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OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents

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arxiv 2306.16527 v2 pith:R5QWLZJH submitted 2023-06-21 cs.IR cs.CV

Hugo Laurençon , Lucile Saulnier , Léo Tronchon , Stas Bekman , Amanpreet Singh , Anton Lozhkov , Thomas Wang , Siddharth Karamcheti
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Alexander M. Rush Douwe Kiela Matthieu Cord Victor Sanh
This is my paper
classification cs.IRcs.CV
keywords datasetmodelsdocumentsimage-textmultimodalobelicsbeenbenchmarks
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Large multimodal models trained on natural documents, which interleave images and text, outperform models trained on image-text pairs on various multimodal benchmarks. However, the datasets used to train these models have not been released, and the collection process has not been fully specified. We introduce the OBELICS dataset, an open web-scale filtered dataset of interleaved image-text documents comprising 141 million web pages extracted from Common Crawl, 353 million associated images, and 115 billion text tokens. We describe the dataset creation process, present comprehensive filtering rules, and provide an analysis of the dataset's content. To show the viability of OBELICS, we train vision and language models of 9 and 80 billion parameters named IDEFICS, and obtain competitive performance on different multimodal benchmarks. We release our dataset, models and code.

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