Update README.md

README.md

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----
-license: mit
----
+---
+tags:
+- regression
+- pytorch
+license: mit
+---
+
+## Model Description
+
+`NumAdd-v2.0` is an optimized feed-forward neural network (FNN) in PyTorch for numerical sum prediction.
+**Architecture:** 2-input, 1-output, with two hidden layers (32, 64 neurons) and ReLU activations.
+**Parameters:** 2,273 trainable.
+**Precision:** Requires `torch.float64` (double precision).
+**Training Config:** Optimal batch size: 2048, Final tuning learning rate: 1.0e-12.
+
+## Evaluation
+
+Benchmarked on 120,000 samples across six input magnitude ranges. Metrics: MAE, MSE, RMSE, R2.
+
+| Range (Input Max) | MAE     | MSE      | RMSE    | R2      |
+|-------------------|---------|----------|---------|---------|
+| 0-50              | 0.004   | 0.000    | 0.004   | 1.000   |
+| 51-500            | 0.003   | 0.000    | 0.004   | 1.000   |
+| 501-5000          | 0.004   | 0.000    | 0.004   | 1.000   |
+| 5001-50000        | 0.004   | 0.000    | 0.005   | 1.000   |
+| 50001-500000      | 0.010   | 0.001    | 0.028   | 1.000   |
+| 500001-50000000   | 0.706   | 6.333    | 2.517   | 1.000   |
+
+## Limitations
+
+Precision degrades for extremely large magnitude inputs (e.g., >500,000), indicated by increased MAE/MSE, although R2 remains high.