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Question & Answer
Choose the Best Answer
It allows the model to assign different weights to different input elements based on their relevance.
It reduces the size of the model by simplifying the architecture.
It increases the number of training epochs required for fine-tuning.
It limits the model's ability to generalize to new tasks.
Understanding the Answer
Let's break down why this is correct
Self‑attention lets each word look at every other word and decide how important each one is. Other options are incorrect because Some people think self‑attention shrinks the model, but it actually adds more calculations; It is easy to imagine that more attention means more training time, but the opposite is true.
Key Concepts
Transformer Architecture
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Deep Dive: 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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