Name: mC4
Description: A colossal, cleaned version of Common Crawl's web crawl corpus.
Provider: Google
Paper Title: A colossal, cleaned version of Common Crawl's web crawl corpus.
Tasks: text generation, language modeling
Venue Name: Journal of Machine Learning Research
Authors: Colin Raffel, Noam Shazeer, Adam Roberts, Ktherine 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 finetuned 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 all text-based
  language problems into a text-to-text format. Our systematic study compares pre-training
  objectives, architectures, unlabeled data sets, 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 data set,
  pre-trained models, and code