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The Zero-Shot Learning (ZSL) task pertains to the identification of entitiesor relations in texts that were not seen during training. ZSL has emerged as acritical research area due to the scarcity of labeled data in specific domains,and its applications have grown significantly in recent years. With the adventof large pretrained language models, several novel methods have been proposed,resulting in substantial improvements in ZSL performance. There is a growingdemand, both in the research community and industry, for a comprehensive ZSLframework that facilitates the development and accessibility of the latestmethods and pretrained models.In this study, we propose a novel ZSL frameworkcalled Zshot that aims to address the aforementioned challenges. Our primaryobjective is to provide a platform that allows researchers to compare differentstate-of-the-art ZSL methods with standard benchmark datasets. Additionally, wehave designed our framework to support the industry with readily available APIsfor production under the standard SpaCy NLP pipeline. Our API is extendible andevaluable, moreover, we include numerous enhancements such as boosting theaccuracy with pipeline ensembling and visualization utilities available as aSpaCy extension.

Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction