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Programmer-RD-AI/sinhala-english-singlish-translation

Sinhala–English–Singlish Translation Dataset A parallel corpus of Sinhala sentences, their English translations, and romanized Sinhala (“Singlish”) transliterations. 📋 Table of Contents Dataset Overview Installation Quick Start Dataset Structure Usage Examples Citation License Credits Dataset Overview Description: 34,500 aligned triplets of Sinhala (native script) English (human translation) Singlish (romanized Sinhala)… See the full description on the dataset page: https://huggingface.co/datasets/Programmer-RD-AI/sinhala-english-singlish-translation.

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Sinhala–English–Singlish Translation Dataset

A parallel corpus of Sinhala sentences, their English translations, and romanized Sinhala (“Singlish”) transliterations.

Dataset Overview

—Description: 34,500 aligned triplets of
—Sinhala (native script)
—English (human translation)
—Singlish (romanized Sinhala)
—Source:
—📊 Kaggle dataset: programmerrdai/sinhala-english-singlish-translation-dataset
—🛠️ Collection pipeline: GitHub Sinenglish-LLM-Data-Collection
—DOI: 10.57967/hf/5605
—Released: 2025 (Revision c6560ff)
—License: MIT

Installation

bash

pip install datasets

Quick Start

python

from datasets import load_dataset

ds = load_dataset(
    "Programmer-RD-AI/sinhala-english-singlish-translation", 
    split="train"
)
print(ds[0])
# {
#   "sinhala": "මෙය මගේ ප්‍රධාන අයිතියයි",
#   "english": "This is my headright.",
#   "singlish": "meya mage pradhana ayithiyayi"
# }

Dataset Structure

Column	Type	Description
`sinhala`	`string`	Original sentence in Sinhala script
`english`	`string`	Corresponding English translation
`singlish`	`string`	Romanized (“Singlish”) transliteration

—Rows: 34,500
—Format: CSV (viewed as Parquet on HF)

Usage Examples

Load into Pandas

python

import pandas as pd
from datasets import load_dataset

df = load_dataset(
    "Programmer-RD-AI/sinhala-english-singlish-translation", 
    split="train"
).to_pandas()

print(df.head())

Fine-tuning a Translation Model

python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, Trainer, TrainingArguments

# 1. Tokenizer & model
tokenizer = AutoTokenizer.from_pretrained("t5-small")
model     = AutoModelForSeq2SeqLM.from_pretrained("t5-small")

# 2. Preprocess
def preprocess(ex):
    inputs  = "translate Sinhala to English: " + ex["sinhala"]
    targets = ex["english"]
    tokenized = tokenizer(inputs, text_target=targets, truncation=True)
    return tokenized

train_dataset = ds.map(preprocess, remove_columns=ds.column_names)
  
# 3. Training
args = TrainingArguments(
    output_dir="outputs",
    num_train_epochs=3,
    per_device_train_batch_size=16,
)
trainer = Trainer(
    model=model,
    args=args,
    train_dataset=train_dataset,
    tokenizer=tokenizer
)
trainer.train()

Citation

bibtex

@misc{ranuga_disansa_gamage_2025,
	author       = { Ranuga Disansa Gamage and Sasvidu Abesinghe and Sheneli Fernando and Thulana Vithanage },
	title        = { sinhala-english-singlish-translation (Revision b6bde25) },
	year         = 2025,
	url          = { https://huggingface.co/datasets/Programmer-RD-AI/sinhala-english-singlish-translation },
	doi          = { 10.57967/hf/5626 },
	publisher    = { Hugging Face }
}

License

This dataset is released under the CC License. See the LICENSE file for details.