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# Copyright 2020-2025 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 typing import Optional
from datasets import load_dataset
from huggingface_hub import ModelCard
from transformers import HfArgumentParser
@dataclass
class ScriptArguments:
r"""
Arguments for the script.
Args:
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/tldr-preference"`):
Hugging Face repository ID to push the dataset to.
dataset_num_proc (`int` or `None`, *optional*, defaults to `None`):
Number of workers to use for dataset processing.
"""
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/tldr-preference",
metadata={"help": "Hugging Face repository ID to push the dataset to."},
)
dataset_num_proc: Optional[int] = field(
default=None,
metadata={"help": "Number of workers to use for dataset processing."},
)
def to_preference(example):
info = example["info"]
if example["batch"] in ["batch0_cnndm", "cnndm0", "cnndm2"]: # CNN Daily Mail batches
article = info["article"].replace("\n\n", "\n")
prompt = f"TITLE: {info['title']}\n\n{article}\n\nTL;DR:"
elif example["batch"] in [f"batch{i}" for i in range(3, 23)] + ["edit_b2_eval_test"]: # Reddit batches
post = info["post"].replace("\n\n", "\n")
prompt = f"SUBREDDIT: r/{info['subreddit']}\n\nTITLE: {info['title']}\n\nPOST: {post}\n\nTL;DR:"
else:
raise ValueError(f"Unknown batch: {example['batch']}")
chosen_idx = example["choice"]
rejected_idx = 1 - chosen_idx
chosen = example["summaries"][chosen_idx]["text"]
rejected = example["summaries"][rejected_idx]["text"]
return {"prompt": prompt, "chosen": chosen, "rejected": rejected}
model_card = ModelCard("""
---
tags: [trl]
---
# TL;DR Dataset for Preference Learning
## Summary
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.
## Data Structure
- **Format**: [Standard](https://huggingface.co/docs/trl/main/dataset_formats#standard)
- **Type**: [Preference](https://huggingface.co/docs/trl/main/dataset_formats#preference)
Columns:
- `"prompt"`: The unabridged Reddit post.
- `"chosen"`: The concise "TL;DR" summary appended by the author.
- `"rejected"`: An alternative summary or response that was not selected.
This structure enables models to learn the relationship between detailed content and its abbreviated form, enhancing their summarization capabilities.
## Generation script
The script used to generate this dataset can be found [here](https://github.com/huggingface/trl/blob/main/examples/datasets/tldr_preference.py).
""")
if __name__ == "__main__":
parser = HfArgumentParser(ScriptArguments)
script_args = parser.parse_args_into_dataclasses()[0]
dataset = load_dataset("openai/summarize_from_feedback", "comparisons")
dataset = dataset.map(
to_preference,
num_proc=script_args.dataset_num_proc,
remove_columns=["info", "summaries", "choice", "worker", "batch", "split", "extra"],
)
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")
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