Transformer Models Introduced Attention-Only Architecture

The original Transformer replaced recurrence and convolutions with self-attention, enabling parallel processing of sequences.

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🤯 Did You Know (click to read)

Self-attention enables Transformers to capture dependencies between tokens that are hundreds of positions apart in a sequence.

The Transformer architecture uses multi-head self-attention layers to capture relationships between all tokens in a sequence simultaneously. This design allows efficient training on GPUs and TPUs and overcomes the limitations of sequential processing in RNNs. Positional encodings provide order information, preserving sequence structure without recurrence.

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💥 Impact (click to read)

Attention-only design accelerates training for NLP tasks like translation and summarization, making large-scale models feasible.

Developers and researchers can exploit parallelism and context-aware embeddings for faster experimentation and deployment in AI systems.

Source

Vaswani et al., 2017 - Attention is All You Need

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