diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..1ef325f1b111266a6b26e0196871bd78baa8c2f3 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,59 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.lz4 filter=lfs diff=lfs merge=lfs -text +*.mds filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +# Audio files - uncompressed +*.pcm filter=lfs diff=lfs merge=lfs -text +*.sam filter=lfs diff=lfs merge=lfs -text +*.raw filter=lfs diff=lfs merge=lfs -text +# Audio files - compressed +*.aac filter=lfs diff=lfs merge=lfs -text +*.flac filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text +# Image files - uncompressed +*.bmp filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text +*.tiff filter=lfs diff=lfs merge=lfs -text +# Image files - compressed +*.jpg filter=lfs diff=lfs merge=lfs -text +*.jpeg filter=lfs diff=lfs merge=lfs -text +*.webp filter=lfs diff=lfs merge=lfs -text +# Video files - compressed +*.mp4 filter=lfs diff=lfs merge=lfs -text +*.webm filter=lfs diff=lfs merge=lfs -text diff --git a/DATASHEET.md b/DATASHEET.md new file mode 100644 index 0000000000000000000000000000000000000000..d5f61276d0a4c885b24be701b2e7a9df284d3996 --- /dev/null +++ b/DATASHEET.md @@ -0,0 +1,262 @@ +# Datasheet: German Commons + +This is a datasheet compliant with the recommendations of [Gebru et al. (2018)](https://arxiv.org/abs/1803.09010v8), describing the properties of the **German Commons** dataset. + +## Motivation + +### Why was the dataset created? + +German Commons addresses the critical gap in large-scale open German +text for language model training. Existing German corpora either lack +explicit licensing, contain web-scraped content of uncertain provenance, +or provide insufficient scale. + +### Has the dataset been used already? + +This represents the initial release of German Commons. No external usage +has occurred prior to publication. Constituent dataset may have already been used prior. + +### What (other) tasks could the dataset be used for? + +Beyond language model pretraining, German Commons supports all German +NLP research requiring clean, license-compliant text, multilingual model +development, or linguistic analysis of German text across domains. The +diverse domain coverage (legal, cultural, scientific, etc.) further +enables domain-specific model development and cross-domain evaluation +studies. + +### Who funded the creation of the dataset? + +Dataset compilation was supported by German and European research +grants: German Federal Ministry of Research, Technology, and Space +(BMFTR) under Grants  `01IS24077A`,  `01IS24077B`, and  `01IS24077D`, by +the ScaDS.AI Center for Scalable Data Analytics and Artificial +Intelligence, funded by the BMFTR and by the Sächsische +Staatsministerium für Wissenschaft, Kultur und Tourismus under Grant + `ScaDS.AI`, and by the OpenWeb-Search.eu project, funded by the +European Union under Grant  `GA 101070014`. Constituent datasets +originate primarily from state-funded institutions across Germany and +Austria. + +## Dataset Composition + +### What are the instances? + +Each instance represents a single German-language document with +associated metadata and licensing information. + +### How many instances are there in total? + +The dataset contains 35,778,211 documents comprising 154,558,196,961 +GPT-2 tokens. + +### What data does each instance consist of? + +Each instance includes: a unique identifier for source +cross-referencing, source dataset name, quality-filtered and +paragraph-deduplicated raw text, canonical SPDX license URL, thematic +domain key, GPT-2 token count, a perplexity score calculated using a +KenLM model trained on German Wikipedia text, and a OCR quality score +calculated using [OCRoscope](https://github.com/Pleias/OCRoscope). + +### Is there a label or target associated with each instance? + +No supervised labels exist. However, each instance contains metadata +labels for thematic domain classification, licensing information, and +document length statistics. + +### Is any information missing from individual instances? + +Paragraph-level deduplication may alter texts from their original form. +Personally identifiable information has been systematically removed. + +### Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? + +The dataset represents a filtered subset of source collections. +Filtering removes OCR errors, extraction artifacts, and low-quality or +duplicated content, creating a curated selection. + +### Are there recommended data splits? + +No predefined splits are provided. All data is intended for pretraining. + +### Are there any errors, sources of noise, or redundancies in the dataset? + +Despite quality filtering and deduplication, residual issues may remain: +cross-corpus text duplicates from overlapping sources, and extraction +artifacts from OCR and PDF-to-text processing. + +### Is the dataset self-contained, or does it link to or otherwise rely on external resources? + +The dataset is self-contained and centrally downloadable. The Source +dataset references provided enable reproducible reconstruction. + +## Collection Process + +### What mechanisms or procedures were used to collect the data? + +Data collection employed multiple automated procedures: direct download +from institutional repositories and open platforms, programmatic +crawling via APIs where available, and automated text extraction from +PDF and other document formats using specialized libraries. Then, the +open source processing pipelines were applied for quality filtering and +deduplication all sources. Validation occurred through manual inspection +of sample outputs, cross-verification against source repositories, and +automated consistency checks. + +### How was the data associated with each instance acquired? + +All text data represents directly observable content from original +sources; no inference or derivation occurred. Metadata (licensing, +thematic classification, source attribution) was extracted directly from +source repository information or explicitly provided by institutional +datasets. Where PDF extraction was required, raw text underwent +validation against source documents to verify accuracy. + +### If the dataset is a sample from a larger set, what was the sampling strategy? + +Sampling was deterministic based on explicit criteria: German language +content as per automated classification explicit open licensing, quality +thresholds, and institutional source verification. No probabilistic +sampling occurred; all content meeting