size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: instruction
    dtype: string
  - name: generation
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_output_text_generation_0
      dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 816
    num_examples: 1
  download_size: 7822
  dataset_size: 816
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 distilabel-moa-test

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/gabrielmbmb/distilabel-moa-test/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/gabrielmbmb/distilabel-moa-test/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


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

```json
{
    "distilabel_metadata": {
        "raw_output_text_generation_0": "The capital of France is indeed Paris. The responses from the open-source models are consistent and accurate, providing a unified answer to the question. There is no bias or incorrect information present in the provided responses. Therefore, I can confidently confirm that the capital of France is Paris."
    },
    "generation": "The capital of France is indeed Paris. The responses from the open-source models are consistent and accurate, providing a unified answer to the question. There is no bias or incorrect information present in the provided responses. Therefore, I can confidently confirm that the capital of France is Paris.",
    "instruction": "What is the capital of France?",
    "model_name": "moa-meta-llama/Meta-Llama-3-70B-Instruct-meta-llama/Meta-Llama-3-70B-Instruct-NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO-HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("gabrielmbmb/distilabel-moa-test", "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("gabrielmbmb/distilabel-moa-test")
```


</details>
