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English(EN) Unleashing the Power of ONNX for Speedier SBERT Inference

ONNX框架加速Sentence-BERT推理

本文探讨了ONNX框架如何加速Sentence-BERT (SBERT) 模型的推理时间,SBERT模型常用于生成句子嵌入。作者通过将`all-MiniLM-L6-v2` SBERT模型转换为ONNX格式,并在CPU和GPU上使用Kaggle的1000条电影描述数据集,将其推理速度与原始模型进行比较,以此来展示这一点。文章提供了ONNX及相关库的安装说明,并概述了用于测量性能的实验设置。 AI

影响 使用ONNX优化SBERT推理可以加快需要句子嵌入的应用中的文本数据处理速度。

排序理由 文章详细介绍了一种优化现有模型性能的技术方法,类似于研究论文对方法论和结果的关注。 [lever_c_demoted from research: ic=1 ai=1.0]

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ONNX框架加速Sentence-BERT推理

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文章详细介绍了一种优化现有模型性能的技术方法,类似于研究论文对方法论和结果的关注。 [lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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infra, paper
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完整方法见我们的编辑标准。

报道来源 [1]

  1. Towards AI TIER_1 English(EN) · Swaraj Patil ·

    利用 ONNX 加速 SBERT 推理

    <p><strong>SBERT</strong>, also known as <strong>Sentence-Bert</strong>, is a widely used approach for obtaining sentence embeddings that aim to retain the contextual information within the sentences. However, generating these embeddings can be slow when dealing with large amount…