--- license: mit dataset_info: - config_name: default features: - name: id dtype: string - name: question dtype: string - name: question_chinese dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: equation dtype: string splits: - name: test num_bytes: 1153807 num_examples: 1785 - name: train num_bytes: 111628273 num_examples: 195179 - name: validation num_bytes: 1169676 num_examples: 1783 download_size: 50706818 dataset_size: 113951756 - config_name: original-splits features: - name: id dtype: string - name: question dtype: string - name: question_chinese dtype: string - name: chain dtype: string - name: result dtype: string - name: result_float dtype: float64 - name: equation dtype: string splits: - name: test num_bytes: 2784396 num_examples: 4867 - name: train num_bytes: 111628273 num_examples: 195179 - name: validation num_bytes: 2789481 num_examples: 4867 download_size: 52107586 dataset_size: 117202150 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* - split: validation path: data/validation-* - config_name: original-splits data_files: - split: test path: original-splits/test-* - split: train path: original-splits/train-* - split: validation path: original-splits/validation-* --- # Dataset Card for Calc-ape210k ## Summary This dataset is an instance of Ape210K 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 to the mathematical problem (a number) ## 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 First, we translated the questions into English using Google Translate. Next, we parsed the equations and the results. We linearized the equations into a sequence of elementary steps and evaluated them using a sympy-based calculator. We numerically compare the output with the result in the data and remove all examples where they do not match (less than 3% loss in each split). Finally, we save the chain of steps in the HTML-like language in the `chain` column. We keep the original columns in the dataset for convenience. 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 Ape210k, we removed parts of the validation and test split, with around 1700 remaining in each. You can read more information about this process in our [Calc-X paper](https://arxiv.org/abs/2305.15017). ## Data splits The default config contains filtered splits with data leaks removed. You can load it using: ```python datasets.load_dataset("MU-NLPC/calc-ape210k") ``` In the `original-splits` config, the data splits are unfiltered and correspond to the original Ape210K dataset. See [ape210k dataset github](https://github.com/Chenny0808/ape210k) and [the paper](https://arxiv.org/abs/2009.11506) for more info. You can load it using: ```python datasets.load_dataset("MU-NLPC/calc-ape210k", "original-splits") ``` ## Attributes - **id** - id of the example - **question** - the description of the math problem. Automatically translated from the `question_chinese` column into English using Google Translate - **question_chinese** - the original description of the math problem in Chinese - **chain** - linearized `equation`, sequence of arithmetic steps in HTML-like language that can be evaluated using our sympy-based calculator - **result** - result as a string (can be an integer, float, or a fraction) - **result_float** - result, converted to a float - **equation** - a nested expression that evaluates to the correct answer Attributes **id**, **question**, **chain**, and **result** are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483). ## 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. - [**Calc-X collection**](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) - datasets for training Calcformers - [**Calcformers collection**](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) - calculator-using models we trained and published on HF - [**Calc-X and Calcformers paper**](https://arxiv.org/abs/2305.15017) - [**Calc-X and Calcformers repo**](https://github.com/prompteus/calc-x) Here are links to the original dataset: - [**original Ape210k dataset and repo**](https://github.com/Chenny0808/ape210k) - [**original Ape210k paper**](https://arxiv.org/abs/2009.11506) ## Licence MIT, consistently with the original dataset. ## Cite If you use this version of the dataset in research, please cite the [original Ape210k paper](https://arxiv.org/abs/2009.11506), and the [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", } ```