sklearn

2. **Model Selection**: Use a more sophisticated model that handles multiple languages effectively. 3. **Hyperparameter Tuning**: Fine-tune hyperparameters to improve model performance. 4. **Evaluation Metrics**: Use additional evaluation m…

dense_scores = get_embeddings([query]).dot(embeddings.T) combined_scores = 0.5 * sparse_scores + 0.5 * dense_scores return combined_scores # Example usage documents = ["This is a sample document.", "Este es un documento de mues…

SVMs can be effective, especially with the right kernel and parameter tuning. ### 4. **Decision Tree Classifier** Decision Trees are simple yet effective for certain types of data and can be used as a baseline. ### 5. **Naive Bayes Classi…

predictions.append(predicted_label) return predictions # Make predictions predictions = predict_labels(test_df, bm25, train_df) # Calculate the recall score recall = recall_score(test_df['label'], predictions, average='binary'…

- **User Segmentation**: Segment users based on their behavior and preferences, and tailor the feedback algorithm for each segment. ### 4. **Evaluate and Iterate** Regularly evaluate your model's performance and iterate based on the result…

logging.info(f"Iteration {iteration}: Model accuracy = {accuracy:.4f}") # Example usage: model = RandomForestClassifier(n_estimators=100) for i in range(5): # Example: Fine-tune and evaluate the model 5 times fine_tuned_model = fi…

logging_dir='./logs', logging_steps=10, evaluation_strategy="epoch", save_total_limit=2, ) # Define Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=test_…

3. **Data Augmentation**: Apply data augmentation techniques to further improve the model's performance. 4. **Evaluate and Monitor**: Continuously evaluate and monitor the model's performance. Would you like to proceed with these steps or …