size_categories:100K<n<1M format:parquet modality:text library:datasets library:dask library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
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>




