
# 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)
annotations_creators:no-annotation language_creators:found multilinguality:monolingual size_categories:1M<n<10M source_datasets:rendered|BookCorpusOpen language:en license:unknown arxiv:1506.06724 arxiv:2207.06991 arxiv:2105.05241 region:us
{"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "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", "pretty_name": "Team-PIXEL/rendered-bookcorpus"}
TAGS
#annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-rendered|BookCorpusOpen #language-English #license-unknown #arxiv-1506.06724 #arxiv-2207.06991 #arxiv-2105.05241 #region-us 

Dataset Card for Team-PIXEL/rendered-bookcorpus
===============================================


Dataset Description
-------------------


* Homepage: URL
* Repository: URL
* Papers: Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
, Language Modelling with Pixels
* Point of Contact: Phillip Rust
* 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 URL 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 and contains 17868 books of various genres. The rendered BookCorpus was used to train the PIXEL model introduced in the paper Language Modelling with Pixels 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 URL 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:


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.


### 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



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


The books have been crawled from URL, see their terms of service 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


### Contact Person


This dataset was added by Phillip Rust.


Github: @xplip


Twitter: @rust\_phillip



### Dataset Summary


This dataset is a version of the BookCorpus available at URL 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 and contains 17868 books of various genres. The rendered BookCorpus was used to train the PIXEL model introduced in the paper Language Modelling with Pixels 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 URL 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:


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. ### 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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


The books have been crawled from URL, see their terms of service 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 ### Contact Person


This dataset was added by Phillip Rust.


Github: @xplip


Twitter: @rust\_phillip
TAGS
#annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-1M<n<10M #source_datasets-rendered|BookCorpusOpen #language-English #license-unknown #arxiv-1506.06724 #arxiv-2207.06991 #arxiv-2105.05241 #region-us 
 ### Dataset Summary


This dataset is a version of the BookCorpus available at URL 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 and contains 17868 books of various genres. The rendered BookCorpus was used to train the PIXEL model introduced in the paper Language Modelling with Pixels 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 URL 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:


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. ### 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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information


The books have been crawled from URL, see their terms of service 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 ### Contact Person


This dataset was added by Phillip Rust.


Github: @xplip


Twitter: @rust\_phillip