update

pipeline.py

@@ -7,9 +7,10 @@ class PreTrainedPipeline():
         # Preload all the elements you are going to need at inference.
         # For instance your model, processors, tokenizer that might be needed.
         # This function is only called once, so do all the heavy processing I/O here"""
-        raise NotImplementedError(
-            "Please implement PreTrainedPipeline __init__ function"
-        )
+        # raise NotImplementedError(
+        #     "Please implement PreTrainedPipeline __init__ function"
+        # )
+        pass
 
     def __call__(self, inputs: str) -> List[List[Dict[str, float]]]:
         """
@@ -22,6 +23,7 @@ class PreTrainedPipeline():
                 - "score": A score between 0 and 1 describing how confident the model is for this label/class.
         """
         # IMPLEMENT_THIS
-        raise NotImplementedError(
-            "Please implement PreTrainedPipeline __call__ function"
-        )
+        # raise NotImplementedError(
+        #     "Please implement PreTrainedPipeline __call__ function"
+        # )
+        return [[{"label": inputs, "score":0.2}]]