Datasets:

Languages:
English
Multilinguality:
monolingual
Size Categories:
10M<n<100M
Language Creators:
crowdsourced
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
Tags:
License:
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+ ---
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+ annotations_creators:
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+ - no-annotation
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+ language_creators:
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+ - crowdsourced
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+ language:
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+ - en
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+ license:
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+ - cc-by-sa-3.0
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+ - gfdl
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+ multilinguality:
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+ - monolingual
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+ pretty_name: Team-PIXEL/rendered-wikipedia-english
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+ size_categories:
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+ - 10M<n<100M
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - masked-auto-encoding
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+ - rendered-language-modelling
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+ task_ids:
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+ - masked-auto-encoding
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+ - rendered-language-modeling
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+ paperswithcode_id: null
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+ ---
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+
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+ # Dataset Card for Team-PIXEL/rendered-wikipedia-english
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+
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+
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+ ## Dataset Description
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+
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+ - **Homepage:** [https://github.com/xplip/pixel](https://github.com/xplip/pixel)
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+ - **Repository:** [https://github.com/xplip/pixel](https://github.com/xplip/pixel)
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+ - **Paper:** [Language Modelling with Pixels](https://arxiv.org/abs/2207.06991)
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+ - **Point of Contact:** [Phillip Rust](mailto:p.rust@di.ku.dk)
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+ - **Size of downloaded dataset files:** 125.66 GB
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+ - **Size of the generated dataset:** 125.56 GB
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+ - **Total amount of disk used:** 251.22 GB
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+
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+ ### Dataset Summary
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+
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+ This dataset contains the full English Wikipedia from February 1, 2018, rendered into images of 16x8464 resolution.
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+
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+ The original text dataset was built from a [Wikipedia dump](https://dumps.wikimedia.org/). Each example in the original *text* dataset contained the content of one full Wikipedia article with cleaning to strip markdown and unwanted sections (references, etc.). Each *rendered* example contains a subset of one full article. This rendered English Wikipedia was used to train the [PIXEL](https://huggingface.co/Team-PIXEL/pixel-base) model introduced in the paper [Language Modelling with Pixels](https://arxiv.org/abs/2207.06991) by Phillip Rust, Jonas F. Lotz, Emanuele Bugliarello, Elizabeth Salesky, Miryam de Lhoneux, and Desmond Elliott.
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+
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+ The original Wikipedia text dataset was rendered article-by-article into 11.4M examples containing approximately 2B words in total. The dataset is stored as a collection of 338 parquet files.
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+
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+ It was rendered using the script openly available at [https://github.com/xplip/pixel/blob/main/scripts/data/prerendering/prerender_wikipedia.py](https://github.com/xplip/pixel/blob/main/scripts/data/prerendering/prerender_wikipedia.py). The text renderer uses a PyGame backend and a collection of merged Google Noto Sans fonts. The PyGame backend does not support complex text layouts (e.g. ligatures and right-to-left scripts) or emoji, so occurrences of such text in the Wikipedia data have not been rendered accurately.
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+ Each example consists of a "pixel_values" field which stores a 16x8464 (height, width) grayscale image containing the rendered text, and an integer value "num_patches" which stores how many image patches (when splitting the image into 529 non-overlapping patches of resolution 16x16 pixels) in the associated images contain actual text, i.e. are neither blank (fully white) nor are the fully black end-of-sequence patch.
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+
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+ You can load the dataset as follows:
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ # Download the full dataset to disk
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+ load_dataset("Team-PIXEL/rendered-wikipedia-english", split="train")
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+
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+ # Stream the dataset directly from the hub
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+ load_dataset("Team-PIXEL/rendered-wikipedia-english", split="train", streaming=True)
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+ ```
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+
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+ ## Dataset Structure
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+
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+ ### Data Instances
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+
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+ - **Size of downloaded dataset files:** 125.66 GB
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+ - **Size of the generated dataset:** 125.56 GB
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+ - **Total amount of disk used:** 251.22 GB
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+
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+ An example of 'train' looks as follows.
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+ ```
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+ {
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+ "pixel_values": <PIL.PngImagePlugin.PngImageFile image mode=L size=8464x16
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+ "num_patches": "469"
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+ }
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+ ```
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+
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+ ### Data Fields
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+
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+ The data fields are the same among all splits.
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+
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+ - `pixel_values`: an `Image` feature.
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+ - `num_patches`: a `Value(dtype="int64")` feature.
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+
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+ ### Data Splits
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+
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+ |train|
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+ |:----|
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+ |11446535|
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+
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+ ## Dataset Creation
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+
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+ ### Curation Rationale
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+
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+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+
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+ ### Source Data
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+
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+ #### Initial Data Collection and Normalization
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+
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+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+
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+ #### Who are the source language producers?
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+
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+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+
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+ ### Annotations
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+
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+ #### Annotation process
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+
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+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+
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+ #### Who are the annotators?
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+
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+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+
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+ ### Personal and Sensitive Information
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+
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+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+
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+ ## Considerations for Using the Data
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+
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+ ### Social Impact of Dataset
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+
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+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+
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+ ### Discussion of Biases
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+
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+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+
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+ ### Other Known Limitations
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+
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+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+
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+ ## Additional Information
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+
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+ ### Dataset Curators
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+
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+ [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
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+
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+ ### Licensing Information
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+
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+ Most of Wikipedia's text and many of its images are co-licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License (CC BY-SA) and the GNU Free Documentation License (GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts).
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+
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+ Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes the text.
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+
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+ ### Citation Information
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+
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+ ```bibtex
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+ @article{rust-etal-2022-pixel,
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+ title={Language Modelling with Pixels},
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+ author={Phillip Rust and Jonas F. Lotz and Emanuele Bugliarello and Elizabeth Salesky and Miryam de Lhoneux and Desmond Elliott},
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+ journal={arXiv preprint},
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+ year={2022},
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+ url={https://arxiv.org/abs/2207.06991}
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+ }
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+ ```
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+
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+
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+ ### Contact Person
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+
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+ This dataset was added by Phillip Rust.
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+
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+ Github: [@xplip](https://github.com/xplip)
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+
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+ Twitter: [@rust_phillip](https://twitter.com/rust_phillip)