DeepL Translator – AI Assistance for Language

On a slight tangent, are there any fully-trained ready-to-work state-of-the-art open source distributions of translation systems available?

I've never been able to find one, but maybe I just haven't looked hard enough.

Not that I know of, but the source code is available to train a Transformer model in a single day.

https://github.com/tensorflow/tensor2tensor#walkthrough

I think Mozilla and Safari should be interested in having local translation for better privacy and speed.

It probably does well when translating general human speech, but I was rather interested in its performance on a paragraph of a scientific text (it being a much more challenging task, obviously).

* "The impact of the solar wind protons on the surface of Mercury" became "Der Einfluss der solaren Windprotonen auf die Oberfläche von Quecksilber". Note that 'solar wind protons' should have been translated as 'Sonnenwindprotonen' instead, i.e. the word 'solar' was to be a part of the noun's modifier, but it was pushed out.

* The lack of domain-specific training is especially obvious with the case of the planet's name being translated as "Quecksilber" instead of "Merkur" (Quecksilber being the name of the metal).

* "pure northward interplanetary magnetic field (IMF)" became "reines interplanetares interplanetares Magnetfeld nach Norden (IWF)". Aside from this being a poor translation, it's worth noting that DeepL didn't properly process the introduction of an abbreviation (IWF being the abbrev. for the International Monetary Fund in German).

Yes there are a number of models, but since the quality of the results depends a lot on the training data as well, which I wouldn't know how to find or evaluate, and possibly might require tweaking algorithms for different languages, which I wouldn't know how to do, it's not really 'usable' (for me).

I figured someone would have gone to the trouble of combining models with a maintained collection of datasets to produce an open source alternative to Google Translate by now. I've been wondering that for years and it never seems to happen. Not saying anyone should feel obligated - I'm just curious why we don't see this, when we see so many other open source software projects that are competetive with their commercial alternatives.

Is it difficult/expensive to acquire these datasets? Is it a lot of effort to actually fine-tune the algorithms to reach passable results?

It seems (without knowing the details myself) that the state of the art in actually usable machine translation tools is always locked up in commercial IP, even though it feels (at least to me) like something that should be a free public service and therefore an ideal candidate for the 'open source' treatment.