Content Comparison

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1. What is Training?

   • Training is the process of optimizing a model to make better predictions by minimizing a loss function.

   • For your BERT-based model:

     • Intent Classification: Predict the intent of a user query (e.g., addFilter or recipeRequest).

     • Slot Filling: Assign BIO tags to each token in the query (e.g., B-shortTimeKeyWord for "fast").

2. How Does the Training Loop Work?

   • Forward Pass: The input data flows through the model to generate predictions.

   • Loss Calculation: The predictions are compared with ground truth labels to calculate the loss.

   • Backward Pass (Backpropagation): The model adjusts its weights based on the loss to improve predictions in the next iteration.

3. What Are We Optimizing?

   • Intent Loss: Measures how well the model predicts the intent.

   • Slot Loss: Measures how well the model predicts slot tags for each token.

4. Why Do We Use Two Loss Functions?

   • Your model performs two tasks simultaneously, so you need separate losses for each task. These are combined to train the model in a balanced way.

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Steps to Complete the train_model Function

The train_model function is incomplete, and your task is to fill in the missing pieces. This function is the backbone of the training process for a dual-task model that handles intent classification and slot filling. Through this exercise, you’ll learn how to integrate loss functions, perform forward passes, and optimize a model’s weights effectively.

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