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.

Dataset consists of 5 emotion labels. These labels are anger, happy, distinguish, surprise and fear. There are 800 tweets in the dataset for each label. Hence, total tweet count is 4000 for dataset.

You can use the data set in many areas such as sentiment, emotion analysis and topic modeling.

Info: Hashtags and usernames was removed in the dataset. Dataset has used many studies and researches. These researches are followed as: -(please citation this article) Güven, Z. A., Diri, B., & Cąkaloglu, T. (2020). Comparison of n-stage Latent Dirichlet Allocation versus other topic modeling methods for emotion analysis. Journal of the Faculty of Engineering and Architecture of Gazi University. https://doi.org/10.17341/gazimmfd.556104 -Güven, Z. A., Diri, B., & Çakaloğlu, T. (2019). Emotion Detection with n-stage Latent Dirichlet Allocation for Turkish Tweets. Academic Platform Journal of Engineering and Science. https://doi.org/10.21541/apjes.459447 -Guven, Z. A., Diri, B., & Cakaloglu, T. (2019). Comparison Method for Emotion Detection of Twitter Users. Proceedings - 2019 Innovations in Intelligent Systems and Applications Conference, ASYU 2019. https://doi.org/10.1109/ASYU48272.2019.8946435

Turkish Texts for Toxic Language Detection

  Dataset Description





  Dataset Summary

This text dataset is a collection of Turkish texts that have been merged from various existing offensive language datasets found online. The dataset contains a total of 77,800 instances, each labeled as either offensive or not offensive. To ensure the dataset's completeness, we utilized multiple transformer models to augment the dataset with pseudo labels. The resulting dataset is… See the full description on the dataset page: https://huggingface.co/datasets/Overfit-GM/turkish-toxic-language.

A human-annotated morphosyntactic treebank for Turkish.

Home Turkish Turkey DatasetTürkiye Türkiye Veri KümesiHigh-Quality Turkish Turkey TTS Dataset for AI & Speech Models Contact Us OverviewTitleTurkish Turkey Language DatasetDataset TypeTTSDescriptionSingle-utterance recordings, which tend to fall in the…

Dataset Card for "turkishneuralvoice"

  Dataset Overview

Dataset Name: Turkish Neural Voice Description: This dataset contains Turkish audio samples generated using Microsoft Text to Speech services. The dataset includes audio files and their corresponding transcriptions.

  Dataset Structure

Configs:

default

Data Files:

Split: train Path: data/train-*

Dataset Info:

Features: audio: Audio file transcription: Corresponding text transcription

Splits: train… See the full description on the dataset page: https://huggingface.co/datasets/erenfazlioglu/turkishvoicedataset.

Dataset Card for Turkish Product Reviews

  Dataset Summary

This Turkish Product Reviews Dataset contains 235.165 product reviews collected online. There are 220.284 positive, 14881 negative reviews.

  Supported Tasks and Leaderboards

[More Information Needed]

  Languages

The dataset is based on Turkish.

  Dataset Structure





  Data Instances

Example 1: sentence: beklentimin altında bir ürün kaliteli değil sentiment: 0 (negative) Example 2:… See the full description on the dataset page: https://huggingface.co/datasets/fthbrmnby/turkish_product_reviews.

Home Wake Word Turkish DatasetHigh-Quality Turkish Wake Word Dataset for AI & Speech Models Contact Us OverviewTitleWake Word Turkish Language DatasetDataset TypeWake WordDescriptionWake Words / Voice Command / Trigger Word…

Introduction

The Turkish General Domain Chat Dataset is a high-quality, text-based dataset designed to train and evaluate conversational AI, NLP models, and smart assistants in real-world Turkish usage. Collected through FutureBeeAI’s trusted crowd community, this dataset reflects natural, native-level Turkish conversations covering a broad spectrum of everyday topics.

Conversational Text Data

This dataset includes over 15000 chat transcripts, each featuring free-flowing dialogue between two native Turkish speakers. The conversations are spontaneous, context-rich, and mimic informal, real-life texting behavior.

Diversity and Domain Coverage

Conversations span a wide variety of general-domain topics to ensure comprehensive model exposure:

This diversity ensures the dataset is useful across multiple NLP and language understanding applications.

Linguistic Authenticity

Chats reflect informal, native-level Turkish usage with:

Metadata

Every chat instance is accompanied by structured metadata, which includes:

This metadata supports model filtering, demographic-specific evaluation, and more controlled fine-tuning workflows.

Data Quality Assurance

All chat records pass through a rigorous QA process to maintain consistency and accuracy:

This ensures a clean, reliable dataset ready for high-performance AI model training.

Applications

This dataset is ideal for training and evaluating a wide range of text-based AI systems:

Turkish Number Plates

## Overview

Turkish Number Plates is a dataset for object detection tasks - it contains Plate annotations for 2,246 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).

