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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
ref: https://arxiv.org/abs/1910.10683
This research paper introduces T5, a text-to-text transfer transformer model that achieves state-of-the-art results on various natural language processing benchmarks. The authors present a unified framework converting diverse NLP tasks into a text-to-text format, enabling systematic comparison of different transfer learning techniques. A new large-scale dataset, the Colossal Clean Crawled Corpus (C4), is introduced and released, along with pre-trained models and code. The study explores the effects of different pre-training objectives, architectures, and data sets on model performance, demonstrating the significant impact of scale on results. Finally, the authors discuss their findings and suggest avenues for future research in this area.
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By KnowledgeDBref: https://arxiv.org/abs/1910.10683
This research paper introduces T5, a text-to-text transfer transformer model that achieves state-of-the-art results on various natural language processing benchmarks. The authors present a unified framework converting diverse NLP tasks into a text-to-text format, enabling systematic comparison of different transfer learning techniques. A new large-scale dataset, the Colossal Clean Crawled Corpus (C4), is introduced and released, along with pre-trained models and code. The study explores the effects of different pre-training objectives, architectures, and data sets on model performance, demonstrating the significant impact of scale on results. Finally, the authors discuss their findings and suggest avenues for future research in this area.