Datasets:
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README.md
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were evaluated using a sympy-based calculator, the outputs were checked to be consistent with the intermediate results and exported
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into a simple html-like language that BeautifulSoup can parse.
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## Content and Data splits
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For convenience, we created a validation set by sampling 200 random examples from the original train split.
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```python3
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datasets.load_dataset("MU-NLPC/Calc-gsm8k")
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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).
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## Licence
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MIT, consistently with the original dataset.
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title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems",
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author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek",
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booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track",
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month =
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year = "2023",
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address = "Singapore, Singapore",
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publisher = "Association for Computational Linguistics",
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were evaluated using a sympy-based calculator, the outputs were checked to be consistent with the intermediate results and exported
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into a simple html-like language that BeautifulSoup can parse.
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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)
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However, in case of gsm8k, we found no data leaks and removed no examples from the data.
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## Content and Data splits
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For convenience, we created a validation set by sampling 200 random examples from the original train split. This is the default variant:
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```python3
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datasets.load_dataset("MU-NLPC/Calc-gsm8k")
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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).
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## Related work
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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.
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- [**Calc-X collection**](https://huggingface.co/collections/MU-NLPC/calc-x-652fee9a6b838fd820055483) - datasets for training Calcformers
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- [**Calcformers collection**](https://huggingface.co/collections/MU-NLPC/calcformers-65367392badc497807b3caf5) - calculator-using models we trained and published on HF
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- [**Calc-X and Calcformers paper**](https://arxiv.org/abs/2305.15017)
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- [**Calc-X and Calcformers repo**](https://github.com/prompteus/calc-x)
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Here are links to the original dataset:
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- [**original gsm8k dataset**](https://huggingface.co/datasets/gsm8k)
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- [**original gsm8k paper**](https://arxiv.org/abs/2110.14168)
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- [**original gsm8k repo**](https://github.com/openai/grade-school-math)
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## Licence
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MIT, consistently with the original dataset.
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title = "Calc-X and Calcformers: Empowering Arithmetical Chain-of-Thought through Interaction with Symbolic Systems",
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author = "Marek Kadlčík and Michal Štefánik and Ondřej Sotolář and Vlastimil Martinek",
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booktitle = "Proceedings of the The 2023 Conference on Empirical Methods in Natural Language Processing: Main track",
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month = dec,
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year = "2023",
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address = "Singapore, Singapore",
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publisher = "Association for Computational Linguistics",
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