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Transformer Architecture
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What is the primary reason that the Transformer architecture has revolutionized natural language processing compared to earlier models?

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A

It uses attention mechanisms to process data in parallel

B

It relies on convolutional layers for image processing

C

It applies recurrent layers for sequence modeling

D

It is based on a simple feedforward neural network

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Transformers use attention to look at all words at once. Other options are incorrect because The idea that Transformers rely on convolutional layers is a misconception; Some think Transformers use recurrent layers.

Key Concepts

Transformer Architecture
Attention Mechanisms
Parallel Processing
Topic

Transformer Architecture

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Definition
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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