Update README.md

README.md

@@ -48,7 +48,7 @@ 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 stores in `bfloat16`. 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, 20 [max_depth],)* 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). 
 
@@ -56,7 +56,7 @@ The thin film layer sequence is ordered from top to bottom, i.e., the lowest ind
 ## Details
 
 - All spectra have been simulated with [tmm_fast](https://github.com/MLResearchAtOSRAM/tmm_fast)
-- The *n* (refractive index) and *k* (extinction coefficients) of all materials can be found in CSV files in the `/materials` folder
+- The *n* (refractive index), *k* (extinction coefficients) and *wl* (wavelength) of all materials can be found in CSV files in the `/materials` folder
 - The training and test data are pre-split and can be found in the `/train` and `/test` folder respectively
 - 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)