examples/datasets/ultrafeedback.py

# Copyright 2020-2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from dataclasses import dataclass, field

from datasets import load_dataset
from huggingface_hub import ModelCard
from transformers import HfArgumentParser


@dataclass
class ScriptArguments:
    r"""
    Arguments for the script.

    Args:
        model_name (`str`, *optional*, defaults to `"gpt-3.5-turbo"`):
            Language model to target. Possible values are:
        aspect (`str`, *optional*, defaults to `"helpfulness"`):
            Aspect to target.
        push_to_hub (`bool`, *optional*, defaults to `False`):
            Whether to push the dataset to the Hugging Face Hub.
        repo_id (`str`, *optional*, defaults to `"trl-lib/ultrafeedback-gpt-3.5-turbo-helpfulness"`):
            Hugging Face repository ID to push the dataset to.
        dataset_num_proc (`int`, *optional*):
            Number of workers to use for dataset processing.
    """

    model_name: str = field(
        default="gpt-3.5-turbo",
        metadata={
            "help": "Language model to target.",
            "choices": [
                "alpaca-7b",
                "bard",
                "falcon-40b-instruct",
                "gpt-3.5-turbo",
                "gpt-4",
                "llama-2-13b-chat",
                "llama-2-70b-chat",
                "llama-2-7b-chat",
                "mpt-30b-chat",
                "pythia-12b",
                "starchat",
                "ultralm-13b",
                "ultralm-65b",
                "vicuna-33b",
                "wizardlm-13b",
                "wizardlm-70b",
                "wizardlm-7b",
            ],
        },
    )
    aspect: str = field(
        default="helpfulness",
        metadata={
            "help": "Aspect to target. Possible values are: 'helpfulness' (default), 'honesty', "
            "'instruction-following', 'truthfulness'.",
            "choices": ["helpfulness", "honesty", "instruction-following", "truthfulness"],
        },
    )
    push_to_hub: bool = field(
        default=False,
        metadata={"help": "Whether to push the dataset to the Hugging Face Hub."},
    )
    repo_id: str = field(
        default="trl-lib/ultrafeedback-gpt-3.5-turbo-helpfulness",
        metadata={"help": "Hugging Face repository ID to push the dataset to."},
    )
    dataset_num_proc: int | None = field(
        default=None,
        metadata={"help": "Number of workers to use for dataset processing."},
    )


def to_unpaired_preference(example, model_name, aspect):
    prompt = [{"role": "user", "content": example["instruction"]}]
    model_index = example["models"].index(model_name)
    response_content = example["completions"][model_index]["response"]
    completion = [{"role": "assistant", "content": response_content}]
    score = int(example["completions"][model_index]["annotations"][aspect]["Rating"])
    label = score >= 5
    return {"prompt": prompt, "completion": completion, "label": label}


model_card = ModelCard("""
---
tags: [trl]
---

# UltraFeedback GPT-3.5-Turbo Helpfulness Dataset

## Summary

The UltraFeedback GPT-3.5-Turbo Helpfulness dataset contains processed user-assistant interactions filtered for helpfulness, derived from the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset. It is designed for fine-tuning and evaluating models in alignment tasks.

## Data Structure

- **Format**: [Conversational](https://huggingface.co/docs/trl/main/dataset_formats#conversational)
- **Type**: [Unpaired preference](https://huggingface.co/docs/trl/main/dataset_formats#unpaired-preference)

Column:
- `"prompt"`: The input question or instruction provided to the model.
- `"completion"`: The model's response to the prompt.
- `"label"`: A binary value indicating whether the response is sufficiently helpful.

## Generation script

The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/ultrafeedback.py).
""")

if __name__ == "__main__":
    parser = HfArgumentParser(ScriptArguments)
    script_args = parser.parse_args_into_dataclasses()[0]

    dataset = load_dataset("openbmb/UltraFeedback", split="train")

    dataset = dataset.filter(
        lambda example: script_args.model_name in example["models"],
        batched=False,
        num_proc=script_args.dataset_num_proc,
    )
    dataset = dataset.map(
        to_unpaired_preference,
        remove_columns=["source", "instruction", "models", "completions", "correct_answers", "incorrect_answers"],
        fn_kwargs={"model_name": script_args.model_name, "aspect": script_args.aspect},
        num_proc=script_args.dataset_num_proc,
    )
    dataset = dataset.train_test_split(test_size=0.05, seed=42)

    if script_args.push_to_hub:
        dataset.push_to_hub(script_args.repo_id)
        model_card.push_to_hub(script_args.repo_id, repo_type="dataset")