/trænsˈfɔːrmər/
noun … “a neural network architecture that models relationships using attention mechanisms.”
Transformer is a deep learning architecture designed to process sequential or structured data by modeling dependencies between elements through self-attention mechanisms rather than relying solely on recurrence or convolutions. Introduced in 2017, the Transformer fundamentally changed natural language processing (NLP), computer vision, and multimodal AI tasks by enabling highly parallelizable computation and capturing long-range relationships effectively.
The core innovation of a Transformer is the self-attention mechanism, which computes a weighted representation of each element in a sequence relative to all others. Input tokens are mapped to query, key, and value vectors, and attention scores determine how much each token influences the representation of others. Stacking multiple self-attention layers with feed-forward networks allows the model to learn hierarchical patterns and complex contextual relationships across sequences of arbitrary length.
Transformer architectures typically consist of an encoder, decoder, or both. The encoder maps input sequences to contextual embeddings, while the decoder generates output sequences by attending to encoder representations and previous outputs. This design underpins models such as BERT for masked-language understanding, GPT for autoregressive text generation, and Vision Transformers (ViT) for image classification.
Transformer interacts naturally with other deep learning concepts. It is often combined with CNN layers in multimodal tasks, and its training relies heavily on large-scale datasets, gradient optimization, and parallel computation on GPUs or TPUs. Transformers also support transfer learning and fine-tuning, enabling pretrained models to adapt to diverse tasks such as machine translation, summarization, question answering, and image captioning.
Conceptually, Transformer differs from recurrent models like RNN and LSTM by avoiding sequential dependency bottlenecks. It emphasizes global context via attention, providing efficiency and scalability advantages. Related architectures include BERT, GPT, and Autoencoders for unsupervised sequence learning, showing how self-attention generalizes across modalities and domains.
An example of a Transformer in practice using Julia’s Flux:
using Flux
model = Transformer(
encoder=EncoderLayer(512, 8, 2048),
decoder=DecoderLayer(512, 8, 2048),
vocab_size=10000
)
x = rand(Int, 10, 1) # example token sequence
y_pred = model(x) # generates contextual embeddings or predictions The intuition anchor is that a Transformer acts like a dynamic network of relationships: every element in a sequence “looks at” all others to determine influence, enabling the model to capture both local and global patterns efficiently. It transforms raw sequences into rich, contextual representations, allowing machines to understand and generate complex structured data at scale.