{"id":479386,"date":"2023-08-09T10:35:54","date_gmt":"2023-08-09T10:35:54","guid":{"rendered":""},"modified":"2023-09-05T11:18:41","modified_gmt":"2023-09-05T11:18:41","slug":"transformer-xl","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/ir\/wiki\/transformer-xl\/","title":{"rendered":"\u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631-XL"},"content":{"rendered":"<p>\u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0645\u062e\u062a\u0635\u0631\u06cc \u062f\u0631 \u0645\u0648\u0631\u062f Transformer-XL<\/p>\n<p>Transformer-XL\u060c \u0645\u062e\u0641\u0641 Transformer Extra Long\u060c \u06cc\u06a9 \u0645\u062f\u0644 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0631 \u0627\u0633\u0627\u0633 \u0645\u0639\u0645\u0627\u0631\u06cc \u0627\u0635\u0644\u06cc \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631 \u0633\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a. &quot;XL&quot; \u062f\u0631 \u0646\u0627\u0645 \u062e\u0648\u062f \u0628\u0647 \u062a\u0648\u0627\u0646\u0627\u06cc\u06cc \u0645\u062f\u0644 \u0628\u0631\u0627\u06cc \u0645\u062f\u06cc\u0631\u06cc\u062a \u062a\u0648\u0627\u0644\u06cc \u0647\u0627\u06cc \u0637\u0648\u0644\u0627\u0646\u06cc \u062a\u0631 \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0645\u06a9\u0627\u0646\u06cc\u0632\u0645\u06cc \u0628\u0647 \u0646\u0627\u0645 \u0628\u0627\u0632\u06af\u0634\u062a \u0627\u0634\u0627\u0631\u0647 \u062f\u0627\u0631\u062f. \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0645\u062f\u06cc\u0631\u06cc\u062a \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0645\u062a\u0648\u0627\u0644\u06cc \u0631\u0627 \u0627\u0641\u0632\u0627\u06cc\u0634 \u0645\u06cc \u062f\u0647\u062f \u0648 \u0632\u0645\u06cc\u0646\u0647 \u0622\u06af\u0627\u0647\u06cc \u0648 \u062f\u0631\u06a9 \u0628\u0647\u062a\u0631 \u0648\u0627\u0628\u0633\u062a\u06af\u06cc \u0647\u0627 \u0631\u0627 \u062f\u0631 \u062a\u0648\u0627\u0644\u06cc \u0647\u0627\u06cc \u0637\u0648\u0644\u0627\u0646\u06cc \u0641\u0631\u0627\u0647\u0645 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<h2>\u062a\u0627\u0631\u06cc\u062e\u0686\u0647 \u067e\u06cc\u062f\u0627\u06cc\u0634 Transformer-XL \u0648 \u0627\u0648\u0644\u06cc\u0646 \u0630\u06a9\u0631 \u0622\u0646<\/h2>\n<p>Transformer-XL \u062a\u0648\u0633\u0637 \u0645\u062d\u0642\u0642\u0627\u0646 Google Brain \u062f\u0631 \u0645\u0642\u0627\u0644\u0647 \u0627\u06cc \u0628\u0627 \u0639\u0646\u0648\u0627\u0646 &quot;Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context&quot; \u06a9\u0647 \u062f\u0631 \u0633\u0627\u0644 2019 \u0645\u0646\u062a\u0634\u0631 \u0634\u062f\u060c \u0645\u0639\u0631\u0641\u06cc \u0634\u062f. \u0628\u0631 \u0627\u0633\u0627\u0633 \u0645\u0648\u0641\u0642\u06cc\u062a \u0645\u062f\u0644 \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631 \u067e\u06cc\u0634\u0646\u0647\u0627\u062f \u0634\u062f\u0647 \u062a\u0648\u0633\u0637 Vaswani \u0648 \u0647\u0645\u06a9\u0627\u0631\u0627\u0646. \u062f\u0631 \u0633\u0627\u0644 2017\u060c Transformer-XL \u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u063a\u0644\u0628\u0647 \u0628\u0631 \u0645\u062d\u062f\u0648\u062f\u06cc\u062a\u200c\u0647\u0627\u06cc \u0632\u0645\u06cc\u0646\u0647 \u0628\u0627 \u0637\u0648\u0644 \u062b\u0627\u0628\u062a \u0628\u0648\u062f \u0648 \u062f\u0631 \u0646\u062a\u06cc\u062c\u0647 \u062a\u0648\u0627\u0646\u0627\u06cc\u06cc \u0645\u062f\u0644 \u0631\u0627 \u0628\u0631\u0627\u06cc \u06af\u0631\u0641\u062a\u0646 \u0648\u0627\u0628\u0633\u062a\u06af\u06cc\u200c\u0647\u0627\u06cc \u0628\u0644\u0646\u062f\u0645\u062f\u062a \u0628\u0647\u0628\u0648\u062f \u0628\u062e\u0634\u06cc\u062f.<\/p>\n<h2>\u0627\u0637\u0644\u0627\u0639\u0627\u062a \u062f\u0642\u06cc\u0642 \u062f\u0631\u0628\u0627\u0631\u0647 Transformer-XL: \u06af\u0633\u062a\u0631\u0634 \u0645\u0648\u0636\u0648\u0639 Transformer-XL<\/h2>\n<p>Transformer-XL \u0628\u0627 \u062a\u0648\u0627\u0646\u0627\u06cc\u06cc \u0622\u0646 \u062f\u0631 \u06af\u0631\u0641\u062a\u0646 \u0648\u0627\u0628\u0633\u062a\u06af\u06cc \u0647\u0627 \u0628\u0631 \u0631\u0648\u06cc \u062a\u0648\u0627\u0644\u06cc \u0647\u0627\u06cc \u06af\u0633\u062a\u0631\u062f\u0647\u060c \u0628\u0647\u0628\u0648\u062f \u062f\u0631\u06a9 \u0632\u0645\u06cc\u0646\u0647 \u062f\u0631 \u06a9\u0627\u0631\u0647\u0627\u06cc\u06cc \u0645\u0627\u0646\u0646\u062f \u062a\u0648\u0644\u06cc\u062f \u0645\u062a\u0646\u060c \u062a\u0631\u062c\u0645\u0647 \u0648 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0645\u0634\u062e\u0635 \u0645\u06cc \u0634\u0648\u062f. \u0637\u0631\u0627\u062d\u06cc \u062c\u062f\u06cc\u062f\u060c \u0639\u0648\u062f \u062f\u0631 \u0628\u062e\u0634\u200c\u0647\u0627 \u0648 \u06cc\u06a9 \u0637\u0631\u062d \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a\u06cc \u0646\u0633\u0628\u06cc \u0631\u0627 \u0645\u0639\u0631\u0641\u06cc \u0645\u06cc\u200c\u06a9\u0646\u062f. \u0627\u06cc\u0646\u0647\u0627 \u0628\u0647 \u0645\u062f\u0644 \u0627\u062c\u0627\u0632\u0647 \u0645\u06cc\u200c\u062f\u0647\u062f \u062a\u0627 \u062d\u0627\u0644\u062a\u200c\u0647\u0627\u06cc \u067e\u0646\u0647\u0627\u0646 \u0631\u0627 \u062f\u0631 \u0628\u062e\u0634\u200c\u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u0628\u0647 \u062e\u0627\u0637\u0631 \u0628\u0633\u067e\u0627\u0631\u062f \u0648 \u0631\u0627\u0647 \u0631\u0627 \u0628\u0631\u0627\u06cc \u062f\u0631\u06a9 \u0639\u0645\u06cc\u0642\u200c\u062a\u0631 \u062f\u0646\u0628\u0627\u0644\u0647\u200c\u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u0637\u0648\u0644\u0627\u0646\u06cc \u0647\u0645\u0648\u0627\u0631 \u06a9\u0646\u062f.<\/p>\n<h2>\u0633\u0627\u062e\u062a\u0627\u0631 \u062f\u0627\u062e\u0644\u06cc Transformer-XL: Transformer-XL \u0686\u06af\u0648\u0646\u0647 \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f<\/h2>\n<p>Transformer-XL \u0627\u0632 \u0686\u0646\u062f\u06cc\u0646 \u0644\u0627\u06cc\u0647 \u0648 \u0627\u062c\u0632\u0627 \u062a\u0634\u06a9\u06cc\u0644 \u0634\u062f\u0647 \u0627\u0633\u062a\u060c \u0627\u0632 \u062c\u0645\u0644\u0647:<\/p>\n<ol>\n<li><strong>\u0639\u0648\u062f \u0628\u062e\u0634:<\/strong> \u0628\u0647 \u062d\u0627\u0644\u062a \u0647\u0627\u06cc \u067e\u0646\u0647\u0627\u0646 \u0627\u0632 \u0628\u062e\u0634 \u0647\u0627\u06cc \u0642\u0628\u0644\u06cc \u0627\u062c\u0627\u0632\u0647 \u0645\u06cc \u062f\u0647\u062f \u062a\u0627 \u062f\u0631 \u0628\u062e\u0634 \u0647\u0627\u06cc \u0628\u0639\u062f\u06cc \u0645\u062c\u062f\u062f\u0627\u064b \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0634\u0648\u0646\u062f.