Encoder-Only vs Decoder-Only vs Encoder-Decoder Transformer

People keep asking me about, what is the difference between encoder, decoder, and normal transformer (with self-attention). It is a simple thing, you can master quickly. BERT has Encoder-only architecture. Input is text and output is sequence of embeddings. Use cases are sequence classification (class token), token classification. It uses bidirectional attention, so the model can see forwards and backwards. Another encoder-only model example is ViT (Vision Transformer) for image classification. GPT-2 has Decoder-only architecture. Input is text and output is the next word (token), which is then appended to the input. Use cases are mostly text generation (autoregressive), but with prompting we can do many things including sequence classification. The attention is almost always causal (unidirectional), so the model can see only previous tokens (prefix). T5 has Encoder-Decoder or Full-Transformer. Input is text and output is the next word (token), which is then appended to the decoder-inp

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related reading

• The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time.jalammar.github.io
• A Mathematical Framework for Transformer Circuitstransformer-circuits.pub
• The Annotated Transformernlp.seas.harvard.edu
• Transformers from Scratche2eml.school
• Transformer Explainer: LLM Transformer Model Visually Explainedpoloclub.github.io
• transformer_attention.pdfarxiv.org
• Illustrating Reinforcement Learning from Human Feedback (RLHF)huggingface.co
• Transformer Architectures · Hugging Facehuggingface.co
• The Annotated Transformernlp.seas.harvard.edu
• Transformer (deep learning) - Wikipediaen.wikipedia.org
• The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time.jalammar.github.io
• Encoder Decoder Models · Hugging Facehuggingface.co