OpenMath-GSM8K-masked

# OpenMath GSM8K Masked

We release a *masked* version of the [GSM8K](https://github.com/openai/grade-school-math) solutions.
This data can be used to aid synthetic generation of additional solutions for GSM8K dataset
as it is much less likely to lead to inconsistent reasoning compared to using
the original solutions directly.

This dataset was used to construct [OpenMathInstruct-1](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1):
a math instruction tuning dataset with 1.8M problem-solution pairs
generated using permissively licensed [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) model.

For details of how the masked solutions were created, see our [paper](https://arxiv.org/abs/2402.10176).

You can re-create this dataset or apply similar techniques to mask solutions for other datasets
by using our [open-sourced code](https://github.com/Kipok/NeMo-Skills).

## Citation

If you find our work useful, please consider citing us!

```bibtex
@article{toshniwal2024openmath,
title = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset},
author = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
year = {2024},
journal = {arXiv preprint arXiv: Arxiv-2402.10176}
}
```

## License

The use of this dataset is governed by the [NVIDIA License](LICENSE) which permits commercial usage.

# OpenMath GSM8K 掩码版数据集
我们发布了[GSM8K](https://github.com/openai/grade-school-math)解题方案的掩码版本。该数据集可用于辅助合成生成GSM8K数据集的更多解题方案，相较于直接使用原始解题方案，其导致推理不一致的概率显著更低。

本数据集曾用于构建[OpenMathInstruct-1](https://huggingface.co/datasets/nvidia/OpenMathInstruct-1)——一款包含180万道题解对的数学指令微调数据集，其通过使用许可宽松的[Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)模型生成。

有关掩码解题方案的具体构建细节，请参阅我们的[论文](https://arxiv.org/abs/2402.10176)。您可通过我们的[开源代码](https://github.com/Kipok/NeMo-Skills)复现本数据集，或采用类似技术为其他数据集的解题方案添加掩码。

## 引用
若您认为本工作对您有所帮助，请考虑引用我们的研究成果：
bibtex
@article{toshniwal2024openmath,
title = {OpenMathInstruct-1: A 1.8 Million Math Instruction Tuning Dataset},
author = {Shubham Toshniwal and Ivan Moshkov and Sean Narenthiran and Daria Gitman and Fei Jia and Igor Gitman},
year = {2024},
journal = {arXiv preprint arXiv: Arxiv-2402.10176}
}

## 许可
本数据集的使用受[NVIDIA许可协议](LICENSE)约束，该协议允许商业用途。