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from typing import Dict, List, Any
from optimum.onnxruntime import ORTModelForSequenceClassification
from transformers import pipeline, AutoTokenizer
class PreTrainedPipeline():
def __init__(self, path=""):
# load the optimized model
model = ORTModelForSequenceClassification.from_pretrained(path)
tokenizer = AutoTokenizer.from_pretrained(path, model_max_length=128)
# create inference pipeline
self.pipeline = pipeline("feature-extraction", model=model, tokenizer=tokenizer)
def __call__(self, inputs: Any) -> List[List[Dict[str, float]]]:
"""
Args:
data (:obj:`str`):
a string containing some text
Return:
A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
- "label": A string representing what the label/class is. There can be multiple labels.
- "score": A score between 0 and 1 describing how confident the model is for this label/class.
"""
# pop inputs for pipeline
def cls_pooling(pipeline_output):
"""
Return the [CLS] token embedding
"""
return [_h[0] for _h in pipeline_output]
embeddings = cls_pooling(self.pipeline(inputs))
return embeddings |