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': publishable
          '1': non-publishable
  splits:
  - name: train
    num_bytes: 5657
    num_examples: 10
  download_size: 6895
  dataset_size: 5657
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/harshalmalani/my-distiset-be899639/raw/main/pipeline.yaml"
```

or explore the configuration:

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

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 0,
    "text": "The proposed novel architecture integrates graph neural networks with variational autoencoders to effectively capture the topological and geometrical properties of complex networks, enabling the discovery of hidden patterns and structures in high-dimensional data. This methodology is demonstrated through a series of experiments on large-scale networks, showcasing its potential applications in network science and machine learning."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

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


</details>




