What is token representation?

Token representation refers to the process of converting discrete elements, such as Words or subWords, into numerical vectors that can be processed by machine learning models, particularly in natural language processing (NLP). Each token is assigned a unique identifier or vector, which captures its meaning and context within a larger dataset. This representation allows algorithms to analyze and understand language more effectively by facilitating operations like similarity measurement and clustering. Common methods for token representation include one-hot encoding, Word embeddings (like Word2Vec and GloVe), and contextual embeddings (like BERT).