annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: OpenWebText
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: openwebtext
dataset_info:
features:
- name: text
dtype: string
config_name: plain_text
splits:
- name: train
num_bytes: 39769491688
num_examples: 8013769
download_size: 12880189440
dataset_size: 39769491688
Dataset Card for "openwebtext"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://skylion007.github.io/OpenWebTextCorpus/
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- Size of downloaded dataset files: 13.51 GB
- Size of the generated dataset: 41.70 GB
- Total amount of disk used: 55.21 GB
Dataset Summary
An open-source replication of the WebText dataset from OpenAI, that was used to train GPT-2.
This distribution was created by Aaron Gokaslan and Vanya Cohen of Brown University.
Supported Tasks and Leaderboards
Languages
Dataset Structure
Data Instances
plain_text
- Size of downloaded dataset files: 13.51 GB
- Size of the generated dataset: 41.70 GB
- Total amount of disk used: 55.21 GB
An example of 'train' looks as follows.
This example was too long and was cropped:
{
"text": "\"A magazine supplement with an image of Adolf Hitler and the title 'The Unreadable Book' is pictured in Berlin. No law bans “Mei..."
}
Data Fields
The data fields are the same among all splits.
plain_text
text: astringfeature.
Data Splits
| name | train |
|---|---|
| plain_text | 8013769 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
The authors started by extracting all Reddit post urls from the Reddit submissions dataset. These links were deduplicated, filtered to exclude non-html content, and then shuffled randomly. The links were then distributed to several machines in parallel for download, and all web pages were extracted using the newspaper python package. Using Facebook FastText, non-English web pages were filtered out.
Subsequently, near-duplicate documents were identified using local-sensitivity hashing (LSH). Documents were hashed into sets of 5-grams and all documents that had a similarity threshold of greater than 0.5 were removed. The the remaining documents were tokenized, and documents with fewer than 128 tokens were removed. This left 38GB of text data (40GB using SI units) from 8,013,769 documents.
Who are the source language producers?
Annotations
The dataset doesn't contain annotations.
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
These data are released under this licensing scheme from the original authors (source):
We do not own any of the text from which these data has been extracted.
We license the actual packaging of these parallel data under the [Creative Commons CC0 license (“no rights reserved”)](https://creativecommons.org/share-your-work/public-domain/cc0/)
Notice policy
Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:
Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
Clearly identify the copyrighted work claimed to be infringed.
Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.
And contact us at the following email address: openwebtext at gmail.com and datasets at huggingface.co
Take down policy
The original authors will comply to legitimate requests by removing the affected sources from the next release of the corpus. Hugging Face will also update this repository accordingly.
Citation Information
@misc{Gokaslan2019OpenWeb,
title={OpenWebText Corpus},
author={Aaron Gokaslan and Vanya Cohen},
howpublished={\url{http://Skylion007.github.io/OpenWebTextCorpus}},
year={2019}
}
Contributions
Thanks to @richarddwang for adding this dataset.