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: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 1429752
    num_examples: 100
  download_size: 638888
  dataset_size: 1429752
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-v2

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-v2/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/fine-preferences-magpie-v2/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\n## 7. User Role Refinement\n- The user should primarily ask follow-up questions based on the AI\u0027s previous response.\n- User questions should build on the information just provided, showing curiosity and engagement with the topic at hand.\n- Avoid having the user abruptly introduce new topics from the document without context.\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": "How do galaxies maintain their structure?",
            "role": "user"
        },
        {
            "content": "Galaxies, like spiral galaxies, have a delicate balance of energies and processes that help maintain their structure. One key aspect is the interstellar medium, or ISM, which is the material that fills the space between stars. The ISM plays a crucial role in shaping the galaxy\u0027s structure.\n\nYou see, when massive stars explode as supernovae or when Gamma-Ray Bursts (GRBs) occur, they release an enormous amount of energy into the ISM. This energy can heat the local gas to incredibly high temperatures, exceeding 10 million degrees Kelvin! As this hot gas expands, it creates large bubbles or shells that can grow to sizes of several kiloparsecs.\n\nNow, you might think that these bubbles would just dissipate and lose their energy, but that\u0027s not the case. During the expansion phase, the bubble can acquire a significant amount of momentum, which allows it to \"snowplow\" through the surrounding material, even reaching sizes of up to a kiloparsec in diameter. This process can have a profound impact on the galaxy\u0027s structure.\n\nIn fact, some researchers suggest that these GRB-driven bubbles could be responsible for creating the giant shells we observe in some spiral galaxies. These shells can be massive, with energies of around 10^53 ergs, and they can persist for a long time. Interestingly, some of these shells don\u0027t seem to have any obvious central star clusters, which has led some to speculate that they might be the remnants of ancient GRBs.\n\nSo, to answer your question, the structure of galaxies is maintained through a complex interplay of energetic processes, including supernovae, GRBs, and the resulting interactions with the interstellar medium. These processes help shape the galaxy\u0027s morphology and can even create the large-scale structures we observe.",
            "role": "assistant"
        },
        {
            "content": "Do these GRBs have any connection to star formation or are they separate events?",
            "role": "user"
        },
        {
            "content": "Gamma-Ray Bursts (GRBs) are indeed connected to star formation, but they\u0027re not directly related to the process of star formation itself. GRBs are thought to occur when massive stars collapse or when neutron stars or black holes merge. These events are typically associated with the death of massive stars, which are often found in star-forming regions.\n\nIn fact, many GRBs are found to occur in galaxies with high star formation rates. This is because the same conditions that lead to the formation of massive stars, such as the presence of dense gas and dust, also increase the likelihood of GRBs.\n\nHowever, not all star-forming regions produce GRBs, and not all GRBs occur in star-forming regions. GRBs are more closely tied to the collapse of individual massive stars or the merger of compact objects, rather than the star formation process as a whole.\n\nThat being said, the connection between GRBs and star formation is important because it can help us understand the history of star formation in galaxies. For example, the rate of GRBs can be used to infer the rate of star formation in the past. Additionally, the presence of GRBs in certain galaxies can indicate that those galaxies have experienced a period of intense star formation in the past.\n\nSo, while GRBs are not directly caused by star formation, they are connected to the same underlying processes that drive the formation of massive stars. And by studying GRBs, we can gain insights into the history of star formation in galaxies and the role of massive stars in shaping the universe as we know it.",
            "role": "assistant"
        },
        {
            "content": "How does the energy released by these GRBs affect the galaxy they occur in?",
            "role": "user"
        }
    ],
    "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,
    "model_name": "meta-llama/Meta-Llama-3-70B-Instruct",
    "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\n## 7. User Role Refinement\n- The user should primarily ask follow-up questions based on the AI\u0027s previous response.\n- User questions should build on the information just provided, showing curiosity and engagement with the topic at hand.\n- Avoid having the user abruptly introduce new topics from the document without context.\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-v2", "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-v2")
```


</details>
