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

pip install -r requirements.txt
python main.py

To fine-tune:

python fine_tune_template.py --data your_dataset.csv