Update README.md

README.md

@@ -36,10 +36,10 @@ The original dataset can be found in the [OptoGPT publication 📝](https://arxi
 │   ├── ...
 │   └── ZnSe.csv
 ├── train/
-│   ├── train-00.safetensors
-│   ├── train-01.safetensors
+│   ├── train-0.safetensors
+│   ├── train-1.safetensors
 │   ├── ...
-│   └── train-09.safetensors
+│   └── train-9.safetensors
 ├── test/
 │   └── test.safetensors
 └── tokens.json
@@ -47,10 +47,26 @@ The original dataset can be found in the [OptoGPT publication 📝](https://arxi
 
 Each `*.safetensors` file contains 1 million thin film structures (indexed by `tokens.json`) as well as their simulated absorption, reflection and transmission (RAT) spectrum. 
 
-The spectrum tensors have the shape *(n_samples, 3 [RAT], 171 [bins],)* and are stored in `float16`. The thin film layers have the shape *(n_samples, 20 [max_depth],)* and are stored as `long`.
+The spectrum tensors have the shape *(n_samples, 3 [RAT], 171 [bins],)* and are stored in `float16`. The thin film layers have the shape *(n_samples, 21 [max_depth incl. EOS and PAD],)* and are stored as `long`.
 
 The thin film layer sequence is ordered from top to bottom, i.e., the lowest index is the top of the thin film layers (touches the air), and the highest index is the bottom layer (touching the back substrate). 
 
+## Loading Data
+
+This shows an example on how to load (a subset) of the entire data
+
+```python
+from safetensors.torch import load_file
+
+data = load_file("train/train-0.safetensors")
+
+spectra = data['spectra']
+thin_films = data['thin_films']
+print(spectra.shape, thin_films.shape)
+
+>>> torch.Size([1000000, 3, 171]) torch.Size([1000000, 21])
+```
+
 
 ## Details
 
@@ -60,9 +76,9 @@ The thin film layer sequence is ordered from top to bottom, i.e., the lowest ind
 - A full list of the vocabular, i.e., all possible tokens, can be found in the `tokens.json`, with the the format of `<material>_<thickness_in_nm>`
 - There are additional tokens for end of sequences (EOS), padding (PAD) and masking (MASK)
 
-| Train samples   | Test samples |
-| :-------------- | -----------: |
-|      10,000,000 |    1,000,000 |
+| Train samples   | Test samples | Test samples (cropped) |
+| --------------: | -----------: | ---------------------: |
+|      10,000,000 |    1,000,000 |                128,000 |
 
 ## Acknowledgements