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---
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 to 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](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
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](https://arxiv.org/abs/2305.15017).
## Attributes
- `id` - id of the example
- `question` - the description of the math problem. Automatically translated from the `question_chinese` column into English using Google Translate
- `question_chinese` - description of the math problem in Chinese
- `chain` - linearized `equation`, sequence of arithmetic steps in HTML-like language that can be evaluated using our sympy-based calculator
- `result` - result as a string (can be an integer, float, or a fraction)
- `result_float` - result as a float
- `equation` - a nested expression that evaluates to the correct answer
Attributes `id`, `question`, `chain`, and `result` are present in all datasets in [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483).
## Data splits
The default config contains filtered splits with data leaks removed.
You can load it using:
```python3
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](https://github.com/Chenny0808/ape210k) and [the paper](https://arxiv.org/abs/2009.11506) for more info.
You can load it using:
```python3
datasets.load_dataset("MU-NLPC/calc-ape210k", "original-splits")
```
## Licence
MIT, consistently with the original dataset.
## Cite
If you use this version of the dataset in research, please cite the [original Ape210k paper](https://arxiv.org/abs/2009.11506), and the [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",
}
``` |