README.md

---
tags:
- translation,
- text-generation,
---

# Sinhala–English–Singlish Translation Dataset

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

---

## 📋 Table of Contents

1. [Dataset Overview](#dataset-overview)  
2. [Installation](#installation)  
3. [Quick Start](#quick-start)  
4. [Dataset Structure](#dataset-structure)  
5. [Usage Examples](#usage-examples)  
6. [Citation](#citation)  
7. [License](#license)  
8. [Credits](#credits)  

---

## 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](https://github.com/Programmer-RD-AI-Archive/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](LICENSE) file for details.