BERT fine-tuning for classification in NLP - Model Pipeline Trace

Model Pipeline - BERT fine-tuning for classification

This pipeline fine-tunes a pre-trained BERT model to classify text into categories. It starts with raw text data, processes it into tokens BERT understands, trains the model to learn from labeled examples, and then predicts categories for new text.

Data Flow - 5 Stages

1Raw Text Input

1000 rows x 1 column→Collect sentences or documents with labels→1000 rows x 1 column

"I love this movie!"

↓

2Tokenization

1000 rows x 1 column→Convert text to BERT tokens with special tokens and padding→1000 rows x 128 tokens

[CLS] i love this movie ! [SEP] [PAD] ... [PAD]

↓

3Input IDs and Attention Masks

1000 rows x 128 tokens→Create numerical IDs and masks for tokens→1000 rows x 128 columns (IDs), 1000 rows x 128 columns (masks)

IDs: [101, 1045, 2293, 2023, 3185, 999, 102, 0, ..., 0]

↓

4Train/Test Split

1000 rows x 128 columns (IDs and masks)→Split data into 800 training and 200 testing samples→800 rows x 128 columns (train), 200 rows x 128 columns (test)

Train sample: IDs and masks for "I love this movie!"

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5Model Fine-tuning

800 rows x 128 columns (IDs and masks)→Train BERT with classification head on training data→Fine-tuned BERT model

Model learns to predict sentiment labels

Training Trace - Epoch by Epoch

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

Epoch	Loss ↓	Accuracy ↑	Observation
1	0.65	0.60	Model starts learning, loss is high, accuracy moderate
2	0.45	0.75	Loss decreases, accuracy improves as model learns patterns
3	0.30	0.85	Model shows good learning, loss low, accuracy high
4	0.25	0.88	Further improvement, model converging
5	0.22	0.90	Training stabilizes with high accuracy and low loss

Prediction Trace - 5 Layers

Layer 1: Tokenization

Layer 2: Input IDs and Attention Masks

Layer 3: BERT Encoder

Layer 4: Classification Head

Layer 5: Prediction

Model Quiz - 3 Questions

What does the tokenization stage add to the input text?

A Numerical IDs for words

B Special tokens like [CLS] and [SEP]

C Predicted labels

D Loss values

Key Insight

Fine-tuning BERT adapts its deep language understanding to a specific task by adjusting weights using labeled examples. This process improves classification accuracy as the model learns task-specific patterns while retaining general language knowledge.