aimlresearch2023/distilabel_4

---
size_categories: n<1K
dataset_info:
features:
- name: instruction
dtype: string
- name: generations
sequence: 'null'
- name: generation_models
sequence: string
- name: ratings
sequence: 'null'
- name: rationales
sequence: 'null'
splits:
- name: train
num_bytes: 59771
num_examples: 100
download_size: 34956
dataset_size: 59771
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 distilabel_4

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/aimlresearch2023/distilabel_4/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/aimlresearch2023/distilabel_4/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:

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

```json
{
"generation_models": [
"microsoft/Phi-3-mini-128k-instruct",
"microsoft/Phi-3-mini-128k-instruct"
],
"generations": [
null,
null
],
"instruction": "Provide step-by-step instructions on how to make a safe and effective homemade all-purpose cleaner from common household ingredients. The guide should include measurements, tips for storing the cleaner, and additional variations or scents that can be added. Additionally, the guide should be written in clear and concise language, with helpful visuals or photographs to aid in the process.",
"ratings": [
null,
null
],
"rationales": [
null,
null
]
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("aimlresearch2023/distilabel_4", "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("aimlresearch2023/distilabel_4")
```

</details>

原始信息汇总

数据集概述

数据集结构

特征

• instruction: 类型为字符串。
• generations: 序列类型为空。
• generation_models: 序列类型为字符串。
• ratings: 序列类型为空。
• rationales: 序列类型为空。

数据分割

• train: 包含100个样本，总字节数为59771。

数据大小

• 下载大小: 34956字节。
• 数据集大小: 59771字节。

配置

• default: 包含训练数据文件，路径为data/train-*。

标签

• synthetic
• distilabel
• rlaif