Datasets:

MU-NLPC
/

Calc-ape210k

	---
	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",
	}
	```