model_predictions

Ensure that the training data is clean, representative, and annotated correctly. Poor data quality can significantly impact model performance. - **Tools**: Use spaCy's `spacy lookups` to inspect and validate the training data. - **Techniqu…

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…

- **Error Handling**: Use try-except blocks to catch and print errors, which helps in debugging. - **Verification**: Verify that the model and optimizer were loaded correctly after attempting to load them. This approach should help you deb…

x = self.fc2(x) return x # Initialize the model and optimizer model = MyModel() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # Define the feedback loop logic def feedback_loop(model, optimizer, data): # U…

for epoch in range(100): for batch in data_loader: inputs = batch['query'].float().to(device) labels = batch['label'].long().to(device) optimizer.zero_grad() outputs = model(input…

- **Continuous Evaluation**: Continuously evaluate the model's performance on a validation set to identify areas for improvement. - **Feedback Loop**: Implement a feedback loop where the model's predictions are reviewed and used to up…