Middle-English-PD

# 🇬🇧 Midlle-English Public Domain 🇬🇧

**Middle-English-Public Domain** or **Middle-English-PD** is a large collection aiming to aggregate all midlle-age English monographies, periodicals and texts in the public domain. As of March 2024, it is the biggest middle-age English open corpus.

## Dataset summary
The collection contains 204,472 individual titles making up 12,100,748,108 words recovered from multiple sources, including Internet Archive and various European national libraries and cultural heritage institutions. Each parquet file has the full text of 2,000 books selected at random.

## Curation method
The composition of the dataset adheres to the criteria for public domain works in the EU and, consequently, all Berne-countries for EU authors: any publication whose author is dead for more than 70 years. Additionally, the initial consolidation of public domain status for cultural heritage operates in the EU under the 2019 Copyright Directive (art. 14).

As of March 2024, to limit rights verification, we have retained exclusively titles published prior to 1884.

The corpus will be expanded at a later stage to encompass late 19th century and early 20th century publications, after checking for public domain validity.

## Uses
The collection aims to expand the availability of open works for the training of Large Language Models. The text can be used for model training and republished without restriction for reproducibility purposes.

The rationales for creation of this collection are multifold:

* **Scientific**: We observe that the closure of training corpora represents a major barrier to AI research. Large language models face a real crisis of reproducibility.
* **Legal**: With the adoption of the AI Act with its obligations in terms of copyright law compliance for the pretraining corpora, the European AI ecosystem will have to change its provenance practices.
* **Cultural**: The linguistic diversity of the European Union is currently underrepresented. Unlike web archives, open, heritage, administrative, or scientific texts are often of high quality: they are long, multilingual, and editorialized publications.
* **Economical**: Today, value capture is concentrated on players whose financial resources are already considerable, allowing them to collect or purchase data at a high price. Making a royalty-free corpus available to as many people as possible frees innovation in uses and minimizes economic dependencies on dominant actors.

## License
The entire collection is in the public domain in all regions. This means that the patrimonial rights of each individual or collective right holders have expired.

There has been a debate for years in Europe over the definition of public domain and the possibility to restrict its use. Since 2019, the EU Copyright Directive states that "Member States shall provide that, when the term of protection of a work of visual art has expired, any material resulting from an act of reproduction of that work is not subject to copyright or related rights, unless the material resulting from that act of reproduction is original in the sense that it is the author's own intellectual creation." (art. 14)

## Future work
This dataset is not a one-time work but will continue to evolve significantly in three directions:
* Expansion of the dataset to the late 19th and early 20th century works and its further enhancement with currently unexploited collections coming from European patrimonial data repositories.
* Correction of computer generated errors in the text. All the texts have been transcribed automatically through the use of Optical Character Recognition (OCR) software. The original files have been digitized over a long time period (since the mid-2000s) and some documents should be. Future versions will strive either to re-OCRize the original text or use experimental LLM models for partial OCR correction.
* Enhancement of the structure/editorial presentation of the original text. Some parts of the original documents are likely unwanted for large scale analysis or model training (header, page count…). Additionally, some advanced document structures like tables or multi-column layout are unlikely to be well-formatted.

## Acknowledgements
The corpus was stored and processed with the generous support of Scaleway. It was built up with the support and concerted efforts of the state start-up LANGU:IA (start-up d’Etat), supported by the French Ministry of Culture and DINUM, as part of the prefiguration of the service offering of the Alliance for Language technologies EDIC (ALT-EDIC).

Corpus collection has been largely facilitated thanks to the open science LLM community insights and cooperation (Occiglot, Eleuther AI, Allen AI).

# 🇬🇧 中古英语公共领域数据集（Middle-English Public Domain）

**中古英语公共领域数据集（Middle-English-Public Domain，简称Middle-English-PD）** 是一个大型聚合项目，旨在收录所有处于公共领域的中世纪英语专著、期刊及文本。截至2024年3月，它已是规模最大的中世纪英语开放语料库。

## 数据集概览
该语料库共收录204,472部独立作品，总字数达12,100,748,108词，数据源自多个渠道，包括互联网档案馆（Internet Archive）及欧洲多国国家图书馆与文化遗产机构。每个Parquet文件均包含随机选取的2,000部书籍的完整文本。

## 遴选规则
本数据集的构成遵循欧盟公共领域作品标准，同时适配伯尔尼公约成员国中欧盟作者的相关规则：即作者去世超过70年的已出版作品。此外，欧盟2019年版权指令（第14条）也为文化遗产作品的公共领域身份认定提供了依据。
截至2024年3月，为简化权利核验流程，我们仅保留1884年之前出版的作品。
后续我们将在验证其公共领域合法性后，拓展语料库至19世纪末至20世纪初的出版物。

## 应用场景
本数据集旨在提升开放作品的可及性，以供大语言模型（Large Language Model，LLM）训练使用。文本可无限制用于模型训练与再发布，以保障研究可重复性。
构建该数据集的动因涵盖多维度：
* **学术层面**：当前训练语料库的封闭化已成为人工智能研究的重大阻碍，大语言模型正面临严峻的可重复性危机。
* **法律层面**：随着《人工智能法案》的通过，其对预训练语料库的版权合规提出了明确要求，欧洲人工智能生态必须调整其数据源获取实践。
* **文化层面**：欧盟当前的语言多样性未得到充分体现。与网页档案不同，开放的文化遗产、行政或学术文本往往具备高质量特征：篇幅较长、多语言且经过编辑加工。
* **经济层面**：当前数据价值的获取高度集中于财力雄厚的头部玩家，他们能够以高昂成本收集或采购数据。向尽可能多的群体提供免版税语料库，能够解放创新应用空间，并降低对主导企业的经济依赖。

## 授权协议
本数据集所有内容在全球范围内均处于公共领域。这意味着所有个人或集体权利持有者的财产性权利均已过期。
欧洲多年来围绕公共领域的定义及限制其使用的可能性存在争议。自2019年起，欧盟版权指令明确规定："各成员国应规定，当视觉艺术作品的保护期届满时，对该作品进行复制行为所产生的材料不受版权或相关权利约束，除非该复制材料具有独创性，即属于作者的原创智力成果。"（第14条）

## 未来规划
本数据集并非一次性项目，将从三个方向持续迭代优化：
* **语料扩容**：将语料库拓展至19世纪末至20世纪初的作品，并引入欧洲遗产数据仓库中尚未开发的馆藏资源进行丰富。
* **文本错误修正**：所有文本均通过光学字符识别（Optical Character Recognition，OCR）软件自动转录而来。原始文件自2000年代中期起历经多年数字化，部分文档存在转录误差。后续版本将要么对原始文本重新进行OCR识别，要么通过实验性大语言模型实现部分OCR错误修正。
* **文本结构优化**：原始文档中的部分内容（如页眉、页码等）可能不适用于大规模分析或模型训练。此外，部分高级文档结构（如表格或多栏布局）往往格式不佳，后续将对此进行优化完善。

## 致谢
本语料库的存储与处理得到了Scaleway的慷慨支持。数据集的构建得到了国家初创企业LANGU:IA（法国文化部与国家数字化与创新总署（DINUM）支持的国家初创企业）的协助与协同努力，作为语言技术联盟（ALT-EDIC）服务预配置的一部分。
语料库的收集工作在很大程度上得益于开放科学大语言模型社区的见解与合作（包括Occiglot、Eleuther AI、Allen AI）。