philschmid HF staff commited on
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e071055
1 Parent(s): 0c86405

Delete pipeline.py

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  1. pipeline.py +0 -34
pipeline.py DELETED
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- from typing import Dict, List, Any
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- from optimum.onnxruntime import ORTModelForSequenceClassification
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- from transformers import pipeline, AutoTokenizer
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-
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-
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- class PreTrainedPipeline():
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- def __init__(self, path=""):
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- # load the optimized model
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- model = ORTModelForSequenceClassification.from_pretrained(path)
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- tokenizer = AutoTokenizer.from_pretrained(path)
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- # create inference pipeline
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- self.pipeline = pipeline("text-classification", model=model, tokenizer=tokenizer)
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-
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-
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- def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
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- """
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- Args:
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- data (:obj:):
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- includes the input data and the parameters for the inference.
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- Return:
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- A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
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- - "label": A string representing what the label/class is. There can be multiple labels.
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- - "score": A score between 0 and 1 describing how confident the model is for this label/class.
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- """
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- inputs = data.pop("inputs", data)
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- parameters = data.pop("parameters", None)
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-
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- # pass inputs with all kwargs in data
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- if parameters is not None:
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- prediction = self.pipeline(inputs, **parameters)
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- else:
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- prediction = self.pipeline(inputs)
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- # postprocess the prediction
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- return prediction