BERT for text classification in PyTorch - Model Pipeline Trace

Model Pipeline - BERT for text classification

This pipeline uses BERT, a smart language model, to read sentences and decide their category, like sorting emails into spam or not spam.

Data Flow - 7 Stages

1Raw Text Input

1000 sentences→Collect raw sentences for classification→1000 sentences

"I love this movie!", "This is terrible."

↓

2Tokenization

1000 sentences→Split sentences into tokens and add special tokens→1000 sequences x 128 tokens

[CLS] I love this movie ! [SEP]

↓

3Convert Tokens to IDs

1000 sequences x 128 tokens→Map tokens to numbers BERT understands→1000 sequences x 128 token IDs

[101, 1045, 2293, 2023, 3185, 999, 102]

↓

4Attention Masks

1000 sequences x 128 token IDs→Create masks to tell model which tokens to focus on→1000 sequences x 128 masks

[1, 1, 1, 1, 1, 1, 1, 0, 0, ...]

↓

5BERT Model Forward Pass

1000 sequences x 128 token IDs + masks→Process tokens to get sentence meaning vectors→1000 sequences x 768 features

[0.12, -0.34, 0.56, ..., 0.01]

↓

6Classification Layer

1000 sequences x 768 features→Predict class scores from BERT features→1000 sequences x 2 classes

[2.3, -1.7]

↓

7Softmax Activation

1000 sequences x 2 classes→Convert scores to probabilities→1000 sequences x 2 probabilities

[0.91, 0.09]

Training Trace - Epoch by Epoch

Loss
0.7 |*       
0.6 | *      
0.5 |  *     
0.4 |   *    
0.3 |    *   
0.2 |     *  
0.1 |       
    +--------
     1 2 3 4 5 Epochs

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning, loss high, accuracy low
2	0.45	0.75	Loss decreases, accuracy improves
3	0.35	0.82	Model learns better features
4	0.28	0.87	Loss continues to drop, accuracy rises
5	0.22	0.90	Model converges with good accuracy

Prediction Trace - 6 Layers

Layer 1: Tokenization

Layer 2: Convert Tokens to IDs

Layer 3: Attention Mask Creation

Layer 4: BERT Model Forward Pass

Layer 5: Classification Layer

Layer 6: Softmax Activation

Model Quiz - 3 Questions

What does the attention mask do in the BERT pipeline?

A It splits sentences into words

B It tells the model which tokens to focus on

C It converts tokens to numbers

D It predicts the final class

Key Insight

BERT transforms sentences into meaningful vectors, then a simple layer predicts classes. Training reduces loss and improves accuracy steadily, showing the model learns well.