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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.

πŸ“‚ 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

πŸ“¦ 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

πŸš€ Quick Start

pip install -r requirements.txt
python main.py

To fine-tune:

python fine_tune_template.py --data your_dataset.csv