What is a Transformer decoder?
Transformer Model. On a high level, the encoder maps an input sequence into an abstract continuous representation that holds all the learned information of that input. The decoder then takes that continuous representation and step by step generates a single output while also being fed the previous output.
What exactly is happening inside the Transformer?
Transformer converts token indices into vector representations thought embedding and positional encoding. Positional encoding is used instead of convolutional layers or recurrent mechanism to record relative position information of data points in sequence.
What is embedding in Transformer?
If you have ever worked with word embeddings using the Word2Vec algorithm, the input and output embeddings are basically just embedding layers. The embedding layer takes a sequence of words and learns a vector representation for each word. Word embedding of a sentence with 5-dimensional vectors for each word.
What is ADD and Norm?
Add and norm: The inputs and outputs of both the multi-head attention layer or the position-wise feed-forward network, are processed by two “add and norm” layer that contains a residual structure and a layer normalization layer.
What liquid is inside a transformer?
Transformer oil or insulating oil is an oil that is stable at high temperatures and has excellent electrical insulating properties. It is used in oil-filled transformers, some types of high-voltage capacitors, fluorescent lamp ballasts, and some types of high-voltage switches and circuit breakers.
What is the first input to the decoder in a transformer model?
At each decoding time step, the decoder receives 2 inputs: the encoder output: this is computed once and is fed to all layers of the decoder at each decoding time step as key (K e n d e c) and value (V e n d e c) for the encoder-decoder attention blocks.
What does encoderdecoderconfig do in Transformers 4?
EncoderDecoderConfig is the configuration class to store the configuration of a EncoderDecoderModel. It is used to instantiate an Encoder Decoder model according to the specified arguments, defining the encoder and decoder configs. Configuration objects inherit from PretrainedConfig and can be used to control the model outputs.
Which is a part of the Transformer architecture?
The Transformer architecture has two parts, the Encoder (left) and the Decoder (right). Let’s examine the Transformer architecture. In the Encoder, it inputs an English sentence, and the output will be a set of encoded vectors for every word. Each word in the input English sentence is converted into an embedding to represent meaning.
Which is the evolution of the encoder-decoder model?
The Transformer model is the evolution of the encoder-decoder architecture, proposed in the paper Attention is All You Need. While encoder-decoder architecture has been relying on recurrent neural networks (RNNs) to extract sequential information, the Transformer doesn’t use RNN.