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README.md
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- qsa
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- common-crawl
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- PDF
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pretty_name:
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size_categories:
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- 1M<n<10M
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---
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#
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## Dataset Summary
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**
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The dataset is designed for training and studying natural-language mathematical reasoning, with a strong emphasis on university-level content.
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- **Sample count**: approximately 4 million Q&A pairs
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## Use
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- **continued pretraining** on mathematical text
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- supervised fine-tuning on math Q&A data
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- filtering, ablation, and multilingual analysis of math corpora
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- language-specific training tasks, such as translation of mathematical texts
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In our paper, we provide several ablation experiments for different use cases using
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showing significant gains on CPT for various pre-trained models, such as Qwen3-1.7B, LLama-3.2-1B,and 7B, etc.
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## Acknowledgments
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- qsa
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- common-crawl
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- PDF
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pretty_name: PolyUniMath
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size_categories:
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# PolyUniMath
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## Dataset Summary
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**PolyUniMath** is a large multilingual mathematics dataset of question-solution-answer pairs extracted from mathematical PDFs.
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The dataset is designed for training and studying natural-language mathematical reasoning, with a strong emphasis on university-level content.
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- **Sample count**: approximately 4 million Q&A pairs
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## Use
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PolyUniMath is intended for:
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- **continued pretraining** on mathematical text
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- supervised fine-tuning on math Q&A data
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- filtering, ablation, and multilingual analysis of math corpora
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- language-specific training tasks, such as translation of mathematical texts
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In our paper, we provide several ablation experiments for different use cases using PolyUniMath,
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showing significant gains on CPT for various pre-trained models, such as Qwen3-1.7B, LLama-3.2-1B,and 7B, etc.
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## Acknowledgments
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