---
size_categories: n<1K
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: generations
    sequence: 'null'
  - name: generation_models
    sequence: string
  - name: ratings
    sequence: 'null'
  - name: rationales
    sequence: 'null'
  splits:
  - name: train
    num_bytes: 59771
    num_examples: 100
  download_size: 34956
  dataset_size: 59771
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_3

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/aimlresearch2023/distilabel_3/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/aimlresearch2023/distilabel_3/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "generation_models": [
        "microsoft/Phi-3-mini-128k-instruct",
        "microsoft/Phi-3-mini-128k-instruct"
    ],
    "generations": [
        null,
        null
    ],
    "instruction": "Provide step-by-step instructions on how to make a safe and effective homemade all-purpose cleaner from common household ingredients. The guide should include measurements, tips for storing the cleaner, and additional variations or scents that can be added. Additionally, the guide should be written in clear and concise language, with helpful visuals or photographs to aid in the process.",
    "ratings": [
        null,
        null
    ],
    "rationales": [
        null,
        null
    ]
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("aimlresearch2023/distilabel_3", "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("aimlresearch2023/distilabel_3")
```


</details>