size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel arxiv:2310.01377 region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
- config_name: format_text_generation_d_p_o_0
  features:
  - name: instruction
    dtype: string
  - name: generations
    sequence: string
  - name: distilabel_metadata
    struct:
    - name: raw_output_ultra_feedback_0
      dtype: 'null'
  - name: model_names
    sequence: string
  - name: ratings
    sequence: 'null'
  - name: rationales
    sequence: 'null'
  - name: model_name
    dtype: string
  - name: prompt
    dtype: 'null'
  - name: prompt_id
    dtype: 'null'
  - name: chosen
    dtype: 'null'
  - name: chosen_model
    dtype: 'null'
  - name: chosen_rating
    dtype: 'null'
  - name: rejected
    dtype: 'null'
  - name: rejected_model
    dtype: 'null'
  - name: rejected_rating
    dtype: 'null'
  splits:
  - name: train
    num_bytes: 6891
    num_examples: 4
  download_size: 16781
  dataset_size: 6891
- config_name: preference_to_argilla_0
  features:
  - name: instruction
    dtype: string
  - name: generations
    sequence: string
  - name: distilabel_metadata
    struct:
    - name: raw_output_ultra_feedback_0
      dtype: 'null'
  - name: model_names
    sequence: string
  - name: ratings
    sequence: 'null'
  - name: rationales
    sequence: 'null'
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 6891
    num_examples: 4
  download_size: 13803
  dataset_size: 6891
configs:
- config_name: format_text_generation_d_p_o_0
  data_files:
  - split: train
    path: format_text_generation_d_p_o_0/train-*
- config_name: preference_to_argilla_0
  data_files:
  - split: train
    path: preference_to_argilla_0/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 ex-pref-data1

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/VidyaPeddinti/ex-pref-data1/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/VidyaPeddinti/ex-pref-data1/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: preference_to_argilla_0 </summary><hr>

```json
{
    "distilabel_metadata": {
        "raw_output_ultra_feedback_0": null
    },
    "generations": [
        "I would label this paragraph as Claim.\n\nA Claim is a statement that makes a specific assertion or statement about a topic. In this case, \"Regular exercise improves cardiovascular health\" is a clear and concise statement that makes a claim about the relationship between exercise and cardiovascular health.\n\nThere is no Evidence or Corroboration in this paragraph, as it is a standalone statement that does not provide any supporting data or information. Contextualization is also not applicable, as the paragraph does not provide any additional information or background context about the topic.",
        " Claim: Regular exercise improves cardiovascular health.\n\nExplanation: A claim is a statement or assertion that expresses an opinion, takes a position, or asserts a truth. In this case, the statement \"Regular exercise improves cardiovascular health\" is expressing an opinion or asserting a truth about the benefits of exercise on heart health. It is not directly presenting evidence or providing context for the claim, so it is labeled as a claim."
    ],
    "instruction": "Label the paragraph as either Claim, Evidence, Corroboration, or Contextualization. paragraph: Regular exercise improves cardiovascular health.",
    "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct",
    "model_names": [
        "meta-llama/Meta-Llama-3-8B-Instruct",
        "mistralai/Mixtral-8x7B-Instruct-v0.1"
    ],
    "ratings": [
        null,
        null
    ],
    "rationales": [
        null,
        null
    ]
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("VidyaPeddinti/ex-pref-data1", "preference_to_argilla_0")
```


</details>

<details><summary> Configuration: format_text_generation_d_p_o_0 </summary><hr>

```json
{
    "chosen": null,
    "chosen_model": null,
    "chosen_rating": null,
    "distilabel_metadata": {
        "raw_output_ultra_feedback_0": null
    },
    "generations": [
        "I would label this paragraph as Claim.\n\nA Claim is a statement that makes a specific assertion or statement about a topic. In this case, \"Regular exercise improves cardiovascular health\" is a clear and concise statement that makes a claim about the relationship between exercise and cardiovascular health.\n\nThere is no Evidence or Corroboration in this paragraph, as it is a standalone statement that does not provide any supporting data or information. Contextualization is also not applicable, as the paragraph does not provide any additional information or background context about the topic.",
        " Claim: Regular exercise improves cardiovascular health.\n\nExplanation: A claim is a statement or assertion that expresses an opinion, takes a position, or asserts a truth. In this case, the statement \"Regular exercise improves cardiovascular health\" is expressing an opinion or asserting a truth about the benefits of exercise on heart health. It is not directly presenting evidence or providing context for the claim, so it is labeled as a claim."
    ],
    "instruction": "Label the paragraph as either Claim, Evidence, Corroboration, or Contextualization. paragraph: Regular exercise improves cardiovascular health.",
    "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct",
    "model_names": [
        "meta-llama/Meta-Llama-3-8B-Instruct",
        "mistralai/Mixtral-8x7B-Instruct-v0.1"
    ],
    "prompt": null,
    "prompt_id": null,
    "ratings": [
        null,
        null
    ],
    "rationales": [
        null,
        null
    ],
    "rejected": null,
    "rejected_model": null,
    "rejected_rating": null
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("VidyaPeddinti/ex-pref-data1", "format_text_generation_d_p_o_0")
```


</details>



## References


```
@misc{cui2024ultrafeedbackboostinglanguagemodels,
    title={UltraFeedback: Boosting Language Models with Scaled AI Feedback},
    author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Bingxiang He and Wei Zhu and Yuan Ni and Guotong Xie and Ruobing Xie and Yankai Lin and Zhiyuan Liu and Maosong Sun},
    year={2024},
    eprint={2310.01377},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2310.01377},
}
```


