{"id":196313,"date":"2025-07-09T06:19:00","date_gmt":"2025-07-09T05:19:00","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=196313"},"modified":"2025-07-09T14:37:09","modified_gmt":"2025-07-09T13:37:09","slug":"all-about-transformers-models","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/all-about-transformers-models","title":{"rendered":"What are Transformers Models? Why are they important in AI?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"196313\" class=\"elementor elementor-196313\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3ceab4f e-flex e-con-boxed e-con\" data-id=\"3ceab4f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0da6069 elementor-widget elementor-widget-text-editor\" data-id=\"0da6069\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><b>Since their introduction in 2017, Transformer models have dramatically transformed the AI landscape, particularly in the field of natural language processing (NLP).<\/b><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9e5594a elementor-widget elementor-widget-text-editor\" data-id=\"9e5594a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Created to address the limitations of <\/span><a href=\"https:\/\/datascientest.com\/en\/recurrent-neural-network-what-is-it\"><span style=\"font-weight: 400;\">recurrent neural networks (RNNs)<\/span><\/a><span style=\"font-weight: 400;\">, Transformer models utilize self-attention mechanisms, enabling parallel data processing. Employed by renowned systems like ChatGPT, BERT, and <\/span><span style=\"font-weight: 400;\">ViT<\/span><span style=\"font-weight: 400;\">, they have paved the way for applications ranging from real-time <\/span><b>translation<\/b><span style=\"font-weight: 400;\"> to <\/span><b>genomic analysis<\/b><span style=\"font-weight: 400;\">. This article delves into their operation, their impact, and the associated challenges.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-68f0f06 e-flex e-con-boxed e-con\" data-id=\"68f0f06\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-1f2c388 elementor-widget elementor-widget-heading\" data-id=\"1f2c388\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-xl\">What preceded Transformers?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-01fe991 elementor-widget elementor-widget-text-editor\" data-id=\"01fe991\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Prior to 2017, the prevailing models for processing sequences (text, speech) were <\/span><a href=\"https:\/\/datascientest.com\/en\/recurrent-neural-network-what-is-it\"><span style=\"font-weight: 400;\">recurrent neural networks (RNNs)<\/span><\/a><span style=\"font-weight: 400;\"> and their variants, such as LSTM (Long Short-Term Memory). These architectures handled data sequentially, maintaining a &#8220;memory state&#8221; updated with each step. However, they faced two significant issues:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Gradient vanishing problem<\/b><span style=\"font-weight: 400;\">: In long sequences, information from the initial tokens (words) was lost.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Prolonged training time<\/b><span style=\"font-weight: 400;\">: Sequential processing curtailed parallelization, slowing learning on large data sets.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7db02a1 elementor-widget elementor-widget-text-editor\" data-id=\"7db02a1\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">To mitigate these issues, researchers introduced <\/span><b>attention layers<\/b><span style=\"font-weight: 400;\">, allowing models to focus on pertinent segments of the input. For instance, in an English-French translation task, the model could directly access crucial words of the source sentence to produce an accurate output. Nevertheless, these mechanisms remained coupled with RNNs&#8230; until the <\/span><b>Transformer revolution<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ce375e elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"6ce375e\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Become an expert in AI<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-313e11b e-flex e-con-boxed e-con\" data-id=\"313e11b\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-32f1a58 elementor-widget elementor-widget-heading\" data-id=\"32f1a58\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">How were Transformers developed?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a299721 elementor-widget elementor-widget-text-editor\" data-id=\"a299721\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Discussed in the pivotal paper <\/span><a href=\"https:\/\/arxiv.org\/abs\/1706.03762\"><i><span style=\"font-weight: 400;\">&#8220;Attention Is All You Need&#8221;<\/span><\/i><\/a><span style=\"font-weight: 400;\"> (Vaswani et al., 2017), this architecture eschews RNNs in favor of pure attention, combined with novel techniques.<\/span><\/p><p><span style=\"font-weight: 400;\">It comprises these <\/span><b>essential components:<\/b><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-56d9cc4 elementor-widget elementor-widget-heading\" data-id=\"56d9cc4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">1. Positional Encoding<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7a3e6ff elementor-widget elementor-widget-text-editor\" data-id=\"7a3e6ff\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Unlike RNNs, Transformers <\/span><b>do not process tokens sequentially<\/b><span style=\"font-weight: 400;\">. To maintain sequential information, each word receives a positional vector (sinusoidal or learned) denoting its position in the sentence.