Transformers are a type of deep learning architecture that revolutionized natural language processing (NLP) and other sequential data tasks. They were introduced in the 2017 paper "Attention is All You Need" by Vaswani et al., and have since become the backbone of many state-of-the-art NLP models.
The core innovation of transformers lies in their attention mechanism, which allows them to process input data in parallel and capture long-range dependencies effectively. Traditional sequential models, like recurrent neural networks (RNNs), process input data one step at a time, leading to computational inefficiencies and difficulty in capturing long-term dependencies.
The main components of a transformer are:
Encoder: The encoder takes the input sequence and converts it into a set of hidden representations. Each input token is embedded into a vector and then processed through multiple layers of self-attention and feed-forward neural networks.
Decoder (optional): In applications like machine translation, a transformer can also include a decoder. The decoder generates the output sequence based on the encoder's hidden representations, attending to the relevant parts of the input during the generation process.
Key concepts of transformers:
Self-Attention: Self-attention allows a transformer to weigh the importance of different input tokens while processing each token. It computes a weighted sum of all input tokens, where the weights are learned based on the similarity between the current token and other tokens in the sequence.
Multi-Head Attention: Transformers use multiple self-attention heads, allowing them to capture different aspects of the relationships between tokens. Each head learns different attention patterns and contributes to a more expressive model.
Positional Encoding: Since transformers do not have inherent notions of word order like RNNs, they rely on positional encoding to convey the relative positions of tokens in the input sequence.
Applications of transformers:
Natural Language Processing: Transformers have been widely used in NLP tasks, including language translation, sentiment analysis, named entity recognition, question-answering systems, language modeling, and more. Models like BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) have achieved remarkable performance in various NLP benchmarks.
Computer Vision: Transformers have been adapted to computer vision tasks, such as image captioning and object detection. Vision Transformers (ViTs) treat image pixels as tokens and process them using the transformer architecture.
Speech Recognition and Synthesis: Transformers have shown promise in speech recognition and speech synthesis tasks, contributing to advances in automatic speech recognition (ASR) and text-to-speech (TTS) systems.
Recommender Systems: Transformers have been used to build recommendation models by encoding user-item interactions and learning personalized recommendations.
Drug Discovery: In the pharmaceutical industry, transformers have been applied to predict molecular properties and identify potential drug candidates.
Transformers' ability to capture long-range dependencies and their parallel processing nature has made them a versatile and powerful architecture, driving advancements in various domains beyond natural language processing.