Tensor2Tensor for Neural Machine Translation

WS 2018 · Ashish Vaswani, Samy Bengio, Eugene Brevdo, Francois Chollet, Aidan N. Gomez, Stephan Gouws, Llion Jones, Łukasz Kaiser, Nal Kalchbrenner, Niki Parmar, Ryan Sepassi, Noam Shazeer, Jakob Uszkoreit ·

Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.

PDF Abstract WS 2018 PDF WS 2018 Abstract

Code

Add Remove Mark official

tensorflow/tensor2tensor official

↳ Quickstart in

Colab

14,883

inzva/fake-academic-paper-generation

artitw/tensor2tensor

↳ Quickstart in

Colab

gitboyzorro5/tensor2tensor

↳ Quickstart in

Colab

qijimrc/tensor2tensor

↳ Quickstart in

Colab

See all 14 implementations

Tasks

Add Remove

Machine Translation

Translation

Datasets

Add Datasets introduced or used in this paper

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods

Add Remove

Absolute Position Encodings • Adam • BPE • Dense Connections • Dropout • Label Smoothing • Layer Normalization • Linear Layer • Multi-Head Attention • Position-Wise Feed-Forward Layer • ReLU • Residual Connection • Scaled Dot-Product Attention • Softmax • Transformer

Edit Social Preview

Tensor2Tensor for Neural Machine Translation

Code Edit Add Remove Mark official

Tasks Edit Add Remove

Datasets Edit

Results from the Paper Edit

Methods Edit Add Remove

Code

Add Remove Mark official

Tasks

Add Remove

Datasets

Results from the Paper

Edit

Methods

Add Remove