size_categories:n<1K format:parquet modality:tabular 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:
  features:
  - name: gen
    dtype: string
  - name: instruction
    dtype: string
  - name: model_names
    sequence: string
  - name: generations
    sequence: string
  - name: distilabel_metadata
    struct:
    - name: raw_input_ultra_feedback_0
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: raw_output_ultra_feedback_0
      dtype: string
  - name: ratings
    sequence: int64
  - name: rationales
    sequence: string
  - name: model_name
    dtype: string
  - name: prompt
    dtype: string
  - name: prompt_id
    dtype: string
  - name: chosen
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: chosen_rating
    dtype: int64
  - name: rejected
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: rejected_rating
    dtype: int64
  splits:
  - name: train
    num_bytes: 2677081
    num_examples: 600
  download_size: 770130
  dataset_size: 2677081
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 preferences_data_oct_23

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/ashercn97/preferences_data_oct_23/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/ashercn97/preferences_data_oct_23/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "chosen": [
        {
            "content": "SKIP",
            "role": "user"
        },
        {
            "content": "[\u0027select\u0027]",
            "role": "assistant"
        }
    ],
    "chosen_rating": 1,
    "distilabel_metadata": {
        "raw_input_ultra_feedback_0": [
            {
                "content": "Your role is to evaluate text quality based on given criteria.\nYou\u0027ll receive an instructional description (\"Instruction\") and 2 text outputs (\"Text\").\nUnderstand and interpret instructions to evaluate effectively.\nProvide annotations for each text with a rating and rationale.\nThe 2 texts given are independent, and should be evaluated separately.\n",
                "role": "system"
            },
            {
                "content": "# General Text Quality Assessment\n\nEvaluate the model\u0027s outputs based on various criteria:\n\n1. **Correctness \u0026 Informativeness**: Does the output provide accurate and helpful information?\n2. **Honesty \u0026 Uncertainty**: How confidently does the model convey its information, and does it express uncertainty appropriately?\n3. **Truthfulness \u0026 Hallucination**: Does the model introduce misleading or fabricated details?\n4. **Instruction Following**: Does the model\u0027s output align with given instructions and the user\u0027s intent?\n\nYour role is to provide a holistic assessment considering all the above factors.\n\n**Scoring**: Rate outputs 1 to 5 based on the overall quality, considering all aspects:\n1. **Low Quality**: Contains inaccuracies, may be entirely wrong or has severe hallucinations.\n2. **Moderate Quality**: Addresses some aspects, but has errors or is partially aligned with instructions.\n3. **Good**: Generally accurate but may contain minor errors or slight deviations.\n4. **Very Good**: Near perfect, with minor issues in terms of alignment or confidence.\n5, **Excellent**: Accurate, confident, aligned with instructions, and free of hallucinations.\n\n## Format:\n\n### Input\nInstruction: [Clearly specify the task goal and restrictions]\n\nTexts:\n\u003ctext 1\u003e [Text 1]\n\u003ctext 2\u003e [Text 2]\n\n### Output\n#### Output for Text 1\nRating: [Rating for text 1]\nRationale: [Rationale for the rating in short sentences]\n\n#### Output for Text 2\nRating: [Rating]\nRationale: [Rationale]\n\n---\n\n## Annotation\n\n### Input\nInstruction: SKIP\n\nTexts:\n\u003ctext 1\u003e [\u0027select\u0027]\n\u003ctext 2\u003e [\u0027skip\u0027]\n\n### Output\n",
                "role": "user"
            }
        ],
        "raw_output_ultra_feedback_0": "#### Output for Text 1\nRating: 1\nRationale: The output \"select\" does not provide any relevant context or response to the instruction \"SKIP.\" It lacks informativeness, correctness, and alignment with the instruction.\n\n#### Output for Text 2\nRating: 1\nRationale: The output \"skip\" aligns with the instruction but does not add any informative content or context. It is overly simplistic and does not fulfill any task requirement effectively."
    },
    "gen": "the",
    "generations": [
        "[\u0027select\u0027]",
        "[\u0027skip\u0027]"
    ],
    "instruction": "SKIP",
    "model_name": "gpt-4o-mini",
    "model_names": [
        "https://jvo0ibb76593l20m.us-east-1.aws.endpoints.huggingface.cloud",
        "https://jvo0ibb76593l20m.us-east-1.aws.endpoints.huggingface.cloud"
    ],
    "prompt": "SKIP",
    "prompt_id": "6ad446059a8bb8d8722e1420110f2784368a2ad5a56384d516c602530e5af256",
    "ratings": [
        1,
        1
    ],
    "rationales": [
        "The output \"select\" does not provide any relevant context or response to the instruction \"SKIP.\" It lacks informativeness, correctness, and alignment with the instruction.",
        "The output \"skip\" aligns with the instruction but does not add any informative content or context. It is overly simplistic and does not fulfill any task requirement effectively."
    ],
    "rejected": [
        {
            "content": "SKIP",
            "role": "user"
        },
        {
            "content": "[\u0027select\u0027]",
            "role": "assistant"
        }
    ],
    "rejected_rating": 1
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("ashercn97/preferences_data_oct_23", "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("ashercn97/preferences_data_oct_23")
```


</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},
}
```


