

# 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 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

The answers in the original dataset were 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 exported
into a simple html-like language that BeautifulSoup can parse.

We also perform in-dataset and cross-dataset data-leak detection within the [Calc-X collection](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483)
However, in case of gsm8k, we found no data leaks and removed no examples from the data.


## Content and Data splits

For convenience, we created a validation set by sampling 200 random examples from the original train split. This is the default variant:

```python
datasets.load_dataset("MU-NLPC/Calc-gsm8k")
```

The original data splits can be loaded using:

```python
datasets.load_dataset("MU-NLPC/Calc-gsm8k", "original-splits")
```

For more info about the content of the dataset, see [gsm8k HF dataset](https://huggingface.co/datasets/gsm8k) and the [official repository](https://github.com/openai/grade-school-math).



## 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 gsm8k dataset**](https://huggingface.co/datasets/gsm8k)
- [**original gsm8k paper**](https://arxiv.org/abs/2110.14168)
- [**original gsm8k repo**](https://github.com/openai/grade-school-math)



## Licence

MIT, consistently with the original dataset.


## Cite

If you use this version of the dataset in research, please cite the [original GSM8K paper](https://arxiv.org/abs/2110.14168), and [Calc-X collection](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",
}
```
task_categories:text-generation task_categories:question-answering size_categories:1K<n<10K language:en license:mit arxiv:2305.15017 arxiv:2110.14168 region:us
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TAGS
#task_categories-text-generation #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-mit #arxiv-2305.15017 #arxiv-2110.14168 #region-us 



# 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 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

The answers in the original dataset were 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 exported
into a simple html-like language that BeautifulSoup can parse.

We also perform in-dataset and cross-dataset data-leak detection within the Calc-X collection
However, in case of gsm8k, we found no data leaks and removed no examples from the data.


## Content and Data splits

For convenience, we created a validation set by sampling 200 random examples from the original train split. This is the default variant:



The original data splits can be loaded using:



For more info about the content of the dataset, see gsm8k HF dataset and the official repository.



## 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:

- original gsm8k dataset
- original gsm8k paper
- original gsm8k repo



## Licence

MIT, consistently with the original dataset.


## Cite

If you use this version of the dataset in research, please cite the original GSM8K paper, and Calc-X collection as follows:


# 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 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

The answers in the original dataset were 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 exported
into a simple html-like language that BeautifulSoup can parse.

We also perform in-dataset and cross-dataset data-leak detection within the Calc-X collection
However, in case of gsm8k, we found no data leaks and removed no examples from the data. ## Content and Data splits

For convenience, we created a validation set by sampling 200 random examples from the original train split. This is the default variant:



The original data splits can be loaded using:



For more info about the content of the dataset, see gsm8k HF dataset and the official repository. ## 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:

- original gsm8k dataset
- original gsm8k paper
- original gsm8k repo ## Licence

MIT, consistently with the original dataset. ## Cite

If you use this version of the dataset in research, please cite the original GSM8K paper, and Calc-X collection as follows:
TAGS
#task_categories-text-generation #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-mit #arxiv-2305.15017 #arxiv-2110.14168 #region-us 
 # 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 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

The answers in the original dataset were 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 exported
into a simple html-like language that BeautifulSoup can parse.

We also perform in-dataset and cross-dataset data-leak detection within the Calc-X collection
However, in case of gsm8k, we found no data leaks and removed no examples from the data. ## Content and Data splits

For convenience, we created a validation set by sampling 200 random examples from the original train split. This is the default variant:



The original data splits can be loaded using:



For more info about the content of the dataset, see gsm8k HF dataset and the official repository. ## 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:

- original gsm8k dataset
- original gsm8k paper
- original gsm8k repo ## Licence

MIT, consistently with the original dataset. ## Cite

If you use this version of the dataset in research, please cite the original GSM8K paper, and Calc-X collection as follows:
passage: TAGS
#task_categories-text-generation #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-mit #arxiv-2305.15017 #arxiv-2110.14168 #region-us 
# 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 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

The answers in the original dataset were 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 exported
into a simple html-like language that BeautifulSoup can parse.

We also perform in-dataset and cross-dataset data-leak detection within the Calc-X collection
However, in case of gsm8k, we found no data leaks and removed no examples from the data.## Content and Data splits

For convenience, we created a validation set by sampling 200 random examples from the original train split. This is the default variant:



The original data splits can be loaded using:



For more info about the content of the dataset, see gsm8k HF dataset and the official repository.