T5 base fine tuned for Sarcasm Detection 🙄 Google's T5 base fine tuned on Twitter Sarcasm Dataset for Sequence classification (as text generation) downstream task. Details of T5 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, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu in Here the abstract: Transfer learning, where a model is first pre trained on a data rich task before being fine tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text to text format. Our systematic study compares pre training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy