In natural language processing, embeddings are crucial for understanding textual data. In this talk, we’ll explore sentence embeddings and their application in dialog systems. We'll focus on a use case involving the classification of dialogs. We'll demonstrate the necessity of sentence transformers for this problem, specifically utilizing one of the top-performing small-sized sentence transformers. We will show how to fine-tune this model with both labeled and unlabeled dialog data, using the SentenceTransformers Python framework. This talk is practical, packed with easy-to-follow examples, and aimed at building intuition around this topic. While some basic knowledge of Transformers would be beneficial, it is not required. Newcomers are also welcome.