Transformers, Pytorch, Machine Learning, Deep Dives, Data Science.
range(n_encoder_blocks) ]) def forward(self, x, padding_mask=None): x = self.embedding(x) * math.sqrt(self.n_dim) x = self.positional_encoding(x) for block in self.encoder_blocks: x = block(x=x, src_padding_mask=padding_mask) return x Decoders The decoder part is the part on the left and requires a