Update README.md

README.md

@@ -294,7 +294,7 @@ By selecting either `cls_emb` or `channel_emb`, you leverage the pre-trained mod
 If your dataset requires labels, you can easily generate them using DeepMIMO data. Here's an example to create labels for either LoS/NLoS classification or beam prediction, depending on the scenario selected:
 ```python
 from input_preprocess import create_labels
-tasks = ['LoS/NLoS Classifcation', 'Beam Prediction']
+tasks = ['LoS/NLoS Classification', 'Beam Prediction']
 task = tasks[1] # Choose 0 for LoS/NLoS labels or 1 for beam prediction labels.
 labels = create_labels(task, selected_scenario_names, n_beams=64) # For beam prediction, n_beams specifies the number of beams in the codebook. If you're generating labels for LoS/NLoS classification, you can leave this value unchanged as it doesn't impact the label generation.
 ```