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Update technical dataset information
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README.md
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RefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while only relying on web data.
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```python
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from datasets import load_dataset
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### Data Instances
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### Data Fields
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### Data Splits
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## Dataset Creation
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RefinedWeb is built through stringent filtering and large-scale deduplication of CommonCrawl; we found models trained on RefinedWeb to achieve performance in-line or better than models trained on curated datasets, while only relying on web data.
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RefinedWeb is also "multimodal-friendly": it contains links and alt texts for images in processed samples.
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This public extract should contain 500-650GT depending on the tokenizer you use, and can be enhanced with the curated corpora of your choosing. This public extract is about ~500GB to download, requiring 2.8TB of local storage once unpacked.
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```python
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from datasets import load_dataset
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### Data Instances
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Each data instance corresponds to an individual web page which has been crawled, processed, and deduplicated against all other instances.
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This public extract of RefinedWeb contains about 1B instances (968M individual web pages), for a total of 2.8TB of clean text data.
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### Data Fields
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* `content`: the processed and cleaned text contained in the page;
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* `url`: the url of the webpage crawled to produce the sample;
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* `timestamp`: timestamp of when the webpage was crawled by CommonCrawl;
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* `dump`: the CommonCrawl dump the sample is a part of;
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* `segment`: the CommonCrawl segment the sample is a part of;
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* `image_urls`: a list of elements in the type [`image_url`, `image_alt_text`] for all the images found in the content of the sample.
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### Data Splits
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We do not provide any canonical splits for RefinedWeb.
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## Dataset Creation
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