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: [email protected]
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
Data Fields
There are 4 fields: images, texts, metadata and general_metadata.
For each example, the data in the columns images and texts are two lists of the same size, where for each index, one element and only one is not None.
For example, for the web document <image_1>text<image_2>, in images, we have [image_1,None,image_2] and in texts we have [None,text,None].
The images are replaced by their URLs, and the users have to download them themselves, for example with the library img2dataset.
In metadata, there is a string that can be transformed into a list with json.loads(example["metadata"]). This list will have the same size as the lists of images and texts, and will have a dictionary for each index where there is an image, and a None value when there is a text. This dictionary will contain the metadata of the images (original source document, unformatted source, alt-text if present, ...).
Finally, in general_metadata, there is a string that can be transformed into a dictionary, containing the URL of the document, and information about its location in the Common Crawl data.
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}
}