---
size_categories: n<1K
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': positive
          '1': negative
  splits:
  - name: train
    num_bytes: 29799
    num_examples: 100
  download_size: 13924
  dataset_size: 29799
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-be899639

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/kfhfardin/my-distiset-be899639/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/kfhfardin/my-distiset-be899639/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 1,
    "text": "The new smartwatch\u0027s battery life is decent, but the lack of water resistance is a major concern. The GPS tracking feature is accurate, but the user interface could be more intuitive."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("kfhfardin/my-distiset-be899639", "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("kfhfardin/my-distiset-be899639")
```


</details>