task_categories:text-classification 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
task_categories:
- text-classification
dataset_info:
  features:
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': product_color
          '1': shipping_option_name
          '2': product_variation_size_id
          '3': currency_buyer
  splits:
  - name: train
    num_bytes: 5242
    num_examples: 20
  download_size: 5842
  dataset_size: 5242
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-34a5d1ce

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/HS2611Adraca/my-distiset-34a5d1ce/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/HS2611Adraca/my-distiset-34a5d1ce/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "labels": [],
    "text": "This summer, the brand is releasing a limited edition collection of lightweight and breathable tops made from eco-friendly materials, perfect for hot summer days spent lounging by the pool or hiking in the mountains. The collection features bold, bright colors and comfortable designs that are both stylish and practical."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("HS2611Adraca/my-distiset-34a5d1ce", "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("HS2611Adraca/my-distiset-34a5d1ce")
```


</details>




