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<p class="kicker">03 &middot; METHOD</p>
<h1>Model Architecture: Stacked Self-Attention</h1>
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<li><b>Encoder&ndash;decoder</b> structure built from stacked self-attention and point-wise, fully connected layers (left and right halves of Figure 1).</li>
<li><b>Encoder</b>: N = 6 identical layers &mdash; each with multi-head self-attention, then a position-wise feed-forward network.</li>
<li><b>Decoder</b>: N = 6 layers; adds a <b>masked</b> multi-head self-attention sub-layer (positions only attend to earlier positions) plus attention over the encoder output.</li>
<li>A <b>residual connection</b> wraps every sub-layer, followed by <b>layer normalization</b>.</li>
<li>Inputs/outputs are embedded (d_model = 512) and combined with <b>positional encodings</b>, since the model has no notion of order on its own.</li>
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<p class="figcap">Encoder (left) and decoder (right) stacks.<br>Figure 1, paper p. 3.</p>
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<p class="foot">Visual: Figure 1, &ldquo;The Transformer - model architecture&rdquo; (paper p. 3); text: &sect;3.1 (paper p. 3).</p>
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