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MarianMT has a lot of pre-trained models from one language to another, here is one for Polish to Ukrainian https://huggingface.co/Helsinki-NLP/opus-mt-pl-uk ht

MarianMT has a lot of pre-trained models from one language to another, here is one for Polish to Ukrainian https://huggingface.co/Helsinki-NLP/opus-mt-pl-uk https://huggingface.co/Helsinki-NLP/opus-mt-pl-uk and English to Ukrainian https://huggingface.co/Helsinki-NLP/opus-mt-en-uk https://huggingface.co/Helsinki-NLP/opus-mt-en-uk You can test the models with the form input on the right-hand side And here's a site with docs and code examples for basic usage in your app https://huggingface.co/transformers/model_doc/marian.html https://huggingface.co/transformers/model_doc/marian.html You can also use Marian's training code on PyTorch with custom source and target texts.

drran 6y ago
Translation from just English language creates a lot of ambiguity. The idea is to use two or more input languages to reduce ambiguity. Currently, volunteers manually translate from English to all other languages. It's possible to use AI to help, but quality of translation is too low. Dumb Translation Memory database is much more helpful than AI translator. In the new system, one volunteer will translate messages from English to his native language, while all others will be able to use this translation as the additional constraint, to improve quality of translation to their native language. More input languages -> more constraints -> better quality of translation. My best idea, so far, is to use a multilingual tokenizer (SpaCy looks good) and a linear transformer, because linear transformers are able to accept large inputs, with thousands of tokens. IMHO, I can input multiple translations of messages (they are short), and define loss as expected Ukrainian translation. However, I'm completely new to this field. I completed just one AI project so far: recognition of animals at video. I have no idea how to start. For example, I have no idea how to hint transformer that input messages are the same, just in 4 different languages. Should I interleave messages, like "Cannot Не можу open відкрити file файл : : ", or put them aside, via separator: "\0Cannot open file: \0Не можу відкрити файл: \0", or create few independent inputs? Or use a memory and improve quality of output message incrementally? Any hints where to start?

drran 6y ago

Translation from just English language creates a lot of ambiguity. The idea is to use two or more input languages to reduce ambiguity. Currently, volunteers manually translate from English to all other languages. It's possible to use AI to help, but quality of translation is too low. Dumb Translation Memory database is much more helpful than AI translator. In the new system, one volunteer will translate messages from English to his native language, while all others will be able to use this translation as the additional constraint, to improve quality of translation to their native language. More input languages -> more constraints -> better quality of translation. My best idea, so far, is to use a multilingual tokenizer (SpaCy looks good) and a linear transformer, because linear transformers are able to accept large inputs, with thousands of tokens. IMHO, I can input multiple translations of messages (they are short), and define loss as expected Ukrainian translation. However, I'm completely new to this field. I completed just one AI project so far: recognition of animals at video. I have no idea how to start. For example, I have no idea how to hint transformer that input messages are the same, just in 4 different languages. Should I interleave messages, like "Cannot Не можу open відкрити file файл : : ", or put them aside, via separator: "\0Cannot open file: \0Не можу відкрити файл: \0", or create few independent inputs? Or use a memory and improve quality of output message incrementally? Any hints where to start?