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2e608b elementor-widget elementor-widget-image\" data-id=\"a2e608b\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1000\" height=\"571\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-datascientest-1.webp\" class=\"attachment-full size-full wp-image-196316\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-datascientest-1.webp 1000w, https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-datascientest-1-300x171.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-datascientest-1-768x439.webp 768w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3ba2224 elementor-widget elementor-widget-heading\" data-id=\"3ba2224\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2. Self-Attention<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-72ba7cb elementor-widget elementor-widget-text-editor\" data-id=\"72ba7cb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The essence of the Transformer lies in self-attention layers, where each token interacts with all others via three learned matrices:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Query<\/b><span style=\"font-weight: 400;\">: Represents what the token seeks.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Key<\/b><span style=\"font-weight: 400;\">: Determines what the token can provide.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5328b17 elementor-widget elementor-widget-text-editor\" data-id=\"5328b17\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\"><strong>Value:<\/strong> Encloses the information to be transmitted.<\/span><\/p><p><span style=\"font-weight: 400;\">Attention weights are calculated by dot product between queries and keys, then normalized by a <\/span><i><span style=\"font-weight: 400;\">softmax<\/span><\/i><span style=\"font-weight: 400;\"> function.<\/span><\/p><p><span style=\"font-weight: 400;\">This mechanism allows each token to draw on the entire context of the sentence, independent of its position, thus fostering a better understanding of linguistic relationships.\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bfacaf5 elementor-widget elementor-widget-heading\" data-id=\"bfacaf5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">3. Multi-Head Attention<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3a417bd elementor-widget elementor-widget-text-editor\" data-id=\"3a417bd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">To capture various relationships (syntactic, semantic), each layer employs multiple attention heads in parallel.<\/span><\/p><p><span style=\"font-weight: 400;\">Each attention head learns a distinct representation, allowing the model to concurrently extract multiple levels of meaning, such as grammatical dependencies and semantic relations.<\/span><\/p><p><span style=\"font-weight: 400;\">The results are concatenated and transformed through a feed-forward neural network.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-605b9f1 elementor-widget elementor-widget-heading\" data-id=\"605b9f1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">4. Encoder-Decoder<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aed2717 elementor-widget elementor-widget-text-editor\" data-id=\"aed2717\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Encoder<\/b><span style=\"font-weight: 400;\">: Processes the input to create a contextual representation.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Decoder<\/b><span style=\"font-weight: 400;\">: Utilizes this representation and previous tokens to generate the output incrementally (e.g., translation).<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-71e03ab elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"71e03ab\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\"> Understanding how AI works<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-43a3b71 e-flex e-con-boxed e-con\" data-id=\"43a3b71\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d5cefd6 elementor-widget elementor-widget-heading\" data-id=\"d5cefd6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">How are Transformer Models applied?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-460954c elementor-widget elementor-widget-text-editor\" data-id=\"460954c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Firstly, there is <\/span><b>ChatGPT and LLMs<\/b><span style=\"font-weight: 400;\">. Generative Transformers (GPT, <\/span><a href=\"https:\/\/datascientest.com\/en\/google-unveils-palm-2-its-revolutionary-ai-model\"><span style=\"font-weight: 400;\">PaLM<\/span><\/a><span style=\"font-weight: 400;\">) generate coherent text by predicting the next token. ChatGPT, trained via reinforcement learning, excels in dialogue and content creation.<\/span><\/p><p><span style=\"font-weight: 400;\">We also see <\/span><b>contextual comprehension with BERT<\/b><span style=\"font-weight: 400;\">. Unlike GPT, BERT employs a bidirectional encoder to capture the global context. By 2019, it enhanced 70% of Google searches.<\/span><\/p><p><span style=\"font-weight: 400;\">Additionally, there are <\/span><b>Vision Transformers (ViT)<\/b><span style=\"font-weight: 400;\">: by dividing an image into 16\u00d716 patches, ViT rivals CNNs in classification, object detection, etc., thanks to its ability to model long-range relationships.