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': vraag
          '1': case informatie
          '2': aanpak informatie
          '3': artikel informatie
          '4': klant informatie
          '5': algemene informatie
          '6': product informatie
          '7': contact
          '8': oriëntatie op een baan
          '9': dienst informatie
          '10': geen vraag
  splits:
  - name: train
    num_bytes: 1111
    num_examples: 10
  download_size: 2655
  dataset_size: 1111
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 dataset_sr_0_2

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/deising/dataset_sr_0_2/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/deising/dataset_sr_0_2/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 2,
    "text": "Ik wil weten hoe ik mijn website kunnen laten updaten zodat deze mijn actuele producten en diensten weergeeft."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("deising/dataset_sr_0_2", "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("deising/dataset_sr_0_2")
```


</details>




