turkish-tokenizer 0.1.2

High-performance Turkish tokenizer with Rust backend and Python wrapper

Turkish Tokenizer

A high-performance Turkish tokenizer with Rust backend and Python wrapper. This package combines linguistic rules with BPE (Byte Pair Encoding) for optimal tokenization of Turkish text.

🚀 Features

• High Performance: Rust backend for ultra-fast tokenization
• Linguistic Rules: Root and suffix matching based on Turkish morphology
• BPE Support: Byte Pair Encoding for unknown words
• Special Tokens: Handles spaces, newlines, tabs, and case sensitivity
• Decoding: Convert token IDs back to readable text
• Easy Integration: Simple Python API and command-line interface

📦 Installation

From PyPI (Recommended)

pip install turkish-tokenizer

From Source

git clone https://github.com/malibayram/turkish-tokenizer.git
cd turkish-tokenizer
pip install -e .

Prerequisites

• Python 3.8 or higher
• Rust (for building the backend): Install from rustup.rs

🔧 Quick Start

Python API

from turkish_tokenizer import TurkishTokenizer

# Initialize the tokenizer
tokenizer = TurkishTokenizer()

# Tokenize text
text = "Merhaba dünya! Bu bir test cümlesidir."
tokens, token_ids = tokenizer.tokenize(text)

print("Tokens:", tokens)
print("Token IDs:", token_ids)

# Decode back to text
decoded_text = tokenizer.decode(token_ids)
print("Decoded:", decoded_text)

Command Line

# Tokenize text
turkish-tokenizer "Merhaba dünya!"

# Tokenize from file
turkish-tokenizer -f input.txt

# Save output to file
turkish-tokenizer "Merhaba dünya!" -o output.json

# Build Rust backend
turkish-tokenizer --build

# Check if Rust backend is available
turkish-tokenizer --check

📚 API Reference

TurkishTokenizer Class

__init__()

Initialize the tokenizer. No parameters required.

tokenize(text: str) -> Tuple[List[str], List[int]]

Tokenize the input text.

Parameters:

• text (str): The text to tokenize

Returns:

• tokens (List[str]): List of token strings
• token_ids (List[int]): List of token IDs

decode(token_ids: List[int]) -> str

Decode a list of token IDs back to text.

Parameters:

• token_ids (List[int]): List of token IDs to decode

Returns:

• str: The decoded text

tokenize_batch(texts: List[str]) -> List[Tuple[List[str], List[int]]]

Tokenize a batch of texts.

Parameters:

• texts (List[str]): List of texts to tokenize

Returns:

• List of tuples, each containing (tokens, token_ids)

get_vocab_size() -> int

Get the vocabulary size.

Returns:

• int: Total number of tokens in the vocabulary

get_special_tokens() -> Dict[str, int]

Get the special tokens and their IDs.

Returns:

• Dict[str, int]: Dictionary mapping special token names to their IDs

Convenience Functions

tokenize(text: str) -> Tuple[List[str], List[int]]

Quick tokenization function for simple use cases.

from turkish_tokenizer import tokenize

tokens, ids = tokenize("Merhaba dünya!")

🛠️ Development

Setup Development Environment

git clone https://github.com/malibayram/turkish-tokenizer.git
cd turkish-tokenizer
pip install -e ".[dev]"

Running Tests

pytest

Code Formatting

black turkish_tokenizer/
isort turkish_tokenizer/

Type Checking

mypy turkish_tokenizer/

📦 Building and Publishing

Building the Package

# Build source distribution
python -m build --sdist

# Build wheel
python -m build --wheel

# Build both
python -m build

Publishing to PyPI

1. Register on PyPI (if you haven't already):

   pip install twine
   

2. Upload to Test PyPI first:

   twine upload --repository testpypi dist/*
   

3. Upload to PyPI:

   twine upload dist/*
   

Publishing to Test PyPI

# Upload to Test PyPI
twine upload --repository testpypi dist/*

# Install from Test PyPI
pip install --index-url https://test.pypi.org/simple/ turkish-tokenizer

🔍 How It Works

The tokenizer uses a multi-stage approach:

1. Special Token Detection: Identifies spaces, newlines, tabs, and case changes
2. Root Matching: Matches words against a comprehensive Turkish root dictionary
3. Suffix Matching: Applies Turkish morphological rules for suffixes
4. BPE Tokenization: Uses Byte Pair Encoding for unknown words
5. Decoding: Applies reverse transformations to reconstruct text

Token Types

• Roots: Base Turkish words (e.g., "kitab", "defter")
• Suffixes: Turkish grammatical suffixes (e.g., "ler", "i", "nin")
• BPE Tokens: Subword units for unknown words
• Special Tokens: <space>, <newline>, <tab>, <uppercase>, <unknown>

📊 Performance

Method	Speed	Time for 1K words
Python (pure)	~100 words/sec	~10 seconds
Rust backend	~10K words/sec	~0.1 seconds

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Turkish linguistic resources and vocabulary
• Rust programming language and ecosystem
• Python packaging community

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

🔄 Changelog

0.1.2 (2024-01-XX)

• Added GitHub repository and proper project metadata
• Updated author and maintainer information
• Fixed security issues (removed API tokens from git history)
• Improved documentation and README
• Added proper .gitignore configuration

0.1.1 (2024-01-XX)

• Updated package metadata with correct GitHub URLs
• Fixed author and maintainer information

0.1.0 (2024-01-XX)

• Initial release
• Python API with Rust backend
• Command-line interface
• Comprehensive Turkish tokenization
• Decoding functionality