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dhivehi-byt5-thaana2latin-v1
Neobe/dhivehi-byt5-thaana2latin-v1
dhivehi-byt5-thaana2latin-v1 is a machine learning model from Neobe. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This model is a specialized Thaana-to-Latin transliteration model optimized for Maldivian news media.
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At a glance
- License
- apache-2.0
- Model type
- t5
- Access
- Public
- Created
- Dec 29, 2025
- Updated
- Feb 7, 2026
- SHA
- fea6b74b
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Model card
From the Hugging Face model README
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
- Base Model: google/byt5-small
- General Fine-tuning: Tuned on the alakxender/dhivehi-transliteration-pairs dataset (~180k pairs) to learn general transliteration rules.
- 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
- Type
- t5
- License
- apache-2.0
- Languages
- dv
- Created
- Dec 29, 2025
- Updated
- Feb 7, 2026