Update README.md

README.md

@@ -59,21 +59,18 @@ documents = [
 ]
 input_texts = queries + documents
 
-
 max_length = 512
 # Tokenize the input texts
 batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
 
 model.eval()
 with torch.no_grad():
-outputs = model(**batch_dict)
-embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
-
+  outputs = model(**batch_dict)
+  embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
 ```
 
 Then similarity scores between the different sentences are obtained with a dot product between the embeddings:
 ```python
-
 scores = (embeddings[:2] @ embeddings[2:].T)
 print(scores.tolist())
 ```