---
license: mit
language:
- tt
tags:
- tokenizer
- tatar-language
- wordpiece
- unigram
- bpe
- bbpe
- huggingface
metrics:
- unknown_rate
- compression_ratio
- word_coverage
- tokens_per_second
---

# TatarTokenizer: Tokenizers for the Tatar Language

This repository contains a comprehensive collection of pre-trained tokenizers for the Tatar language. We provide **four different tokenization algorithms** (WordPiece, Unigram, BPE, and BBPE) with **multiple vocabulary sizes** (25k and 50k), trained on a large Tatar corpus. All tokenizers achieve **0% unknown rate** on test data and are ready to use with the `tokenizers` library or Hugging Face Transformers.

## 📦 Available Tokenizers

The following tokenizers are included:

| Tokenizer          | Type      | Vocab Size | Compression Ratio | Speed (tokens/sec) | Notes |
|--------------------|-----------|------------|-------------------|---------------------|-------|
| `wp_50k`           | WordPiece | 50,000     | 4.67              | 378,751             | Best overall balance |
| `wp_25k`           | WordPiece | 25,000     | 4.36              | **496,273**         | Fastest tokenizer |
| `uni_50k`          | Unigram   | 50,000     | 4.59              | 189,623             | Probabilistic model |
| `uni_25k`          | Unigram   | 25,000     | 4.30              | 260,403             | Good for smaller vocab |
| `bpe_50k`          | BPE       | 50,000     | 4.60              | 247,421             | Standard BPE |
| `bpe_50k_freq5`    | BPE       | 50,000     | 4.60              | 226,591             | Higher frequency threshold |
| `bbpe_50k`         | BBPE      | 50,000     | 4.60              | 227,322             | Byte-level BPE |
| `bbpe_25k`         | BBPE      | 25,000     | 4.28              | 257,104             | Compact byte-level |
| `bbpe_fixed_50k`   | BBPE*     | 50,000     | **5.17**          | 315,922             | Best compression ratio |
| `bpe_fixed_50k`    | BPE*      | 50,000     | 4.75              | 337,247             | Fast BPE variant |

\* *Fixed versions with improved Unicode handling*

**Key observations:**
- All tokenizers except `bpe_fixed_50k` achieve **0% unknown rate** on test data
- `bbpe_fixed_50k` offers the **best compression** (5.17 chars/token)
- `wp_25k` is the **fastest** (nearly 500k tokens/second)
- WordPiece models provide the most **human-readable tokens**

## 📁 Repository Structure

The files are organized in subdirectories for each tokenizer type and size:

```
TatarTokenizer/
├── tokenizers/
│   ├── wordpiece/
│   │   ├── 50k/          # wp_50k.json
│   │   └── 25k/          # wp_25k.json
│   ├── unigram/
│   │   ├── 50k/          # uni_50k.json
│   │   └── 25k/          # uni_25k.json
│   ├── bpe/
│   │   ├── 50k/          # bpe_50k.json
│   │   └── 50k_freq5/    # bpe_50k_freq5.json
│   ├── bbpe/
│   │   ├── 50k/          # bbpe_50k.json
│   │   └── 25k/          # bbpe_25k.json
│   ├── bpe_fixed/
│   │   └── 50k/          # bpe_fixed_50k.json
│   └── bbpe_fixed/
│       └── 50k/          # bbpe_fixed_50k.json
└── test_results/          # Evaluation reports and visualizations
    ├── tokenizer_test_report.csv
    ├── test_summary_*.txt
    ├── comparison_*.png
    ├── token_length_dist_*.png
    ├── correlation_*.png
    └── top10_score_*.png
```

Each tokenizer is saved as a single `.json` file compatible with the Hugging Face `tokenizers` library.

## 🚀 Usage

### Installation

First, install the required libraries:

```bash
pip install huggingface_hub tokenizers
```

### Load a Tokenizer

```python
from huggingface_hub import hf_hub_download
from tokenizers import Tokenizer

# Download and load the WordPiece 50k tokenizer
tokenizer_file = hf_hub_download(
    repo_id="TatarNLPWorld/TatarTokenizer",
    filename="tokenizers/wordpiece/50k/wp_50k.json"
)

tokenizer = Tokenizer.from_file(tokenizer_file)

# Test it
text = "Казан - Татарстанның башкаласы"
encoding = tokenizer.encode(text)
print(f"Text: {text}")
print(f"Tokens: {encoding.tokens}")
print(f"Token IDs: {encoding.ids}")
print(f"Decoded: {tokenizer.decode(encoding.ids)}")
```

