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: conversation
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: model_name
    dtype: 'null'
  - name: enhanced_prompt
    dtype: 'null'
  splits:
  - name: train
    num_bytes: 431
    num_examples: 1
  download_size: 5016
  dataset_size: 431
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 magpie-reflection

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/dvilasuero/magpie-reflection/raw/main/pipeline.yaml"
```

or explore the configuration:

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

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "conversation": [
        {
            "content": "import pandas as pd\n\n# creating a DataFrame with missing values\ndf = pd.DataFrame({\u0027A\u0027: [1, 2, 3, 4, 5],\n                   \u0027B\u0027: [6, 7, None, 9, 10],\n                   \u0027C\u0027: [10, 11, 12, 13, 14]})\n\n# find the index of the missing value\nindex_of_missing_value = df[\u0027B\u0027].index[df[\u0027B\u0027].isnull()]\nprint(index_of_missing_value)\n\n# replace the missing value with the above value\ndf.loc[index_of_missing_value, \u0027B\u0027] = df[\u0027",
            "role": "user"
        }
    ],
    "enhanced_prompt": null,
    "model_name": null
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/magpie-reflection", "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("dvilasuero/magpie-reflection")
```


</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},
}
```


