OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text
  Documents
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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Hugo Laurençon
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Hugo Laurençon
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Lucile Saulnier
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Léo Tronchon
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Leo Tronchon
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Stas Bekman
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2023-08-28T15:08:06.714Z
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Stas Bekman
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Amanpreet Singh
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Amanpreet Singh
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Anton Lozhkov
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Anton Lozhkov
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Thomas Wang
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Thomas Wang
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Siddharth Karamcheti
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Siddharth Karamcheti
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Alexander M. Rush
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2023-06-30T09:52:19.798Z
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Sasha Rush
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Douwe Kiela
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2023-06-30T09:52:31.092Z
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Douwe Kiela
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Matthieu Cord
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Victor Sanh
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2023-06-30T09:52:03.972Z
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Victor Sanh
user
VictorSanh
OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text
  Documents
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.
The OBELICS dataset, containing interleaved image-text documents, is introduced and used to train competitive multimodal models.
multimodal models natural documents interleave images image-text pairs multimodal benchmarks OBELICS dataset Common Crawl vision and language models IDEFICS