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The Text-To-Text Transfer Transformer (T5) is a revolutionary development in the field of natural language processing (NLP). In recent years, transfer learning has brought about a significant transformation in NLP, with models being pre-trained on abundant unlabeled text data and then fine-tuned on smaller labeled datasets for improved performance. T5 builds on this approach and presents a large-scale empirical survey to determine the most effective transfer learning techniques. It also introduces the Colossal Clean Crawled Corpus (C4), a new open-source pre-training dataset. T5's text-to-text framework is a key feature that allows it to handle a wide variety of NLP tasks, including machine translation, document summarization, question answering, and classification tasks. By framing all NLP tasks into a unified text-to-text format, T5 can use the same model, loss function, and hyperparameters, providing a consistent and efficient approach. The C4 dataset is an important component of T5's success. It is a cleaned version of Common Crawl that is two orders of magnitude larger than Wikipedia, ensuring high quality, diversity, and a large volume of data for pre-training. This helps the model to avoid overfitting and achieve better results on downstream tasks. Through a systematic study of transfer learning methodology, T5 examines various aspects such as model architectures, pre-training objectives, unlabeled datasets, training strategies, and scale. The findings show that encoder-decoder models generally outperform "decoder-only" language models, and fill-in-the-blank-style denoising objectives work best. T5 achieves state-of-the-art results on several NLP benchmarks, including GLUE, SuperGLUE, SQuAD, and CNN/Daily Mail. Notably, it achieves a near-human score on the SuperGLUE natural language understanding benchmark. T5 is also highly flexible and can be easily modified for application to many other tasks. For example, it has been successfully applied to closed-book question answering and fill-in-the-blank text generation with variable-sized blanks. In closed-book question answering, T5 is able to answer questions based on the knowledge it internalized during pre-training, without access to any external knowledge. In fill-in-the-blank text generation, T5 is able to replace a blank with a specified number of words, producing realistic outputs. Overall, T5 represents a significant advancement in the field of NLP, offering a powerful tool for a wide range of applications and opening up new possibilities for future research and development.