Saibo-creator/bookcorpus_deduplicated

--- dataset_info: features: - name: text dtype: string splits: - name: train num_bytes: 2867856394 num_examples: 38832894 download_size: 1794567875 dataset_size: 2867856394 --- # Dataset Card for "bookcorpus_deduplicated" ## Dataset Summary This is a deduplicated version of the original [Book Corpus dataset](https://huggingface.co/datasets/bookcorpus). The Book Corpus (Zhu et al., 2015), which was used to train popular models such as BERT, has a substantial amount of exact-duplicate documents according to [Bandy and Vincent (2021)](https://arxiv.org/abs/2105.05241) [Bandy and Vincent (2021)](https://arxiv.org/abs/2105.05241) find that thousands of books in BookCorpus are duplicated, with only 7,185 unique books out of 11,038 total. Effect of deduplication - Num of lines: 38832894 VS 74004228 - Dataset size: 2.91GB VS 4.63GB The duplicate text has been droped and only the first appearance is kept. The order of text appearance is kept. ## Why deduplicate? Deduplication of training data has showed various advantages, including: - require fewer training steps to achieve the same or better accuracy - train models that emit memorized text ten times less frequently - reduce carbon emission and energy consumption cf [Deduplicating Training Data Makes Language Models Better](https://arxiv.org/abs/2107.06499) ## Deduplication script ```python import pandas as pd from datasets import load_dataset dataset = load_dataset("bookcorpus")["train"]["text"] df = pd.Dataframe({"text":dataset}) # drop duplicates(exact match) df_filtered = df["text"].drop_duplicates() df_filtered.to_csv("bookcorpus_filtered.csv","index"=False,"header"=False) new_dataset = load_dataset("text",data_files={"train":"bookcorpus_filtered.csv"}) ``` The running time is short, less than several minutes. More sophicated deduplication algorithms can be applied to improve the performance, such as https://github.com/google-research/deduplicate-text-datasets ## Reference ```bib @misc{https://doi.org/10.48550/arxiv.2105.05241, doi = {10.48550/ARXIV.2105.05241}, url = {https://arxiv.org/abs/2105.05241}, author = {Bandy, Jack and Vincent, Nicholas}, keywords = {Computation and Language (cs.CL), Computers and Society (cs.CY), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus}, publisher = {arXiv}, year = {2021}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` ```bib @misc{https://doi.org/10.48550/arxiv.2107.06499, doi = {10.48550/ARXIV.2107.06499}, url = {https://arxiv.org/abs/2107.06499}, author = {Lee, Katherine and Ippolito, Daphne and Nystrom, Andrew and Zhang, Chiyuan and Eck, Douglas and Callison-Burch, Chris and Carlini, Nicholas}, keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Deduplicating Training Data Makes Language Models Better}, publisher = {arXiv}, year = {2021}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` ```bib @misc{https://doi.org/10.48550/arxiv.2209.00099, doi = {10.48550/ARXIV.2209.00099}, url = {https://arxiv.org/abs/2209.00099}, author = {Treviso, Marcos and Ji, Tianchu and Lee, Ji-Ung and van Aken, Betty and Cao, Qingqing and Ciosici, Manuel R. and Hassid, Michael and Heafield, Kenneth and Hooker, Sara and Martins, Pedro H. and Martins, André F. T. and Milder, Peter and Raffel, Colin and Simpson, Edwin and Slonim, Noam and