distilabel-internal-testing/distilabel-artifacts-example

---
size_categories: n<1K
dataset_info:
features:
- name: text
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: category
dtype: string
- name: completion
dtype: string
- name: id
dtype: int64
- name: input
dtype: string
- name: motivation_app
dtype: string
- name: prompt
dtype: string
- name: source
dtype: string
- name: subcategory
dtype: string
- name: text_character_count
dtype: int64
splits:
- name: train
num_bytes: 432981
num_examples: 327
download_size: 292263
dataset_size: 432981
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-artifacts-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/distilabel-internal-testing/distilabel-artifacts-example/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/distilabel-artifacts-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",
"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"
},
"text": "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?",
"text_character_count": 172
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("distilabel-internal-testing/distilabel-artifacts-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("distilabel-internal-testing/distilabel-artifacts-example")
```

</details>

## Artifacts

* **Step**: `count_text_characters_0`

* **Artifact name**: `text_character_count_distribution`

* `type`: image

* `library`: matplotlib

size_categories: 样本量小于1000

dataset_info:
features:
- name: text
dtype: 字符串（string）
- name: completion
dtype: 字符串（string）
- name: meta
struct:
- name: category
dtype: 字符串（string）
- name: completion
dtype: 字符串（string）
- name: id
dtype: 整数（int64）
- name: input
dtype: 字符串（string）
- name: motivation_app
dtype: 字符串（string）
- name: prompt
dtype: 字符串（string）
- name: source
dtype: 字符串（string）
- name: subcategory
dtype: 字符串（string）
- name: text_character_count
dtype: 整数（int64）

splits:
- name: train
num_bytes: 432981
num_examples: 327

download_size: 292263

dataset_size: 432981

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

tags:
- synthetic（合成数据集）
- distilabel
- rlaif（强化学习AI反馈，Reinforcement Learning from AI Feedback）

---

<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="基于distilabel构建" width="200" height="32"/>
</a>
</p>

# distilabel-artifacts-example 数据集卡片

本数据集由[distilabel](https://distilabel.argilla.io/)构建。

## 数据集概述

本数据集包含一个`pipeline.yaml`文件，可通过distilabel命令行界面（CLI）运行该配置，复现生成本数据集的流水线：

console
distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/distilabel-artifacts-example/raw/main/pipeline.yaml"

或查看该配置详情：

console
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/distilabel-artifacts-example/raw/main/pipeline.yaml"

## 数据集结构

每个配置对应的样本结构如下：

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

json
{
"completion": "以人名首字母表示各人拥有的巧克力数量。已知：
A = D + 12
D = R + 6
A = 2 * R

因此，A = (R + 6) + 12 = R + 18
又因A = 2 * R，故2 * R = R + 18
因此R = 18
因此D = 18 + 6 = 24",
"meta": {
"category": "问答（Question Answering）",
"completion": "以人名首字母表示各人拥有的巧克力数量。已知：
A = D + 12
D = R + 6
A = 2 * R

因此，A = (R + 6) + 12 = R + 18
又因A = 2 * R，故2 * R = R + 18
因此R = 18
因此D = 18 + 6 = 24",
"id": 0,
"input": null,
"motivation_app": null,
"prompt": "Arianna比Danny多12颗巧克力。Danny比Robbie多6颗巧克力。Arianna拥有的巧克力数量是Robbie的两倍。请问Danny有多少颗巧克力？",
"source": "surge",
"subcategory": "数学（Math）"
},
"text": "Arianna比Danny多12颗巧克力。Danny比Robbie多6颗巧克力。Arianna拥有的巧克力数量是Robbie的两倍。请问Danny有多少颗巧克力？",
"text_character_count": 172
}

该子集可通过以下代码加载：

python
from datasets import load_dataset

ds = load_dataset("distilabel-internal-testing/distilabel-artifacts-example", "default")

或采用更简洁的方式加载，由于仅存在一个名为default的配置：

python
from datasets import load_dataset

ds = load_dataset("distilabel-internal-testing/distilabel-artifacts-example")

</details>

## 制品

* **处理步骤**：`count_text_characters_0`

* **制品名称**：`text_character_count_distribution`

* 类型：图像

* 依赖库：matplotlib