ALL TURKISH WORDS DATASET

This dataset contains all the Turkish words I've managed to fetch from the web. The dataset has approximately 7 million lines of Turkish word tokens, each seperated by " " so it is easier to read.

Some words are different variations of the same word e.g. "araba", "arabada", "arabadan". Feel free to use lemmatization algorithms to reduce the data size.

I believe this dataset could be improved upon. It certainly is not finished. I will update this dataset if I can get my hands on new words in the future.

My Linkedin: https://www.linkedin.com/in/enistuna/ My Github: https://github.com/enistuna

STSb Turkish

Semantic textual similarity dataset for the Turkish language. It is a machine translation (Azure) of the STSb English dataset. This dataset is not reviewed by expert human translators. Uploaded from this repository.

Dataset

This dataset contains positive , negative and notr sentences from several data sources given in the references. In the most sentiment models , there are only two labels; positive and negative. However , user input can be totally notr sentence. For such cases there were no data I could find. Therefore I created this dataset with 3 class. Positive and negative sentences are listed below. Notr examples are extraced from turkish wiki dump. In addition, added some random text… See the full description on the dataset page: https://huggingface.co/datasets/winvoker/turkish-sentiment-analysis-dataset.

Enhance your Conversational AI model with our Off-the-Shelf Turkish Language Dataset (Turkish Language Speech Datasets). Shaip high-quality audio datasets are a quick and effective solution for model training.

The GlobalPhone corpus developed in collaboration with the Karlsruhe Institute of Technology (KIT) was designed to provide read speech data for the development and evaluation of large continuous speech recognition systems in the most widespread languages of the world, and to provide a uniform, multilingual speech and text database for language independent and language adaptive speech recognition as well as for language identification tasks.

The entire GlobalPhone corpus enables the acquisition of acoustic-phonetic knowledge of the following 22 spoken languages: Arabic (ELRA-S0192), Bulgarian (ELRA-S0319), Chinese-Mandarin (ELRA-S0193), Chinese-Shanghai (ELRA-S0194), Croatian (ELRA-S0195), Czech (ELRA-S0196), French (ELRA-S0197), German (ELRA-S0198), Hausa (ELRA-S0347), Japanese (ELRA-S0199), Korean (ELRA-S0200), Polish (ELRA-S0320), Portuguese (Brazilian) (ELRA-S0201), Russian (ELRA-S0202), Spanish (Latin America) (ELRA-S0203), Swahili (ELRA-S0375), Swedish (ELRA-S0204), Tamil (ELRA-S0205), Thai (ELRA-S0321), Turkish (ELRA-S0206), Ukrainian (ELRA-S0377), and Vietnamese (ELRA-S0322).

In each language about 100 sentences were read from each of the 100 speakers. The read texts were selected from national newspapers available via Internet to provide a large vocabulary. The read articles cover national and international political news as well as economic news. The speech is available in 16bit, 16kHz mono quality, recorded with a close-speaking microphone (Sennheiser 440-6). The transcriptions are internally validated and supplemented by special markers for spontaneous effects like stuttering, false starts, and non-verbal effects like laughing and hesitations. Speaker information like age, gender, occupation, etc. as well as information about the recording setup complement the database. The entire GlobalPhone corpus contains over 450 hours of speech spoken by more than 2100 native adult speakers.

Data is shortened by means of the shorten program written by Tony Robinson. Alternatively, the data could be delivered unshorten.

The Turkish corpus was produced using the Zaman newspaper. It contains recordings of 100 speakers (28 males, 72 females) recorded in Istanbul, Turkey. The following age distribution has been obtained: 30 speakers are below 19, 30 speakers are between 20 and 29, 23 speakers are between 30 and 39, 14 speakers are between 40 and 49, and 3 speakers are over 50.

• The articles in this dataset are not specifically tagged for a particular task and the dataset is untagged.
• This dataset is written in Turkish and was created by a team of volunteers using community engagement methods.
• This dataset is an original dataset created from the Turkish Wikipedia.

Thanks for using the Turkish Wikipedia dataset! We hope it will be useful for your language modeling and text generation tasks.

Since the Turkish Wikipedia dataset is not on Kaggle, I took a shared dataset on Huggingface. I merged the shared dataset as 2 parquet files and shared it on Kaggle. You can go to the version of the dataset shared on Huggingface from the link below. I would also like to thank https://huggingface.co/musabg for creating this dataset.

Original link to this dataset: https://huggingface.co/datasets/musabg/wikipedia-tr

pnrr/data-turkish-class dataset hosted on Hugging Face and contributed by the HF Datasets community

Introduction

Welcome to the Turkish Scripted Monologue Speech Dataset for the General Domain. This meticulously curated dataset is designed to advance the development of General domain Turkish language speech recognition models.

Speech Data

This training dataset comprises over 6,000 high-quality scripted prompt recordings in Turkish. These recordings cover various General domain topics and scenarios, designed to build robust and accurate speech technology.

Each scripted prompt is crafted to reflect real-life scenarios encountered in the General domain, ensuring applicability in training robust natural language processing and speech recognition models.

Transcription Data

In addition to high-quality audio recordings, the dataset includes meticulously prepared text files with verbatim transcriptions of each audio file. These transcriptions are essential for training accurate and robust speech recognition models.

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Introduction

Welcome to the Turkish Call Center Speech Dataset for the Real Estate domain designed to enhance the development of call center speech recognition models specifically for the Real Estate industry. This dataset is meticulously curated to support advanced speech recognition, natural language processing, conversational AI, and generative voice AI algorithms.

Speech Data:

This training dataset comprises 30 Hours of call center audio recordings covering various topics and scenarios related to the Real Estate domain, designed to build robust and accurate customer service speech technology.