Balasubramanian, Niranjan and Derczynski, Leon and Schwartz, Roy}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Methods for Natural Language Processing: A Survey}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } ``` [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)dataset_info: 数据集信息 features: 特征字段： - name: text dtype: string 翻译为：- 文本字段：数据类型为字符串 splits: 数据集划分： - name: train num_bytes: 2867856394 num_examples: 38832894 翻译为：- 训练集：总字节数2867856394，样本量38832894 download_size: 1794567875 dataset_size: 2867856394 --- # 数据集卡片："bookcorpus_deduplicated" ## 数据集概览 本数据集为原始[Book Corpus数据集（Book Corpus Dataset）](https://huggingface.co/datasets/bookcorpus)的去重版本。 据[Bandy与Vincent（2021）](https://arxiv.org/abs/2105.05241)的研究，曾用于训练BERT等主流模型的Book Corpus语料库（Zhu等人，2015年）存在大量完全重复的文档。 [Bandy与Vincent（2021）](https://arxiv.org/abs/2105.05241)的研究显示，BookCorpus中数千本图书存在重复，在总计11038本图书中仅包含7185本独特的图书。 ## 去重效果 - 文本行数：38832894 vs 74004228 - 数据集大小：2.91GB vs 4.63GB 已移除所有重复文本，仅保留每份重复文本的首次出现版本，且保留文本的原始出现顺序。 ## 为何进行数据去重？ 对训练数据进行去重已被证实具备多项显著优势，具体包括： - 以相同或更优的精度完成训练所需的迭代步数更少 - 训练得到的模型泄露记忆文本的概率降至原有的十分之一 - 降低碳排放与能源消耗 参考论文：[《去重训练数据可优化大语言模型性能》（Deduplicating Training Data Makes Language Models Better）](https://arxiv.org/abs/2107.06499) ## 去重脚本 python import pandas as pd from datasets import load_dataset dataset = load_dataset("bookcorpus")["train"]["text"] df = pd.DataFrame({"text":dataset}) # 移除完全匹配的重复项 df_filtered = df["text"].drop_duplicates() df_filtered.to_csv("bookcorpus_filtered.csv", index=False, header=False) new_dataset = load_dataset("text", data_files={"train":"bookcorpus_filtered.csv"}) 该脚本运行耗时极短，仅需数分钟即可完成。亦可采用更先进的去重算法以进一步优化性能，例如https://github.com/google-research/deduplicate-text-datasets ## 参考文献 bib @misc{https://doi.org/10.48550/arxiv.2105.05241, doi = {10.48550/ARXIV.2105.05241}, url = {https://arxiv.org/abs/2105.05241}, author = {Bandy, Jack and Vincent, Nicholas}, keywords = {Computation and Language (cs.CL), Computers and Society (cs.CY), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus}, publisher = {arXiv}, year = {2021}, copyright = {arXiv.org perpetual, non-exclusive license} } bib @misc{https://doi.org/10.48550/arxiv.2107.06499, doi = {10.48550/ARXIV.2107.06499}, url = {https://arxiv.org/abs/2107.06499}, author = {Lee, Katherine and Ippolito, Daphne and Nystrom, Andrew and Zhang, Chiyuan and Eck, Douglas and Callison-Burch, Chris and Carlini, Nicholas}, keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Deduplicating Training Data Makes Language Models Better}, publisher = {arXiv}, year = {2021}, copyright = {arXiv.org perpetual, non-exclusive license} } bib @misc{https://doi.org/10.48550/arxiv.2209.00099, doi = {10.48550/ARXIV.2209.00099}, url = {https://arxiv.org/abs/2209.00099}, author = {Treviso, Marcos and Ji, Tianchu and Lee, Ji-Ung and van Aken, Betty and Cao, Qingqing and Ciosici, Manuel R. and Hassid, Michael and Heafield, Kenneth and Hooker, Sara and Martins, Pedro H. and Martins, André F. T. and Milder, Peter and Raffel, Colin and Simpson, Edwin and Slonim, Noam and Balasubramanian, Niranjan and Derczynski, Leon and Schwartz, Roy}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Methods for Natural Language Processing: A Survey}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} } 【更多信息需求】(https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

