Upload pipeline.py

pipeline.py

@@ -0,0 +1,33 @@
+from typing import  Dict, List, Any
+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import pipeline, AutoTokenizer
+
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        # load the optimized model
+        model = ORTModelForSequenceClassification.from_pretrained(path)
+        tokenizer = AutoTokenizer.from_pretrained(path, model_max_length=128)
+        # create inference pipeline
+        self.pipeline = pipeline("feature-extraction", model=model, tokenizer=tokenizer)
+
+
+    def __call__(self, inputs: Any) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            data (: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.
+        """
+        # pop inputs for pipeline
+        def cls_pooling(pipeline_output):
+            """
+            Return the [CLS] token embedding
+            """
+            return [_h[0] for _h in pipeline_output]
+        
+        embeddings = cls_pooling(self.pipeline(inputs))
+        return embeddings