In this guide we use T5, a pre-trained and very large (e.g., roughly twice the size of BERT-base) encoder-decoder Transformer model for a classification task. T5, a model devised by Google, is an important advancement in the field of Transformers because it achieves near human-level performance on a variety of benchmarks like GLUE and SQuAD. Another important advancement is that it treats NLP as a text-to-text problem, whereby our inputs are text and our outputs are also text. In this universal framework, T5 can therefore handle any NLP task (in English). T5 was pre-trained on the C4 (Colossal Clean Crawled Corpus) corpus which amounts to roughly 750GB of clean English text. For comparative purpsoes, BERT was trained on roughly 13GB of text and XLNet was trained on roughly 126GB of text. For these reasons, T5 is the state of the art and its encoder-decoder architecture is likely the future of NLP models.