WordPiecebased

WordPiecebased refers to models or tokenization systems that rely on the WordPiece subword vocabulary to segment text into smaller units. It is used to represent words in neural networks with a fixed-size vocabulary while preserving the ability to represent unseen terms.

WordPiece originated as a subword tokenization method developed by researchers at Google for large-scale language models. It enables robust handling of multilingual data and morphologically rich languages by representing words as compositions of frequent subword units rather than as indivisible words. This approach helps reduce the impact of out-of-vocabulary words on model performance.

The WordPiece algorithm builds its vocabulary by starting with individual characters (and sometimes small symbols) and iteratively merging adjacent symbols that maximize the likelihood of the training data under a probabilistic model. The process continues until a predefined vocabulary size is reached. At tokenization time, text is segmented into the longest possible subword units found in the vocabulary; words not fully covered by the vocabulary are broken down into smaller pieces. In many implementations, subword continuations are denoted by a marker (such as a prefix like "##") to indicate that a token attaches to the previous one, though markers can vary by framework.

WordPiece-based tokenization has been widely adopted in transformer models, most notably in BERT and its variants, and is commonly used in various language and domain-specific models. It helps balance vocabulary size, computational efficiency, and linguistic expressiveness, enabling effective modeling of large and diverse corpora. Implementations are available across major ML libraries and tokenizers, facilitating pretrained models and downstream tasks.