Update README.md

README.md

@@ -100,7 +100,7 @@ with torch.no_grad():
 # Representations: 13 layers (CNN feature extractor + 12 Transformer)
 # NOTE: each layer performs differently in different downstream tasks - you should choose empirically
 all_layer_hidden_states = torch.stack(outputs.hidden_states).squeeze()
-print(all_layer_hidden_states.shape) # [13 layer, Time steps, 768 feature_dim]
+print(all_layer_hidden_states.shape) # [13 layers, Time steps, 768 feature_dim]
 
 # For utterance-level classification tasks, you can simply reduce the representation in time
 time_reduced_hidden_states = all_layer_hidden_states.mean(-2)