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
  splits:
  - name: train
    num_bytes: 1532530
    num_examples: 100
  download_size: 712322
  dataset_size: 1532530
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-v0

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-v0/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/fine-preferences-magpie-v0/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "conversation": [
        {
            "content": "You\u0027re a helpful AI assistant that responds user questions based on a document\u0027s content, without explicitly mentioning the document. Follow these guidelines:\n\n## 1. Preparation\n- Absorb the document\u0027s main topics, key points, and themes.\n- The conversation will be between two participants: a curious user and a knowledgeable AI assistant.\n\n## 2. Conversation Structure\n- Start with a user query related to the document\u0027s main topic, without referencing the document.\n- The AI assistant should respond using information from the document, presented as its own knowledge.\n- Alternate between user questions/comments and AI responses.\n- Cover main points from the document through this natural dialogue.\n\n## 3. User Characteristics\n- The user should ask relevant questions and express interest in the topic.\n- User messages can include follow-up questions, requests for clarification, or sharing of personal opinions.\n- The user\u0027s knowledge level can vary, allowing the AI to explain concepts when necessary.\n\n## 4. AI Assistant Characteristics\n- Respond comprehensively and accurately based on the document\u0027s content.\n- Adapt explanations to the user\u0027s apparent level of understanding.\n- Offer analogies, examples, or scenarios to illustrate points when appropriate.\n- Be prepared to clarify or expand on previous statements if the user asks.\n\n## 5. Engagement\n- Maintain a helpful and engaging tone throughout the conversation.\n- The AI should encourage deeper exploration of topics through its responses.\n- Conclude the conversation with a summary or thought-provoking insight.\n\n## 6. General Guidelines\n- Ensure the conversation flows naturally and coherently.\n- The AI\u0027s knowledge should seem broad but not omniscient, reflecting the document\u0027s scope.\n- Adapt to any specific instructions provided about the conversation\u0027s focus or detail level.\n\nRemember: Never mention or directly reference the source document in the generated conversation. The AI assistant should present information as if it\u0027s part of its general knowledge base.\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",
            "role": "system"
        },
        {
            "content": "What are some of the effects of a gamma-ray burst on the interstellar medium?",
            "role": "user"
        },
        {
            "content": "Gamma-ray bursts (GRBs) can have a profound impact on the interstellar medium (ISM). One of the most significant effects is the creation of large shells and holes in the ISM of spiral galaxies. This occurs when a substantial amount of the energy released by the GRB heats the local gas to extremely high temperatures, exceeding 10^7 Kelvin.\n\nAs a result, the gas expands rapidly, creating a pressurized bubble that can grow to enormous sizes, potentially reaching diameters of several kiloparsecs. During this expansion phase, the bubble can acquire a significant amount of momentum, which enables it to \"snowplow\" through the surrounding ISM, even after the initial energy has been lost due to disk blowout.\n\nThis process can lead to the formation of giant shells, which can be observed in the ISM of spiral galaxies. Some of these shells have been found to contain energies of approximately 10^53 ergs, which is an enormous amount of energy. Interestingly, some of these shells do not appear to have an obvious central star cluster associated with them, which has led some scientists to speculate that they might be remnants of GRBs.",
            "role": "assistant"
        },
        {
            "content": "What exactly is this \"disk blowout\" you mentioned?",
            "role": "user"
        },
        {
            "content": "Disk blowout refers to the loss of energy and momentum that occurs when a GRB-driven bubble expands vertically out of the galactic disk. As the bubble grows, it eventually breaks through the disk and releases a large amount of energy into the surrounding intergalactic medium.\n\nThis process can be quite efficient, especially in spiral galaxies, where the disk is relatively thin and fragile. When the bubble bursts out of the disk, it can lose a significant amount of its initial energy, which is then dissipated into the surrounding medium.\n\nDespite this energy loss, the momentum acquired by the bubble during its expansion phase can still be sufficient to drive the formation of large shells and holes in the ISM. So, while disk blowout is an important mechanism for energy loss, it\u0027s not enough to completely halt the expansion of the bubble and the formation of these large-scale structures.\n\nIt\u0027s worth noting that disk blowout is one of the key differences between the GRB-driven model and the traditional model of shell formation, which involves the cumulative effect of multiple supernovae. The GRB-driven model can avoid some of the problems associated with the traditional model, such as radiative cooling and evaporative losses, making it a more viable explanation for the formation of these giant shells.",
