BERT vs Transformer Models: Complete Comparison | Vibepedia

BERT, or Bidirectional Encoder Representations from Transformers, is a specialized encoder-only variant of the original Transformer architecture introduced by Google in 2018, excelling in understanding tasks via bidirectional context like those in ChatGPT-era NLP. Transformer models, from the 2017 'Attention is All You Need' paper by Vaswani et al., feature full encoder-decoder stacks for sequence-to-sequence tasks such as machine translation on platforms like Google Translate. While Transformers enable parallel processing and scalability akin to modern artificial intelligence breakthroughs, BERT's self-supervised masked language modeling outperforms on comprehension benchmarks, as seen in comparisons with GPT on Towards Data Science.