| language: | |
| - en | |
| - fr | |
| - ro | |
| - de | |
| - multilingual | |
| license: apache-2.0 | |
| tags: | |
| - summarization | |
| - translation | |
| datasets: | |
| - c4 | |
| [Google's T5](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) | |
| Pretraining Dataset: [C4](https://huggingface.co/datasets/c4) | |
| Other Community Checkpoints: [here](https://huggingface.co/models?search=t5) | |
| Paper: [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) | |
| Authors: *Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu* | |
| ## 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 achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code. | |
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