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