Typically used for tasks like sentence completion, sentence or token classification, autoencoding models are like a jigsaw puzzle solver. Imagine you have a puzzle where some pieces are missing or misplaced. The solver’s job is to predict what those pieces should be based on the overall picture. Similarly, models like BERT take a sentence with some parts masked (or hidden), and they predict what those masked parts are based on the context provided by the rest of the sentence.
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