Token classification is a fundamental task in natural language processing (NLP) that involves categorizing each token (i.e., word or subword) in a text sequence. This process, also known as sequence tagging or sequence labeling, is essential for a wide range of NLP applications.

Once a model is trained, it can be used to predict the labels for new, unseen sequences of tokens. These predictions can be used for a variety of NLP applications, such as information extraction, machine translation, and text classification.

In summary, token classification AI models are a critical component of NLP, enabling computers to better understand natural language text and extract valuable insights.