task_categories:text-generation task_categories:text2text-generation task_categories:question-answering 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-generation
- text2text-generation
- question-answering
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: completion
    dtype: string
  - name: system_prompt
    dtype: string
  splits:
  - name: train
    num_bytes: 54003
    num_examples: 100
  download_size: 19257
  dataset_size: 54003
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 argusdatatest2

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/bjw333/argusdatatest2/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/bjw333/argusdatatest2/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "completion": "Friday vibes! I\u0027ve got some tasty ideas for you. What kind of cuisine are you in the mood for? Italian, Mexican, or something else?",
    "prompt": "I\u0027m so excited for the weekend. I was thinking of trying out a new recipe for dinner tonight. Do you have any suggestions?",
    "system_prompt": "You are a conversational AI chatbot designed to engage in natural, everyday conversations with users. Keep responses concise, relatable, and short. User inputs are conversational not asking facts, and responses should mirror that style."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("bjw333/argusdatatest2", "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("bjw333/argusdatatest2")
```


</details>




