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README.md
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An open-source Chinese pretraining dataset with a scale of 800 billion tokens, offering the NLP community high-quality Chinese pretraining data.
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## Disclaimer
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This model, developed for academic purposes, employs rigorously compliance-checked training data to uphold the highest standards of integrity and compliance. Despite our efforts, the inherent complexities of data and the broad spectrum of model applications prevent us from ensuring absolute accuracy or appropriateness of the model outputs in every scenario.
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```
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Replace [split] with the name of the dataset component you wish to merge (zh-cc, zh-baike, zh-papers, zh-books, or zh-others). After merging, decompress the .gz file to access the dataset's content.
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<table>
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<tr>
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<td>
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## Dataset Composition
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The dataset consists of several components, each originating from different sources and serving various purposes in language modeling and processing. Below is a brief overview of each component:
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<p>
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<img src="data-ratio.png"
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<strong>zh-cc (Chinese Common Crawl)</strong><br>
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Extracts from the Common Crawl project specifically filtered for Chinese content. This component is rich in diverse internet text, ranging from websites, blogs, news articles, and more.<br><br>
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<strong>zh-baike (Chinese Encyclopedias)</strong><br>
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A collection of articles from various Chinese encyclopedias, similar to Wikipedia but including other encyclopedic sources as well.<br><br>
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<strong>zh-papers (Chinese Academic Papers)</strong><br>
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This component consists of academic and research papers published in Chinese. It covers a wide range of disciplines and offers technical, domain-specific language.<br><br>
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<strong>zh-books (Chinese Books)</strong><br>
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Comprises texts extracted from books published in Chinese. This includes literature, non-fiction, textbooks, and more.<br><br>
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<strong>zh-others</strong><br>
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This category is a collection of miscellaneous texts, notably including a substantial amount of QA (Question and Answer) data, alongside a variety of other texts
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</p>
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## Citation
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```
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@misc{du2024chinese,
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An open-source Chinese pretraining dataset with a scale of 800 billion tokens, offering the NLP community high-quality Chinese pretraining data.
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+
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## Disclaimer
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This model, developed for academic purposes, employs rigorously compliance-checked training data to uphold the highest standards of integrity and compliance. Despite our efforts, the inherent complexities of data and the broad spectrum of model applications prevent us from ensuring absolute accuracy or appropriateness of the model outputs in every scenario.
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```
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Replace [split] with the name of the dataset component you wish to merge (zh-cc, zh-baike, zh-papers, zh-books, or zh-others). After merging, decompress the .gz file to access the dataset's content.
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## Dataset Composition
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The dataset consists of several components, each originating from different sources and serving various purposes in language modeling and processing. Below is a brief overview of each component:
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<p>
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+
<img src="data-ratio.png" style="float: right; width: 400px; margin-left: 10px;">
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<strong>zh-cc (Chinese Common Crawl)</strong><br>
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Extracts from the Common Crawl project specifically filtered for Chinese content. This component is rich in diverse internet text, ranging from websites, blogs, news articles, and more.<br><br>
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+
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<strong>zh-baike (Chinese Encyclopedias)</strong><br>
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A collection of articles from various Chinese encyclopedias, similar to Wikipedia but including other encyclopedic sources as well.<br><br>
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<strong>zh-papers (Chinese Academic Papers)</strong><br>
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This component consists of academic and research papers published in Chinese. It covers a wide range of disciplines and offers technical, domain-specific language.<br><br>
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<strong>zh-books (Chinese Books)</strong><br>
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Comprises texts extracted from books published in Chinese. This includes literature, non-fiction, textbooks, and more.<br><br>
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<strong>zh-others</strong><br>
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This category is a collection of miscellaneous texts, notably including a substantial amount of QA (Question and Answer) data, alongside a variety of other texts.<br>
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</p>
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## Citation
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```
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@misc{du2024chinese,
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