Skip to content

Clean an existing preference dataset

Knowledge graph figure

Getting Started

Install the dependencies

To complete this tutorial, you need to install the distilabel SDK and a few third-party libraries via pip. We will be using the free but rate-limited Hugging Face serverless Inference API for this tutorial, so we need to install this as an extra distilabel dependency. You can install them by running the following command:

!pip install "distilabel[hf-inference-endpoints]"

!pip install "transformers~=4.0" "torch~=2.0"

Let's make the required imports:

import random

from datasets import load_dataset

from distilabel.llms import InferenceEndpointsLLM
from distilabel.pipeline import Pipeline
from distilabel.steps import (
    KeepColumns,
    LoadDataFromDicts,
    PreferenceToArgilla,
)
from distilabel.steps.tasks import UltraFeedback

You'll need an HF_TOKEN to use the HF Inference Endpoints. Login to use it directly within this notebook.

import os
from huggingface_hub import login

login(token=os.getenv("HF_TOKEN"), add_to_git_credential=True)

(optional) Deploy Argilla

You can skip this step or replace it with any other data evaluation tool, but the quality of your model will suffer from a lack of data quality, so we do recommend looking at your data. If you already deployed Argilla, you can skip this step. Otherwise, you can quickly deploy Argilla following this guide.

Along with that, you will need to install Argilla as a distilabel extra.

!pip install "distilabel[argilla, hf-inference-endpoints]"

The dataset

In this case, we will clean a preference dataset, so we will use the Intel/orca_dpo_pairs dataset from the Hugging Face Hub.

dataset = load_dataset("Intel/orca_dpo_pairs", split="train[:20]")

Next, we will shuffle the chosen and rejected columns to avoid any bias in the dataset.

def shuffle_and_track(chosen, rejected):
    pair = [chosen, rejected]
    random.shuffle(pair)
    order = ["chosen" if x == chosen else "rejected" for x in pair]
    return {"generations": pair, "order": order}

dataset = dataset.map(lambda x: shuffle_and_track(x["chosen"], x["rejected"]))

dataset = dataset.to_list()
As a custom step

You can also create a custom step in a separate module, import it and add it to the pipeline after loading the orca_dpo_pairs dataset using the LoadDataFromHub step.

shuffle_step.py
from typing import TYPE_CHECKING, List
from distilabel.steps import GlobalStep, StepInput

if TYPE_CHECKING:
    from distilabel.steps.typing import StepOutput

import random

class ShuffleStep(GlobalStep):
    @property
    def inputs(self):
        """Returns List[str]: The inputs of the step."""
        return ["instruction", "chosen", "rejected"]

    @property
    def outputs(self):
        """Returns List[str]: The outputs of the step."""
        return ["instruction", "generations", "order"]

    def process(self, inputs: StepInput):
        """Returns StepOutput: The outputs of the step."""
        outputs = []

        for input in inputs:
            chosen = input["chosen"]
            rejected = input["rejected"]
            pair = [chosen, rejected]
            random.shuffle(pair)
            order = ["chosen" if x == chosen else "rejected" for x in pair]

            outputs.append({"instruction": input["instruction"], "generations": pair, "order": order})

        yield outputs

from shuffle_step import ShuffleStep

Define the pipeline

To clean an existing preference dataset, we will need to define a Pipeline with all the necessary steps. However, a similar workflow can be used to clean a SFT dataset. Below, we will go over each step in detail.

Load the dataset

We will use the dataset we just shuffled as source data.

