Train and Test mode

1. What is the purpose of the train-and-test process?

The train-and-test process is used to develop and evaluate machine learning models. Training involves teaching the model using a dataset, while testing assesses how well the model performs on unseen data. The process ensures that the model is accurate and reliable.

2. How do I begin the training process?

Begin the training process by uploading your training data, defining model parameters, and selecting training options on the Docsumo platform. Ensure that your data is well-prepared and labeled according to the requirements.

3. What types of data are required for training?

You need labeled training data that represents the document types and information you want to extract or classify. The data should be clean, relevant, and diverse to train the model effectively.

4. How can I evaluate the performance of my model?

Evaluate your model's performance using metrics such as accuracy, precision, recall, and F1 score. These metrics help determine how well the model performs on both the training and testing datasets.

5. What is the difference between training data and testing data?

Training data is used to teach the model and adjust its parameters, while testing data is used to evaluate the model’s performance and generalizability. Testing data should be separate from training data to ensure unbiased evaluation.

6. How do I upload data for training?

Upload data through the Docsumo platform by following the data upload instructions provided in the documentation. Ensure that the data is in the supported formats and properly labeled.

7. What are the common issues during the training process?

Common issues include data quality problems, insufficient training data, overfitting, and incorrect parameter settings. Address these issues by reviewing data quality, increasing data diversity, and adjusting model parameters.

8. Can I retrain a model after initial training?

Yes, you can retrain a model with new or additional data to improve its performance or adapt to changes in document types. Retraining allows the model to learn from updated information.

9. How do I set training parameters?

Set training parameters such as learning rate, batch size, and number of epochs through the Docsumo platform's training configuration settings. Adjust these parameters based on your specific requirements.

10. What should I do if my model's performance is not satisfactory?

If the model's performance is not satisfactory, review and enhance your training data, adjust training parameters, and consider additional training iterations. Analyze performance metrics to identify areas for improvement.

11. How often should I test my model?

Test your model periodically throughout the training process and after each significant update to evaluate its performance and make necessary adjustments.

12. What are the benefits of using a validation dataset?

A validation dataset helps assess the model's performance during training and fine-tune parameters. It provides a way to monitor how well the model generalizes to new, unseen data.

13. How can I improve model accuracy?

Improve model accuracy by providing high-quality, diverse training data, optimizing training parameters, and using techniques like data augmentation and regularization.

14. What is the role of hyperparameter tuning in training?

Hyperparameter tuning involves adjusting parameters such as learning rate and batch size to optimize model performance. Proper tuning can enhance the model's accuracy and efficiency.

15. How can I monitor the training progress?

Monitor training progress through dashboards and logs on the Docsumo platform, which provide real-time updates on training status, metrics, and potential issues.

16. Can I use pre-trained models for my specific use case?

Yes, you can use pre-trained models and fine-tune them for your specific document types and requirements. This approach saves time and leverages existing model capabilities.

17. What is overfitting and how can I avoid it?

Overfitting occurs when a model performs well on training data but poorly on unseen data. Avoid overfitting by using regularization techniques, cross-validation, and ensuring diverse training data.

18. How do I handle large datasets during training?

Handle large datasets by using data batching, distributed processing, and optimizing resource usage. This approach helps manage the scale and efficiency of the training process.

19. What are the best practices for model training?

Best practices include using high-quality data, monitoring performance metrics, adjusting parameters based on feedback, and iterating on training to refine the model.

20. How can I get support if I encounter issues during training?

Contact Docsumo’s support team through the platform’s support options or consult the documentation for troubleshooting tips and guidance on resolving training issues.