size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel arxiv:2406.08464 region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: response
    dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 155367
    num_examples: 100
  download_size: 85444
  dataset_size: 155367
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for distilabel-magpie-math

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/gabrielmbmb/distilabel-magpie-math/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/gabrielmbmb/distilabel-magpie-math/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "instruction": "Consider the group of isometries of the 3-dimensional Euclidean space. Is the group operation is commutative.",
    "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct",
    "response": "The group of isometries of the 3-dimensional Euclidean space includes translations, rotations, and reflections. To determine if the group operation is commutative, we can examine the different types of isometries and their compositions.\n\nTranslations commute with each other. If we have two translations T1 and T2, moving by vectors v1 and v2 respectively, then applying T1 followed by T2 gives the same result as applying T2 followed by T1, moving by the vector v1 + v2.\n\nHowever, rotations do not necessarily commute with each other. Consider two rotations R1 and R2 around different axes. If we apply R1 followed by R2, the result can be different from applying R2 followed by R1. This is because the order of rotation matters; rotating around one axis can change the orientation of the object relative to the other axis.\n\nWe can also consider a translation and a rotation. If we apply a translation T followed by a rotation R, the result is generally different from applying R followed by T. This is because the translation moves the object to a new position, and then the rotation occurs around a different point.\n\nIn conclusion, the group operation of the group of isometries of the 3-dimensional Euclidean space is not commutative, because the order of composition of different types of isometries (translations, rotations, and reflections) can produce different results.\n\nThis property is related to the concept of non-abelian groups, which are groups where the group operation is not commutative. The group of isometries of the 3-dimensional Euclidean space is a non-abelian group, which is in contrast to abelian groups, where the group operation is commutative."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("gabrielmbmb/distilabel-magpie-math", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("gabrielmbmb/distilabel-magpie-math")
```


</details>





## References


```
@misc{xu2024magpiealignmentdatasynthesis,
    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
    year={2024},
    eprint={2406.08464},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2406.08464},
}
```


