EthioBBPE 2.0.0

Enterprise-grade Amharic & Ge'ez Biblical Text Tokenizer with Byte Pair Encoding

EthioBBPE

📖 Overview

EthioBBPE is a professional, production-ready Byte Pair Encoding (BPE) tokenizer specifically designed for Amharic, Ge'ez, and biblical texts. It provides state-of-the-art tokenization for Ethiopian languages with support for ancient scripts and special characters.

✨ Key Features

• 🚀 One-line Installation: pip install EthioBBPE
• 🤖 Auto-download: Models automatically downloaded from Hugging Face Hub
• 📦 Compressed Storage: Gzip compression for efficient storage (65%+ size reduction)
• ⚡ High Performance: Optimized for speed with batch processing
• 🎯 Perfect Reconstruction: 100% accuracy on Amharic biblical texts
• 🔧 Professional API: Clean, intuitive interface inspired by Hugging Face
• 📊 Production Ready: Checkpointing, quantization, and comprehensive metrics

🚀 Quick Start

Installation

pip install EthioBBPE

Basic Usage

from ethiobbpe import EthioBBPETokenizer

# Load pretrained tokenizer (auto-downloads from Hugging Face)
tokenizer = EthioBBPETokenizer.from_pretrained()

# Encode text
text = "ሰላም ለኢዮብ ዘኢነበበ ከንቶ ።"
encoded = tokenizer.encode(text)

print(f"Tokens: {encoded.tokens}")
print(f"IDs: {encoded.ids}")

# Decode back to text
decoded = tokenizer.decode(encoded.ids)
print(f"Decoded: {decoded}")
# Output: ሰላም ለኢዮብ ዘኢነበበ ከንቶ ። (100% accurate!)

Advanced Usage

from ethiobbpe import AutoTokenizer

# Alternative: Use AutoTokenizer factory
tokenizer = AutoTokenizer.from_pretrained("Nexuss0781/Ethio-BBPE")

# Batch encoding
texts = [
    "በመዠመሪያ፡እግዚአብሔር፡ሰማይንና፡ምድርን፡ፈጠረ።",
    "ወደ ቍስጥንጥንያ አገርም በደረሰች ጊዜ",
    "ሐዋርያ መንፈስ ይቤ እንዘ ያነክር ሕይወቶ"
]

encodings = tokenizer.encode_batch(texts)
for i, enc in enumerate(encodings):
    print(f"Text {i}: {len(enc.tokens)} tokens")

# Callable interface
result = tokenizer("ሰላም ዓለም")
print(result.tokens)

# Get vocabulary
vocab = tokenizer.get_vocab()
print(f"Vocabulary size: {tokenizer.get_vocab_size()}")

📊 Model Details

Property	Value
Model Name	EthioBBPE_AmharicBible
Vocabulary Size	16,000 tokens
Training Data	61,769 lines (Synaxarium + Canon Biblical)
Compression	Gzip Level 9 (65%+ reduction)
Reconstruction Accuracy	100%
License	Apache 2.0

Training Datasets

1. Synaxarium Dataset: 366 religious texts
2. Canon Biblical Dataset: 61,403 Amharic-English parallel texts

Total corpus: ~27.5 MB of high-quality Amharic biblical text.

🛠️ API Reference

EthioBBPETokenizer

Main tokenizer class with full encoding/decoding capabilities.

Methods

• from_pretrained(model_name, cache_dir, force_download): Load pretrained model
• from_file(file_path): Load from local file
• encode(text, add_special_tokens, truncation, max_length): Encode single text
• encode_batch(texts, ...): Encode multiple texts
• decode(ids, skip_special_tokens): Decode token IDs
• decode_batch(batch_ids, ...): Decode multiple sequences
• get_vocab(): Get vocabulary dictionary
• get_vocab_size(): Get vocabulary size
• save(path): Save tokenizer to file

AutoTokenizer

Factory class for automatic tokenizer loading.

from ethiobbpe import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Nexuss0781/Ethio-BBPE")

Encoding

Wrapper object for encoding results with properties:

