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': history
          '1': environment
          '2': lifestyle
          '3': entertainment
          '4': politics
          '5': business
          '6': social affairs
          '7': technology
          '8': science
          '9': geography
          '10': health
          '11': education
          '12': arts
          '13': sports
  splits:
  - name: train
    num_bytes: 12700
    num_examples: 10
  download_size: 17031
  dataset_size: 12700
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-55a6b53b

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/chartotu19/my-distiset-55a6b53b/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/chartotu19/my-distiset-55a6b53b/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 effects of climate change on marine ecosystems have been well-documented, with rising sea levels and ocean acidification leading to the loss of coral reefs and the decline of marine biodiversity. A study published in the journal Nature found that coral bleaching events, caused by warmer ocean temperatures, have increased by 700% since the 1980s. This has resulted in the loss of over 30% of the world\u0027s coral reefs, which are not only important for the health of the ocean but also provide a source of livelihood for millions of people. In addition to coral bleaching, ocean acidification, caused by the absorption of carbon dioxide by the ocean, has made it more difficult for shellfish and other marine organisms to build their shells and skeletons. This has led to a decline in the populations of these species, which are an important food source for many marine animals. Furthermore, changes in ocean currents and temperature have also affected the distribution and abundance of marine species, leading to changes in the composition of marine ecosystems. For example, the warming of the ocean has led to an increase in the number of jellyfish, which are known to be a nuisance to coastal communities. The impacts of climate change on marine ecosystems are far-reaching and have significant implications for human well-being. They affect not only the health of the ocean but also the livelihoods of people who depend on it for food, income, and recreation. In this article, we will explore the effects of climate change on marine ecosystems and the potential consequences of inaction."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("chartotu19/my-distiset-55a6b53b", "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("chartotu19/my-distiset-55a6b53b")
```


</details>




