Transformer Architecture and Decoding in NLP Quiz

Overview of Transformer Architecture and Decoding in NLP

• The speaker is confirming if everything is working as expected for the class.
• The agenda for the class is to cover Transformers, the decoder module, and the BERT architecture.
• The speaker plans to provide pointers to Transformer code and walk through a real-world problem solved using a pre-trained Transformer.
• The architecture of an encoder in a Transformer includes self-attention, skip connections, add and normalize layers, and feed-forward layers.
• The encoder stack in a Transformer consists of multiple encoders, and the output of the final encoder is passed to each decoder.
• The decoder stack in a Transformer is responsible for generating the final output based on the input from the encoders.
• The decoder stack can have multiple decoders, and the final output of the decoders goes through linear and softmax layers.
• In decoding, the output of the encoder is used as keys and values for each decoder.
• The decoder generates one output per timestamp using the softmax layer.
• At each decoding time step, the input to the decoder is the output of the encoder and the previously generated output.
• The encoder is not re-run at each decoding time step.
• The speaker emphasizes the importance of understanding the encoder architecture before diving into the decoder.