---
size_categories: n<1K
task_categories:
- text-classification
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': government
          '1': politics
          '2': healthcare
          '3': economy
          '4': human-rights
          '5': education
          '6': social-welfare
          '7': transportation
          '8': environment
          '9': environmental-conservation
  splits:
  - name: train
    num_bytes: 21779
    num_examples: 100
  download_size: 13759
  dataset_size: 21779
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-7adcafcd

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/Varsha-20052004/my-distiset-7adcafcd/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/Varsha-20052004/my-distiset-7adcafcd/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 local community center is in dire need of financial support to maintain its programs and services for low-income families. Many children rely on these programs for after-school meals and tutoring."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("Varsha-20052004/my-distiset-7adcafcd", "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("Varsha-20052004/my-distiset-7adcafcd")
```


</details>