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WikiQA Question-Answer Pairs
Data Science and Analytics
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About
This dataset is a valuable collection of question and sentence pairs, specifically curated for research and development in the field of open-domain question answering. It facilitates the discovery of new knowledge by providing structured data for training and evaluating machine learning models. The dataset's primary purpose is to advance the capabilities of systems that can automatically answer questions from a wide range of text sources.
Columns
The dataset features several key columns to support question answering tasks:
- question: This string field contains the specific question that was asked.
- document_title: This string field provides the title of the document, typically a Wikipedia article, from which the question was derived.
- answer: This string field holds the answer to the question.
- label: This string field indicates the relevance of the answer to the corresponding question, often showing whether the answer is supported by the associated document.
The dataset is organised into different splits, such as
test.csv and validation.csv, with consistent data fields across them.Distribution
The dataset is provided in CSV format, which is widely accessible for data processing. While specific total row counts are not explicitly stated, the dataset contains a variety of unique entries across its fields. For instance, there are hundreds of unique questions and document titles, with a significant number of unique label values indicating the breadth of the data.
Usage
This dataset is ideally suited for a variety of applications and use cases in natural language processing and artificial intelligence:
- Training Machine Learning Models: It can be used to train models designed to automatically answer questions.
- Researching Open-Domain Question Answering: The dataset supports investigations into the feasibility and effectiveness of open-domain question answering systems.
- Evaluating Question Answering Models: It serves as a benchmark for assessing the performance of different question answering models.
- Developing NLP Applications: It is highly useful for building and refining applications that require understanding and responding to natural language queries.
Coverage
The dataset has a global geographical scope, making it relevant for a diverse range of users and applications worldwide. Information regarding specific time ranges or demographic scopes for the data itself is not provided. The dataset was listed on 27th June 2025.
License
CC0
Who Can Use It
This dataset is primarily intended for researchers, data scientists, and machine learning engineers who are working on natural language processing and question answering systems. Typical use cases include academic research into AI capabilities, development of commercial AI products, and performance benchmarking for new algorithms in open-domain question answering.
Dataset Name Suggestions
- WikiQA Question-Answer Pairs
- Open-Domain QA Dataset
- Wiki Question-Answering Corpus
- AI Question Answering Data
- WikiQA Benchmark
Attributes
Original Data Source: WikiQA (Open-Domain Q&A)
QUALITY