GPT-2 pretrain loss.

[jiangix-paper]

Hello, Thanks for your great work. I want to know how to calculate the loss given the raw text. For example:
I have a sample in training data: " I want to go to school". When I input the string into the GPT-2 model, every output logits has a loss value. So the total loss is the sum of all output logits loss?

[imostafizur]

No, the aggregate of all output logits loss is not the overall loss. The loss function is usually defined in GPT-2 and other neural network models to calculate the difference between the goal output and the predicted output. Cross-entropy loss or mean squared error metrics are frequently used to quantify this difference.
The GPT-2 algorithm outputs a series of tokens after receiving the phrase "I want to go to school" as input. Given the context of the input, each character in the output has a corresponding probability distribution (logits) that shows how likely each potential token is.
You would match the probability distribution of each generated token to the associated goal token in the training data to calculate the loss for this output. In order to adjust the model's parameters during training, the loss function would quantify the difference between each token's expected and real ranges.