WordPiecepõhiseid
WordPiece-based models are a type of subword tokenization technique used in natural language processing (NLP) to handle the vast vocabulary of human languages. This method was introduced by Google and is widely used in models like BERT (Bidirectional Encoder Representations from Transformers). The core idea behind WordPiece is to break down words into smaller subword units, known as WordPieces, which can be shared across different words. This approach helps in managing out-of-vocabulary words and reduces the size of the vocabulary needed for training models.
The WordPiece algorithm works by iteratively splitting words into smaller pieces based on their frequency in
One of the key advantages of WordPiece-based models is their ability to handle rare or out-of-vocabulary words
However, WordPiece-based models also have some limitations. The subword units may not always correspond to meaningful
In summary, WordPiece-based models are a powerful and widely used technique in NLP for handling the complexity