Update README.md

README.md

@@ -84,16 +84,20 @@ model = T5ForConditionalGeneration.from_pretrained("Voicelab/vlt5-base-keywords"
 tokenizer = T5Tokenizer.from_pretrained("Voicelab/vlt5-base-keywords")
 
 task_prefix = "Keywords: "
-inputs = ["Christina Katrakis, who spoke to the BBC from Vorokhta in western Ukraine, relays the account of one family, who say Russian soldiers shot at their vehicles while they were leaving their village near Chernobyl in northern Ukraine. She says the cars had white flags and signs saying they were carrying children.",
-     "Decays the learning rate of each parameter group by gamma every step_size epochs. Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler. When last_epoch=-1, sets initial lr as lr.",
-    "Hello, I'd like to order a pizza with salami topping."]
+inputs = [
+    "Christina Katrakis, who spoke to the BBC from Vorokhta in western Ukraine, relays the account of one family, who say Russian soldiers shot at their vehicles while they were leaving their village near Chernobyl in northern Ukraine. She says the cars had white flags and signs saying they were carrying children.",
+    "Decays the learning rate of each parameter group by gamma every step_size epochs. Notice that such decay can happen simultaneously with other changes to the learning rate from outside this scheduler. When last_epoch=-1, sets initial lr as lr.",
+    "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 = 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)
+    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