This post is divided into six parts. These are: •Why transformers are superior to SEQ2SEQ • Data preparation and tokenization • Transformer model design • Transformer model design • Causal mask and padding mask • Training and evaluation The traditional SEQ2SEQ model has two major limitations with recurrent neural networks. The 2017 paper, “Attention is everything you need,” overcomes these limitations.
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