language:en size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif datacraft
---
language:
- en
size_categories: n<1K
dataset_info:
  features:
  - name: text
    dtype: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': frame
          '1': gen
          '2': excl
          '3': assump
          '4': unfair
          '5': stereo
  splits:
  - name: train
    num_bytes: 31622
    num_examples: 100
  download_size: 17180
  dataset_size: 31622
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/bhavan2410/my-distiset-be899639/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/bhavan2410/my-distiset-be899639/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "labels": [
        0,
        1,
        3,
        2,
        5
    ],
    "text": "This dichotomy in perceived stereo audio fidelity among audiophiles and casual listeners may be attributed to the former\u0027s increased sensitivity to spatialization cues, which are often compromised in cost-effective home stereos."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("bhavan2410/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("bhavan2410/my-distiset-be899639")
```


</details>