  • Component: LoadDataFromDicts
  • Input columns: system, question, chosen, rejected, generations and order, the same keys as in the loaded list of dictionaries.
  • Output columns: system, instruction, chosen, rejected, generations and order. We will use output_mappings to rename the columns.
load_dataset = LoadDataFromDicts(
    data=dataset[:1],
    output_mappings={"question": "instruction"},
    pipeline=Pipeline(name="showcase-pipeline"),
)
load_dataset.load()
next(load_dataset.process())

([{'system': '',
   'question': "You will be given a definition of a task first, then some input of the task.\nThis task is about using the specified sentence and converting the sentence to Resource Description Framework (RDF) triplets of the form (subject, predicate object). The RDF triplets generated must be such that the triplets accurately capture the structure and semantics of the input sentence. The input is a sentence and the output is a list of triplets of the form [subject, predicate, object] that capture the relationships present in the sentence. When a sentence has more than 1 RDF triplet possible, the output must contain all of them.\n\nAFC Ajax (amateurs)'s ground is Sportpark De Toekomst where Ajax Youth Academy also play.\nOutput:",
   'chosen': '[\n  ["AFC Ajax (amateurs)", "has ground", "Sportpark De Toekomst"],\n  ["Ajax Youth Academy", "plays at", "Sportpark De Toekomst"]\n]',
   'rejected': " Sure, I'd be happy to help! Here are the RDF triplets for the input sentence:\n\n[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst]\n[Ajax Youth Academy, playsAt, Sportpark De Toekomst]\n\nExplanation:\n\n* AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.\n* Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.\n\nNote that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.",
   'generations': [" Sure, I'd be happy to help! Here are the RDF triplets for the input sentence:\n\n[AFC Ajax (amateurs), hasGround, Sportpark De Toekomst]\n[Ajax Youth Academy, playsAt, Sportpark De Toekomst]\n\nExplanation:\n\n* AFC Ajax (amateurs) is the subject of the first triplet, and hasGround is the predicate that describes the relationship between AFC Ajax (amateurs) and Sportpark De Toekomst.\n* Ajax Youth Academy is the subject of the second triplet, and playsAt is the predicate that describes the relationship between Ajax Youth Academy and Sportpark De Toekomst.\n\nNote that there may be other possible RDF triplets that could be derived from the input sentence, but the above triplets capture the main relationships present in the sentence.",
    '[\n  ["AFC Ajax (amateurs)", "has ground", "Sportpark De Toekomst"],\n  ["Ajax Youth Academy", "plays at", "Sportpark De Toekomst"]\n]'],
   'order': ['rejected', 'chosen']}],
 True)

Evaluate the responses

To evaluate the quality of the responses, we will use meta-llama/Meta-Llama-3.1-70B-Instruct, applying the UltraFeedback task that judges the responses according to different dimensions (helpfulness, honesty, instruction-following, truthfulness). For an SFT dataset, you can use PrometheusEval instead.

  • Component: UltraFeedback task with LLMs using InferenceEndpointsLLM
  • Input columns: instruction, generations
  • Output columns: ratings, rationales, distilabel_metadata, model_name

For your use case and to improve the results, you can use any other LLM of your choice.

evaluate_responses = UltraFeedback(
    aspect="overall-rating",
    llm=InferenceEndpointsLLM(
        model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
        tokenizer_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
        generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
    ),
    pipeline=Pipeline(name="showcase-pipeline"),
)
evaluate_responses.load()
next(
    evaluate_responses.process(
        [
            {
                "instruction": "What's the capital of Spain?",
                "generations": ["Madrid", "Barcelona"],
            }
        ]
    )
)

[{'instruction': "What's the capital of Spain?",
  'generations': ['Madrid', 'Barcelona'],
  'ratings': [5, 1],
  'rationales': ["The answer is correct, directly addressing the question, and is free of hallucinations or unnecessary details. It confidently provides the accurate information, aligning perfectly with the user's intent.",
   "The answer is incorrect as Barcelona is not the capital of Spain. This introduces a significant inaccuracy, failing to provide helpful information and deviating entirely from the user's intent."],
  'distilabel_metadata': {'raw_output_ultra_feedback_0': "#### Output for Text 1\nRating: 5 (Excellent)\nRationale: The answer is correct, directly addressing the question, and is free of hallucinations or unnecessary details. It confidently provides the accurate information, aligning perfectly with the user's intent.\n\n#### Output for Text 2\nRating: 1 (Low Quality)\nRationale: The answer is incorrect as Barcelona is not the capital of Spain. This introduces a significant inaccuracy, failing to provide helpful information and deviating entirely from the user's intent."},
  'model_name': 'meta-llama/Meta-Llama-3.1-70B-Instruct'}]

Keep only the required columns

We will get rid of the unneeded columns.

