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': pro-ai-reasoning
          '1': anti-ai-reasoning
          '2': neutral-ai-reasoning
  splits:
  - name: train
    num_bytes: 3028
    num_examples: 9
  download_size: 4476
  dataset_size: 3028
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 can-ai-reason

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/eligapris/can-ai-reason/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/eligapris/can-ai-reason/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 1,
    "text": "The existential implications of advanced AI systems\u0027 reasoning capabilities are often overlooked, yet they pose a significant threat to human agency in the decision-making process. By internalizing vast amounts of knowledge and data, AI systems are increasingly able to bypass human intuition and arrive at conclusions that are detached from human values and ethics. This could lead to a loss of control and a diminished capacity for human beings to make informed decisions."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("eligapris/can-ai-reason", "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("eligapris/can-ai-reason")
```


</details>




