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The Transformer allows for efficient training because it processes all tokens simultaneously rather than sequentially.
RNNs have been proven to be more effective for long sequences due to their recurrent nature.
The Transformer relies heavily on convolutional layers for feature extraction, which are essential for translation tasks.
The performance of RNNs in translation tasks is superior due to their ability to maintain state across time steps.
Understanding the Answer
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Transformers use attention to see all words at once. Other options are incorrect because Some think RNNs are better for long text, but they often forget earlier words; Transformers do not use convolution layers.
Key Concepts
Transformer Architecture
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Definition
The Transformer is a network architecture based solely on attention mechanisms, eliminating the need for recurrent or convolutional layers. It connects encoder and decoder through attention, enabling parallelization and faster training. The model has shown superior performance in machine translation tasks.
Topic Definition
The Transformer is a network architecture based solely on attention mechanisms, eliminating the need for recurrent or convolutional layers. It connects encoder and decoder through attention, enabling parallelization and faster training. The model has shown superior performance in machine translation tasks.
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