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@@ -73,16 +73,16 @@ tags:
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  - qsa
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  - common-crawl
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  - PDF
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- pretty_name: B2-UniMath
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  size_categories:
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  - 1M<n<10M
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  ---
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- # B2-UniMath
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  ## Dataset Summary
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- **B2-UniMath** 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
@@ -167,14 +167,14 @@ Translation and verification steps are conducted using [Qwen3-32B](https://huggi
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  ## Use
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- B2-UniMath 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 B2-UniMath,
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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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  - 1M<n<10M
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  ---
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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