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--- |
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dataset_info: |
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features: |
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- name: id |
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dtype: int64 |
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- name: text |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 225647910.0 |
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num_examples: 2886810 |
|
- name: test |
|
num_bytes: 23848817.0 |
|
num_examples: 311298 |
|
download_size: 131762427 |
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dataset_size: 249496727.0 |
|
--- |
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# Dataset Card for "math_formulas" |
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Mathematical dataset containing formulas based on the [AMPS](https://drive.google.com/file/d/1hQsua3TkpEmcJD_UWQx8dmNdEZPyxw23) Khan dataset and the [ARQMath](https://drive.google.com/drive/folders/1YekTVvfmYKZ8I5uiUMbs21G2mKwF9IAm) dataset V1.3. Based on the retrieved LaTeX formulas, more equivalent versions have been generated by applying randomized LaTeX printing with this [SymPy fork](https://github.com/jdrechsel13/sympy-random-LaTeX) using [Math Mutator (MAMUT)](https://github.com/aieng-lab/math-mutator). The formulas are intended to be well applicable for MLM. For instance, a masking for a formula like `(a+b)^2 = a^2 + 2ab + b^2` makes sense (e.g., `(a+[MASK])^2 = a^2 + [MASK]ab + b[MASK]2` -> masked tokens are deducable by the context), in contrast, formulas such as `f(x) = 3x+1` are not (e.g., `[MASK](x) = 3x[MASK]1` -> [MASK] tokens are ambigious). |
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You can find more information in [MAMUT: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training](https://arxiv.org/abs/2502.20855). |
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## Citation |
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``` |
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@misc{drechsel2025mamutnovelframeworkmodifying, |
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title={{MAMUT}: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training}, |
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author={Jonathan Drechsel and Anja Reusch and Steffen Herbold}, |
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year={2025}, |
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eprint={2502.20855}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2502.20855}, |
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} |
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``` |