### Using with Hugging Face Transformers

You can easily convert any tokenizer to Hugging Face format:

```python
from transformers import PreTrainedTokenizerFast

hf_tokenizer = PreTrainedTokenizerFast(
    tokenizer_object=tokenizer,
    unk_token='[UNK]',
    pad_token='[PAD]',
    cls_token='[CLS]',
    sep_token='[SEP]',
    mask_token='[MASK]'
)

# Now you can use it with any transformer model
```

### Download All Files for a Specific Tokenizer

```python
from huggingface_hub import snapshot_download

# Download all files for WordPiece 50k
model_path = snapshot_download(
    repo_id="TatarNLPWorld/TatarTokenizer",
    allow_patterns="tokenizers/wordpiece/50k/*",
    local_dir="./tatar_tokenizer_wp50k"
)
```

## 📊 Evaluation Results

We conducted extensive testing on a held-out corpus of 10,000 documents (19.5 million characters). Here are the key findings:

### Best Tokenizers by Category

| Category | Winner | Value |
|----------|--------|-------|
| **Best Compression** | `bbpe_fixed_50k` | 5.17 chars/token |
| **Fastest** | `wp_25k` | 496,273 tokens/sec |
| **Best Overall** | `wp_50k` | Balanced performance |
| **Most Readable** | WordPiece family | Human-readable tokens |

### Performance Summary

All tokenizers (except `bpe_fixed_50k`) achieve:
- **0% unknown rate** on test data
- **100% word coverage** for common vocabulary
- Compression ratios between 4.28 and 5.17

### Visualizations

The repository includes comprehensive evaluation visualizations in the `test_results/` folder:
- **Comparison plots** showing unknown rate, compression ratio, and speed by tokenizer type
- **Token length distributions** for each best-in-class tokenizer
- **Correlation matrices** between different metrics
- **Top-10 rankings** by composite score

Both Russian and English versions of all plots are available.

## 🧪 Test Results Summary

| Model | Type | Unknown Rate | Compression | Word Coverage | Speed (tokens/sec) |
|-------|------|--------------|-------------|---------------|-------------------|
| wp_50k | WordPiece | 0.0000 | 4.67 | 1.0000 | 378,751 |
| wp_25k | WordPiece | 0.0000 | 4.36 | 1.0000 | **496,273** |
| uni_50k | Unigram | 0.0000 | 4.59 | 1.0000 | 189,623 |
| uni_25k | Unigram | 0.0000 | 4.30 | 1.0000 | 260,403 |
| bpe_50k | BPE | 0.0000 | 4.60 | 1.0000 | 247,421 |
| bbpe_fixed_50k | BBPE_fixed | 0.0000 | **5.17** | 1.0000 | 315,922 |

## 🎯 Recommendations

Based on our evaluation, we recommend:

1. **For BERT-like models**: Use `wp_50k` (WordPiece) - best balance of readability and performance
2. **For maximum speed**: Use `wp_25k` - fastest tokenizer, ideal for high-throughput applications
3. **For maximum compression**: Use `bbpe_fixed_50k` - most efficient tokenization
4. **For GPT-like models**: Use `bpe_50k` or `bbpe_50k` - compatible with modern LLM architectures
5. **For research**: All tokenizers are provided for comparative studies

## 📝 License

All tokenizers are released under the **MIT License**. You are free to use, modify, and distribute them for any purpose, with proper attribution.

## 🤝 Citation

If you use these tokenizers in your research, please cite:

```bibtex
@software{tatartokenizer_2026,
    title = {TatarTokenizer: A Comprehensive Collection of Tokenizers for the Tatar Language},
    author = {Arabov, Mullosharaf Kurbonvoich},
    year = {2026},
    publisher = {Kazan Federal University},
    url = {https://huggingface.co/TatarNLPWorld/TatarTokenizer}
}
```

## 🌐 Language

All tokenizers are trained on Tatar text and are intended for use with the Tatar language (language code `tt`). They handle Tatar-specific characters perfectly (`ә`, `Ә`, `ү`, `Ү`, `җ`, `Җ`, `ң`, `Ң`, `һ`, `Һ`, `ө`, `Ө`).

## 🙌 Acknowledgements

These tokenizers were trained and evaluated by [TatarNLPWorld](https://huggingface.co/TatarNLPWorld) as part of an effort to advance NLP resources for the Tatar language. We thank the open-source community for the tools and libraries that made this work possible.

Special thanks to the Hugging Face team for the `tokenizers` library and the Hugging Face Hub platform.