---
dataset_info:
  features:
  - name: conversations
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  splits:
  - name: train
    num_bytes: 513536088
    num_examples: 7122355
  download_size: 66753003
  dataset_size: 513536088
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
language:
- en
- si
tags:
- code
- ai
- singlish
- sinhala
- llm
size_categories:
- 1M<n<10M
---

## Dataset Summary

The **Singlish-to-Sinhala Dataset** is a large-scale dataset designed to facilitate the translation and understanding of Romanized Sinhala (Singlish) text. It consists of over 7 million rows of Romanized Sinhala phrases alongside their Sinhala script equivalents. This dataset is particularly useful for tasks such as:

- Translation from Singlish to Sinhala.
- Building and evaluating NLP models for low-resource languages.
- Fine-tuning conversational AI models.

This dataset is a reformatted version of the [Swabhasha Romanized Sinhala Dataset](https://huggingface.co/datasets/deshanksuman/Swabhasha_RomanizedSinhala_Dataset). The original dataset comprises over 7 million entries, each containing a Romanized Sinhala phrase and its corresponding Sinhala script equivalent, formatted as "Singlish/Sinhala." In the Singlish-to-Sinhala Dataset, each entry has been transformed into a conversational format to better support tasks such as training conversational AI models.

### Key Features
- **Dataset Name**: Singlish-to-Sinhala Dataset
- **Rows**: 7,122,355
- **Columns**: `conversations`
- **License**: None

## Dataset Structure

### Data Instances
Each instance in the dataset contains:

- `conversations`: A list of dictionaries with `content` and `role` keys.

Example:
```json
{
  "conversations": [
    {"content": "yoghurt", "role": "user"},
    {"content": "යෝගට්", "role": "assistant"}
  ]
}
```

### Dataset Statistics
- **Total Rows**: 7,122,355
- **Features**: One column (`conversations`)
- **Languages**: Romanized Sinhala (Singlish) and Sinhala Script

## Usage

### Loading the Dataset
The dataset can be easily loaded using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("mayurasandakalum/singlish-to-sinhala-dataset", split="train")
print(dataset)
```

### Dataset Exploration
The dataset includes samples in a conversational format. Here's how you can explore the first few rows:

```python
for i in range(5):
    print(dataset[i]["conversations"])
```

Output:
```
[
  {"content": "yoghurt", "role": "user"},
  {"content": "යෝගට්", "role": "assistant"}
]
```

### DataLoader Example
For large-scale processing, the dataset can be wrapped in a PyTorch DataLoader:

```python
from torch.utils.data import Dataset, DataLoader

class TextDataset(Dataset):
    def __init__(self, conversations):
        self.conversations = conversations

    def __len__(self):
        return len(self.conversations)

    def __getitem__(self, idx):
        return self.conversations[idx]

text_dataset = TextDataset(dataset["conversations"])
dataloader = DataLoader(text_dataset, batch_size=1024, num_workers=4)
```

### Model Training
The dataset is suitable for training conversational AI models. Here’s an example preprocessing pipeline for model input:

```python
def format_conversations(example):
    return {
        "input_text": example["conversations"][0]["content"],
        "target_text": example["conversations"][1]["content"]
    }

formatted_dataset = dataset.map(format_conversations)
```

## Dataset Creation

### Source
The dataset was derived from the [Swabhasha Romanized Sinhala Dataset](https://huggingface.co/datasets/deshanksuman/Swabhasha_RomanizedSinhala_Dataset) and reformatted to align in a conversational format. The original dataset comprises over 7 million entries, formatted as "Singlish/Sinhala," and this structured format enhances the dataset's applicability for machine translation, transliteration, and conversational AI tasks.

### Processing Steps
1. Loaded the raw dataset.
2. Converted each "Singlish/Sinhala" pair into a structured conversation format.
3. Batch processed using PyTorch DataLoader for scalability.
4. Uploaded to Hugging Face using the `push_to_hub` API.

### Citation
If you use this dataset, please cite it as follows:

```
@dataset{mayurasandakalum2025singlish,
  author = "Mayura Sandakalum",
  title = "Singlish-to-Sinhala Dataset",
  year = 2025,
  publisher = "Hugging Face",
  howpublished = "https://huggingface.co/datasets/mayurasandakalum/singlish-to-sinhala-dataset"
}
```

## License
This dataset does not have a specific license associated with it.