--- dataset_info: features: - name: id dtype: int64 - name: text dtype: string splits: - name: train num_bytes: 225647910.0 num_examples: 2886810 - name: test num_bytes: 23848817.0 num_examples: 311298 download_size: 131762427 dataset_size: 249496727.0 --- # Dataset Card for "math_formulas" 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). 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). 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). ## Citation ``` @misc{drechsel2025mamutnovelframeworkmodifying, title={{MAMUT}: A Novel Framework for Modifying Mathematical Formulas for the Generation of Specialized Datasets for Language Model Training}, author={Jonathan Drechsel and Anja Reusch and Steffen Herbold}, year={2025}, eprint={2502.20855}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2502.20855}, } ```