Datasets:

Team-PIXEL
/

rendered-bookcorpus

	---
	annotations_creators:
	- no-annotation
	language_creators:
	- found
	language:
	- en
	license:
	- unknown
	multilinguality:
	- monolingual
	pretty_name: Team-PIXEL/rendered-bookcorpus
	size_categories:
	- 1M<n<10M
	source_datasets:
	- rendered\|BookCorpusOpen
	task_categories:
	- masked-auto-encoding
	- rendered-language-modelling
	task_ids:
	- masked-auto-encoding
	- rendered-language-modeling
	paperswithcode_id: bookcorpus
	---

	# Dataset Card for Team-PIXEL/rendered-bookcorpus

	## Dataset Description

	- Homepage: [https://github.com/xplip/pixel](https://github.com/xplip/pixel)
	- Repository: [https://github.com/xplip/pixel](https://github.com/xplip/pixel)
	- Papers: [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
	](https://arxiv.org/abs/1506.06724), [Language Modelling with Pixels](https://arxiv.org/abs/2207.06991)
	- Point of Contact: [Phillip Rust](mailto:p.rust@di.ku.dk)
	- Size of downloaded dataset files: 63.58 GB
	- Size of the generated dataset: 63.59 GB
	- Total amount of disk used: 127.17 GB

	### Dataset Summary

	This dataset is a version of the BookCorpus available at [https://huggingface.co/datasets/bookcorpusopen](https://huggingface.co/datasets/bookcorpusopen) with examples rendered as images with resolution 16x8464 pixels.

	The original BookCorpus was introduced by Zhu et al. (2015) in [Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books](https://arxiv.org/abs/1506.06724) and contains 17868 books of various genres. The rendered BookCorpus 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.

	The BookCorpusOpen dataset was rendered book-by-book into 5.4M examples containing approximately 1.1B words in total. The dataset is stored as a collection of 162 parquet files. It was rendered using the script openly available at [https://github.com/xplip/pixel/blob/main/scripts/data/prerendering/prerender_bookcorpus.py](https://github.com/xplip/pixel/blob/main/scripts/data/prerendering/prerender_bookcorpus.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 BookCorpus have not been rendered accurately.
	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.

	The rendered BookCorpus can be loaded via the datasets library as follows:

	```python
	from datasets import load_dataset

	# Download the full dataset to disk
	load_dataset("Team-PIXEL/rendered-bookcorpus", split="train")

	# Stream the dataset directly from the hub
	load_dataset("Team-PIXEL/rendered-bookcorpus", split="train", streaming=True)
	```

	## Dataset Structure

	### Data Instances

	- Size of downloaded dataset files: 63.58 GB
	- Size of the generated dataset: 63.59 GB
	- Total amount of disk used: 127.17 GB

	An example of 'train' looks as follows.
	```
	{
	"pixel_values": <PIL.PngImagePlugin.PngImageFile image mode=L size=8464x16
	"num_patches": "498"
	}
	```

	### Data Fields

	The data fields are the same among all splits.

	- `pixel_values`: an `Image` feature.
	- `num_patches`: a `Value(dtype="int64")` feature.

	### Data Splits

	\|train\|
	\|:----\|
	\|5400000\|

	## Dataset Creation

	### Curation Rationale

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	#### Who are the source language producers?

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Annotations

	#### Annotation process

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	#### Who are the annotators?

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Personal and Sensitive Information

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Discussion of Biases

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Other Known Limitations

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	## Additional Information

	### Dataset Curators

	[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

	### Licensing Information

	The books have been crawled from smashwords.com, see their [terms of service](https://www.smashwords.com/about/tos) for more information.

	A data sheet for this dataset has also been created and published in [Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus](https://arxiv.org/abs/2105.05241)

	### Citation Information

	```bibtex
	@InProceedings{Zhu_2015_ICCV,
	title = {Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books},
	author = {Zhu, Yukun and Kiros, Ryan and Zemel, Rich and Salakhutdinov, Ruslan and Urtasun, Raquel and Torralba, Antonio and Fidler, Sanja},
	booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
	month = {December},
	year = {2015}
	}
	```

	```bibtex
	@article{rust-etal-2022-pixel,
	title={Language Modelling with Pixels},
	author={Phillip Rust and Jonas F. Lotz and Emanuele Bugliarello and Elizabeth Salesky and Miryam de Lhoneux and Desmond Elliott},
	journal={arXiv preprint},
	year={2022},
	url={https://arxiv.org/abs/2207.06991}
	}
	```


	### Contact Person

	This dataset was added by Phillip Rust.

	Github: [@xplip](https://github.com/xplip)

	Twitter: [@rust_phillip](https://twitter.com/rust_phillip)