upaya07/distilabel-gpt35_example

---
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: 23265
num_examples: 10
download_size: 30122
dataset_size: 23265
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-gpt35_example

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/upaya07/distilabel-gpt35_example/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/upaya07/distilabel-gpt35_example/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 denote the number of chocolates Robbie has as R. \n\nAccording to the information given, Arianna has 12 chocolates more than Danny, so we can represent the number of chocolates Arianna has as D + 12.\n\nDanny has 6 chocolates more than Robbie, so we can represent the number of chocolates Danny has as R + 6.\n\nSince Arianna has twice as many chocolates as Robbie, we can write the following equation:\n\nD + 12 = 2R\n\nNow, let\u0027s substitute D + 12 for A and R + 6 for D in the equation above:\n\nR + 6 + 12 = 2R\nR + 18 = 2R\n18 = R\n\nNow that we know Robbie has 18 chocolates, we can find out how many chocolates Danny has:\n\nD = R + 6\nD = 18 + 6\nD = 24\n\nTherefore, Danny 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("upaya07/distilabel-gpt35_example", "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("upaya07/distilabel-gpt35_example")
```

</details>

原始信息汇总

数据集概述

数据集基本信息

• 大小分类: n<1K
• 下载大小: 30122字节
• 数据集大小: 23265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，包含以下字段：
  • category: 字符串类型
  • completion: 字符串类型
  • id: int64类型
  • input: null类型
  • motivation_app: null类型
  • 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: 包含10个示例，总大小为23265字节

配置信息

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

标签

• synthetic
• distilabel
• rlaif