Multi-head attention in PyTorch - Model Metrics & Evaluation

Metrics & Evaluation - Multi-head attention

Which metric matters for Multi-head attention and WHY

Multi-head attention is often used in tasks like language translation or text understanding. The key metrics to check are accuracy for classification tasks, BLEU score for translation quality, and loss to see how well the model learns. Accuracy tells us how many predictions are correct. Loss shows how far off predictions are during training. For sequence tasks, BLEU score measures how close the output is to the correct sentence. These metrics help us know if the attention mechanism is helping the model focus on important parts of the input.

Confusion matrix example for classification with Multi-head attention

      | Predicted Positive | Predicted Negative |
      |--------------------|--------------------|
      | True Positive (TP) = 85  | False Negative (FN) = 15 |
      | False Positive (FP) = 10 | True Negative (TN) = 90  |

      Total samples = 85 + 15 + 10 + 90 = 200

      Precision = TP / (TP + FP) = 85 / (85 + 10) = 0.8947
      Recall = TP / (TP + FN) = 85 / (85 + 15) = 0.85
      F1 Score = 2 * (Precision * Recall) / (Precision + Recall) = 2 * (0.8947 * 0.85) / (0.8947 + 0.85) ≈ 0.871
    

This matrix helps us understand how well the multi-head attention model classifies inputs by showing correct and incorrect predictions.

Precision vs Recall tradeoff with Multi-head attention

Imagine a spam email detector using multi-head attention to focus on important words. If we want high precision, the model marks emails as spam only when very sure, so fewer good emails are wrongly marked. But it might miss some spam emails (lower recall).

If we want high recall, the model catches almost all spam emails but might wrongly mark some good emails as spam (lower precision).

Choosing precision or recall depends on what is worse: missing spam or wrongly blocking good emails. Multi-head attention helps by focusing on key parts of the email to improve both metrics.

Good vs Bad metric values for Multi-head attention use cases

• Good: Accuracy above 85%, Precision and Recall both above 80%, and steady loss decrease during training.
• Bad: Accuracy below 60%, Precision or Recall below 50%, or loss that does not improve or fluctuates wildly.

Good metrics mean the multi-head attention is helping the model understand important input parts. Bad metrics suggest the model is not learning well or focusing incorrectly.

Common pitfalls in metrics for Multi-head attention

• Accuracy paradox: High accuracy can be misleading if data is imbalanced (e.g., many more negatives than positives).
• Data leakage: If test data leaks into training, metrics look better but model fails in real use.
• Overfitting: Low training loss but high test loss means model memorizes training data but does not generalize.
• Ignoring sequence metrics: For tasks like translation, only accuracy is not enough; use BLEU or similar.

Self-check question

Your multi-head attention model has 98% accuracy but only 12% recall on fraud detection. Is it good for production? Why or why not?

Answer: No, it is not good. The model misses 88% of fraud cases (low recall), which is dangerous. High accuracy is misleading because most transactions are not fraud. For fraud detection, high recall is critical to catch as many frauds as possible.

Key Result

Precision, recall, and loss are key metrics to evaluate multi-head attention models, ensuring they focus well and generalize.