Update README.md

README.md

@@ -4,7 +4,6 @@
 Pre-trained **Fourier Neural Operator (FNO)** models for predicting multi-floor acceleration response time histories of MDOF shear buildings subjected to seismic ground motions.
 
 **Code:** [github.com/HKUJasonJiang/Seismic-FNO](https://github.com/HKUJasonJiang/Seismic-FNO)  
-**Dataset:** [<!-- TODO: HuggingFace dataset link -->]
 
 ---
 
@@ -59,37 +58,12 @@ Each model folder contains:
 
 ### Install dependencies
 
-```bash
-git clone https://github.com/HKUJasonJiang/Seismic-FNO.git
-cd Seismic-FNO
-pip install -r requirement.txt   # Windows
-# pip install -r requirement_linux.txt  # Linux
-```
-
-### Load a checkpoint and run inference
-
 Refer to github repo.
 
 ### Quick review notebook
 
 Open `quick_inference.ipynb` in the cloned repository to run inference on the held-out test set and visualise time-history and Fourier amplitude spectrum comparisons interactively.
 
----
-
-## Training Details
-
-| Property | Value |
-|----------|-------|
-| Framework | PyTorch ≥ 2.0 + [neuralop](https://github.com/neuraloperator/neuraloperator) |
-| Loss | MSE |
-| Optimizer | AdamW |
-| LR schedule | StepLR (step=20, γ=0.5) |
-| Batch size | 2 560 |
-| Epochs | 50 (baseline models) |
-| Train / Val / Test split | 70 % / 20 % / 10 % (grouped by GM to prevent leakage) |
-| Dataset | KNET — 3 474 GMs × 57 amplitude scale factors × 250 buildings |
-
----
 
 ## License