metadata
license: cc-by-sa-4.0
task_categories:
- text-generation
- question-answering
language:
- en
pretty_name: AutoMathText
size_categories:
- 10B<n<100B
configs:
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data_files:
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path:
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- data/web/0.90-0.95.jsonl
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default: true
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data_files:
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tags:
- mathematical-reasoning
- reasoning
- finetuning
- pretraining
- llm
AutoMathText
AutoMathText 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.
Objective
The primary aim of the AutoMathText 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:
- Facilitating advanced research in the intersection of mathematics and artificial intelligence.
- Serving as an educational tool for learning and teaching complex mathematical concepts.
- Providing a foundation for developing and training AI models specialized in processing and understanding mathematical content.
Features
- Volume: Approximately 200 GB of text data (in natural language and programming language).
- Content: A diverse collection of mathematical texts, including but not limited to research papers, educational articles, and code documentation.
- Labeling: Every text is scored by Qwen-72B, a sophisticated language model, ensuring a high standard of relevance and accuracy.
- Scope: Covers a wide spectrum of mathematical topics, making it suitable for various applications in advanced research and education.
References
Citation
We appreciate your use of AutoMathText 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.
@misc{automathtext2024,
title={AutoMathText: Autonomous Data Selection with Language Models for Mathematical Texts},
author={Zhang, Yifan and Luo, Yifan},
year={2024},
}