Transformer<\/strong> is the architecture that has shaken up artificial intelligence since 2017. Conceived by Google researchers in the seminal article “Attention is All You Need”,<\/em> it paved the way for large language models (LLMs)<\/strong> like GPT, BERT, LLaMA or Gemini<\/strong>. <\/p>\n Why such an impact? Because Transformer has replaced conventional sequential approaches (RNN, LSTM) with anattention<\/strong> mechanism capable of processing words in parallel<\/strong> and understanding their relationships in the overall context of a text<\/strong>. The result: faster, more accurate and infinitely more powerful models for translating, generating, summarizing or analyzing natural language<\/strong>. <\/p>\n Today, the Transformer is no longer just an academic innovation: it’s the bedrock of modern AI<\/strong>, used in machine translation, virtual assistants, document analysis, code generation and even computer vision.<\/p>\n \ud83d\udc49 In this article, we’ll clearly define what a Transformer is<\/strong>, understand how it works, its benefits and limitations, and explore its concrete applications in sectors such as banking, insurance, healthcare and customer relations<\/strong>.<\/p>\n Before the Transformer architecture, the dominant architectures for processing sequential data (such as sentences) were RNNs<\/strong> (recurrent neural networks), LSTMs (Long Short-Term Memory) or GRUs, sometimes combined with attention mechanisms. These architectures had several limitations: <\/p>\n they processed sequences iteratively<\/strong> (word by word), making parallelization difficult,<\/p>\n<\/li>\n long-distance dependencies could fade (vanishing gradient problem) or be poorly modeled,<\/p>\n<\/li>\n convolution-based architectures (as in some variants) had difficulty effectively capturing global relationships in the sequence.<\/p>\n<\/li>\n<\/ul>\n The seminal article “Attention Is All You Need”<\/strong> (Vaswani et al., 2017) proposes an architecture based entirely on attention, without recourse to recurrent or convolutional mechanisms. DeanHub | Code Is Life<\/span>+3<\/span><\/span>arXiv<\/span>+3<\/span><\/span>Wikipedia<\/span>+3<\/span><\/span><\/span><\/a><\/span><\/span> Since its publication, the Transformer architecture has become the cornerstone of many modern language processing models: BERT, GPT, T5, etc. Wikipedia<\/span>+3<\/span><\/span>Wikipedia<\/span>+3<\/span><\/span>neuron.ai<\/span>+3<\/span><\/span><\/span><\/a><\/span><\/span><\/p>\n The basic architecture of the Transformer follows an encoder-decoder<\/strong> scheme, as in many classic seq2seq (sequence-to-sequence) models. neuron.ai<\/span>+3<\/span><\/span>arXiv<\/span>+3<\/span><\/span>Jay Alammar<\/span>+3<\/span><\/span><\/span><\/a><\/span><\/span><\/p>\n The encoder takes a sequence as input (for example, a sentence in the source language).<\/p>\n<\/li>\n It consists of a stack of<\/strong> NN<\/span>N<\/span><\/span><\/span><\/span> identical layers (often N=6N = 6<\/span>N<\/span>=<\/span><\/span>6<\/span><\/span><\/span><\/span> in the “base” version of the original article). arXiv<\/span>+2<\/span><\/span>neuron.ai<\/span>+2<\/span><\/span><\/span><\/a><\/span><\/span><\/p>\n<\/li>\n Each encoder layer consists of two main sub-blocks:<\/p>\n Self-Attention (multi-head)<\/strong><\/p>\n<\/li>\n Positional feed-forward<\/strong> (feed-forward applied to each position)<\/p>\n<\/li>\n<\/ol>\n<\/li>\n Each sub-block is surrounded by a residual connection + layer normalization. arXiv<\/span>+2<\/span><\/span>neuron.ai<\/span>+2<\/span><\/span><\/span><\/a><\/span><\/span><\/p>\n<\/li>\n<\/ul>\n At the encoder’s output, contextualized vector representations are obtained for each of the input tokens.<\/p>\n The decoder generates the output (e.g. a translated sentence) autoregressively (word by word).<\/p>\n<\/li>\n It also includes a stack of NN<\/span>N<\/span><\/span><\/span><\/span> identical layers. arXiv<\/span>+2<\/span><\/span>neuron.ai<\/span>+2<\/span><\/span><\/span><\/a><\/span><\/span><\/p>\n<\/li>\n Each decoder layer contains three sub-blocks:<\/p>\n Masked self-attention<\/strong>: so that the position ii<\/span>i<\/span><\/span><\/span><\/span> cannot “watch” future positions > ii<\/span>i<\/span><\/span><\/span><\/span>.<\/p>\n<\/li>\n Encoder-decoder cross-attention<\/strong>: each decoder position can be attached to encoder positions.<\/p>\n<\/li>\n Positional feed-forward<\/strong><\/p>\n<\/li>\n<\/ol>\n<\/li>\n Here too, each sub-block is equipped with residual connections + layer normalization. arXiv<\/span>+2<\/span><\/span>neuron.ai<\/span>+2<\/span><\/span><\/span><\/a><\/span><\/span><\/p>\n<\/li>\n<\/ul>\n Finally, after the last decoder layer, we apply a linear + softmax layer to obtain the probability distribution on the vocabulary for the next token. arXiv<\/span>+2<\/span><\/span>neuron.ai<\/span>+2<\/span><\/span><\/span><\/a><\/span><\/span><\/p>\n<\/h2>\n
1. Background, motivation and origins<\/h2>\n
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The authors show that this model is not only easier to parallelize, but also achieves better performance on machine translation tasks. arXiv<\/span><\/span><\/span><\/a><\/span><\/span><\/p>\n
\n2. Global overview: encoder \/ decoder<\/h2>\n
2.1 Encoder<\/h3>\n
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2.2 Decoder<\/h3>\n
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