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We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.
Answered from this document
Earlier models read a sentence in order, one word at a time, so each step waited on the last. This one drops that: every word looks at every other word at once. That is what the paper calls multi-head self-attention in §3.2.
Grounded in §1 and §3.2 — not from the model's memory.
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…dispensing with recurrence entirely.
Answer cites §3.2 · 94 words
Fact-checks the claim you highlighted against the live web and lists its sources. Highlight a heading or half a sentence and it says so, instead of inventing a verdict for something that isn’t a claim.
Checked against 3 sources
The 3.57% figure is the ensemble error, not single-model.
2 sources cite the single-model number instead.
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Verdict
Worth reading
4 substantive claims · ~11 min · covers ground you haven’t read
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