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---
size_categories: n<1K
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': budget-friendly
'1': practical
'2': fashionable
'3': high-end
splits:
- name: train
num_bytes: 2988
num_examples: 10
download_size: 4866
dataset_size: 2988
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-64eab852
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/apol/my-distiset-64eab852/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/apol/my-distiset-64eab852/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: default </summary><hr>
```json
{
"label": 3,
"text": "This sleek and luxurious Italian leather backpack features an intricate gold buckle and premium stitching, perfect for carrying your laptop, tablet, and other essentials to and from work or travel."
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("apol/my-distiset-64eab852", "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("apol/my-distiset-64eab852")
```
</details>
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