size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: instruction
    dtype: string
  splits:
  - name: train
    num_bytes: 13007
    num_examples: 45
  download_size: 9982
  dataset_size: 13007
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 final_agent_instruct

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/hassaan-qaisar/final_agent_instruct/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/hassaan-qaisar/final_agent_instruct/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "generation": "Based on the suggestions, here are three refined and complex questions:\n\n1. What are the primary renewable energy sources dominant in different regions around the world, and how do their unique characteristics and geographical advantages contribute to combating climate change globally, particularly in areas with limited infrastructure or resource availability?\n\n(This question combines the regional focus suggestion with an added consideration of challenges and limitations, encouraging the reader to think about innovative solutions for less developed regions.)\n\n2. How do government policies, international agreements, and regulatory frameworks influence the development and deployment of renewable energy sources, and what are the most effective strategies for promoting a global transition to a low-carbon economy while also addressing issues like energy justice, indigenous rights, and socioeconomic equity?\n\n(This question incorporates the policy and governance suggestion, while adding a layer of complexity by considering social and environmental justice concerns. It asks the reader to weigh the trade-offs between competing priorities.)\n\n3. Can integrated approaches to renewable energy development, such as rooftop solar programs, community-based wind projects, or district heating systems, contribute to achieving broader sustainable development objectives like improving energy access, reducing poverty, and enhancing food security, particularly in rural or marginalized communities, and what are the key success factors and scalability challenges associated with these efforts?\n\n(This question explores the intersection with other sustainability goals, as"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("hassaan-qaisar/final_agent_instruct", "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("hassaan-qaisar/final_agent_instruct")
```


</details>
