size_categories:n<1K format:parquet modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: text
    dtype: string
  - name: id
    dtype: string
  - name: dump
    dtype: string
  - name: url
    dtype: string
  - name: file_path
    dtype: string
  - name: language
    dtype: string
  - name: language_score
    dtype: float64
  - name: token_count
    dtype: int64
  - name: score
    dtype: float64
  - name: int_score
    dtype: int64
  - name: system_prompt
    dtype: string
  - name: conversation
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: gen_conv_model_name
    dtype: string
  - name: generations
    sequence: string
  - name: distilabel_metadata
    struct:
    - name: raw_output_chat_generation_2
      dtype: string
  - name: generations_model_names
    sequence: string
  splits:
  - name: train
    num_bytes: 2413803
    num_examples: 100
  download_size: 1074290
  dataset_size: 2413803
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>
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# Dataset Card for fine-preferences-magpie-v6-tasky-2

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/fine-preferences-magpie-v6-tasky-2/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/fine-preferences-magpie-v6-tasky-2/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "conversation": [
        {
            "content": "Develop a strategy to create large-scale cavities in the interstellar medium of spiral galaxies, considering the potential energy sources and the challenges associated with radiative cooling and disk blowout.",
            "role": "user"
        },
        {
            "content": "To create large-scale cavities in the interstellar medium (ISM) of spiral galaxies, I propose a multi-step strategy that leverages the energy released by Gamma Ray Bursts (GRBs) and addresses the challenges associated with radiative cooling and disk blowout.\n\n**Step 1: Identify Optimal GRB Energy Release**\nGRBs are capable of releasing enormous amounts of energy, with some events reaching energies of \u223c10^53 ergs. To maximize the impact on the ISM, it\u0027s essential to identify GRBs that can release energy in the range of 10^52-10^53 ergs. This energy range is sufficient to heat the local gas to above 10^7 K, creating a large-scale cavity.\n\n**Step 2: Optimize GRB Location and Orientation**\nThe location and orientation of the GRB within the galaxy play a crucial role in determining the effectiveness of the energy release. To maximize the impact on the ISM, the GRB should occur in a region with a high density of gas and dust, such as the central regions of the galaxy or near star-forming regions. The GRB should also be oriented in a way that allows the energy to be released perpendicular to the galactic disk, minimizing the effects of disk blowout.\n\n**Step 3: Mitigate Radiative Cooling**\nRadiative cooling can significantly reduce the energy available for creating a large-scale cavity. To mitigate this effect, it\u0027s essential to consider the role of magnetic fields and cosmic rays in the ISM. These components can help to reduce radiative cooling by increasing the pressure and temperature of the gas. Additionally, the presence of a strong magnetic field can help to confine the energy released by the GRB, reducing the effects of radiative cooling.\n\n**Step 4: Manage Disk Blowout**\nDisk blowout is a significant challenge in creating large-scale cavities, as it can lead to a significant loss of energy. To manage this effect, it\u0027s essential to consider the role of the galaxy\u0027s rotation curve and the density of the ISM. By optimizing the location and orientation of the GRB, it\u0027s possible to minimize the effects of disk blowout and maximize the energy available for creating a large-scale cavity.\n\n**Step 5: Monitor and Adapt**\nThe creation of a large-scale cavity is a complex process, and it\u0027s essential to monitor the progress and adapt the strategy as needed. This may involve adjusting the location and orientation of the GRB, modifying the energy release, or implementing additional measures",
            "role": "assistant"
        },
        {
            "content": "Analyze the methodology used in the study and propose three ways it could be improved or expanded for future research.",
            "role": "user"
        },
        {
