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111 changes: 69 additions & 42 deletions FAQ.md
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---
layout: page
title: FAQ
title: Frequently asked questions
order: 4
---

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## General

### Why neural machine translation (NMT)?
### Why should I use OpenNMT?

We care about neural machine translation for several reasons.
OpenNMT has been successfully used in [many research and industry applications](/publications). It is now a robust and complete ecosystem for machine translation.

1) Results show that NMT produces automatic translations that are
significantly preferred by humans to other machine translation
outputs. This has led several companies to switch NMT-based
translation systems.
Here are some reasons to use OpenNMT:

2) Similar methods (often called seq2seq) are also effective for many
other NLP and language-related applications such as dialogue, image
captioning, and summarization. A recent talk from HarvardNLP describing some of these recent
advances is available <a
href="https://harvardnlp.github.io/seq2seq-talk/slidescmu.pdf">here</a>.
* The 2 supported implementations, [OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) and [OpenNMT-tf](https://github.com/OpenNMT/OpenNMT-tf), give the choice between [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/) which are 2 of the most popular deep learning toolkits.
* The implementations are packed with many configurable models and features including for other generation tasks: summarization, speech to text, image to text, etc.
* The [OpenNMT ecosystem](https://github.com/OpenNMT) includes tools and projects to cover the full NMT workflow: from advanced tokenization to training automation.
* The NMT systems are able to reach high translation quality and speed, on par with the best online translation offerings.
* You get reactive and free support on the [community forum](http://forum.opennmt.net/).

3) NMT has been used as a representative application of the recent
success of deep learning-based artificial intelligence. For instance a
recent <a href="http://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html">NYT
magazine cover story</a> focused on Google's NMT system.
### Who is behind OpenNMT?

OpenNMT is currently supported by the companies [SYSTRAN](http://www.systransoft.com/) and [Ubiqus](https://www.ubiqus.com/). The main maintainers are:

### Where can I go to learn about NMT?
* Guillaume Klein (SYSTRAN)
* Vincent Nguyen (Ubiqus)
* Jean Senellart (SYSTRAN)

We recommend starting with the ACL'16 NMT <a
href="https://sites.google.com/site/acl16nmt/home">tutorial</a>
produced by researchers at Stanford and NYU. The tutorial also
includes a detailed bibliography describing work in the field.
The project was initiated in December 2016 by the [Harvard NLP](https://nlp.seas.harvard.edu/) group and SYSTRAN.

### What do I need to train an NMT model?
### Which OpenNMT implementation should I use?

You just need two files: a source file and a target file. Each with
one sentence per line with words space separated. These files can come from
standard free translation corpora such a WMT, or it can be any other sources
you want to train from.
OpenNMT has 2 main implementations: [OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py) and [OpenNMT-tf](https://github.com/OpenNMT/OpenNMT-tf).

You first need to decide between [PyTorch](https://pytorch.org/) and [TensorFlow](https://www.tensorflow.org/). Both frameworks have strengths and weaknesses, see which one is more suited and easier for you to integrate.

## Installation / Setup
Then, each OpenNMT implementation has its own design and set of unique features. For example OpenNMT-py has better support for other tasks (summarization, speech, image) and is generally faster while OpenNMT-tf supports modular architectures and language modeling. See their respective GitHub repository for more details.

### How to cite the OpenNMT project?

If you are using the project for academic work, please cite the initial [system demonstration paper](https://www.aclweb.org/anthology/P17-4012):

```
@inproceedings{klein-etal-2017-opennmt,
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 = "https://www.aclweb.org/anthology/P17-4012",
pages = "67--72",
}
```

### Where can I get support?

The [OpenNMT forum](http://forum.opennmt.net/) is the place to go for asking general questions or requesting support. You can usually expect a reply within 1 or 2 work days.

For bugs, please report them on the GitHub directly.

### Where can I go to learn about NMT?

We recommend starting with the [ACL'16 NMT](https://sites.google.com/site/acl16nmt/home) produced by researchers at Stanford and NYU. The tutorial also includes a detailed bibliography describing work in the field.