<\/li>\n<li><strong>\u06a9\u062f\u06af\u0630\u0627\u0631\u06cc \u0647\u0627\u06cc \u0645\u0648\u0642\u0639\u06cc\u062a \u0646\u0633\u0628\u06cc:<\/strong> \u0628\u0647 \u0645\u062f\u0644 \u06a9\u0645\u06a9 \u0645\u06cc \u06a9\u0646\u062f \u062a\u0627 \u0645\u0648\u0642\u0639\u06cc\u062a \u0647\u0627\u06cc \u0646\u0633\u0628\u06cc \u0646\u0634\u0627\u0646\u0647 \u0647\u0627 \u0631\u0627 \u062f\u0631 \u06cc\u06a9 \u062f\u0646\u0628\u0627\u0644\u0647\u060c \u0628\u062f\u0648\u0646 \u062a\u0648\u062c\u0647 \u0628\u0647 \u0645\u0648\u0642\u0639\u06cc\u062a \u0645\u0637\u0644\u0642 \u0622\u0646\u0647\u0627 \u062f\u0631\u06a9 \u06a9\u0646\u062f.<\/li>\n<li><strong>\u0644\u0627\u06cc\u0647 \u0647\u0627\u06cc \u062a\u0648\u062c\u0647:<\/strong> \u0627\u06cc\u0646 \u0644\u0627\u06cc\u0647 \u0647\u0627 \u0645\u062f\u0644 \u0631\u0627 \u0642\u0627\u062f\u0631 \u0645\u06cc \u0633\u0627\u0632\u0646\u062f \u062a\u0627 \u062f\u0631 \u0635\u0648\u0631\u062a \u0646\u06cc\u0627\u0632 \u0631\u0648\u06cc \u0642\u0633\u0645\u062a \u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u062f\u0646\u0628\u0627\u0644\u0647 \u0648\u0631\u0648\u062f\u06cc \u062a\u0645\u0631\u06a9\u0632 \u06a9\u0646\u062f.<\/li>\n<li><strong>\u0644\u0627\u06cc\u0647 \u0647\u0627\u06cc \u0641\u06cc\u062f \u0641\u0648\u0631\u0648\u0627\u0631\u062f:<\/strong> \u0645\u0633\u0626\u0648\u0644 \u062a\u0628\u062f\u06cc\u0644 \u062f\u0627\u062f\u0647 \u0647\u0627 \u062f\u0631 \u0647\u0646\u06af\u0627\u0645 \u0639\u0628\u0648\u0631 \u0627\u0632 \u0634\u0628\u06a9\u0647 \u0627\u0633\u062a.<\/li>\n<\/ol>\n<p>\u062a\u0631\u06a9\u06cc\u0628 \u0627\u06cc\u0646 \u0627\u062c\u0632\u0627 \u0628\u0647 Transformer-XL \u0627\u062c\u0627\u0632\u0647 \u0645\u06cc \u062f\u0647\u062f \u062a\u0627 \u062a\u0648\u0627\u0644\u06cc \u0647\u0627\u06cc \u0637\u0648\u0644\u0627\u0646\u06cc \u062a\u0631\u06cc \u0631\u0627 \u0645\u062f\u06cc\u0631\u06cc\u062a \u06a9\u0646\u062f \u0648 \u0648\u0627\u0628\u0633\u062a\u06af\u06cc \u0647\u0627\u06cc\u06cc \u0631\u0627 \u06a9\u0647 \u062f\u0631 \u063a\u06cc\u0631 \u0627\u06cc\u0646 \u0635\u0648\u0631\u062a \u0628\u0631\u0627\u06cc \u0645\u062f\u0644 \u0647\u0627\u06cc \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631 \u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f \u062f\u0634\u0648\u0627\u0631 \u0627\u0633\u062a\u060c \u0636\u0628\u0637 \u06a9\u0646\u062f.<\/p>\n<h2>\u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u06a9\u0644\u06cc\u062f\u06cc Transformer-XL<\/h2>\n<p>\u0628\u0631\u062e\u06cc \u0627\u0632 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u06a9\u0644\u06cc\u062f\u06cc Transformer-XL \u0639\u0628\u0627\u0631\u062a\u0646\u062f \u0627\u0632:<\/p>\n<ul>\n<li><strong>\u062d\u0627\u0641\u0638\u0647 \u0645\u062a\u0646\u06cc \u0637\u0648\u0644\u0627\u0646\u06cc \u062a\u0631:<\/strong> \u0648\u0627\u0628\u0633\u062a\u06af\u06cc \u0647\u0627\u06cc \u0637\u0648\u0644\u0627\u0646\u06cc \u0645\u062f\u062a \u0631\u0627 \u062f\u0631 \u062a\u0648\u0627\u0644\u06cc \u062b\u0628\u062a \u0645\u06cc \u06a9\u0646\u062f.