LLM Datasets

This dataset contains 1.51 million annotated image editing pairs. Editing types include 500,000 sets of portrait/object consistency editing, 300,000 sets of structural edits, 210,000 sets of mixed editing, and 450,000 sets of spatial editing, and 50,000 sets of style transfer editing. The editing targets cover scenes such as people, animals, goods, plants, and landscapes. In terms of annotation, the targets that need to be edited in the image are edited according to the editing instructions. The data can be used for tasks such as image synthesis, data augmentation, and virtual scene generation.

This dataset contains 2.4 million structured Korean exam questions covering primary, middle, and high school subjects including Korean, Mathematics, English, Social Studies, Science, Physics, Chemistry, Biology, History, and Geography. Each record includes question type (multiple-choice, fill-in-the-blank, true/false, short answer), the question itself, standard answers, and detailed explanations. The data is professionally annotated and categorized by subject and academic level, making it ideal for training AI models in educational applications such as question answering systems, tutoring bots, academic reasoning, and subject-level knowledge enhancement. It is widely applicable for natural language processing tasks involving structured QA, exam-style NLP training, and educational content generation. All data is collected and processed in compliance with GDPR, CCPA, and PIPL standards, ensuring privacy and legal integrity throughout the lifecycle.

50,000 Sets - Image Editing Data. The editing types include human attribute editing, image semantic editing, and image structure editing. The editing targets cover scenes such as people, animals, goods, plants, and landscapes. In terms of annotation, based on the editing instructions, the targets that need to be edited in the image are edited. The data can be used for tasks such as image synthesis, data augmentation, and virtual scene generation.

32 million structured science questions covering mathematics, physics, chemistry, and biology across primary, middle, high school, and university levels. Each question entry includes a title, answer, solution parsing, question type, subject category, and corresponding grade level. The dataset is designed to support AI training tasks such as large language model development, subject-specific knowledge enhancement, machine reading comprehension, and question-answering systems. It provides a rich resource for educational NLP applications and has been validated for quality and completeness. All data complies with global data protection standards including GDPR, CCPA, and PIPL.

This dataset contains 1 million Chinese programming questions with corresponding answers, detailed parses (explanations), and programming language labels. It includes a wide range of questions in C, C++, Python, Java, and JavaScript, making it ideal for training large language models (LLMs) on multilingual code understanding and generation. The questions cover fundamental to advanced topics, supporting AI applications such as code completion, bug fixing, and programming reasoning. This structured dataset enhances model performance in natural language programming tasks and helps reinforce code logic skills in AI systems. All data complies with international privacy regulations including GDPR, CCPA, and PIPL.

The product contains a total of 89,007 data samples, with each sample consisting of one image and one JSON document. The JSON document may contain an image caption, a visual question-answering pair, OCR results extracted from the image, or a visual question-answering pair based on the OCR results. The dataset covers Arabic and Japanese languages and spans six domains:① Business and Finance, ②Coding and Computer Science, ③Law, Government, and Politics, ④Science, Technology, Engineering, and Mathematics (STEM), ⑤Society, Culture, Humanities, and Religion, ⑥ Sports, Lifestyle, and Leisure. The accuracy of image domain classification（per-image accuracy） is above 95%;The matching degree between image and text description is greater than 95%；OCR recognition accuracy (per-sentence accuracy) must exceed 95%. Suitable for multilingual OCR, multimodal LLM training, image captioning, and multilingual VQA tasks.

20,011 Image Caption Data of OCR in Natural Scenes, including Asian and European languages, a total of 14 languages, the collection environment includes shop plaques, stop signs, posters, road signs and other scenes, including a variety of shooting angles. The description language is English, which mainly describes the text arrangement, text content, color and other information.

This dataset contains 200,000 sets of images and bilingual captions (Chinese and English) featuring landmark buildings from over 20 countries, including the United States, United Kingdom, France, Germany, and Russia. Each set includes 1–10 images of a specific landmark, captured from different angles, distances, and time periods. The dataset covers approximately 80,000 domestic landmarks and 120,000 international ones. Types of landmarks include commercial buildings, ancient architecture, monuments, libraries, and scenic spots. Annotations include landmark country, city, location, category, and descriptive captions. This high-quality dataset is ideal for training models in landmark recognition, image classification, multilingual image captioning, and image-to-text retrieval.

This dataset contains 1.5 million English science and engineering test questions, including mathematics, physics, chemistry, biology, and other STEM subjects at the university level. Each questions contain title, answer, parse, type, subject, grade. The dataset can be used for large model subject knowledge enhancement tasks.