{"id":8360,"date":"2025-08-04T06:27:57","date_gmt":"2025-08-04T06:27:57","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2025\/08\/04\/building-a-transformer-model-for-language-translation\/"},"modified":"2025-08-04T06:27:57","modified_gmt":"2025-08-04T06:27:57","slug":"building-a-transformer-model-for-language-translation","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2025\/08\/04\/building-a-transformer-model-for-language-translation\/","title":{"rendered":"Building a Transformer Model for Language Translation"},"content":{"rendered":"<p>Author: Adrian Tam<\/p>\n<div>This post is divided into six parts; they are: \u2022 Why Transformer is Better than Seq2Seq \u2022 Data Preparation and Tokenization \u2022 Design of a Transformer Model \u2022 Building the Transformer Model \u2022 Causal Mask and Padding Mask \u2022 Training and Evaluation Traditional seq2seq models with recurrent neural networks have two main limitations: \u2022 Sequential processing prevents parallelization \u2022 Limited ability to capture long-term dependencies since hidden states are overwritten whenever an element is processed The Transformer architecture, introduced in the 2017 paper &#8220;Attention is All You Need&#8221;, overcomes these limitations.<\/div>\n<p><a href=\"https:\/\/machinelearningmastery.com\/building-a-transformer-model-for-language-translation\/\">Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Author: Adrian Tam This post is divided into six parts; they are: \u2022 Why Transformer is Better than Seq2Seq \u2022 Data Preparation and Tokenization \u2022 [&hellip;] <span class=\"read-more-link\"><a class=\"read-more\" href=\"https:\/\/www.aiproblog.com\/index.php\/2025\/08\/04\/building-a-transformer-model-for-language-translation\/\">Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":470,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[24],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/8360"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=8360"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/8360\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/466"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=8360"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=8360"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=8360"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}