<\/li>\n<li><strong>\u0627\u0641\u0632\u0627\u06cc\u0634 \u06a9\u0627\u0631\u0627\u06cc\u06cc:<\/strong> \u0627\u0632 \u0645\u062d\u0627\u0633\u0628\u0627\u062a \u0628\u062e\u0634 \u0647\u0627\u06cc \u0642\u0628\u0644\u06cc \u0645\u062c\u062f\u062f\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u06a9\u0627\u0631\u0627\u06cc\u06cc \u0631\u0627 \u0628\u0647\u0628\u0648\u062f \u0645\u06cc \u0628\u062e\u0634\u062f.<\/li>\n<li><strong>\u062b\u0628\u0627\u062a \u062a\u0645\u0631\u06cc\u0646\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647:<\/strong> \u0645\u0634\u06a9\u0644 \u0646\u0627\u067e\u062f\u06cc\u062f \u0634\u062f\u0646 \u06af\u0631\u0627\u062f\u06cc\u0627\u0646 \u0647\u0627 \u062f\u0631 \u062a\u0648\u0627\u0644\u06cc \u0647\u0627\u06cc \u0637\u0648\u0644\u0627\u0646\u06cc \u062a\u0631 \u0631\u0627 \u06a9\u0627\u0647\u0634 \u0645\u06cc \u062f\u0647\u062f.<\/li>\n<li><strong>\u0627\u0646\u0639\u0637\u0627\u0641 \u067e\u0630\u06cc\u0631\u06cc:<\/strong> \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0628\u0631\u0627\u06cc \u06a9\u0627\u0631\u0647\u0627\u06cc \u0645\u062a\u0648\u0627\u0644\u06cc \u0645\u062e\u062a\u0644\u0641 \u0627\u0632 \u062c\u0645\u0644\u0647 \u062a\u0648\u0644\u06cc\u062f \u0645\u062a\u0646 \u0648 \u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc \u0627\u0639\u0645\u0627\u0644 \u0634\u0648\u062f.<\/li>\n<\/ul>\n<h2>\u0627\u0646\u0648\u0627\u0639 Transformer-XL<\/h2>\n<p>\u0628\u0647 \u0637\u0648\u0631 \u0639\u0645\u062f\u0647 \u06cc\u06a9 \u0645\u0639\u0645\u0627\u0631\u06cc \u0628\u0631\u0627\u06cc Transformer-XL \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f\u060c \u0627\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0622\u0646 \u0631\u0627 \u0628\u0631\u0627\u06cc \u06a9\u0627\u0631\u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641\u06cc \u0637\u0631\u0627\u062d\u06cc \u06a9\u0631\u062f\u060c \u0645\u0627\u0646\u0646\u062f:<\/p>\n<ol>\n<li><strong>\u0645\u062f\u0644 \u0633\u0627\u0632\u06cc \u0632\u0628\u0627\u0646:<\/strong> \u062f\u0631\u06a9 \u0648 \u062a\u0648\u0644\u06cc\u062f \u0645\u062a\u0646 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc.<\/li>\n<li><strong>\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc:<\/strong> \u062a\u0631\u062c\u0645\u0647 \u0645\u062a\u0646 \u0628\u06cc\u0646 \u0632\u0628\u0627\u0646 \u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641<\/li>\n<li><strong>\u062e\u0644\u0627\u0635\u0647 \u0633\u0627\u0632\u06cc \u0645\u062a\u0646:<\/strong> \u062e\u0644\u0627\u0635\u0647 \u06a9\u0631\u062f\u0646 \u0642\u0637\u0639\u0627\u062a \u0628\u0632\u0631\u06af \u0645\u062a\u0646<\/li>\n<\/ol>\n<h2>\u0631\u0627\u0647 \u0647\u0627\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 Transformer-XL\u060c \u0645\u0634\u06a9\u0644\u0627\u062a \u0648 \u0631\u0627\u0647 \u062d\u0644 \u0647\u0627\u06cc \u0645\u0631\u0628\u0648\u0637 \u0628\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0622\u0646\u0647\u0627<\/h2>\n<p><strong>\u0631\u0627\u0647 \u0647\u0627\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647:<\/strong><\/p>\n<ul>\n<li>\u062f\u0631\u06a9 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc<\/li>\n<li>\u062a\u0648\u0644\u06cc\u062f \u0645\u062a\u0646<\/li>\n<li>\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc<\/li>\n<\/ul>\n<p><strong>\u0645\u0634\u06a9\u0644\u0627\u062a \u0648 \u0631\u0627\u0647 \u062d\u0644 \u0647\u0627:<\/strong><\/p>\n<ul>\n<li><strong>\u0645\u0633\u0626\u0644\u0647:<\/strong> \u0645\u0635\u0631\u0641 \u062d\u0627\u0641\u0638\u0647\n<ul>\n<li><strong>\u0631\u0627\u0647 \u062d\u0644:<\/strong> \u0627\u0632 \u0645\u0648\u0627\u0632\u06cc \u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u06cc\u0627 \u0633\u0627\u06cc\u0631 \u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc \u0628\u0647\u06cc\u0646\u0647 \u0633\u0627\u0632\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f.<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u0645\u0633\u0626\u0644\u0647:<\/strong> \u067e\u06cc\u0686\u06cc\u062f\u06af\u06cc \u062f\u0631 \u0622\u0645\u0648\u0632\u0634\n<ul>\n<li><strong>\u0631\u0627\u0647 \u062d\u0644:<\/strong> \u0627\u0632 \u0645\u062f\u0644 \u0647\u0627\u06cc \u0627\u0632 \u067e\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f \u06cc\u0627 \u06a9\u0627\u0631\u0647\u0627\u06cc \u062e\u0627\u0635 \u0631\u0627 \u0628\u0647 \u062f\u0642\u062a \u062a\u0646\u0638\u06cc\u0645 \u06a9\u0646\u06cc\u062f.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2>\u0648\u06cc\u0698\u06af\u06cc \u0647\u0627\u06cc \u0627\u0635\u0644\u06cc \u0648 \u0645\u0642\u0627\u06cc\u0633\u0647 \u0647\u0627\u06cc \u062f\u06cc\u06af\u0631 \u0628\u0627 \u0627\u0635\u0637\u0644\u0627\u062d\u0627\u062a \u0645\u0634\u0627\u0628\u0647<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u0648\u06cc\u0698\u06af\u06cc<\/th>\n<th>\u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631-XL<\/th>\n<th>\u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631 \u0627\u0635\u0644\u06cc<\/th>\n<th>LSTM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u062d\u0627\u0641\u0638\u0647 \u0645\u062a\u0646\u06cc<\/td>\n<td>\u062a\u0645\u062f\u06cc\u062f \u0634\u062f\u0647<\/td>\n<td>\u0637\u0648\u0644 \u062b\u0627\u0628\u062a<\/td>\n<td>\u06a9\u0648\u062a\u0627\u0647<\/td>\n<\/tr>\n<tr>\n<td>\u06a9\u0627\u0631\u0627\u06cc\u06cc \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc<\/td>\n<td>\u0628\u0627\u0644\u0627\u062a\u0631<\/td>\n<td>\u0645\u062a\u0648\u0633\u0637<\/td>\n<td>\u067e\u0627\u06cc\u06cc\u0646 \u062a\u0631<\/td>\n<\/tr>\n<tr>\n<td>\u062b\u0628\u0627\u062a \u062a\u0645\u0631\u06cc\u0646<\/td>\n<td>\u0628\u0647\u0628\u0648\u062f \u06cc\u0627\u0641\u062a\u0647<\/td>\n<td>\u0627\u0633\u062a\u0627\u0646\u062f\u0627\u0631\u062f<\/td>\n<td>\u067e\u0627\u06cc\u06cc\u0646 \u062a\u0631<\/td>\n<\/tr>\n<tr>\n<td>\u0627\u0646\u0639\u0637\u0627\u0641 \u067e\u0630\u06cc\u0631\u06cc<\/td>\n<td>\u0628\u0627\u0644\u0627<\/td>\n<td>\u0645\u062a\u0648\u0633\u0637<\/td>\n<td>\u0645\u062a\u0648\u0633\u0637<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u062f\u06cc\u062f\u06af\u0627\u0647 \u0647\u0627 \u0648 \u0641\u0646\u0627\u0648\u0631\u06cc \u0647\u0627\u06cc \u0622\u06cc\u0646\u062f\u0647 \u0645\u0631\u062a\u0628\u0637 \u0628\u0627 Transformer-XL<\/h2>\n<p>Transformer-XL \u0631\u0627\u0647 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647\u200c\u062a\u0631\u06cc \u0647\u0645\u0648\u0627\u0631 \u0645\u06cc\u200c\u06a9\u0646\u062f \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u0646\u062f \u062f\u0646\u0628\u0627\u0644\u0647\u200c\u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u0637\u0648\u0644\u0627\u0646\u06cc \u0631\u0627 \u062f\u0631\u06a9 \u0648 \u062a\u0648\u0644\u06cc\u062f \u06a9\u0646\u0646\u062f. \u062a\u062d\u0642\u06cc\u0642\u0627\u062a \u0622\u06cc\u0646\u062f\u0647 \u0645\u0645\u06a9\u0646 \u0627\u0633\u062a \u0628\u0631 \u06a9\u0627\u0647\u0634 \u067e\u06cc\u0686\u06cc\u062f\u06af\u06cc \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc\u060c \u0627\u0641\u0632\u0627\u06cc\u0634 \u0628\u06cc\u0634\u062a\u0631 \u06a9\u0627\u0631\u0627\u06cc\u06cc \u0645\u062f\u0644 \u0648 \u06af\u0633\u062a\u0631\u0634 \u06a9\u0627\u0631\u0628\u0631\u062f\u0647\u0627\u06cc \u0622\u0646 \u062f\u0631 \u062d\u0648\u0632\u0647\u200c\u0647\u0627\u06cc \u062f\u06cc\u06af\u0631 \u0645\u0627\u0646\u0646\u062f \u067e\u0631\u062f\u0627\u0632\u0634 \u062a\u0635\u0648\u06cc\u0631\u06cc \u0648 \u0635\u0648\u062a\u06cc \u062a\u0645\u0631\u06a9\u0632 \u06a9\u0646\u062f.<\/p>\n<h2>\u0686\u06af\u0648\u0646\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 \u0633\u0631\u0648\u0631\u0647\u0627\u06cc \u067e\u0631\u0648\u06a9\u0633\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f \u06cc\u0627 \u0628\u0627 Transformer-XL \u0645\u0631\u062a\u0628\u0637 \u0634\u062f<\/h2>\n<p>\u0627\u0632 \u0633\u0631\u0648\u0631\u0647\u0627\u06cc \u067e\u0631\u0627\u06a9\u0633\u06cc \u0645\u0627\u0646\u0646\u062f OneProxy \u0645\u06cc \u062a\u0648\u0627\u0646 \u062f\u0631 \u062c\u0645\u0639 \u0622\u0648\u0631\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0647\u0627\u06cc Transformer-XL \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f. \u0628\u0627 \u0646\u0627\u0634\u0646\u0627\u0633 \u06a9\u0631\u062f\u0646 \u062f\u0631\u062e\u0648\u0627\u0633\u062a\u200c\u0647\u0627\u06cc \u062f\u0627\u062f\u0647\u060c \u0633\u0631\u0648\u0631\u0647\u0627\u06cc \u067e\u0631\u0648\u06a9\u0633\u06cc \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u0646\u062f \u0645\u062c\u0645\u0648\u0639\u0647\u200c\u0627\u06cc \u0627\u0632 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0628\u0632\u0631\u06af \u0648 \u0645\u062a\u0646\u0648\u0639 \u0631\u0627 \u062a\u0633\u0647\u06cc\u0644 \u06a9\u0646\u0646\u062f. \u0627\u06cc\u0646 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0628\u0647 \u062a\u0648\u0633\u0639\u0647 \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0642\u0648\u06cc\u200c\u062a\u0631 \u0648 \u0647\u0645\u0647\u200c\u06a9\u0627\u0631\u0647\u200c\u062a\u0631 \u06a9\u0645\u06a9 \u06a9\u0646\u062f \u0648 \u0639\u0645\u0644\u06a9\u0631\u062f \u0631\u0627 \u062f\u0631 \u0648\u0638\u0627\u06cc\u0641 \u0648 \u0632\u0628\u0627\u0646\u200c\u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u0627\u0641\u0632\u0627\u06cc\u0634 \u062f\u0647\u062f.