Update README.md

README.md

@@ -45,7 +45,7 @@ def max_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()
     token_embeddings[input_mask_expanded == 0] = -1e9  # Set padding tokens to large negative value
-    return torch.max(token_embeddings, 1)[0]
+    return torch.mean(token_embeddings, 1)[0]
 
 # Sentences we want sentence embeddings for
 sentences = [