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: string
  splits:
  - name: train
    num_bytes: 5450
    num_examples: 2
  download_size: 8416
  dataset_size: 5450
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 magpieui-climate-assistant

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/magpieui-climate-assistant/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dvilasuero/magpieui-climate-assistant/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "conversation": [
        {
            "content": "What are some eco-friendly alternatives to traditional air conditioning during the hot summer months? \n\nWhile I\u0027m at it, what are some eco-friendly ways to repel flies and mosquitoes and rid my home of rodents?",
            "role": "user"
        },
        {
            "content": "Here are some eco-friendly alternatives to traditional air conditioning to help you stay cool during the hot summer months:\n\n1.  **Natural Ventilation**: Open windows and doors to allow cool air to enter and hot air to escape. This is the most energy-efficient way to cool your home.\n2.  ** Ceiling Fans**: Install ceiling fans to circulate air and make your home feel cooler. They use significantly less energy than air conditioners.\n3.  **Whole House Fans**: Whole house fans can pull hot air out of your home and bring cooler air in through windows.\n4.  **Shading**: Close curtains, blinds, or",
            "role": "assistant"
        },
        {
            "content": "You seems to have gotten cut off. Here is what I requested:  What are some eco-friendly alternatives to traditional air conditioning during the hot summer months? \n\nWhile I\u0027m at it, what are some eco-friendly ways to repel flies and mosquitoes and rid my home of rodents?\n\n1.  **Shading**: Close curtains, blinds, or shades during the hottest parts of the day to block sunlight and keep your home cool.\n\n2.  **Evaporative Coolers**: Evaporative coolers use water to cool the air, which is more efficient than traditional air conditioners. They\u0027re also known as swamp coolers.\n\n",
            "role": "user"
        },
        {
            "content": "Here are the complete answers to your questions:\n\n**Eco-Friendly Alternatives to Traditional Air Conditioning:**\n\n1.  **Natural Ventilation**: Open windows and doors to allow cool air to enter and hot air to escape. This is the most energy-efficient way to cool your home.\n2.  **Ceiling Fans**: Install ceiling fans to circulate air and make your home feel cooler. They use significantly less energy than air conditioners.\n3.  **Whole House Fans**: Whole house fans can pull hot air out of your home and bring cooler air in through windows.\n4.  **Shading**: Close curtains, blinds",
            "role": "assistant"
        },
        {
            "content": "How can our community be more supportive of choosing organic foods and buying from local farmers.",
            "role": "user"
        },
        {
            "content": "Here are some strategies to foster a supportive community for choosing organic foods and buying from local farmers:\n\n\n\n1.  **Community-Supported Agriculture (CSA) Programs**: Encourage local farmers to start CSA programs, where community members buy shares of the farm\u0027s produce on a regular basis. This model helps farmers financially and provides members with fresh, organic produce.\n2.  **Farmers\u0027 Markets**: Organize and promote local farmers\u0027 markets, where farmers can sell their organic produce directly to consumers. This platform allows consumers to connect with farmers, learn about their practices, and purchase fresh, seasonal produce.\n3.  **Online Platforms**:",
            "role": "assistant"
        }
    ],
    "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/magpieui-climate-assistant", "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/magpieui-climate-assistant")
```


</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},
}
```


