metadata

dataset_info:
  features:
    - name: images
      sequence: string
    - name: metadata
      dtype: string
    - name: general_metadata
      dtype: string
    - name: texts
      sequence: string
  splits:
    - name: train
      num_bytes: 715724717192
      num_examples: 141047697
  download_size: 71520629655
  dataset_size: 715724717192
license: cc-by-4.0
language:
  - en
pretty_name: OBELISC
size_categories:
  - 100M<n<1B

Dataset Card for OBELISC

Dataset Description

Repository: https://github.com/huggingface/OBELISC
Paper: OBELISC: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents
Point of Contact: hugo@huggingface.co

Dataset Summary

OBELISC is an open, massive and curated collection of interleaved image-text web documents, containing 141M documents, 115B text tokens and 353M images.

Languages

English

Visualization of OBELISC documents

https://huggingface.co/spaces/HuggingFaceM4/obelisc_visualization

GitHub repository

https://github.com/huggingface/OBELISC

Terms of Use

By using the dataset, you agree to comply with the original licenses of the source content as well as the dataset license (CC-BY-4.0). Additionally, if you use this dataset to train a Machine Learning model, you agree to disclose your use of the dataset when releasing the model or an ML application using the model.

Licensing Information

License CC-BY-4.0.

Citation Information

If you are using this dataset, please cite

@inproceedings{
lauren{\c{c}}on2023obe,
title={OBELISC: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents},
author={Hugo Lauren{\c{c}}on and Lucile Saulnier and L{\'e}o Tronchon and Stas Bekman and Amanpreet Singh and Anton Lozhkov and Thomas Wang and Siddharth Karamcheti and Alexander M Rush and Douwe Kiela and Matthieu Cord and Victor Sanh},
year={2023}
}