---
size_categories: n<1K
dataset_info:
  features:
  - name: messages
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: generation
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_text_generation_vllm
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_text_generation_vllm
      dtype: string
    - name: statistics_text_generation_vllm
      struct:
      - name: input_tokens
        dtype: int64
      - name: output_tokens
        dtype: int64
  - name: llm_text_generation_vllm_logprobs
    list:
      list:
      - name: logprob
        dtype: float64
      - name: token
        dtype: string
  - name: model_name
    dtype: string
  - name: messages_combined
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  splits:
  - name: train
    num_bytes: 100604089716
    num_examples: 74174
  download_size: 34383797181
  dataset_size: 100604089716
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 evolkit-logprobs-pipeline-75k-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/winglian/evolkit-logprobs-pipeline-75k-v2/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/winglian/evolkit-logprobs-pipeline-75k-v2/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "distilabel_metadata": {
        "raw_input_text_generation_vllm": [
            {
                "content": "In an interdisciplinary research scenario where knot invariants are essential, critically analyze their robustness in distinguishing knots across diverse conditions and explore the theoretical frameworks underlying these invariants, highlighting their significance in real-world applications and potential to inform other theoretical models.",
                "role": "user"
            }
        ],
        "raw_output_text_generation_vllm": "Knot invariants are mathematical constructs that are used to distinguish and classify knots, which are essential topological features in various physical systems, such as materials science, biology, and physics. The robustness of knot invariants in distinguishing knots across diverse conditions is a critical aspect of their application in interdisciplinary research. In this analysis, we will explore the theoretical frameworks underlying knot invariants, their significance in real-world applications, and their potential to inform other theoretical models.\n\n**Theoretical Frameworks:**\n\nKnot invariants are based on various theoretical frameworks, including:\n\n1. **Topological Invariants:** These invariants are derived from the topological properties of knots, such as the fundamental group, homology, and homotopy. Examples include the Jones polynomial, Alexander polynomial, and knot group.\n2. **Geometric Invariants:** These invariants are based on the geometric properties of knots, such as the length, width, and curvature of the knot. Examples include the knot energy and the total curvature.\n3. **Combinatorial Invariants:** These invariants are derived from the combinatorial properties of knots, such as the number of crossings, twists, and turns. Examples include the crossing number and the twist number.\n\n**Robustness of Knot Invariants:**\n\nThe robustness of knot invariants in distinguishing knots across diverse conditions depends on various factors, including:\n\n1. **Topological equivalence:** Knot invariants are sensitive to topological equivalence, which means that they can distinguish knots that are not topologically equivalent.\n2. **Geometric perturbations:** Knot invariants can be robust against small geometric perturbations, such as slight changes in the length or width of the knot.\n3. **Noise and errors:** Knot invariants can be sensitive to noise and errors in the data, which can affect their ability to distinguish knots.\n\n**Real-World Applications:**\n\nKnot invariants have significant applications in various fields, including:\n\n1. **Materials Science:** Knot invariants are used to study the topological properties of materials, such as knotting and linking in polymers and proteins.\n2. **Biology:** Knot invariants are used to study the topological properties of biological molecules, such as DNA and proteins.\n3. **Physics:** Knot invariants are used to study the topological properties of physical systems, such as quantum field theory and condensed matter physics.\n4. **Computer Science:** Knot invariants are used in computer science to study the topological properties of data and networks.\n\n**Potential to Inform Other Theoretical Models:**\n\nKnot invariants have the potential to inform other theoretical models, including:\n\n1. **Topological Quantum Field Theory:** Knot invariants can be used to study the topological properties of quantum field theory and the behavior of particles in different topological environments.\n2. **Network Science:** Knot invariants can be used to study the topological properties of networks and the behavior of complex systems.\n3. **Machine Learning:** Knot invariants can be used to develop new machine learning algorithms that can recognize and classify topological features in data.\n\n**Challenges and Future Directions:**\n\nWhile knot invariants have been successful in distinguishing knots across diverse conditions, there are still challenges and future directions to be explored, including:\n\n1. **Developing new knot invariants:** There is a need to develop new knot invariants that can capture more subtle topological features of knots.\n2. **Improving the robustness of knot invariants:** There is a need to improve the robustness of knot invariants against noise, errors, and geometric perturbations.\n3. **Applying knot invariants to real-world problems:** There is a need to apply knot invariants to real-world problems, such as studying the topological properties of materials, biological molecules, and physical systems.\n\nIn conclusion, knot invariants are powerful tools for distinguishing knots across diverse conditions, and their theoretical frameworks, robustness, and real-world applications make them essential in interdisciplinary research. Their potential to inform other theoretical models, such as topological quantum field theory, network science, and machine learning, highlights their significance in advancing our understanding of complex systems and phenomena.",
