size_categories:n<1K format:parquet modality:image 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: persona
    dtype: string
  - name: model_name
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 309385.0
    num_examples: 3
  download_size: 312256
  dataset_size: 309385.0
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 test-finepersonas-v0.1-tiny-flux-schnell-v3

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/plaguss/test-finepersonas-v0.1-tiny-flux-schnell-v3/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/plaguss/test-finepersonas-v0.1-tiny-flux-schnell-v3/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "image": "",
    "model_name": "black-forest-labs/FLUX.1-schnell",
    "persona": "A local art historian and museum professional interested in 19th-century American art and the local cultural heritage of Cincinnati."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("plaguss/test-finepersonas-v0.1-tiny-flux-schnell-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("plaguss/test-finepersonas-v0.1-tiny-flux-schnell-v3")
```


</details>




