TREC_WebTrack:临时检索的相关性排名。 这是用于在TREC Web Track上采用机器学习模型的存储库

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TREC WebTrack 这是一个用于在即席任务上采用机器学习模型的存储库。 任何问题,PR或建议都将受到欢迎。 更具体地说,这些模型是查询文档对的重新排序模型。 由于计算每个查询文档对的相关性得分的成本太高,因此我们的目的是对每年的QL提交进行排名,您可以在找到它们。 这些模型能够根据文本文档与特定查询的相关性对文本文档列表进行排序。 可以使用此存储库来训练您的重新模型,或对定制数据(即一组查询和文档)使用预训练。 当前,实现了2个模型,其描述如下: Kai Hui,Andrew Yates,Klaus Berberich,Gerard de Melo。 。 在EMNLP中,2017年。 Kai Hui,Andrew Yates,Klaus Berberich,Gerard de Melo。 。 在WSDM中,2018年。 它们的实现改编自。 要安装运行(Python 3

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