Datasets:

Modalities:
Text
Formats:
parquet
Languages:
English
ArXiv:
Libraries:
Datasets
pandas
License:
Calc-mawps / README.md
prompteus's picture
Update README.md
38c1005
---
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:
```python
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](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
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:
```python
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 for `x`
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 MAWPS dataset**](http://lang.ee.washington.edu/MAWPS)
- [**MAWPS dataset variant in Arabic**](https://huggingface.co/datasets/omarxadel/MaWPS-ar)
- [**original MAWPS paper**](https://aclanthology.org/N16-1136/)
- [**original MAWPS repo**](https://github.com/sroy9/mawps)
## 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](https://aclanthology.org/N16-1136/), and [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",
}
```