原始信息汇总

数据集卡片 "bookcorpus_deduplicated"

数据集概述

这是一个原始Book Corpus数据集的去重版本。根据Bandy和Vincent (2021)的研究，Book Corpus数据集中有大量完全重复的文档。去重后，数据集仅保留了7,185本独特的书籍，而原始数据集共有11,038本书籍。

去重效果：

• 行数：38832894 VS 74004228
• 数据集大小：2.91GB VS 4.63GB

去重过程中，重复的文本被删除，仅保留首次出现的文本，并保持文本出现的顺序。

为什么去重？

训练数据的去重显示出多种优势，包括：

• 需要更少的训练步骤来达到相同或更好的准确性
• 训练出的模型产生记忆文本的频率降低十倍
• 减少碳排放和能源消耗

参考文献：Deduplicating Training Data Makes Language Models Better

去重脚本

python import pandas as pd from datasets import load_dataset

dataset = load_dataset("bookcorpus")["train"]["text"] df = pd.Dataframe({"text":dataset})

删除重复项（完全匹配）

df_filtered = df["text"].drop_duplicates()

df_filtered.to_csv("bookcorpus_filtered.csv","index"=False,"header"=False) new_dataset = load_dataset("text",data_files={"train":"bookcorpus_filtered.csv"})

运行时间短，不到几分钟。更复杂的去重算法可以应用于提高性能，例如google-research/deduplicate-text-datasets。

参考文献

bib @misc{https://doi.org/10.48550/arxiv.2105.05241, doi = {10.48550/ARXIV.2105.05241}, url = {https://arxiv.org/abs/2105.05241}, author = {Bandy, Jack and Vincent, Nicholas}, keywords = {Computation and Language (cs.CL), Computers and Society (cs.CY), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Addressing "Documentation Debt" in Machine Learning Research: A Retrospective Datasheet for BookCorpus}, publisher = {arXiv}, year = {2021}, copyright = {arXiv.org perpetual, non-exclusive license} }

bib @misc{https://doi.org/10.48550/arxiv.2107.06499, doi = {10.48550/ARXIV.2107.06499}, url = {https://arxiv.org/abs/2107.06499}, author = {Lee, Katherine and Ippolito, Daphne and Nystrom, Andrew and Zhang, Chiyuan and Eck, Douglas and Callison-Burch, Chris and Carlini, Nicholas}, keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Deduplicating Training Data Makes Language Models Better}, publisher = {arXiv}, year = {2021}, copyright = {arXiv.org perpetual, non-exclusive license} }

bib @misc{https://doi.org/10.48550/arxiv.2209.00099, doi = {10.48550/ARXIV.2209.00099}, url = {https://arxiv.org/abs/2209.00099}, author = {Treviso, Marcos and Ji, Tianchu and Lee, Ji-Ung and van Aken, Betty and Cao, Qingqing and Ciosici, Manuel R. and Hassid, Michael and Heafield, Kenneth and Hooker, Sara and Martins, Pedro H. and Martins, André F. T. and Milder, Peter and Raffel, Colin and Simpson, Edwin and Slonim, Noam and Balasubramanian, Niranjan and Derczynski, Leon and Schwartz, Roy}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Methods for Natural Language Processing: A Survey}, publisher = {arXiv}, year = {2022}, copyright = {arXiv.org perpetual, non-exclusive license} }

搜集汇总

数据集介绍

构建方式

在自然语言处理领域，高质量的训练数据是模型性能的基石。BookCorpus作为BERT等经典模型的预训练语料库，其原始版本却存在大量完全重复的文档。Saibo-creator/bookcorpus_deduplicated数据集正是为解决这一缺陷而生，通过精确的去重处理，仅保留每条文本的首次出现，并维持原始文本顺序，从而构建出一个更为纯净、高效的语料库。具体而言，该数据集借助Pandas库中的drop_duplicates函数，对原始BookCorpus中的文本行进行精确匹配去重，最终将74,004,228条文本精简至38,832,894条，数据集大小也从4.63GB压缩至2.91GB，显著提升了数据质量与可用性。

特点

该数据集的核心特点在于其经过严谨去重处理后展现出的优越性。一方面，它大幅减少了训练数据中的冗余信息，使得模型在达到相同或更优精度时所需的训练步数更少，从而有效降低碳排放与能源消耗。另一方面，去重后的数据集能够显著减少模型对记忆化文本的生成频率，频率降低达十倍之多，这对于提升模型的泛化能力与鲁棒性至关重要。此外，该数据集保留了原始文本的排列顺序，确保了语料的时间连贯性与结构完整性，为下游任务提供了可靠的数据基础。

使用方法

使用Saibo-creator/bookcorpus_deduplicated数据集极为便捷，研究人员可直接通过HuggingFace的datasets库进行加载。具体操作时，只需调用load_dataset函数并指定数据集名称，即可轻松获取训练集。该数据集仅包含一个文本字段，格式简洁，适用于多种自然语言处理任务，如语言模型预训练、文本分类或序列生成等。对于希望进一步提升去重效果的用户，还可参考Google Research的deduplicate-text-datasets工具，应用更复杂的去重算法，以优化数据质量。整个加载与使用过程高效流畅，仅需数分钟即可完成。

