Datasets:

commoncrawl
/

gneissweb-annotation-url-testing-v1

	# GneissWeb Annotations

	GneissWeb Annotations, powered by [IBM Research's GneissWeb](https://arxiv.org/abs/2502.14907) methodology, is a dataset of quality and category annotations applied to the Common Crawl corpus.

	This dataset enables precise filtering of web content across medical, educational, technology, and scientific domains, making it easier to build high-quality corpora for research projects, language models, and specialized applications.

	Learn more about the annotation process and methodology in our [official blog post](https://www.commoncrawl.org/blog/announcing-gneissweb-annotations).

	## What's Inside

	GneissWeb Annotations uses the [GneissWeb bloom filter](https://huggingface.co/ibm-granite/GneissWeb.bloom) made publicly available by IBM, along with IBM’s [Data Prep Kit](https://github.com/data-prep-kit/data-prep-kit) (now a Linux Foundation AI & Data project) and the GneissWeb groups’ category classifiers.

	- Medical - Health information, medical research, and clinical content
	- Education - Learning materials, academic resources, and educational platforms
	- Technology - Software documentation, technical guides, and tech industry content
	- Science - Research publications, scientific articles, and academic work

	You can access annotations at two levels of granularity:

	- URL-level - Individual URL classifications for precise content selection (This dataset)
	- Host-level - Aggregate statistics for entire domains, perfect for broad filtering

	## Getting the Data

	Access the dataset through:

	- Hugging Face: [https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-url-testing-v1](https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-url-testing-v1) (URL)
	- Hugging Face: [https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-host-testing-v1](https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-host-testing-v1) (Host)
	- AWS S3: `s3://commoncrawl/projects/gneissweb-annotation-testing-v1`

	## Example usage

	Check out the gneissweb examples in the [cc-index-annotations](https://github.com/commoncrawl/cc-index-annotations) github repository.

	## Schema

	### URL Index Files

	Granular annotations for individual URLs.

	\| Column \| Description \|
	\|--------\|-------------\|
	\| `crawl` \| Common Crawl archive ID (e.g., CC-MAIN-2024-10) \|
	\| `in_gneissweb` \| Boolean flag for GneissWeb inclusion \|
	\| `url_surkey` \| SURT-formatted URL key \|
	\| `surt_host_name` \| SURT-formatted hostname \|
	\| `fetch_time` \| TIMESTAMP of the page fetch \|
	\| `gneissweb_medical` \| Medical content quality score \|
	\| `gneissweb_technology` \| Technology content quality score \|
	\| `gneissweb_education` \| Educational content quality score \|
	\| `gneissweb_science` \| Scientific content quality score \|


	### Host Index Files

	Aggregated domain-level annotations for efficient filtering by source.

	\| Column \| Description \|
	\|--------\|-------------\|
	\| `crawl` \| Common Crawl archive ID (e.g., CC-MAIN-2024-10) \|
	\| `in_gneissweb` \| Boolean flag for GneissWeb inclusion \|
	\| `surt_host_name` \| SURT-formatted hostname \|
	\| `gneissweb_medical` \| Medical content quality score \|
	\| `gneissweb_technology` \| Technology content quality score \|
	\| `gneissweb_education` \| Educational content quality score \|
	\| `gneissweb_science` \| Scientific content quality score \|


	## Applications

	This dataset opens up numerous possibilities:

	- Train domain-specific language models with curated web data
	- Conduct research on content quality distribution across the web
	- Create filtered datasets for specific industries or use cases
	- Combine with other Common Crawl signals (language, TLD, etc.) for multi-dimensional filtering

	## Attribution

	When using our data in your work, please cite the [Common Crawl Foundation](https://commoncrawl.org) and let us know, we'd love to hear from you!

	## Licensing

	Common Crawl Foundation's standard terms and condition apply, see [Common Crawl Terms of Use](https://commoncrawl.org/terms-of-use) for more details.

	# GneissWeb Annotations

	GneissWeb Annotations, powered by [IBM Research's GneissWeb](https://arxiv.org/abs/2502.14907) methodology, is a dataset of quality and category annotations applied to the Common Crawl corpus.

	This dataset enables precise filtering of web content across medical, educational, technology, and scientific domains, making it easier to build high-quality corpora for research projects, language models, and specialized applications.

	Learn more about the annotation process and methodology in our [official blog post](https://www.commoncrawl.org/blog/announcing-gneissweb-annotations).

	## What's Inside

	GneissWeb Annotations uses the [GneissWeb bloom filter](https://huggingface.co/ibm-granite/GneissWeb.bloom) made publicly available by IBM, along with IBM’s [Data Prep Kit](https://github.com/data-prep-kit/data-prep-kit) (now a Linux Foundation AI & Data project) and the GneissWeb groups’ category classifiers.

	- Medical - Health information, medical research, and clinical content
	- Education - Learning materials, academic resources, and educational platforms
	- Technology - Software documentation, technical guides, and tech industry content
	- Science - Research publications, scientific articles, and academic work

	You can access annotations at two levels of granularity:

	- URL-level - Individual URL classifications for precise content selection (This dataset)
	- Host-level - Aggregate statistics for entire domains, perfect for broad filtering

	## Getting the Data

	Access the dataset through:

	- Hugging Face: [https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-url-testing-v1](https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-url-testing-v1) (URL)
	- Hugging Face: [https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-host-testing-v1](https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-host-testing-v1) (Host)
	- AWS S3: `s3://commoncrawl/projects/gneissweb-annotation-testing-v1`

	## Example usage

	Check out the gneissweb examples in the [cc-index-annotations](https://github.com/commoncrawl/cc-index-annotations) github repository.

	## Schema

	### URL Index Files

	Granular annotations for individual URLs.

	\| Column \| Description \|
	\|--------\|-------------\|
	\| `crawl` \| Common Crawl archive ID (e.g., CC-MAIN-2024-10) \|
	\| `in_gneissweb` \| Boolean flag for GneissWeb inclusion \|
	\| `url_surkey` \| SURT-formatted URL key \|
	\| `surt_host_name` \| SURT-formatted hostname \|
	\| `fetch_time` \| TIMESTAMP of the page fetch \|
	\| `gneissweb_medical` \| Medical content quality score \|
	\| `gneissweb_technology` \| Technology content quality score \|
	\| `gneissweb_education` \| Educational content quality score \|
	\| `gneissweb_science` \| Scientific content quality score \|


	### Host Index Files

	Aggregated domain-level annotations for efficient filtering by source.

	\| Column \| Description \|
	\|--------\|-------------\|
	\| `crawl` \| Common Crawl archive ID (e.g., CC-MAIN-2024-10) \|
	\| `in_gneissweb` \| Boolean flag for GneissWeb inclusion \|
	\| `surt_host_name` \| SURT-formatted hostname \|
	\| `gneissweb_medical` \| Medical content quality score \|
	\| `gneissweb_technology` \| Technology content quality score \|
	\| `gneissweb_education` \| Educational content quality score \|
	\| `gneissweb_science` \| Scientific content quality score \|


	## Applications

	This dataset opens up numerous possibilities:

	- Train domain-specific language models with curated web data
	- Conduct research on content quality distribution across the web
	- Create filtered datasets for specific industries or use cases
	- Combine with other Common Crawl signals (language, TLD, etc.) for multi-dimensional filtering

	## Attribution

	When using our data in your work, please cite the [Common Crawl Foundation](https://commoncrawl.org) and let us know, we'd love to hear from you!

	## Licensing

	Common Crawl Foundation's standard terms and condition apply, see [Common Crawl Terms of Use](https://commoncrawl.org/terms-of-use) for more details.