Personalized and feedback-based learning frequently produces better learning outcomes in the classroom. Digital adaptive learning systems, capable of recreating personalized tutoring in classrooms, rely upon the categorization of problems and course materials to accurately recommend personalized content to users. While adaptive learning systems have already been widely adopted in math, the problems are often manually categorized and created. This paper discusses the multi-label classification application for categorizing competitive math problems into widely recognized categories using problem text, answer choices, and user-generated solutions with DistilBERT, a Transformer-based language representation model. Additionally, by adding LaTeX control sequences to the tokenizer vocabulary, the trained model was able to recognize symbolic math equations and thus more accurately predicted problem categorization compared to using a tokenizer without additional vocabulary. The trained model with additional vocabulary mostly accurately predicted problem labels across all five general categories, thus demonstrating DistilBERT’s ability for problem categorization and the significance of representing symbolic equations with vocabulary.