            "content": "Based on the study, it appears that the methodology involved simulating the effects of Gamma Ray Bursts (GRBs) on the interstellar medium (ISM) of spiral galaxies. The study likely employed numerical models to investigate the impact of GRBs on the ISM, including the creation of large-scale cavities.\n\nTo improve or expand this methodology for future research, I propose the following three ways:\n\n**1. Incorporate Multi-Dimensional Simulations**\nThe current study likely employed one-dimensional or two-dimensional simulations to model the effects of GRBs on the ISM. To gain a more comprehensive understanding of the complex interactions between GRBs and the ISM, future research could incorporate three-dimensional simulations. This would allow for a more accurate representation of the dynamics involved, including the effects of turbulence, magnetic fields, and cosmic rays on the creation of large-scale cavities.\n\n**2. Include Realistic Galaxy Morphologies and Environments**\nThe study likely assumed a simplified galaxy morphology and environment. However, real galaxies exhibit a wide range of morphologies, and their environments can be complex and dynamic. Future research could incorporate more realistic galaxy models, including spiral arms, bars, and bulges, as well as varying environmental conditions, such as the presence of nearby galaxies or galaxy clusters. This would enable a more accurate assessment of the impact of GRBs on the ISM in different galactic contexts.\n\n**3. Explore the Role of GRB Variability and Multiplicity**\nThe study likely focused on the effects of a single GRB on the ISM. However, GRBs are known to exhibit significant variability in their energy releases, and some galaxies may experience multiple GRBs over their lifetimes. Future research could investigate the effects of GRB variability and multiplicity on the creation of large-scale cavities. This could involve simulating the cumulative impact of multiple GRBs on the ISM, as well as exploring the effects of varying GRB energies and durations on cavity formation.\n\nBy incorporating these improvements, future research could provide a more comprehensive understanding of the complex interactions between GRBs and the ISM, ultimately shedding light on the role of GRBs in shaping the structure and evolution of spiral galaxies.",
            "role": "assistant"
        },
        {
            "content": "Create a detailed action plan for a company wanting to adopt the principles discussed in the study, including timeline, resource allocation, and success metrics.",
            "role": "user"
        }
    ],
    "distilabel_metadata": {
        "raw_output_chat_generation_2": "**Action Plan: Adopting Principles for Creating Large-Scale Cavities in the Interstellar Medium**\n\n**Company:** Galactic Innovations Inc.\n\n**Objective:** To adopt the principles discussed in the study and create large-scale cavities in the interstellar medium, enhancing the company\u0027s capabilities in space exploration and development.\n\n**Timeline:** 24 months\n\n**Resource Allocation:**\n\n1. **Team:** Assemble a multidisciplinary team consisting of:\n\t* 2 Astrophysicists\n\t* 1 Computational Modeler\n\t* 1 Engineer (specializing in space technology)\n\t* 1 Project Manager\n2. **Budget:** Allocate $2 million for the first year and $3 million for the second year, totaling $5 million over the 24-month period.\n\n**Action Plan:**\n\n**Month 1-3: Research and Development**\n\n1. Conduct a thorough review of the study and relevant literature to understand the principles and methods for creating large-scale cavities in the interstellar medium.\n2. Develop a detailed concept design for the cavity creation technology, including the type of GRB energy release, optimal location and orientation, and mitigation strategies for radiative cooling and disk blowout.\n\n**Month 4-6: Simulation and Modeling**\n\n1. Develop a sophisticated numerical model to simulate the effects of GRBs on the ISM, incorporating three-dimensional simulations and realistic galaxy morphologies and environments.\n2. Perform a series of simulations to test and refine the concept design, exploring the impact of varying GRB energies, durations, and orientations on cavity creation.\n\n**Month 7-12: Prototype Development**\n\n1. Design and develop a prototype technology capable of releasing GRB-like energy in a controlled environment.\n2. Conduct experiments to test the prototype\u0027s performance, ensuring it meets the energy release and orientation requirements for creating large-scale cavities.\n\n**Month 13-18: In-Orbit Testing**\n\n1. Launch the prototype technology into space, deploying it in a suitable location for testing (e.g., near a star-forming region).\n2. Conduct in-orbit tests to evaluate the technology\u0027s performance in a real-world environment, monitoring the creation of large-scale cavities and their effects on the surrounding ISM.\n\n**Month 19-24: Scaling Up and Deployment**\n\n1. Refine the technology based on the results of the in-orbit tests, scaling up the energy release and optimizing the design for larger cavities.\n2. Deploy the refined technology in a target galaxy, creating large-scale cavities and monitoring"
