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@@ -5,7 +5,7 @@ task_categories:
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  - question-answering
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  language:
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  - en
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- pretty_name: MathText
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  size_categories:
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  - 10B<n<100B
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  configs:
@@ -58,19 +58,36 @@ tags:
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  # MathText
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- MathText is an extensive and carefully curated dataset encompassing **200 GB** of mathematical texts. It's a unique compilation sourced from a diverse range of platforms including various websites, arXiv, and GitHub (OpenWebMath, RedPajama, Algebraic Stack). This rich repository has been **autonomously labeled by the state-of-the-art open-sourced language model**, Qwen-72B. Each piece of content in the dataset is assigned **a score `lm_q1q2_score` within the range of [0, 1]**, reflecting its relevance, quality and educational value in the context of mathematical intelligence.
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  ## Objective
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  The primary aim of the MathText dataset is to provide a comprehensive and reliable resource for a wide array of users - from academic researchers and educators to AI practitioners and mathematics enthusiasts. This dataset is particularly geared towards:
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- - Facilitating advanced research in the intersection of mathematics and artificial intelligence.
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- - Serving as an educational tool for learning and teaching complex mathematical concepts.
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- - Providing a robust foundation for developing and training AI models specialized in processing and understanding mathematical content.
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  ## Features
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- - **Volume**: Approximately 200 GB of text data.
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  - **Content**: A diverse collection of mathematical texts, including but not limited to research papers, educational articles, and code documentation.
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- - **Labeling**: Every text is scored by Qwen-72B, a sophisticated language model, ensuring a high standard of relevance and accuracy.
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- - **Scope**: Covers a wide spectrum of mathematical topics, making it suitable for various applications in research and education.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - question-answering
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  language:
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  - en
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+ pretty_name: AutoMathText
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  size_categories:
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  - 10B<n<100B
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  configs:
 
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  # MathText
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+ MathText is an extensive and carefully curated dataset encompassing **200 GB** of mathematical texts. It's a unique compilation sourced from a diverse range of platforms including various websites, arXiv, and GitHub (OpenWebMath, RedPajama, Algebraic Stack). This rich repository has been **autonomously selected (labeled) by the state-of-the-art open-sourced language model**, Qwen-72B. Each piece of content in the dataset is assigned **a score `lm_q1q2_score` within the range of [0, 1]**, reflecting its relevance, quality and educational value in the context of mathematical intelligence.
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  ## Objective
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  The primary aim of the MathText dataset is to provide a comprehensive and reliable resource for a wide array of users - from academic researchers and educators to AI practitioners and mathematics enthusiasts. This dataset is particularly geared towards:
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+ - Facilitating advanced research in **the intersection of mathematics and artificial intelligence**.
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+ - Serving as an educational tool for **learning and teaching complex mathematical concepts**.
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+ - Providing **a foundation for developing and training AI models** specialized in processing and understanding **mathematical content**.
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  ## Features
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+ - **Volume**: Approximately 200 GB of text data (in natural language and programming language).
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  - **Content**: A diverse collection of mathematical texts, including but not limited to research papers, educational articles, and code documentation.
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+ - **Labeling**: Every text is **scored** by Qwen-72B, a sophisticated language model, ensuring a high standard of relevance and accuracy.
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+ - **Scope**: Covers a wide spectrum of mathematical topics, making it suitable for various applications in advanced research and education.
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+
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+ ## References
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+
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+ - OpenWebMath [(link)]()
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+ - Redpajama [(link)]()
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+ - Algebraick Stack [(link)]()
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+
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+ ## Citation
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+ We appreciate your use of MathText in your work. If you find this repository helpful, please consider citing it and star this repo. Feel free to contact zhangyif21@tsinghua.edu.cn or open an issue if you have any questions.
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+
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+ ```bibtex
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+ @misc{automathtext2024,
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+ title={AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts},
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+ author={Zhang, Yifan and Luo, Yifan},
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+ year={2024},
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+ }
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+ ```