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from typing import Dict, List, Any
from optimum.onnxruntime import ORTModelForQuestionAnswering
from transformers import AutoTokenizer, pipeline
class EndpointHandler():
def __init__(self, path=""):
# load the optimized model
self.model = ORTModelForQuestionAnswering.from_pretrained(path, file_name="model_optimized_quantized.onnx")
self.tokenizer = AutoTokenizer.from_pretrained(path)
# create pipeline
self.pipeline = pipeline("question-answering", model=self.model, tokenizer=self.tokenizer)
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 answer and scores of the inference inputs
"""
inputs = data.get("inputs", data)
# run the model
prediction = self.pipeline(**inputs)
# return prediction
return prediction
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