The zip file contains two folders related to the German Credit Data (https://archive.ics.uci.edu/dataset/144/statlog+german+credit+data). The `german_credit` folder contains the CSV file of the dataset. The `german_data` contains 5 different folds of the dataset.

:::: Ten Thousand German News Articles Dataset ::::

A dataset for topic extraction from 10k German News Articles and NLP for German language. English text classification datasets are common. Examples are the big AG News, the class-rich 20 Newsgroups and the large-scale DBpedia ontology datasets for topic classification and for example the commonly used IMDb and Yelp datasets for sentiment analysis. Non-english datasets, especially German datasets, are less common. There is a collection of sentiment analysis datasets assembled by the Interest Group on German Sentiment Analysis. To my knowledge the MLDoc contains German documents for classification. Due to grammatical differences between the English and the German language, a classifier might be effective on a English dataset, but not as effective on a German dataset. The German language has a higher inflection and long compound words are quite common compared to the English language. One would need to evaluate a classifier on multiple German datasets to get a sense of it’s effectiveness.

:::: What It Cointains ::::

The 10kGNAD dataset is intended to solve part of this problem as the first german topic classification dataset. It consists of 10273 german language news articles from an austrian online newspaper categorized into nine topics. These articles are a till now unused part of the One Million Posts Corpus. In the One Million Posts Corpus each article has a topic path. For example Newsroom/Wirtschaft/Wirtschaftpolitik/Finanzmaerkte/Griechenlandkrise. The 10kGNAD uses the second part of the topic path, here Wirtschaft, as class label. The article titles and texts are concatenated into one text and the authors are removed to avoid a keyword like classification on autors frequent in a class. I created and used this dataset in my thesis to train and evaluate four text classifiers on the German language. By publishing the dataset I hope to support the advancement of tools and models for the German language. Additionally this dataset can be used as a benchmark dataset for german topic classification.

Citations:

@InProceedings{Schabus2017, Author = {Dietmar Schabus and Marcin Skowron and Martin Trapp}, Title = {One Million Posts: A Data Set of German Online Discussions}, Booktitle = {Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)}, Pages = {1241--1244}, Year = {2017}, Address = {Tokyo, Japan}, Doi = {10.1145/3077136.3080711}, Month = aug } @InProceedings{Schabus2018, author = {Dietmar Schabus and Marcin Skowron}, title = {Academic-Industrial Perspective on the Development and Deployment of a Moderation System for a Newspaper Website}, booktitle = {Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC)}, year = {2018}, address = {Miyazaki, Japan}, month = may, pages = {1602-1605}, abstract = {This paper describes an approach and our experiences from the development, deployment and usability testing of a Natural Language Processing (NLP) and Information Retrieval system that supports the moderation of user comments on a large newspaper website. We highlight some of the differences between industry-oriented and academic research settings and their influence on the decisions made in the data collection and annotation processes, selection of document representation and machine learning methods. We report on classification results, where the problems to solve and the data to work with come from a commercial enterprise. In this context typical for NLP research, we discuss relevant industrial aspects. We believe that the challenges faced as well as the solutions proposed for addressing them can provide insights to others working in a similar setting.}, url = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/8885.html}, }

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Dataset

This dataset was created by Kikoy

Released under Other (specified in description)

Contents

Dataset Card for "German LER"

  Dataset Summary

A dataset of Legal Documents from German federal court decisions for Named Entity Recognition. The dataset is human-annotated with 19 fine-grained entity classes. The dataset consists of approx. 67,000 sentences and contains 54,000 annotated entities. NER tags use the BIO tagging scheme. The dataset includes two different versions of annotations, one with a set of 19 fine-grained semantic classes (ner_tags) and another one… See the full description on the dataset page: https://huggingface.co/datasets/elenanereiss/german-ler.

Tsev German DatasetDeutscher DatensatzHigh-Quality German Hu-Center, thiab IVR Dataset rau AI & Speech Models Hu rau peb OverviewTitle (Language) German Language DatasetDataset TypesCall Center, General Conversation, Music, Scripted MonologueCountryGermanyDescriptionTitle (Language) German Language DatasetDataset TypesCall Center, General Conversation, Music, Scripted MonologueCountryGermanyDescriptionTitle Unscripted, kev sib tham...

