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6.9M Chinese Educational QA Dataset for LLM Training (K12 to University)
educational dataset
math dataset
stem dataset
k12 dataset
instruction dataset for llm
This dataset contains 6.9 million Chinese educational question-answer pairs covering multiple disciplines from primary school to university levels, including mathematics, science, and other academic subjects. Each question includes a title, answer, explanation, question type, subject, and grade level. The dataset is suitable for LLM instruction tuning, educational AI systems, tutoring platforms, math reasoning models, and general knowledge enhancement tasks.
This is a paid datasets for commercial use, research purpose and more. Licensed ready made datasets help jump-start AI projects.
Nexdata’s LLM datasets can support a wide range of large language model development tasks, including pre-training, supervised fine-tuning, instruction tuning, preference optimization, evaluation, and domain-specific model development. Depending on the dataset, data may include text, instruction-response pairs, conversations, question-answer pairs, reasoning data, and other structured or annotated content.
Can Nexdata customize LLM datasets based on our specific model and requirements?
Yes. If our off-the-shelf LLM datasets do not fully match your requirements, Nexdata provides flexible custom data collection, generation, annotation, curation, and quality control services. We can customize datasets based on your target languages, domains, use cases, data formats, task types, volume, and quality requirements to support specific LLM training and evaluation projects.
Can Nexdata provide large-scale and high-quality data for LLM development?
Yes. Nexdata can support large-scale LLM data projects across multiple languages, domains, and data types. Our data services include multi-stage quality control, data cleaning, annotation, validation, and curation to help ensure consistency and usability. For projects requiring data beyond our existing datasets, our customized data services can be scaled according to the required volume, specifications, and delivery schedule.
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