OpenNMT is a full-featured, open-source (MIT) neural machine translation system utilizing the Torch mathematical toolkit.
The system is designed to be simple to use and easy to extend , while maintaining efficiency and state-of-the-art translation accuracy. Features include:
- Speed and memory optimizations for high-performance GPU training.
- Simple general-purpose interface, only requires and source/target data files.
- C++ implementation of the translator for easy deployment.
- Extensions to allow other sequence generation tasks such as summarization and image captioning.
OpenNMT only requires a vanilla Torch install with few dependencies. Alternatively there is a (CUDA) Docker container.
nnnngraphtdspenlight
GPU training requires:
cunncutorch
Multi-GPU training additionally requires:
threads
OpenNMT consists of three commands:
- Preprocess the data.
th preprocess.lua -train_src data/src-train.txt -train_tgt data/tgt-train.txt -valid_src data/src-val.txt -valid_tgt data/tgt-val.txt -save_data data/demo
- Train the model.
th train.lua -data data/demo-train.t7 -save_model model
- Translate sentences.
th translate.lua -model model_final.t7 -src data/src-test.txt -output pred.txt
See the guide for more details.
A technical report on OpenNMT is available. If you use the system for academic work, please cite:
@ARTICLE{2017opennmt,
author = { {Klein}, G. and {Kim}, Y. and {Deng}, Y.
and {Senellart}, J. and {Rush}, A.~M.},
title = "{OpenNMT: Open-Source Toolkit
for Neural Machine Translation}",
journal = {ArXiv e-prints},
eprint = {1701.02810} }