Introduction to Transformer Model
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Transformer Overview
Transformer Overview
1. Introduction to Transformer Model
The Transformer is a neural network architecture that uses self-attention mechanisms for sequence transduction, replacing recurrent layers.
Transformer Overview
2. Transformer Architecture
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Self-Attention
Self-Attention
3. Self-Attention Mechanism
Self-attention relates different positions of a single sequence to compute representations without recurrence, improving parallelization and efficiency.
Self-Attention
4. Scaled Dot-Product Attention
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Self-Attention
5. Multi-Head Attention
Combines several attention mechanisms in parallel, allowing the model to focus on different parts of the sequence. It uses different learned linear projections.
Positional Encoding
Positional Encoding
6. Positional Encoding in Transformer
Transformer uses sinusoidal positional encodings to incorporate token position information into embeddings.
Training and Optimization
Training and Optimization
7. Training the Transformer
The model is trained using the Adam optimizer with a warm-up learning rate strategy over 100,000 steps on 8 GPUs.
Model Advantages
Model Advantages
8. Advantages of Transformer Architecture
The Transformer improves computational efficiency and translation quality by reducing sequential operations and path lengths in dependency learning.
Model Performance
Model Performance
9. Results and Performance
The Transformer achieves state-of-the-art BLEU scores for English-to-German and English-to-French translation tasks, outperforming other models.
Model Performance
10. Generalization to Other Tasks
The Transformer generalizes well to tasks like English constituency parsing, demonstrating performance improvements over previous models.
Introduction to Transformer Model