Sample-wise Negative Log-Likelihood Loss for a Sub-sequence

Sequence-Level Loss

An engineer is training a model on a large dataset. They are monitoring two metrics:

• Metric A: A value calculated for each individual data sample. This value fluctuates significantly from one sample to the next.
• Metric B: A single, aggregate value calculated after the model has processed the entire training dataset. This value shows a steady, downward trend over multiple passes through the dataset.

Based on the standard terminology for measuring a model's performance, what is the most accurate way to classify these two metrics?

Interpreting Training Metrics

Match each term to its most accurate description regarding how a model's performance is measured during training.