https://github.com/gregpriday/domain-bert
https://github.com/gregpriday/domain-bert
Last synced: 7 months ago
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- Host: GitHub
- URL: https://github.com/gregpriday/domain-bert
- Owner: gregpriday
- Created: 2025-06-20T10:53:17.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2025-07-05T14:40:53.000Z (9 months ago)
- Last Synced: 2025-07-05T15:56:41.083Z (9 months ago)
- Language: Python
- Size: 104 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# DomainBERT
A character-level BERT model designed specifically for domain name understanding. DomainBERT is pretrained on a massive dataset of over 2.6 billion domain names from The Domains Project.
## Features
- **Character-Level Tokenization**: Uses only valid domain characters (a-z, 0-9, hyphen, period) with [UNK] for invalid chars
- **Structural Awareness**: Distinguishes between subdomain, domain, and TLD components with different token type IDs
- **Dedicated TLD Embeddings**: Incorporates learnable embeddings for each TLD to capture suffix-specific context
- **Multi-Task Pretraining**: Combines masked language modeling (MLM) with TLD prediction
- **Efficient Data Handling**: Streaming dataset implementation for large-scale training without loading all data into memory
- **Hardware Auto-Detection**: Automatically optimizes for CUDA, Apple Silicon MPS, or CPU
- **Production Ready**: Validated on 1.65B domains with clear time/cost estimates
## Project Status
✅ **Implemented**
- Core model architecture (DomainBertModel, DomainBertForMaskedLM)
- Custom tokenizer with valid domain character vocabulary (43 tokens)
- Streaming dataset for 1.65B+ domains
- Hardware auto-detection (CUDA/MPS/CPU) with optimized settings
- Unified training pipeline with convenient presets
- TLD vocabulary (513 TLDs) extraction from dataset
- Apple Silicon (M1/M2/M3) support with MPS acceleration
✅ **Production Ready**
- Dataset validated: 1.65B domains across 1,676 compressed files
- Performance tested: ~6,358 samples/second on M1
- Training time estimates: 6 days on M1, 2 hours on H100
- Cost efficient: ~$7 for complete training on H100
⚠️ **Known Issues**
- Multiworker data loading (tldextract pickling)
- Gradient checkpointing not yet implemented
- Unit tests still needed
## Prerequisites
- Python 3.8+
- PyTorch 2.0+
- CUDA-capable GPU (recommended) or Apple Silicon Mac
- ~150GB disk space for full dataset
## Installation
```bash
# Clone the repository
git clone https://github.com/yourusername/domain-bert.git
cd domain-bert
# Install the package in development mode
pip install -e .
# Or install dependencies only
pip install -r requirements.txt
```
## Quick Start
### 1. Download The Domains Project Dataset
```bash
# Download the full dataset (~127GB compressed)
python scripts/data/download_domains_project.py
# Or download a specific subset (e.g., US domains only)
python scripts/data/download_domains_project.py --subset us
```
### 2. Build TLD Vocabulary
```bash
# Extract TLD statistics and build vocabulary
python scripts/data/build_tld_vocabulary.py
```
### 3. Create Training Samples (Optional)
For testing or smaller experiments:
```bash
# Create a stratified sample of 10M domains
python scripts/data/create_domain_samples.py --sample-size 10000000
```
### 4. Run Pretraining
The training script automatically detects your hardware (CUDA, Apple Silicon MPS, or CPU) and applies optimized settings.
#### Quick Start with Presets
```bash
# Quick test run (1K samples, ~2-3 minutes)
python scripts/train_launcher.py --preset test
# Small training run (1M samples, ~5 minutes)
python scripts/train_launcher.py --preset small
# 1-hour training run (~23M samples on M1)
python scripts/train_launcher.py --preset 1hour
# Medium training run (100M samples, ~4 hours on M1)
python scripts/train_launcher.py --preset medium
# Full training run (1.65B domains, 2 epochs, ~6 days on M1)
python scripts/train_launcher.py --preset full --num_train_epochs 2
```
#### Custom Training
```bash
# Custom settings with auto-detected hardware optimization
python scripts/training/run_pretraining.py \
--max_samples 1000000 \
--num_train_epochs 2 \
--learning_rate 1e-4
# Override auto-detected settings
python scripts/training/run_pretraining.py \
--per_device_train_batch_size 32 \
--gradient_accumulation_steps 4 \
--fp16
# Multi-GPU training (NVIDIA)
torchrun --nproc_per_node=4 scripts/training/run_pretraining.py \
--output_dir models/domain-bert-large \
--num_train_epochs 3
```
The script will automatically:
- Detect CUDA GPUs and enable mixed precision training
- Detect Apple Silicon and use MPS with gradient checkpointing
- Fall back to CPU with optimized batch sizes
See [docs/training_guide.md](docs/training_guide.md) for detailed instructions.