• ids: Token IDs (List[int])
• tokens: Token strings (List[str])
• attention_mask: Attention mask (List[int])
• type_ids: Token type IDs (List[int])
• offsets: Character offsets (List[tuple])
• special_tokens_mask: Special tokens mask (List[int])

📈 Performance

Compression Benefits

Format	Size	Compression
tokenizer.json	1.3 MB	Baseline
vocab.json.gz	136 KB	89.8%
tokenizer_quantized_8bit.json.gz	56 KB	95.7%

Speed

• Single encoding: < 1ms
• Batch encoding (32 texts): < 10ms
• Model download: Automatic caching for subsequent uses

🔬 Technical Details

Architecture

• Algorithm: Byte Pair Encoding (BPE)
• Pre-tokenization: Whitespace splitting
• Special Tokens: [PAD], [CLS], [SEP], [MASK], [UNK]
• Vocabulary: 16,000 tokens optimized for Amharic/Ge'ez

Production Features

1. Checkpointing: SHA256 integrity verification for fault-tolerant training
2. Quantization: 8-bit and 4-bit options for deployment optimization
3. Metrics Tracking: Comprehensive training statistics in JSON format
4. Multiple Export Formats: tokenizer.json, vocab.json.gz, quantized versions

📝 Examples

Ge'ez Punctuation Handling

tokenizer = EthioBBPETokenizer.from_pretrained()

# Ancient Ge'ez punctuation marks
geez_text = "፠፠፠፠፠፠፠፠፠፠፠፠፠፠፠፠፠፠"
encoded = tokenizer.encode(geez_text)
print(f"Ge'ez punctuation: {encoded.tokens}")
# Single token for repeated marks!

decoded = tokenizer.decode(encoded.ids)
assert decoded == geez_text  # Perfect reconstruction

Biblical Text Processing

biblical_text = """
ሰላም ለኢዮብ ዘኢነበበ ከንቶ ። 
አመ አኀዞ አበቅ ወአመ አህጎለ ጥሪቶ ። 
ሐዋርያ መንፈስ ይቤ እንዘ ያነክር ሕይወቶ ።
"""

encoded = tokenizer.encode(biblical_text)
print(f"Token count: {len(encoded.tokens)}")
print(f"Perfect reconstruction: {tokenizer.decode(encoded.ids) == biblical_text}")

🤝 Integration

With Hugging Face Transformers

from transformers import AutoModel, AutoTokenizer
from ethiobbpe import EthioBBPETokenizer

# Use EthioBBPE as the tokenizer
ethio_tokenizer = EthioBBPETokenizer.from_pretrained()

# Integrate with your pipeline
def tokenize_for_model(text):
    encoding = ethio_tokenizer.encode(text)
    return {
        "input_ids": encoding.ids,
        "attention_mask": encoding.attention_mask
    }

With PyTorch DataLoader

import torch
from torch.utils.data import Dataset
from ethiobbpe import EthioBBPETokenizer

class AmharicDataset(Dataset):
    def __init__(self, texts, tokenizer, max_length=512):
        self.texts = texts
        self.tokenizer = tokenizer
        self.max_length = max_length
    
    def __len__(self):
        return len(self.texts)
    
    def __getitem__(self, idx):
        encoding = self.tokenizer.encode(
            self.texts[idx],
            truncation=True,
            max_length=self.max_length
        )
        return {
            "input_ids": torch.tensor(encoding.ids),
            "attention_mask": torch.tensor(encoding.attention_mask)
        }

📦 Installation Options

Basic Installation

pip install EthioBBPE

Development Installation

pip install EthioBBPE[dev]

From Source

git clone https://github.com/nexuss0781/Ethio_BBPE.git
cd Ethio_BBPE
pip install -e .

🧪 Testing

# Run tests
pytest tests/

# With coverage
pytest tests/ --cov=ethiobbpe

📚 Resources

• Hugging Face Model Page
• GitHub Repository
• Issue Tracker

🙏 Acknowledgments

• Training data from Synaxarium Dataset
• Training data from Canon Biblical Dataset
• Built with Hugging Face Tokenizers

📄 License

Apache License 2.0 - See LICENSE for details.

👤 Author

Nexuss0781
Email: [e-mail]

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Made with ❤️ for Ethiopian Language NLP