Loading GPT-2 Model Weights into GPTModel
Exercise 5.5 asks you to calculate two losses for a GPTModel with pretrained OpenAI weights, both on the 'The Verdict' dataset.
A Built Model Meets Real Text
By this point in the chapter, a GPTModel has been built and its pretrained weights from OpenAI have been loaded into it. The natural next question is how well the model handles text. This exercise answers that question with a single number called loss, and it asks you to compute that number twice: once on a training set and once on a validation set. Both sets come from the 'The Verdict' dataset.
What do you think happens?
Before reading on: if you measure a model on only one portion of a dataset, what can you tell about how it behaves on the rest of that dataset?
Reveal answer
You learn about the portion you measured, but not necessarily about other portions.
A single loss value reflects only the data it was computed on. Measuring a second, separate portion shows whether the result looks similar across portions or changes depending on which one is used.
What Each Loss Measures
Training set loss is the loss calculated on the training dataset. Validation set loss is the loss calculated on the validation dataset. Both are a single number summarizing how the model performs on the data portion it is given.
The word loss in this exercise names a number that the model is evaluated with. The source does not give the calculation steps or specific values for this exercise, so what you need to remember is the role each loss plays: the training set loss is computed on the portion labeled for training, and the validation set loss is computed on a separate portion labeled for validation. The labels describe which data is evaluated, not a different kind of measurement.
Holding the Model Fixed
Notice what is held constant and what changes between the two calculations. The model is the same in both cases, and so are its weights. Only the data being evaluated changes. That is the key to reading the two values correctly: any difference between them comes from the data portion, not from a different model.
Follow the same sequence twice. The model stays exactly as it was, so the only thing you vary is the data passed in. The source does not supply the specific loss values for this exercise, so the task is to compute them yourself rather than read them off the page.
Keeping the Validation Data Separate
The validation portion must not simply repeat the training data. If the two portions overlap, the two losses are no longer measuring two different portions, and the comparison loses its meaning. The source is explicit that the validation data should be kept separate from the training data before you compare the two recorded values.
Reading the Two Numbers Together
Once you have both numbers, the useful question is what their relationship tells you. Because the model is fixed, a difference between the training and validation values reflects how the same model responds to two different data portions. Two values that are close suggest the model behaves similarly on both portions. Two values that are far apart suggest it behaves differently on them.
Because the source frames the comparison as a question about how one model behaves across two portions, a small gap is best read as evidence of similar behavior on both portions, and a large gap as evidence of different behavior. The source does not go beyond that reading, so you should not claim more from the gap than this.
A Hypothetical Reading of Two Losses
Interpreting an illustrative pair of losses
Suppose a learner computes a training set loss and a validation set loss for the same GPTModel. The numbers below are invented for illustration only and are not values from the source exercise.
- 1Confirm the model is fixed
Both calculations use the same GPTModel with the same weights, so only the data portion differs.
- 2Confirm the split is clean
The validation portion is separate from the training portion, so the two numbers measure two different portions.
- 3Read the gap
If the two values are close, the model behaves similarly on both portions. If they are far apart, the model behaves differently on the two portions.
The comparison answers how consistent the model's behavior is across the two data portions, and that is the purpose of computing both numbers.
Mistakes When Reading the Two Losses
Common mistake Treating the two values as if they came from two different models
Why it failsThe model and its weights stay the same across both calculations, so the difference comes from the data portion.
Do this insteadDescribe any difference as a difference in how the same model responds to each data portion.
Common mistake Comparing the two values before computing them yourself
Why it failsThe source asks you to calculate both losses, and it does not supply the values for you to compare.
Do this insteadCompute both losses first, then compare the results you obtained.
Common mistake Using overlapping data for the validation set
Why it failsThe validation portion must not simply repeat the training data, or the two numbers do not measure two separate portions.
Do this insteadKeep the validation data separate from the training data before computing the validation loss.
Common mistake Reading any gap between the two values as a fixed rule without considering that it depends on the data portions being separate
Trace the Two Calculations
Write out the sequence of the two calculations in your own words. For each one, name the model, name the data portion, and state the loss it produces. Then state in one sentence what changed between the two calculations and what stayed the same.
Hints
- The model is the same in both calculations, so name it once and mark it as shared.
- Only the data portion changes between the two calculations.
- Check that the validation portion is separate from the training portion before you write your comparison.
Key Takeaways
- The training set loss is computed on the training portion of 'The Verdict' dataset, and the validation set loss is computed on the validation portion.
- The GPTModel and its pretrained OpenAI weights stay the same across both calculations, so only the data changes.
- Compare the two values only after computing them yourself and only when the validation data is kept separate from the training data.
- A small gap between the two losses suggests similar behavior on both portions, and a large gap suggests different behavior.