I'm currently comparing various pre-trained NMT models and can't help but wonder what the difference between MarianMT and OpusMT is. According to OpusMT's Github it is based on MarianMT. However in the Huggingface transformers implementation all pretrained MarianMT models start with "Helsinki-NLP/opus-mt". So I thought it was the same, but even though they're roughly the same size, they yield different translation results.
If someone could please shed some light on what the differences are I would be very thankful.
Marian is an open-source tool for training and serving neural machine translation, mostly developed at the University of Edinburgh, Adam Mickiewicz University in Poznań and at Microsoft. It is implemented in C++ and is heavily optimized for MT, unlike PyTorch-based Huggingface Transformers that aim for generality rather than efficiency in a specific use case.
The NLP group at the University of Helsinki trained many translation models using Marian on parallel data collected at Opus, and open-sourced those models. Later, they also did a conversion of the trained model into Huggingface Transformers and made them available via the Huggingface Hub.
MarianMT is a class in Huggingface Transformers for imported Marian models. You can train a model in Marian and convert it yourself. OpusMT models are Marian models trained on the Opus data in Helsinki converted to the PyTorch models. If you search the Huggingface Hub for Marian, you will find other MarianMT models than those from Helsinki.