  • Component: KeepColumns
  • Input columns: system, instruction, chosen, rejected, generations, ratings, rationales, distilabel_metadata and model_name
  • Output columns: instruction, chosen, rejected, generations and order
keep_columns = KeepColumns(
    columns=[
        "instruction",
        "generations",
        "order",
        "ratings",
        "rationales",
        "model_name",
    ],
    pipeline=Pipeline(name="showcase-pipeline"),
)
keep_columns.load()
next(
    keep_columns.process(
        [
            {
                "system": "",
                "instruction": "What's the capital of Spain?",
                "chosen": "Madrid",
                "rejected": "Barcelona",
                "generations": ["Madrid", "Barcelona"],
                "order": ["chosen", "rejected"],
                "ratings": [5, 1],
                "rationales": ["", ""],
                "model_name": "meta-llama/Meta-Llama-3.1-70B-Instruct",
            }
        ]
    )
)

[{'instruction': "What's the capital of Spain?",
  'generations': ['Madrid', 'Barcelona'],
  'order': ['chosen', 'rejected'],
  'ratings': [5, 1],
  'rationales': ['', ''],
  'model_name': 'meta-llama/Meta-Llama-3.1-70B-Instruct'}]

(Optional) Further data curation

You can use Argilla to further curate your data.

  • Component: PreferenceToArgilla step
  • Input columns: instruction, generations, generation_models, ratings
  • Output columns: instruction, generations, generation_models, ratings
to_argilla = PreferenceToArgilla(
    dataset_name="cleaned-dataset",
    dataset_workspace="argilla",
    api_url="https://[your-owner-name]-[your-space-name].hf.space",
    api_key="[your-api-key]",
    num_generations=2
)

Run the pipeline

Below, you can see the full pipeline definition:

with Pipeline(name="clean-dataset") as pipeline:

    load_dataset = LoadDataFromDicts(
        data=dataset, output_mappings={"question": "instruction"}
    )

    evaluate_responses = UltraFeedback(
        aspect="overall-rating",
        llm=InferenceEndpointsLLM(
            model_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
            tokenizer_id="meta-llama/Meta-Llama-3.1-70B-Instruct",
            generation_kwargs={"max_new_tokens": 512, "temperature": 0.7},
        ),
    )

    keep_columns = KeepColumns(
        columns=[
            "instruction",
            "generations",
            "order",
            "ratings",
            "rationales",
            "model_name",
        ]
    )

    to_argilla = PreferenceToArgilla(
        dataset_name="cleaned-dataset",
        dataset_workspace="argilla",
        api_url="https://[your-owner-name]-[your-space-name].hf.space",
        api_key="[your-api-key]",
        num_generations=2,
    )

    load_dataset.connect(evaluate_responses)
    evaluate_responses.connect(keep_columns)
    keep_columns.connect(to_argilla)

Let's now run the pipeline and clean our preference dataset.

distiset = pipeline.run()

Let's check it! If you have loaded the data to Argilla, you can start annotating in the Argilla UI.

You can push the dataset to the Hub for sharing with the community and embed it to explore the data.

distiset.push_to_hub("[your-owner-name]/example-cleaned-preference-dataset")

Conclusions

In this tutorial, we showcased the detailed steps to build a pipeline for cleaning a preference dataset using distilabel. However, you can customize this pipeline for your own use cases, such as cleaning an SFT dataset or adding custom steps.

We used a preference dataset as our starting point and shuffled the data to avoid any bias. Next, we evaluated the responses using a model through the serverless Hugging Face Inference API, following the UltraFeedback standards. Finally, we kept the needed columns and used Argilla for further curation.