mirror of
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104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass, field
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from typing import Optional
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from datasets import load_dataset
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from huggingface_hub import ModelCard
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from transformers import HfArgumentParser
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@dataclass
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class ScriptArguments:
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r"""
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Arguments for the script.
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Args:
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push_to_hub (`bool`, *optional*, defaults to `False`):
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Whether to push the dataset to the Hugging Face Hub.
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repo_id (`str`, *optional*, defaults to `"trl-lib/ultrafeedback-prompt"`):
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Hugging Face repository ID to push the dataset to.
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dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
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Number of workers to use for dataset processing.
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"""
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push_to_hub: bool = field(
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default=False,
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metadata={"help": "Whether to push the dataset to the Hugging Face Hub."},
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)
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repo_id: str = field(
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default="trl-lib/ultrafeedback-prompt",
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metadata={"help": "Hugging Face repository ID to push the dataset to."},
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)
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dataset_num_proc: Optional[int] = field(
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default=None,
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metadata={"help": "Number of workers to use for dataset processing."},
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)
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def to_unpaired_preference(example):
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prompt = [{"role": "user", "content": example["instruction"]}]
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return {"prompt": prompt}
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def drop_long_prompt(example):
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if len(example["prompt"][0]["content"]) > 512:
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return False
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else:
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return True
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model_card = ModelCard("""
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---
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tags: [trl]
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---
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# UltraFeedback - Prompts Dataset
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## Summary
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The UltraFeedback - Prompts dataset is a processed version of the [UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset for model evaluation on specific aspects like helpfulness, honesty, and instruction-following.
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## Data Structure
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- **Format**: [Conversational](https://huggingface.co/docs/trl/main/dataset_formats#conversational)
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- **Type**: [Prompt-only](https://huggingface.co/docs/trl/main/dataset_formats#prompt-only)
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Column:
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- `"prompt"`: The input question or instruction provided to the model.
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## Generation script
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The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/ultrafeedback-prompt.py).
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""")
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if __name__ == "__main__":
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parser = HfArgumentParser(ScriptArguments)
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script_args = parser.parse_args_into_dataclasses()[0]
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dataset = load_dataset("openbmb/UltraFeedback", split="train")
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dataset = dataset.map(
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to_unpaired_preference,
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remove_columns=["source", "instruction", "models", "completions", "correct_answers", "incorrect_answers"],
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num_proc=script_args.dataset_num_proc,
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)
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dataset = dataset.filter(drop_long_prompt)
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dataset = dataset.train_test_split(test_size=0.05, seed=42)
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if script_args.push_to_hub:
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dataset.push_to_hub(script_args.repo_id)
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model_card.push_to_hub(script_args.repo_id, repo_type="dataset")
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