  The Zero-Shot Learning (ZSL) task pertains to the identification of entities
or relations in texts that were not seen during training. ZSL has emerged as a
critical research area due to the scarcity of labeled data in specific domains,
and its applications have grown significantly in recent years. With the advent
of large pretrained language models, several novel methods have been proposed,
resulting in substantial improvements in ZSL performance. There is a growing
demand, both in the research community and industry, for a comprehensive ZSL
framework that facilitates the development and accessibility of the latest
methods and pretrained models.In this study, we propose a novel ZSL framework
called Zshot that aims to address the aforementioned challenges. Our primary
objective is to provide a platform that allows researchers to compare different
state-of-the-art ZSL methods with standard benchmark datasets. Additionally, we
have designed our framework to support the industry with readily available APIs
for production under the standard SpaCy NLP pipeline. Our API is extendible and
evaluable, moreover, we include numerous enhancements such as boosting the
accuracy with pipeline ensembling and visualization utilities available as a
SpaCy extension.