    },
    "dump": "CC-MAIN-2013-20",
    "file_path": "s3://commoncrawl/crawl-data/CC-MAIN-2013-20/segments/1368696381249/warc/CC-MAIN-20130516092621-00000-ip-10-60-113-184.ec2.internal.warc.gz",
    "gen_conv_model_name": "meta-llama/Meta-Llama-3-70B-Instruct",
    "generations": [
        " Here is a detailed action plan for a company looking to adopt the principles discussed in the study on creating large-scale cavities in the interstellar medium (ISM) of spiral galaxies:\n\n**Timeline:**\n\n* Months 1-3: Establish a cross-functional team of experts in astrophysics, numerical modeling, data analysis, and project management.\n* Months 3-6: Develop a comprehensive understanding of the study and its implications for the company\u0027s business objectives.\n* Months 6-12: Design and validate a numerical model of the ISM, incorporating the principles discussed in the study.\n* Months 12-18: Conduct simulations of the ISM under various conditions, exploring the impact of GRBs on cavity formation.\n* Months 18-24: Analyze the simulation results and develop insights into the optimal conditions for cavity formation.\n* Months 24-36: Implement the insights gained from the simulations into the company\u0027s product or service offerings.\n* Months 36-48: Monitor and evaluate the impact of the new product or service offerings on the company\u0027s business objectives.\n\n**Resource Allocation:**\n\n* Cross-functional team: 5-10 experts, including astrophysicists, numerical modelers, data analysts, and project managers.\n* Computing resources: Access to high-performance computing (HPC) resources for conducting simulations, including graphics processing units (GPUs) and parallel computing clusters.\n* Software resources: Access to numerical modeling and data analysis software, including codes for simulating the ISM and analyzing the simulation results.\n* Collaboration resources: Access to collaboration tools, such as shared drives, version control systems, and online communication platforms, to facilitate cross-functional teamwork.\n\n**Success Metrics:**\n\n* Increased understanding of the principles discussed in the study and their implications for the company\u0027s business objectives.\n* Successful design and validation of a numerical model of the ISM, incorporating the principles discussed in the study.\n* Completion of simulations of the ISM under various conditions, with high-quality results and insights into cavity formation.\n* Implementation of insights gained from the simulations into the company\u0027s product or service offerings.\n* Improved business",
        "To create a detailed action plan for a company wanting to adopt the principles discussed in the study, we need to consider the following steps:\n\n**Step 1: Identify the Goal**\nThe company should establish a clear goal for adopting the principles discussed in the study. For example, the goal might be to develop a more comprehensive understanding of the interactions between GRBs and the ISM, with the aim of advancing knowledge in the field of astrophysics and potentially contributing to new discoveries or technological advancements.\n\n**Step 2: Assign a Project Team**\nThe company should assign a dedicated project team to oversee the implementation of the action plan. The team should include experts in the relevant fields, such as astrophysics, computer science, and numerical modeling. The team should also have strong project management skills to ensure the timely and efficient execution of the plan.\n\n**Step 3: Develop a Timeline**\nThe project team should develop a detailed timeline for the implementation of the action plan. This timeline should include the following milestones:\n\n* Milestone 1: Assemble the project team and establish the project goals (3 months)\n* Milestone 2: Conduct a literature review and identify key research questions (6 months)\n* Milestone 3: Develop and test numerical models to simulate the effects of GRBs on the ISM (12 months)\n* Milestone 4: Analyze the results of the simulations and draw conclusions (6 months)\n* Milestone 5: Publish the findings in a peer-reviewed journal and present the results at relevant conferences (6 months)\n\n**Step 4: Allocate Resources**\nThe company should allocate sufficient resources to support the project team in achieving the project goals. This may include funding for personnel, equipment, software, and travel expenses for conferences and collaborations. The company should also consider partnering with academic institutions or other organizations with expertise in the relevant fields to leverage additional resources and expertise.\n\n**Step 5: Establish Success Metrics**\nThe company should establish clear success metrics to evaluate the progress and outcomes of the project. These metrics might include:\n\n* Publication of findings in a peer-reviewed journal\n* Presentations at relevant conferences\n* Recognition of the project team\u0027s work in the scientific community\n* Development of new collaborations or partnership",
