license:apache-2.0 size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
license: apache-2.0
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: 4882
    num_examples: 10
  download_size: 5388
  dataset_size: 4882
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 Spider_gpt4o

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/Spider_gpt4o/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/mjerome89/Spider_gpt4o/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "generation_models": [
        "Mistral_7B_Inst",
        "gretel",
        "GPT_3_5_Turbo"
    ],
    "generations": [
        "SELECT salesperson_id, SUM(volume) AS total_volume\n     FROM timber_sales\n     GROUP BY salesperson_id\n     ORDER BY salesperson_id;",
        "SELECT salesperson_id, name, SUM(volume) as total_volume FROM timber_sales JOIN salesperson ON timber_sales.salesperson_id = salesperson.salesperson_id GROUP BY salesperson_id, name ORDER BY total_volume DESC;",
        "SELECT salesperson.name, SUM(timber_sales.volume) AS total_volume\nFROM salesperson\nJOIN timber_sales ON salesperson.salesperson_id = timber_sales.salesperson_id\nGROUP BY salesperson.name\nORDER BY salesperson.name;"
    ],
    "instruction": "What is the total volume of timber sold by each salesperson, sorted by salesperson?",
    "ratings": [
        4,
        5
    ],
    "ultrafeedback_model": "gpt-4"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("mjerome89/Spider_gpt4o", "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/Spider_gpt4o")
```


</details>
