As the number of open and shared scientific datasets on the Internet increases under the open science movement, efficiently retrieving these datasets is a crucial task in information retrieval (IR) research. In recent years, the development of large models, particularly the pre-training and fine-tuning paradigm, which involves pre-training on large models and fine-tuning on downstream tasks, has provided new solutions for IR match tasks. In this study, we use the original BERT token in the embedding layer, improve the Sentence-BERT model structure in the model layer by introducing the SimCSE and K-Nearest Neighbors method, and use the cosent loss function in the optimization phase to optimize the target output. Our experimental results show that our model outperforms other competing models on both public and self-built datasets through comparative experiments and ablation implementations. This study explores and validates the feasibility and efficiency of pre-training techniques for semantic retrieval of Chinese scientific datasets.

翻译：随着开放科学运动的推进，互联网上开放共享的科学数据集数量不断增加，高效检索这些数据集成为信息检索（IR）研究中的一项关键任务。近年来，大规模模型的发展，特别是预训练与微调范式——即在大模型上进行预训练并在下游任务上进行微调——为信息检索匹配任务提供了新的解决方案。在本研究中，我们在嵌入层使用原始BERT词元，在模型层通过引入SimCSE和K近邻方法改进Sentence-BERT模型结构，并在优化阶段采用cosent损失函数对目标输出进行优化。实验结果表明，通过对比实验和消融实现，我们的模型在公开数据集和自建数据集上均优于其他对比模型。本研究探索并验证了预训练技术用于中文科学数据集语义检索的可行性和有效性。