update

README.md

@@ -19,6 +19,7 @@ It can be used for:
 ## Usage
 ```python
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
 
 model_name = 'doc2query/msmarco-german-mt5-base-v1'
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -37,7 +38,7 @@ def create_queries(para):
             do_sample=True,
             top_p=0.95,
             top_k=10, 
-            num_return_sequences=10
+            num_return_sequences=5
             )
         
         # Here we use Beam-search. It generates better quality queries, but with less diversity
@@ -54,12 +55,12 @@ def create_queries(para):
     print("Paragraph:")
     print(para)
     
-    print("\Beam Outputs:")
+    print("\nBeam Outputs:")
     for i in range(len(beam_outputs)):
         query = tokenizer.decode(beam_outputs[i], skip_special_tokens=True)
         print(f'{i + 1}: {query}')
 
-    print("\Sampling Outputs:")
+    print("\nSampling Outputs:")
     for i in range(len(sampling_outputs)):
         query = tokenizer.decode(sampling_outputs[i], skip_special_tokens=True)
         print(f'{i + 1}: {query}')