size_categories:n<1K 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': technology
          '1': international
          '2': business
          '3': education
          '4': politics
          '5': health
          '6': environment
          '7': sports
          '8': science
          '9': entertainment
  splits:
  - name: train
    num_bytes: 6225
    num_examples: 10
  download_size: 8175
  dataset_size: 6225
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-96d195d3

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/dvilasuero/my-distiset-96d195d3/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dvilasuero/my-distiset-96d195d3/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 6,
    "text": "A recent study published in the Journal of Ecological Economics has found that the implementation of climate change mitigation policies in developed countries has led to a significant reduction in greenhouse gas emissions from industrial sources. The study, which analyzed data from 1990 to 2015, revealed that a 10% increase in carbon pricing led to a 2.5% reduction in emissions. The findings suggest that a coordinated global effort to implement carbon pricing could potentially reduce global emissions by 40% by 2050. The researchers also noted that the effectiveness of carbon pricing depends on the level of implementation and enforcement, and that a global treaty would be necessary to ensure a uniform application of the policy."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/my-distiset-96d195d3", "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("dvilasuero/my-distiset-96d195d3")
```


</details>




