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Transformers use a mechanism called self-attention to process and generate sequences of data. This allows them to weigh the importance of different words in a sentence relative to one another, capturing contextual relationships effectively. By stacking multiple layers of self-attention and feedforward networks, transformers can learn complex patterns in data, making them highly effective for tasks like natural language processing and machine translation. Additionally, they utilize positional encodings to retain the order of words in a sequence.

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2mo ago

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