Transformers are a type of deep learning model architecture that has gained significant popularity in natural language processing (NLP) tasks. They rely on a mechanism called self-attention to analyze input sequences and capture dependencies between different elements. Unlike traditional recurrent or convolutional neural networks, transformers can process input data in parallel, making them highly efficient for handling long sequences. They have been instrumental in achieving state-of-the-art results in various NLP tasks, including language translation, text generation, sentiment analysis, and more. Transformer-based models, such as BERT, GPT, and T5, have become foundational in NLP research and applications, driving advancements in language understanding and generation.

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