Tsev Wake Lo Lus Turkish DatasetHigh-Quality Turkish Wake Word Dataset rau AI & Cov Qauv Hais Lus Hu rau Peb Txheej TxheemTitleWake Lo Lus Turkish Lus DatasetDataset HomWake WordDescriptionWake Words / Voice Command / Trigger Word…

We have selected two most popular movie and hotel recommendation websites from those which attain a high rate in the Alexa website. We selected “beyazperde.com” and “otelpuan.com” for movie and hotel reviews, respectively. The reviews of 5,660 movies were investigated. The all 220,000 extracted reviews had been already rated by own authors using stars 1 to 5. As most of the reviews were positive, we selected the positive reviews as much as the negative ones to provide a balanced situation. The total of negative reviews rated by 1 or 2 stars were 26,700, thus, we randomly selected 26,700 out of 130,210 positive reviews rated by 4 or 5 stars. Overall, 53,400 movie reviews by the average length of 33 words were selected. The similar manner was used to hotel reviews with the difference that the hotel reviews had been rated by the numbers between 0 and 100 instead of stars. From 18,478 reviews extracted from 550 hotels, a balanced set of positive and negative reviews was selected. As there were only 5,802 negative hotel reviews using 0 to 40 rating, we selected 5800 out of 6499 positive reviews rated from 80 to 100. The average length of all 11,600 selected positive and negative hotel reviews were 74 which is more than two times of the movie reviews.

Ikhaya Lasekhaya laseTurkey I-Turkey DatasetTürkiye Türkiye Veri KümesiIkhwalithi ephezulu ye-Turkish Turkey Idathasethi ye-TTS ye-AI namamodeli wenkulumo Xhumana nathi UhlolojikeleleTitleTurkish Turkey Language DatasetIdathasethi UhloboTTSDescriptionUkurekhodwa kwezwi elilodwa, okuvamise ukuwa...

Turkish Sign Language

## Overview

Turkish Sign Language is a dataset for object detection tasks - it contains A annotations for 331 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).

What’s Included

Introducing the Turkish Newspaper, Books, and Magazine Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Turkish language.

Containing a total of 5000 images, this Turkish OCR dataset offers an equal distribution across newspapers, books, and magazines. Within, you'll find a diverse collection of content, including articles, advertisements, cover pages, headlines, call outs, and author sections from a variety of newspapers, books, and magazines. Images in this dataset showcases distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personal identifiable information (PII), and in each image a minimum of 80% space is contain visible Turkish text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, further enhancing dataset diversity. The collection features images in portrait and landscape modes.

All these images were captured by native Turkish people to ensure the text quality, avoid toxic content and PII text. We used latest iOS and android mobile devices above 5MP camera to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data you will also receive detailed structured metadata in CSV format. For each image it includes metadata like device information, source type like newspaper, magazine or book image, and image type like portrait or landscape etc. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of Turkish text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native Turkish crowd community.

If you require a custom dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this image dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the Turkish language. Your journey to enhanced language understanding and processing starts here.

Ikhaya Wake IZwi I-Turkish DatasetIkhwalithi ephezulu ye-Turkish Wake Word Dataset ye-AI namamodeli Enkulumo Xhumana nathi UhlolojikeleleTitleWake Izwi Lase-Turkish Language DatasetDataset TypeWake WordDescriptionWake Amagama / Umyalo Wezwi / Qalisa Igama...

Home Tiorka Tiorkia DatasetTürkiye Türkiye Veri Kümesi Tiorka Tiorka TTS manana kalitao avo lenta ho an'ny AI sy maodely kabary Mifandraisa aminay OverviewTitleTitle Tiorkia Fiteny Dataset TypeTTSDescriptionFiraketana fitenenana tokana, izay mirona amin'ny…

What’s Included

Introducing the Turkish Shopping List Image Dataset - a diverse and comprehensive collection of handwritten text images carefully curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Turkish language.

Containing more than 2000 images, this Turkish OCR dataset offers a wide distribution of different types of shopping list images. Within this dataset, you'll discover a variety of handwritten text, including sentences, and individual item name words, quantity, comments, etc on shopping lists. The images in this dataset showcase distinct handwriting styles, fonts, font sizes, and writing variations.

To ensure diversity and robustness in training your OCR model, we allow limited (less than three) unique images in a single handwriting. This ensures we have diverse types of handwriting to train your OCR model on. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible Turkish text.

The images have been captured under varying lighting conditions, including day and night, as well as different capture angles and backgrounds. This diversity helps build a balanced OCR dataset, featuring images in both portrait and landscape modes.

All these shopping lists were written and images were captured by native Turkish people to ensure text quality, prevent toxic content, and exclude PII text. We utilized the latest iOS and Android mobile devices with cameras above 5MP to maintain image quality. Images in this training dataset are available in both JPEG and HEIC formats.

In addition to the image data, you will receive structured metadata in CSV format. For each image, this metadata includes information on image orientation, country, language, and device details. Each image is correctly named to correspond with the metadata.

This metadata serves as a valuable resource for understanding and characterizing the data, aiding informed decision-making in the development of Turkish text recognition models.

We are committed to continually expanding this dataset by adding more images with the help of our native Turkish crowd community.

If you require a customized OCR dataset containing shopping list images tailored to your specific guidelines or device distribution, please don't hesitate to contact us. We have the capability to curate specialized data to meet your unique requirements.

Additionally, we can annotate or label the images with bounding boxes or transcribe the text in the images to align with your project's specific needs using our crowd community.

This image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage this shopping list image OCR dataset to enhance the training and performance of text recognition, text detection, and optical character recognition models for the Turkish language. Your journey to improved language understanding and processing begins here.

Kovid

Home Wake Word DatasetTurkiga-Tayo-sare oo tayo leh Qalabka Tooska ah ee Turkiga ee AI & Qaababka Hadalka Nala soo xidhiidh DulmarkaTitleWake Word Xogta Luuqadda Turkiga