WordPiece
WordPiece is a subword tokenization algorithm used in natural language processing (NLP) to break down text into smaller units called subwords or tokens. It works by iteratively merging the most frequent character pairs in a corpus to create a vocabulary of subword units, balancing between character-level and word-level representations. This approach helps handle out-of-vocabulary words and reduces vocabulary size compared to word-based tokenization.
Developers should learn WordPiece when building NLP models, especially for transformer-based architectures like BERT, as it improves model efficiency and handles rare or unseen words effectively. It is particularly useful in multilingual or domain-specific applications where vocabulary coverage is critical, such as in machine translation, text classification, or question-answering systems.