Original data from: https://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data), using the file "german.data-numeric" version produced by Strathclyde University. Changes made: - changed the ordering of Attribute 3 (Credit History) to try to extract monotone relationship: ORIGINAL ORDERING: A30 : no credits taken/all credits paid back duly, A31 : all credits at this bank paid back duly, A32 : existing credits paid back duly till now, A33 : delay in paying off in the past, A34 : critical account/other credits existing (not at this bank)) NEW ORDERING: 0=all credits paid back (A31) 1=all credits paid back duly til now (A32) 2= no credits taken (A30) 3= delay in past (A33) 4=critical acct (A34). ATTRIBUTES: 0 CLASS Credit Rating: +1 is bad / -1 is good 1 BalanceCheque 2 Loan NurnMonth 3 CreditHistory 4 CreditAmt 5 SavingsBalance 6 Mths in PresentEmployment 7 PersonStatusSex 8 PresentResidenceSince 9 Property 10 AgeInYears 11 OtherInstallmentPlans (highest val is NO other installment plans) 12 NumExistingCreditsThisBank 13 NumPplLiablMaint 14 Telephone 15 ForeignWorker 16 Purpose-CarNew 17 Purpose-CarOld 18 otherdebtor-none (compared to guarantor) 19 otherdebt-coappl (compared to guarantor) 20 house-rent (compared to 'for free') 21 house-owns (compared to 'for free') 22 job-unemployed (vs mgt) 23 jobs-unskilled (vs mgt) 24 job-skilled (vs mgt)

Home Dataset GermanDeutscher DatensatzHigh-Quality German Call-Center, ndi IVR Dataset for AI & Speech Models Lumikizanani Nafe OverviewTitle (Language) German Language DatasetDataset TypesCall Center, General Conversation, Music, Scripted MonologueCountryGermanyDescriptionUnscripted, synthetic telephonic conversations...

Home UK German DatasetHigh-Quality UK German Call Center, and Utterance Dataset for AI & Speech Models Contact Us OverviewTitle (Language)UK German Language DatasetDataset TypesCall Center, UtteranceCountryUnited KingdomDescriptionThis dataset includes unscripted…

German Traffic Sign Recognition

## Overview

German Traffic Sign Recognition is a dataset for object detection tasks - it contains Traffic Signs annotations for 1,102 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Home German Wake Word DatasetHigh-Quality German Wake Word Dataset for AI & Speech Models Contact Us OverviewTitle (Language)German Language DatasetDataset TypesWake WordCountryGermanyDescriptionWake Words / Voice Command / Trigger Word /…

Tsev UK German DatasetHigh-Quality UK German Hu Xov Tooj Center, thiab Utterance Dataset rau AI & Speech Models Hu rau Peb Txheej TxheemTitle (Language)UK German Language DatasetDataset TypesCall Center, UtteranceCountryUnited KingdomDescriptionThis dataset include unscripted…

German Railway Signaling Dataset

## Overview

German Railway Signaling Dataset is a dataset for object detection tasks - it contains Signals annotations for 1,605 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

🇩🇪 German Public Domain 🇩🇪

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

  Dataset summary

The collection contains 260,638 individual texts making up 37,650,706,611 words recovered from multiple sources, including Internet Archive and various European national libraries and cultural heritage institutions. Each parquet file… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/German-PD.

German

## Overview

German is a dataset for object detection tasks - it contains Sign annotations for 898 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

(see https://tblock.github.io/10kGNAD/ for the original dataset page)

This page introduces the 10k German News Articles Dataset (10kGNAD) german topic classification dataset. The 10kGNAD is based on the One Million Posts Corpus and avalaible under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. You can download the dataset here.

Why a German dataset?

English text classification datasets are common. Examples are the big AG News, the class-rich 20 Newsgroups and the large-scale DBpedia ontology datasets for topic classification and for example the commonly used IMDb and Yelp datasets for sentiment analysis. Non-english datasets, especially German datasets, are less common. There is a collection of sentiment analysis datasets assembled by the Interest Group on German Sentiment Analysis. However, to my knowlege, no german topic classification dataset is avaliable to the public.

Due to grammatical differences between the English and the German language, a classifyer might be effective on a English dataset, but not as effectiv on a German dataset. The German language has a higher inflection and long compound words are quite common compared to the English language. One would need to evaluate a classifyer on multiple German datasets to get a sense of it's effectivness.

The dataset

The 10kGNAD dataset is intended to solve part of this problem as the first german topic classification dataset. It consists of 10273 german language news articles from an austrian online newspaper categorized into nine topics. These articles are a till now unused part of the One Million Posts Corpus.

In the One Million Posts Corpus each article has a topic path. For example Newsroom/Wirtschaft/Wirtschaftpolitik/Finanzmaerkte/Griechenlandkrise. The 10kGNAD uses the second part of the topic path, here Wirtschaft, as class label. In result the dataset can be used for multi-class classification.

I created and used this dataset in my thesis to train and evaluate four text classifyers on the German language. By publishing the dataset I hope to support the advancement of tools and models for the German language. Additionally this dataset can be used as a benchmark dataset for german topic classification.

Numbers and statistics

As in most real-world datasets the class distribution of the 10kGNAD is not balanced. The biggest class Web consists of 1678, while the smalles class Kultur contains only 539 articles. However articles from the Web class have on average the fewest words, while artilces from the culture class have the second most words.

Splitting into train and test

I propose a stratifyed split of 10% for testing and the remaining articles for training. To use the dataset as a benchmark dataset, please used the train.csv and test.csv files located in the project root.

