language:
- en
license: mit
size_categories:
- 1K<n<10K
task_categories:
- text-generation
tags:
- math world problems
- math
- arithmetics
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: equation
dtype: string
- name: expression
dtype: string
splits:
- name: train
num_bytes: 298347
num_examples: 1089
- name: validation
num_bytes: 285321
num_examples: 1040
- name: test
num_bytes: 142648
num_examples: 520
download_size: 0
dataset_size: 726316
- 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: equation
dtype: string
- name: expression
dtype: string
splits:
- name: train
num_bytes: 1000546
num_examples: 3636
- name: test
num_bytes: 142648
num_examples: 520
- name: validation
num_bytes: 285321
num_examples: 1040
download_size: 128730
dataset_size: 1428515
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
- config_name: original-splits
data_files:
- split: train
path: original-splits/train-*
- split: test
path: original-splits/test-*
- split: validation
path: original-splits/validation-*
Dataset Card for Calc-MAWPS
Summary
The dataset is a collection of simple math word problems focused on arithmetics. It is derived from https://huggingface.co/datasets/omarxadel/MaWPS-ar.
The main addition in this dataset variant is the chain
column. It was created by converting the solution 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
This variant of 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.
Data splits
We provide 2 variants of the dataset. In the first one, the data splits correspond to the original one and can be loaded using:
datasets.load_dataset("MU-NLPC/calc-mawps", "original-splits")
The second one is filtered to prevent data leaks (overly similar examples in train and test/val splits) in between and across datasets in Calc-X collection. Specifically, we filtered out around 2,500 near-duplicates from the train set that were similar to some instances in the MAWPS val and test splits and ASDiv-A test split. You can load this variant via:
datasets.load_dataset("MU-NLPC/calc-mawps")
Attributes:
- id: id of the example
- question: problem description in English
- question_arabic: problem description in Arabic
- chain: series of simple operations (derived from expression) that lead to the solution
- result: the solution for x as a number or fraction (string)
- result_float: same as
result
but converted to a float - equation: an equation that needs to be solved for
x
to obtain the result. Usually in the form of "x = ..." but not always. - expression: arithmetic expression derived from
equation
that solves it forx
Attributes id, question, chain, and result are present in all datasets in Calc-X collection.
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 - datasets for training Calcformers
- Calcformers collection - calculator-using models we trained and published on HF
- Calc-X and Calcformers paper
- Calc-X and Calcformers repo
Here are links to the original dataset:
Licence
MIT, consistent with the original source dataset linked above.
Cite
If you use this version of the dataset in research, please cite the original MAWPS paper, and Calc-X paper as follows:
@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",
}