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: labels
    sequence:
      class_label:
        names:
          '0': not-innovative
          '1': breakthrough
          '2': incremental-innovation
  splits:
  - name: train
    num_bytes: 5662
    num_examples: 10
  download_size: 6812
  dataset_size: 5662
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-a6a9374c

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/AIProjectDevice/my-distiset-a6a9374c/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/AIProjectDevice/my-distiset-a6a9374c/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "labels": [
        2,
        1
    ],
    "text": "The introduction of Quantum Computing has sparked significant interest in the scientific community, with the potential to revolutionize computational complexity theory and solve long-standing problems in fields such as cryptography and optimization. However, the noise-resistant properties of Quantum Computing are still in its infancy and have yet to be fully understood, requiring the development of new error correction techniques and robust quantum algorithms to overcome the noisy nature of these devices. Furthermore, the scalability and cost-effectiveness of Quantum Computing remain significant challenges, making it difficult to envision a widespread adoption of this technology in the near future."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("AIProjectDevice/my-distiset-a6a9374c", "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("AIProjectDevice/my-distiset-a6a9374c")
```


</details>




