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: train
num_bytes: 111988047
num_examples: 195179
- name: validation
num_bytes: 1172933
num_examples: 1783
- name: test
num_bytes: 1157061
num_examples: 1785
download_size: 50827709
dataset_size: 114318041
- 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: train
num_bytes: 111988047
num_examples: 195179
- name: validation
num_bytes: 2798479
num_examples: 4867
- name: test
num_bytes: 2793355
num_examples: 4867
download_size: 52234086
dataset_size: 117579881
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: validation
path: original-splits/validation-*
- split: test
path: original-splits/test-*
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 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 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.
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.
Content and Data splits
The default config contains filtered splits with data leaks removed. You can load it using:
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 and the paper for more info.
You can load it using:
datasets.load_dataset("MU-NLPC/calc-ape210k", "original-splits")
Columns:
id
- id of the examplequestion
- the description of the math problem. Automatically translated from thequestion_chinese
column into English using Google Translatequestion_chinese
- description of the math problem in Chinesechain
- linearizedequation
, sequence of arithmetic steps in HTML-like language that can be evaluated using our sympy-based calculatorresult
- result as a string (can be an integer, float, or a fraction)result_float
- result as a floatequation
- a nested expression that evaluates to the correct answer
Columns id
, question
, chain
, and result
are present in all datasets in Calc-X collection.
Licence
MIT, consistently with the original dataset.
Cite
If you use this version of the dataset in research, please cite the original Ape210k 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 = December,
year = "2023",
address = "Singapore, Singapore",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2305.15017",
}