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from typing import Dict, List, Any |
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from transformers import AutoTokenizer, AutoModel |
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from optimum.pipelines import pipeline |
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import torch |
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def mean_pooling(model_output, attention_mask): |
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token_embeddings = model_output[0] |
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input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
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return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.onnx_extractor = pipeline("feature-extraction", model="optimum/sbert-all-MiniLM-L6-with-pooler", accelerator="ort") |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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""" |
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data args: |
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inputs (:obj: `str` | `PIL.Image` | `np.array`) |
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kwargs |
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Return: |
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A :obj:`list` | `dict`: will be serialized and returned |
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""" |
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sentences = data.pop("inputs",data) |
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pred = self.onnx_extractor(sentences) |
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return pred |
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