<\/p>\n<h2>\u0644\u06cc\u0646\u06a9 \u0647\u0627\u06cc \u0645\u0631\u0628\u0648\u0637\u0647<\/h2>\n<ol>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1901.02860\" target=\"_new\" rel=\"noopener nofollow\">\u06a9\u0627\u063a\u0630 \u0627\u0635\u0644\u06cc Transformer-XL<\/a><\/li>\n<li><a href=\"https:\/\/ai.googleblog.com\/2019\/01\/transformer-xl-unleashing-potential-of.html\" target=\"_new\" rel=\"noopener nofollow\">\u067e\u0633\u062a \u0648\u0628\u0644\u0627\u06af \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u06af\u0648\u06af\u0644 \u062f\u0631 Transformer-XL<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/tensorflow\/tensor2tensor\/tree\/master\/tensor2tensor\/models\/research\/transformer_xl\" target=\"_new\" rel=\"noopener nofollow\">\u0627\u062c\u0631\u0627\u06cc TensorFlow Transformer-XL<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/ir\/\" target=\"_new\" rel=\"noopener\">\u0648\u0628 \u0633\u0627\u06cc\u062a OneProxy<\/a><\/li>\n<\/ol>\n<p>Transformer-XL \u06cc\u06a9 \u067e\u06cc\u0634\u0631\u0641\u062a \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647 \u062f\u0631 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0627\u0633\u062a \u06a9\u0647 \u0642\u0627\u0628\u0644\u06cc\u062a \u0647\u0627\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0627\u06cc \u0631\u0627 \u062f\u0631 \u062f\u0631\u06a9 \u0648 \u062a\u0648\u0644\u06cc\u062f \u062f\u0646\u0628\u0627\u0644\u0647 \u0647\u0627\u06cc \u0637\u0648\u0644\u0627\u0646\u06cc \u0627\u0631\u0627\u0626\u0647 \u0645\u06cc \u062f\u0647\u062f. \u06a9\u0627\u0631\u0628\u0631\u062f\u0647\u0627\u06cc \u0622\u0646 \u0628\u0633\u06cc\u0627\u0631 \u06af\u0633\u062a\u0631\u062f\u0647 \u0627\u0633\u062a \u0648 \u0637\u0631\u0627\u062d\u06cc \u0646\u0648\u0622\u0648\u0631\u0627\u0646\u0647 \u0622\u0646 \u0627\u062d\u062a\u0645\u0627\u0644\u0627\u064b \u0628\u0631 \u062a\u062d\u0642\u06cc\u0642\u0627\u062a \u0622\u06cc\u0646\u062f\u0647 \u062f\u0631 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u0648 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 \u062a\u0623\u062b\u06cc\u0631 \u0645\u06cc \u06af\u0630\u0627\u0631\u062f.<\/p>","protected":false},"featured_media":470729,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-479386","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Transformer-XL: An In-Depth Exploration<\/mark>","faq_items":[{"question":"What is Transformer-XL?","answer":"<p>Transformer-XL, or Transformer Extra Long, is a deep learning model that builds upon the original Transformer architecture. It's designed to handle longer sequences of data by using a mechanism known as recurrence. This allows for better understanding of context and dependencies in long sequences, particularly useful in natural language processing tasks.