mirror of
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112 lines
4.4 KiB
Python
112 lines
4.4 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/tldr-preference"`):
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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/tldr-preference",
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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_preference(example):
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info = example["info"]
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if example["batch"] in ["batch0_cnndm", "cnndm0", "cnndm2"]: # CNN Daily Mail batches
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article = info["article"].replace("\n\n", "\n")
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prompt = f"TITLE: {info['title']}\n\n{article}\n\nTL;DR:"
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elif example["batch"] in [f"batch{i}" for i in range(3, 23)] + ["edit_b2_eval_test"]: # Reddit batches
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post = info["post"].replace("\n\n", "\n")
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prompt = f"SUBREDDIT: r/{info['subreddit']}\n\nTITLE: {info['title']}\n\nPOST: {post}\n\nTL;DR:"
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else:
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raise ValueError(f"Unknown batch: {example['batch']}")
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chosen_idx = example["choice"]
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rejected_idx = 1 - chosen_idx
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chosen = example["summaries"][chosen_idx]["text"]
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rejected = example["summaries"][rejected_idx]["text"]
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return {"prompt": prompt, "chosen": chosen, "rejected": rejected}
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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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# TL;DR Dataset for Preference Learning
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## Summary
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The TL;DR dataset is a processed version of Reddit posts, specifically curated to train models using the [TRL library](https://github.com/huggingface/trl) for preference learning and Reinforcement Learning from Human Feedback (RLHF) tasks. It leverages the common practice on Reddit where users append "TL;DR" (Too Long; Didn't Read) summaries to lengthy posts, providing a rich source of paired text data for training models to understand and generate concise summaries.
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## Data Structure
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- **Format**: [Standard](https://huggingface.co/docs/trl/main/dataset_formats#standard)
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- **Type**: [Preference](https://huggingface.co/docs/trl/main/dataset_formats#preference)
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Columns:
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- `"prompt"`: The unabridged Reddit post.
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- `"chosen"`: The concise "TL;DR" summary appended by the author.
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- `"rejected"`: An alternative summary or response that was not selected.
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This structure enables models to learn the relationship between detailed content and its abbreviated form, enhancing their summarization capabilities.
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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/tldr_preference.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("openai/summarize_from_feedback", "comparisons")
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dataset = dataset.map(
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to_preference,
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num_proc=script_args.dataset_num_proc,
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remove_columns=["info", "summaries", "choice", "worker", "batch", "split", "extra"],
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)
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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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