Topic Diversity

This dataset offers a diverse range of conversation topics, call types, and outcomes, including both inbound and outbound calls with positive, neutral, and negative outcomes.

This extensive coverage ensures the dataset includes realistic call center scenarios, which is essential for developing effective customer support speech recognition models.

Transcription

To facilitate your workflow, the dataset includes manual verbatim transcriptions of each call center audio file in JSON format. These transcriptions feature:

These ready-to-use transcriptions accelerate the development of the Real Estate domain call center conversational AI and ASR models for the Turkish language.

Metadata

The dataset provides comprehensive metadata for each conversation and participant:

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Turkish call center speech recognition models.

Usage and

TWNERTC and EWNERTC are collections of automatically categorized and annotated sentences obtained from Turkish and English Wikipedia for named-entity recognition and text categorization.

Firstly, we construct large-scale gazetteers by using a graph crawler algorithm to extract relevant entity and domain information from a semantic knowledge base, Freebase. The final gazetteers has 77 domains (categories) and more than 1000 fine-grained entity types for both languages. Turkish gazetteers contains approximately 300K named-entities and English gazetteers has approximately 23M named-entities.

By leveraging large-scale gazetteers and linked Wikipedia articles, we construct TWNERTC and EWNERTC. Since the categorization and annotation processes are automated, the raw collections are prone to ambiguity. Hence, we introduce two noise reduction methodologies: (a) domain-dependent (b) domain-independent. We produce two different versions by post-processing raw collections. As a result of this process, we introduced 3 versions of TWNERTC and EWNERTC: (a) raw (b) domain-dependent post-processed (c) domain-independent post-processed. Turkish collections have approximately 700K sentences for each version (varies between versions), while English collections contain more than 7M sentences.

We also introduce "Coarse-Grained NER" versions of the same datasets. We reduce fine-grained types into "organization", "person", "location" and "misc" by mapping each fine-grained type to the most similar coarse-grained version. Note that this process also eliminated many domains and fine-grained annotations due to lack of information for coarse-grained NER. Hence, "Coarse-Grained NER" labelled datasets contain only 25 domains and number of sentences are decreased compared to "Fine-Grained NER" versions.

All processes are explained in our published white paper for Turkish; however, major methods (gazetteers creation, automatic categorization/annotation, noise reduction) do not change for English.

naytin/turkish-sentiment-analysis-dataset_ENG dataset hosted on Hugging Face and contributed by the HF Datasets community

hazal/Turkish-Biomedical-corpus-trM dataset hosted on Hugging Face and contributed by the HF Datasets community

Natural Language Inference in Turkish (NLI-TR) provides translations of two large English NLI datasets into Turkish and had a team of experts validate their translation quality and fidelity to the original labels.

The Wake Word Turkish Dataset is a collection of audio recordings specifically curated for training and evaluating wake word detection systems in the Turkish language. This dataset includes a variety of speakers, environments, and scenarios to ensure robustness and effectiveness in wake word detection algorithms. It serves as a valuable resource for researchers and developers working on voice-controlled systems and natural language processing applications in Turkish.

The Turkish web corpus MaCoCu-tr 1.0 was built by crawling the ".tr" internet top-level domain in 2021, extending the crawl dynamically to other domains as well (https://github.com/macocu/MaCoCu-crawler).

Considerable efforts were devoted into cleaning the extracted text to provide a high-quality web corpus. This was achieved by removing boilerplate (https://corpus.tools/wiki/Justext) and near-duplicated paragraphs (https://corpus.tools/wiki/Onion), discarding very short texts as well as texts that are not in the target language. The dataset is characterized by extensive metadata which allows filtering the dataset based on text quality and other criteria (https://github.com/bitextor/monotextor), making the corpus highly useful for corpus linguistics studies, as well as for training language models and other language technologies.

Each document is accompanied by the following metadata: title, crawl date, url, domain, file type of the original document, distribution of languages inside the document, and a fluency score (based on a language model). The text of each document is divided into paragraphs that are accompanied by metadata on the information whether a paragraph is a heading or not, metadata on the paragraph quality and fluency, the automatically identified language of the text in the paragraph, and information whether the paragraph contains personal information.

This action has received funding from the European Union's Connecting Europe Facility 2014-2020 - CEF Telecom, under Grant Agreement No. INEA/CEF/ICT/A2020/2278341. This communication reflects only the author’s view. The Agency is not responsible for any use that may be made of the information it contains.

Turkish(Turkey) Real-world Casual Conversation and Monologue speech dataset, covers self-media, conversation, live and other generic domains, mirrors real-world interactions. Transcribed with text content, speaker's ID, gender and other attributes. Our dataset was collected from extensive and diversify speakers, geographicly speaking, enhancing model performance in real and complex tasks. Quality tested by various AI companies. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Graph and download economic data for Turkish Intervention: Central Bank of Turkey Purchases of USD (Millions of USD) (TRINTDEXR) from 2002-01-01 to 2025-01-02 about intervention, Turkey, banks, and depository institutions.

What’s Included

The Turkish Closed-Ended Question Answering Dataset is a meticulously curated collection of 5000 comprehensive Question-Answer pairs. It serves as a valuable resource for training Large Language Models (LLMs) and question-answering models in the Turkish language, advancing the field of artificial intelligence.

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 Turkish people, and references were taken from diverse sources like books, news articles, websites, web forums, 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.

The Turkish versions is grammatically accurate without any spelling or grammatical errors. No toxic or harmful content is used while building this dataset.

hasancanonder/turkish-english-translate dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Enes Kulak

Contents