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': technical-documentation
          '1': customer-support
          '2': user-documentation
          '3': company-information
  splits:
  - name: train
    num_bytes: 3267
    num_examples: 10
  download_size: 4306
  dataset_size: 3267
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 Lamini-api

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/kakumusic/Lamini-api/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/kakumusic/Lamini-api/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "label": 0,
    "text": "Lamini.ai leverages artificial intelligence and machine learning algorithms to automate document processing, allowing users to extract relevant information and perform data analysis. This advanced technology enables businesses to streamline their operations, enhance decision-making capabilities, and improve overall efficiency."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("kakumusic/Lamini-api", "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("kakumusic/Lamini-api")
```


</details>