<\/span><\/p><p><span style=\"font-weight: 400;\">The figure below depicts the architecture of Transformers alongside GPT and BERT for comparison, both utilizing elements of the Transformer architecture:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-251003b elementor-widget elementor-widget-image\" data-id=\"251003b\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1920\" height=\"1294\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model.webp\" class=\"attachment-full size-full wp-image-196317\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model.webp 1920w, https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-300x202.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-1024x690.webp 1024w, https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-768x518.webp 768w, https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-1536x1035.webp 1536w\" sizes=\"(max-width: 1920px) 100vw, 1920px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-6df7544 e-flex e-con-boxed e-con\" data-id=\"6df7544\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4fbaca1 elementor-widget elementor-widget-heading\" data-id=\"4fbaca1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">What are the benefits of Transformer Models?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b432857 elementor-widget elementor-widget-text-editor\" data-id=\"b432857\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">By <\/span><b>parallelizing<\/b><span style=\"font-weight: 400;\"> the processes, they become more <\/span><b>efficient<\/b><span style=\"font-weight: 400;\">: by bypassing sequential processing, Transformers fully harness <a href=\"https:\/\/datascientest.com\/en\/harnessing-the-power-of-gpus-in-data-science-what-you-need-to-know\">GPUs<\/a>\/TPUs, reducing training times by 50 to 80% compared to RNNs.<\/span><\/p><p><span style=\"font-weight: 400;\">Their architecture allows for <\/span><b>extensive pre-training on unlabeled corpora<\/b><span style=\"font-weight: 400;\">, such as Wikipedia or book contents. Models like BERT or GPT-3 achieve unprecedented performance thanks to hundreds of billions of parameters.<\/span><\/p><p><span style=\"font-weight: 400;\">Originally crafted for <\/span><a href=\"https:\/\/datascientest.com\/en\/natural-language-processing-definition-and-principles\"><span style=\"font-weight: 400;\">NLP<\/span><\/a><span style=\"font-weight: 400;\">, Transformers today are <\/span><b>versatile<\/b><span style=\"font-weight: 400;\">, expanding into:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><a href=\"https:\/\/datascientest.com\/en\/deep-learning-course\/formation-computer-vision\">Computer vision<\/a><span style=\"font-weight: 400;\">: ViT (Vision Transformer) divides images into patches and processes them as sequences.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Biology<\/b><span style=\"font-weight: 400;\">: analyzing DNA or protein sequences.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Multimodal<\/b><span style=\"font-weight: 400;\">: models that integrate text, image, and sound, like DALL-E.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3feb3a0 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"3feb3a0\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\"> Learn how to develop AI<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0b42298 e-flex e-con-boxed e-con\" data-id=\"0b42298\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-c5dcbae elementor-widget elementor-widget-heading\" data-id=\"c5dcbae\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">What are the constraints of Transformer Models?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f36aeaf elementor-widget elementor-widget-text-editor\" data-id=\"f36aeaf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">First, we consider the <\/span><b>computational and environmental cost<\/b><span style=\"font-weight: 400;\">: training models like GPT-3 consumes several megawatt-hours, raising ethical and ecological concerns.<\/span><\/p><p><span style=\"font-weight: 400;\">Moreover, Transformers perpetuate the <\/span><b>biases<\/b><span style=\"font-weight: 400;\"> present in their training data, presenting significant risks when used for critical decisions, such as recruitment through resume filtering or medical decision support, as implicit biases can sustain and even amplify. Additionally, they can generate false yet plausible statements, such as fabricating nonexistent academic references or asserting a fictional event actually occurred. These statements are referred to as <\/span><b>hallucinations<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">An inevitable limitation is the <\/span><b>complexity of interpretation<\/b><span style=\"font-weight: 400;\">. Indeed, attention mechanisms, although potent, remain &#8220;black boxes,&#8221; complicating the detection of systemic errors.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-7c1af02 e-flex e-con-boxed e-con\" data-id=\"7c1af02\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7465f21 elementor-widget elementor-widget-heading\" data-id=\"7465f21\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">What are the future prospects?<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-05a4cf7 elementor-widget elementor-widget-text-editor\" data-id=\"05a4cf7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">The swift evolution of Transformers has profoundly influenced numerous fields, making research on optimization and reducing their energy footprint essential. Today, promising prospects regarding the use of Transformers include:<\/span><\/p><ul><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Eco-Efficient Models<\/b><span style=\"font-weight: 400;\">: Exploring resource-efficient architectures prioritizing optimization of resource consumption (energy, memory, computing power, data volume\u2026), like <\/span><i><span style=\"font-weight: 400;\">Sparse Transformers<\/span><\/i><span style=\"font-weight: 400;\">, or employing techniques like LoRA (Low-Rank Adaptation), which enables refining models without necessitating complete retraining.