README.md

metadata

size_categories: n<1K
dataset_info:
  features:
    - name: instruction
      dtype: string
    - name: generations
      sequence: 'null'
    - name: generation_models
      sequence: string
    - name: ratings
      sequence: 'null'
    - name: rationales
      sequence: 'null'
  splits:
    - name: train
      num_bytes: 59771
      num_examples: 100
  download_size: 34956
  dataset_size: 59771
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
tags:
  - synthetic
  - distilabel
  - rlaif

Dataset Card for distilabel_3

This dataset has been created with distilabel.

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:

distilabel pipeline run --config "https://huggingface.co/datasets/aimlresearch2023/distilabel_3/raw/main/pipeline.yaml"

or explore the configuration:

distilabel pipeline info --config "https://huggingface.co/datasets/aimlresearch2023/distilabel_3/raw/main/pipeline.yaml"

Dataset structure

The examples have the following structure per configuration:

Configuration: default

---

{
    "generation_models": [
        "microsoft/Phi-3-mini-128k-instruct",
        "microsoft/Phi-3-mini-128k-instruct"
    ],
    "generations": [
        null,
        null
    ],
    "instruction": "Provide step-by-step instructions on how to make a safe and effective homemade all-purpose cleaner from common household ingredients. The guide should include measurements, tips for storing the cleaner, and additional variations or scents that can be added. Additionally, the guide should be written in clear and concise language, with helpful visuals or photographs to aid in the process.",
    "ratings": [
        null,
        null
    ],
    "rationales": [
        null,
        null
    ]
}

This subset can be loaded as:

from datasets import load_dataset

ds = load_dataset("aimlresearch2023/distilabel_3", "default")

Or simply as it follows, since there's only one configuration and is named default:

from datasets import load_dataset

ds = load_dataset("aimlresearch2023/distilabel_3")