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Quickstart [Lua]


OpenNMT only requires a vanilla Torch install. It makes use of the libraries: nn, nngraph, cunn and bit32. We highly recommend use of GPU-based training.

Alternatively there is a Docker image available.


OpenNMT consists of three steps. (These steps assume you data is already tokenized. If not see the tokenization section below.)

1) 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

2) Train the model.

th train.lua -data data/demo-train.t7 -save_model model [-gpuid 1]

3) Translate sentences.

th translate.lua -model model_final.t7 -src data/src-val.txt -output file-tgt.tok [-gpuid 1]

Let’s walk through each of these commands in more detail.

Step 1: Preprocess 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

Here we are working with example data in data/ folder. The data consists of a source (src) and target (tgt) data. This will take the source/target train/valid files (src-train.txt, tgt-train.txt, src-val.txt, tgt-val.txt).

Get the full options list on the features page.

Es geht nicht an , dass über Ausführungsbestimmungen , deren Inhalt , Zweck und Ausmaß vorher nicht bestimmt ist , zusammen mit den nationalen Bürokratien das Gesetzgebungsrecht des Europäischen Parlaments ausgehebelt wird .
Meistertrainer und leitender Dozent des italienischen Fitnessverbands für Aerobic , Gruppenfitness , Haltungsgymnastik , Stretching und Pilates; arbeitet seit 2004 bei Antiche Terme als Personal Trainer und Lehrer für Stretching , Pilates und Rückengymnastik .
Also kam ich nach Südafrika " , erzählte eine Frau namens Grace dem Human Rights Watch-Mitarbeiter Gerry Simpson , der die Probleme der zimbabwischen Flüchtlinge in Südafrika untersucht .
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 .

After running the system will build the following files:

The *.dict files are needed to check vocabulary, or to preprocess data with fixed vocabularies. These files are simple human-readable dictionaries.

<blank> 1
<unk> 2
<s> 3
</s> 4
It 5
is 6
not 7
acceptable 8
that 9
, 10
with 11

Internally the system never touches the words themselves, but uses these indices.

Step 2: Train the model

th train.lua -data data/demo-train.t7 -save_model demo-model

The main train command is quite simple. Minimally it takes a data file and a save file. This will run the default model, which consists of a 2-layer LSTM with 500 hidden units on both the encoder/decoder. You can also add -gpuid 1 to use (say) GPU 1.

Get the full options list on the features page.

Step 3: Translate

th translate.lua -model demo-model_final.t7 -src data/src-val.txt -output file-tgt.tok [-gpuid 1]

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 file-tgt.tok. 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.

Get the full options list on the features page.


Our demo data comes pre-tokenized. If you data is not tokenized, we also provide a simple language-independent tokenizer/detokenizer that we have found to be effective for machine translation tasks. You can run the tokenizer with the following commands:

1) Tokenize your data (file is src-train.txt, tgt-train.txt, valid and test sets as well)

th tools/tokenize.lua -joiner_annotate < file > file.tok

The tokenize.lua script can also handle following options:

Get the full options list on the features page.

2) Detokenize the output.

th tools/detokenize.lua < file-tgt.tok > file-tgt.detok

The detokenize.lua script can also handle following options: