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----
-license: mit
-tags:
-  - pytorch
-  - anomaly-detection
-  - time-series
-  - gru
-  - sequence-model
-  - binary-classification
-model-index:
-  - name: GRU Sequence Anomaly Detector
-    results: []
----
-
-# GRU Sequence Anomaly Detector
-
-This model uses a bidirectional GRU (Gated Recurrent Unit) architecture to detect anomalies in sequential tabular data — such as transaction records, log events, or sensor readings. It's designed for general-purpose anomaly detection and can be fine-tuned on domain-specific datasets.
-
----
-
-## 🧠 Model Architecture
-
-- **Type:** Bidirectional GRU
-- **Input:** Sequence of numerical feature vectors `(batch_size, time_steps, input_dim)`
-- **Output:** Binary classification (0 = normal, 1 = anomaly)
-- **Layers:** 2-layer GRU → BatchNorm → Dense → Sigmoid
-
----
-
-## 🛠️ Intended Use
-
-This model is ideal for:
-- Transaction anomaly detection
-- Time-series pattern disruption
-- Sequential event log monitoring
-
-It is **open for fine-tuning** using your labeled anomaly dataset via `fine_tune_template.py`.
-
----
-
-## 🚀 How to Use
-
-```python
-import torch
-from models.model import TxnAnomalyGRU
-
-model = TxnAnomalyGRU(input_dim=32)
-model.load_state_dict(torch.load("models/txn_anomaly_model.pt"))
-model.eval()
-```
-
-Or use the ONNX version with ONNX Runtime:
-
-```python
-import onnxruntime
-session = onnxruntime.InferenceSession("models/txn_anomaly_model.onnx")
-outputs = session.run(None, {"input": your_input_array})
-```
-
----
-
-## 🔄 Fine-Tuning
-
-To fine-tune on your own dataset:
-
-```bash
-python fine_tune_template.py --data your_dataset.csv
-```
-
-Ensure your data is preprocessed into sequences of the same input dimension (`input_dim=32` by default).
-
----
-
-## 📦 Files Included
-
-- `models/txn_anomaly_model.pt` – Pretrained PyTorch model
-- `models/txn_anomaly_model.onnx` – ONNX export
-- `fine_tune_template.py` – Script to fine-tune on your dataset
-- `pipeline/main.py` – End-to-end pipeline
-
----
-
-## 📝 License
-
-This model is released under the **MIT License**. You are free to use, modify, and distribute it for research or commercial purposes.
-
----
-
-## 👤 Author
-
-Developed by [Your Name]  
-For contributions, bug reports, or questions, please use GitHub Issues.