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Transformer Architecture
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In the context of transformer architecture, what is the main purpose of fine-tuning a pre-trained model for a specific business application?

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A

To reduce the model size

B

To adapt the model to specialized data for improved performance

C

To create a completely new model from scratch

D

To eliminate the need for training data

Understanding the Answer

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Fine‑tuning takes a model that already knows general language patterns and tweaks its weights so it works better on the data the business uses. Other options are incorrect because Some think fine‑tuning shrinks the model, but it only changes the numbers inside; Fine‑tuning does not build a brand‑new model.

Key Concepts

Fine-Tuning
Topic

Transformer Architecture

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easy level question

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Deep Dive: 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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