Closed Ended Question Answer Dataset in Gujarati
This Q&A dataset consist of closed ended questions and answers in Gujarati language from wide range of domains. Along with that it includes the context text and detailed annotation for each question and answer.
Category
Prompt-Completion Closed Ended QA Dataset
Total volume
5000+ Assets
Last Updated
Sept 2023
Number of participants
50+ people
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About This OTS Dataset
What’s Included
The Gujarati 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 Gujarati language, advancing the field of artificial intelligence.
Dataset Content:
This closed-ended QA dataset comprises a diverse set of context paragraphs and questions paired with corresponding answers in Gujarati. There is a context paragraph given for each question to get the answer from. The questions cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more.
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 Gujarati 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.
Question Diversity:
To ensure diversity, this Q&A dataset includes questions with varying complexity levels, ranging from easy to medium and hard. Different types of questions, such as multiple-choice, direct, and true/false, are included. The QA dataset also contains questions with constraints, which makes it even more useful for LLM training.
Answer Formats:
To accommodate varied learning experiences, the dataset incorporates different types of answer formats. These formats include single-word, short phrases, single sentences, and paragraphs types of answers. The answers contain text strings, numerical values, date and time formats as well. Such diversity strengthens the language model's ability to generate coherent and contextually appropriate answers.
Data Format and Annotation Details:
This fully labeled Gujarati Closed-Ended Question Answer Dataset is available in JSON and CSV formats. It includes annotation details such as a unique id, context paragraph, context reference link, question, question type, question complexity, question category, domain, prompt type, answer, answer type, and rich text presence.
Quality and Accuracy:
The dataset upholds the highest standards of quality and accuracy. Each question undergoes careful validation, and the corresponding answers are thoroughly verified. To prioritize inclusivity, the dataset incorporates questions and answers representing diverse perspectives and writing styles, ensuring it remains unbiased and avoids perpetuating discrimination.
The Gujarati versions is grammatically accurate without any spelling or grammatical errors. No toxic or harmful content is used while building this dataset.
Continuous Updates and Customization:
The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Continuous efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to collect custom question-answer data tailored to specific needs, providing flexibility and customization options.
License:
The dataset, created by FutureBeeAI, is now ready for commercial use. Researchers, data scientists, and developers can utilize this fully labeled and ready-to-deploy Gujarati Closed-Ended Question Answer Dataset to enhance the language understanding capabilities of their generative AI models, improve response generation, and explore new approaches to NLP question-answering tasks.
Use Cases
Language Model Training
Question Answering Systems
Natural Language Understanding
Chatbots and Virtual Assistants
Dataset Sample(s)
CHANGE VIEW
Simplified View
JSON View
ATTRIBUTES
| id | fb_gu_8213 |
| language | Gujarati |
| domain | Finance |
| prompt_type | Instructive |
| question_type | Direct |
| complexity | Medium |
| answer_type | Short Phrase |
| rich_text | Table |
| reference_link | Link |
Dataset Details
Dataset type
Closed Ended Question Answer Dataset
Volume
5000+
Media type
Text
Language
Gujarati
Domain
science, history, technology,...more
File Details
Format
JSON, CSV
Annotation
Yes
Schema Element
unique_id, context_text,...more
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