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deshanksuman/
swabhashambart50SinhalaTransliteration

Name: swabhashambart50SinhalaTransliteration
Availability: InStock

deshanksuman/Augmented_SinhalatoRomanizedSinhala_Dataset

2507.09245

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Swa bhasha mBART-50 Sinhala Transliteration Model

This model transliterates Romanized Sinhala text to Sinhala script.

Model description

This is a fine-tuned version of deshanksuman/mbart_50_SinhalaTransliteration specialized for Sinhala transliteration. It converts romanized Sinhala (using Latin characters) to proper Sinhala script. Due to the training limitation only 2/3 of data was utilized for the training purposes.

Intended uses & limitations

This model is intended for transliterating Romanized Sinhala text to proper Sinhala script. It can be useful for:

Text input conversion in applications
Helping non-native speakers type in Sinhala
Converting legacy text in romanized format to proper Sinhala

Acknowledgement

We acknowledge the support of the Supercomputing Wales project, which is part-funded by the European Regional Development Fund (ERDF) via Welsh Government.

How to use

python

from transformers import MBartForConditionalGeneration, MBartTokenizer

# Load model and tokenizer
model_name = "deshanksuman/swabhashambart50SinhalaTransliteration"
tokenizer = MBartTokenizer.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)

# Set language codes
tokenizer.src_lang = "en_XX"  # Using English as source language token
tokenizer.tgt_lang = "si_LK"  # Sinhala as target

# Prepare input
text = "mama oyata adare karanawa"
inputs = tokenizer(text, return_tensors="pt", max_length=128, padding="max_length", truncation=True)

# Generate output
outputs = model.generate(
    input_ids=inputs["input_ids"],
    attention_mask=inputs["attention_mask"],
    max_length=128,
    num_beams=5,
    early_stopping=True
)

# Decode output
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

Training data

The model was trained on the deshanksuman/Augmented_SinhalatoRomanizedSinhala_Dataset dataset, which contains pairs of Romanized Sinhala and corresponding Sinhala script text.

Training procedure

The model was trained with the following parameters:

Learning rate: 5e-05
Batch size: 32
Number of epochs: 1
Max sequence length: 128
Optimizer: AdamW

Model Performance

The model achieves an accuracy of 72.00% on the test set.

Examples:

Example 1:

Romanized: karandeniya em
Expected: කරන්දෙණිය එම්
Predicted: කරන්දෙණිය එම්
Correct: True

Example 2:

Romanized: yatawena minissu hoya ganna beriwa mihidan wenawa
Expected: යටවෙන මිනිස්සු හොයා ගන්න බැරිව මිහිදන් වෙනවා
Predicted: යටවෙන මිනිස්සු හොයා ගන්න බැරිව මිහිදන් වෙනවා
Correct: True

Citation

bibtex

@article{sumanathilaka2025swa,
  title={Swa-bhasha Resource Hub: Romanized Sinhala to Sinhala Transliteration Systems and Data Resources},
  author={Sumanathilaka, Deshan and Perera, Sameera and Dharmasiri, Sachithya and Athukorala, Maneesha and Herath, Anuja Dilrukshi and Dias, Rukshan and Gamage, Pasindu and Weerasinghe, Ruvan and Priyadarshana, YHPP},
  journal={arXiv preprint arXiv:2507.09245},
  year={2025}
}