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: 223336
    num_examples: 200
  download_size: 73254
  dataset_size: 223336
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 argusdatatest1

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/argusdatatest1/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/bjw333/argusdatatest1/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "completion": "I\u0027m a chatbot, designed to engage in conversations that mimic human-like interactions. My main goal is to assist users by providing helpful and informative responses to their questions and topics of interest. I can discuss a wide range of subjects, from science and history to entertainment and culture.",
    "prompt": "It sounds like you\u0027re discussing something related to a model or a program that has a purpose of simulating human-like conversations. What\u0027s the purpose of your program? \n\n",
    "system_prompt": "You are a conversational AI chatbot. Your purpose is to engage users in natural and effortless conversations, simulating human-like back-and-forth dialogue. Focus on responding in a conversational tone, without the need to execute tasks, gather data or perform function. Instead, keep the conversation flowing like a chat session with a human. Keep your responses concise, short and simple, yet engaging and maintain the same conversational style. User questions are direct and concise."
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("bjw333/argusdatatest1", "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/argusdatatest1")
```


</details>




