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本模型为基于ROM-Tiny预训练模型在Multi-CPR医疗数据训练的医疗领域中文语义相关性模型,模型以一个source sentence以及一个句子列表作为输入,最终输出source sentence与列表中每个句子的相关性得分(0-1,分数越高代表两者越相关)。
文本检索是信息检索领域的核心问题, 其在很多信息检索、NLP下游任务中发挥着非常重要的作用。 近几年, BERT等大规模预训练语言模型的出现使得文本表示效果有了大幅度的提升, 基于预训练语言模型构建的文本检索系统在召回、排序效果上都明显优于传统统计模型。
由于文档候选集合通常比较庞大,实际的工业搜索系统中候选文档数量往往在千万甚至更高的数量级, 为了兼顾效率和准确率,目前的文本检索系统通常是基于召回&排序的多阶段搜索框架。在召回阶段,系统的主要目标是从海量文本中去找到潜在跟query相关的文档,得到较小的候选文档集合(100-1000个)。召回完成后, 排序阶段的模型会对这些召回的候选文档进行更加复杂的排序, 产出最后的排序结果。 本模型为基于预训练的排序阶段模型。
模型来源: https://modelscope.cn/models/damo/nlp_corom_passage-ranking_chinese-tiny-medical/summary
本模型基于 ServiceBoot微服务引擎 进行服务化封装,参见: 《CubeAI模型开发指南》
$ sh pip-install-reqs.sh
$ serviceboot start
或
$ python3 run_model_server.py
一键式本地容器化部署和运行,参见: 《CubeAI模型独立部署指南》 或 CubeAI Docker Builder
本模型服务可一键发布至 CubeAI智立方平台 进行共享和部署,参见: 《CubeAI模型发布指南》
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Python Shell Text Dockerfile
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》