Update README.md

README.md

@@ -65,14 +65,12 @@ Without sentence-transformers, you can use the model like this: First, you pass
 from transformers import AutoTokenizer, AutoModel
 import torch
 
-
 #Mean Pooling - Take attention mask into account for correct averaging
 def mean_pooling(model_output, attention_mask):
     token_embeddings = model_output[0] #First element of model_output contains all token embeddings
     input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
     return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
 
-
 # Sentences we want sentence embeddings for
 sentences = ['This is an example sentence', 'Each sentence is converted']