back to normal, removed additional bad words argument

handler.py

@@ -11,7 +11,7 @@ class EndpointHandler():
         self.tokenizer = tokenizer
         self.stopping_criteria = StoppingCriteriaList([StopAtPeriodCriteria(tokenizer)])
 
-    def __call__(self, data: Dict[str, Any], additional_bad_words_ids: List[List[int]] = None) -> List[Dict[str, Any]]:
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """
        data args:
             inputs (:obj: `str`)
@@ -23,9 +23,7 @@ class EndpointHandler():
 
 
         # Bad word: id 3070, 10456 corresponds to "(*", and we do not want to output a comment
-        bad_words_ids = [[3070], [313, 334], [10456], [29871, 25956, 413, 325, 301, 29889]]
-        if additional_bad_words_ids:
-            bad_words_ids.extend(additional_bad_words_ids)
+        bad_words_ids = [[3070], [313, 334], [10456]]
 
         input_ids = self.tokenizer.encode(inputs, return_tensors="pt")
 
@@ -39,11 +37,8 @@ class EndpointHandler():
             stopping_criteria=self.stopping_criteria,
         )
 
-        # Slice the generated_ids to only include the new tokens generated, excluding the input tokens
-        generated_ids = generated_ids[:, input_ids.shape[1]:]
-
-        generated_text = self.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
-        prediction = [{"generated_text": generated_text, "generated_ids": generated_ids[0].tolist()}]
+        generated_text = self.tokenizer.decode(generated_ids[0, input_ids.shape[1]:], skip_special_tokens=True)
+        prediction = [{"generated_text": generated_text}]
         return prediction