T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for whichĮach task is converted into a text-to-text format. NLP, we release our dataset, pre-trained models, and code. To facilitate future work on transfer learning for Summarization, question answering, text classification, and more. With scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering By combining the insights from our exploration Our systematic study compares pretraining objectives, architectures, unlabeled datasets, transferĪpproaches, and other factors on dozens of language understanding tasks. Transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a In this paper, we explore the landscape of Has given rise to a diversity of approaches, methodology, and practice. Task, has emerged as a powerful technique in natural language processing (NLP). Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream The abstract from the paper is the following: Michael Matena, Yanqi Zhou, Wei Li, Peter J. The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang,
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