Tokeniserijad

Tokeniserijad, often referred to as tokenizers, are fundamental components in natural language processing (NLP) and computational linguistics. Their primary function is to break down a sequence of text, such as a sentence or a document, into smaller units called tokens. These tokens can be words, punctuation marks, or even sub-word units, depending on the specific tokenization strategy employed.

The process of tokenization is a crucial preprocessing step before most NLP tasks, including machine translation, sentiment analysis, and text classification. By segmenting text into manageable units, tokenizers enable machines to better understand and process human language. Different languages may require different tokenization approaches due to variations in their grammatical structures and writing systems. For example, languages without explicit word separators like spaces might need more sophisticated tokenization methods.

Common tokenization techniques include whitespace tokenization, which splits text based on spaces, and punctuation-based tokenization, which separates words from punctuation. More advanced methods, such as sub-word tokenization (e.g., Byte Pair Encoding or WordPiece), are used to handle rare words and improve the model's ability to generalize by breaking down unknown words into known sub-word units. The choice of tokenizer can significantly impact the performance of downstream NLP models.