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: prompt
    dtype: string
  - name: quality_generation
    dtype: string
  - name: style_generation
    dtype: string
  - name: complexity_generation
    dtype: string
  - name: image
    dtype: 'null'
  - name: grouped_model_name
    sequence: 'null'
  splits:
  - name: train
    num_bytes: 383833
    num_examples: 500
  download_size: 179361
  dataset_size: 383833
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 imgsys-augmented-v3

This dataset has been created with [distilabel](https://distilabel.argilla.io/).


The pipeline script was uploaded to easily reproduce the dataset:
[augmentation_generation.py](https://huggingface.co/datasets/dvilasuero/imgsys-augmented-v3/raw/main/augmentation_generation.py).

It can be run directly using the CLI:

```console
distilabel pipeline run --script "https://huggingface.co/datasets/dvilasuero/imgsys-augmented-v3/raw/main/augmentation_generation.py"
```


## 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/imgsys-augmented-v3/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dvilasuero/imgsys-augmented-v3/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "complexity_generation": "intelligence manifesting as a luminous, ethereal network, intertwining with the fabric of a cybernetic forest, during a solar eclipse, blending organic and mechanical elements, transcending dimensions, and radiating a sense of profound enigma.",
    "grouped_model_name": [
        null,
        null,
        null,
        null
    ],
    "image": null,
    "prompt": "intelligence",
    "quality_generation": "intelligent AI, high resolution, photorealistic, vibrant color palette, soft golden hour light, dynamic perspective, intricate details, serene mood",
    "style_generation": "Intelligence, depicted as a glowing neural network in a cyberpunk cityscape, neon lights reflecting off rain-soaked streets, digital painting, octane render, cinematic lighting, wide lens, trending on ArtStation"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/imgsys-augmented-v3", "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/imgsys-augmented-v3")
```


</details>




