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': not review-related
          '1': review-related
  splits:
  - name: train
    num_bytes: 261849
    num_examples: 453
  download_size: 132092
  dataset_size: 261849
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-55a6b53b

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/lallu-lalla-ji/my-distiset-55a6b53b/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/lallu-lalla-ji/my-distiset-55a6b53b/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 0,
    "text": "The rapid growth of the city has led to an increased focus on sustainable infrastructure and green energy projects which include the installation of solar panels on rooftops and the implementation of a high-speed public transportation system."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("lallu-lalla-ji/my-distiset-55a6b53b", "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("lallu-lalla-ji/my-distiset-55a6b53b")
```


</details>




