true_labels

def update_weights(engine1_accuracy, engine2_accuracy): total_accuracy = engine1_accuracy + engine2_accuracy if total_accuracy == 0: return (0.5, 0.5) # Default equal weights if both accuracies are zero new_weights = (e…

# Compute ensemble scores ensemble_scores = compute_weighted_ensemble_scores(scores1, scores2, weights=weights) print("Current Ensemble Scores:", ensemble_scores) # Calculate predictions predictions1 = np.argmax(scores1…

predicted_labels = [tokenizer.decode(pred, skip_special_tokens=True) for pred in predictions] # Ground truth labels true_labels = [item['text'] for item in tokenized_datasets['test']] # Calculate accuracy accuracy = accuracy_score(true_la…

dense_scores = np.array([0.7, 0.3, 0.1]) # Normalize and compute hybrid scores hybrid_scores = hybrid_ranking(sparse_scores, dense_scores) print(hybrid_scores) # Optionally, sort documents based on hybrid scores sorted_indices = np.argsor…

dense_scores = np.random.rand(num_queries, num_documents) # Test queries test_queries = np.random.rand(num_queries, num_documents) predictions = [] for i in range(num_queries): query = test_queries[i] sparse_scores_i = sparse_scor…

prediction = rank_documents(query, sparse_scores_i, dense_scores_i) if prediction is not None: predictions.append(prediction) # Evaluate precision true_labels = np.random.randint(0, 2, size=(num_queries, num_documents)) # …

precision = precision_score(true_labels.ravel(), predicted_labels.ravel()) print(f"Precision: {precision:.2f}") ``` ### Explanation 1. **Hybrid Search Function:** - Combines sparse and dense scores using adaptive weights. - Handles …

# Early stopping if val_loss < best_val_loss: best_val_loss = val_loss counter = 0 else: counter += 1 if counter >= patience: print("Early stopping") break ``` #### 4. Ev…

accuracy = evaluate_system(expanded_query, documents, true_labels) print(f"Accuracy: {accuracy}") ``` ### Conclusion By following these steps and implementing the techniques described, you can significantly enhance the results for your 11…