vonewman/instruction-dataset-mini-with-generations

---
size_categories: n<1K
dataset_info:
features:
- name: instruction
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: category
dtype: string
- name: completion
dtype: string
- name: id
dtype: int64
- name: input
dtype: 'null'
- name: motivation_app
dtype: 'null'
- name: prompt
dtype: string
- name: source
dtype: string
- name: subcategory
dtype: string
- name: model_name
dtype: string
- name: generation
dtype: string
splits:
- name: train
num_bytes: 22643
num_examples: 10
download_size: 30649
dataset_size: 22643
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 instruction-dataset-mini-with-generations

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/vonewman/instruction-dataset-mini-with-generations/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/vonewman/instruction-dataset-mini-with-generations/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:

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

```json
{
"completion": "Denote the number of chocolates each person has by the letter of their first name. We know that\nA = D + 12\nD = R + 6\nA = 2 * R\n\nThus, A = (R + 6) + 12 = R + 18\nSince also A = 2 * R, this means 2 * R = R + 18\nHence R = 18\nHence D = 18 + 6 = 24",
"generation": "Let\u0027s start by defining variables for each person:\nLet x be the number of chocolates Robbie has.\nThen Danny has x + 6 chocolates.\nAnd Arianna has x + 6 + 12 chocolates, which is x + 18 chocolates.\n\nWe are given that Arianna has twice as many chocolates as Robbie, so:\nx + 18 = 2x\n18 = x\n\nSo, Robbie has 18 chocolates.\nTherefore, Danny has 18 + 6 = 24 chocolates. \n\nDanny has 24 chocolates.",
"instruction": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"meta": {
"category": "Question Answering",
"completion": "Denote the number of chocolates each person has by the letter of their first name. We know that\nA = D + 12\nD = R + 6\nA = 2 * R\n\nThus, A = (R + 6) + 12 = R + 18\nSince also A = 2 * R, this means 2 * R = R + 18\nHence R = 18\nHence D = 18 + 6 = 24",
"id": 0,
"input": null,
"motivation_app": null,
"prompt": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"source": "surge",
"subcategory": "Math"
},
"model_name": "gpt-3.5-turbo"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("vonewman/instruction-dataset-mini-with-generations", "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("vonewman/instruction-dataset-mini-with-generations")
```

</details>

原始信息汇总

数据集概述

数据集基本信息

• 数据集大小: 小于1KB
• 下载大小: 30649字节
• 数据集大小: 22643字节

数据集特征

• instruction: 字符串类型
• completion: 字符串类型
• meta: 结构化数据
  • category: 字符串类型
  • completion: 字符串类型
  • id: 整数类型（64位）
  • input: 空值
  • motivation_app: 空值
  • prompt: 字符串类型
  • source: 字符串类型
  • subcategory: 字符串类型
• model_name: 字符串类型
• generation: 字符串类型

数据集分割

• train:
  • num_bytes: 22643字节
  • num_examples: 10个样本

配置信息

• config_name: default
• data_files:
  • split: train
  • path: data/train-*

标签

• synthetic
• distilabel
• rlaif

数据集加载示例

python from datasets import load_dataset

ds = load_dataset("vonewman/instruction-dataset-mini-with-generations")