Transformer Concepts Overview

Transformers

Transformers have revolutionized natural language processing and sequence modeling tasks since their introduction in the paper "Attention Is All You Need" by Vaswani et al. in 2017. Unlike traditional sequence models such as RNNs and LSTMs, Transformers rely entirely on self-attention mechanisms to draw global dependencies between input and output sequences.

Why Transformers?

Traditional sequence models like RNNs suffer from limitations in capturing long-range dependencies due to their sequential nature. Transformers address this by employing self-attention, allowing them to process tokens in parallel and maintain relationships between distant words more effectively.

Read the Paper for a detailed understanding of the Transformer architecture and its mathematical underpinnings.

Attention Mechanisms

Attention in "Attention Is All You Need" refers to the model's dynamic focusing ability on different input elements, improving efficiency and accuracy.

Scaled Dot-Product Attention and Multi-Head Attention

• Scaled Dot-Product Attention: Uses scaled dot products to compute attention weights, preventing them from becoming too large.

• Multi-Head Attention: Employs multiple scaled dot-product attention heads in parallel to focus on different parts simultaneously, enhancing data representations.

Transformer Application Example

Machine Translation:

• The Transformer encodes input sentences using self-attention to capture relationships.
• Multi-head attention facilitates translation by focusing on diverse parts of the encoded input.

Self-Attention

• Self-Attention: Enables a model to attend to different parts of its own input using the same query, key, and value matrices.

• Applications: Effective for NLP tasks such as machine translation and text summarization, capturing long-range dependencies.

Transformer Model Overview

What are Transformer Models Built Of?

Embedding Layer

The embedding layer converts the input text into a sequence of vectors, representing the meaning of words in the text.

Self-Attention Layers

Self-attention layers enable the model to learn long-range dependencies between words in a sentence by computing scores for word pairs and aggregating them into weighted sums.

Positional Encoding

The positional encoding layer adds positional information to word embeddings, crucial for learning sequence order and dependencies.

Decoder

The decoder generates output tokens based on the input from self-attention layers, producing the final output sequence.

Training Techniques

Masked Language Modeling

Masked language modeling trains Transformers to predict missing words in sentences, enhancing the model's ability to focus on relevant context.

Attention Masking

Attention masking prevents the model from attending to future words, aiding in learning dependencies without introducing circular references.

Gradient Clipping

Gradient clipping limits gradient magnitudes during training to stabilize the process and prevent overfitting.

---

Transformers leverage attention layers to facilitate learning of long-range dependencies within sequences, overcoming limitations of sequential models like RNNs. The architecture comprises an encoder and decoder, each equipped with self-attention layers for comprehensive context understanding and sequence generation.