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OpenNMT: Open-Source Neural Machine Translation with Torch Mathematical Toolkit

59 points by dragonsh 6y ago

heyitsguay 6y ago
Looks cool, but it hasn't been updated in a bit and as they note, the Torch framework is no longer maintained. There is a PyTorch alternative that appears more active: https://github.com/OpenNMT/OpenNMT-py https://github.com/OpenNMT/OpenNMT-py.
ai_ja_nai 6y ago
This is much more used, it's based on FAIRSeq Transformer: https://github.com/modernmt/modernmt https://github.com/modernmt/modernmt. Plus, it's a publicly funded project
srush 6y ago
Hi all, I'm one of the original authors of OpenNMT and an nlp prof, always nice to see it trending :). I work with Hugging Face (https://huggingface.co/ https://huggingface.co/) now, we do similar things for NLP generally as well as supporting NMT applications. Main Versions (supported by folks at systran): https://github.com/OpenNMT/OpenNMT-tf https://github.com/OpenNMT/OpenNMT-tf https://github.com/opennmt/opennmt-py https://github.com/opennmt/opennmt-py Happy to answer any questions about NLP, neural models, open-source ML.
- zitterbewegung 6y ago
  I noticed that your swift libraries will be ready so that refined attention based models can be put on device for iOS . Will you also support android also ?
sqrt17 6y ago
There are many high-performing machine translation toolkits that use PyTorch these days: - OpenNMT-py (OpenNMT, but Python!) - fairseq - JoeyNMT In addition, you can also try - tensor2tensor (Tensorflow) - Sockeye (MXNet / Gluon)
- totetsu 6y ago
  Are any of these any good at JP/EN? Google translate is a big thorn in my privacy behind.
  - sqrt17 6y ago
    You may be lucky - people from NTT have published a model based on JParaCrawl - a large JP/EN parallel corpus, which can be used together with fairseq http://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/ http://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/
j88439h84 6y ago
How many sentence examples do you need to train one of these? How much compute?
theblackcat1002 6y ago
As someone who has work with all three NLP toolkit: huggingface, openmt-py and fairseq. I always have trouble juggling through the heavy abstraction of openmt-py. For example in openmt-py you need to write fields, reader and raw datasets before you even load into their complex dataset class. Each item is heavily abstracted through several layers of classes. I understand this improve code reuse, but introduce a huge steep curve for newcomer. Huggingface approach on the other hand is slightly more "messy" [2] but easier to understand and add your own tweak. [1] https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/bin/preprocess.py#L296 https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/bin/p... [2] https://github.com/huggingface/transformers/tree/master/src/transformers https://github.com/huggingface/transformers/tree/master/src/...

59 points by dragonsh 6y ago

Looks cool, but it hasn't been updated in a bit and as they note, the Torch framework is no longer maintained. There is a PyTorch alternative that appears more active: https://github.com/OpenNMT/OpenNMT-py https://github.com/OpenNMT/OpenNMT-py.

ai_ja_nai 6y ago

This is much more used, it's based on FAIRSeq Transformer: https://github.com/modernmt/modernmt https://github.com/modernmt/modernmt. Plus, it's a publicly funded project

srush 6y ago

Hi all, I'm one of the original authors of OpenNMT and an nlp prof, always nice to see it trending :). I work with Hugging Face (https://huggingface.co/ https://huggingface.co/) now, we do similar things for NLP generally as well as supporting NMT applications. Main Versions (supported by folks at systran): https://github.com/OpenNMT/OpenNMT-tf https://github.com/OpenNMT/OpenNMT-tf https://github.com/opennmt/opennmt-py https://github.com/opennmt/opennmt-py Happy to answer any questions about NLP, neural models, open-source ML.

zitterbewegung 6y ago
I noticed that your swift libraries will be ready so that refined attention based models can be put on device for iOS . Will you also support android also ?

sqrt17 6y ago

There are many high-performing machine translation toolkits that use PyTorch these days: - OpenNMT-py (OpenNMT, but Python!) - fairseq - JoeyNMT In addition, you can also try - tensor2tensor (Tensorflow) - Sockeye (MXNet / Gluon)

totetsu 6y ago
Are any of these any good at JP/EN? Google translate is a big thorn in my privacy behind.
- sqrt17 6y ago
  You may be lucky - people from NTT have published a model based on JParaCrawl - a large JP/EN parallel corpus, which can be used together with fairseq http://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/ http://www.kecl.ntt.co.jp/icl/lirg/jparacrawl/

j88439h84 6y ago

How many sentence examples do you need to train one of these? How much compute?

theblackcat1002 6y ago

As someone who has work with all three NLP toolkit: huggingface, openmt-py and fairseq. I always have trouble juggling through the heavy abstraction of openmt-py. For example in openmt-py you need to write fields, reader and raw datasets before you even load into their complex dataset class. Each item is heavily abstracted through several layers of classes. I understand this improve code reuse, but introduce a huge steep curve for newcomer. Huggingface approach on the other hand is slightly more "messy" [2] but easier to understand and add your own tweak. [1] https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/bin/preprocess.py#L296 https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/bin/p... [2] https://github.com/huggingface/transformers/tree/master/src/transformers https://github.com/huggingface/transformers/tree/master/src/...