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<p class="kicker">07 &middot; EXPERIMENTS</p>
<h1>Training Setup</h1>
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<p class="ct">Data &amp; Batching</p>
<ul>
<li><b>WMT 2014 English&ndash;German</b>: 4.5M sentence pairs; <b>English&ndash;French</b>: 36M sentences.</li>
<li>Byte-pair encoding with a <b>shared source&ndash;target vocabulary of ~37K tokens</b>.</li>
<li>Batches of ~25,000 source and ~25,000 target tokens, grouped by approximate sequence length.</li>
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<p class="ct">Optimization &amp; Regularization</p>
<ul>
<li>Adam (&beta;&#x2081; = 0.9, &beta;&#x2082; = 0.98, &epsilon; = 10&#x207B;&#x2079;) with <b>warmup</b>: 4,000 linearly increasing steps, then inverse-square-root decay.</li>
<li>Residual dropout 0.1 (base) / 0.3 (big); attention dropout on each sub-layer.</li>
<li>Label smoothing &epsilon;_ls = 0.1 &mdash; hurts perplexity but <b>improves accuracy and BLEU</b>.</li>
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<p><b>Hardware &amp; time:</b> 8 &times; NVIDIA P100 GPUs &mdash; base model: 12 hours (100K steps); big model: 3.5 days (300K steps). Beam search with beam size 4, length penalty &alpha; = 0.6.</p>
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<p class="foot">Source: &sect;5 Training (paper p. 7); &sect;6.1 (paper p. 8).</p>
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