论文标题
基于改进的模型串联的中国文本分类方法具有低硬件要求
A Chinese Text Classification Method With Low Hardware Requirement Based on Improved Model Concatenation
论文作者
论文摘要
为了提高具有低硬件要求的中国文本分类模型的准确性性能，本文设计了改进的基于串联的模型，这是5种不同的子模型的串联，包括TextCNN，LSTM和BI-LSTM。与现有的合奏学习方法相比，对于文本分类任务，该模型的准确性提高了2％。同时，该模型的硬件要求远低于基于BERT的模型。
In order to improve the accuracy performance of Chinese text classification models with low hardware requirements, an improved concatenation-based model is designed in this paper, which is a concatenation of 5 different sub-models, including TextCNN, LSTM, and Bi-LSTM. Compared with the existing ensemble learning method, for a text classification mission, this model's accuracy is 2% higher. Meanwhile, the hardware requirements of this model are much lower than the BERT-based model.