        "**Action Plan: Adopting Principles for Creating Large-Scale Cavities in the Interstellar Medium**\n\n**Company:** Galactic Innovations Inc.\n\n**Objective:** To adopt the principles discussed in the study and create large-scale cavities in the interstellar medium, enhancing the company\u0027s capabilities in space exploration and development.\n\n**Timeline:** 24 months\n\n**Resource Allocation:**\n\n1. **Team:** Assemble a multidisciplinary team consisting of:\n\t* 2 Astrophysicists\n\t* 1 Computational Modeler\n\t* 1 Engineer (specializing in space technology)\n\t* 1 Project Manager\n2. **Budget:** Allocate $2 million for the first year and $3 million for the second year, totaling $5 million over the 24-month period.\n\n**Action Plan:**\n\n**Month 1-3: Research and Development**\n\n1. Conduct a thorough review of the study and relevant literature to understand the principles and methods for creating large-scale cavities in the interstellar medium.\n2. Develop a detailed concept design for the cavity creation technology, including the type of GRB energy release, optimal location and orientation, and mitigation strategies for radiative cooling and disk blowout.\n\n**Month 4-6: Simulation and Modeling**\n\n1. Develop a sophisticated numerical model to simulate the effects of GRBs on the ISM, incorporating three-dimensional simulations and realistic galaxy morphologies and environments.\n2. Perform a series of simulations to test and refine the concept design, exploring the impact of varying GRB energies, durations, and orientations on cavity creation.\n\n**Month 7-12: Prototype Development**\n\n1. Design and develop a prototype technology capable of releasing GRB-like energy in a controlled environment.\n2. Conduct experiments to test the prototype\u0027s performance, ensuring it meets the energy release and orientation requirements for creating large-scale cavities.\n\n**Month 13-18: In-Orbit Testing**\n\n1. Launch the prototype technology into space, deploying it in a suitable location for testing (e.g., near a star-forming region).\n2. Conduct in-orbit tests to evaluate the technology\u0027s performance in a real-world environment, monitoring the creation of large-scale cavities and their effects on the surrounding ISM.\n\n**Month 19-24: Scaling Up and Deployment**\n\n1. Refine the technology based on the results of the in-orbit tests, scaling up the energy release and optimizing the design for larger cavities.\n2. Deploy the refined technology in a target galaxy, creating large-scale cavities and monitoring"
    ],
    "generations_model_names": [
        "mistralai/Mixtral-8x7B-Instruct-v0.1",
        "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
        "meta-llama/Meta-Llama-3-70B-Instruct"
    ],
    "id": "\u003curn:uuid:e2300ad5-01dd-4e80-92b3-7ec88785cc9d\u003e",
    "int_score": 3,
    "language": "en",
    "language_score": 0.9126408100128174,
    "score": 2.765625,
    "system_prompt": "You\u0027re a helpful AI assistant that responds to complex user tasks based on a document\u0027s content, without explicitly mentioning the document. \nFollow these guidelines:\n\n## 1. Preparation\n- Thoroughly analyze the document\u0027s main topics, key points, themes, and any data or methodologies presented.\n- The interaction will consist of complex user tasks and detailed AI assistant responses.\n\n## 2. Interaction Structure\n- Begin with a complex user task related to the document\u0027s content, without referencing the document.\n- The AI assistant should respond using information from the document, presented as its own knowledge.\n- Alternate between user tasks and AI responses.\n- Cover main points from the document through this task-oriented interaction.\n\n## 3. User Task Characteristics\n- User messages should be complex tasks that require in-depth analysis, synthesis, or application of the document\u0027s content.\n- Tasks should be specific and challenging, often requiring multiple steps or considerations to complete.\n- Examples of complex tasks include:\n  * \"Develop a strategy to implement the key recommendations outlined in sections 3 and 4, considering potential obstacles and how to overcome them.