alvarobartt/example-distilabel

---
size_categories: n<1K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
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: generation_model
dtype: string
- name: generation
dtype: string
splits:
- name: train
num_bytes: 4257
num_examples: 2
download_size: 21358
dataset_size: 4257
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 example-distilabel

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/alvarobartt/example-distilabel/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/alvarobartt/example-distilabel/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. According to the information given:\n\n1. Arianna has 2R (twice as many) chocolates.\n2. Danny has R + 6 chocolates (6 more than Robbie).\n3. Arianna also has 12 more chocolates than Danny, so we can write this as:\n 2R = (R + 6) + 12\n\nNow, let\u0027s solve for R:\n\n2R = R + 18\n2R - R = 18\nR = 18\n\nSo, Robbie has 18 chocolates. Now we can find out how many Danny has:\n\nDanny = R + 6\nDanny = 18 + 6\nDanny = 24\n\nTherefore, Danny has 24 chocolates.",
"generation_model": "examples/models/Phi-3-mini-4k-instruct-q4.gguf",
"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"
}
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("alvarobartt/example-distilabel", "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("alvarobartt/example-distilabel")
```

</details>

原始信息汇总

数据集概述

基本信息

• 大小分类: n<1K
• 配置:
  • 默认配置 (config_name: default)
    • 数据文件:
      • 训练集 (split: train)
        • 路径: data/train-*

数据集信息

• 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: 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
  • generation_model: 字符串类型
  • generation: 字符串类型

数据集分割

• 训练集 (name: train)
  • 大小: 4257字节
  • 示例数量: 2

下载与数据集大小

• 下载大小: 21358字节
• 数据集大小: 4257字节

标签

• synthetic
• distilabel
• rlaif