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: label
    dtype:
      class_label:
        names:
          '0': health
          '1': entertainment
          '2': politics-right
          '3': politics-left
          '4': business
          '5': sports
          '6': neutral-politics
  splits:
  - name: train
    num_bytes: 65340
    num_examples: 98
  download_size: 38186
  dataset_size: 65340
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-4f6936cb

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/karteek/my-distiset-4f6936cb/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/karteek/my-distiset-4f6936cb/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 6,
    "text": "The proposed Green New Deal aims to transition the United States to 100% renewable energy sources by 2050, which could lead to significant job creation in the clean energy sector, as well as reduce carbon emissions and greenhouse gas pollution. However, critics argue that this ambitious plan could lead to increased energy costs and a potential strain on the national grid. Furthermore, some experts suggest that the timeframe for achieving 100% renewable energy is overly ambitious and may not be technologically feasible in the near future, requiring substantial advancements in energy storage and grid management technologies."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("karteek/my-distiset-4f6936cb", "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("karteek/my-distiset-4f6936cb")
```


</details>