背景与挑战

背景概述

在自然语言处理领域，大规模文本语料库的构建与质量保障始终是推动模型性能跃升的关键基石。BookCorpus数据集由Zhu等人于2015年创建，曾作为BERT等里程碑式模型的预训练语料，对深度学习在语言理解任务中的突破产生了深远影响。然而，2021年Bandy与Vincent的研究揭示了该数据集存在严重的文档重复问题，在总计11,038本书籍中仅有7,185部为唯一内容，重复率高达约35%。这一发现催生了Saibo-creator/bookcorpus_deduplicated数据集的诞生，其核心研究问题在于通过精确去重优化语料纯净度，以提升下游模型训练效率与泛化能力。该去重版本由社区研究者基于公开脚本构建，旨在为语言模型提供更可靠的数据基础，其影响力体现在对数据质量重要性的重新审视——不仅降低了模型对重复文本的记忆风险，还通过减少训练步数与碳排放，呼应了高效自然语言处理方法的时代需求。

当前挑战

数据集当前面临的核心挑战可分为两个层面。在领域问题层面，原始BookCorpus的重复文档直接导致了语言模型训练中的偏差放大与泛化能力衰减，例如模型可能过度拟合高频重复内容，从而在未见文本上表现不稳定。去重虽能缓解此问题，但精确的去重算法（如基于字符串匹配的简单方法）可能遗漏语义相似的重复或变体表达，而更复杂的模糊去重技术（如MinHash或SimHash）又需权衡计算开销与精度，这构成了平衡数据纯净度与资源消耗的技术难题。在构建过程中，挑战则体现在数据溯源与处理流程的标准化不足：原始语料来源的版权与元数据缺失（即所谓的“文档债务”）使得去重后的数据集仍可能隐含遗留的噪声或非均匀分布，同时，简单的去重脚本仅移除完全相同的文本行，未考虑跨文档的段落级重复或结构重排，这限制了语料库的语义一致性。此外，如何在不破坏原始语料叙事连贯性的前提下实施去重，并确保处理结果的可复现性，也是当前版本需进一步优化的方向。

常用场景

经典使用场景

在自然语言处理领域，BookCorpus去重版本为大规模无监督预训练提供了更为纯净的文本语料。该数据集剔除了原始BookCorpus中约68%的重复书籍，保留了38,832,894条文本行，体积缩减至2.91GB。其核心价值在于作为BERT等经典模型的训练基座，通过消除冗余信息使模型能够从更精炼的语义结构中学习语言规律，显著提升训练效率与泛化能力。研究者在进行语言模型预训练、文本生成任务或语义表征学习时，常以此数据集作为标准对照，验证去重策略对模型性能的增益效果。

解决学术问题

该数据集直面机器学习社区长期忽视的数据文档化缺陷与训练数据冗余问题。Bandy与Vincent（2021）揭示原始BookCorpus中仅有7,185本独特书籍，大量重复文本导致模型在训练时陷入记忆化陷阱，而非真正的语言理解。去重版本通过消除精确重复文档，有效解决了数据偏差引发的过拟合与泛化能力下降问题。Lee等人（2021）进一步证实，基于此数据训练的语言模型不仅在同等精度下减少训练步数，更将记忆化文本生成频率降低十倍，为构建高效、可信赖的AI系统奠定了数据基础，推动了数据质量评估标准的学术讨论。

衍生相关工作

围绕该数据集衍生出一系列具有里程碑意义的研究工作。Lee等人（2021）的《Deduplicating Training Data Makes Language Models Better》首次系统论证了去重对模型效率与安全性的双重提升，该成果直接影响了后续GPT-3、T5等大规模预训练模型的数据处理规范。Treviso等人（2022）在《Efficient Methods for Natural Language Processing: A Survey》中将其作为数据高效利用的典型案例。此外，Google Research开发的去重工具包（deduplicate-text-datasets）以此数据集为测试基准，推动了MinHash、SimHash等近似去重算法在NLP领域的标准化应用，形成从数据清洗到模型评估的完整方法论体系。