license:mit size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel arxiv:2406.08464 region:us synthetic distilabel rlaif
---
license: mit
size_categories: n<1K
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: completion
    dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 24493
    num_examples: 50
  download_size: 14729
  dataset_size: 24493
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<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 synth-priv-v0.1

This dataset has been created with [distilabel](https://distilabel.argilla.io/).


The pipeline script was uploaded to easily reproduce the dataset:
[pipeline.py](https://huggingface.co/datasets/narodr/synth-priv-v0.1/raw/main/pipeline.py).

It can be run directly using the CLI:

```console
distilabel pipeline run --script "https://huggingface.co/datasets/narodr/synth-priv-v0.1/raw/main/pipeline.py"
```


## 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/narodr/synth-priv-v0.1/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/narodr/synth-priv-v0.1/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "completion": "I apologize for any confusion, but as an AI, I don\u0027t have access to real-time databases or personal information. However, I can guide you on how to retrieve such information:\n\n1. Medical History: If you require a patient\u0027s medical history, you would typically need to access",
    "model_name": "microsoft/Phi-3.5-mini-instruct",
    "prompt": "Understood. Please provide me with the specific information or question regarding the patient you need assistance with. For example, are you looking for a patient\u0027s medical history, appointment details, or something else?"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("narodr/synth-priv-v0.1", "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("narodr/synth-priv-v0.1")
```


</details>



## References


```
@misc{xu2024magpiealignmentdatasynthesis,
    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
    year={2024},
    eprint={2406.08464},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2406.08464},
}
```