        "statistics_text_generation_vllm": {
            "input_tokens": 83,
            "output_tokens": 844
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    },
    "generation": "Knot invariants are mathematical constructs that are used to distinguish and classify knots, which are essential topological features in various physical systems, such as materials science, biology, and physics. The robustness of knot invariants in distinguishing knots across diverse conditions is a critical aspect of their application in interdisciplinary research. In this analysis, we will explore the theoretical frameworks underlying knot invariants, their significance in real-world applications, and their potential to inform other theoretical models.\n\n**Theoretical Frameworks:**\n\nKnot invariants are based on various theoretical frameworks, including:\n\n1. **Topological Invariants:** These invariants are derived from the topological properties of knots, such as the fundamental group, homology, and homotopy. Examples include the Jones polynomial, Alexander polynomial, and knot group.\n2. **Geometric Invariants:** These invariants are based on the geometric properties of knots, such as the length, width, and curvature of the knot. Examples include the knot energy and the total curvature.\n3. **Combinatorial Invariants:** These invariants are derived from the combinatorial properties of knots, such as the number of crossings, twists, and turns. Examples include the crossing number and the twist number.\n\n**Robustness of Knot Invariants:**\n\nThe robustness of knot invariants in distinguishing knots across diverse conditions depends on various factors, including:\n\n1. **Topological equivalence:** Knot invariants are sensitive to topological equivalence, which means that they can distinguish knots that are not topologically equivalent.\n2. **Geometric perturbations:** Knot invariants can be robust against small geometric perturbations, such as slight changes in the length or width of the knot.\n3. **Noise and errors:** Knot invariants can be sensitive to noise and errors in the data, which can affect their ability to distinguish knots.\n\n**Real-World Applications:**\n\nKnot invariants have significant applications in various fields, including:\n\n1. **Materials Science:** Knot invariants are used to study the topological properties of materials, such as knotting and linking in polymers and proteins.\n2. **Biology:** Knot invariants are used to study the topological properties of biological molecules, such as DNA and proteins.\n3. **Physics:** Knot invariants are used to study the topological properties of physical systems, such as quantum field theory and condensed matter physics.\n4. **Computer Science:** Knot invariants are used in computer science to study the topological properties of data and networks.\n\n**Potential to Inform Other Theoretical Models:**\n\nKnot invariants have the potential to inform other theoretical models, including:\n\n1. **Topological Quantum Field Theory:** Knot invariants can be used to study the topological properties of quantum field theory and the behavior of particles in different topological environments.\n2. **Network Science:** Knot invariants can be used to study the topological properties of networks and the behavior of complex systems.\n3. **Machine Learning:** Knot invariants can be used to develop new machine learning algorithms that can recognize and classify topological features in data.\n\n**Challenges and Future Directions:**\n\nWhile knot invariants have been successful in distinguishing knots across diverse conditions, there are still challenges and future directions to be explored, including:\n\n1. **Developing new knot invariants:** There is a need to develop new knot invariants that can capture more subtle topological features of knots.\n2. **Improving the robustness of knot invariants:** There is a need to improve the robustness of knot invariants against noise, errors, and geometric perturbations.\n3. **Applying knot invariants to real-world problems:** There is a need to apply knot invariants to real-world problems, such as studying the topological properties of materials, biological molecules, and physical systems.\n\nIn conclusion, knot invariants are powerful tools for distinguishing knots across diverse conditions, and their theoretical frameworks, robustness, and real-world applications make them essential in interdisciplinary research. Their potential to inform other theoretical models, such as topological quantum field theory, network science, and machine learning, highlights their significance in advancing our understanding of complex systems and phenomena.",
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    ],
    "messages": [
        {
