What is Transformer?
Transformer is a deep learning architecture introduced in the landmark paper 'Attention Is All You Need' (2017) by Google researchers, which revolutionized natural language processing by replacing recurrent neural networks with a self-attention mechanism that enables parallel processing of sequential data and captures long-range dependencies more effectively.
Quick Facts
| Created | 2017 by Google (Vaswani et al.) |
|---|---|
| Specification | Official Specification |
How It Works
The Transformer architecture fundamentally changed how neural networks process sequential data by eliminating the need for recurrence and convolutions. At its core, the model uses multi-head self-attention mechanisms that allow each position in a sequence to attend to all other positions simultaneously, enabling the model to capture complex relationships regardless of distance. The architecture consists of an encoder-decoder structure, where the encoder processes the input sequence and the decoder generates the output sequence. Positional encodings are added to input embeddings to provide information about token positions since the model has no inherent notion of sequence order. Transformers have become the foundation for most state-of-the-art models in NLP, including BERT, GPT, and T5, and have been successfully adapted to computer vision, audio processing, and multimodal applications. Recent innovations include Flash Attention for memory-efficient attention computation, Mixture of Experts (MoE) for scaling model capacity without proportional compute increase, and architectural variants like Mamba (state space models) that challenge the attention-based paradigm.
Key Characteristics
- Self-attention mechanism enabling parallel computation across all sequence positions
- Multi-head attention for capturing different types of relationships simultaneously
- Positional encoding to inject sequence order information into the model
- Encoder-decoder architecture for sequence-to-sequence tasks
- Layer normalization and residual connections for stable deep network training
- Highly parallelizable compared to recurrent architectures like RNN and LSTM
Common Use Cases
- Large language models (GPT, BERT, LLaMA) for text generation and understanding
- Machine translation and multilingual natural language processing
- Vision Transformers (ViT) for image classification and object detection
- Multimodal models combining text, image, and audio understanding
- Speech recognition and text-to-speech synthesis systems
Example
Loading code...Frequently Asked Questions
What is the self-attention mechanism in Transformers?
Self-attention allows each position in a sequence to attend to all other positions, computing weighted representations based on relevance. It uses Query, Key, and Value vectors: attention scores are computed by comparing queries with keys, then used to weight values. This enables capturing long-range dependencies without recurrence.
What is the difference between encoder-only, decoder-only, and encoder-decoder Transformers?
Encoder-only models (like BERT) process input bidirectionally for understanding tasks. Decoder-only models (like GPT) generate text autoregressively, seeing only previous tokens. Encoder-decoder models (like T5) combine both for sequence-to-sequence tasks like translation, where the encoder processes input and decoder generates output.
Why are positional encodings necessary in Transformers?
Unlike RNNs that process sequences in order, Transformers process all positions simultaneously and have no inherent notion of sequence order. Positional encodings add information about token positions, either through learned embeddings or sinusoidal functions, enabling the model to understand word order and relative positions.
What is multi-head attention and why is it used?
Multi-head attention runs multiple attention operations in parallel, each with different learned projections. This allows the model to jointly attend to information from different representation subspaces at different positions. For example, one head might focus on syntax while another captures semantic relationships.
How do Vision Transformers (ViT) adapt the architecture for images?
Vision Transformers divide images into fixed-size patches (e.g., 16x16 pixels), flatten them into sequences, and add positional embeddings. These patch embeddings are processed like word tokens in NLP Transformers. A classification token aggregates information for image-level predictions. ViT has achieved state-of-the-art results on image classification.