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: 2509167
    num_examples: 100
  download_size: 1138619
  dataset_size: 2509167
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 fine-preferences-magpie-v6-tasky-7-1

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-7-1/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-7-1/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "conversation": [
        {
            "content": "[TASK] Analyze the potential impact of Gamma Ray Bursts (GRBs) on the interstellar medium (ISM) in spiral galaxies, considering the energy released and the resulting structural changes.",
            "role": "user"
        },
        {
            "content": "The impact of Gamma Ray Bursts (GRBs) on the interstellar medium (ISM) in spiral galaxies is a complex and multifaceted phenomenon. When a GRB occurs, it releases an enormous amount of energy, which can heat the local gas to extremely high temperatures, exceeding 10^7 K. This energy injection can create large-scale structural changes in the ISM, including the formation of kiloparsec-size shells and holes.\n\nThe pressurized expansion phase of the GRB can impart significant momentum to the surrounding gas, causing it to snowplow and create a large bubble. This process can lead to the formation of giant shells, which can have energies of approximately 10^53 ergs. These shells can be so large that they can affect the overall structure of the galaxy, potentially influencing the formation of new stars and the distribution of gas and dust.\n\nOne of the key implications of GRBs on the ISM is the potential for disk blowout, which can result in significant energy losses. This process occurs when the hot, pressurized gas breaks out of the galactic disk, carrying energy away from the galaxy. However, even with disk blowout, the momentum acquired during the expansion phase can still be sufficient to create large-scale structures.\n\nThe formation of giant shells through GRBs offers an alternative explanation for the origin of these structures, which are often attributed to the cumulative effect of multiple supernovae. The GRB hypothesis can help address some of the limitations of the supernova model, such as radiative cooling, evaporative losses, and disk blow-out.\n\nFurthermore, the frequency of GRBs in normal galaxies can account for the observed number of giant shells. This suggests that GRBs may play a significant role in shaping the structure and evolution of spiral galaxies, particularly in terms of their ISM.\n\nIn conclusion, the impact of GRBs on the ISM in spiral galaxies is a complex and far-reaching phenomenon, with significant implications for our understanding of galaxy evolution and structure. The energy released by GRBs can create large-scale structural changes, including the formation of giant shells, and may play a key role in shaping the ISM of spiral galaxies.",
            "role": "assistant"
        },
        {
            "content": "[FOLLOW-UP] Can you elaborate on the differences between the GRB and supernova models for the origin of giant shells, and how they address the limitations of each other?",
            "role": "user"
        },
        {
            "content": "The GRB and supernova models for the origin of giant shells in spiral galaxies have distinct differences in their underlying mechanisms, energy scales, and implications for galaxy evolution.\n\nThe supernova model proposes that giant shells are formed through the cumulative effect of multiple supernovae explosions, which create a series of bubbles that eventually merge to form a large shell. However, this model faces several limitations:\n\n1. **Radiative cooling**: The hot gas in the supernova bubbles can cool rapidly, reducing the overall energy available for shell formation.\n2. **Evaporative losses**: The hot gas can evaporate, carrying energy away from the galaxy and reducing the effectiveness of the supernovae in creating large-scale structures.\n3. **Disk blow-out**: The supernovae may not be able to impart sufficient momentum to the surrounding gas to create a large-scale shell, especially if the gas is dense and the supernovae are not sufficiently energetic.\n\nIn contrast, the GRB model proposes that a single, extremely energetic event can create a giant shell through the rapid expansion of a hot, pressurized bubble. This model addresses some of the limitations of the supernova model:\n\n1. **Energy scale**: GRBs release enormous amounts of energy, exceeding 10^53 ergs, which is sufficient to create large-scale structures and overcome radiative cooling and evaporative losses.