SQuADtekstitehtävissä

SQuADtekstitehtävissä, often abbreviated as SQuAD, is a popular reading comprehension dataset used in natural language processing research. The dataset was introduced by researchers at the University of Washington in 2016. Its primary purpose is to train and evaluate machine reading comprehension models.

The dataset consists of a collection of questions posed on Wikipedia articles. For each question, there is a corresponding passage from a Wikipedia article that contains the answer. The answer is always a contiguous span of text within the provided passage. This format makes SQuAD suitable for training models to identify and extract specific information from text.

SQuAD has been instrumental in advancing the field of question answering. Its large scale and the nature of the tasks have driven the development of more sophisticated deep learning architectures, such as transformer-based models. Researchers use SQuAD to benchmark the performance of their models and compare different approaches to natural language understanding.

The original SQuAD dataset has since been followed by several variations, including SQuAD 2.0, which introduces unanswerable questions, further challenging models to distinguish between questions that can be answered from the text and those that cannot. The ongoing development and use of SQuAD and its successors continue to be a significant area of focus in AI research.