Neobe/dhivehi-byt5-thaana2latin-v1

Dhivehi ByT5 News Transliteration (v1)

This model is a specialized Thaana-to-Latin transliteration model optimized for Maldivian news media.

It is designed to handle the complexities of journalistic text, focusing on the correct preservation of context, English loan words, and named entities (e.g., Country names, Acronyms like UN, Sports teams).

🧠 Training Strategy

1. Base Model: google/byt5-small
2. General Fine-tuning: Tuned on the alakxender/dhivehi-transliteration-pairs dataset (~180k pairs) to learn general transliteration rules.
3. Domain Adaptation: Further fine-tuned on a high-quality dataset of 10k News headlines to capture journalistic style and entities.

📊 Performance Samples

Category	Thaana Input	Model Output
Global Politics	ސީރިއާގެ ކަންކަމުގެ ތެރެއަށް އިސްރާއީލް ނުވަނުމަށް	Syria ge kankamuge thereah Israel nuvanumah
Acronyms	ރާއްޖޭގެ ރެކޯޑުތައް އދ އިން ބަލަނީ	Raajjeyge record thah UN in balanee
English Loan Words	ސްކޭމާއި ދެކޮޅަށް ހިންގި ޚާއްސަ އޮޕަރޭޝަނެއް	Scam aai dhekolhah hingi khaassa operation eh
Sports	ވެސްޓް ހޭމް އަތުން ޗެލްސީ އަށް މޮޅެއް	West Ham athun Chelsea ah molheh
Dates	އޮކްޓޯބަރ 3 ގެ މުޒާހަރާ	October 3 ge muzaaharaa

💻 Usage

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Neobe/dhivehi-byt5-thaana2latin-v1")
model = AutoModelForSeq2SeqLM.from_pretrained("Neobe/dhivehi-byt5-thaana2latin-v1")

text = "ސީރިއާގެ ކަންކަމުގެ ތެރެއަށް އިސްރާއީލް ނުވަނުމަށް ޓްރަމްޕް ގޮވާލައްވައިފި"
inputs = tokenizer(text, return_tensors="pt")

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

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# Output: Syria ge kankamuge thereah Israel nuvanumah Trump govaalavvaifi