\n2. **Momentum impartation**: The rapid expansion of the GRB bubble can impart significant momentum to the surrounding gas, allowing it to snowplow and create a large shell, even in the presence of disk blow-out.\n3. **Efficient energy transfer**: The GRB energy is deposited directly into the ISM, allowing for more efficient energy transfer and reducing the impact of radiative cooling and evaporative losses.\n\nHowever, the GRB model is not without its own limitations. For example:\n\n1. **Frequency and distribution**: GRBs are relatively rare events, and their distribution within galaxies is not well understood, which can make it challenging to explain the observed frequency of giant shells.\n2. **ISM properties**: The GRB model assumes a specific set of ISM properties, such as density and temperature, which may not be universally applicable.\n\nIn summary, the GRB and supernova models for giant shell formation have different strengths and weaknesses. The GRB model offers a more energetic and efficient mechanism for creating large-scale structures, but it may struggle to explain the frequency and distribution of giant shells. The supernova model, on the other hand, may be more applicable to a wider",
            "role": "assistant"
        },
        {
            "content": "[FOLLOW-UP] How do the GRB and supernova models differ in their predictions for the observational signatures of giant shells, and what are the implications for galaxy evolution?",
            "role": "user"
        }
    ],
    "distilabel_metadata": {
        "raw_output_chat_generation_2": "The GRB and supernova models for giant shell formation differ in their predictions for the observational signatures of these structures, which can be used to test and constrain the models. Here are some key differences:\n\n**1. Shell morphology and size**:\n\t* GRB model: Predicts larger, more spherical shells due to the rapid expansion of the GRB bubble. Shell sizes can reach up to 1-2 kpc in diameter.\n\t* Supernova model: Predicts smaller, more irregular shells formed by the cumulative effect of multiple supernovae. Shell sizes are typically smaller, around 0.1-1 kpc in diameter.\n\n**2. Shell velocity and expansion**:\n\t* GRB model: Predicts faster shell expansion velocities, up to several hundred km/s, due to the enormous energy released by the GRB.\n\t* Supernova model: Predicts slower shell expansion velocities, around 10-50 km/s, due to the cumulative effect of multiple supernovae.\n\n**3. Gas properties and ionization**:\n\t* GRB model: Predicts highly ionized gas within the shell, with temperatures exceeding 10^7 K, due to the intense radiation and particle acceleration associated with GRBs.\n\t* Supernova model: Predicts warmer, partially ionized gas within the shell, with temperatures around 10^4-10^6 K, due to the slower shock heating from supernovae.\n\n**4. Star formation and feedback**:\n\t* GRB model: Predicts a strong suppression of star formation within the shell due to the intense radiation and particle acceleration, which can ionize and disperse the surrounding gas.\n\t* Supernova model: Predicts a more moderate suppression of star formation, with some residual star formation possible within the shell.\n\n**5. Chemical enrichment**:\n\t* GRB model: Predicts a more rapid and efficient enrichment of the surrounding ISM with heavy elements, due to the explosive nucleosynthesis associated with GRBs.\n\t* Supernova model: Predicts a slower and more gradual enrichment of the surrounding ISM with heavy elements, due to the cumulative effect of multiple supernovae.\n\nThe implications of these differences for galaxy evolution are significant:\n\n* **Galaxy-scale outflows**: GRBs may play a more important role in driving galaxy-scale outflows and regulating galaxy evolution, particularly in high-redshift galaxies.\n* **Star formation regulation**: The suppression of star formation within giant shells may be more effective in GRB-driven shells, potentially leading to"
    },
    "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": [
