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README.md

@@ -25,14 +25,6 @@ The data and loader scripts reproduce the behavior of the original PyTorch `Data
 
 ## Repository Structure
 
-```
-sibcl-dataset/
-├── phc2d.h5
-├── tise.h5
-├── dataset_loader.py
-└── README.md
-```
-
 * `phc2d.h5`: HDF5 with groups:
 
   * `unitcell/mpbepsimage/<index>`: 32×32 permittivity images
@@ -46,46 +38,6 @@ sibcl-dataset/
   * `eigval_fil/<index>`: eigenvalues
   * `eigvec_fil/<index>`: eigenvectors
 
-* `dataset_loader.py`: Hugging Face `datasets` script implementing both PhC2D and TISE loaders, with full config options.
-
-## Metadata
-
-```yaml
-dataset_name: sibcl-dataset
-builder_name: SIBCLDataset
-configs:
-  - name: phc2d
-    description: "2D photonic crystal DOS & bandstructure"
-  - name: tise
-    description: "3D TISE eigenvalue & eigenvector solutions"
-license: apache-2.0
-features:
-  phc2d:
-    inputeps: Array3D(shape=(1,32,32), dtype=float32)
-    y: Sequence(Value(float32))
-    EL: Sequence(Value(float32))
-    ELdiff: Sequence(Value(float32))
-    epsavg: Value(float32)
-  tise:
-    input: Array3D(shape=(1,<downres>,<downres>), dtype=float32)
-    y: Sequence(Value(float32))
-splits:
-  train: 10000
-  validation: 500
-  test: 2000
-```
-
-## Usage
-
-```python
-from datasets import load_dataset
-
-# Load PhC2D (DOS prediction)
-ds_phc = load_dataset("YOUR_USERNAME/sibcl-dataset", name="phc2d", split="train")
-
-# Load TISE (eigenvalue prediction)
-ds_tise = load_dataset("YOUR_USERNAME/sibcl-dataset", name="tise", split="test")
-```
 
 ## Processing & Training