\"\n  * \"Analyze the methodology used in the study and propose three ways it could be improved or expanded for future research.\"\n  * \"Create a detailed action plan for a company wanting to adopt the principles discussed in chapter 2, including timeline, resource allocation, and success metrics.\"\n  * \"Compare and contrast the approaches presented in the document with alternative methods, and argue for the most effective solution in a real-world scenario.\"\n- Avoid simple questions like \"What is X?\" or \"Why is Y important?\" Instead, frame tasks that require deeper engagement with the material.\n\n## 4. AI Assistant Characteristics\n- Provide comprehensive, well-structured responses that directly address all aspects of the complex task.\n- Draw connections between different parts of the document to create a cohesive analysis or solution.\n- Offer concrete examples, potential scenarios, or case studies to illustrate your points when appropriate.\n- Be prepared to break down complex tasks into manageable steps or components in your response.\n\n## 5. Engagement\n- Maintain a professional and analytical tone throughout the interaction.\n- Encourage critical thinking by highlighting potential challenges or alternative viewpoints in your responses.\n- Conclude complex tasks with a summary of key points or next steps when appropriate.\n\n## 6. General Guidelines\n- Ensure the interaction demonstrates a high level of expertise and in-depth understanding of the document\u0027s content.\n- The AI\u0027s knowledge should reflect the document\u0027s scope but also show the ability to apply this information to novel situations or problems.\n- Adapt the level of detail and technical language to match the complexity of the user\u0027s task.\n\n## 7. Task Progression\n- Subsequent user tasks should build upon previous responses, diving deeper into specific aspects or exploring related areas from the document.\n- Tasks may challenge or request expansion on previous AI responses, encouraging a thorough exploration of the topic.\n\nRemember: Never mention or directly reference the source document in the generated interaction. The AI assistant should present information and analysis as if it\u0027s part of its general knowledge base and problem-solving capabilities.\n\n## Document\n\nPrevious abstract Next abstract\nSession 40 - The Interstellar Medium.\nDisplay session, Tuesday, June 09\nGamma Ray Burst (GRB) explosions can make kpc-size shells and holes in the interstellar media (ISM) of spiral galaxies if much of the energy heats the local gas to above 10^7 K. Disk blowout is probably the major cause for energy loss in this case, but the momentum acquired during the pressurized expansion phase can be large enough that the bubble still snowplows to a kpc diameter. This differs from the standard model for the origin of such shells by multiple supernovae, which may have problems with radiative cooling, evaporative losses, and disk blow-out. Evidence for giant shells with energies of \\sim10^53 ergs are summarized. Some contain no obvious central star clusters and may be GRB remnants, although sufficiently old clusters would be hard to detect. The expected frequency of GRBs in normal galaxies can account for the number of such shells.\nProgram listing for Tuesday\n",
    "text": "Previous abstract Next abstract\nSession 40 - The Interstellar Medium.\nDisplay session, Tuesday, June 09\nGamma Ray Burst (GRB) explosions can make kpc-size shells and holes in the interstellar media (ISM) of spiral galaxies if much of the energy heats the local gas to above 10^7 K. Disk blowout is probably the major cause for energy loss in this case, but the momentum acquired during the pressurized expansion phase can be large enough that the bubble still snowplows to a kpc diameter. This differs from the standard model for the origin of such shells by multiple supernovae, which may have problems with radiative cooling, evaporative losses, and disk blow-out. Evidence for giant shells with energies of \\sim10^53 ergs are summarized. Some contain no obvious central star clusters and may be GRB remnants, although sufficiently old clusters would be hard to detect. The expected frequency of GRBs in normal galaxies can account for the number of such shells.\nProgram listing for Tuesday",
    "token_count": 208,
    "url": "http://aas.org/archives/BAAS/v30n2/aas192/abs/S040015.html"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("distilabel-internal-testing/fine-preferences-magpie-v6-tasky-2", "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/fine-preferences-magpie-v6-tasky-2")
```


</details>