## Requirements

### What do I need to train a NMT model?

You just need two files: a source file and a target file. Each with one sentence per line with words that are space separated. These files can come from standard free translation corpora such a WMT, or it can be any other sources you want to train from.

### What type of computer do I need to train with?

While in theory you can train on any machine; in practice for all but
trivally small data sets you will need a GPU that supports CUDA if you
want training to finish in a reasonable amount of time. For
medium-size models you will need at least 4GB; for full-size
state-of-the-art models 8-12GB is recommend.
While in theory you can train on any machine, in practice for all but trivally small data sets you will need a GPU that supports CUDA if you want training to finish in a reasonable amount of time.

## Models
We recommend a GPU with at least 8GB of memory.

### How can I replicate the full-scale NMT translation results?
## Models

We have posted a complete <a href="http://forum.opennmt.net/t/training-english-german-wmt15-nmt-engine/29">tutorial</a> for training a German-to-English translation system on standard data.
### How can I replicate the full-scale NMT translation results?

We published a [baseline training script](https://github.com/OpenNMT/OpenNMT-tf/tree/master/scripts/wmt) to train a robust German-English translation model using OpenNMT-tf. If you are using OpenNMT-py, you can apply the same preprocessing and train a Transformer model with the [recommended options](http://opennmt.net/OpenNMT-py/FAQ.html#how-do-i-use-the-transformer-model-do-you-support-multi-gpu).

### Are there pretrained translation models that I can try?

There are several different pretrained models available on the <a href="/Models">pretrained models</a> page for the Torch version.
There are several pretrained models available for [OpenNMT-py](/Models-py) and [OpenNMT-tf](/Models-tf).

### Where can I get training data for translation from X-to-X?
### Where can I get training data for translation?

Try the <a href="http://opus.lingfil.uu.se/">OPUS</a> Project. An open-source collection of parallel corpora. After stripping XML tags, you should be able to use the raw files directly in OpenNMT.
Try the [OPUS](http://opus.lingfil.uu.se/) project. An open-source collection of parallel corpora. After stripping XML tags, you should be able to use the raw files directly in OpenNMT.

### I am interested in other seq2seq-like problems such as summarization, dialogue, tree-generation. Can OpenNMT work for these?

Yes. OpenNMT is a general-purpose attention-based seq2seq system. There is very little code that is translation specific, and so it should be effective for many of these applications.
Yes. OpenNMT is a general-purpose attention-based seq2seq system. There is very little code that is translation specific, and so it should be effective for many of these applications.

For the case of summarization, OpenNMT has been shown to be more effective than neural systems like <a href="https://github.com/facebook/NAMAS">NAMAS</a>, and will be supported going forward. See the <a href="/Models">models</a> page for a pretrained summarization system on the Gigaword dataset.
For the case of summarization, OpenNMT has been shown to be more effective than neural systems like [NAMAS](https://github.com/facebook/NAMAS), and will be supported going forward. See the [OpenNMT-py models](/Models-py) page for a pretrained summarization system on the Gigaword dataset.

### I am interested in variants of seq2seq such as image-to-sequence generation. Can OpenNMT work for these?
### I am interested in variants of seq2seq such as image-to-text generation. Can OpenNMT work for these?

Yes. As an example, we have implemented a relatively general-purpose <a href="http://github.com/OpenNMT/im2text">im2text</a> system, with a small amount of additional code. Feel free to use this as a model for extending OpenNMT.
Yes. OpenNMT-py includes a relatively general-purpose [im2text](http://opennmt.net/OpenNMT-py/im2text.html) system, with a small amount of additional code. Feel free to use this as a model for extending OpenNMT.
6 changes: 3 additions & 3 deletions LICENSE.md → LICENSE
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# Released under MIT License
MIT License

Copyright (c) 2013 Mark Otto.
Copyright (c) 2019 The OpenNMT authors.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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