Datasets:
File size: 3,556 Bytes
ba4c215 a3440bc ba4c215 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 | ---
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. |