CasRel implements a novel binary tagging approach where relations are modeled as functions mapping subjects to objects, rather than discrete classification labels. The framework first identifies all possible subjects in a sentence, then applies relation-specific taggers to simultaneously extract corresponding objects. Built on Keras and BERT, it supports multiple relation extraction benchmarks including NYT, WebNLG, and ACE04 datasets.