Transformers, Pytorch, Machine Learning, Deep Dives, Data Science.
float('-inf')) # (B, S, E) encoder_output = self.encoder( x, padding_mask=encoder_padding_mask ) return encoder_output, encoder_padding_mask def decode( self, tgt: torch.Tensor, memory: torch.Tensor, memory_padding_mask=None ) -> torch.Tensor: """ B = Batch size S = Source sequence length L =