Language models for text classification: From bag-of-words to Jev

The article discusses the evolution of language models for text classification, from the traditional bag-of-words approach to the more advanced Joint Embeddings and Vocabulary (JEV) model. It highlights the limitations of the bag-of-words model, including its inability to capture word order and context, and how it can be improved upon by using more sophisticated models like JEV. The article also touches on the concept of embeddings, which is a crucial component of JEV, and explains how it can be used to represent words as vectors in a high-dimensional space. The author also mentions the importance of using pre-trained language models like BERT and RoBERTa to fine-tune for specific tasks.