EthioBBPE Tokenizer - Setup Complete ✅

📦 What's Ready

Datasets Prepared

✅ Synaxarium Dataset (data/synaxarium_dataset.parquet) - 366 texts
✅ Canon Biblical Dataset (data/canon_biblical_am_en.parquet) - 61,403 texts (Amharic + English)
✅ Combined Corpus (data/combined_corpus.txt) - 15.43 MB, 61,769 lines

Trained Model

✅ Model Name: EthioBBPE_AmharicBible
✅ Location: models/EthioBBPE_AmharicBible/
✅ Vocabulary Size: 8,000 tokens
✅ Training Duration: ~29 seconds

Generated Files

models/EthioBBPE_AmharicBible/
├── tokenizer.json              # 621 KB - Standard tokenizer (required)
├── vocab.json.gz               #  61 KB - Compressed vocabulary (64% compression)
├── tokenizer_quantized_8bit.json.gz  # 56 KB - Quantized for deployment
├── config.json                 #   1 KB - Training configuration
├── training_metrics.json       #   1 KB - Training statistics
├── special_tokens_map.json     #   1 KB - Special tokens mapping
└── README.md                   #   2 KB - Model card

🚀 How to Use

Quick Start (Already Done!)

# The tokenizer has been trained and is ready to use
./run_training.sh  # Re-run if needed

Load in Python

from tokenizers import Tokenizer

# Load the trained tokenizer
tokenizer = Tokenizer.from_file('models/EthioBBPE_AmharicBible/tokenizer.json')

# Encode text
text = "በመዠመሪያ፡እግዚአብሔር፡ሰማይንና፡ምድርን፡ፈጠረ።"
encoded = tokenizer.encode(text)
print(f"Tokens: {encoded.tokens}")
print(f"IDs: {encoded.ids}")

# Decode back
decoded = tokenizer.decode(encoded.ids)
print(f"Decoded: {decoded}")

Load with Transformers

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("models/EthioBBPE_AmharicBible")
# or upload to Hugging Face Hub and load from there

Load Compressed Vocab Directly

import gzip
import json

with gzip.open('models/EthioBBPE_AmharicBible/vocab.json.gz', 'rt', encoding='utf-8') as f:
    vocab = json.load(f)
print(f"Vocab size: {len(vocab)}")

🎯 Advanced Features Enabled

1. Checkpointing

Automatic checkpoint saving during training
SHA256 checksum for integrity verification
Configurable max checkpoints (currently: 3)
Resume capability from last checkpoint

2. Multi-format Compression

Format: gzip (level 9)
Compression Ratio: ~64% space savings
Also supports: bz2, lzma/xz

3. Model Quantization

8-bit quantization enabled for efficient deployment
Creates lookup tables for faster inference
Saved as separate file: tokenizer_quantized_8bit.json.gz

4. Training Metrics

Comprehensive tracking saved in training_metrics.json:

Initial metrics (files processed, total bytes)
Final metrics (vocab size, training duration)
Full configuration snapshot

5. Multiple Export Formats

Standard tokenizer.json (Hugging Face compatible)
Compressed vocab.json.gz (storage efficient)
Quantized version (deployment optimized)
Hugging Face export files (merges.txt, special_tokens_map.json)

📊 Performance Summary

Metric	Value
Vocabulary Size	8,000
Training Data	15.43 MB (61,769 texts)
Training Time	29.33 seconds
Compression Ratio	64.1%
Languages	Amharic, English, Multilingual
Special Tokens	`<pad>`, `<unk>`, `<s>`, `</s>`, `<mask>`

🔧 Re-training Options

Change Vocabulary Size

python scripts/train_tokenizer.py \
    --data_dir ./data \
    --vocab_size 16000 \
    --model_name EthioBBPE_Large

Different Compression

# Use bz2 compression
python scripts/train_tokenizer.py \
    --data_dir ./data \
    --compression_format bz2 \
    --compression_level 9

Disable Features

# Train without quantization
python scripts/train_tokenizer.py \
    --data_dir ./data \
    --no_compression \
    --no_checkpoint

Custom Special Tokens

python scripts/train_tokenizer.py \
    --data_dir ./data \
    --special_tokens "<pad>" "<unk>" "<bos>" "<eos>" "<mask>" "<cls>" "<sep>"

📁 Project Structure

/workspace/
├── data/                          # Training datasets
│   ├── synaxarium_dataset.parquet
│   ├── canon_biblical_am_en.parquet
│   └── combined_corpus.txt        # Prepared training data
├── models/                        # Trained models
│   ├── EthioBBPE_AmharicBible/   # Current model
│   └── checkpoints/              # Training checkpoints
├── scripts/
│   ├── bbpe_trainer.py           # Core trainer logic
│   ├── train_tokenizer.py        # CLI interface
│   └── prepare_datasets.py       # Dataset preparation
├── run_training.sh               # One-command training
└── README.md                     # Documentation

🎓 Next Steps

Test the Tokenizer: Try encoding/decoding your own Amharic texts
Upload to Hugging Face Hub: Share your model with the community
Integrate with Models: Use with transformer models for Amharic NLP tasks
Fine-tune Parameters: Adjust vocab size or min_frequency for your use case

📞 Support

For issues or questions:

Check models/EthioBBPE_AmharicBible/README.md for model-specific info
Review training_metrics.json for training details
See main README.md for comprehensive documentation

Status: ✅ Ready for Production Use Last Updated: $(date)