Building a Transformer Model for Language Translation
This post is divided into six parts; they are: • Why Transformer is Better than Seq2Seq • Data Preparation and Tokenization • Design of a Transformer Model • Building the Transformer Model • Causal Mask and Padding Mask • Training and Evaluation Traditional seq2seq models with recurrent neural networks have two main limitations: • Sequential processing prevents parallelization • Limited ability to capture long-term dependencies since hidden states are overwritten whenever an element is processed The Transformer architecture, introduced in the 2017 paper "Attention is All You Need", overcomes these limitations.
This post is divided into six parts; they are: • Why Transformer is Better than Seq2Seq • Data Preparation and Tokenization • Design of a Transformer Model • Building the Transformer Model • Causal Mask and Padding Mask • Training and Evaluation Traditional seq2seq models with recurrent neural networks have two main limitations: • Sequential processing prevents parallelization • Limited ability to capture long-term dependencies since hidden states are overwritten whenever an element is processed The Transformer architecture, introduced in the 2017 paper "Attention is All You Need", overcomes these limitations.
