🤯 Did You Know (click to read)
Multi-head attention in BERT allows simultaneous focus on multiple parts of a sentence, capturing complex dependencies.
BERT employs multi-head self-attention within transformer encoders, enabling it to assign different weights to each token based on context. This mechanism allows the model to focus on key words while understanding relationships between distant tokens. Attention enhances performance on tasks such as translation, summarization, and question answering by preserving context and meaning across the sequence.
💥 Impact (click to read)
Attention mechanisms improve NLP task accuracy and interpretability, supporting AI applications that require nuanced language understanding.
For users, outputs appear contextually precise and coherent. The irony is that focus emerges from weighted statistical calculations, not conscious attention.
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