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Tired in I suggest getting some sleep after all |
You're going away in morning |
Oh, you are it? Decided did to change your mind below a sudden? Oh, but I thought you were totally ready to set off |
You would dead set on that warranty |
You want to stay here |
With me, I'm so shocked that preposition How long would that be for that you want to stay with me? Forever, that is so kind of you |
Do you want to keep me company here |
For forever |
We don't have to be lonely anymore |
Do we |
Not to we have each other |
There's just such a shame |
That it had to take a left potion |
To bind and love together |
I was feeling slightly bad that I tricked you into taking this patient, but There was no other way if I told you the truth I it was see potion to make you fall love with me |
You wouldn't have taken and you would have missed out And that would have been a very big shape |
So I hope you're thankful for me doing that |
I don't think you mine this at home |
And now we have company |
Someone does stay with me and to talk to give me a love and affection |
It's all I have |
I'm going to look after you |
And carefully |
I hope you know of course, and protect you from everything and anything that tries to come between |
Q like you didn't end up in my part of the forest fastest asleep by accident |
No |
It is destiny |
It was meant to be |
Let's be honest |
You're out there just asking to be taken away by me |
Maybe one day |
I will tell you about what actually happened to this human |
But do you would just lucky enough to be so cute and catch me and my little struggle of companionship the past few |
Come here |
My dear |
You are looking very like |
I think I shall good you just sleep now |
We are together after all |
It wouldn't be weird if I joined you either |
Also, we have a very busy day tomorrow |
Going to create some memories together |
Teach you the way of the forest |
While doing lots of lovely, things on top of that of course |
I have a lot to learn about that |
I'm sure you can me |
Come on my dear |
Try to find the trousers |
Go to sleep It will make you feel |
When wake, you'll be feeling fit |
And then a energized to |
I love my dearAttention Is All You Need |
Ashish Vaswani∗ |
Google Brain |
avaswani@google.com |
Noam Shazeer∗ |
Google Brain |
noam@google.com |
Niki Parmar∗ |
Google Research |
nikip@google.com |
Jakob Uszkoreit∗ |
Google Research |
usz@google.com |
Llion Jones∗ |
Google Research |
llion@google.com |
Aidan N. Gomez∗ † |
University of Toronto |
aidan@cs.toronto.edu |
Łukasz Kaiser∗ |
Google Brain |
lukaszkaiser@google.com |
Illia Polosukhin∗ ‡ |
illia.polosukhin@gmail.com |
Abstract |
The dominant sequence transduction models are based on complex recurrent or |
convolutional neural networks that include an encoder and a decoder. The best |
performing models also connect the encoder and decoder through an attention |
mechanism. We propose a new simple network architecture, the Transformer, |
based solely on attention mechanisms, dispensing with recurrence and convolutions |
entirely. Experiments on two machine translation tasks show these models to |
be superior in quality while being more parallelizable and requiring significantly |
less time to train. Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including |
ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, |
our model establishes a new single-model state-of-the-art BLEU score of 41.8 after |
training for 3.5 days on eight GPUs, a small fraction of the training costs of the |
best models from the literature. We show that the Transformer generalizes well to |
other tasks by applying it successfully to English constituency parsing both with |
large and limited training data. |
1 Introduction |
Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks |
in particular, have been firmly established as state of the art approaches in sequence modeling and |
∗Equal contribution. Listing order is random. Jakob proposed replacing RNNs with self-attention and started |
the effort to evaluate this idea. Ashish, with Illia, designed and implemented the first Transformer models and |
has been crucially involved in every aspect of this work. Noam proposed scaled dot-product attention, multi-head |
attention and the parameter-free position representation and became the other person involved in nearly every |
detail. Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and |
tensor2tensor. Llion also experimented with novel model variants, was responsible for our initial codebase, and |
efficient inference and visualizations. Lukasz and Aidan spent countless long days designing various parts of and |
implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating |
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