| from typing import Dict, List, Any | |
| from sentence_transformers import SentenceTransformer | |
| class EndpointHandler(): | |
| def __init__(self, path=""): | |
| model_name = "all-MiniLM-L6-v2" | |
| self.model = SentenceTransformer( | |
| model_name, | |
| backend="onnx", | |
| model_kwargs={ | |
| "file_name": "model_O3.onnx", | |
| "provider": "CUDAExecutionProvider", | |
| } | |
| ) | |
| 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 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. | |
| """ | |
| inputs = data.pop("inputs", data) | |
| prediction = self.model.encode(inputs) | |
| return prediction.tolist() |