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@@ -69,11 +69,11 @@ It was built on top of CommonCrawl, leveraging stringent filtering and extensive
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  ### Supported Tasks and Leaderboards
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- RefinedWeb is intended to be primarly used as a pretraining dataset for large language models. Practitioners may leverage it for upstream evaluation with a validation loss, but we do not provide any canonical split.
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  ### Languages
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- RefinedWeb primarly contains English.
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  ## Dataset Structure
@@ -120,7 +120,7 @@ RefinedWeb is built from [CommonCrawl](https://commoncrawl.org) dumps. These dum
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  We applied extensive preprocessing and cleaning of the data, using our Macrodata Refinement Pipeline.
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- We first filter URLs to remove adult content using a blocklist and a score system, we then use `trafilatura` to extract content from pages, and perform language identification with the `fastText` classifier from CCNet ([Wenzek et al., 2019](https://arxiv.org/abs/1911.00359)). After this first preprocessing stage, we filter data using heuristics from MassiveWeb ([Rae et al., 2021](https://arxiv.org/abs/2112.11446)), and our own line-wise corrections.
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  Finally, we run extensive deduplication, removing URLs revisited across dumps and performing subsequently fuzzy and exact substring deduplication.
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@@ -169,7 +169,7 @@ This public extract is made available under an [ODC-By 1.0](https://opendatacomm
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  ### Opt-out request
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- RefinedWeb is based on [CommonCrawl](https://commoncrawl.org/). Their crawler honors opt-out requests in the `robots.txt`, see the [CC FAQ](https://commoncrawl.org/big-picture/frequently-asked-questions/) for details.
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  To remove a document from RefinedWeb, please message falconllm@tii.ae.
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  ### Supported Tasks and Leaderboards
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+ RefinedWeb is intended to be primarily used as a pretraining dataset for large language models. Practitioners may leverage it for upstream evaluation with a validation loss, but we do not provide any canonical split.
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  ### Languages
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+ RefinedWeb primarily contains English.
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  ## Dataset Structure
 
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  We applied extensive preprocessing and cleaning of the data, using our Macrodata Refinement Pipeline.
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+ We first filter URLs to remove adult content using a blocklist and a scoring system, we then use `trafilatura` to extract content from pages and perform language identification with the `fastText` classifier from CCNet ([Wenzek et al., 2019](https://arxiv.org/abs/1911.00359)). After this first preprocessing stage, we filter data using heuristics from MassiveWeb ([Rae et al., 2021](https://arxiv.org/abs/2112.11446)), and our own line-wise corrections.
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  Finally, we run extensive deduplication, removing URLs revisited across dumps and performing subsequently fuzzy and exact substring deduplication.
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  ### Opt-out request
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+ RefinedWeb is based on [CommonCrawl](https://commoncrawl.org/). Their crawler honours opt-out requests in the `robots.txt`, see the [CC FAQ](https://commoncrawl.org/big-picture/frequently-asked-questions/) for details.
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  To remove a document from RefinedWeb, please message falconllm@tii.ae.
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