This model is a specialized Latin-to-Thaana transliteration model optimized for Maldives news media, but also capable of handling both formal journalistic text and casual social media typing.

It functions as a "Hybrid" model: it respects the casual spacing of chat messages while correctly applying formal grammatical rules (such as compounding verbs) when it detects a news context.

🧠 Training Strategy

Base Model: google/byt5-small
General Fine-tuning: Tuned on the alakxender/dhivehi-transliteration-pairs dataset (~150k pairs) to learn general phonetics and spelling.
Domain Adaptation: Further fine-tuned on a high-quality dataset of 10k News headlines to recognize formal entities and apply correct grammatical spacing for official terms.

📊 Performance Samples

Category	Latin Input	Model Output
Formal Grammar	`Raeesul jumhooriyya... thasdheegu kuravvaifi`	`ރައީސުލް ޖުމްހޫރިއްޔާ... ތަސްދީގުކުރައްވައިފި`
Casual / Chat	`Aharen miadhu varah ban'du hai`	`އަހަރެން މިއަދު ވަރަށް ބަނޑުހައި`
Official Titles	`Minister of Foreign Affairs Moosa Zameer`	`މިނިސްޓަރު އޮފް ފޮރިން އެފެއާސް މޫސަ ޒަމީރު`
News Phrasing	`Police service in vanee ekan kuhveri kohfa`	`ޕޮލިސް ސާވިސްއިން ވަނީ އެކަން ކުށްވެރިކޮށްފައި`

💻 Usage

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Neobe/dhivehi-byt5-latin2thaana-v1")
model = AutoModelForSeq2SeqLM.from_pretrained("Neobe/dhivehi-byt5-latin2thaana-v1")

text = "Raeesul jumhooriyya miadhu ganoonu thasdheegu kuravvaifi"
inputs = tokenizer(text, return_tensors="pt")

outputs = model.generate(**inputs, max_length=256)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Output: ރައީސުލް ޖުމްހޫރިއްޔާ މިއަދު ގާނޫނު ތަސްދީގުކުރައްވައިފި