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SQuAD2.0 Question Answering Dataset
Social Media and Networking
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About
The SQuAD2.0 dataset is designed for training and evaluating machine learning models on question answering tasks. It merges the SQuAD1.1 dataset, containing 100,000 questions, with an additional 50,000 unanswerable questions. These unanswerable questions were crafted by crowdworkers to appear similar to answerable questions, posing a significant challenge for systems. To perform well with SQuAD2.0, models must not only accurately answer questions when a response is supported by the provided text but also identify when no answer can be deduced from the paragraph and, consequently, abstain from providing one. The dataset is built upon content from Wikipedia articles, supplying the complete text of each document, along with human-generated questions and their corresponding answers.
Columns
The dataset typically includes the following columns in files such as
train.csv and validation.csv:- title: This string column contains the title of the Wikipedia article from which the context and questions are derived.
- context: This string column provides the full text of the Wikipedia article, serving as the passage from which answers to questions should be extracted or deemed unanswerable.
- question: This string column holds the specific question that the machine learning model is intended to answer.
- answers: This string column contains the human-generated answer to the corresponding question. For unanswerable questions, this field would indicate the lack of a direct answer.
- id: An identifier for each question-answer pair.
Distribution
The SQuAD2.0 dataset is typically distributed in CSV file format, with distinct files for training and validation data, such as
train.csv and validation.csv. It comprises over 150,000 questions in total, combining the original 100,000 answerable questions with more than 50,000 adversarially created unanswerable questions. While specific row counts for individual files are not provided, unique values across the dataset include approximately 130,319 unique IDs, 442 unique article titles, 19,029 unique questions, and 130,217 unique answers. The dataset's quality is rated highly.Usage
This dataset is ideally suited for various applications in natural language processing and machine learning:
- Training Question Answering Models: It can be used to train machine learning models to automatically generate answers to questions based on a given context.
- Generating Questions: The dataset facilitates the training of models to automatically generate questions based on provided text contexts.
- Improving QA System Accuracy: It is valuable for enhancing the accuracy and robustness of existing question answering systems, particularly in their ability to discern unanswerable questions.
Coverage
The SQuAD2.0 dataset draws its content from Wikipedia articles, offering a broad scope of topics. Its region of coverage is global. While specific time ranges for the Wikipedia articles are not detailed, the dataset's focus is on general knowledge and information found within Wikipedia at the time of its creation. No specific demographic scope is provided.
License
CCO
Who Can Use It
This dataset is primarily intended for:
- Machine Learning Researchers: Those developing and refining algorithms for natural language understanding and question answering.
- AI/NLP Developers: Individuals building or improving automated systems that need to process text and respond to user queries accurately.
- Data Scientists: Professionals working with large text datasets for insights, model training, and performance evaluation in the domain of textual information retrieval.
Dataset Name Suggestions
- SQuAD2.0 Question Answering Dataset
- Contextual Question Answering Data with Unanswerable Questions
- Wikipedia-based QA Benchmark Dataset
- Advanced Question Answering Corpus
Attributes
Original Data Source: SQuAD2.0
QUALITY