subwordtokener

A subword tokenizer is a technique used in natural language processing (NLP) to break down text into smaller units called subwords. Unlike traditional word tokenizers that split text at spaces or punctuation, subword tokenizers can divide words into meaningful parts. This is particularly useful for handling rare words, out-of-vocabulary (OOV) words, and morphologically rich languages.

The main idea behind subword tokenization is to represent words as sequences of subword units. Common subword tokenization algorithms include Byte Pair Encoding (BPE), WordPiece, and SentencePiece. These algorithms learn a vocabulary of subwords from a large corpus of text. For instance, a word like "tokenization" might be broken down into "token", "iz", and "ation".

Subword tokenization offers several advantages. It can effectively handle unseen words by composing them from known subwords, thus reducing the problem of OOV words. This leads to more robust models that perform better on diverse datasets. Furthermore, it can capture morphological variations within words, enabling models to understand the relationship between related words like "run," "running," and "ran" by sharing common subword units. This approach also helps in reducing the overall vocabulary size while maintaining coverage. Subword tokenization is a fundamental preprocessing step in many modern NLP models, including transformers like BERT and GPT.