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Original PDF
Attention Is All You Need
Ashish Vaswani · Noam Shazeer · Niki Parmar · Jakob Uszkoreit · Llion Jones · Aidan N. Gomez · Łukasz Kaiser · Illia Polosukhin Google Brain · Google Research · University of Toronto
Abstract
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.
Inputs Outputs N× N× Softmax Encoder Decoder
Figure 1: The Transformer — model architecture.
(1)
Scaled dot-product attention.
| Table 2: BLEU on newstest2014. | Model | EN–DE | EN–FR | | ------------------------------- | ----- | ----- | ----- | | ByteNet | 23.75 | — | | | GNMT + RL | 24.6 | 39.92 | | | Transformer (big) | 28.4 | 41.8 | |
Markdown output
\# Attention Is All You Need Ashish Vaswani · Noam Shazeer · Niki Parmar · Jakob Uszkoreit · Llion Jones · Aidan N. Gomez · Łukasz Kaiser · Illia Polosukhin Google Brain · Google Research · University of Toronto \## Abstract
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.
!\Figure 1: The Transformer — model architecture\
✓ Inputs Outputs N× N× Softmax Encoder Decoder
$$
✓
$$ Table 2: BLEU on newstest2014.
✓
| Model | EN–DE | EN–FR | | ----------------- | ----- | ----- | | ByteNet | 23.75 | — | | GNMT + RL | 24.6 | 39.92 | | Transformer (big) | 28.4 | 41.8 |
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| How it compares. | | pdfmarkdown.appTHIS TOOL | pdf2md.morethan.io | CloudConvert | MarkItDown | | ------------------------------ | ------------------------------ | ------------------------ | ------------------------------ | -------------------------- | ---------- | | Stays on your device | Yes | Yes in-browser | No uploads to a server | Yes local, but a CLI | | | Works without a command line | Yes | Yes | Yes | No Python library | | | Side-by-side with the original | Yes flags scanned / hard pages | No | No | No | | | Tables stay clean | Yes | Limited | Limited | Limited | | | Formulas (LaTeX / math) | Yes | No | No | Limited | | | Images extracted & placed | Yes | No | No | Limited | | | Scanned PDFs (OCR) | Yes on-device | No | Limited fails on rotated pages | Limited needs extra plugin | | | Encrypted PDFs | Yes | No | No | No | | | No signup | Yes | Yes | Limited limits / credits | Yes | | | Free | Yes | Yes | Limited | Yes | |
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