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: label
    dtype:
      class_label:
        names:
          '0': fair
          '1': poor
          '2': good
  splits:
  - name: train
    num_bytes: 37308
    num_examples: 60
  download_size: 21329
  dataset_size: 37308
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-72e4d8b2

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/Annarao/my-distiset-72e4d8b2/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/Annarao/my-distiset-72e4d8b2/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 2,
    "text": "Our company utilizes a diverse range of carriers for its logistical operations, including DHL, UPS, and FedEx, while also relying on regional and local carriers to meet specific regional demands. The average delivery time for standard packages is approximately 3-5 business days, with an estimated 97% on-time delivery rate. However, our analysis reveals that 1.4% of packages experience delays, primarily due to restrictions imposed by carriers on packages weighing over 70 pounds or measuring more than 48 inches in length and 40 inches in width. Unfortunately, 0.2% of our packages are misrouted, resulting in loss. Our final shipping cost is calculated based on weight, dimensions, and destination. We prioritize secure and efficient transportation while addressing the complexities of supply chain logistics."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("Annarao/my-distiset-72e4d8b2", "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("Annarao/my-distiset-72e4d8b2")
```


</details>




