| --- |
| 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. |
|
|
| 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. |
|
|
| ### 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 image (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. |
|
|
| ## Data Splits |
|
|
| There is only one split, `train`, that contains 141,047,697 examples. |
|
|
| ## Size |
|
|
| `OBELISC` with images replaced by their URLs weighs 666.6 GB (unwanted!) in arrow format and 377 GB in this uploaded `parquet` format. |
|
|
| ### Visualization of OBELISC documents |
|
|
| https://huggingface.co/spaces/HuggingFaceM4/obelisc_visualization |
| |
| ### Research paper |
| |
| https://arxiv.org/abs/2306.16527 |
| |
| ### 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} |
| } |
| ``` |