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Multimodal AI: Seamlessly integrating text-image-video like GPT-4 or Gemini, which handle multiple modalities within a single model.<\/span><\/li><li style=\"font-weight: 400;\" aria-level=\"1\"><b>Ethical Personalization<\/b><span style=\"font-weight: 400;\">: Adapting LLMs to specific needs without bias.<\/span><\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-73c1b7b elementor-widget elementor-widget-image\" data-id=\"73c1b7b\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"1000\" height=\"571\" src=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-datascientest-2.webp\" class=\"attachment-full size-full wp-image-196315\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-datascientest-2.webp 1000w, https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-datascientest-2-300x171.webp 300w, https:\/\/datascientest.com\/en\/files\/2025\/05\/transformers-model-datascientest-2-768x439.webp 768w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ea9bd91 e-flex e-con-boxed e-con\" data-id=\"ea9bd91\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-fccdcd0 elementor-widget elementor-widget-heading\" data-id=\"fccdcd0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-xl\">Conclusion<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8b1b58b elementor-widget elementor-widget-text-editor\" data-id=\"8b1b58b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Transformers have <\/span><b>revolutionized the field of AI<\/b><span style=\"font-weight: 400;\">, combining efficiency, versatility, and power. Confronting technical and ethical challenges, they remain fundamental to ongoing advancements, from virtual assistants to medical research and diagnostic tools. Their progression towards more responsible and less energy-intensive systems is likely to define <\/span><a href=\"https:\/\/datascientest.com\/en\/all-about-anthropic\"><span style=\"font-weight: 400;\">the next decade of artificial intelligence<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fffde77 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"fffde77\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Find a course for you<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-bd04e71 e-flex e-con-boxed e-con\" data-id=\"bd04e71\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f5cf24d elementor-widget elementor-widget-html\" data-id=\"f5cf24d\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What preceded Transformers?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Before 2017, recurrent neural networks (RNNs) and variants like LSTM processed sequences sequentially but faced gradient vanishing and slow training issues.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"How were Transformers developed?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Transformers use self-attention mechanisms with positional encoding, multi-head attention, and encoder-decoder architecture, introduced in the paper 'Attention Is All You Need' (2017).\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"How are Transformer Models applied?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"They power generative models like ChatGPT, bidirectional models like BERT for search, and Vision Transformers for image tasks, among other applications.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What are the benefits of Transformer Models?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Transformers enable parallel processing, reduce training times, allow large-scale pre-training, and are versatile across NLP, vision, biology, and multimodal tasks.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What are the constraints of Transformer Models?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"They have high computational and environmental costs, can perpetuate biases, produce plausible but false outputs (hallucinations), and are difficult to interpret.\"\r\n      }\r\n    },\r\n    {\r\n      \"@type\": \"Question\",\r\n      \"name\": \"What are the future prospects for Transformer Models?\",\r\n      \"acceptedAnswer\": {\r\n        \"@type\": \"Answer\",\r\n        \"text\": \"Future prospects include eco-efficient models, multimodal AI integrating text, image and video, and ethical personalization to reduce biases.\"\r\n      }\r\n    }\r\n  ]\r\n}\r\n<\/script>\r\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Since their introduction in 2017, Transformer models have dramatically transformed the AI landscape, particularly in the field of natural language processing (NLP). Created to address the limitations of recurrent neural networks (RNNs), Transformer models utilize self-attention mechanisms, enabling parallel data processing. Employed by renowned systems like ChatGPT, BERT, and ViT, they have paved the way [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":196314,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2427],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>What are Transformers Models? 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