T5 (Text-to-Text Transfer Transformer)

T5 is a unified sequence-to-sequence deep learning framework introduced by Raffel et al. at Google Brain in 2020, published in the Journal of Machine Learning Research (Vol. 21, No. 140). It reframes every NLP task — classification, translation, summarisation, question answering, and more — as a text-to-text problem: both input and output are always character strings, enabling a single encoder-decoder Transformer to be pre-trained once and fine-tuned across tasks with a consistent interface. T5 introduced span-corruption pre-training and the C4 corpus, and its largest variant (11B parameters) achieved state-of-the-art results across a wide range of NLP benchmarks at the time of publication.