Dark Mode
Question Answering Generation Choices
Machine Learning and AI
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset contains questions and answers paired with their respective contexts. It is designed for applications involving natural language processing (NLP), such as question-answering systems, machine reading comprehension, and conversational AI. The dataset's purpose is to provide examples of human-like comprehension and response generation, focusing on understanding the relationship between context and derived answers.
Dataset Features
- QA_ID: Unique identifier for each question-answer pair in the dataset.
- Context: A passage or text that provides background information relevant to the question.
- Question: The query derived from the context that requires comprehension for answering.
- Answer: The human-provided response to the question based on the context.
Distribution
- Data Volume: 60000 rows, 4 columns.
- Format: Tabular structure, each row representing a unique question-answer context.
Usage
This dataset is ideal for a variety of applications:
- Application: Training machine learning models for question-answering tasks.
- Application: Benchmarking NLP systems for comprehension accuracy.
- Application: Enhancing conversational AI systems with better context-driven responses.
Coverage
- Geographic Coverage: general textual content.
- Time Range: No specific time range; timeless general knowledge and narratives.
License
CC0 (Public Domain)
Who Can Use It
- Data Scientists: For developing and fine-tuning QA systems and NLP models.
- Researchers: For studying context-aware language processing or model evaluation.
- Businesses: For integrating intelligent question-answering capabilities into customer support or other services.
QUALITY