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---
license: apache-2.0
configs:
- 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: 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 replace 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. The sequence of steps is then saved in HTML-like language
in the `chain` column. We keep the original columns in the dataset for convenience.
You can read more information about this process in our [technical report](https://arxiv.org/abs/2305.15017).
## Content and Data splits
Content and splits correspond to the original math_qa dataset.
See [mathqa HF dataset](https://huggingface.co/datasets/math_qa) and [official website](https://math-qa.github.io/) for more info.
Columns:
- `question` - th description of a mathematical problem in natural language
- `options` - dictionary with choices 'A' to 'E' as possible solutions
- `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
- `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
- `index` - index of the example in the original math_qa dataset
## 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 also [our technical report](https://arxiv.org/abs/2305.15017) as follows:
```bibtex
@article{kadlcik2023calcx,
title={Calc-X: Enriching Arithmetical Chain-of-Thoughts Datasets by Interaction with Symbolic Systems},
author={Marek Kadlčík and Michal Štefánik},
year={2023},
eprint={2305.15017},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
``` |