Polish-PD

# 🇵🇱 Polish Public Domain 🇵🇱

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

## Dataset summary
The collection contains 247,491 individual texts making up 2,697,414,811 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, cooperation and support (Occiglot, Eleuther AI, OpenLLM France, Allen AI).

# 🇵🇱 波兰公共领域数据集 🇵🇱

**波兰公共领域数据集（Polish Public Domain，简称Polish-PD）**是一款旨在聚合所有处于公共领域的波兰专著与期刊的大型数据集。截至2024年3月，它已是规模最大的波兰开放语料库。

## 数据集概览
该语料库包含247,491篇独立文本，总计2,697,414,811词，数据来源于互联网档案（Internet Archive）、欧洲多国国家图书馆及文化遗产机构等多个渠道。每个Parquet文件随机收录2000部图书的完整文本。

## 遴选规范
本数据集的构成遵循欧盟公共领域作品判定标准，同时适用于伯尔尼公约成员国中欧盟作者的作品：即作者逝世超过70年的已出版作品。此外，欧盟范围内的文化遗产作品公共领域身份认定依据2019年版权指令第14条执行。
截至2024年3月，为简化版权验证流程，本次收录的作品均为1884年之前出版的内容。
后续将在验证公共领域合法性后，拓展收录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获取性，以供大语言模型（Large Language Model）训练使用。其文本可用于模型训练，且可无限制复刻重发布，以保障研究可重复性。
构建该数据集的核心动因涵盖多维度：
* **科研维度**：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，大语言模型正面临切实的可重复性危机。
* **法律维度**：随着《人工智能法案》（AI Act）的通过，其对预训练语料库的版权合规要求将迫使欧洲人工智能生态体系变革数据溯源实践。
* **文化维度**：欧盟的语言多样性目前在语料库中代表性不足。与网页档案不同，开放型、文化遗产类、行政类及学术类文本往往具备高质量特征：篇幅较长、多语言属性且经过专业编辑加工。
* **经济维度**：当下数据价值的获取高度集中于少数财力雄厚的巨头企业，它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，可推动相关应用创新，降低对头部平台的经济依赖。

## 授权协议
本数据集全部内容在全球范围内均属于公共领域，即所有个人或集体权利持有人的财产性权利均已过期。
欧洲范围内关于公共领域定义及使用限制的争论已持续多年。自2019年起，欧盟版权指令明确规定：“当美术作品的保护期届满后，对该作品进行复制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，属于作者的原创智力成果。”（第14条）

## 后续规划
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**：拓展收录19世纪末至20世纪初的作品，并引入欧洲文化遗产数据仓库中尚未被利用的馆藏资源。
* **文本纠错**：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，部分文档存在识别误差。后续版本将对原始文本进行重新光学字符识别，或借助实验性大语言模型完成部分OCR纠错工作。
* **结构优化**：原始文档中的部分内容（如页眉、页码等）可能不适合大规模分析或模型训练，且部分复杂文档结构（如表格、多栏布局）的格式规范性不足，未来将对文本结构与编辑呈现形式进行优化。

## 致谢
本语料库的存储与处理工作得益于Scaleway的慷慨支持。数据集的构建得到了法国文化部与DINUM支持的国家级初创企业LANGU:IA的协助，该项目隶属于语言技术联盟（Alliance for Language technologies EDIC，ALT-EDIC）的服务预研计划。
语料收集工作还得益于开放科学大语言模型社区的洞见、协作与支持，包括Occiglot、Eleuther AI、OpenLLM France、Allen AI等团队。