few-shot-htc

Few-shot-htc is an advanced technique in the field of natural language processing, designed to achieve efficient text classification with a small number of labeled samples. This method leverages deep learning and transfer learning to enhance the model's generalization ability on small datasets, thereby enabling it to quickly adapt to new tasks and achieve high accuracy. The core objective is to reduce reliance on large-scale labeled data, improve learning efficiency, and increase application flexibility. Few-shot-htc has significant application value in specialized domains such as healthcare and law, effectively addressing issues of data scarcity and accelerating the implementation and optimization of text classification tasks.