Datasets:
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---
language:
- en
license: mit
size_categories:
- 1K<n<10K
task_categories:
- text-generation
- question-answering
dataset_info:
config_name: original-splits
features:
- name: question
dtype: string
- name: chain
dtype: string
- name: result
dtype: string
- name: result_float
dtype: float64
splits:
- name: train
num_bytes: 5318579
num_examples: 7473
- name: test
num_bytes: 957406
num_examples: 1319
download_size: 2949137
dataset_size: 6275985
configs:
- config_name: original-splits
data_files:
- split: train
path: original-splits/train-*
- split: test
path: original-splits/test-*
---
# Dataset Card for "Calc-gsm8k"
## Summary
This dataset is an instance of gsm8k 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 (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 out-source the computations in the reasoning chain to a calculator.
## Construction Process
The answers in the original dataset was in in a structured but non-standard format. So, the answers were parsed, all arithmetical expressions
were evaluated using a sympy-based calculator, the outputs were checked to be consistent with the intermediate results and finally exported
into a simple html-like language that BeautifulSoup can parse.
## Content and Data splits
Content corresponds to the original gsm8k dataset.
In this version, we created validation set by sampling 200 random examples from the original train split. The original data splits can be downloaded using:
```
datasets.load_dataset("MU-NLPC/Calc-gsm8k", "original-splits")
```
See [gsm8k HF dataset](https://huggingface.co/datasets/gsm8k) and [official repository](https://github.com/openai/grade-school-math) for more info.
## Licence
MIT, consistently with the original dataset.
## Cite
If you use this version of dataset in research, please cite the [original GSM8K paper](https://arxiv.org/abs/2110.14168) and our report 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}
}
``` |