license:wtfpl size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
license: wtfpl
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: ultrafeedback_model
    dtype: string
  splits:
  - name: train
    num_bytes: 37836
    num_examples: 50
  download_size: 29377
  dataset_size: 37836
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 SQL-Ultrafeedback

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/mjerome89/SQL-Ultrafeedback/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/mjerome89/SQL-Ultrafeedback/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "generation_models": [
        "gpt-4o"
    ],
    "generations": [
        "To solve the problem, let\u0027s define some variables:\n\n- Let \\( R \\) be the number of chocolates Robbie has.\n- Since Danny has 6 more chocolates than Robbie, Danny has \\( R + 6 \\) chocolates.\n- Arianna has 12 more chocolates than Danny, so Arianna has \\( (R + 6) + 12 \\) chocolates.\n- We are also told that Arianna has twice as many chocolates as Robbie. Therefore, Arianna has \\( 2R \\) chocolates.\n\nWe can set up the following equation to represent the information given about Arianna\u0027s chocolates:\n\n\\[\n(R + 6"
    ],
    "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": [
        3
    ],
    "ultrafeedback_model": "gpt-4o"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("mjerome89/SQL-Ultrafeedback", "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("mjerome89/SQL-Ultrafeedback")
```


</details>
