Update README.md

README.md

@@ -89,14 +89,12 @@ inputs = ["Christina Katrakis, who spoke to the BBC from Vorokhta in western Ukr
     "Hello, I'd like to order a pizza with salami topping."]
 
 for sample in inputs:
-       	input_sequences = [task_prefix + sample]
-        input_ids = tokenizer(input_sequences, return_tensors='pt', truncation=True).input_ids
-        output = vlt5.generate(input_ids, no_repeat_ngram_size=3, num_beams=4)
-        predicted = tokenizer.decode(output[0], skip_special_tokens=True)
-        print(sample, "\n --->", predicted)
+  input_sequences = [task_prefix + sample]
+  input_ids = tokenizer(input_sequences, return_tensors='pt', truncation=True).input_ids
+  output = model.generate(input_ids, no_repeat_ngram_size=3, num_beams=4)
+  predicted = tokenizer.decode(output[0], skip_special_tokens=True)
+  print(sample, "\n --->", predicted)
 
-
-  
 ```
 # Inference
 Our results showed that the best generation results were achieved with `no_repeat_ngram_size=3, num_beams=4`