Evaluating Model Performance for Spam Classification

Suppose you are building a spam classifier. Your report card is the fraction of messages it labels correctly. That fraction is the number you ultimately care about. Yet when you train the model, you do not hand the training procedure that fraction and ask it to climb. Instead, you hand it a different quantity, a loss, and ask it to shrink that. This section explains why that swap happens and how the rest of the evaluation process depends on it.

Two quantities play different roles in this article. Accuracy is the measure of performance you report. Cross-entropy loss is the quantity that training reduces, chosen because it can be optimized where accuracy cannot. Keeping these two roles separate is the key to reading everything that follows.

Training adjusts a model by nudging its internal numbers in a direction that makes the score better. That nudging works smoothly only when the score changes gradually as the numbers change. Classification accuracy does not behave that way. It is a count of correct decisions, so it changes in discrete steps: a message either flips from wrong to right, or it does not.

Between two nearby settings of the model, the accuracy may stay exactly the same and then jump. A function with that stepped behavior is not differentiable, which means there is no usable slope to follow when deciding which way to adjust. A gradient-based procedure needs that slope, so it has nothing reliable to follow when the target is raw accuracy.

Cross-entropy loss is a loss function, so it produces a value that training tries to reduce. Lowering it is meant to push the model toward higher accuracy, even though the loss and the accuracy are not the same number. Training reduces the loss in the hope of improving accuracy. The loss is the lever training can pull; accuracy is the outcome you care about.

The function that computes the loss for a batch of data is named calc_loss_batch. The source describes its structure as staying the same when the model is adapted to classification. There is one change, and it concerns which part of the model output feeds into the loss. Before adaptation the loss used every token position. After adaptation it uses only the last token position.

The adjustment is the only change to calc_loss_batch. Everything else about how a batch flows into the loss stays the same. That is why the single slice is so important to read carefully: it decides which slice of the output the training signal is attached to.

A slice with three positions separated by colons selects along three axes. Read the expression [:, -1, :] one axis at a time. Each part of the slice has its own job, and reading them in order prevents most mistakes.

Training a language model means repeatedly updating its weights and watching the loss. A falling loss is encouraging, but it does not by itself answer the question that matters: does the training actually improve the model? To answer that, the training process needs a separate step that measures the model's loss on both the training data and held-out validation data after each model update.

The loss that reflects the updated model is the one measured after the weight update. Measuring after each update is what lets the training process be judged rather than assumed.

In this course, the evaluation step is handled by a function named evaluate_model. It takes the model, a training data loader, a validation data loader, the device the computation runs on, and an eval_iter value that controls how many batches are used. It returns two numbers: a training loss and a validation loss.

The two losses form a pair. The training loss shows how the model performs on data it has learned from. The validation loss shows how it performs on data kept aside for checking. Comparing them after each model update is what allows training progress to be judged.

The function does more than compute numbers. It changes how the model behaves for the duration of the calculation. Two effects are separate ideas. Dropout is a behavior of the model's layers, and calling model.eval() switches it off so that repeated evaluations are stable and reproducible. Gradient tracking is a bookkeeping behavior of the computation, and it is switched off by the no-gradient context because evaluation only needs the loss value, not the gradients used to update weights.

The order of operations is significant. The mode is set before any loss is computed, so both losses are measured under the same conditions. Once the model is in evaluation mode, evaluate_model wraps the loss calculations in a no-gradient context named torch.no_grad(). Inside that context, the function calls calc_loss_loader once on the training loader and once on the validation loader. Both calls receive the same model and device, and both use eval_iter as the number of batches. The mode is restored only after both losses have been computed.

A faulty evaluation step would show up in the reported losses at the point where the mode or the context is wrong. For example, if the restoring step were moved ahead of the loss calculations, the model would be back in training mode while the losses were being measured, so the two numbers would no longer be taken under the same conditions. This is a diagnostic inference from the function's structure, not a fixed procedure stated in the source.