What are the three different embeddings that are generated from an input sentence in a Transformer model?Recurrent, feedforward, and attention embeddingsEmbedding, classification, and next sentence embeddingsToken, segment, and position embeddingsConvolution, pooling, and recurrent embeddings

What are the two sublayers of each encoder in a Transformer model?Embedding and classificationSelf-attention and feedforwardRecurrent and feedforwardConvolution and pooling

Which model architecture introduced the concept of transformers in NLP?*1 pointConvolutional Neural Networks (CNN)B) Recurrent Neural Networks (RNN)Long Short-Term Memory (LSTM)Attention Is All You Need

What are the encoder and decoder components of a transformer model?The encoder ingests an input sequence and produces a sequence of tokens. The decoder takes in the tokens from the encoder and produces an output sequence.The encoder ingests an input sequence and produces a single hidden state. The decoder takes in the hidden state from the encoder and produces an output sequence.The encoder ingests an input sequence and produces a sequence of hidden states. The decoder takes in the hidden states from the encoder and produces an output sequence.The encoder ingests an input sequence and produces a sequence of images. The decoder takes in the images from the encoder and produces an output sequence.

What is the main role of the decoder in a Transformer model?Question 14Answera.To generate output tokens based on the final encoder representation.b.To compute attention scores between input and output tokens.c.Learning positional encodings.d.To encode the input sequence.

What is the name of the language modeling technique that is used in Bidirectional Encoder Representations from Transformers (BERT)?Recurrent Neural Network (RNN)TransformerLong Short-Term Memory (LSTM)Gated Recurrent Unit (GRU)