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:
  features:
  - name: instruction
    dtype: string
  - name: generations
    sequence: string
  - name: generation_models
    sequence: string
  - name: ratings
    sequence: int64
  - name: rationales
    sequence: string
  - name: ultrafeedback_model
    dtype: string
  splits:
  - name: train
    num_bytes: 14144
    num_examples: 10
  download_size: 15678
  dataset_size: 14144
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 instruction-dataset-mini-with-generations

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/Gunther520/instruction-dataset-mini-with-generations/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/Gunther520/instruction-dataset-mini-with-generations/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "generation_models": [
        "NousResearch/Hermes-3-Llama-3.1-8B",
        "Qwen/Qwen2.5-7B-Instruct"
    ],
    "generations": [
        "Create a story where three friends, Arianna, Danny, and Robbie, are given a task to distribute 36 chocolates equally among themselves. However, they end up with different numbers of chocolates due to a counting error. Write a narrative explaining how they resolved the issue and shared the chocolates in a fair manner.",
        "If Sarah has 3 fewer apples than Tom, Tom has 5 more apples than Uma, and Sarah has half as many apples as Uma, how many apples does Tom have?"
    ],
    "instruction": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
    "ratings": [
        4,
        0
    ],
    "rationales": [
        "This prompt answered the input question directly rather than generating a narrative. However, it provided a clear and concise solution to the problem. The response is accurate and aligned with the instructions.",
        "This output failed to address the question directly and instead focused on unrelated information. It missed addressing the counting error and the distribution of chocolates. It also contained inaccuracies and failed to provide a clear solution to the problem."
    ],
    "ultrafeedback_model": "meta-llama/Llama-3.1-8B-Instruct"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("Gunther520/instruction-dataset-mini-with-generations", "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("Gunther520/instruction-dataset-mini-with-generations")
```


</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},
}
```


