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README.md

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+---
+library_name: clearn
+tags:
+  - continual-learning
+  - catastrophic-forgetting
+  - ewc
+  - pytorch
+license: mit
+---
+
+# clearn Demo: EWC Continual Learning
+
+This model was trained with [clearn](https://github.com/itisrmk/clearn) — a continual learning library for PyTorch.
+
+## What is this?
+
+A simple MLP trained on **3 sequential fraud detection tasks** using Elastic Weight Consolidation (EWC). Despite learning 3 tasks sequentially, the model retains 100% accuracy on all previous tasks.
+
+## How to use
+
+```bash
+pip install clearn-ai
+```
+
+```python
+import clearn
+import torch.nn as nn
+
+# Recreate the architecture
+model = nn.Sequential(nn.Linear(128, 256), nn.ReLU(), nn.Linear(256, 10))
+
+# Load the continual learning checkpoint
+cl_model = clearn.load("./checkpoint", model=model)
+
+# See retention across all tasks
+print(cl_model.diff())
+```
+
+## Retention Report
+
+```
+RetentionReport
+├── fraud_q1: 100.0% retained
+├── fraud_q2: 100.0% retained
+├── fraud_q3: 100.0% retained
+├── plasticity_score: 1.00
+├── stability_score: 1.00
+└── recommendation: "stable — no action needed"
+```
+
+## Strategy
+
+- **Strategy**: EWC (Elastic Weight Consolidation)
+- **Lambda**: 5000
+- **Tasks**: 3 sequential fraud detection tasks
+- **Architecture**: MLP (128 → 256 → 10)
+
+Built with [clearn](https://github.com/itisrmk/clearn) — *Wrap once. Train forever.*