BramVanroy/CommonCrawl-CreativeCommons-v0.1

Raw CommonCrawl crawls, annotated with potential Creative Commons license information

The licensing information is extracted from the web pages based on whether they link to Creative Commons licenses but false positives may occur! While further filtering based on the location type of the license should improve the precision (e.g. by removing hyperlink (a_tag) references), false positives may still occur.

Usage

from datasets import load_dataset

# Everything
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons")

# Single dump, all languages
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "CC-MAIN-2024-51")

# Single language, all dumps
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "nl")

# Single language, single dump
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "CC-MAIN-2024-51-nl")

Fields

In some cases, multiple licenses are found on a single page. All licenses are collected in potential_licenses. From these, the "best guess" is selected based on three criteria:

1. location_preference_order: meta_tag, json-ld, link_tag, a_tag
2. head_preference_order: True, False
3. footer_preference_order: True, False

Based on these criteria, the "best guessed" license is picked as the one in the license_* columns. Potential disagreement between multiple licenses is given in license_disagreement.

• text: the extracted text (unmodified)
• id: WARC-Record-ID
• dump: Common Crawl crawl
• url: original url for document
• date: crawl date
• file_path: file path on the S3 bucket
• license_abbr: the license type. Possible values: "cc-unknown" (recommended to filter this one out), "by", "by-sa", "by-nd", "by-nc", "by-nc-sa", "by-nc-nd", "zero", "certification", "mark". If multiple licenses were found (potential_licenses)
• license_version: the license version, e.g. "4.0"
• license_location: the location where the license was found. Possible values: "meta_tag", "json-ld", "link_tag", "a_tag"
• license_in_head: whether the license was found inside a head HTML element
• license_in_footer: whether the license was found inside a footer HTML element, or an HTML element that had footer in the ID or class name
• potential_licenses:
  • abbr: list of all found license abbreviations
  • version: list of all found license versions
  • location: list of all found license locations
  • in_head: list of whether licenses were found in the head
  • in_footer: list of whether licenses were found in a footer
• license_parse_error: whether there was a problem when trying to extract the license, e.g. an unparseable HTML document
• license_disagreement: whether the potential_licenses["abbr"] disagree, i.e., different types of licenses were found. License versions are not included in the comparison!
• language: the language, as detected by fastText ft176
• language_score: the language identification confidence score

Progress

The attempt is to at least process all five RedPyjama crawls + CC-MAIN-2024-51.

Done:

• CC-MAIN-2024-51

Running:

• CC-MAIN-2023-06
• CC-MAIN-2020-05

To do:

• CC-MAIN-2021-04
• CC-MAIN-2022-05
• CC-MAIN-2019-30

Languages

The following languages are included.

• nl
• fr
• es
• fy
• de
• af
• en
• it

Recommendations

• Raw CommonCrawl data is processed in an attempt to extract licensing information. No quality filtering is done!! It is highly recommended to filter this data further on quality, fluency, toxicity, etc.
• Similarly, the data has not been deduplicated.
• The licenses include all possible Creative Commons licenses, including non-commercial ones. Take care about what kind of data you wish to use, and filter out non-commercial licenses when needed.
• The column license_disagreement indicates whether multiple licenses were found that have not the same abbreviation, e.g. cc-by and cc-by-nc. It is recommended to filter these out.
• The column license_parse_error indicates whether an error occurred when parsing the license. You probably want to filter out documents where this was the case, though this should be extremely rare.
• Unsurpisingly, the data contains a lot of Wikipedia/Wikimedia content. Depending on what you need, you may wish to filter those out. For Wikipedia specifically, you may opt to use the more thoroughly parsed (but potentially more outdated) wikimedia/wikipedia set.
• In exceptional cases, a link to creativecommons.org is found but the exact license could not be found. These are under license_abbr="cc-unknown" which you may wish to filter out.

Acknowledgments

• TNO, who funded the work hours to accomplish this collection. They intend to use parts of this material for the GPT-NL project.
• Flemish Supercomputer Center for part of the compute under grant 2024-107
• Guilherme Penedo (@guipenedo) and the rest of the FineWeb and datatrove team for the help and insights
• ML6 and specifically Robin Van Craenenbroek for their Fondant Creative Commons filter for image datasets. While my approach is different, their code did serve as inspiration.