CHERCHE: A New Tool to Rapidly Implement Pipelines in Information Retrieval
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval(2022)
摘要
In this demo paper, we present a new open-source python module for building information retrieval pipelines with transformers namely CHERCHE. Our aim is to propose an easy to plug tool capable to execute, simple but strong, state-of-the-art information retrieval models. To do so, we have integrated classical models based on lexical matching but also recent models based on semantic matching. Indeed, a large number of models available on public hubs can be now tested on information retrieval tasks with only a few lines. CHERCHE is oriented to newcomers into the neural information retrieval field that want to use transformer-based models in small collections without struggling with heavy tools. The code and documentation of CHERCHE is public available at https://github.com/raphaelsty/cherche
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关键词
Neural Information Retrieval,Python Library,Information Retrieval Pipelines
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