arch.py

#architecture
print(model)

#No. of trianable parameters
def count_parameters(model):
    return sum(p.numel() for p in model.parameters() if p.requires_grad)
    
print(f'The model has {count_parameters(model):,} trainable parameters')

#Initialize the pretrained embedding
pretrained_embeddings = TEXT.vocab.vectors
model.embedding.weight.data.copy_(pretrained_embeddings)

print(pretrained_embeddings.shape)