BERT tokenization (WordPiece) in NLP - Model Pipeline Trace

Model Pipeline - BERT tokenization (WordPiece)

This pipeline shows how BERT breaks text into smaller pieces called WordPieces. It helps the model understand words, even if they are new or rare.

Data Flow - 4 Stages

1Raw Text Input

1 sentence (string)→Input sentence as plain text→1 sentence (string)

"Playing football is fun!"

↓

2Basic Tokenization

1 sentence (string)→Split sentence into words and punctuation→List of tokens (words and punctuation)

["Playing", "football", "is", "fun", "!"]

↓

3WordPiece Tokenization

List of tokens→Break words into subword pieces using WordPiece vocabulary→List of WordPiece tokens

["Play", "##ing", "football", "is", "fun", "!"]

↓

4Convert Tokens to IDs

List of WordPiece tokens→Map each token to a unique number from vocabulary→List of token IDs (integers)

[1234, 567, 4321, 29, 876, 17]

Training Trace - Epoch by Epoch

Loss
0.9 |****
0.8 |*** 
0.7 |**  
0.6 |**  
0.5 |*   
0.4 |*   
0.3 |    
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.85	0.60	Model starts learning basic token patterns.
2	0.65	0.72	Loss decreases as model learns subword relationships.
3	0.50	0.80	Model improves understanding of word pieces.
4	0.40	0.85	Training converges with better token predictions.
5	0.35	0.88	Final epoch shows stable loss and high accuracy.

Prediction Trace - 4 Layers

Layer 1: Input Sentence

Layer 2: Basic Tokenization

Layer 3: WordPiece Tokenization

Layer 4: Convert Tokens to IDs

Model Quiz - 3 Questions

What does the '##' symbol mean in WordPiece tokens?

A It marks the start of a new word.

B It indicates punctuation.

C It shows the token is a continuation of the previous word piece.

D It means the token is a stop word.

Key Insight

BERT's WordPiece tokenization helps the model understand words by breaking them into smaller parts. This allows it to handle new or rare words better, improving language understanding.