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docs: updated dataset urls

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  1. README.md +11 -8
README.md CHANGED
@@ -17,14 +17,15 @@ GneissWeb Annotations uses the [GneissWeb bloom filter](https://huggingface.co/i
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  You can access annotations at two levels of granularity:
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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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  ## Getting the Data
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  Access the dataset through:
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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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  ## Example usage
@@ -33,36 +34,38 @@ Check out the gneissweb examples in the [cc-index-annotations](https://github.co
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  ## Schema
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- ### Host Index Files
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- Aggregated domain-level annotations for efficient filtering by source.
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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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- ### URL Index Files
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- Granular annotations for individual URLs.
 
 
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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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  ## Applications
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  This dataset opens up numerous possibilities:
 
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  You can access annotations at two levels of granularity:
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+ - **URL-level** - Individual URL classifications for precise content selection (This dataset)
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  - **Host-level** - Aggregate statistics for entire domains, perfect for broad filtering
 
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  ## Getting the Data
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  Access the dataset through:
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+ - **Hugging Face**: [https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-url-testing-v1](https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-url-testing-v1) (URL-level)
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+ - **Hugging Face**: [https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-host-testing-v1](https://huggingface.co/datasets/commoncrawl/gneissweb-annotation-host-testing-v1) (Host-level)
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  - **AWS S3**: `s3://commoncrawl/projects/gneissweb-annotation-testing-v1`
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  ## Example usage
 
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  ## Schema
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+ ### URL Index Files
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+ Granular annotations for individual URLs.
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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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+ ### Host Index Files
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+
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+ Aggregated domain-level annotations for efficient filtering by source.
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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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  ## Applications
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  This dataset opens up numerous possibilities: