Delete pipeline.py

pipeline.py

@@ -1,39 +0,0 @@
-from typing import  Dict, List, Any
-from optimum.onnxruntime import ORTModelForFeatureExtraction
-from transformers import AutoTokenizer
-import torch.nn.functional as F
-import torch
-
-# copied from the model card
-def mean_pooling(model_output, attention_mask):
-    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
-    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=""):
-        # load the optimized model
-        self.model = ORTModelForFeatureExtraction.from_pretrained(path, file_name="model-quantized.onnx")
-        self.tokenizer = AutoTokenizer.from_pretrained(path)
-
-    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()}