README.md

---
license: cc
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: ©️ Common Crawl Creative Commons
language:
- afr
- deu
- eng
- fra
- fry
- ita
- nld
- spa
- af
- de
- en
- fr
- fy
- it
- nl
- es
configs:
- config_name: default
  data_files: data/**/*.parquet
# Languages
- config_name: afr
  data_files: data/**/afr/*.parquet
- config_name: deu
  data_files: data/**/deu/*.parquet
- config_name: eng
  data_files: data/**/eng/*.parquet
- config_name: spa
  data_files: data/**/spa/*.parquet
- config_name: fra
  data_files: data/**/fra/*.parquet
- config_name: fry
  data_files: data/**/fry/*.parquet
- config_name: ita
  data_files: data/**/ita/*.parquet
- config_name: nld
  data_files: data/**/nld/*.parquet
# Per-crawl
# CC-MAIN-2019-30
- config_name: CC-MAIN-2019-30
  data_files: data/CC-MAIN-2019-30/**/*.parquet
- config_name: CC-MAIN-2019-30-afr
  data_files: data/CC-MAIN-2019-30/afr/*.parquet
- config_name: CC-MAIN-2019-30-deu
  data_files: data/CC-MAIN-2019-30/deu/*.parquet
- config_name: CC-MAIN-2019-30-eng
  data_files: data/CC-MAIN-2019-30/eng/*.parquet
- config_name: CC-MAIN-2019-30-spa
  data_files: data/CC-MAIN-2019-30/spa/*.parquet
- config_name: CC-MAIN-2019-30-fra
  data_files: data/CC-MAIN-2019-30/fra/*.parquet
- config_name: CC-MAIN-2019-30-fry
  data_files: data/CC-MAIN-2019-30/fry/*.parquet
- config_name: CC-MAIN-2019-30-ita
  data_files: data/CC-MAIN-2019-30/ita/*.parquet
- config_name: CC-MAIN-2019-30-nld
  data_files: data/CC-MAIN-2019-30/nld/*.parquet
# CC-MAIN-2020-05
- config_name: CC-MAIN-2020-05
  data_files: data/CC-MAIN-2020-05/**/*.parquet
- config_name: CC-MAIN-2020-05-afr
  data_files: data/CC-MAIN-2020-05/afr/*.parquet
- config_name: CC-MAIN-2020-05-deu
  data_files: data/CC-MAIN-2020-05/deu/*.parquet
- config_name: CC-MAIN-2020-05-eng
  data_files: data/CC-MAIN-2020-05/eng/*.parquet
- config_name: CC-MAIN-2020-05-spa
  data_files: data/CC-MAIN-2020-05/spa/*.parquet
- config_name: CC-MAIN-2020-05-fra
  data_files: data/CC-MAIN-2020-05/fra/*.parquet
- config_name: CC-MAIN-2020-05-fry
  data_files: data/CC-MAIN-2020-05/fry/*.parquet
- config_name: CC-MAIN-2020-05-ita
  data_files: data/CC-MAIN-2020-05/ita/*.parquet
- config_name: CC-MAIN-2020-05-nld
  data_files: data/CC-MAIN-2020-05/nld/*.parquet
# CC-MAIN-2023-06
- config_name: CC-MAIN-2023-06
  data_files: data/CC-MAIN-2023-06/**/*.parquet
- config_name: CC-MAIN-2023-06-afr
  data_files: data/CC-MAIN-2023-06/afr/*.parquet
- config_name: CC-MAIN-2023-06-deu
  data_files: data/CC-MAIN-2023-06/deu/*.parquet
- config_name: CC-MAIN-2023-06-eng
  data_files: data/CC-MAIN-2023-06/eng/*.parquet
- config_name: CC-MAIN-2023-06-spa
  data_files: data/CC-MAIN-2023-06/spa/*.parquet
- config_name: CC-MAIN-2023-06-fra
  data_files: data/CC-MAIN-2023-06/fra/*.parquet
- config_name: CC-MAIN-2023-06-fry
  data_files: data/CC-MAIN-2023-06/fry/*.parquet
- config_name: CC-MAIN-2023-06-ita
  data_files: data/CC-MAIN-2023-06/ita/*.parquet
- config_name: CC-MAIN-2023-06-nld
  data_files: data/CC-MAIN-2023-06/nld/*.parquet
# CC-MAIN-2024-51
- config_name: CC-MAIN-2024-51
  data_files: data/CC-MAIN-2024-51/**/*.parquet
- config_name: CC-MAIN-2024-51-afr
  data_files: data/CC-MAIN-2024-51/afr/*.parquet
- config_name: CC-MAIN-2024-51-deu
  data_files: data/CC-MAIN-2024-51/deu/*.parquet
- config_name: CC-MAIN-2024-51-eng
  data_files: data/CC-MAIN-2024-51/eng/*.parquet
- config_name: CC-MAIN-2024-51-spa
  data_files: data/CC-MAIN-2024-51/spa/*.parquet
- config_name: CC-MAIN-2024-51-fra
  data_files: data/CC-MAIN-2024-51/fra/*.parquet
- config_name: CC-MAIN-2024-51-fry
  data_files: data/CC-MAIN-2024-51/fry/*.parquet
- config_name: CC-MAIN-2024-51-ita
  data_files: data/CC-MAIN-2024-51/ita/*.parquet
- config_name: CC-MAIN-2024-51-nld
  data_files: data/CC-MAIN-2024-51/nld/*.parquet
  # CC-MAIN-2024-46
- config_name: CC-MAIN-2024-46
  data_files: data/CC-MAIN-2024-46/**/*.parquet
- config_name: CC-MAIN-2024-46-afr
  data_files: data/CC-MAIN-2024-46/afr/*.parquet
- config_name: CC-MAIN-2024-46-deu
  data_files: data/CC-MAIN-2024-46/deu/*.parquet
- config_name: CC-MAIN-2024-46-eng
  data_files: data/CC-MAIN-2024-46/eng/*.parquet
- config_name: CC-MAIN-2024-46-spa
  data_files: data/CC-MAIN-2024-46/spa/*.parquet
- config_name: CC-MAIN-2024-46-fra
  data_files: data/CC-MAIN-2024-46/fra/*.parquet
- config_name: CC-MAIN-2024-46-fry
  data_files: data/CC-MAIN-2024-46/fry/*.parquet
- config_name: CC-MAIN-2024-46-ita
  data_files: data/CC-MAIN-2024-46/ita/*.parquet
- config_name: CC-MAIN-2024-46-nld
  data_files: data/CC-MAIN-2024-46/nld/*.parquet
---

