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The Pile: An 800GB Dataset of Diverse Text for Language Modeling

2020/12/31 by Leo Gao, Gao, Leo, Stella Biderman +22 · 6 voices · 271 citations
Computer Science · #Topic Modeling #Natural Language Processing Techniques #Speech Recognition and Synthesis

paper · pdf · doi:10.48550/arxiv.2101.00027

Abstract

Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language models. With this in mind, we present the Pile: an 825 GiB English text corpus targeted at training large-scale language models. The Pile is constructed from 22 diverse high-quality subsets -- both existing and newly constructed -- many of which derive from academic or professional sources. Our evaluation of the untuned performance of GPT-2 and GPT-3 on the Pile shows that these models struggle on many of its components, such as academic writing. Conversely, models trained on the Pile improve significantly over both Raw CC and CC-100 on all components of the Pile, while improving performance on downstream evaluations. Through an in-depth exploratory analysis, we document potentially concerning aspects of the data for prospective users. We make publicly available the code used in its construction.

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