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

### Load in Python
```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
```python
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
```python
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
```bash
python scripts/train_tokenizer.py \
    --data_dir ./data \
    --vocab_size 16000 \
    --model_name EthioBBPE_Large
```

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

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

### Custom Special Tokens
```bash
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

1. **Test the Tokenizer**: Try encoding/decoding your own Amharic texts
2. **Upload to Hugging Face Hub**: Share your model with the community
3. **Integrate with Models**: Use with transformer models for Amharic NLP tasks
4. **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)