NumAdd
Collection
2 items
•
Updated
Model Description
NumAdd-v1.0 is a lightweight feed-forward neural network (FNN) implemented 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.
Evaluation
Benchmarked on 120,000 samples across five input magnitude ranges. Metrics: MAE, MSE, RMSE, R2.
| Range (Input Max) | MAE | MSE | RMSE | R2 |
|---|---|---|---|---|
| 0-50 | 0.003 | 0.000 | 0.004 | 1.000 |
| 51-500 | 0.003 | 0.000 | 0.004 | 1.000 |
| 501-5000 | 0.004 | 0.000 | 0.006 | 1.000 |
| 5001-50000 | 0.016 | 0.003 | 0.050 | 1.000 |
| 50001-500000 | 0.1525 | 0.2377 | 0.4876 | 1.000 |
| 500001-50000000 | 12.947 | 2143.782 | 46.301 | 1.000 |
Limitations
Performance degrades significantly for large magnitude inputs (>50,000), evidenced by increased MAE/MSE, despite maintaining high R2.
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
🙋
Ask for provider support