Create pipeline.py

pipeline.py

@@ -0,0 +1,27 @@
+from typing import  Dict
+import numpy as np
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        # IMPLEMENT_THIS
+        # 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"
+        )
+
+     def __call__(self, inputs: str) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            inputs (:obj:`str`):
+                a string containing some text
+        Return:
+            A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
+                - "label": A string representing what the label/class is. There can be multiple labels.
+                - "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"
+        )