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Datasets:
dvilasuero
/
img-prefs-distilabel-artifacts-sample

Modalities:
Image
Text
Formats:
parquet
Size:
< 1K
Tags:
Synthetic
distilabel
rlaif
Libraries:
Datasets
pandas
Dataset card Viewer Files Community
img-prefs-distilabel-artifacts-sample
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---
size_categories: n<1K
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: models
    sequence: string
  - name: images
    list:
    - name: path
      dtype: string
  splits:
  - name: train
    num_bytes: 615
    num_examples: 2
  download_size: 3357
  dataset_size: 615
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 img-prefs-distilabel-artifacts-sample

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/dvilasuero/img-prefs-distilabel-artifacts-sample/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dvilasuero/img-prefs-distilabel-artifacts-sample/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "images": [
        {
            "path": "artifacts/flux_schnell/images/90b884933d23c4d57ca01dbe2898d405.jpeg"
        },
        {
            "path": "artifacts/flux_dev/images/90b884933d23c4d57ca01dbe2898d405.jpeg"
        }
    ],
    "models": [
        "black-forest-labs/FLUX.1-schnell",
        "black-forest-labs/FLUX.1-dev"
    ],
    "prompt": "intelligence"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/img-prefs-distilabel-artifacts-sample", "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/img-prefs-distilabel-artifacts-sample")
```


</details>



## Artifacts


* **Step**: `flux_dev`
  
    * **Artifact name**: `images`
      
        * `type`: image
      
        * `library`: diffusers
      
  

* **Step**: `flux_schnell`
  
    * **Artifact name**: `images`
      
        * `type`: image
      
        * `library`: diffusers