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Open Source Neural Machine Translation in PyTorch

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OpenNMT-py: Open-Source Neural Machine Translation

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OpenNMT-py is the PyTorch version of the OpenNMT project, an open-source (MIT) neural machine translation framework. It is designed to be research friendly to try out new ideas in translation, summary, morphology, and many other domains. Some companies have proven the code to be production ready.

We love contributions! Please look at issues marked with the contributions welcome tag.

Before raising an issue, make sure you read the requirements and the documentation examples.

Unless there is a bug, please use the forum or Gitter to ask questions.

Announcement - OpenNMT-py 2.0

We're happy to announce the upcoming release v2.0 of OpenNMT-py.

The major idea behind this release is the -- almost -- complete makeover of the data loading pipeline. A new 'dynamic' paradigm is introduced, allowing to apply on the fly transforms to the data.

This has a few advantages, amongst which:

  • remove or drastically reduce the preprocessing required to train a model;
  • increase the possibilities of data augmentation and manipulation through on-the fly transforms.

These transforms can be specific tokenization methods, filters, noising, or any custom transform users may want to implement. Custom transform implementation is quite straightforward thanks to the existing base class and example implementations.

You can check out how to use this new data loading pipeline in the updated docs.

All the readily available transforms are described here.


Given sufficient CPU resources according to GPU computing power, most of the transforms should not slow the training down. (Note: for now, one producer process per GPU is spawned -- meaning you would ideally need 2N CPU threads for N GPUs).

Breaking changes

For now, the new data loading paradigm does not support Audio, Video and Image inputs.

A few features are also dropped, at least for now:

  • audio, image and video inputs;
  • source word features.

For any user that still need these features, the previous codebase will be retained as

in a separate branch. It will no longer receive extensive development from the core team but PRs may still be accepted.

Feel free to check it out and let us know what you think of the new paradigm!

Table of Contents


OpenNMT-py requires:

  • Python >= 3.6
  • PyTorch == 1.6.0


pip install OpenNMT-py

or from the sources:

git clone
cd OpenNMT-py
pip install -e .

Note: if you encounter a

during installation, try to use

(Optional) Some advanced features (e.g. working pretrained models or specific transforms) require extra packages, you can install them with:

pip install -r requirements.opt.txt



Full Documentation

Step 1: Prepare the data

To get started, we propose to download a toy English-German dataset for machine translation containing 10k tokenized sentences:

tar xf toy-ende.tar.gz
cd toy-ende

The data consists of parallel source (

) and target (
) data containing one sentence per line with tokens separated by a space:
  • src-train.txt
  • tgt-train.txt
  • src-val.txt
  • tgt-val.txt

Validation files are used to evaluate the convergence of the training. It usually contains no more than 5k sentences.

$ head -n 3 toy-ende/src-train.txt
It is not acceptable that , with the help of the national bureaucracies , Parliament 's legislative prerogative should be made null and void by means of implementing provisions whose content , purpose and extent are not laid down in advance .
Federal Master Trainer and Senior Instructor of the Italian Federation of Aerobic Fitness , Group Fitness , Postural Gym , Stretching and Pilates; from 2004 , he has been collaborating with Antiche Terme as personal Trainer and Instructor of Stretching , Pilates and Postural Gym .
" Two soldiers came up to me and told me that if I refuse to sleep with them , they will kill me . They beat me and ripped my clothes .

We need to build a YAML configuration file to specify the data that will be used:

# toy_en_de.yaml

Where the samples will be written

save_data: toy-ende/run/example

Where the vocab(s) will be written

src_vocab: toy-ende/run/example.vocab.src tgt_vocab: toy-ende/run/example.vocab.tgt

Prevent overwriting existing files in the folder

overwrite: False

Corpus opts:

data: corpus_1: path_src: toy-ende/src-train.txt path_tgt: toy-ende/tgt-train.txt valid: path_src: toy-ende/src-val.txt path_tgt: toy-ende/tgt-val.txt ...

From this configuration, we can build the vocab(s) that will be necessary to train the model:

onmt_build_vocab -config toy_en_de.yaml -n_sample 10000

Notes: -

is required here -- it represents the number of lines sampled from each corpus to build the vocab. - This configuration is the simplest possible, without any tokenization or other transforms. See other example configurations for more complex pipelines.

Step 2: Train the model

To train a model, we need to add the following to the YAML configuration file: - the vocabulary path(s) that will be used: can be that generated by onmtbuildvocab; - training specific parameters.

# toy_en_de.yaml


Vocabulary files that were just created

src_vocab: toy-ende/run/example.vocab.src tgt_vocab: toy-ende/run/example.vocab.tgt

Train on a single GPU

world_size: 1 gpu_ranks: [0]

Where to save the checkpoints

save_model: toy-ende/run/model save_checkpoint_steps: 500 train_steps: 1000 valid_steps: 500

Then you can simply run:

onmt_train -config toy_en_de.yaml

This configuration will run the default model, which consists of a 2-layer LSTM with 500 hidden units on both the encoder and decoder. It will run on a single GPU (

world_size 1
gpu_ranks [0]

Before the training process actually starts, the

together with
will be dumpped to
with configurations specified in
yaml file. We'll also generate transformed samples to simplify any potentially required visual inspection. The number of sample lines to dump per corpus is set with the

For more advanded models and parameters, see other example configurations or the FAQ.

Step 3: Translate

onmt_translate -model toy-ende/run/ -src toy-ende/src-test.txt -output toy-ende/pred_1000.txt -gpu 0 -verbose

Now you have a model which you can use to predict on new data. We do this by running beam search. This will output predictions into



The predictions are going to be quite terrible, as the demo dataset is small. Try running on some larger datasets! For example you can download millions of parallel sentences for translation or summarization.

(Optional) Step 4: Release

When you are satisfied with your trained model, you can release it for inference. The release process will remove training-only parameters from the checkpoint:

onmt_release_model -model toy-ende/run/ -output toy-ende/run/

The release script can also export checkpoints to CTranslate2, a fast inference engine for Transformer models. See the

command line option.

Alternative: Run on FloydHub

Run on FloydHub

Click this button to open a Workspace on FloydHub for training/testing your code.

Pretrained embeddings (e.g. GloVe)

Please see the FAQ: How to use GloVe pre-trained embeddings in OpenNMT-py

Pretrained models

Several pretrained models can be downloaded and used with



OpenNMT-py is run as a collaborative open-source project. The original code was written by Adam Lerer (NYC) to reproduce OpenNMT-Lua using PyTorch.

Major contributors are: * Sasha Rush (Cambridge, MA) * Vincent Nguyen (Ubiqus) * Ben Peters (Lisbon) * Sebastian Gehrmann (Harvard NLP) * Yuntian Deng (Harvard NLP) * Guillaume Klein (Systran) * Paul Tardy (Ubiqus / Lium) * François Hernandez (Ubiqus) * Linxiao Zeng (Ubiqus) * Jianyu Zhan (Shanghai) * Dylan Flaute (University of Dayton) * ... and more!

OpenNMT-py is part of the OpenNMT project.


If you are using OpenNMT-py for academic work, please cite the initial system demonstration paper published in ACL 2017:

    title = "{O}pen{NMT}: Open-Source Toolkit for Neural Machine Translation",
    author = "Klein, Guillaume  and
      Kim, Yoon  and
      Deng, Yuntian  and
      Senellart, Jean  and
      Rush, Alexander",
    booktitle = "Proceedings of {ACL} 2017, System Demonstrations",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "",
    pages = "67--72",

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