This articleis approximately 5200 words, recommended reading time is10+minutes We know that, to some extent, the deeper the network, the more parameters it has, and the more complex the model, the better its final performance. The compression algorithm for neural networks aims to transform a large and complex pre-trained model into a streamlined smaller model. … Read more
Approximately 3500 words, recommended reading time 10 minutes. Today, we will explore the core technologies behind large models! 1. Transformer The Transformer model is undoubtedly the solid foundation of large language models, ushering in a new era in deep learning. In the early stages, Recurrent Neural Networks (RNNs) were the core means of handling sequential … Read more
©PaperWeekly Original · Author｜Su Jianlin School｜Zhuiyi Technology Research Direction｜NLP, Neural Networks Recently, I learned about a BERT model compression method called “BERT-of-Theseus”, derived from the paper BERT-of-Theseus: Compressing BERT by Progressive Module Replacing. This is a model compression scheme built on the concept of “replaceability”. Compared to conventional methods like pruning and distillation, it appears … Read more
Baido NLP Column Author: Baido NLP Introduction In recent years, we have been deeply engaged in the integration of neural network models with NLP tasks, achieving significant progress in various areas such as syntactic analysis, semantic similarity computation, and chat generation. In search engines, semantic similarity features have also become one of the most important … Read more
Big Data Digest authorized reprint from Data Pie Compiled by:Sun Siqi, Cheng Yu, Gan Zhe, Liu Jingjing In the past year, there have been many breakthrough advancements in the research of language models, such as GPT, which generates sentences that are convincingly realistic [1]; BERT, XLNet, RoBERTa [2,3,4], etc., have swept various NLP rankings as … Read more
Large models based on the Transformer architecture are playing an increasingly important role in artificial intelligence, especially in the fields of natural language processing (NLP) and computer vision (CV). Model compression methods reduce their memory and computational costs, which is a necessary step for implementing Transformer models on practical devices. Given the unique architecture of … Read more