Problem: Most popular Vietnamese input method is Telex, that require additional keypress to create marks and tones for Vietnamese syllable. E.g: tooi => tôi, nois => nói, tieengs => tiếng, Vieetj => Việt. It’s work just fine it you have are using laptop, desktop that have a physical QWERTY keyboard (typing with 8 fingers). But it’s slow and error-prone while using smartphones (virtual keyboard typing with 2 fingers) or featured-phones (using T9 with only 0-9 keys). By using ML to map the minimal Ascii version of syllables to utf-8 with full marks and tone syllables, I hope that we can improve the situation. Beside pre-train the model using big data from pre-defined text corpus, on-the-fly learning / adapting from the document user is typing also help to improve the accuracy since we tend to repeating the same terms, keywords or phrases …
Model: I’m quite new to the field so have no idea which model is best for Vietnamese in general. Please discuss.
Data: GitHub - binhvq/news-corpus: Corpus tiếng việt 4
around 18.6 GB of internet news/articles crawled from 130 Vietnamese online news websites.
Method: As you see from the example in the project title. We can treat the problem as a sub-problem of spelling correction (deletion only). I guest can follow below project proposal as a foundation for the final solution.

  
    
    
    Pre-training/fine-tuning Seq2Seq model for spelling and/or grammar correction in English Flax/JAX Projects
  
  
    Pre-training/fine-tuning Seq2Seq model for spelling and/or grammar correction
For this project, one can use a randomly initialized or a pre-trained BART/T5 model. 

Model
Pre-trained BART, T5 models can be found on the model hub. 

Datasets
The dataset for this model can be prepared as described in this blog post. 
One can make use OSCAR . The dataset is also available through the datasets library here: oscar · Datasets at Hugging Face. 

Available training scripts
As this will be a Seq2Seq mod…