---
license: apache-2.0
datasets:
- Navanjana/sinhala-articles
language:
- si
pipeline_tag: token-classification
---

---

# Sinhala BPE Tokenizer

A Byte-Pair Encoding (BPE) tokenizer tailored for Sinhala text, designed for seamless integration with Hugging Face's Transformers library. Optimized for modern NLP tasks, it ensures efficient and accurate tokenization of Sinhala language data.

## 🔍 Overview

* **Tokenizer Type**: Byte-Pair Encoding (BPE)
* **Vocabulary Size**: 32,000 tokens
* **Training Dataset**: [Navanjana/sinhala-articles](https://huggingface.co/datasets/Navanjana/sinhala-articles)
* **Language**: Sinhala (සිංහල)
* **License**: Apache 2.0

## 🔧 Features

* Utilizes Unicode NFD normalization for accurate Sinhala character segmentation
* Employs whitespace and punctuation pre-tokenization for precise token boundaries
* Fully compatible with Hugging Face's `transformers` and `tokenizers` libraries
* Optimized for tasks like text generation, classification, and translation

## 🧹 Special Tokens

| Token    | Purpose                     |     |                           |
| -------- | --------------------------- | --- | ------------------------- |
| `<bos>`  | Beginning of sequence       |     |                           |
| `<eos>`  | End of sequence             |     |                           |
| `</s>`   | Separator token             |     |                           |
| `<unk>`  | Unknown token               |     |                           |
| `<pad>`  | Padding token               |     |                           |
| `[MASK]` | Masking token for MLM tasks |     |                           |
| \`<      | startoftext                 | >\` | Start of raw text segment |
| \`<      | endoftext                   | >\` | End of raw text segment   |

## 🚀 Getting Started

### ✨ Installation

```bash
pip install transformers tokenizers
```

### 🛠️ Usage

```python
from transformers import AutoTokenizer

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("your-username/sinhala-bpe-tokenizer")

# Sample text
text = "මේ සිංහල පාඨයක්"

# Tokenize
tokens = tokenizer.tokenize(text)
print("Tokens:", tokens)

# Encode
token_ids = tokenizer.encode(text)
print("Token IDs:", token_ids)

# Decode
decoded = tokenizer.decode(token_ids)
print("Decoded:", decoded)

# Encode with special tokens
with_special = tokenizer.encode(text, add_special_tokens=True)
print("With special tokens:", with_special)
```

### 🧠 Advanced Usage

```python
texts = [
    "ශ්‍රී ලංකාව අපේ රට",
    "අද හොඳ දිනයක්",
    "සිංහල භාෂාව ලස්සන"
]

# Batch encode
encoded = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
print(encoded)
```

## ⚙️ Training Specifications

* **Vocabulary Size**: 32,000
* **Minimum Frequency**: 2
* **Normalization**: NFD
* **Pre-tokenizer**: Whitespace and Punctuation
* **End-of-word marker**: `</w>`

### Configuration Example

```python
tokenizer_config = {
    "vocab_size": 32000,
    "min_frequency": 2,
    "normalizer": "NFD",
    "pre_tokenizer": ["Whitespace", "Punctuation"],
    "decoder": "BPEDecoder",
    "post_processor": "TemplateProcessing"
}
```

## 🌐 Use Cases

### NLP Tasks

* Language Modeling
* Text Generation
* Machine Translation
* Text Classification
* Named Entity Recognition (NER)

### Research

* Low-resource NLP
* Multilingual and cross-lingual model training

## 🎯 Example Tokenizations

```python
text = "මම සිංහල ඉගෙන ගන්නවා"
# Output: ['▁මම', '▁සිංහල', '▁ඉගෙන', '▁ගන්නවා']

text = "ශ්‍රී ලංකාවේ ඓතිහාසික නගරයක් වන අනුරාධපුරය"
# Output: ['▁ශ්‍රී', '▁ලංකාවේ', '▁ඓතිහාසික', '▁නගරයක්', '▁වන', '▁අනුරාධපුරය']

text = "2024 වර්ෂයේ ශ්‍රී ලංකාව"
# Output: ['▁2024', '▁වර්ෂයේ', '▁ශ්‍රී', '▁ලංකාව']
```

## 🧐 Model Integration

### GPT-style Models

```python
from transformers import GPT2LMHeadModel, GPT2Config

config = GPT2Config(vocab_size=tokenizer.vocab_size)
model = GPT2LMHeadModel(config)
```

### BERT-style Models

```python
from transformers import BertForMaskedLM, BertConfig

config = BertConfig(vocab_size=tokenizer.vocab_size)
model = BertForMaskedLM(config)
```

## 📊 Evaluation Metrics

| Metric              | Value  | Description                        |
| ------------------- | ------ | ---------------------------------- |
| Vocabulary Coverage | >95%   | Coverage of common Sinhala words   |
| Compression Ratio   | \~3.2  | Average characters per token       |
| Special Token Ratio | 0.025% | Ratio of special to regular tokens |
| OOV Rate            | <2%    | Out-of-vocabulary rate on test set |

## 🗒️ Version History

* **v1.0**: Initial release with 32K vocabulary

  * Trained on Navanjana/sinhala-articles dataset
  * Compatible with Transformers 4.0+

## 📚 Citation

If you use this tokenizer in your research, please cite:

```bibtex
@misc{sinhala-bpe-tokenizer-2025,
  title={Sinhala BPE Tokenizer: A Specialized Tokenizer for Sinhala Text Processing},
  author={Your Name},
  year={2025},
  url={https://huggingface.co/your-username/sinhala-bpe-tokenizer},
  note={Hugging Face Model Hub}
}
```

## 🤝 Contributing

Contributions are welcome! You can:

* Report issues with specific edge cases
* Suggest improvements or optimizations
* Submit evaluation results on downstream Sinhala NLP tasks
* Share model training results using this tokenizer

## 🌍 License

This tokenizer is released under the Apache 2.0 License. See the LICENSE file for details.

## 🙏 Acknowledgments

* Built using Hugging Face's Tokenizers library
* Trained on the Navanjana/sinhala-articles dataset
* Inspired by modern tokenization best practices
* Special thanks to the Sinhala NLP community

## 📬 Contact

For questions, issues, or collaboration:

* Open an issue in this repository
* Email: \[[your-email@example.com](mailto:your-email@example.com)]
* Twitter: \[@your-twitter-handle]