---
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}, 
}
```