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
  - name: generations
    sequence: string
  - name: generation_models
    sequence: string
  - name: ratings
    sequence: 'null'
  - name: rationales
    sequence: 'null'
  splits:
  - name: train
    num_bytes: 42101
    num_examples: 100
  download_size: 26409
  dataset_size: 42101
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 distillama3-prompts100

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/berkekavak/distillama3-prompts100/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/berkekavak/distillama3-prompts100/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "generation_models": [
        "meta-llama/Meta-Llama-3-8B-Instruct"
    ],
    "generations": [
        "It"
    ],
    "instruction": "This spray is really nice. It smells really good, goes on really fine, and does the trick. I will say it feels like you need a lot of it though to get the texture I want. I have a lot of hair, medium thickness. I am comparing to other brands with yucky chemicals so I\u0027m gonna stick with this. Try it!",
    "ratings": [
        null
    ],
    "rationales": [
        null
    ]
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("berkekavak/distillama3-prompts100", "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("berkekavak/distillama3-prompts100")
```


</details>
