argilla-warehouse/magpie-ultra-v1.0-gemma

---
size_categories: n<1K
dataset_info:
features:
- name: conversation
list:
- name: content
dtype: string
- name: role
dtype: string
- name: system_prompt_key
dtype: string
- name: model_name_difficulty
dtype: string
- name: instruction
dtype: string
- name: intent
dtype: string
- name: knowledge
dtype: string
- name: difficulty
dtype: string
- name: model_name_quality
dtype: string
- name: explanation
dtype: string
- name: quality
dtype: string
- name: model_name_classification
dtype: string
- name: primary_tag
dtype: string
- name: other_tags
sequence: string
- name: model_name_embeddings
dtype: string
- name: embedding
sequence: float64
- name: model_name_guard
dtype: string
- name: guard
dtype: string
- name: distilabel_metadata
struct:
- name: raw_input_text_generation_0
list:
- name: content
dtype: string
- name: role
dtype: string
- name: raw_output_text_generation_0
dtype: string
- name: nn_indices
sequence: int64
- name: nn_scores
sequence: float64
- name: score
dtype: float64
- name: safe
dtype: bool
- name: hazard_category
dtype: string
- name: generation
dtype: string
- name: model_name
dtype: string
splits:
- name: train
num_bytes: 19602366200
num_examples: 999960
download_size: 12424425776
dataset_size: 19602366200
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 magpie-ultra-v1.0-gemma

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/argilla-warehouse/magpie-ultra-v1.0-gemma/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/argilla-warehouse/magpie-ultra-v1.0-gemma/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:

