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': manufacturing
          '1': construction
          '2': food and beverage production
          '3': industrial maintenance and repair
          '4': quality assurance and control
          '5': operations management
  splits:
  - name: train
    num_bytes: 26561
    num_examples: 99
  download_size: 12517
  dataset_size: 26561
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-38bf4541

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/mmalo/my-distiset-38bf4541/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/mmalo/my-distiset-38bf4541/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 5,
    "text": "The Operations Department oversees supply chain management, logistics, quality control, and inventory management, ensuring efficient use of resources and minimizing waste. This department also handles customer service, procurement, and employee safety."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("mmalo/my-distiset-38bf4541", "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("mmalo/my-distiset-38bf4541")
```


</details>




