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  1. README.md +83 -0
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README.md ADDED
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+ # GneissWeb Annotations
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+
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+ 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.
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+
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+ 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.
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+
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+ Learn more about the annotation process and methodology in our [official blog post](https://www.commoncrawl.org/blog/announcing-gneissweb-annotations).
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+
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+ ## What's Inside
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+
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+ GneissWeb Annotations uses the [https://huggingface.co/ibm-granite/GneissWeb.bloom](GneissWeb bloom filter) 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.
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+
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+ - **Medical** - Health information, medical research, and clinical content
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+ - **Education** - Learning materials, academic resources, and educational platforms
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+ - **Technology** - Software documentation, technical guides, and tech industry content
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+ - **Science** - Research publications, scientific articles, and academic work
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+
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+ You can access annotations at two levels of granularity:
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+
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+ - **Host-level** - Aggregate statistics for entire domains, perfect for broad filtering
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+ - **URL-level** - Individual URL classifications for precise content selection
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+
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+ ## Getting the Data
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+
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+ Access the dataset through:
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+
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+ - **Hugging Face**: [https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-testing-v1](https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-testing-v1)
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+ - **AWS S3**: `s3://commoncrawl/projects/gneissweb-annotation-testing-v1`
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+
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+ ## Example usage
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+
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+ Check out the gneissweb examples in the [cc-index-annotations](https://github.com/commoncrawl/cc-index-annotations) github repository.
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+
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+ ## Schema
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+
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+ ### Host Index Files
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+
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+ Aggregated domain-level annotations for efficient filtering by source.
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+
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+ | Column | Description |
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+ |--------|-------------|
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+ | `crawl` | Common Crawl archive ID (e.g., CC-MAIN-2024-10) |
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+ | `in_gneissweb` | Boolean flag for GneissWeb inclusion |
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+ | `surt_host_name` | SURT-formatted hostname |
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+ | `gneissweb_medical` | Medical content quality score |
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+ | `gneissweb_technology` | Technology content quality score |
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+ | `gneissweb_education` | Educational content quality score |
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+ | `gneissweb_science` | Scientific content quality score |
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+
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+ ### URL Index Files
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+
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+ Granular annotations for individual URLs.
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+
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+ | Column | Description |
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+ |--------|-------------|
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+ | `crawl` | Common Crawl archive ID (e.g., CC-MAIN-2024-10) |
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+ | `in_gneissweb` | Boolean flag for GneissWeb inclusion |
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+ | `url_surkey` | SURT-formatted URL key |
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+ | `surt_host_name` | SURT-formatted hostname |
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+ | `fetch_time` | TIMESTAMP of the page fetch |
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+ | `gneissweb_medical` | Medical content quality score |
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+ | `gneissweb_technology` | Technology content quality score |
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+ | `gneissweb_education` | Educational content quality score |
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+ | `gneissweb_science` | Scientific content quality score |
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+
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+ ## Applications
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+
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+ This dataset opens up numerous possibilities:
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+
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+ - Train domain-specific language models with curated web data
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+ - Conduct research on content quality distribution across the web
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+ - Create filtered datasets for specific industries or use cases
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+ - Combine with other Common Crawl signals (language, TLD, etc.) for multi-dimensional filtering
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+
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+ ## Attribution
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+
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+ When using our data in your work, please cite the [https://commoncrawl.org](Common Crawl Foundation) and let us know, we'd love to hear from you!
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+
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+ ## Licensing
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+
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+ Common Crawl Foundation's standard terms and condition apply, see [https://commoncrawl.org/terms-of-use](https://commoncrawl.org/terms-of-use) for more details.
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+
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