The library provides reusable embedding layers and graph/network building blocks for text classification tasks including long-text classification, short-sentence classification, and multi-label classification. It implements over 15 model architectures ranging from classic methods like FastText and TextCNN to modern transformer-based approaches including BERT, Albert, and Xlnet, with support for techniques like attention, capsule networks, and hierarchical attention networks.