Upload model_card.md

model_card.md

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+---
+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