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from typing import Dict,List, Any
from optimum.onnxruntime import ORTModelForFeatureExtraction
from transformers import AutoTokenizer
def cls_pooling(model_output):
return model_output.last_hidden_state[:,0]
class PreTrainedPipeline():
def __init__(self, path=""):
# load the optimized model
self.model = ORTModelForFeatureExtraction.from_pretrained(path)
self.tokenizer = AutoTokenizer.from_pretrained(path, model_max_length=128)
def __call__(self, inputs: Any) -> Dict[str, List[float]]:
# tokenize the input
encoded_input = self.tokenizer(inputs, padding="longest", truncation=True, return_tensors='pt')
# run the model
model_output = self.model(**encoded_input, return_dict=True)
embeddings = cls_pooling(model_output)
return {"vectors": embeddings[0].tolist()}
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