# GRU Sequence Anomaly Detector

This project provides an open-source, bidirectional GRU-based deep learning model to detect anomalies in time-series transactional data. It is designed to be general-purpose and supports transfer learning.

## 📂 Project Structure

- `models/` – Contains the model architecture and trained weights
- `pipeline/` – Core training, evaluation, and export logic
- `utils/` – Logging and utility functions
- `notebooks/` – Example usage and exploration
- `tests/` – Unit and integration tests
- `main.py` – Entry script to run training/evaluation
- `fine_tune_template.py` – Script for model fine-tuning on external datasets
- `model_card.md` – Model documentation and expected usage
- `requirements.txt` – All required dependencies

## 📦 Pretrained Models

- `models/txn_anomaly_model.pt` – PyTorch model file for fine-tuning or loading
- `models/txn_anomaly_model.onnx` – ONNX model file for deployment in other runtimes

## 🚀 Quick Start

```bash
pip install -r requirements.txt
python main.py
```

To fine-tune:

```bash
python fine_tune_template.py --data your_dataset.csv
```