tokenizálással

Tokenization is a fundamental process in natural language processing (NLP) and computer science. It involves breaking down a sequence of text, such as a sentence or a document, into smaller units called tokens. These tokens can be words, sub-word units, punctuation marks, or even individual characters, depending on the specific tokenization strategy employed. The primary goal of tokenization is to prepare raw text data for further analysis or processing by converting it into a structured format that can be easily understood by machines.

Different tokenization methods exist, each with its own advantages and disadvantages. Word tokenization, perhaps the most common, splits text based on whitespace and punctuation. For example, the sentence "Hello, world!" might be tokenized into ["Hello", ",", "world", "!"]. Sub-word tokenization, on the other hand, breaks down words into smaller meaningful units, which can be particularly useful for handling rare words or morphologically rich languages. Byte Pair Encoding (BPE) and WordPiece are popular sub-word tokenization algorithms. Character tokenization, the simplest form, treats each character as a token.

The choice of tokenization method significantly impacts the performance of downstream NLP tasks, such as machine translation, sentiment analysis, and text classification. Proper tokenization ensures that the input data is consistently represented, allowing NLP models to learn meaningful patterns and relationships within the text more effectively. It is often one of the very first steps in any NLP pipeline.