Attention mechanism basics in NLP - Model Pipeline Trace

Model Pipeline - Attention mechanism basics

This pipeline shows how the attention mechanism helps a model focus on important words when understanding a sentence. It improves how the model learns and predicts by weighing words differently.

Data Flow - 6 Stages

1Input sentence

1 sentence x 6 words→Tokenize sentence into words→1 sentence x 6 tokens

"The cat sat on the mat" -> ['The', 'cat', 'sat', 'on', 'the', 'mat']

↓

2Word embeddings

1 sentence x 6 tokens→Convert tokens to vectors→1 sentence x 6 vectors (each 8 dims)

['cat'] -> [0.2, 0.1, ..., 0.05]

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3Calculate attention scores

1 sentence x 6 vectors→Compute similarity scores between words→6 x 6 matrix (attention scores)

Score between 'cat' and 'sat' = 0.8

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4Apply softmax to scores

6 x 6 matrix→Turn scores into probabilities→6 x 6 matrix (attention weights)

Row for 'cat': [0.1, 0.4, 0.3, 0.1, 0.05, 0.05]

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5Weighted sum of vectors

6 x 6 matrix and 1 sentence x 6 vectors→Multiply weights by vectors and sum→1 sentence x 6 new vectors

New vector for 'cat' focuses more on 'sat' vector

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6Output for next layer

1 sentence x 6 new vectors→Pass weighted vectors forward→1 sentence x 6 vectors

Vectors now contain context-aware info

Training Trace - Epoch by Epoch

Loss
1.2 |*       
0.9 | *      
0.7 |  *     
0.5 |   *    
0.4 |    *   
    +---------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Model starts learning, loss high, accuracy low
2	0.9	0.60	Loss decreases, accuracy improves as attention helps
3	0.7	0.72	Model better focuses on important words
4	0.5	0.80	Attention weights refine, improving predictions
5	0.4	0.85	Training converges with good attention learning

Prediction Trace - 5 Layers

Layer 1: Input tokens

Layer 2: Embedding layer

Layer 3: Attention score calculation

Layer 4: Softmax normalization

Layer 5: Weighted sum

Model Quiz - 3 Questions

What does the attention mechanism do with word vectors?

A It changes words into numbers randomly

B It deletes unimportant words

C It weighs them to focus on important words

D It sorts words alphabetically

Key Insight

The attention mechanism helps the model learn which words to focus on by assigning weights. This focus improves understanding and prediction accuracy over training.