The Transformer model architecture has evolved to use either an encoder-only or decoder-only structure, depending on the task. Encoder-only models like BERT are used for tasks requiring understanding of input text, such as classification and named entity recognition. Decoder-only models like GPT are used for generating text one token at a time. This division stems from the bidirectional nature of encoders (allowing tokens to see all other tokens) versus the causal structure of decoders (preventing tokens from seeing future tokens), which suits different task requirements effectively.
Read the full article at Towards AI - Medium
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Ali NematiWritten by Ali
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