Neural networks have become an integral part of everyday life, finding applications in various domestic and industrial tasks. Generative models based on the Transformer architecture play a particularly significant role in natural language processing. These models have achieved, and in some cases surpassed, human-level performance in several tasks. However, despite their high performance, generative models can sometimes produce unexpected results. Understanding the principles behind the decisions of such models is an important and relevant challenge. In this article, we investigate how effectively the T5 model explains its answers in classification tasks. We also compare its interpretative capabilities with those of the BERT model using well-known interpretation methods such as SHAP, LIME, and the attention mechanism.