<\/p>"},{"question":"What are the key features of Transformer-XL?","answer":"<p>The key features of Transformer-XL include longer contextual memory, increased efficiency, enhanced training stability, and flexibility. These features enable it to capture long-term dependencies in sequences, reuse computations, reduce vanishing gradients in longer sequences, and be applied to various sequential tasks.<\/p>"},{"question":"How does the Transformer-XL work?","answer":"<p>The Transformer-XL consists of several components including segment recurrence, relative positional encodings, attention layers, and feed-forward layers. These components work together to allow Transformer-XL to handle longer sequences, improve efficiency, and capture dependencies that are otherwise difficult for standard Transformer models.<\/p>"},{"question":"How is Transformer-XL different from other models like the original Transformer and LSTM?","answer":"<p>Transformer-XL is known for its extended contextual memory, higher computational efficiency, improved training stability, and high flexibility. This contrasts with the original Transformer's fixed-length context and LSTM's shorter contextual memory. The comparative table in the main article provides a detailed comparison.<\/p>"},{"question":"What types of Transformer-XL exist and what are its applications?","answer":"<p>There is mainly one architecture for Transformer-XL, but it can be tailored for different tasks such as language modeling, machine translation, and text summarization.<\/p>"},{"question":"What problems might arise with Transformer-XL and how can they be solved?","answer":"<p>Some challenges include memory consumption and complexity in training. These can be addressed through techniques like model parallelism, optimization techniques, using pre-trained models, or fine-tuning on specific tasks.<\/p>"},{"question":"How can proxy servers like OneProxy be associated with Transformer-XL?","answer":"<p>Proxy servers like OneProxy can be used in data gathering for training Transformer-XL models. They facilitate the collection of large, diverse datasets by anonymizing data requests, aiding in the development of robust and versatile models.<\/p>"},{"question":"What are the future perspectives related to Transformer-XL?","answer":"<p>The future of Transformer-XL may focus on reducing computational complexity, enhancing efficiency, and expanding its applications to domains like video and audio processing. It's paving the way for advanced models that can understand and generate long textual sequences.<\/p>"},{"question":"Where can I find more information about Transformer-XL?","answer":"<p>You can find more detailed information through the original Transformer-XL paper, Google's AI blog post on Transformer-XL, the TensorFlow implementation of Transformer-XL, and the OneProxy website. Links to these resources are provided in the related links section of the article.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/wiki\/479386","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/wiki\/479386\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/media\/470729"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/ir\/wp-json\/wp\/v2\/media?parent=479386"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}