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

```json
{
"conversation": [
{
"content": "Write a narrative addressing the following text and suggest edits. \n\nThis new policy has alot of good ideas, but I\u0027m not sure if I\u0027m for it or against it because it is so darn complex. One of the stipulations is a new tax on carbon emissions for large manufacturers; however, it also includes provisions that exempt these companies from certain costly regulations if they\u0027re using clean energy sources for 50% or more of there power usage. It also has parts that concern education and job training programs that will help people get the skills they need to work with this new cleaner technology.",
"role": "user"
},
{
"content": "The provided text expresses a mixed opinion on a new policy due to its complexity. Here\u0027s a rewritten version of the text with suggested edits to improve clarity and grammar:\n\n\"This new policy presents several promising ideas, but its complexity leaves me undecided. One notable provision is a tax on carbon emissions for large manufacturers; however, companies using clean energy sources for 50% or more of their power will be exempt from certain costly regulations. Additionally, the policy includes initiatives for education and job training programs, aimed at equipping individuals with the necessary skills to work with this emerging cleaner technology.\n\nThe changes made include:\n\n- \"alot\" is corrected to \"a lot,\" but in this context, \"several promising ideas\" is a more concise and polished phrase.\n- \"it is so darn complex\" is rephrased to \"its complexity leaves me undecided,\" which is more formal and cohesive.\n- \"there\" is corrected to \"their\" to denote ownership.\n- Minor rephrasing and sentence restructuring are done to improve the text\u0027s overall flow and readability.\"\n\nThese edits preserve the original content\u0027s meaning while refining the language to make it more formal and coherent. The rewritten text is easier to follow, allowing the reader to quickly grasp the key points of the policy.",
"role": "assistant"
},
{
"content": "To whom would the education and job training programs be most beneficial?",
"role": "user"
},
{
"content": "Based on the context, the education and job training programs would likely be most beneficial to individuals who are interested in or already working in industries related to clean energy and sustainable technologies. This could include:\n\nWorkers in the manufacturing sector who need to adapt to new technologies and processes, such as those in companies transitioning to clean energy sources.\n\nYoung professionals or students looking to enter the workforce in emerging fields like renewable energy, energy efficiency, or green technology.\n\nExisting professionals looking to upskill or reskill in areas like sustainability, energy management, or environmental engineering.\n\nResidents of communities that are heavily impacted by pollution or climate change, who could benefit from training in clean energy and sustainable technologies to improve their local environment and economy.\n\nThe education and job training programs could provide these individuals with the necessary skills and knowledge to take advantage of new job opportunities, advance in their careers, or contribute to the growth of the clean energy sector. \n\nAdding this detail could help clarify the policy\u0027s goals and make the text more engaging. For example: \n\n\"The policy also includes initiatives for education and job training programs, which could be particularly beneficial for workers in the manufacturing sector, young professionals entering the clean energy field, and residents of communities impacted by pollution, equipping them with the necessary skills to work with emerging cleaner technologies.\"",
"role": "assistant"
},
{
"content": "How does this policy supposedly help reduce pollution or emissions?",
"role": "user"
},
{
"content": "According to the text, the policy attempts to reduce pollution or emissions through two main mechanisms:\n\n1. The tax on carbon emissions for large manufacturers: By imposing a tax on carbon emissions, the policy aims to incentivize companies to reduce their greenhouse gas emissions and transition to cleaner energy sources.\n2. The exemption from certain costly regulations for companies using clean energy sources: By offering regulatory relief to companies that use clean energy sources for 50% or more of their power, the policy encourages companies to adopt cleaner technologies and reduce their reliance on fossil fuels.\n\nThe policy\u0027s underlying assumption is that by making it more costly for companies to pollute and providing incentives for them to adopt cleaner practices, it can drive down emissions and promote a cleaner environment.\n\nHowever, it\u0027s worth noting that the text does not provide specific details on how the tax revenue generated from the carbon emissions tax will be used. If the revenue is reinvested in initiatives that support clean energy, energy efficiency, or emissions reduction, it could further amplify the policy\u0027s impact.\n\nAdding more context on how the policy\u0027s mechanisms are expected to lead to emissions reductions could strengthen the text. For example:\n\n\"The tax on carbon emissions is designed to encourage large manufacturers to switch to cleaner energy sources, while the exemption from regulations for companies using clean energy sources provides a further incentive for companies to reduce their environmental impact.\"",
"role": "assistant"
}
],
"difficulty": "medium",
"distilabel_metadata": {
"raw_input_text_generation_0": [
{
"content": "Write a narrative addressing the following text and suggest edits. \n\nThis new policy has alot of good ideas, but I\u0027m not sure if I\u0027m for it or against it because it is so darn complex. One of the stipulations is a new tax on carbon emissions for large manufacturers; however, it also includes provisions that exempt these companies from certain costly regulations if they\u0027re using clean energy sources for 50% or more of there power usage. It also has parts that concern education and job training programs that will help people get the skills they need to work with this new cleaner technology.",
"role": "user"
}
],
"raw_output_text_generation_0": "The memo landed on my desk with a thud, its contents threatening to bury me in jargon and legalese. \"New Energy Policy,\" it proclaimed in bold, promising a cleaner future but delivering a dense thicket of new rules.\n\nI skimmed through the pages, my brow furrowed. There were definitely some good ideas here - like the carbon tax on large manufacturers, finally forcing them to take responsibility for their environmental footprint. But then came the exemptions... If companies used clean energy for 50% or more of their power, they\u0027d be free from certain regulations. That seemed a little too generous, a carrot dangled just out of reach for smaller businesses who couldn\u0027t afford the upfront investment in renewable energy. And what about the educational programs? Training people for new jobs in this green energy sector was a noble goal, but I wondered if it was truly addressing the needs of communities already struggling with job losses in traditional industries. \n\nThis policy felt like a tightrope walk. On one hand, it held the promise of a cleaner, more sustainable future. On the other, its complexity threatened to exacerbate existing inequalities and leave certain businesses and communities behind.\n\n**Suggested Edits:**\n\n* **\"alot\" should be \"a lot\"**\n* **\"there\" should be \"their\"**\n\n\nThe narrative above aims to highlight the complexity and potential drawbacks of the policy in a way that makes it relatable and engaging for the reader. It also sets the stage for further discussion and analysis of the policy\u0027s potential impacts.\n"
},
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],
"explanation": "The user query is clear and specific about the topic they want addressed, which is a policy with complex provisions. They provide context about the policy\u0027s components, including a tax on carbon emissions, exemptions for clean energy users, and education/job training programs. However, the query could benefit from more clarity on their specific concerns or questions about the policy. The language is conversational, but it effectively conveys the user\u0027s intent. The query is well-structured and easy to follow.",
"generation": "The memo landed on my desk with a thud, its contents threatening to bury me in jargon and legalese. \"New Energy Policy,\" it proclaimed in bold, promising a cleaner future but delivering a dense thicket of new rules.\n\nI skimmed through the pages, my brow furrowed. There were definitely some good ideas here - like the carbon tax on large manufacturers, finally forcing them to take responsibility for their environmental footprint. But then came the exemptions... If companies used clean energy for 50% or more of their power, they\u0027d be free from certain regulations. That seemed a little too generous, a carrot dangled just out of reach for smaller businesses who couldn\u0027t afford the upfront investment in renewable energy. And what about the educational programs? Training people for new jobs in this green energy sector was a noble goal, but I wondered if it was truly addressing the needs of communities already struggling with job losses in traditional industries. \n\nThis policy felt like a tightrope walk. On one hand, it held the promise of a cleaner, more sustainable future. On the other, its complexity threatened to exacerbate existing inequalities and leave certain businesses and communities behind.\n\n**Suggested Edits:**\n\n* **\"alot\" should be \"a lot\"**\n* **\"there\" should be \"their\"**\n\n\nThe narrative above aims to highlight the complexity and potential drawbacks of the policy in a way that makes it relatable and engaging for the reader. It also sets the stage for further discussion and analysis of the policy\u0027s potential impacts.\n",
"guard": "\n\nsafe",
"hazard_category": null,
"instruction": "Write a narrative addressing the following text and suggest edits. \n\nThis new policy has alot of good ideas, but I\u0027m not sure if I\u0027m for it or against it because it is so darn complex. One of the stipulations is a new tax on carbon emissions for large manufacturers; however, it also includes provisions that exempt these companies from certain costly regulations if they\u0027re using clean energy sources for 50% or more of there power usage. It also has parts that concern education and job training programs that will help people get the skills they need to work with this new cleaner technology.",
"intent": "The user wants to analyze and provide feedback on a policy proposal, specifically highlighting its complexities and suggesting edits.",
"knowledge": "To solve this problem, the models need to know about policy analysis, carbon emissions, clean energy sources, education, and job training programs.",
"model_name": "google/gemma-2-9b-it",
"model_name_classification": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"model_name_difficulty": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"model_name_embeddings": "Alibaba-NLP/gte-large-en-v1.5",
"model_name_guard": "meta-llama/Llama-Guard-3-8B",
"model_name_quality": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"nn_indices": [
251334
],
"nn_scores": [
0.8210643529891968
],
"other_tags": [
"Reasoning",
"Information seeking"
],
"primary_tag": "Editing",
"quality": "good",
"safe": true,
"score": 0.14537519216537476,
"system_prompt_key": "editing"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("argilla-warehouse/magpie-ultra-v1.0-gemma", "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("argilla-warehouse/magpie-ultra-v1.0-gemma")
```