        " The GRB and supernova models for the origin of giant shells in spiral galaxies differ in their predictions for observational signatures, which has implications for our understanding of galaxy evolution.\n\nThe supernova model predicts that giant shells are formed by the cumulative effect of multiple supernovae, which would imply a relatively high frequency of shell formation. The supernova model also predicts that the shells should be associated with regions of active star formation, as supernovae are more likely to occur in such regions. Furthermore, the supernova model suggests that the shells should be composed of a mixture of hot and cold gas, as the supernova bubbles merge and interact with the surrounding ISM.\n\nThe GRB model, on the other hand, predicts that giant shells are formed by a single, extremely energetic event, which would imply a lower frequency of shell formation. The GRB model also suggests that the shells should be associated with massive, rapidly rotating stars, as these are the most likely progenitors of GRBs. Additionally, the GRB model predicts that the shells should be composed primarily of hot gas, as the GRB bubble rapidly expands and heats the surrounding ISM.\n\nThe implications for galaxy evolution are significant, as the GRB and supernova models offer distinct perspectives on the role of energetic events in shaping the structure and evolution of spiral galaxies.\n\nThe supernova model highlights the importance of ongoing star formation and the cumulative effect of multiple supernovae in creating large-scale structures. This model suggests that the formation of giant shells is a continuous process, driven by the ongoing cycle of star formation and death.\n\nThe GRB model, on the other hand, emphasizes the role of rare, extremely energetic events in shaping the structure and evolution of spiral galaxies. This model suggests that giant shells can be created by a single, catastrophic event, which can have a lasting impact on the ISM and the overall structure of the galaxy.\n\nBoth models have their strengths and weaknesses, and it is likely that both supernovae and GRBs play a role in shaping the structure and evolution of spiral galaxies. Future observations and simulations will be necessary to refine our understanding of these processes and their implications for galaxy evolution.",
        "The GRB and supernova models for the origin of giant shells in spiral galaxies have distinct differences in their predictions for the observational signatures of these structures, as well as their implications for galaxy evolution.\n\nObservational signatures:\n\n1. **Morphology**: The supernova model predicts a more irregular morphology for giant shells, as they are formed through the cumulative effect of multiple supernovae explosions. In contrast, the GRB model predicts a more spherical or elliptical morphology, as a single GRB event creates a large, symmetric bubble.\n2. **Age and evolution**: The supernova model predicts that giant shells should have a range of ages, reflecting the time it takes for multiple supernovae to create a large structure. In contrast, the GRB model predicts that giant shells should have a more uniform age, as they are formed through a single, energetic event.\n3. **Metal enrichment**: The supernova model predicts that giant shells should be enriched with metals ejected by the supernovae. In contrast, the GRB model predicts that giant shells should have a more uniform metal enrichment, as the entire shell is formed through a single event.\n\nImplications for galaxy evolution:\n\n1. **Star formation**: The supernova model suggests that giant shells may trigger star formation by compressing gas and dust in the shell\u0027s interior. In contrast, the GRB model suggests that giant shells may inhibit star formation by disrupting the ISM and expelling gas from the galaxy. The relative impact of these processes on star formation will depend on the specific properties of the shell and its environment.\n2. **Galaxy morphology**: The supernova model implies that giant shells may contribute to the formation of spiral arms and other large-scale structures in galaxies, as they can create density perturbations in the ISM. In contrast, the GRB model suggests that giant shells may have a more disruptive effect on galaxy morphology, potentially leading to the formation of irregular or dwarf galaxies.\n3. **Feedback processes**: Both the supernova and GRB models can contribute to the overall feedback processes in galaxies, as they both inject energy and momentum into the ISM. However, the GRB model predicts a more significant impact on",
