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Questions and Answers
What is the purpose of the self-attention layer in the encoding component?
What is the purpose of the self-attention layer in the encoding component?
- To determine the length of the longest sentence in the training dataset
- To connect the encoder and decoder components
- To help the encoder look at other words in the input sentence as it encodes a specific word (correct)
- To calculate the word embeddings directly
What is the primary function of the embedding layer in a Transformer model?
What is the primary function of the embedding layer in a Transformer model?
- Capture the meaning of each word or token in a vector space (correct)
- Model complex relationships between input tokens
- Perform self-attention calculations
- Apply non-linear transformations to the input sequence
Where does the embedding algorithm operate in the encoder-decoder model described?
Where does the embedding algorithm operate in the encoder-decoder model described?
- In the decoder's attention layer
- Only in the decoder layers
- In the bottom-most encoder (correct)
- It operates after the self-attention layer
In the Transformer architecture, what are the two sub-layers present in each encoder or decoder layer?
In the Transformer architecture, what are the two sub-layers present in each encoder or decoder layer?
How does the multi-head attention mechanism in Transformers handle attending to different parts of the input sequence simultaneously?
How does the multi-head attention mechanism in Transformers handle attending to different parts of the input sequence simultaneously?
What is the purpose of the attention layer between the decoder's self-attention and feed-forward layers?
What is the purpose of the attention layer between the decoder's self-attention and feed-forward layers?
What is common to all the encoders described in the text?
What is common to all the encoders described in the text?
What is the purpose of the feedforward neural network component in the Transformer architecture?
What is the purpose of the feedforward neural network component in the Transformer architecture?
How does the self-attention mechanism in Transformers allow the model to focus on different parts of the input sequence?
How does the self-attention mechanism in Transformers allow the model to focus on different parts of the input sequence?
How does each word in the input sequence flow through an encoder?
How does each word in the input sequence flow through an encoder?
Which component of Transformer models helps in capturing the semantic meaning of individual words or tokens?
Which component of Transformer models helps in capturing the semantic meaning of individual words or tokens?
What determines the length of the list of vectors received by each encoder?
What determines the length of the list of vectors received by each encoder?
What is the purpose of the Output layer in the described model architecture?
What is the purpose of the Output layer in the described model architecture?
What role does the Decoder stack play in the processing of the target sequence?
What role does the Decoder stack play in the processing of the target sequence?
How does a pre-trained model benefit downstream NLP tasks?
How does a pre-trained model benefit downstream NLP tasks?
In the described model architecture, what happens after taking the last word of the output sequence as the predicted word?
In the described model architecture, what happens after taking the last word of the output sequence as the predicted word?
What is the primary purpose of training a model on a general task before fine-tuning it on a specific downstream task?
What is the primary purpose of training a model on a general task before fine-tuning it on a specific downstream task?
Why is it unnecessary to repeat steps #1 and #2 for each iteration in the described model architecture?
Why is it unnecessary to repeat steps #1 and #2 for each iteration in the described model architecture?
