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: 22717
    num_examples: 10
  download_size: 23030
  dataset_size: 22717
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-generations2

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-generations2/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/Gunther520/instruction-dataset-mini-with-generations2/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",
        "microsoft/Phi-3.5-mini-instruct"
    ],
    "generations": [
        "A bakery has 48 chocolates distributed among three friends, Arianna, Danny, and Robbie. The number of chocolates each friend receives follows a specific pattern. Arianna receives twice as many chocolates as Robbie, while Danny has 6 more chocolates than Robbie. How many chocolates does Arianna have?",
        " Prompt: In a trio of friends, Rachel has 15 rings, which is 10 more than Martin. Martin has 5 rings less than Ashley. If Rachel owns three times the number of rings Ashley has, establish the count of rings that belongs to Martin."
    ],
    "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": [
        5,
        5
    ],
    "rationales": [
        "This prompt is well-structured and clearly specifies the task goal, providing necessary information for the model to solve the problem. The format is easy to follow, and the restrictions are clearly stated.",
        "This prompt is well-written, with a clear task goal, well-defined restrictions, and easy-to-understand language. The format is logical and easy to follow, and the necessary details for solving the problem are provided."
    ],
    "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-generations2", "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-generations2")
```


</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},
}
```


