--- license: apache-2.0 configs: - config_name: default data_files: - split: train path: data/train-* - config_name: original-splits data_files: - split: train path: original-splits/train-* - split: validation path: original-splits/validation-* - split: test path: original-splits/test-* dataset_info: - config_name: default features: - name: id dtype: string - name: question dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: question_without_options dtype: string - name: options struct: - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: E dtype: string - name: annotated_formula dtype: string - name: linear_formula dtype: string - name: rationale dtype: string - name: category dtype: string splits: - name: train num_bytes: 25058735 num_examples: 20868 download_size: 11157481 dataset_size: 25058735 - config_name: original-splits features: - name: id dtype: string - name: question dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: question_without_options dtype: string - name: options struct: - name: A dtype: string - name: B dtype: string - name: C dtype: string - name: D dtype: string - name: E dtype: string - name: annotated_formula dtype: string - name: linear_formula dtype: string - name: rationale dtype: string - name: category dtype: string splits: - name: train num_bytes: 25058735 num_examples: 20868 - name: validation num_bytes: 3722848 num_examples: 3102 - name: test num_bytes: 2423833 num_examples: 2029 download_size: 13928430 dataset_size: 31205416 --- # Dataset Card for Calc-math_qa ## Summary This dataset is an instance of math_qa dataset, converted to a simple HTML-like language that can be easily parsed (e.g. by BeautifulSoup). The data contains 3 types of tags: - gadget: A tag whose content is intended to be evaluated by calling an external tool (sympy-based calculator in this case) - output: An output of the external tool - result: The final answer of the mathematical problem (correct option) ## Supported Tasks The dataset is intended for training Chain-of-Thought reasoning **models able to use external tools** to enhance the factuality of their responses. This dataset presents in-context scenarios where models can outsource the computations in the reasoning chain to a calculator. ## Construction Process We took the original math_qa dataset, parsed the nested formulas, linearized them into a sequence (chain) of operations, and replaced all advanced function calls (such as `circle_area`) with explicit elementary operations. We evaluate all the steps in each example and filter out examples if their evaluation does not match the answer selected as correct in the data with a 5% tolerance, with about 26k examples remaining. The sequence of steps is then saved in HTML-like language in the `chain` column. We also perform in-dataset and cross-dataset data-leak detection within [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). Specifically for MathQA, we found that majority of validation and test examples are near-duplicates of some example in the train set, and that all validation and test examples likely originate from the Aqua-RAT train split. We do not recommend to original validation and test splits of the MathQA dataset. You can read more information about this process in our [Calc-X paper](https://arxiv.org/abs/2305.15017). ## Data splits In our default configuration, test and validation splits are removed and we recommend using MathQA for training only. You can load it using: ```python datasets.load_dataset("MU-NLPC/calc-math_qa") ``` If you want to use the original dataset splits, you can load it using: ```python datasets.load_dataset("MU-NLPC/calc-math_qa", "original-splits") ``` ## Atributes - **id** - id of the example - **question** - the description of a mathematical problem in natural language, and includes the options to be selected from - **chain** - solution in the form of step-by-step calculations encoded in simple html-like language. computed from `annotated_formula` column - **result** - the correct option - **result_float** - the result converted to a float - **question_without_options** - same as `question`, but does not contain the options - **options** - dictionary of options to choose from, one is correct, keys are "A".."E" - **annotated_formula** - human-annotated nested expression that (approximately) evaluates to the selected correct answer - **linear_formula** - same as `annotated_formula`, but linearized by original math_qa authors - **rationale** - human-annotated free-text reasoning that leads to the correct answer - **category** - category of the math problem Attributes **id**, **question**, **chain**, and **result** are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). ## Sources - [mathqa HF dataset](https://huggingface.co/datasets/math_qa) - [official website](https://math-qa.github.io/) ## Related work This dataset was created as a part of a larger effort in training models capable of using a calculator during inference, which we call Calcformers. We have released a collection of datasets on solving math problems with calculator interactions on HuggingFace called [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). You can find the models we trained in the [Calcformers collection](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5). You can read more in our paper [Calc-X and Calcformers](https://arxiv.org/abs/2305.15017). ## Licence Apache 2.0, consistently with the original dataset. ## Cite If you use this version of dataset in research, please cite the [original MathQA paper](https://arxiv.org/abs/1905.13319), and [Calc-X paper](https://arxiv.org/abs/2305.15017) as follows: ```bibtex @inproceedings{kadlcik-etal-2023-soft, title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems", author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek", booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track", month = dec, year = "2023", address = "Singapore, Singapore", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2305.15017", } ```