e.g., Word2Vec, GloVe, or contextual embeddings from Transformers) is where semantic meaning is encoded for the model.