The Pile is an 825 GiB English text corpus designed for training large-scale language models. It consists of 22 diverse, high-quality datasets, including both existing and newly created sources. These datasets include academic, professional, and public sources such as PubMed Central, ArXiv, GitHub, FreeLaw, Stack Exchange, and others. The Pile is intended to improve the general cross-domain knowledge and downstream generalization capabilities of language models compared to models trained on fewer data sources. The Pile is also used as a benchmark for evaluating language models across various domains. The Pile includes a filtered subset of Common Crawl, Pile-CC, with improved extraction quality. Evaluations show that models trained on the Pile significantly outperform models trained on raw Common Crawl data. The Pile is also used to assess the performance of GPT-2 and GPT-3 on various components, revealing that these models struggle with certain components like academic writing. The Pile is publicly available, with code for its construction and alternative versions provided. The Pile is also used to evaluate the performance of language models on tasks such as WikiText and LAMBADA, showing significant improvements over previous datasets. The Pile is also used to investigate the structural statistics of the dataset, including document lengths, tokenization, and language distribution. The Pile is also used to analyze the presence of pejorative content, bias, and sentiment co-occurrence. The Pile is also used to discuss the legal implications of using copyrighted data, and the potential impact of the Pile on AI timelines and alignment. The Pile is also used to address ethical concerns about the data, and to promote the practice of engaging with AI ethics literature. The Pile is also used to document the methods and data used in its construction, and to provide a detailed picture of the data for researchers. The Pile is also used to provide a framework for evaluating the performance of language models on various tasks, and to highlight the importance of diverse data in improving the generalization capabilities of language models.The Pile is an 825 GiB English text corpus designed for training large-scale language models. It consists of 22 diverse, high-quality datasets, including both existing and newly created sources. These datasets include academic, professional, and public sources such as PubMed Central, ArXiv, GitHub, FreeLaw, Stack Exchange, and others. The Pile is intended to improve the general cross-domain knowledge and downstream generalization capabilities of language models compared to models trained on fewer data sources. The Pile is also used as a benchmark for evaluating language models across various domains. The Pile includes a filtered subset of Common Crawl, Pile-CC, with improved extraction quality. Evaluations show that models trained on the Pile significantly outperform models trained on raw Common Crawl data. The Pile is also used to assess the performance of GPT-2 and GPT-3 on various components, revealing that these models struggle with certain components like academic writing. The Pile is publicly available, with code for its construction and alternative versions provided. The Pile is also used to evaluate the performance of language models on tasks such as WikiText and LAMBADA, showing significant improvements over previous datasets. The Pile is also used to investigate the structural statistics of the dataset, including document lengths, tokenization, and language distribution. The Pile is also used to analyze the presence of pejorative content, bias, and sentiment co-occurrence. The Pile is also used to discuss the legal implications of using copyrighted data, and the potential impact of the Pile on AI timelines and alignment. The Pile is also used to address ethical concerns about the data, and to promote the practice of engaging with AI ethics literature. The Pile is also used to document the methods and data used in its construction, and to provide a detailed picture of the data for researchers. The Pile is also used to provide a framework for evaluating the performance of language models on various tasks, and to highlight the importance of diverse data in improving the generalization capabilities of language models.