A tokenizer is a fundamental component in natural language processing (NLP) that converts raw text into a sequence of tokens—such as words, subwords, or characters—that a machine learning model can process. This transformation is crucial for tasks like text classification, translation, and sentiment analysis.
Tokenizers operate by segmenting text based on predefined rules or statistical models. For example, they might split sentences into words or break down complex words into subword units to handle rare or out-of-vocabulary terms. This segmentation enables models to manage and understand language more effectively.
In practice, tokenizers are implemented in various programming languages and libraries. For instance, Python’s tokenize module provides a lexical scanner for Python source code, while the Hugging Face Transformers library offers tokenizers tailored for different models and languages.
By converting text into tokens, tokenizers bridge the gap between human language and machine-readable formats, enabling more accurate and efficient language understanding and generation.
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