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---
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
---
# Mathematical Formulas (MF)
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/aieng-lab/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).
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).
A similar dataset consisting of mathematical texts (i.e., texts containing LaTeX formulas between an inline math environment `$...$`) is [https://huggingface.co/datasets/ddrg/math_text](https://huggingface.co/datasets/ddrg/math_text).
## Citation
```
@article{
drechsel2025mamut,
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},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2025},
url={https://openreview.net/forum?id=khODmRpQEx}
}
``` |