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--- |
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dataset_info: |
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features: |
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- name: images |
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sequence: string |
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- name: metadata |
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dtype: string |
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- name: general_metadata |
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dtype: string |
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- name: texts |
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sequence: string |
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splits: |
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- name: train |
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num_bytes: 715724717192 |
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num_examples: 141047697 |
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download_size: 71520629655 |
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dataset_size: 715724717192 |
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license: cc-by-4.0 |
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language: |
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- en |
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pretty_name: OBELISC |
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size_categories: |
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- 100M<n<1B |
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--- |
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# Dataset Card for OBELISC |
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## Dataset Description |
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- **Repository: https://github.com/huggingface/OBELISC** |
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- **Paper: OBELISC: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents** |
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- **Point of Contact: [email protected]** |
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### Dataset Summary |
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`OBELISC` is an open, massive and curated collection of interleaved image-text web documents, containing 141M documents, 115B text tokens and 353M images. |
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This dataset can be used to train large multimodal models, significantly improving their reasoning abilities compared to models trained solely on image/text pairs. Please refer to our paper for further details about the construction of the dataset, quantitative and qualitative analyses of `OBELISC`, and experiments we conducted. |
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### Languages |
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English |
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## Data Fields |
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There are 4 fields: `images`, `texts`, `metadata` and `general_metadata`. |
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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`. |
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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]`. |
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The images are replaced by their URLs, and the users have to download them themselves, for example with the library `img2dataset`. |
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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 image (original source document, unformatted source, alt-text if present, ...). |
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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. |
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## Data Splits |
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There is only one split, `train`, that contains 141,047,697 examples. |
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## Size |
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`OBELISC` with images replaced by their URLs weighs 666.6 GB (unwanted!) in arrow format and 377 GB in this uploaded `parquet` format. |
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### Visualization of OBELISC documents |
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https://huggingface.co/spaces/HuggingFaceM4/obelisc_visualization |
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### Research paper |
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https://arxiv.org/abs/2306.16527 |
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### GitHub repository |
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https://github.com/huggingface/OBELISC |
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## Terms of Use |
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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. |
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### Licensing Information |
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License CC-BY-4.0. |
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### Citation Information |
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If you are using this dataset, please cite |
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``` |
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@inproceedings{ |
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lauren{\c{c}}on2023obe, |
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title={OBELISC: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents}, |
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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}, |
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year={2023} |
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} |
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``` |
