size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif datacraft
---
size_categories: n<1K
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': value-for-money
          '1': customer-service
          '2': overall-experience
          '3': music-catalog
          '4': user-interface
  splits:
  - name: train
    num_bytes: 2808
    num_examples: 10
  download_size: 4482
  dataset_size: 2808
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
- datacraft
---

<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 my-distiset-1e19c746

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/fxxy100/my-distiset-1e19c746/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/fxxy100/my-distiset-1e19c746/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 3,
    "text": "I\u0027ve been using this music streaming service for months now, and I must say it has become an integral part of my daily routine. The variety of genres is impressive, and the discovery feature is spot on. However, sometimes the audio quality is a bit off, which can be frustrating."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("fxxy100/my-distiset-1e19c746", "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("fxxy100/my-distiset-1e19c746")
```


</details>




