No, sorry for the confusion. I want to be able to type “kanun” or “qanoon”, and have it infer the Hindi word “कानून”, which is an indexed word. It is not neces

No, sorry for the confusion. I want to be able to type “kanun” or “qanoon”, and have it infer the Hindi word “कानून”, which is an indexed word. It is not necessary that there is a one to one correspondence between words. Sometimes two english words may represent one hindi word, or vice versa. I believe I can build up a decent sized training/validation set, for example from Bollywood song lyric databases written in English, and mapping them to the Hindi equivalent (or Tamil, Bengali, etc). As for your last question, I dont know, since I haver implemented an ML model in practice. I saw a tutorial on Bert this morning, where a word has 768 features. That itself sounds huge, leave alone the model itself.