Author: Zen and the Art of Computer Programming
1 Introduction
With the rapid development of artificial intelligence (AI) technology, deep learning (DL) and Transformer models have become the two most representative research directions. In recent years, both have made major breakthroughs in the fields of natural language processing, image recognition, text generation and other fields, and have had a wide impact in all walks of life. This article will start from the perspective of the latest research results and related applications, comprehensively introduce the Transformer model and some commonly used algorithms, and demonstrate the Transformer model through examples to help readers understand the operating principle of the Transformer model and better apply it to actual production. Environment.
2. Explanation of basic concepts and terms
1. Transformer Overview
Transformer is an NLP model based on the Attention mechanism, consisting of an Encoder and a Decoder. The Encoder receives the input sequence (words or symbols), encodes it into a fixed-length vector, and pays attention to the input sequence through the Attention module. Decoder generates output sequences (words or symbols) and also pays attention to contextual information in this way. The entire model does not require memory function and can directly use the self-attention mechanism to achieve sequence-to-sequence (Seq2Seq) mapping conversion. Therefore, the Transformer model is considered to have strong computational efficiency and can solve the long-term dependency problem in sequence modeling.
2. Transformer model structure
Figure 1 Transformer model architecture
3. Attention mechanism
The attention mechanism is a way for the model to automatically "pay attention" to the information at certain positions of the input sequence rather than simply copying the input sequence. Specifically