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handler.py

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+from typing import  Dict, List, Any
+
+import torch
+import torch.nn.functional as F
+from transformers import AutoTokenizer
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+class EndpointHandler():
+
+    def __init__(self, path=""):
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = ORTModelForFeatureExtraction.from_pretrained(path, file_name="model.onnx")
+        
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`list`:. The list contains the embeddings of the inference inputs
+        """
+        inputs = data.get("inputs", data)
+
+        # tokenize the input
+        encoded_inputs = self.tokenizer(inputs, padding=True, truncation=True, return_tensors='pt')
+        # run the model
+        outputs = self.model(**encoded_inputs)
+        # Perform pooling
+        sentence_embeddings = mean_pooling(outputs, encoded_inputs['attention_mask'])
+        # Normalize embeddings
+        sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
+        # postprocess the prediction
+        return {"embeddings": sentence_embeddings.tolist()}

requirements.txt

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+optimum[onnxruntime]
+mkl-include
+mkl