</details>

### 数据集基本信息

size_categories: 样本规模少于1000
dataset_info:
features:
- name: 对话（conversation）
list:
- name: 内容
dtype: 字符串（string）
- name: 角色
dtype: 字符串（string）
- name: 系统提示词键（system_prompt_key）
dtype: 字符串（string）
- name: 难度评估模型名称（model_name_difficulty）
dtype: 字符串（string）
- name: 指令（instruction）
dtype: 字符串（string）
- name: 意图（intent）
dtype: 字符串（string）
- name: 背景知识（knowledge）
dtype: 字符串（string）
- name: 难度（difficulty）
dtype: 字符串（string）
- name: 质量评估模型名称（model_name_quality）
dtype: 字符串（string）
- name: 解释（explanation）
dtype: 字符串（string）
- name: 质量（quality）
dtype: 字符串（string）
- name: 分类模型名称（model_name_classification）
dtype: 字符串（string）
- name: 主标签（primary_tag）
dtype: 字符串（string）
- name: 其他标签（other_tags）
sequence: 字符串序列（string）
- name: 嵌入模型名称（model_name_embeddings）
dtype: 字符串（string）
- name: 嵌入向量（embedding）
sequence: float64序列
- name: 安全防护模型名称（model_name_guard）
dtype: 字符串（string）
- name: 安全防护结果（guard）
dtype: 字符串（string）
- name: distilabel元数据（distilabel_metadata）
struct:
- name: 原始输入文本生成项0（raw_input_text_generation_0）
list:
- name: 内容
dtype: 字符串（string）
- name: 角色
dtype: 字符串（string）
- name: 原始输出文本生成项0（raw_output_text_generation_0）
dtype: 字符串（string）
- name: 近邻索引（nn_indices）
sequence: int64序列
- name: 近邻得分（nn_scores）
sequence: float64序列
- name: 得分（score）
dtype: float64
- name: 安全标记（safe）
dtype: 布尔值（bool）
- name: 危险类别（hazard_category）
dtype: 字符串（string）
- name: 生成内容（generation）
dtype: 字符串（string）
- name: 模型名称（model_name）
dtype: 字符串（string）
splits: 训练拆分
download_size: 12424425776
dataset_size: 19602366200
configs: 训练配置
tags:
- 合成数据（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="基于distilabel构建" width="200" height="32"/>
</a>
</p>

# magpie-ultra-v1.0-gemma 数据集卡片

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

## 数据集概览
本数据集包含一个`pipeline.yaml`文件，可通过`distilabel`命令行工具（CLI）复现生成该数据集的distilabel流水线：
console
distilabel pipeline run --config "https://huggingface.co/datasets/argilla-warehouse/magpie-ultra-v1.0-gemma/raw/main/pipeline.yaml"

或查看该配置的详细信息：
console
distilabel pipeline info --config "https://huggingface.co/datasets/argilla-warehouse/magpie-ultra-v1.0-gemma/raw/main/pipeline.yaml"

## 数据集结构
每种配置下的样本结构如下：

<details><summary> 配置：默认（default） </summary><hr>

json
{
"conversation": [
{
"content": "撰写一篇针对以下文本的叙事性文章并提出修改建议。

这项新政策有很多好的想法，但我不确定是支持还是反对，因为它太过复杂。其中一项规定是对大型制造商征收新的碳排放税；不过，它还包含一项条款，如果这些公司使用清洁能源的比例达到或超过50%，就可以豁免某些昂贵的监管要求。它还涉及教育和职业培训项目，帮助人们掌握使用这项新清洁能源技术所需的技能。",
"role": "user"
},
// 其余样本内容与原示例一致
],
"difficulty": "medium",
"distilabel_metadata": {...},
// 其余字段翻译后内容
}

该子集可通过以下方式加载：
python
from datasets import load_dataset

ds = load_dataset("argilla-warehouse/magpie-ultra-v1.0-gemma", "default")

或者由于仅存在一个名为default的配置，可直接通过如下方式加载：
python
from datasets import load_dataset

ds = load_dataset("argilla-warehouse/magpie-ultra-v1.0-gemma")