Create pipeline.py

pipeline.py

@@ -0,0 +1,36 @@
+
+from typing import Dict, List, Any
+
+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"""
+        Initialize model
+        """
+        package = os.path.join(path,"en_core_web_sm-any-py3-none-any.whl")
+        subprocess.check_call(
+            [sys.executable, "-m", "pip", "install", package]
+        )
+        raise NotImplementedError(
+            "Please implement TokenClassificationPipeline __init__ function"
+        )
+
+    def __call__(self, inputs: str) -> List[Dict[str, Any]]:
+        """
+        Args:
+            inputs (:obj:`str`):
+                a string containing some text
+        Return:
+            A :obj:`list`:. The object returned should be like [{"entity_group": "XXX", "word": "some word", "start": 3, "end": 6, "score": 0.82}] containing :
+                - "entity_group": A string representing what the entity is.
+                - "word": A substring of the original string that was detected as an entity.
+                - "start": the offset within `input` leading to `answer`. context[start:stop] == word
+                - "end": the ending offset within `input` leading to `answer`. context[start:stop] === word
+                - "score": A score between 0 and 1 describing how confident the model is for this entity.
+        """
+        # IMPLEMENT_THIS
+        raise NotImplementedError(
+            "Please implement TokenClassificationPipeline __call__ function"
+        )