
Overview
This dataset is a large-scale collection of Question Answering (QA) data, designed to support the development and training of advanced NLP systems and AI models for scientific understanding, reasoning, problem-solving, and educational learning in Hindi.
The dataset consists of multiple-choice question answering (MCQA) samples across core STEM domains including Physics, Mathematics, Chemistry, Biology, and General Science, enabling models to learn, reason, and generate accurate answers to domain-specific queries. Additionally, this dataset can be used in pipelines for Supervised Fine-Tuning (SFT) and Reinforcement Learning with Human Feedback (RLHF) workflows, improving model performance in multilingual QA and reasoning tasks.
Dataset Specification
-Modality: Hindi text (MCQ-based question-answer pairs with explanations) -Type: Educational / STEM -Data Nature: Real-world and curated data -Content: Questions with options, correct answers, and explanationsKey Use Cases
-Question Answering (QA) in Hindi (MCQ-based) -Named Entity Recognition (NER) in STEM content -Automated tutoring and educational assistants -STEM knowledge retrieval systems -Model evaluation and benchmarkingValue of This Dataset
-Enables learning of STEM concepts in Hindi -Improves reasoning capabilities of AI models -Supports multilingual and domain-specific QA systems -Helps build AI-powered educational platforms -Enhances accuracy and reliability of LLMs in STEM domainsBasic JSON Schema
{ "section": "string", "answer_type": "string", "q_string": "string", "q_option": ["string"], "q_answer": "string", "q_exp": "string", "lang_code": "string", "category": "string" }
Full Dataset Overview
6.7M+ Questions / 1.8B+ Tokens This scale provides extensive domain coverage, rich contextual learning, and significantly improves language understanding, reasoning, and model performance.Data Creation
Procured through formal agreements and generated in the ordinary course of business.Considerations
This dataset is provided for research and educational purposes only. It contains only sample data. For access to the full dataset and enterprise licensing options, please visit our website InfoBay.AI or contact us directly.
-Ph: (91) 8303174762 -Email: <datareq@infobay.ai>Highlights
- Sample from a large-scale multilingual Q&A corpus containing 6.7M+ question-answer pairs across English, Hindi, Arabic, and additional global languages for AI training and research.
- Designed for LLM training, instruction tuning, supervised fine-tuning (SFT), RAG pipelines, conversational AI, NLP, and Generative AI applications requiring high-quality question-answer data.
- Supports development of AI assistants, chatbots, knowledge retrieval systems, educational AI, multilingual foundation models, and human-aligned conversational AI systems.
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You will receive access to the following data sets.
Data set name | Type | Historical revisions | Future revisions | Sensitive information | Data dictionaries | Data samples |
|---|---|---|---|---|---|---|
Question and Answer with Explanation | All historical revisions | All future revisions | Not included | Not included |
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