Text Classification - BERT

This case study demonstrates fine-tuning BERT (Bidirectional Encoder Representations from Transformers) for sentiment classification on movie reviews. BERT's bidirectional architecture captures rich contextual understanding, making it highly effective for natural language understanding tasks.

Dataset: IMDB Movie Reviews

• Source: HuggingFace (stanfordnlp/imdb)
• Type: Binary text classification
• Size: 50,000 reviews (25k train, 25k test)
• Classes: Positive, Negative
• Average Length: 233 words per review
• Language: English

Model Configuration

{
  "model": "bert",
  "category": "nlp",
  "subcategory": "text-classification",
  "model_config": {
    "model_name": "bert-base-uncased",
    "num_labels": 2,
    "max_seq_length": 512,
    "batch_size": 32,
    "epochs": 3,
    "learning_rate": 0.00002,
    "warmup_steps": 500
  }
}

Training Results

Training Progress

Accuracy and loss curves over 3 epochs:

Confusion Matrix

Classification performance on test set:

Prediction Confidence Distribution

How confident is the model in its predictions?

Performance by Review Length

Does review length affect classification accuracy?

Most Important Words

Attention weights for sentiment prediction:

Common Use Cases

• Customer Feedback Analysis: Classify product reviews, support tickets
• Social Media Monitoring: Track brand sentiment, crisis detection
• Content Moderation: Identify toxic or inappropriate comments
• Market Research: Analyze consumer opinions and trends
• Political Analysis: Classify political discourse, news sentiment
• Financial Markets: Sentiment analysis of news for trading signals
• Healthcare: Analyze patient feedback, clinical notes

Key Settings

Essential Parameters

• model_name: Pre-trained model variant (base, large, multilingual)
• max_seq_length: Maximum input tokens (128-512)
• num_labels: Number of classes (2 for binary)
• learning_rate: Fine-tuning rate (1e-5 to 5e-5)
• batch_size: Samples per iteration (16-32)
• epochs: Training iterations (2-4 typical)

Optimization

• warmup_steps: Gradual learning rate increase
• weight_decay: L2 regularization (0.01 typical)
• adam_epsilon: Optimizer stability (1e-8)
• max_grad_norm: Gradient clipping (1.0)

Advanced Configuration

• fp16: Mixed precision training (faster, less memory)
• gradient_accumulation: Simulate larger batch sizes
• early_stopping: Stop if validation improves
• class_weights: Handle imbalanced datasets
• attention_probs_dropout: Regularization

Performance Metrics

• Accuracy: 92.7% on test set
• Precision: 92.4% (positive class)
• Recall: 93.1% (positive class)
• F1 Score: 92.7% (both classes)
• Training Time: 3.2 hours (NVIDIA RTX 3080)
• Inference Speed: ~80 reviews/second
• Model Size: 438 MB (BERT-base-uncased)

Tips for Success

1. Pre-trained Models: Always start with pre-trained BERT
2. Sequence Length: Truncate intelligently (keep important parts)
3. Learning Rate: Start small (2e-5), crucial for fine-tuning
4. Few Epochs: 2-4 epochs usually sufficient
5. Validation: Monitor validation loss for early stopping
6. Batch Size: Larger batches more stable but need more memory
7. Special Tokens: Properly handle [CLS], [SEP], [PAD]

Example Scenarios

Scenario 1: Positive Review

• Input: "This movie is an absolute masterpiece! The acting was brilliant and the plot kept me engaged throughout. Highly recommend!"
• Prediction: Positive (confidence: 98.7%)
• Key Tokens: masterpiece, brilliant, highly recommend

Scenario 2: Negative Review

• Input: "What a waste of time. The plot was confusing, acting was terrible, and I couldn't wait for it to end."
• Prediction: Negative (confidence: 97.3%)
• Key Tokens: waste of time, confusing, terrible

Scenario 3: Mixed Review (Challenging)

• Input: "While the cinematography was stunning, the weak storyline and poor character development ruined the experience."
• Prediction: Negative (confidence: 68.2%)
• Reasoning: Negative aspects outweigh positive mention

Troubleshooting

Problem: Model overfitting (train acc >> val acc)

• Solution: Reduce epochs (use 2 instead of 3-4), add dropout, increase data

Problem: Poor performance on sarcastic reviews

• Solution: Add sarcasm examples to training, use context-aware features

Problem: Slow training or OOM errors

• Solution: Reduce batch_size or max_seq_length, use fp16 training

Problem: Biased predictions (favors one class)

• Solution: Balance dataset, adjust class_weights, check label distribution

Problem: Low confidence on short texts

• Solution: Train on more short examples, consider different models for short text

Model Architecture Highlights

BERT-base consists of:

• 12 Transformer Layers: Stacked encoder blocks
• 768 Hidden Units: Dense representation dimension
• 12 Attention Heads: Multi-head self-attention
• Parameters: 110 million trainable parameters
• WordPiece Tokenization: 30,522 vocabulary size
• Bidirectional Context: Captures left and right context
• Special Tokens: [CLS] for classification, [SEP] for separation

BERT Variants Comparison

Next Steps

After training your BERT classifier, you can:

• Deploy as REST API for real-time predictions
• Fine-tune on domain-specific data (medical, legal, etc.)
• Multi-task learning (sentiment + emotion + topic)
• Export to ONNX for faster inference
• Distill to smaller model (DistilBERT)
• Ensemble with other models for higher accuracy
• Build interpretability tools (attention visualization)
• Adapt to other languages (multilingual BERT)