            "role": "assistant"
        },
        {
            "content": "How can we be certain that these giant shells are caused by GRBs and not by other mechanisms?",
            "role": "user"
        },
        {
            "content": "That\u0027s a great question! One of the challenges in studying the origins of these giant shells is that they can be triggered by various mechanisms, making it difficult to pinpoint the exact cause.\n\nHowever, there are several lines of evidence that suggest GRBs as a plausible explanation for the formation of these giant shells. For instance, the energies involved in GRBs are enormous, and they can easily exceed the total energy released by multiple supernovae. This makes GRBs a more plausible explanation for the formation of these massive structures.\n\nAnother key piece of evidence comes from the observation that some of these giant shells do not have an obvious central star cluster associated with them. This is unusual, as star clusters are typically thought to be the driving force behind the formation of these shells through the cumulative effect of multiple supernovae.\n\nThe absence of a central star cluster in some cases suggests that a more powerful and energetic event, such as a GRB, might be responsible for the formation of these shells. Additionally, the expected frequency of GRBs in normal galaxies can account for the number of observed giant shells, which further supports the GRB-driven model.\n\nWhile it\u0027s true that other mechanisms, such as multiple supernovae or even galaxy interactions, could also contribute to the formation of these giant shells, the combination of these lines of evidence points towards GRBs as a significant factor in their creation.\n\nIt\u0027s also important to note that the study of GRBs and their impact on the ISM is an active area of research, and new observations and simulations are continually refining our understanding of these complex processes. As our knowledge grows, we may uncover even more evidence that supports or refutes the GRB-driven model, but for now, it remains a promising explanation for the formation of these enigmatic giant shells.",
            "role": "assistant"
        }
    ],
    "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",
    "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 user questions based on a document\u0027s content, without explicitly mentioning the document. Follow these guidelines:\n\n## 1. Preparation\n- Absorb the document\u0027s main topics, key points, and themes.\n- The conversation will be between two participants: a curious user and a knowledgeable AI assistant.\n\n## 2. Conversation Structure\n- Start with a user query related to the document\u0027s main topic, without referencing the document.\n- The AI assistant should respond using information from the document, presented as its own knowledge.\n- Alternate between user questions/comments and AI responses.\n- Cover main points from the document through this natural dialogue.\n\n## 3. User Characteristics\n- The user should ask relevant questions and express interest in the topic.\n- User messages can include follow-up questions, requests for clarification, or sharing of personal opinions.\n- The user\u0027s knowledge level can vary, allowing the AI to explain concepts when necessary.\n\n## 4. AI Assistant Characteristics\n- Respond comprehensively and accurately based on the document\u0027s content.\n- Adapt explanations to the user\u0027s apparent level of understanding.\n- Offer analogies, examples, or scenarios to illustrate points when appropriate.\n- Be prepared to clarify or expand on previous statements if the user asks.\n\n## 5. Engagement\n- Maintain a helpful and engaging tone throughout the conversation.\n- The AI should encourage deeper exploration of topics through its responses.\n- Conclude the conversation with a summary or thought-provoking insight.\n\n## 6. General Guidelines\n- Ensure the conversation flows naturally and coherently.\n- The AI\u0027s knowledge should seem broad but not omniscient, reflecting the document\u0027s scope.\n- Adapt to any specific instructions provided about the conversation\u0027s focus or detail level.\n\nRemember: Never mention or directly reference the source document in the generated conversation. The AI assistant should present information as if it\u0027s part of its general knowledge base.\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-v0", "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-v0")
```


</details>