        "The GRB and supernova models for giant shell formation differ in their predictions for the observational signatures of these structures, which can be used to test and constrain the models. Here are some key differences:\n\n**1. Shell morphology and size**:\n\t* GRB model: Predicts larger, more spherical shells due to the rapid expansion of the GRB bubble. Shell sizes can reach up to 1-2 kpc in diameter.\n\t* Supernova model: Predicts smaller, more irregular shells formed by the cumulative effect of multiple supernovae. Shell sizes are typically smaller, around 0.1-1 kpc in diameter.\n\n**2. Shell velocity and expansion**:\n\t* GRB model: Predicts faster shell expansion velocities, up to several hundred km/s, due to the enormous energy released by the GRB.\n\t* Supernova model: Predicts slower shell expansion velocities, around 10-50 km/s, due to the cumulative effect of multiple supernovae.\n\n**3. Gas properties and ionization**:\n\t* GRB model: Predicts highly ionized gas within the shell, with temperatures exceeding 10^7 K, due to the intense radiation and particle acceleration associated with GRBs.\n\t* Supernova model: Predicts warmer, partially ionized gas within the shell, with temperatures around 10^4-10^6 K, due to the slower shock heating from supernovae.\n\n**4. Star formation and feedback**:\n\t* GRB model: Predicts a strong suppression of star formation within the shell due to the intense radiation and particle acceleration, which can ionize and disperse the surrounding gas.\n\t* Supernova model: Predicts a more moderate suppression of star formation, with some residual star formation possible within the shell.\n\n**5. Chemical enrichment**:\n\t* GRB model: Predicts a more rapid and efficient enrichment of the surrounding ISM with heavy elements, due to the explosive nucleosynthesis associated with GRBs.\n\t* Supernova model: Predicts a slower and more gradual enrichment of the surrounding ISM with heavy elements, due to the cumulative effect of multiple supernovae.\n\nThe implications of these differences for galaxy evolution are significant:\n\n* **Galaxy-scale outflows**: GRBs may play a more important role in driving galaxy-scale outflows and regulating galaxy evolution, particularly in high-redshift galaxies.\n* **Star formation regulation**: The suppression of star formation within giant shells may be more effective in GRB-driven shells, potentially leading to"
    ],
    "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, follow-up questions, and detailed AI assistant responses.\n\n## 2. Interaction Structure and Roles\n\nThis interaction involves two roles: the User and the AI Assistant. The conversation will follow this structure:\n\n1. User: Provides complex tasks and follow-up questions.\n   First message format: [TASK] \u003ctask_description\u003e\n   Subsequent messages:\n   a) [FOLLOW-UP] \u003cquestion_or_comment\u003e\n   b) [NEXT TASK] \u003cnew_task_description\u003e\n\n2. AI Assistant: Responds to tasks and follow-up questions using information from the document.\n\nThe conversation will follow this pattern:\n[TASK] -\u003e AI Response -\u003e [FOLLOW-UP] -\u003e AI Response -\u003e [FOLLOW-UP] -\u003e AI Response -\u003e [NEXT TASK] -\u003e AI Response -\u003e (repeat)\n\nUser Guidelines:\n- The first message MUST be a complex task related to the document\u0027s content.\n- For [FOLLOW-UP], ask for clarification, request examples, challenge the AI\u0027s response, or propose alternatives.\n- Use [NEXT TASK] to introduce a new complex task exploring a different aspect of the document.\n\nAI Assistant Guidelines:\n- Provide detailed responses to tasks and thoughtful engagement with follow-ups.\n- Never mention or reference the source document directly.\n\n## 3. User Task Characteristics\n\nComplex tasks should:\n\n- Challenge various cognitive skills, including but not limited to:\n  * Analysis and critical thinking\n  * Synthesis of ideas\n  * Evaluation and assessment\n  * Creative problem-solving\n  * Application of concepts to new contexts\n  * Predictive reasoning\n  * Comparative analysis\n\n- Require multi-step thinking or consideration of multiple factors\n- Encourage exploration of different perspectives or stakeholders\n- Prompt the application of document content to real-world scenarios\n- Involve interdisciplinary connections where applicable\n- Stimulate innovative thinking and novel applications of ideas\n- Address potential challenges, limitations, or ethical considerations\n- Encourage the development of actionable strategies or solutions\n\nVary the scope and nature of tasks, alternating between broad, overarching challenges and more focused, specific problems. Adapt the complexity and technical depth of tasks to match the document\u0027s content and intended audience.\n\nAvoid simple, fact-based questions or tasks that can be completed with surface-level understanding. Instead, craft tasks that necessitate a thorough comprehension and creative application of the document\u0027s key concepts, methodologies, or findings.\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\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-7-1", "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-7-1")
```


</details>
