TabLib: A Dataset of 627M Tables with Context
Gus Eggert Kevin Huo Mike Biven Justin Waugh
It is well-established that large, diverse datasets play a pivotal role in
the performance of modern AI systems for text and image modalities. However,
there are no datasets for tabular data of comparable size and diversity to
those available for text and images. Thus we present "TabLib'', a compilation
of 627 million tables totaling 69 TiB, along with 867B tokens of context.
TabLib was extracted from numerous file formats, including CSV, HTML, SQLite,
PDF, Excel, and others, sourced from GitHub and Common Crawl. The size and
diversity of TabLib offer considerable promise in the table modality,
reminiscent of the original promise of foundational datasets for text and
images, such as The Pile and LAION.