use pipelines in generator

generator.py

@@ -43,7 +43,7 @@ def load_model():
   return hfm, hft, tok, model
 
 hfmodel, hftokenizer, tok, model = load_model()
-
+nlp = pipeline("e2e-qg")
 def run_model(input_string, **generator_args):
   generator_args = {
   "max_length": 256,
@@ -53,11 +53,12 @@ def run_model(input_string, **generator_args):
   "early_stopping": False,
   }
   # tokenizer = att.from_pretrained("ThomasSimonini/t5-end2end-question-generation")
-  input_string = "generate questions: " + input_string + " </s>"
-  input_ids = hftokenizer.encode(input_string, return_tensors="pt")
-  res = hfmodel.generate(input_ids, **generator_args)
-  output = hftokenizer.batch_decode(res, skip_special_tokens=True)
-  output = [item.split("<sep>") for item in output]
+  output = nlp(input_string)
+  # input_string = "generate questions: " + input_string + " </s>"
+  # input_ids = hftokenizer.encode(input_string, return_tensors="pt")
+  # res = hfmodel.generate(input_ids, **generator_args)
+  # output = hftokenizer.batch_decode(res, skip_special_tokens=True)
+  # output = [item.split("<sep>") for item in output]
   return output