How to Build a PyTorch training loop for a Transformer-based encoder-decoder model

Question

Can i know How to Build a PyTorch training loop for a Transformer-based encoder-decoder model.

Answer 1

You can build a PyTorch training loop for a Transformer-based encoder-decoder model by setting up forward passes, computing loss, and updating parameters in each iteration.

Here is the code snippet below:

In the above code we are using the following key points:

• PyTorch DataLoader to iterate over batches.

• Transformer model with encoder and decoder layers.

• CrossEntropyLoss and Adam optimizer for training.

• Teacher forcing with shifted target sequences for decoder input.

Hence, this setup provides a complete and efficient framework to train Transformer-based encoder-decoder models in PyTorch.