            "content": "In an interdisciplinary research scenario where knot invariants are essential, critically analyze their robustness in distinguishing knots across diverse conditions and explore the theoretical frameworks underlying these invariants, highlighting their significance in real-world applications and potential to inform other theoretical models.",
            "role": "user"
        }
    ],
    "messages_combined": [
        {
            "content": "In an interdisciplinary research scenario where knot invariants are essential, critically analyze their robustness in distinguishing knots across diverse conditions and explore the theoretical frameworks underlying these invariants, highlighting their significance in real-world applications and potential to inform other theoretical models.",
            "role": "user"
        },
        {
            "content": "Knot invariants are mathematical constructs that are used to distinguish and classify knots, which are essential topological features in various physical systems, such as materials science, biology, and physics. The robustness of knot invariants in distinguishing knots across diverse conditions is a critical aspect of their application in interdisciplinary research. In this analysis, we will explore the theoretical frameworks underlying knot invariants, their significance in real-world applications, and their potential to inform other theoretical models.\n\n**Theoretical Frameworks:**\n\nKnot invariants are based on various theoretical frameworks, including:\n\n1. **Topological Invariants:** These invariants are derived from the topological properties of knots, such as the fundamental group, homology, and homotopy. Examples include the Jones polynomial, Alexander polynomial, and knot group.\n2. **Geometric Invariants:** These invariants are based on the geometric properties of knots, such as the length, width, and curvature of the knot. Examples include the knot energy and the total curvature.\n3. **Combinatorial Invariants:** These invariants are derived from the combinatorial properties of knots, such as the number of crossings, twists, and turns. Examples include the crossing number and the twist number.\n\n**Robustness of Knot Invariants:**\n\nThe robustness of knot invariants in distinguishing knots across diverse conditions depends on various factors, including:\n\n1. **Topological equivalence:** Knot invariants are sensitive to topological equivalence, which means that they can distinguish knots that are not topologically equivalent.\n2. **Geometric perturbations:** Knot invariants can be robust against small geometric perturbations, such as slight changes in the length or width of the knot.\n3. **Noise and errors:** Knot invariants can be sensitive to noise and errors in the data, which can affect their ability to distinguish knots.\n\n**Real-World Applications:**\n\nKnot invariants have significant applications in various fields, including:\n\n1. **Materials Science:** Knot invariants are used to study the topological properties of materials, such as knotting and linking in polymers and proteins.\n2. **Biology:** Knot invariants are used to study the topological properties of biological molecules, such as DNA and proteins.\n3. **Physics:** Knot invariants are used to study the topological properties of physical systems, such as quantum field theory and condensed matter physics.\n4. **Computer Science:** Knot invariants are used in computer science to study the topological properties of data and networks.\n\n**Potential to Inform Other Theoretical Models:**\n\nKnot invariants have the potential to inform other theoretical models, including:\n\n1. **Topological Quantum Field Theory:** Knot invariants can be used to study the topological properties of quantum field theory and the behavior of particles in different topological environments.\n2. **Network Science:** Knot invariants can be used to study the topological properties of networks and the behavior of complex systems.\n3. **Machine Learning:** Knot invariants can be used to develop new machine learning algorithms that can recognize and classify topological features in data.\n\n**Challenges and Future Directions:**\n\nWhile knot invariants have been successful in distinguishing knots across diverse conditions, there are still challenges and future directions to be explored, including:\n\n1. **Developing new knot invariants:** There is a need to develop new knot invariants that can capture more subtle topological features of knots.\n2. **Improving the robustness of knot invariants:** There is a need to improve the robustness of knot invariants against noise, errors, and geometric perturbations.\n3. **Applying knot invariants to real-world problems:** There is a need to apply knot invariants to real-world problems, such as studying the topological properties of materials, biological molecules, and physical systems.\n\nIn conclusion, knot invariants are powerful tools for distinguishing knots across diverse conditions, and their theoretical frameworks, robustness, and real-world applications make them essential in interdisciplinary research. Their potential to inform other theoretical models, such as topological quantum field theory, network science, and machine learning, highlights their significance in advancing our understanding of complex systems and phenomena.",
            "role": "assistant"
        }
    ],
    "model_name": "winglian/Llama-3.3-70B-Instruct-FP8-Dynamic"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("winglian/evolkit-logprobs-pipeline-75k-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("winglian/evolkit-logprobs-pipeline-75k-v2")
```


</details>