> **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. **See Recommendations and Caveats below!**

## Code

I am very grateful to the Flemish Supercomputer to provide compute necessary to create this dataset, but as you can tell there is still a lot of data left to be processed. Therefore, I am happy to collaborate to process as many Common Crawl crawls as possible. [Shoot me a message](mailto:bram.vanroy@kuleuven.be) if you want to sponsor this project with compute! You can also simply run the code yourself if you'd like. You can find the whole code base, based on `datatrove`, on [Github](https://github.com/BramVanroy/CommonCrawl-CreativeCommons). If you use the code, please [reference my work](https://github.com/BramVanroy/CommonCrawl-CreativeCommons?tab=readme-ov-file#citation) accordingly and share your processed crawls with the rest of the world (or get in touch with me so I can add them to this repo).

## Usage

```python
from datasets import load_dataset

# Everything -- massive, you will need streaming
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", streaming=True)

# Single dump, all languages -- large, you may need streaming on non-server hardware
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "CC-MAIN-2019-30")

# Single language, all dumps -- very large, you will likely need streaming
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "nld", streaming=True)

# Single language, single dump
ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "CC-MAIN-2019-30-nld")
```

## 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 glotlid
- language_score: the language identification confidence score
- found_in_fw: whether this sample was found in FineWeb(-2). For non-English, crawls that are more recent than FW2 (everything after 2024-18) is marked as None. For English, crawls that are more recent than FW v1.3 is marked as None (after 2024-51).


## Progress

The attempt is to at least process all five RedPyjama crawls + `CC-MAIN-2019-30`. 

Done:

- CC-MAIN-2019-30
- CC-MAIN-2020-05
- CC-MAIN-2023-06
- CC-MAIN-2024-51
- CC-MAIN-2024-46
- CC-MAIN-2025-05

Running:

- CC-MAIN-2021-04
- CC-MAIN-2022-05

## Languages

The following languages are included.

- Afrikaans: afr
- German: deu
- English: eng
- French: fra
- Frysian: fry
- Italian: ita
- Dutch: nld
- Spanish: spa


## Recommendations and Caveats

- 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](https://huggingface.co/datasets/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.


Recommendation:

```python
from datasets import load_dataset


ds = load_dataset("BramVanroy/CommonCrawl-CreativeCommons", "CC-MAIN-2019-30", split="train")
ds = ds.filter(
    lambda x: (
        (not x["license_disagreement"]) and    # Only use pages with a consistent license
        x["found_in_fw"] and                   # Only use pages that are in FineWeb(-2)
        "nc" not in x["license_abbr"] and      # Exclude non-commercial licenses
        x["license_abbr"] != "cc-unknown" and  # Exclude unknown licenses
        "wiki" not in x["url"]                 # Exclude Wiki-like pages (best to get those from a more reliable parser)
    ), 
    num_proc=16
)
```


## Citation

```bibtex
@software{Vanroy_CommonCrawl-CreativeCommons_2025,
  author = {Vanroy, Bram},
  license = {GPL-3.0},
  month = feb,
  title = {{CommonCrawl-CreativeCommons}},
  url = {https://github.com/BramVanroy/CommonCrawl-CreativeCommons},
  version = {1.3.0},
  year = {2025}
}
```


## Acknowledgments

- The [Common Crawl](https://commoncrawl.org/) non-profit organization. 
- [TNO](https://www.tno.nl/nl/), who funded the work hours to accomplish this code. They intend to use (parts of) [the generated material](https://huggingface.co/datasets/BramVanroy/CommonCrawl-CreativeCommons) for the [GPT-NL project](https://gpt-nl.nl/).
- [Flemish Supercomputer Center](https://www.vscentrum.be/) for part of the compute under grant 2024-107
- Guilherme Penedo ([@guipenedo](https://huggingface.co/guipenedo)) and the rest of the [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb) and [datatrove](https://github.com/huggingface/datatrove) team for the help and insights
- ML6 and specifically Robin Van Craenenbroek for their [Fondant Creative Commons](https://github.com/ml6team/fondant-usecase-filter-creative-commons/tree/add-fondant-usecase-cc-image-extraction) filter for image datasets. While my approach is different, their code did serve as inspiration.