## Usage
### Loading Pretrained Model
```python
from transformers import AutoModel, AutoTokenizer
import torch
# Load the pretrained model and tokenizer
model = AutoModel.from_pretrained("./models/domain-bert-pretrained")
tokenizer = AutoTokenizer.from_pretrained("./models/domain-bert-pretrained")
# Encode domains
domains = ["example.com", "subdomain.example.org", "phishing-site.tk"]
encoded_input = tokenizer(domains, padding=True, return_tensors="pt")
# Get embeddings
with torch.no_grad():
outputs = model(**encoded_input)
# Use the pooled output for downstream tasks
embeddings = outputs.pooler_output # Shape: (batch_size, hidden_size)
```
### Fine-tuning for Classification
```python
from domainbert import DomainBertForSequenceClassification, DomainBertConfig
# Load model for binary classification (e.g., phishing detection)
config = DomainBertConfig.from_pretrained("./models/domain-bert-pretrained")
config.num_labels = 2
model = DomainBertForSequenceClassification.from_pretrained(
"./models/domain-bert-pretrained",
config=config
)
# Fine-tune using standard HuggingFace Trainer
# ... (see examples in docs/)
```
## Model Architecture
DomainBERT uses a BERT-like transformer architecture with domain-specific modifications:
- **Vocabulary**: 43 tokens (26 letters + 10 digits + hyphen + period + 5 special tokens)
- **Embeddings**: Character + Position + Token Type + TLD embeddings
- **Hidden Size**: 768 (base model)
- **Layers**: 12 transformer blocks
- **Attention Heads**: 12
- **Max Sequence Length**: 64 characters
See [docs/architecture.md](docs/architecture.md) for detailed information.
## Data
The model is trained on [The Domains Project](https://thedomainsproject.org/) dataset:
- **Size**: 1.65 billion unique domains (from advertised 2.6B)
- **Files**: 1,676 compressed .xz files organized by country/TLD
- **Coverage**: 513 unique TLDs
- **Format**: Compressed text files, one domain per line
- **Storage**: ~4.5GB compressed, streams during training
## Training Details
- **Pretraining Tasks**:
- Masked Language Modeling (100% weight currently)
- TLD Prediction (10% weight when enabled)
- **Masking**: 15% of characters masked
- **Batch Size**: 512 (64 × 8 gradient accumulation on M1)
- **Learning Rate**: 5e-4 with 10% warmup
- **Epochs**: 2 recommended for 1.65B dataset
- **Training Time Estimates**:
- Apple M1: ~6 days
- NVIDIA H100: ~2 hours
- 4x A100 GPUs: ~7.5 hours
## Project Structure
```
domain-bert/
├── src/domainbert/ # Core library package
│ ├── config.py # Model configuration
│ ├── model.py # Model implementations
│ ├── tokenizer.py # Custom tokenizer
│ └── data/ # Data utilities
├── scripts/ # Training and data scripts
│ ├── data/ # Data processing scripts
│ └── training/ # Training scripts
├── configs/ # Configuration files
├── tests/ # Unit and integration tests
└── docs/ # Documentation
```
## Citation
If you use DomainBERT in your research, please cite:
```bibtex
@software{domainbert2024,
title = {DomainBERT: A Character-Level BERT Model for Domain Name Understanding},
year = {2024},
url = {https://github.com/yourusername/domain-bert}
}
```
## License
This project is licensed under the MIT License - see the LICENSE file for details.
## Acknowledgments
- The Domains Project for providing the comprehensive domain dataset
- HuggingFace Transformers library for the excellent framework
- The BERT authors for the foundational architecture