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- question-answering
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The base architecture is BERT for Question-Answering.
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## Dataset
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The Stanford Question Answering Dataset (SQuAD) is a widely used benchmark dataset for the task of machine reading comprehension. It consists of over 100,000 question-answer pairs based on a set of Wikipedia articles. The goal is to train models that can answer questions based on their understanding of the given text passages. SQuAD has played a significant role in advancing the state-of-the-art in this field and remains a popular choice for researchers and practitioners alike.
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# QA-BERT
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QA-BERT is a Question Answering Model. This model is a lighter version of any of the question-answering models out there.
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## Dataset
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The Stanford Question Answering Dataset (SQuAD) is a widely used benchmark dataset for the task of machine reading comprehension. It consists of over 100,000 question-answer pairs based on a set of Wikipedia articles. The goal is to train models that can answer questions based on their understanding of the given text passages. SQuAD has played a significant role in advancing the state-of-the-art in this field and remains a popular choice for researchers and practitioners alike.
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