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- W4387635083 abstract "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." @default.
- W4387635083 created "2023-10-14" @default.
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- W4387635083 date "2023-10-11" @default.
- W4387635083 modified "2023-10-15" @default.
- W4387635083 title "TabLib: A Dataset of 627M Tables with Context" @default.
- W4387635083 doi "https://doi.org/10.48550/arxiv.2310.07875" @default.
- W4387635083 hasPublicationYear "2023" @default.
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