Code

Python scripts to extract the articles and split them into a train- and a testset avaliable in the code directory of this project. Make sure to install the requirements. The original corpus.sqlite3 is required to extract the articles (download here (compressed) or here (uncompressed)).

License

This dataset is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Please consider citing the authors of the One Million Post Corpus if you use the dataset.

The German Open-Ended Question Answering Dataset is a meticulously curated collection of comprehensive Question-Answer pairs. It serves as a valuable resource for training Large Language Models (LLMs) and Question-answering models in the German language, advancing the field of artificial intelligence.

Dataset Content:

This QA dataset comprises a diverse set of open-ended questions paired with corresponding answers in German. There is no context paragraph given to choose an answer from, and each question is answered without any predefined context content. The questions cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more.

Each question is accompanied by an answer, providing valuable information and insights to enhance the language model training process. Both the questions and answers were manually curated by native German people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This question-answer prompt completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains questions and answers with different types of rich text, including tables, code, JSON, etc., with proper markdown.

Question Diversity:

To ensure diversity, this Q&A dataset includes questions with varying complexity levels, ranging from easy to medium and hard. Different types of questions, such as multiple-choice, direct, and true/false, are included. Additionally, questions are further classified into fact-based and opinion-based categories, creating a comprehensive variety. The QA dataset also contains the question with constraints and persona restrictions, which makes it even more useful for LLM training.

Answer Formats:

To accommodate varied learning experiences, the dataset incorporates different types of answer formats. These formats include single-word, short phrases, single sentences, and paragraph types of answers. The answer contains text strings, numerical values, date and time formats as well. Such diversity strengthens the Language model's ability to generate coherent and contextually appropriate answers.

Data Format and Annotation Details:

This fully labeled German Open Ended Question Answer Dataset is available in JSON and CSV formats. It includes annotation details such as id, language, domain, question_length, prompt_type, question_category, question_type, complexity, answer_type, rich_text.

Quality and Accuracy:

The dataset upholds the highest standards of quality and accuracy. Each question undergoes careful validation, and the corresponding answers are thoroughly verified. To prioritize inclusivity, the dataset incorporates questions and answers representing diverse perspectives and writing styles, ensuring it remains unbiased and avoids perpetuating discrimination.

Both the question and answers in German are grammatically accurate without any word or grammatical errors. No copyrighted, toxic, or harmful content is used while building this dataset.

Continuous Updates and Customization:

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Continuous efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to collect custom question-answer data tailored to specific needs, providing flexibility and customization options.

License:

The dataset, created by FutureBeeAI, is now ready for commercial use. Researchers, data scientists, and developers can utilize this fully labeled and ready-to-deploy German Open Ended Question Answer Dataset to enhance the language understanding capabilities of their generative ai models, improve response generation, and explore new approaches to NLP question-answering tasks.

In order to raise the bar for non-English QA, we are releasing a high-quality, human-labeled German QA dataset consisting of 13 722 questions, incl. a three-way annotated test set. The creation of GermanQuAD is inspired by insights from existing datasets as well as our labeling experience from several industry projects. We combine the strengths of SQuAD, such as high out-of-domain performance, with self-sufficient questions that contain all relevant information for open-domain QA as in the NaturalQuestions dataset. Our training and test datasets do not overlap like other popular datasets and include complex questions that cannot be answered with a single entity or only a few words.

Tsev German Wake Word DatasetHigh-Quality German Wake Word Dataset rau AI & Cov Qauv Hais Lus Hu rau Peb Txheej TxheemTitle (Language) German Language DatasetDataset TypesWake WordCountryGermanyDescriptionWake Words / Voice Command / Trigger Word /…

The historical development of road infrastructure has relied on the human visual system as the primary method for perceiving and controlling vehicles. In order for an autonomous driving system to operate safely in existing road environments, it is crucial for it to possess visual recognition capabilities equivalent to those of humans. Visual classification, which includes tasks like object detection and semantic segmentation, plays a critical role in the visual perception of autonomous vehicles, particularly for accurately identifying road signs.

To facilitate research in this area, the German Traffic Sign Recognition Benchmark (GTSRB) dataset has been created. This dataset consists of images of traffic signs captured from German roads, with each image labeled according to its corresponding class. The dataset focuses on single-image, multi-class classification challenges and does not include any temporal information from the original video footage.

Here are some details about the GTSRB dataset:

1.Each image in the dataset has a resolution of 32 x 32 pixels and is represented in RGB format with three color channels. The pixel values are stored as unsigned 8-bit integers, providing a total of 256 possible values for each pixel.

2.The dataset comprises a total of 43 distinct classes or labels, based on the design or meaning of the traffic signs.

3.The training set consists of 34,799 images, each associated with its corresponding label.

4.The validation set contains 4,410 images, also accompanied by their respective labels.

5.Lastly, the test set comprises 12,630 images, each labeled with its corresponding class.

In CSV file,there are two columns. One is ClassID (represents the class ID of traffic signs) and another is SignName (represents the name of traffic sign).