Transformer architecture in NLP - Model Pipeline Trace

Model Pipeline - Transformer architecture

The Transformer architecture processes text data by converting words into numbers, then learning relationships between words using attention. It trains to predict the next word or classify text, improving accuracy over time.

Data Flow - 7 Stages

1Input Text

1 sentence x variable length→Raw text input→1 sentence x variable length

"The cat sat on the mat."

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2Tokenization

1 sentence x variable length→Split sentence into tokens (words or subwords)→1 sentence x 6 tokens

["The", "cat", "sat", "on", "the", "mat"]

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3Embedding

1 sentence x 6 tokens→Convert tokens to vectors of size 512→1 sentence x 6 tokens x 512 features

[[0.1, 0.3, ..., 0.2], ..., [0.05, 0.4, ..., 0.1]]

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4Positional Encoding

1 sentence x 6 tokens x 512 features→Add position info to embeddings→1 sentence x 6 tokens x 512 features

Embedding vectors with added position signals

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5Multi-Head Self-Attention

1 sentence x 6 tokens x 512 features→Calculate attention scores and weighted sums→1 sentence x 6 tokens x 512 features

Attention output vectors showing word relationships

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6Feed-Forward Network

1 sentence x 6 tokens x 512 features→Apply two linear layers with ReLU activation→1 sentence x 6 tokens x 512 features

Processed feature vectors for each token

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7Output Layer

1 sentence x 6 tokens x 512 features→Project to vocabulary size for prediction→1 sentence x 6 tokens x 10000 classes

Probabilities for each word in vocabulary

Training Trace - Epoch by Epoch

Loss
5.2 |**************
4.0 |**********
2.8 |*******
1.6 |****
0.4 |*
    +----------------
     1  3  5  7  10 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	5.2	0.12	Model starts with high loss and low accuracy
2	3.8	0.28	Loss decreases, accuracy improves as model learns
3	2.7	0.45	Model captures basic word relationships
4	1.9	0.60	Attention mechanism helps improve predictions
5	1.3	0.72	Model learns complex context and syntax
6	0.9	0.81	Loss steadily decreases, accuracy rises
7	0.7	0.86	Model converges with good performance
8	0.6	0.89	Fine tuning improves accuracy further
9	0.55	0.91	Model predictions become more confident
10	0.50	0.93	Training converges with low loss and high accuracy

Prediction Trace - 6 Layers

Layer 1: Tokenization

Layer 2: Embedding

Layer 3: Positional Encoding

Layer 4: Multi-Head Self-Attention

Layer 5: Feed-Forward Network

Layer 6: Output Layer

Model Quiz - 3 Questions

What does the positional encoding add to the token embeddings?

A Random noise to improve generalization

B Information about the order of words

C Labels for each token's part of speech

D The final prediction probabilities

Key Insight

The Transformer architecture uses attention to understand relationships between words regardless of their position. This allows it to learn complex language patterns efficiently, shown by decreasing loss and increasing accuracy during training.