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https://github.com/google/seq2seq
A general-purpose encoder-decoder framework for Tensorflow
https://github.com/google/seq2seq
deeplearning machine-translation neural-network tensorflow translation
Last synced: about 1 month ago
JSON representation
A general-purpose encoder-decoder framework for Tensorflow
- Host: GitHub
- URL: https://github.com/google/seq2seq
- Owner: google
- License: apache-2.0
- Archived: true
- Created: 2017-03-02T22:49:20.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-10-15T08:00:39.000Z (about 4 years ago)
- Last Synced: 2024-09-23T07:33:08.886Z (about 2 months ago)
- Topics: deeplearning, machine-translation, neural-network, tensorflow, translation
- Language: Python
- Homepage: https://google.github.io/seq2seq/
- Size: 1.59 MB
- Stars: 5,607
- Watchers: 245
- Forks: 1,298
- Open Issues: 198
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
[![CircleCI](https://circleci.com/gh/google/seq2seq.svg?style=svg)](https://circleci.com/gh/google/seq2seq)
---
**[READ THE DOCUMENTATION](https://google.github.io/seq2seq)**
**[CONTRIBUTING](https://google.github.io/seq2seq/contributing/)**
---
A general-purpose encoder-decoder framework for Tensorflow that can be used for Machine Translation, Text Summarization, Conversational Modeling, Image Captioning, and more.
![Translation Model](https://3.bp.blogspot.com/-3Pbj_dvt0Vo/V-qe-Nl6P5I/AAAAAAAABQc/z0_6WtVWtvARtMk0i9_AtLeyyGyV6AI4wCLcB/s1600/nmt-model-fast.gif)
---
The official code used for the [Massive Exploration of Neural Machine Translation Architectures](https://arxiv.org/abs/1703.03906) paper.
If you use this code for academic purposes, please cite it as:
```
@ARTICLE{Britz:2017,
author = {{Britz}, Denny and {Goldie}, Anna and {Luong}, Thang and {Le}, Quoc},
title = "{Massive Exploration of Neural Machine Translation Architectures}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprinttype = {arxiv},
eprint = {1703.03906},
primaryClass = "cs.CL",
keywords = {Computer Science - Computation and Language},
year = 2017,
month = mar,
}
```This is not an official Google product.