🤯 Did You Know (click to read)
Multi-head attention, an extension of self-attention, allows the model to attend to information from multiple representation subspaces simultaneously.
In self-attention, each token in the input generates query, key, and value vectors. Attention scores are computed by dot products of queries and keys, scaled and normalized via softmax. These scores weight the values to produce context-aware embeddings. This mechanism enables the model to capture long-range dependencies and relationships between words, even in long sentences, without sequential processing.
💥 Impact (click to read)
Self-attention enables Transformers to outperform recurrent models in translation, summarization, and text classification by capturing global context efficiently.
For AI practitioners, understanding self-attention provides insight into modern NLP architectures and helps in designing more interpretable and effective models.
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