| --- |
| dataset_info: |
| features: |
| - name: text |
| dtype: string |
| - name: id |
| dtype: string |
| - name: doc_id |
| dtype: string |
| - name: qa_nr_in_doc |
| dtype: string |
| - name: problem |
| dtype: string |
| - name: options |
| dtype: string |
| - name: solution |
| dtype: string |
| - name: math_type |
| dtype: string |
| - name: answer |
| dtype: string |
| - name: dataset |
| dtype: string |
| - name: paper_score |
| dtype: float64 |
| - name: score_elementary |
| dtype: int64 |
| - name: score_highschool |
| dtype: int64 |
| - name: score_highschool_competition |
| dtype: int64 |
| - name: score_university |
| dtype: int64 |
| - name: score_university_competition |
| dtype: int64 |
| - name: score_research |
| dtype: int64 |
| - name: self_contained |
| dtype: string |
| - name: qwen_translated_problem |
| dtype: string |
| - name: qwen_translated_solution |
| dtype: string |
| - name: qwen_translated_answer |
| dtype: string |
| - name: lang |
| dtype: string |
| - name: model_answer |
| dtype: string |
| - name: question_type |
| dtype: string |
| - name: problem_is_valid |
| dtype: string |
| - name: solution_is_valid |
| dtype: string |
| - name: fine_math_domain |
| dtype: float64 |
| splits: |
| - name: train |
| num_bytes: 11338053874 |
| num_examples: 4290861 |
| download_size: 5225466652 |
| dataset_size: 11338053874 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| tags: |
| - math |
| - university-math |
| - highschool-competition-math |
| - qsa |
| - common-crawl |
| - PDF |
| pretty_name: PolyUniMath |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| # PolyUniMath |
|
|
| ## Dataset Summary |
|
|
| **PolyUniMath** is a large multilingual mathematics dataset of question-solution-answer pairs extracted from mathematical PDFs. |
| The dataset is designed for training and studying natural-language mathematical reasoning, with a strong emphasis on university-level content. |
|
|
| - **Sample count**: approximately 4 million Q&A pairs |
| - **Main focus**: university-level mathematics |
| - **Format**: problem, optional choices, solution, final answer, and auxiliary annotations |
| - **Metadata**: We provide rich metadata for each sample collected from various stages of our data generation pipeline |
| - **Translation**: Each sample contains a translation from the base language into English using Qwen3-32B as the translator |
|
|
| The data itself is collected from PDF data contained in the [CommonCrawl archives](https://commoncrawl.org/), which we refetch, OCR, and then pass through |
| several filtering and processing stages. The full pipeline is described and visualized in our linked paper. |
|
|
| ## Dataset Statistics |
|
|
| ### Math Levels |
|
|
| The dataset is predominantly rated highest as university-level: |
|
|
| - `score_university`: 80.2% |
| - `score_university_competition`: about 19% |
| - `score_highschool_competition`: less than 1% |
|
|
| This is intended, as our goal was to collect data only from high school competition to the university-level math. |
| However, each datapoint is scored on all difficulty levels (see Section 'Features' below), with only the highest of those |
| scores determining the class. |
|
|
| ### Language Distribution |
| Our dataset covers a **wide range of languages**, all of which offer a machine translation for the QSA pairs. |
| This enables the use of our dataset for downstream tasks, such as distilling the translation capability. |
| multi-language math-reasoning or development of language-specific models. |
|
|
| The language distribution is roughly (top 10): |
| - `en`: 60.5% |
| - `fr`: 8.7% |
| - `de`: 4.9% |
| - `ru`: 3.3% |
| - `es`: 3.3% |
| - `ca`: 2.6% |
| - `it`: 2.4% |
| - `pt`: 2.3% |
| - `ro`: 1.5% |
| - `cs`: 1.1% |
| - `other`: 9.5% |
|
|
| ## Dataset Structure |
|
|
| Each row corresponds to one math QSA item. The dataset combines source text with machine-generated metadata, translations, and filtering annotations. |
| For a full description of the various stages and processes that generate the metadata, refer to our linked paper. |
|
|
| ## Features |
|
|
| | Column | Description | |
| |---|---| |
| | `text` | Raw text record for the sample. | |
| | `id` | Unique identifier for the Q&A pair. | |
| | `doc_id` | Identifier of the source document from the CommonCrawl archives. | |
| | `qa_nr_in_doc` | Index of the sample within the source document. | |
| | `problem` | Problem statement. | |
| | `options` | Multiple-choice options, if present. | |
| | `solution` | Worked solution or explanation. | |
| | `math_type` | Coarse mathematical topic label. | |
| | `answer` | Final answer. | |
| | `dataset` | Source subset or release name. | |
| | `paper_score` | Source-document score from the PDF mining pipeline. | |
| | `score_elementary` | Difficulty score for elementary level. | |
| | `score_highschool` | Difficulty score for high-school level. | |
| | `score_highschool_competition` | Difficulty score for high-school competition level. | |
| | `score_university` | Difficulty score for university level. | |
| | `score_university_competition` | Difficulty score for university competition level. | |
| | `score_research` | Difficulty score for research level. | |
| | `self_contained` | Whether the problem is self-contained. | |
| | `qwen_translated_problem` | Machine-translated version of the problem. | |
| | `qwen_translated_solution` | Machine-translated version of the solution. | |
| | `qwen_translated_answer` | Machine-translated version of the final answer. | |
| | `lang` | Detected language code of the original sample. | |
| | `model_answer` | Auxiliary model-produced answer or extraction field. | |
| | `question_type` | Coarse question format, such as `math-word-problem`, `proof`, or `MCQ`. | |
| | `problem_is_valid` | Validity tag for the problem statement. | |
| | `solution_is_valid` | Validity tag for the solution. | |
| | `fine_math_domain` | Fine-grained [FineMath](https://arxiv.org/abs/2403.07747) domain annotation stored in numeric form. | |
|
|
| Translation and verification steps are conducted using [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B). |
|
|
| ## Use |
|
|
| PolyUniMath is intended for: |
|
|
| - **continued pretraining** on mathematical text |
| - supervised fine-tuning on math Q&A data |
| - filtering, ablation, and multilingual analysis of math corpora |
| - language-specific training tasks, such as translation of mathematical texts |
|
|
| In our paper, we provide several ablation experiments for different use cases using PolyUniMath, |
| showing significant gains on CPT for various pre-trained models, such as Qwen3-1.7B, LLama-3.2-1B,and 7B, etc. |
|
|
| ## Acknowledgments |
| Thanks to **BenchXiv**, **HuggingFace data team**, and **Project Numina** for providing the resources (both compute and time) |
| that enabled us to create this dataset. |