inclusion criteria was retained +after deduplication. + +### Who was involved in the data collection process and how were they compensated? + +Data collection was conducted by the author team using automated +systems. No crowdworkers, contractors, or external annotators were +employed. All processing occurred through programmatic methods without +manual content creation or labeling requiring compensation. + +### Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances? + +Collection occurred between January and August 2025, using source +dataset versions available through August 31st, 2025. The underlying +content creation spans multiple centuries, representing a temporal range +that significantly predates and extends beyond the collection timeframe. + +## Data Preprocessing + +### Was any preprocessing/cleaning/labeling of the data done? + +Comprehensive preprocessing included: text extraction from PDFs and OCR +sources with encoding normalization, language detection and filtering +for German content, and quality filtering targeting digitization +artifacts and extraction errors, paragraph-level deduplication using +content hashing, systematic PII removal, format standardization across +all source types. Thematic domain classification was applied based on +source dataset. + +### Was the raw data saved in addition to the preprocessed/cleaned/labeled data? + +Raw data is not provided since all constituent source datasets remain +publicly accessible through their original repositories. + +### Is the software used to preprocess/clean/label the instances available? + +All preprocessing software is open source and available at + , ensuring complete +reproducibility of the dataset. + +### Does this dataset collection/processing procedure achieve the motivation for creating the dataset stated in the first section of this datasheet? + +Yes. The procedure successfully addresses the identified gap by: +providing the largest collection to-date of openly licensed German text, +enabling open German language model development without licensing +uncertainties, and establishing reproducible methodology for future +dataset construction. This directly fulfills the stated motivation of +creating license-compliant, large-scale German training data. + +### How will the dataset be distributed? + +The dataset is distributed as Parquet files through multiple public +repositories for redundancy. Primary distribution occurs via Hugging +Face Hub at . + +### When will the dataset be released/first distributed? What license (if any) is it distributed under? + +Public release occurred on 2025/10/14. Dataset metadata and compilation +are licensed under ODC-BY 1.0 (). Individual document texts retain +their original licenses as specified in each instance's SPDX URL field, +creating a heterogeneous but fully documented licensing structure. + +### Are there any copyrights on the data? + +Yes. Each document retains copyright under its original creator or +institutional provider, governed by the specific license indicated in +the instance metadata. The compilation itself does not claim additional +copyright over constituent texts. + +### Are there any fees or access/export restrictions? + +The dataset is freely accessible without fees or registration +requirements. However, users must comply with individual document +licenses, which may include attribution requirements or share-alike +provisions. Commercial use is permitted by all constituent licenses. + +## Dataset Maintenance + +### Who is supporting/hosting/maintaining the dataset? + +The dataset is maintained by the authors of this report. + +### Will the dataset be updated? If so, how often and by whom? + +Updates may occur when significant new German open-source collections +become available. The original authors will coordinate updates, with +community contributions welcomed through the open-source pipeline. + +### How will updates be communicated? + +Updates will be announced through: versioned releases on hosting +platforms with detailed changelogs, academic publication updates when +substantial changes occur. + +### If the dataset becomes obsolete how will this be communicated? + +Obsolescence will be communicated through deprecation notices on all +hosting platforms. + +### Is there a repository to link to any/all papers/systems that use this dataset? + +No centralized usage repository will be maintained. Usage tracking +occurs through standard academic citation of the dataset paper. Users +are encouraged to cite the dataset publication when reporting results or +building derivative works. + +### If others want to extend/augment/build on this dataset, is there a mechanism for them to do so? + +The open-source `llmdata` pipeline enables community extensions through +standardized data ingestion protocols for new sources and automated +quality assessment and deduplication using established filtering +criteria. Community contributions undergo review by the maintenance +team. + +## Ethical Considerations + +### Were any ethical review processes conducted? + +No formal institutional review board process was conducted. The dataset +relies exclusively on pre-existing, publicly available, and explicitly +licensed materials from established institutional sources. Data +processing incorporated ethical considerations including systematic PII +removal and exclusion of sources lacking clear licensing frameworks. + +### Does the dataset contain data that might be considered confidential? + +No. All included content derives from explicitly open-licensed +institutional sources. + +### Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? + +Potentially yes. The dataset spans centuries of German text documents, +which may include historical perspectives, political viewpoints, or +language that could be considered offensive by contemporary standards. +The scale and temporal range make comprehensive content moderation +infeasible. Users should exercise appropriate caution. + +### Does the dataset relate to people? + +The dataset may contain publicly available information relating to +individuals in various contexts including historical documents, +biographical information, academic citations, and government records. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..31e3b63c689b5657ae971bb204f94a1f8cd4cf96 --- /dev/null +++ b/README.md @@ -0,0 +1,479 @@ +--- +annotations_creators: +- machine-generated +language_creators: +- found +language: +- de +license: +- odc-by +multilinguality: +- monolingual +size_categories: +- 100B