{"id":16096,"date":"2024-01-20T18:47:17","date_gmt":"2024-01-20T15:17:17","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%da%86%d9%86%d8%af-%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c-%d8%a8\/"},"modified":"2024-01-20T18:47:17","modified_gmt":"2024-01-20T15:17:17","slug":"%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%da%86%d9%86%d8%af-%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c-%d8%a8","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%da%86%d9%86%d8%af-%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c-%d8%a8\/","title":{"rendered":"\u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc NLP: \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0628\u0627 Keras"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_85 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\"><p class=\"ez-toc-title\" style=\"cursor:inherit\">\u0633\u0631\u0641\u0635\u0644\u0647\u0627\u06cc \u0645\u0637\u0644\u0628<\/p>\n<\/div><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%da%86%d9%86%d8%af-%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c-%d8%a8\/#%d9%85%d8%b9%d8%b1%d9%81%db%8c\" >\u0645\u0639\u0631\u0641\u06cc<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%da%86%d9%86%d8%af-%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c-%d8%a8\/#%d9%85%d8%ac%d9%85%d9%88%d8%b9%d9%87_%d8%af%d8%a7%d8%af%d9%87\" >\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%da%86%d9%86%d8%af-%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c-%d8%a8\/#%d8%a7%db%8c%d8%ac%d8%a7%d8%af_%d9%85%d8%af%d9%84_%d9%87%d8%a7%db%8c_%d8%af%d8%b3%d8%aa%d9%87_%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%da%86%d9%86%d8%af_%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c\" >\u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644 \u0647\u0627\u06cc \u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%da%86%d9%86%d8%af-%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c-%d8%a8\/#%d9%85%d8%af%d9%84_%d8%b7%d8%a8%d9%82%d9%87%e2%80%8c%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%da%86%d9%86%d8%af_%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c_%d8%a8%d8%a7_%d9%84%d8%a7%db%8c%d9%87_%d8%ae%d8%b1%d9%88%d8%ac%db%8c_%d9%88%d8%a7%d8%ad%d8%af\" >\u0645\u062f\u0644 \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0628\u0627 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0648\u0627\u062d\u062f<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%da%86%d9%86%d8%af-%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c-%d8%a8\/#%d9%85%d8%af%d9%84_%d8%b7%d8%a8%d9%82%d9%87%e2%80%8c%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%da%86%d9%86%d8%af_%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c_%d8%a8%d8%a7_%d9%84%d8%a7%db%8c%d9%87%e2%80%8c%d9%87%d8%a7%db%8c_%d8%ae%d8%b1%d9%88%d8%ac%db%8c_%da%86%d9%86%d8%af%da%af%d8%a7%d9%86%d9%87\" >\u0645\u062f\u0644 \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0628\u0627 \u0644\u0627\u06cc\u0647\u200c\u0647\u0627\u06cc \u062e\u0631\u0648\u062c\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/rasanegaar.com\/blog\/%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86-%d8%a8%d8%b1%d8%a7%db%8c-nlp-%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%da%86%d9%86%d8%af-%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c-%d8%a8\/#%d9%86%d8%aa%db%8c%d8%ac%d9%87\" >\u0646\u062a\u06cc\u062c\u0647<\/a><\/li><\/ul><\/nav><\/div>\n<span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 11<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span><p> <br \/>\n<\/p>\n<div><noscript><\/noscript><\/p>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%b9%d8%b1%d9%81%db%8c\"><\/span>\u0645\u0639\u0631\u0641\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u06cc\u0646 \u0646\u0648\u0632\u062f\u0647\u0645\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0632 \u0633\u0631\u06cc \u0645\u0642\u0627\u0644\u0627\u062a \u0645\u0646 \u0627\u0633\u062a \u0631\u0648\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc NLP.  \u0627\u0632 \u0686\u0646\u062f \u0645\u0642\u0627\u0644\u0647 \u0627\u062e\u06cc\u0631\u060c \u0645\u0627 \u0645\u0641\u0627\u0647\u06cc\u0645 \u0646\u0633\u0628\u062a\u0627\u064b \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 NLP \u0631\u0627 \u0628\u0631\u0631\u0633\u06cc \u06a9\u0631\u062f\u0647 \u0627\u06cc\u0645 \u0631\u0648\u06cc \u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642  \u062f\u0631 \u0622\u062e\u0631\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647\u060c \u0631\u0648\u0634 \u0627\u06cc\u062c\u0627\u062f \u06cc\u06a9 \u0645\u062f\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0648\u0631\u0648\u062f\u06cc \u0647\u0627\u06cc \u0645\u062a\u0639\u062f\u062f \u0627\u0632 \u0627\u0646\u0648\u0627\u0639 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u0631\u0627 \u062f\u06cc\u062f\u06cc\u0645.  \u0645\u0627 \u06cc\u06a9 \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc\u200c\u06a9\u0646\u0646\u062f\u0647 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0645\u062a\u0646\u06cc \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0648\u0631\u0648\u062f\u06cc\u200c\u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u0628\u0647 \u0627\u0636\u0627\u0641\u0647 \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0645\u062a\u0627 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0631\u062f\u06cc\u0645.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0648\u0634 \u0627\u06cc\u062c\u0627\u062f \u06cc\u06a9 \u0645\u062f\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0628\u0627 \u062e\u0631\u0648\u062c\u06cc \u0647\u0627\u06cc \u0645\u062a\u0639\u062f\u062f \u0631\u0627 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u06cc\u062f.  \u0645\u0627 \u062f\u0631 \u062d\u0627\u0644 \u062a\u0648\u0633\u0639\u0647 \u06cc\u06a9 \u0645\u062f\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u062e\u0648\u0627\u0647\u06cc\u0645 \u0628\u0648\u062f \u06a9\u0647 \u06cc\u06a9 \u0646\u0638\u0631 \u0645\u062a\u0646\u06cc \u0631\u0627 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0686\u0646\u062f\u06cc\u0646 \u0628\u0631\u0686\u0633\u0628 \u0645\u0631\u062a\u0628\u0637 \u0628\u0627 \u0646\u0638\u0631 \u0631\u0627 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u06cc \u06a9\u0646\u062f.  \u0645\u0633\u0626\u0644\u0647 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u062f\u0631 \u0648\u0627\u0642\u0639 \u0632\u06cc\u0631\u0645\u062c\u0645\u0648\u0639\u0647 \u0627\u06cc \u0627\u0632 \u0645\u062f\u0644 \u0647\u0627\u06cc \u062e\u0631\u0648\u062c\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647 \u0627\u0633\u062a.  \u062f\u0631 \u067e\u0627\u06cc\u0627\u0646 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0634\u0645\u0627 \u0642\u0627\u062f\u0631 \u062e\u0648\u0627\u0647\u06cc\u062f \u0628\u0648\u062f \u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u062f \u0631\u0648\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0634\u0645\u0627<\/p>\n<p>\u0631\u0648\u06cc\u06a9\u0631\u062f \u062a\u0648\u0636\u06cc\u062d \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u06a9\u0644\u06cc \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u06af\u0633\u062a\u0631\u0634 \u062f\u0627\u062f.  \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u06cc\u06a9 \u0645\u0634\u06a9\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0631\u0627 \u062d\u0644 \u06a9\u0646\u06cc\u062f \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u06cc\u06a9 \u062a\u0635\u0648\u06cc\u0631 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0631\u0648\u062f\u06cc \u062f\u0627\u0631\u06cc\u062f \u0648 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u062a\u0635\u0648\u06cc\u0631 \u0648 \u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u062a\u0635\u0648\u06cc\u0631 \u0631\u0627 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u06a9\u0646\u06cc\u062f.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0631\u062d\u0644\u0647\u060c \u062a\u0648\u0636\u06cc\u062d \u062a\u0641\u0627\u0648\u062a \u0628\u06cc\u0646 \u06cc\u06a9 \u0645\u0633\u0626\u0644\u0647 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0686\u0646\u062f \u0637\u0628\u0642\u0647 \u0648 \u06cc\u06a9 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628 \u0645\u0647\u0645 \u0627\u0633\u062a.  \u062f\u0631 \u0645\u0633\u0627\u0626\u0644 \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0686\u0646\u062f \u06a9\u0644\u0627\u0633\u0647\u060c \u06cc\u06a9 \u0646\u0645\u0648\u0646\u0647 \u06cc\u0627 \u06cc\u06a9 \u0631\u06a9\u0648\u0631\u062f \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0628\u0647 \u06cc\u06a9 \u0648 \u062a\u0646\u0647\u0627 \u06cc\u06a9\u06cc \u0627\u0632 \u06a9\u0644\u0627\u0633\u200c\u0647\u0627\u06cc \u062e\u0631\u0648\u062c\u06cc \u0645\u062a\u0639\u062f\u062f \u062a\u0639\u0644\u0642 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f.  \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u062f\u0631 \u0645\u0633\u0626\u0644\u0647 \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u06a9\u0647 \u062f\u0631 \u0645\u0642\u0627\u0644\u0647 \u06af\u0630\u0634\u062a\u0647 \u0645\u0637\u0627\u0644\u0639\u0647 \u06a9\u0631\u062f\u06cc\u0645\u060c \u06cc\u06a9 \u0645\u0631\u0648\u0631 \u0645\u062a\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f &#8220;\u062e\u0648\u0628&#8221;\u060c &#8220;\u0628\u062f&#8221; \u06cc\u0627 &#8220;\u0645\u062a\u0648\u0633\u0637&#8221; \u0628\u0627\u0634\u062f.  \u0646\u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u062f\u0631 \u0622\u0646 \u0648\u0627\u062d\u062f \u0647\u0645 \u00ab\u062e\u0648\u0628\u00bb \u0648 \u0647\u0645 \u00ab\u0645\u062a\u0648\u0633\u0637\u00bb \u0628\u0627\u0634\u062f.  \u0627\u0632 \u0637\u0631\u0641 \u062f\u06cc\u06af\u0631 \u062f\u0631 \u0645\u0633\u0627\u0626\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc\u060c \u06cc\u06a9 \u0646\u0645\u0648\u0646\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0647\u0645\u0632\u0645\u0627\u0646 \u0686\u0646\u062f\u06cc\u0646 \u062e\u0631\u0648\u062c\u06cc \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f.  \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0645\u062b\u0627\u0644\u060c \u062f\u0631 \u0645\u0633\u0626\u0644\u0647 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u06a9\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u062d\u0644 \u06a9\u0646\u06cc\u0645\u060c \u06cc\u06a9 \u0646\u0638\u0631 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0686\u0646\u062f\u06cc\u0646 \u0628\u0631\u0686\u0633\u0628 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f.  \u0627\u06cc\u0646 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627 \u0647\u0645\u0632\u0645\u0627\u0646 \u0634\u0627\u0645\u0644 \u00ab\u0633\u0645\u06cc\u00bb\u060c \u00ab\u0641\u062d\u0634\u00bb\u060c \u00ab\u062a\u0648\u0647\u06cc\u0646 \u0622\u0645\u06cc\u0632\u00bb \u0648 &#8230; \u0645\u06cc \u0628\u0627\u0634\u062f.<\/p>\n<h2 id=\"thedataset\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%ac%d9%85%d9%88%d8%b9%d9%87_%d8%af%d8%a7%d8%af%d9%87\"><\/span>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0627\u0645\u0644 \u0646\u0638\u0631\u0627\u062a\u06cc \u0627\u0632 <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/en.wikipedia.org\/wiki\/Help:Talk_pages\">\u0628\u062d\u062b \u0648\u06cc\u06a9\u06cc \u067e\u062f\u06cc\u0627 page<\/a>  \u0648\u06cc\u0631\u0627\u06cc\u0634 \u0647\u0627  \u0634\u0634 \u0628\u0631\u0686\u0633\u0628 \u062e\u0631\u0648\u062c\u06cc \u0628\u0631\u0627\u06cc \u0647\u0631 \u0646\u0638\u0631 \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f: \u0633\u0645\u06cc\u060c \u0634\u062f\u06cc\u062f_\u0633\u0645\u06cc\u060c \u0646\u0627\u067e\u0633\u0646\u062f\u060c \u062a\u0647\u062f\u06cc\u062f\u060c \u062a\u0648\u0647\u06cc\u0646 \u0648 \u0647\u0648\u06cc\u062a_\u0646\u0641\u0631\u062a.  \u06cc\u06a9 \u0646\u0638\u0631 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u062f \u0628\u0647 \u0647\u0645\u0647 \u0627\u06cc\u0646 \u062f\u0633\u062a\u0647\u200c\u0647\u0627 \u06cc\u0627 \u0632\u06cc\u0631\u0645\u062c\u0645\u0648\u0639\u0647\u200c\u0627\u06cc \u0627\u0632 \u0627\u06cc\u0646 \u062f\u0633\u062a\u0647\u200c\u0647\u0627 \u062a\u0639\u0644\u0642 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f \u06a9\u0647 \u0622\u0646 \u0631\u0627 \u0628\u0647 \u06cc\u06a9 \u0645\u0634\u06a9\u0644 \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u06cc\u200c\u06a9\u0646\u062f.<\/p>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0632 \u0627\u06cc\u0646\u062c\u0627 \u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u0646\u06cc\u062f <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.kaggle.com\/c\/jigsaw-toxic-comment-classification-challenge\/overview\">\u0644\u06cc\u0646\u06a9 \u06a9\u0627\u06af\u0644<\/a>.  \u0645\u0627 \u0641\u0642\u0637 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f <code>train.csv<\/code> \u0641\u0627\u06cc\u0644\u06cc \u06a9\u0647 \u0634\u0627\u0645\u0644 160000 \u0631\u06a9\u0648\u0631\u062f \u0645\u06cc \u0628\u0627\u0634\u062f.<\/p>\n<p>\u0641\u0627\u06cc\u0644 CSV \u0631\u0627 \u062f\u0631 \u0641\u0647\u0631\u0633\u062a \u0645\u062d\u0644\u06cc \u062e\u0648\u062f \u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u0646\u06cc\u062f.  \u0645\u0646 \u0641\u0627\u06cc\u0644 \u0631\u0627 \u0628\u0647 &#8220;toxic_comments.csv&#8221; \u062a\u063a\u06cc\u06cc\u0631 \u0646\u0627\u0645 \u062f\u0627\u062f\u0647 \u0627\u0645.  \u0634\u0645\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0647\u0631 \u0646\u0627\u0645\u06cc \u0628\u0647 \u0622\u0646 \u0628\u062f\u0647\u06cc\u062f\u060c \u0627\u0645\u0627 \u0641\u0642\u0637 \u0645\u0637\u0645\u0626\u0646 \u0634\u0648\u06cc\u062f \u06a9\u0647 \u0627\u0632 \u0622\u0646 \u0646\u0627\u0645 \u062f\u0631 \u06a9\u062f \u062e\u0648\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f.<\/p>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f import \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0648 \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062f\u0631 \u0628\u0631\u0646\u0627\u0645\u0647 \u0645\u0627.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632 \u0631\u0627 \u0648\u0627\u0631\u062f \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> numpy <span class=\"hljs-keyword\">import<\/span> array\n<span class=\"hljs-keyword\">from<\/span> keras.preprocessing.text <span class=\"hljs-keyword\">import<\/span> one_hot\n<span class=\"hljs-keyword\">from<\/span> keras.preprocessing.sequence <span class=\"hljs-keyword\">import<\/span> pad_sequences\n<span class=\"hljs-keyword\">from<\/span> keras.models <span class=\"hljs-keyword\">import<\/span> Sequential\n<span class=\"hljs-keyword\">from<\/span> keras.layers.core <span class=\"hljs-keyword\">import<\/span> Activation, Dropout, Dense\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> Flatten, LSTM\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> GlobalMaxPooling1D\n<span class=\"hljs-keyword\">from<\/span> keras.models <span class=\"hljs-keyword\">import<\/span> Model\n<span class=\"hljs-keyword\">from<\/span> keras.layers.embeddings <span class=\"hljs-keyword\">import<\/span> Embedding\n<span class=\"hljs-keyword\">from<\/span> sklearn.model_selection <span class=\"hljs-keyword\">import<\/span> train_test_split\n<span class=\"hljs-keyword\">from<\/span> keras.preprocessing.text <span class=\"hljs-keyword\">import<\/span> Tokenizer\n<span class=\"hljs-keyword\">from<\/span> keras.layers <span class=\"hljs-keyword\">import<\/span> Input\n<span class=\"hljs-keyword\">from<\/span> keras.layers.merge <span class=\"hljs-keyword\">import<\/span> Concatenate\n\n<span class=\"hljs-keyword\">import<\/span> pandas <span class=\"hljs-keyword\">as<\/span> pd\n<span class=\"hljs-keyword\">import<\/span> numpy <span class=\"hljs-keyword\">as<\/span> np\n<span class=\"hljs-keyword\">import<\/span> re\n\n<span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u0627\u06a9\u0646\u0648\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u062f\u0631 \u062d\u0627\u0641\u0638\u0647 \u0628\u0627\u0631\u06af\u0630\u0627\u0631\u06cc \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">toxic_comments = pd.read_csv(<span class=\"hljs-string\">\"\/content\/drive\/My Drive\/Colab Datasets\/toxic_comments.csv\"<\/span>)\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0634\u06a9\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0646\u0645\u0627\u06cc\u0634 \u0645\u06cc \u062f\u0647\u062f \u0648 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0647\u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0686\u0627\u067e \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(toxic_comments.shape)\n\ntoxic_comments.head()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">(159571,8)\n<\/code><\/pre>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u0627\u0645\u0644 159571 \u0631\u06a9\u0648\u0631\u062f \u0648 8 \u0633\u062a\u0648\u0646 \u0627\u0633\u062a.  \u0647\u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0628\u0647 \u0634\u06a9\u0644 \u0632\u06cc\u0631 \u0627\u0633\u062a:<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/multi-label-text-classification-with-keras-1.PNG\" alt=\"img1\" title=\"\"><\/p>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u062a\u0645\u0627\u0645 \u0631\u06a9\u0648\u0631\u062f\u0647\u0627\u06cc\u06cc \u0631\u0627 \u06a9\u0647 \u0647\u0631 \u0631\u062f\u06cc\u0641 \u062d\u0627\u0648\u06cc \u0645\u0642\u062f\u0627\u0631 \u062a\u0647\u06cc \u06cc\u0627 \u0631\u0634\u062a\u0647 \u062e\u0627\u0644\u06cc \u0627\u0633\u062a \u062d\u0630\u0641 \u06a9\u0646\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">filter<\/span> = toxic_comments(<span class=\"hljs-string\">\"comment_text\"<\/span>) != <span class=\"hljs-string\">\"\"<\/span>\ntoxic_comments = toxic_comments(<span class=\"hljs-built_in\">filter<\/span>)\ntoxic_comments = toxic_comments.dropna()\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 <code>comment_text<\/code> \u0633\u062a\u0648\u0646 \u062d\u0627\u0648\u06cc \u0646\u0638\u0631\u0627\u062a \u0645\u062a\u0646\u06cc \u0627\u0633\u062a.  \u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f print \u06cc\u06a9 \u0646\u0638\u0631 \u062a\u0635\u0627\u062f\u0641\u06cc \u0648 \u0633\u067e\u0633 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627\u06cc \u0646\u0638\u0631\u0627\u062a \u0631\u0627 \u0628\u0628\u06cc\u0646\u06cc\u062f.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(toxic_comments(<span class=\"hljs-string\">\"comment_text\"<\/span>)(<span class=\"hljs-number\">168<\/span>))\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">You should be fired, you're a moronic wimp who is too lazy to do research. It makes me sick that people like you exist in this world.\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0628\u0647 \u0648\u0636\u0648\u062d \u06cc\u06a9 \u0627\u0638\u0647\u0627\u0631 \u0646\u0638\u0631 \u0633\u0645\u06cc \u0627\u0633\u062a.  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0628\u0631\u0686\u0633\u0628 \u0647\u0627\u06cc \u0645\u0631\u062a\u0628\u0637 \u0628\u0627 \u0627\u06cc\u0646 \u0646\u0638\u0631 \u0631\u0627 \u0628\u0628\u06cc\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Toxic:\"<\/span> + <span class=\"hljs-built_in\">str<\/span>(toxic_comments(<span class=\"hljs-string\">\"toxic\"<\/span>)(<span class=\"hljs-number\">168<\/span>)))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Severe_toxic:\"<\/span> + <span class=\"hljs-built_in\">str<\/span>(toxic_comments(<span class=\"hljs-string\">\"severe_toxic\"<\/span>)(<span class=\"hljs-number\">168<\/span>)))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Obscene:\"<\/span> + <span class=\"hljs-built_in\">str<\/span>(toxic_comments(<span class=\"hljs-string\">\"obscene\"<\/span>)(<span class=\"hljs-number\">168<\/span>)))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Threat:\"<\/span> + <span class=\"hljs-built_in\">str<\/span>(toxic_comments(<span class=\"hljs-string\">\"threat\"<\/span>)(<span class=\"hljs-number\">168<\/span>)))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Insult:\"<\/span> + <span class=\"hljs-built_in\">str<\/span>(toxic_comments(<span class=\"hljs-string\">\"insult\"<\/span>)(<span class=\"hljs-number\">168<\/span>)))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Identity_hate:\"<\/span> + <span class=\"hljs-built_in\">str<\/span>(toxic_comments(<span class=\"hljs-string\">\"identity_hate\"<\/span>)(<span class=\"hljs-number\">168<\/span>)))\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">Toxic:1\nSevere_toxic:0\nObscene:0\nThreat:0\nInsult:1\nIdentity_hate:0\n<\/code><\/pre>\n<p>\u062d\u0627\u0644\u0627 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u062a\u0639\u062f\u0627\u062f \u0646\u0638\u0631\u0627\u062a \u0631\u0627 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0628\u0631\u0686\u0633\u0628 \u062a\u0631\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645.  \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631\u060c \u0627\u0628\u062a\u062f\u0627 \u062a\u0645\u0627\u0645 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627 \u06cc\u0627 \u0633\u062a\u0648\u0646 \u0647\u0627\u06cc \u062e\u0631\u0648\u062c\u06cc \u0631\u0627 \u0641\u06cc\u0644\u062a\u0631 \u0645\u06cc \u06a9\u0646\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\">toxic_comments_labels = toxic_comments((<span class=\"hljs-string\">\"toxic\"<\/span>, <span class=\"hljs-string\">\"severe_toxic\"<\/span>, <span class=\"hljs-string\">\"obscene\"<\/span>, <span class=\"hljs-string\">\"threat\"<\/span>, <span class=\"hljs-string\">\"insult\"<\/span>, <span class=\"hljs-string\">\"identity_hate\"<\/span>))\ntoxic_comments_labels.head()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/multi-label-text-classification-with-keras-2.PNG\" alt=\"img2\" title=\"\"><\/p>\n<p>\u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 <code>toxic_comments_labels<\/code> \u0686\u0627\u0631\u0686\u0648\u0628 \u062f\u0627\u062f\u0647\u060c \u0646\u0645\u0648\u062f\u0627\u0631\u0647\u0627\u06cc \u0646\u0648\u0627\u0631\u06cc \u0631\u0627 \u0631\u0633\u0645 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u062a\u0639\u062f\u0627\u062f \u06a9\u0644 \u0646\u0638\u0631\u0627\u062a \u0631\u0627 \u0628\u0631\u0627\u06cc \u0628\u0631\u0686\u0633\u0628 \u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f.<\/p>\n<pre><code class=\"hljs\">fig_size = plt.rcParams(<span class=\"hljs-string\">\"figure.figsize\"<\/span>)\nfig_size(<span class=\"hljs-number\">0<\/span>) = <span class=\"hljs-number\">10<\/span>\nfig_size(<span class=\"hljs-number\">1<\/span>) = <span class=\"hljs-number\">8<\/span>\nplt.rcParams(<span class=\"hljs-string\">\"figure.figsize\"<\/span>) = fig_size\n\ntoxic_comments_labels.<span class=\"hljs-built_in\">sum<\/span>(axis=<span class=\"hljs-number\">0<\/span>).plot.bar()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/multi-label-text-classification-with-keras-3.PNG\" alt=\"img3\" title=\"\"><\/p>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u06a9\u0627\u0645\u0646\u062a \u00ab\u0633\u0645\u06cc\u00bb \u0628\u06cc\u0634\u062a\u0631\u06cc\u0646 \u0641\u0631\u0627\u0648\u0627\u0646\u06cc \u0631\u0627 \u062f\u0627\u0631\u062f \u0648 \u0628\u0647 \u062a\u0631\u062a\u06cc\u0628 \u00ab\u0641\u062d\u0634\u00bb \u0648 \u00ab\u062a\u0648\u0647\u06cc\u0646\u00bb \u0642\u0631\u0627\u0631 \u062f\u0627\u0631\u0646\u062f.<\/p>\n<p>\u0645\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0628\u0627 \u0645\u0648\u0641\u0642\u06cc\u062a \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u06a9\u0631\u062f\u0647 \u0627\u06cc\u0645\u060c \u062f\u0631 \u0628\u062e\u0634 \u0628\u0639\u062f\u06cc \u0645\u062f\u0644 \u0647\u0627\u06cc \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0627\u06cc\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0627\u06cc\u062c\u0627\u062f \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.<\/p>\n<h2 id=\"creatingmultilabeltextclassificationmodels\"><span class=\"ez-toc-section\" id=\"%d8%a7%db%8c%d8%ac%d8%a7%d8%af_%d9%85%d8%af%d9%84_%d9%87%d8%a7%db%8c_%d8%af%d8%b3%d8%aa%d9%87_%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%da%86%d9%86%d8%af_%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c\"><\/span>\u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644 \u0647\u0627\u06cc \u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u062f\u0648 \u0631\u0627\u0647 \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644\u200c\u0647\u0627\u06cc \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0648\u062c\u0648\u062f \u062f\u0627\u0631\u062f: \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0645\u062a\u0631\u0627\u06a9\u0645 \u0648 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0686\u0646\u062f\u06cc\u0646 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0645\u062a\u0631\u0627\u06a9\u0645.<\/p>\n<p>\u062f\u0631 \u0631\u0648\u06cc\u06a9\u0631\u062f \u0627\u0648\u0644\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0632 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0645\u062a\u0631\u0627\u06a9\u0645 \u0645\u0646\u0641\u0631\u062f \u0628\u0627 \u0634\u0634 \u062e\u0631\u0648\u062c\u06cc \u0628\u0627 \u062a\u0648\u0627\u0628\u0639 \u0641\u0639\u0627\u0644\u200c\u0633\u0627\u0632\u06cc \u0633\u06cc\u06af\u0645\u0648\u0626\u06cc\u062f \u0648 \u062a\u0648\u0627\u0628\u0639 \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646 \u0622\u0646\u062a\u0631\u0648\u067e\u06cc \u0645\u062a\u0642\u0627\u0637\u0639 \u0628\u0627\u06cc\u0646\u0631\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645.  \u0647\u0631 \u0646\u0648\u0631\u0648\u0646 \u062f\u0631 \u0644\u0627\u06cc\u0647 \u0645\u062a\u0631\u0627\u06a9\u0645 \u062e\u0631\u0648\u062c\u06cc \u06cc\u06a9\u06cc \u0627\u0632 \u0634\u0634 \u0628\u0631\u0686\u0633\u0628 \u062e\u0631\u0648\u062c\u06cc \u0631\u0627 \u0646\u0634\u0627\u0646 \u062e\u0648\u0627\u0647\u062f \u062f\u0627\u062f.  \u062a\u0627\u0628\u0639 \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc \u0633\u06cc\u06af\u0645\u0648\u0626\u06cc\u062f \u0645\u0642\u062f\u0627\u0631\u06cc \u0628\u06cc\u0646 0 \u0648 1 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0646\u0648\u0631\u0648\u0646 \u0628\u0631\u0645\u06cc \u06af\u0631\u062f\u0627\u0646\u062f.  \u0627\u06af\u0631 \u0645\u0642\u062f\u0627\u0631 \u062e\u0631\u0648\u062c\u06cc \u0647\u0631 \u0646\u0648\u0631\u0648\u0646 \u0628\u0632\u0631\u06af\u062a\u0631 \u0627\u0632 0.5 \u0628\u0627\u0634\u062f\u060c \u0641\u0631\u0636 \u0645\u06cc \u0634\u0648\u062f \u06a9\u0647 \u0646\u0638\u0631 \u0645\u062a\u0639\u0644\u0642 \u0628\u0647 \u06a9\u0644\u0627\u0633\u06cc \u0627\u0633\u062a \u06a9\u0647 \u062a\u0648\u0633\u0637 \u0622\u0646 \u0646\u0648\u0631\u0648\u0646 \u062e\u0627\u0635 \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a.<\/p>\n<p>\u062f\u0631 \u0631\u0648\u06cc\u06a9\u0631\u062f \u062f\u0648\u0645 \u0645\u0627 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0645\u062a\u0631\u0627\u06a9\u0645 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0628\u0631\u0686\u0633\u0628 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0645\u062c\u0645\u0648\u0639\u0627\u064b 6 \u0644\u0627\u06cc\u0647 \u0645\u062a\u0631\u0627\u06a9\u0645 \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u0634\u062a.  \u0647\u0631 \u0644\u0627\u06cc\u0647 \u062a\u0627\u0628\u0639 \u0633\u06cc\u06af\u0645\u0648\u0626\u06cc\u062f \u062e\u0648\u062f \u0631\u0627 \u062e\u0648\u0627\u0647\u062f \u062f\u0627\u0634\u062a.<\/p>\n<h3 id=\"multilabeltextclassificationmodelwithsingleoutputlayer\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%af%d9%84_%d8%b7%d8%a8%d9%82%d9%87%e2%80%8c%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%da%86%d9%86%d8%af_%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c_%d8%a8%d8%a7_%d9%84%d8%a7%db%8c%d9%87_%d8%ae%d8%b1%d9%88%d8%ac%db%8c_%d9%88%d8%a7%d8%ad%d8%af\"><\/span>\u0645\u062f\u0644 \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0628\u0627 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0648\u0627\u062d\u062f<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0628\u062e\u0634 \u06cc\u06a9 \u0645\u062f\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0628\u0627 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0645\u062b\u0644 \u0647\u0645\u06cc\u0634\u0647\u060c \u0627\u0648\u0644\u06cc\u0646 \u0642\u062f\u0645 \u062f\u0631 \u0645\u062f\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646\u060c \u0627\u06cc\u062c\u0627\u062f \u062a\u0627\u0628\u0639\u06cc \u0627\u0633\u062a \u06a9\u0647 \u0648\u0638\u06cc\u0641\u0647 \u067e\u0627\u06a9\u0633\u0627\u0632\u06cc \u0645\u062a\u0646 \u0631\u0627 \u0628\u0631 \u0639\u0647\u062f\u0647 \u062f\u0627\u0631\u062f.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">preprocess_text<\/span>(<span class=\"hljs-params\">sen<\/span>):<\/span>\n    \n    sentence = re.sub(<span class=\"hljs-string\">'(^a-zA-Z)'<\/span>, <span class=\"hljs-string\">' '<\/span>, sen)\n\n    \n    sentence = re.sub(<span class=\"hljs-string\">r\"\\s+(a-zA-Z)\\s+\"<\/span>, <span class=\"hljs-string\">' '<\/span>, sentence)\n\n    \n    sentence = re.sub(<span class=\"hljs-string\">r'\\s+'<\/span>, <span class=\"hljs-string\">' '<\/span>, sentence)\n\n    <span class=\"hljs-keyword\">return<\/span> sentence\n<\/code><\/pre>\n<p>\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f \u0645\u062c\u0645\u0648\u0639\u0647 \u0648\u0631\u0648\u062f\u06cc \u0648 \u062e\u0631\u0648\u062c\u06cc \u062e\u0648\u062f \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0648\u0631\u0648\u062f\u06cc \u0646\u0638\u0631 \u0627\u0632 <code>comment_text<\/code> \u0633\u062a\u0648\u0646  \u0645\u0627 \u0647\u0645\u0647 \u0646\u0638\u0631\u0627\u062a \u0631\u0627 \u067e\u0627\u06a9 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u062f\u0631 \u0622\u0646 \u0630\u062e\u06cc\u0631\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>X<\/code> \u0645\u062a\u063a\u06cc\u0631.  \u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u06cc\u0627 \u062e\u0631\u0648\u062c\u06cc\u200c\u0647\u0627 \u0642\u0628\u0644\u0627\u064b \u062f\u0631 \u0622\u0646 \u0630\u062e\u06cc\u0631\u0647 \u0634\u062f\u0647\u200c\u0627\u0646\u062f <code>toxic_comments_labels<\/code> \u0686\u0627\u0631\u0686\u0648\u0628 \u062f\u0627\u062f\u0647  \u0645\u0627 \u0627\u0632 \u0622\u0646 \u0645\u0642\u0627\u062f\u06cc\u0631 dataframe \u0628\u0631\u0627\u06cc \u0630\u062e\u06cc\u0631\u0647 \u062e\u0631\u0648\u062c\u06cc \u062f\u0631 <code>y<\/code> \u0645\u062a\u063a\u06cc\u0631.  \u0628\u0647 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0646\u06af\u0627\u0647 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">X = ()\nsentences = <span class=\"hljs-built_in\">list<\/span>(toxic_comments(<span class=\"hljs-string\">\"comment_text\"<\/span>))\n<span class=\"hljs-keyword\">for<\/span> sen <span class=\"hljs-keyword\">in<\/span> sentences:\n    X.append(preprocess_text(sen))\n\ny = toxic_comments_labels.values\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u0645\u0627 \u0646\u06cc\u0627\u0632\u06cc \u0628\u0647 \u0627\u0646\u062c\u0627\u0645 \u0647\u06cc\u0686 \u06a9\u062f\u06af\u0630\u0627\u0631\u06cc \u062a\u06a9 \u062f\u0627\u063a \u0646\u062f\u0627\u0631\u06cc\u0645 \u0632\u06cc\u0631\u0627 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627\u06cc \u062e\u0631\u0648\u062c\u06cc \u0645\u0627 \u0642\u0628\u0644\u0627\u064b \u0628\u0647 \u0634\u06a9\u0644 \u0628\u0631\u062f\u0627\u0631\u0647\u0627\u06cc \u06a9\u062f\u06af\u0630\u0627\u0631\u06cc \u0634\u062f\u0647 \u06cc\u06a9 \u06af\u0631\u0645 \u0647\u0633\u062a\u0646\u062f.<\/p>\n<p>\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u062a\u0642\u0633\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=<span class=\"hljs-number\">0.20<\/span>, random_state=<span class=\"hljs-number\">42<\/span>)\n<\/code><\/pre>\n<p>\u0628\u0627\u06cc\u062f \u0648\u0631\u0648\u062f\u06cc \u0647\u0627\u06cc \u0645\u062a\u0646 \u0631\u0627 \u0628\u0647 \u0628\u0631\u062f\u0627\u0631\u0647\u0627\u06cc \u062c\u0627\u0633\u0627\u0632\u06cc \u0634\u062f\u0647 \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u0645.  \u0628\u0631\u0627\u06cc \u062f\u0631\u06a9 \u062f\u0642\u06cc\u0642 \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0644\u0645\u0627\u062a\u060c \u0644\u0637\u0641\u0627\u064b \u0628\u0647 \u0645\u0642\u0627\u0644\u0647 \u0645\u0646 \u0645\u0631\u0627\u062c\u0639\u0647 \u06a9\u0646\u06cc\u062f \u0631\u0648\u06cc \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0644\u0645\u0627\u062a<\/p>\n<pre><code class=\"hljs\">tokenizer = Tokenizer(num_words=<span class=\"hljs-number\">5000<\/span>)\ntokenizer.fit_on_texts(X_train)\n\nX_train = tokenizer.texts_to_sequences(X_train)\nX_test = tokenizer.texts_to_sequences(X_test)\n\nvocab_size = <span class=\"hljs-built_in\">len<\/span>(tokenizer.word_index) + <span class=\"hljs-number\">1<\/span>\n\nmaxlen = <span class=\"hljs-number\">200<\/span>\n\nX_train = pad_sequences(X_train, padding=<span class=\"hljs-string\">'post'<\/span>, maxlen=maxlen)\nX_test = pad_sequences(X_test, padding=<span class=\"hljs-string\">'post'<\/span>, maxlen=maxlen)\n<\/code><\/pre>\n<p>\u0645\u0627 \u0627\u0632 \u062c\u0627\u0633\u0627\u0632\u06cc\u200c\u0647\u0627\u06cc \u06a9\u0644\u0645\u0647 GloVe \u0628\u0631\u0627\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0648\u0631\u0648\u062f\u06cc\u200c\u0647\u0627\u06cc \u0645\u062a\u0646 \u0628\u0647 \u0647\u0645\u062a\u0627\u06cc\u0627\u0646 \u0639\u062f\u062f\u06cc \u062e\u0648\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> numpy <span class=\"hljs-keyword\">import<\/span> array\n<span class=\"hljs-keyword\">from<\/span> numpy <span class=\"hljs-keyword\">import<\/span> asarray\n<span class=\"hljs-keyword\">from<\/span> numpy <span class=\"hljs-keyword\">import<\/span> zeros\n\nembeddings_dictionary = <span class=\"hljs-built_in\">dict<\/span>()\n\nglove_file = <span class=\"hljs-built_in\">open<\/span>(<span class=\"hljs-string\">'\/content\/drive\/My Drive\/Colab Datasets\/glove.6B.100d.txt'<\/span>, encoding=<span class=\"hljs-string\">\"utf8\"<\/span>)\n\n<span class=\"hljs-keyword\">for<\/span> line <span class=\"hljs-keyword\">in<\/span> glove_file:\n    records = line.split()\n    word = records(<span class=\"hljs-number\">0<\/span>)\n    vector_dimensions = asarray(records(<span class=\"hljs-number\">1<\/span>:), dtype=<span class=\"hljs-string\">'float32'<\/span>)\n    embeddings_dictionary(word) = vector_dimensions\nglove_file.close()\n\nembedding_matrix = zeros((vocab_size, <span class=\"hljs-number\">100<\/span>))\n<span class=\"hljs-keyword\">for<\/span> word, index <span class=\"hljs-keyword\">in<\/span> tokenizer.word_index.items():\n    embedding_vector = embeddings_dictionary.get(word)\n    <span class=\"hljs-keyword\">if<\/span> embedding_vector <span class=\"hljs-keyword\">is<\/span> <span class=\"hljs-keyword\">not<\/span> <span class=\"hljs-literal\">None<\/span>:\n        embedding_matrix(index) = embedding_vector\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u062f\u0644 \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u062f.  \u0645\u062f\u0644 \u0645\u0627 \u062f\u0627\u0631\u0627\u06cc \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0648\u0631\u0648\u062f\u06cc\u060c \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062c\u0627\u0633\u0627\u0632\u06cc\u060c \u06cc\u06a9 \u0644\u0627\u06cc\u0647 LSTM \u0628\u0627 128 \u0646\u0648\u0631\u0648\u0646 \u0648 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0628\u0627 6 \u0646\u0648\u0631\u0648\u0646 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f \u0632\u06cc\u0631\u0627 \u0645\u0627 6 \u0628\u0631\u0686\u0633\u0628 \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u062f\u0627\u0631\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\">deep_inputs = Input(shape=(maxlen,))\nembedding_layer = Embedding(vocab_size, <span class=\"hljs-number\">100<\/span>, weights=(embedding_matrix), trainable=<span class=\"hljs-literal\">False<\/span>)(deep_inputs)\nLSTM_Layer_1 = LSTM(<span class=\"hljs-number\">128<\/span>)(embedding_layer)\ndense_layer_1 = Dense(<span class=\"hljs-number\">6<\/span>, activation=<span class=\"hljs-string\">'sigmoid'<\/span>)(LSTM_Layer_1)\nmodel = Model(inputs=deep_inputs, outputs=dense_layer_1)\n\nmodel.<span class=\"hljs-built_in\">compile<\/span>(loss=<span class=\"hljs-string\">'binary_crossentropy'<\/span>, optimizer=<span class=\"hljs-string\">'adam'<\/span>, metrics=(<span class=\"hljs-string\">'acc'<\/span>))\n<\/code><\/pre>\n<p>\u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f print \u062e\u0644\u0627\u0635\u0647 \u0645\u062f\u0644:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(model.summary())\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">_________________________________________________________________\nLayer (type)                 Output Shape              Param #\n=================================================================\ninput_1 (InputLayer)         (None, 200)               0\n_________________________________________________________________\nembedding_1 (Embedding)      (None, 200, 100)          14824300\n_________________________________________________________________\nlstm_1 (LSTM)                (None, 128)               117248\n_________________________________________________________________\ndense_1 (Dense)              (None, 6)                 774\n=================================================================\nTotal params: 14,942,322\nTrainable params: 118,022\nNon-trainable params: 14,824,300\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u0639\u0645\u0627\u0631\u06cc \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0645\u0627 \u0631\u0627 \u0686\u0627\u067e \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> keras.utils <span class=\"hljs-keyword\">import<\/span> plot_model\nplot_model(model, to_file=<span class=\"hljs-string\">'model_plot4a.png'<\/span>, show_shapes=<span class=\"hljs-literal\">True<\/span>, show_layer_names=<span class=\"hljs-literal\">True<\/span>)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/multi-label-text-classification-with-keras-4.png\" alt=\"img4\" title=\"\"><\/p>\n<p>\u0627\u0632 \u0634\u06a9\u0644 \u0628\u0627\u0644\u0627 \u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0641\u0642\u0637 \u0634\u0627\u0645\u0644 1 \u0644\u0627\u06cc\u0647 \u0645\u062a\u0631\u0627\u06a9\u0645 \u0628\u0627 6 \u0646\u0648\u0631\u0648\u0646 \u0627\u0633\u062a.  \u062d\u0627\u0644 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">history = model.fit(X_train, y_train, batch_size=<span class=\"hljs-number\">128<\/span>, epochs=<span class=\"hljs-number\">5<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>)\n<\/code><\/pre>\n<p>\u0645\u0627 \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u0628\u0631\u0627\u06cc 5 \u062f\u0648\u0631\u0647 \u0622\u0645\u0648\u0632\u0634 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f.  \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0645\u062f\u0644 \u0631\u0627 \u0628\u0627 \u062f\u0648\u0631\u0647 \u0647\u0627\u06cc \u0628\u06cc\u0634\u062a\u0631\u06cc \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u062f \u0648 \u0628\u0628\u06cc\u0646\u06cc\u062f \u0622\u06cc\u0627 \u0646\u062a\u0627\u06cc\u062c \u0628\u0647\u062a\u0631 \u06cc\u0627 \u0628\u062f\u062a\u0631\u06cc \u062f\u0631\u06cc\u0627\u0641\u062a \u0645\u06cc \u06a9\u0646\u06cc\u062f.<\/p>\n<p>\u0646\u062a\u06cc\u062c\u0647 \u0647\u0631 5 \u062f\u0648\u0631\u0647 \u0628\u0647 \u0634\u0631\u062d \u0632\u06cc\u0631 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">rain \u0631\u0648\u06cc 102124 samples, validate \u0631\u0648\u06cc 25532 samples\nEpoch 1\/5\n102124\/102124 (==============================) - 245s 2ms\/step - loss: 0.1437 - acc: 0.9634 - val_loss: 0.1361 - val_acc: 0.9631\nEpoch 2\/5\n102124\/102124 (==============================) - 245s 2ms\/step - loss: 0.0763 - acc: 0.9753 - val_loss: 0.0621 - val_acc: 0.9788\nEpoch 3\/5\n102124\/102124 (==============================) - 243s 2ms\/step - loss: 0.0588 - acc: 0.9800 - val_loss: 0.0578 - val_acc: 0.9802\nEpoch 4\/5\n102124\/102124 (==============================) - 246s 2ms\/step - loss: 0.0559 - acc: 0.9807 - val_loss: 0.0571 - val_acc: 0.9801\nEpoch 5\/5\n102124\/102124 (==============================) - 245s 2ms\/step - loss: 0.0528 - acc: 0.9813 - val_loss: 0.0554 - val_acc: 0.9807\n<\/code><\/pre>\n<p>\u062d\u0627\u0644 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u06a9\u0646\u06cc\u0645 \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">score = model.evaluate(X_test, y_test, verbose=<span class=\"hljs-number\">1<\/span>)\n\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Test Score:\"<\/span>, score(<span class=\"hljs-number\">0<\/span>))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Test Accuracy:\"<\/span>, score(<span class=\"hljs-number\">1<\/span>))\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">31915\/31915 (==============================) - 108s 3ms\/step\nTest Score: 0.054090796736467786\nTest Accuracy: 0.9810642735274182\n<\/code><\/pre>\n<p>\u0645\u062f\u0644 \u0645\u0627 \u0628\u0647 \u062f\u0642\u062a \u062d\u062f\u0648\u062f 98 \u062f\u0631\u0635\u062f \u062f\u0633\u062a \u0645\u06cc \u06cc\u0627\u0628\u062f \u06a9\u0647 \u0628\u0633\u06cc\u0627\u0631 \u0686\u0634\u0645\u06af\u06cc\u0631 \u0627\u0633\u062a.<\/p>\n<p>\u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u0645\u0642\u0627\u062f\u06cc\u0631 \u062a\u0644\u0641\u0627\u062a \u0648 \u062f\u0642\u062a \u0631\u0627 \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u062a\u0631\u0633\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u062a\u0627 \u0628\u0628\u06cc\u0646\u06cc\u0645 \u0622\u06cc\u0627 \u0645\u062f\u0644 \u0645\u0627 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0628\u0631\u0627\u0632\u0634 \u062f\u0627\u0631\u062f \u06cc\u0627 \u062e\u06cc\u0631.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n\nplt.plot(history.history(<span class=\"hljs-string\">'acc'<\/span>))\nplt.plot(history.history(<span class=\"hljs-string\">'val_acc'<\/span>))\n\nplt.title(<span class=\"hljs-string\">'model accuracy'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'accuracy'<\/span>)\nplt.xlabel(<span class=\"hljs-string\">'epoch'<\/span>)\nplt.legend((<span class=\"hljs-string\">'train'<\/span>,<span class=\"hljs-string\">'test'<\/span>), loc=<span class=\"hljs-string\">'upper left'<\/span>)\nplt.show()\n\nplt.plot(history.history(<span class=\"hljs-string\">'loss'<\/span>))\nplt.plot(history.history(<span class=\"hljs-string\">'val_loss'<\/span>))\n\nplt.title(<span class=\"hljs-string\">'model loss'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'loss'<\/span>)\nplt.xlabel(<span class=\"hljs-string\">'epoch'<\/span>)\nplt.legend((<span class=\"hljs-string\">'train'<\/span>,<span class=\"hljs-string\">'test'<\/span>), loc=<span class=\"hljs-string\">'upper left'<\/span>)\nplt.show()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/multi-label-text-classification-with-keras-5.PNG\" alt=\"5\" title=\"\"><\/p>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0645\u062f\u0644 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0645\u0646\u0627\u0633\u0628 \u0646\u06cc\u0633\u062a \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0627\u0639\u062a\u0628\u0627\u0631 \u0633\u0646\u062c\u06cc<\/p>\n<h3 id=\"multilabeltextclassificationmodelwithmultipleoutputlayers\"><span class=\"ez-toc-section\" id=\"%d9%85%d8%af%d9%84_%d8%b7%d8%a8%d9%82%d9%87%e2%80%8c%d8%a8%d9%86%d8%af%db%8c_%d9%85%d8%aa%d9%86_%da%86%d9%86%d8%af_%d8%a8%d8%b1%da%86%d8%b3%d8%a8%db%8c_%d8%a8%d8%a7_%d9%84%d8%a7%db%8c%d9%87%e2%80%8c%d9%87%d8%a7%db%8c_%d8%ae%d8%b1%d9%88%d8%ac%db%8c_%da%86%d9%86%d8%af%da%af%d8%a7%d9%86%d9%87\"><\/span>\u0645\u062f\u0644 \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0628\u0627 \u0644\u0627\u06cc\u0647\u200c\u0647\u0627\u06cc \u062e\u0631\u0648\u062c\u06cc \u0686\u0646\u062f\u06af\u0627\u0646\u0647<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0628\u062e\u0634 \u06cc\u06a9 \u0645\u062f\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0647\u0631 \u0628\u0631\u0686\u0633\u0628 \u062e\u0631\u0648\u062c\u06cc \u062f\u0627\u0631\u0627\u06cc \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0645\u062a\u0631\u0627\u06a9\u0645 \u062e\u0631\u0648\u062c\u06cc \u0627\u062e\u062a\u0635\u0627\u0635\u06cc \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.  \u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f \u0627\u0628\u062a\u062f\u0627 \u0639\u0645\u0644\u06a9\u0631\u062f \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u062e\u0648\u062f \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">preprocess_text<\/span>(<span class=\"hljs-params\">sen<\/span>):<\/span>\n    \n    sentence = re.sub(<span class=\"hljs-string\">'(^a-zA-Z)'<\/span>, <span class=\"hljs-string\">' '<\/span>, sen)\n\n    \n    sentence = re.sub(<span class=\"hljs-string\">r\"\\s+(a-zA-Z)\\s+\"<\/span>, <span class=\"hljs-string\">' '<\/span>, sentence)\n\n    \n    sentence = re.sub(<span class=\"hljs-string\">r'\\s+'<\/span>, <span class=\"hljs-string\">' '<\/span>, sentence)\n\n    <span class=\"hljs-keyword\">return<\/span> sentence\n<\/code><\/pre>\n<p>\u0645\u0631\u062d\u0644\u0647 \u062f\u0648\u0645 \u0627\u06cc\u062c\u0627\u062f \u0648\u0631\u0648\u062f\u06cc \u0648 \u062e\u0631\u0648\u062c\u06cc \u0628\u0631\u0627\u06cc \u0645\u062f\u0644 \u0627\u0633\u062a.  \u0648\u0631\u0648\u062f\u06cc \u0645\u062f\u0644 \u06a9\u0627\u0645\u0646\u062a \u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f\u060c \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u062e\u0631\u0648\u062c\u06cc \u0634\u0634 \u0628\u0631\u0686\u0633\u0628 \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0644\u0627\u06cc\u0647 \u0648\u0631\u0648\u062f\u06cc \u0648 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">X = ()\nsentences = <span class=\"hljs-built_in\">list<\/span>(toxic_comments(<span class=\"hljs-string\">\"comment_text\"<\/span>))\n<span class=\"hljs-keyword\">for<\/span> sen <span class=\"hljs-keyword\">in<\/span> sentences:\n    X.append(preprocess_text(sen))\n\ny = toxic_comments((<span class=\"hljs-string\">\"toxic\"<\/span>, <span class=\"hljs-string\">\"severe_toxic\"<\/span>, <span class=\"hljs-string\">\"obscene\"<\/span>, <span class=\"hljs-string\">\"threat\"<\/span>, <span class=\"hljs-string\">\"insult\"<\/span>, <span class=\"hljs-string\">\"identity_hate\"<\/span>))\n<\/code><\/pre>\n<p>\u0628\u06cc\u0627\u06cc\u06cc\u062f \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=<span class=\"hljs-number\">0.20<\/span>, random_state=<span class=\"hljs-number\">42<\/span>)\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 <code>y<\/code> \u0645\u062a\u063a\u06cc\u0631 \u0634\u0627\u0645\u0644 \u062e\u0631\u0648\u062c\u06cc \u062a\u0631\u06a9\u06cc\u0628\u06cc \u0627\u0632 6 \u0628\u0631\u0686\u0633\u0628 \u0627\u0633\u062a.  \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u0645\u0627 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0628\u0631\u0686\u0633\u0628 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u062c\u062f\u0627\u06af\u0627\u0646\u0647 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645.  \u0645\u0627 6 \u0645\u062a\u063a\u06cc\u0631 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0628\u0631\u0686\u0633\u0628 \u0647\u0627\u06cc \u0641\u0631\u062f\u06cc \u0631\u0627 \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0630\u062e\u06cc\u0631\u0647 \u0645\u06cc \u06a9\u0646\u062f \u0648 6 \u0645\u062a\u063a\u06cc\u0631 \u06a9\u0647 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0628\u0631\u0686\u0633\u0628 \u0641\u0631\u062f\u06cc \u0631\u0627 \u0628\u0631\u0627\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u062a\u0633\u062a \u0630\u062e\u06cc\u0631\u0647 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0628\u0647 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0646\u06af\u0627\u0647 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">\ny1_train = y_train((<span class=\"hljs-string\">\"toxic\"<\/span>)).values\ny1_test =  y_test((<span class=\"hljs-string\">\"toxic\"<\/span>)).values\n\n\ny2_train = y_train((<span class=\"hljs-string\">\"severe_toxic\"<\/span>)).values\ny2_test =  y_test((<span class=\"hljs-string\">\"severe_toxic\"<\/span>)).values\n\n\ny3_train = y_train((<span class=\"hljs-string\">\"obscene\"<\/span>)).values\ny3_test =  y_test((<span class=\"hljs-string\">\"obscene\"<\/span>)).values\n\n\ny4_train = y_train((<span class=\"hljs-string\">\"threat\"<\/span>)).values\ny4_test =  y_test((<span class=\"hljs-string\">\"threat\"<\/span>)).values\n\n\ny5_train = y_train((<span class=\"hljs-string\">\"insult\"<\/span>)).values\ny5_test =  y_test((<span class=\"hljs-string\">\"insult\"<\/span>)).values\n\n\ny6_train = y_train((<span class=\"hljs-string\">\"identity_hate\"<\/span>)).values\ny6_test =  y_test((<span class=\"hljs-string\">\"identity_hate\"<\/span>)).values\n<\/code><\/pre>\n<p>\u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0648\u0631\u0648\u062f\u06cc \u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u0628\u0647 \u0628\u0631\u062f\u0627\u0631\u0647\u0627\u06cc \u062c\u0627\u0633\u0627\u0632\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u0645\u06cc \u062f\u0647\u062f:<\/p>\n<pre><code class=\"hljs\">tokenizer = Tokenizer(num_words=<span class=\"hljs-number\">5000<\/span>)\ntokenizer.fit_on_texts(X_train)\n\nX_train = tokenizer.texts_to_sequences(X_train)\nX_test = tokenizer.texts_to_sequences(X_test)\n\nvocab_size = <span class=\"hljs-built_in\">len<\/span>(tokenizer.word_index) + <span class=\"hljs-number\">1<\/span>\n\nmaxlen = <span class=\"hljs-number\">200<\/span>\n\nX_train = pad_sequences(X_train, padding=<span class=\"hljs-string\">'post'<\/span>, maxlen=maxlen)\nX_test = pad_sequences(X_test, padding=<span class=\"hljs-string\">'post'<\/span>, maxlen=maxlen)\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u062f\u0648\u0628\u0627\u0631\u0647 \u0627\u0632 \u062a\u0639\u0628\u06cc\u0647\u200c\u0647\u0627\u06cc \u06a9\u0644\u0645\u0647 GloVe \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f:<\/p>\n<pre><code class=\"hljs\">glove_file = <span class=\"hljs-built_in\">open<\/span>(<span class=\"hljs-string\">'\/content\/drive\/My Drive\/Colab Datasets\/glove.6B.100d.txt'<\/span>, encoding=<span class=\"hljs-string\">\"utf8\"<\/span>)\n\n<span class=\"hljs-keyword\">for<\/span> line <span class=\"hljs-keyword\">in<\/span> glove_file:\n    records = line.split()\n    word = records(<span class=\"hljs-number\">0<\/span>)\n    vector_dimensions = asarray(records(<span class=\"hljs-number\">1<\/span>:), dtype=<span class=\"hljs-string\">'float32'<\/span>)\n    embeddings_dictionary(word) = vector_dimensions\nglove_file.close()\n\nembedding_matrix = zeros((vocab_size, <span class=\"hljs-number\">100<\/span>))\n<span class=\"hljs-keyword\">for<\/span> word, index <span class=\"hljs-keyword\">in<\/span> tokenizer.word_index.items():\n    embedding_vector = embeddings_dictionary.get(word)\n    <span class=\"hljs-keyword\">if<\/span> embedding_vector <span class=\"hljs-keyword\">is<\/span> <span class=\"hljs-keyword\">not<\/span> <span class=\"hljs-literal\">None<\/span>:\n        embedding_matrix(index) = embedding_vector\n<\/code><\/pre>\n<p>\u0627\u06a9\u0646\u0648\u0646 \u0632\u0645\u0627\u0646 \u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644 \u0645\u0627 \u0627\u0633\u062a.  \u0645\u062f\u0644 \u0645\u0627 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0648\u0631\u0648\u062f\u06cc\u060c \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062c\u0627\u0633\u0627\u0632\u06cc \u0648 \u0628\u0647 \u062f\u0646\u0628\u0627\u0644 \u0622\u0646 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 LSTM \u0628\u0627 128 \u0646\u0648\u0631\u0648\u0646 \u062e\u0648\u0627\u0647\u062f \u062f\u0627\u0634\u062a.  \u062e\u0631\u0648\u062c\u06cc \u0644\u0627\u06cc\u0647 LSTM \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0631\u0648\u062f\u06cc 6 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0645\u062a\u0631\u0627\u06a9\u0645 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u062f \u0634\u062f.  \u0647\u0631 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u062f\u0627\u0631\u0627\u06cc 1 \u0646\u0648\u0631\u0648\u0646 \u0628\u0627 \u0639\u0645\u0644\u06a9\u0631\u062f \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc \u0633\u06cc\u06af\u0645\u0648\u0626\u06cc\u062f \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.  \u0647\u0631 \u062e\u0631\u0648\u062c\u06cc \u0645\u0642\u062f\u0627\u0631 \u0635\u062d\u06cc\u062d \u0628\u06cc\u0646 1 \u0648 0 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0628\u0631\u0686\u0633\u0628 \u0645\u0631\u0628\u0648\u0637\u0647 \u067e\u06cc\u0634 \u0628\u06cc\u0646\u06cc \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u062f\u0644 \u0645\u0627 \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">input_1 = Input(shape=(maxlen,))\nembedding_layer = Embedding(vocab_size, <span class=\"hljs-number\">100<\/span>, weights=(embedding_matrix), trainable=<span class=\"hljs-literal\">False<\/span>)(input_1)\nLSTM_Layer1 = LSTM(<span class=\"hljs-number\">128<\/span>)(embedding_layer)\n\noutput1 = Dense(<span class=\"hljs-number\">1<\/span>, activation=<span class=\"hljs-string\">'sigmoid'<\/span>)(LSTM_Layer1)\noutput2 = Dense(<span class=\"hljs-number\">1<\/span>, activation=<span class=\"hljs-string\">'sigmoid'<\/span>)(LSTM_Layer1)\noutput3 = Dense(<span class=\"hljs-number\">1<\/span>, activation=<span class=\"hljs-string\">'sigmoid'<\/span>)(LSTM_Layer1)\noutput4 = Dense(<span class=\"hljs-number\">1<\/span>, activation=<span class=\"hljs-string\">'sigmoid'<\/span>)(LSTM_Layer1)\noutput5 = Dense(<span class=\"hljs-number\">1<\/span>, activation=<span class=\"hljs-string\">'sigmoid'<\/span>)(LSTM_Layer1)\noutput6 = Dense(<span class=\"hljs-number\">1<\/span>, activation=<span class=\"hljs-string\">'sigmoid'<\/span>)(LSTM_Layer1)\n\nmodel = Model(inputs=input_1, outputs=(output1, output2, output3, output4, output5, output6))\nmodel.<span class=\"hljs-built_in\">compile<\/span>(loss=<span class=\"hljs-string\">'binary_crossentropy'<\/span>, optimizer=<span class=\"hljs-string\">'adam'<\/span>, metrics=(<span class=\"hljs-string\">'acc'<\/span>))\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u062e\u0644\u0627\u0635\u0647 \u0627\u06cc \u0627\u0632 \u0645\u062f\u0644 \u0631\u0627 \u0686\u0627\u067e \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(model.summary())\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to\n==================================================================================================\ninput_1 (InputLayer)            (None, 200)          0\n__________________________________________________________________________________________________\nembedding_1 (Embedding)         (None, 200, 100)     14824300    input_1(0)(0)\n__________________________________________________________________________________________________\nlstm_1 (LSTM)                   (None, 128)          117248      embedding_1(0)(0)\n__________________________________________________________________________________________________\ndense_1 (Dense)                 (None, 1)            129         lstm_1(0)(0)\n__________________________________________________________________________________________________\ndense_2 (Dense)                 (None, 1)            129         lstm_1(0)(0)\n__________________________________________________________________________________________________\ndense_3 (Dense)                 (None, 1)            129         lstm_1(0)(0)\n__________________________________________________________________________________________________\ndense_4 (Dense)                 (None, 1)            129         lstm_1(0)(0)\n__________________________________________________________________________________________________\ndense_5 (Dense)                 (None, 1)            129         lstm_1(0)(0)\n__________________________________________________________________________________________________\ndense_6 (Dense)                 (None, 1)            129         lstm_1(0)(0)\n==================================================================================================\nTotal params: 14,942,322\nTrainable params: 118,022\nNon-trainable params: 14,824,300\n<\/code><\/pre>\n<p>\u0648 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u0639\u0645\u0627\u0631\u06cc \u0645\u062f\u0644 \u0645\u0627 \u0631\u0627 \u0686\u0627\u067e \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">from<\/span> keras.utils <span class=\"hljs-keyword\">import<\/span> plot_model\nplot_model(model, to_file=<span class=\"hljs-string\">'model_plot4b.png'<\/span>, show_shapes=<span class=\"hljs-literal\">True<\/span>, show_layer_names=<span class=\"hljs-literal\">True<\/span>)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/multi-label-text-classification-with-keras-6.png\" alt=\"img6\" title=\"\"><\/p>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0645\u0627 6 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0645\u062e\u062a\u0644\u0641 \u062f\u0627\u0631\u06cc\u0645.  \u0634\u06a9\u0644 \u0628\u0627\u0644\u0627 \u0628\u0647 \u0648\u0636\u0648\u062d \u062a\u0641\u0627\u0648\u062a \u0628\u06cc\u0646 \u0645\u062f\u0644 \u0628\u0627 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u0648\u0631\u0648\u062f\u06cc \u06a9\u0647 \u062f\u0631 \u0642\u0633\u0645\u062a \u0622\u062e\u0631 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0631\u062f\u06cc\u0645 \u0648 \u0645\u062f\u0644 \u0628\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0631\u0627 \u062a\u0648\u0636\u06cc\u062d \u0645\u06cc \u062f\u0647\u062f.<\/p>\n<p>\u062d\u0627\u0644 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">history = model.fit(x=X_train, y=(y1_train, y2_train, y3_train, y4_train, y5_train, y6_train), batch_size=<span class=\"hljs-number\">8192<\/span>, epochs=<span class=\"hljs-number\">5<\/span>, verbose=<span class=\"hljs-number\">1<\/span>, validation_split=<span class=\"hljs-number\">0.2<\/span>)\n<\/code><\/pre>\n<p>\u0645\u0646 \u0633\u0639\u06cc \u06a9\u0631\u062f\u0645 \u0627\u06cc\u0646 \u0645\u062f\u0644 \u0631\u0627 \u0628\u0631\u0627\u06cc \u067e\u0646\u062c \u062f\u0648\u0631\u0647 \u0627\u062c\u0631\u0627 \u06a9\u0646\u0645 \u0627\u0645\u0627 \u0628\u0647 \u0637\u0631\u0632 \u0648\u062d\u0634\u062a\u0646\u0627\u06a9\u06cc \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0645\u0646\u0627\u0633\u0628 \u0628\u0648\u062f \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0627\u0639\u062a\u0628\u0627\u0631 \u0633\u0646\u062c\u06cc  \u0645\u0646 \u0627\u0646\u062f\u0627\u0632\u0647 \u062f\u0633\u062a\u0647 \u0631\u0627 \u0627\u0641\u0632\u0627\u06cc\u0634 \u062f\u0627\u062f\u0645 \u0627\u0645\u0627 \u0647\u0646\u0648\u0632 \u062f\u0642\u062a \u062a\u0633\u062a \u0686\u0646\u062f\u0627\u0646 \u062e\u0648\u0628 \u0646\u0628\u0648\u062f.  \u06cc\u06a9\u06cc \u0627\u0632 \u062f\u0644\u0627\u06cc\u0644 \u0627\u062d\u062a\u0645\u0627\u0644\u06cc \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0628\u0631\u0627\u0632\u0634 \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u0627\u06cc\u0646\u062c\u0627 \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0648\u0631\u062f \u0645\u0627 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u062c\u062f\u0627\u06af\u0627\u0646\u0647 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0628\u0631\u0686\u0633\u0628 \u062f\u0627\u0631\u06cc\u0645 \u06a9\u0647 \u067e\u06cc\u0686\u06cc\u062f\u06af\u06cc \u0645\u062f\u0644 \u0645\u0627 \u0631\u0627 \u0627\u0641\u0632\u0627\u06cc\u0634 \u0645\u06cc \u062f\u0647\u062f.  \u0627\u0641\u0632\u0627\u06cc\u0634 \u067e\u06cc\u0686\u06cc\u062f\u06af\u06cc \u0645\u062f\u0644 \u0627\u063a\u0644\u0628 \u0645\u0646\u062c\u0631 \u0628\u0647 \u0628\u06cc\u0634 \u0627\u0632 \u062d\u062f \u0628\u0631\u0627\u0632\u0634 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u0646\u062a\u06cc\u062c\u0647 \u0647\u0631 \u062f\u0648\u0631\u0647 \u062f\u0631 \u0632\u06cc\u0631 \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a:<\/p>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">Train \u0631\u0648\u06cc 102124 samples, validate \u0631\u0648\u06cc 25532 samples\nEpoch 1\/5\n102124\/102124 (==============================) - 24s 239us\/step - loss: 3.5116 - dense_1_loss: 0.6017 - dense_2_loss: 0.5806 - dense_3_loss: 0.6150 - dense_4_loss: 0.5585 - dense_5_loss: 0.5828 - dense_6_loss: 0.5730 - dense_1_acc: 0.9029 - dense_2_acc: 0.9842 - dense_3_acc: 0.9444 - dense_4_acc: 0.9934 - dense_5_acc: 0.9508 - dense_6_acc: 0.9870 - val_loss: 1.0369 - val_dense_1_loss: 0.3290 - val_dense_2_loss: 0.0983 - val_dense_3_loss: 0.2571 - val_dense_4_loss: 0.0595 - val_dense_5_loss: 0.1972 - val_dense_6_loss: 0.0959 - val_dense_1_acc: 0.9037 - val_dense_2_acc: 0.9901 - val_dense_3_acc: 0.9469 - val_dense_4_acc: 0.9966 - val_dense_5_acc: 0.9509 - val_dense_6_acc: 0.9901\nEpoch 2\/5\n102124\/102124 (==============================) - 20s 197us\/step - loss: 0.9084 - dense_1_loss: 0.3324 - dense_2_loss: 0.0679 - dense_3_loss: 0.2172 - dense_4_loss: 0.0338 - dense_5_loss: 0.1983 - dense_6_loss: 0.0589 - dense_1_acc: 0.9043 - dense_2_acc: 0.9899 - dense_3_acc: 0.9474 - dense_4_acc: 0.9968 - dense_5_acc: 0.9510 - dense_6_acc: 0.9915 - val_loss: 0.8616 - val_dense_1_loss: 0.3164 - val_dense_2_loss: 0.0555 - val_dense_3_loss: 0.2127 - val_dense_4_loss: 0.0235 - val_dense_5_loss: 0.1981 - val_dense_6_loss: 0.0554 - val_dense_1_acc: 0.9038 - val_dense_2_acc: 0.9900 - val_dense_3_acc: 0.9469 - val_dense_4_acc: 0.9965 - val_dense_5_acc: 0.9509 - val_dense_6_acc: 0.9900\nEpoch 3\/5\n102124\/102124 (==============================) - 20s 199us\/step - loss: 0.8513 - dense_1_loss: 0.3179 - dense_2_loss: 0.0566 - dense_3_loss: 0.2103 - dense_4_loss: 0.0216 - dense_5_loss: 0.1960 - dense_6_loss: 0.0490 - dense_1_acc: 0.9043 - dense_2_acc: 0.9899 - dense_3_acc: 0.9474 - dense_4_acc: 0.9968 - dense_5_acc: 0.9510 - dense_6_acc: 0.9915 - val_loss: 0.8552 - val_dense_1_loss: 0.3158 - val_dense_2_loss: 0.0566 - val_dense_3_loss: 0.2074 - val_dense_4_loss: 0.0225 - val_dense_5_loss: 0.1960 - val_dense_6_loss: 0.0568 - val_dense_1_acc: 0.9038 - val_dense_2_acc: 0.9900 - val_dense_3_acc: 0.9469 - val_dense_4_acc: 0.9965 - val_dense_5_acc: 0.9509 - val_dense_6_acc: 0.9900\nEpoch 4\/5\n102124\/102124 (==============================) - 20s 198us\/step - loss: 0.8442 - dense_1_loss: 0.3153 - dense_2_loss: 0.0570 - dense_3_loss: 0.2061 - dense_4_loss: 0.0213 - dense_5_loss: 0.1952 - dense_6_loss: 0.0493 - dense_1_acc: 0.9043 - dense_2_acc: 0.9899 - dense_3_acc: 0.9474 - dense_4_acc: 0.9968 - dense_5_acc: 0.9510 - dense_6_acc: 0.9915 - val_loss: 0.8527 - val_dense_1_loss: 0.3156 - val_dense_2_loss: 0.0558 - val_dense_3_loss: 0.2074 - val_dense_4_loss: 0.0226 - val_dense_5_loss: 0.1951 - val_dense_6_loss: 0.0561 - val_dense_1_acc: 0.9038 - val_dense_2_acc: 0.9900 - val_dense_3_acc: 0.9469 - val_dense_4_acc: 0.9965 - val_dense_5_acc: 0.9509 - val_dense_6_acc: 0.9900\nEpoch 5\/5\n102124\/102124 (==============================) - 20s 197us\/step - loss: 0.8410 - dense_1_loss: 0.3146 - dense_2_loss: 0.0561 - dense_3_loss: 0.2055 - dense_4_loss: 0.0213 - dense_5_loss: 0.1948 - dense_6_loss: 0.0486 - dense_1_acc: 0.9043 - dense_2_acc: 0.9899 - dense_3_acc: 0.9474 - dense_4_acc: 0.9968 - dense_5_acc: 0.9510 - dense_6_acc: 0.9915 - val_loss: 0.8501 - val_dense_1_loss: 0.3153 - val_dense_2_loss: 0.0553 - val_dense_3_loss: 0.2069 - val_dense_4_loss: 0.0226 - val_dense_5_loss: 0.1948 - val_dense_6_loss: 0.0553 - val_dense_1_acc: 0.9038 - val_dense_2_acc: 0.9900 - val_dense_3_acc: 0.9469 - val_dense_4_acc: 0.9965 - val_dense_5_acc: 0.9509 - val_dense_6_acc: 0.9900\n<\/code><\/pre>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0628\u0631\u0627\u06cc \u0647\u0631 \u062f\u0648\u0631\u0647\u060c \u0645\u0627 \u0645\u0642\u0627\u062f\u06cc\u0631\u06cc \u0628\u0631\u0627\u06cc \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646\u060c \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646 \u0627\u0631\u0632\u0634\u060c \u062f\u0642\u062a \u0648 \u062f\u0642\u062a \u0627\u0631\u0632\u0634 \u0628\u0631\u0627\u06cc \u062a\u0645\u0627\u0645 6 \u0644\u0627\u06cc\u0647 \u0645\u062a\u0631\u0627\u06a9\u0645 \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u062f\u0627\u0631\u06cc\u0645.<\/p>\n<p>\u062d\u0627\u0644 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0639\u0645\u0644\u06a9\u0631\u062f \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u0627\u0631\u0632\u06cc\u0627\u0628\u06cc \u06a9\u0646\u06cc\u0645 \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">score = model.evaluate(x=X_test, y=(y1_test, y2_test, y3_test, y4_test, y5_test, y6_test), verbose=<span class=\"hljs-number\">1<\/span>)\n\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Test Score:\"<\/span>, score(<span class=\"hljs-number\">0<\/span>))\n<span class=\"hljs-built_in\">print<\/span>(<span class=\"hljs-string\">\"Test Accuracy:\"<\/span>, score(<span class=\"hljs-number\">1<\/span>))\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">31915\/31915 (==============================) - 111s 3ms\/step\nTest Score: 0.8471985269747015\nTest Accuracy: 0.31425264998511726\n<\/code><\/pre>\n<p>\u062f\u0642\u062a \u062a\u0646\u0647\u0627 31 \u062f\u0631\u0635\u062f \u0628\u0647 \u062f\u0633\u062a \u0622\u0645\u062f\u0647 \u0627\u0633\u062a \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0686\u0646\u062f\u06cc\u0646 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc.<\/p>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0627\u0632 \u062f\u0633\u062a \u062f\u0627\u062f\u0646 \u0648 \u062f\u0642\u062a \u0631\u0627 \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc \u0648 \u0627\u0639\u062a\u0628\u0627\u0631 \u0633\u0646\u062c\u06cc \u0628\u0631\u0627\u06cc \u0627\u0648\u0644\u06cc\u0646 \u0644\u0627\u06cc\u0647 \u0645\u062a\u0631\u0627\u06a9\u0645 \u062a\u0631\u0633\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">import<\/span> matplotlib.pyplot <span class=\"hljs-keyword\">as<\/span> plt\n\nplt.plot(history.history(<span class=\"hljs-string\">'dense_1_acc'<\/span>))\nplt.plot(history.history(<span class=\"hljs-string\">'val_dense_1_acc'<\/span>))\n\nplt.title(<span class=\"hljs-string\">'model accuracy'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'accuracy'<\/span>)\nplt.xlabel(<span class=\"hljs-string\">'epoch'<\/span>)\nplt.legend((<span class=\"hljs-string\">'train'<\/span>,<span class=\"hljs-string\">'test'<\/span>), loc=<span class=\"hljs-string\">'upper left'<\/span>)\nplt.show()\n\nplt.plot(history.history(<span class=\"hljs-string\">'dense_1_loss'<\/span>))\nplt.plot(history.history(<span class=\"hljs-string\">'val_dense_1_loss'<\/span>))\n\nplt.title(<span class=\"hljs-string\">'model loss'<\/span>)\nplt.ylabel(<span class=\"hljs-string\">'loss'<\/span>)\nplt.xlabel(<span class=\"hljs-string\">'epoch'<\/span>)\nplt.legend((<span class=\"hljs-string\">'train'<\/span>,<span class=\"hljs-string\">'test'<\/span>), loc=<span class=\"hljs-string\">'upper left'<\/span>)\nplt.show()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<p><img decoding=\"async\" src=\"https:\/\/rasanegar.com\/blog\/wp-content\/uploads\/2024\/01\/multi-label-text-classification-with-keras-7.PNG\" alt=\"img7\" title=\"\"><\/p>\n<p>\u0627\u0632 \u062e\u0631\u0648\u062c\u06cc \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u062f\u0642\u062a \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a (\u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc) \u0628\u0639\u062f \u0627\u0632 \u0627\u0648\u0644\u06cc\u0646 \u062f\u0648\u0631\u0647 \u0647\u0627 \u0647\u0645\u06af\u0631\u0627 \u0646\u0645\u06cc \u0634\u0648\u062f.  \u0647\u0645\u0686\u0646\u06cc\u0646\u060c \u062a\u0641\u0627\u0648\u062a \u0628\u06cc\u0646 \u062f\u0642\u062a \u0622\u0645\u0648\u0632\u0634 \u0648 \u0627\u0639\u062a\u0628\u0627\u0631\u0633\u0646\u062c\u06cc \u0628\u0633\u06cc\u0627\u0631 \u0646\u0627\u0686\u06cc\u0632 \u0627\u0633\u062a.  \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646\u060c \u0645\u062f\u0644 \u067e\u0633 \u0627\u0632 \u062f\u0648\u0631\u0647 \u0647\u0627\u06cc \u0627\u0648\u0644 \u0634\u0631\u0648\u0639 \u0628\u0647 \u0627\u0636\u0627\u0641\u0647 \u06a9\u0631\u062f\u0646 \u0645\u06cc \u06a9\u0646\u062f \u0648 \u0627\u0632 \u0627\u06cc\u0646 \u0631\u0648 \u0639\u0645\u0644\u06a9\u0631\u062f \u0636\u0639\u06cc\u0641\u06cc \u062f\u0627\u0631\u06cc\u0645 \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u0647\u0627\u06cc \u062f\u06cc\u062f\u0647 \u0646\u0634\u062f\u0647<\/p>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"%d9%86%d8%aa%db%8c%d8%ac%d9%87\"><\/span>\u0646\u062a\u06cc\u062c\u0647<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0627\u06cc\u062c \u062a\u0631\u06cc\u0646 \u0645\u0634\u06a9\u0644\u0627\u062a \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0627\u0633\u062a.  \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647\u060c \u0645\u0627 \u062f\u0648 \u0631\u0648\u06cc\u06a9\u0631\u062f \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0631\u0627 \u0628\u0631\u0627\u06cc \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0645\u0637\u0627\u0644\u0639\u0647 \u06a9\u0631\u062f\u06cc\u0645.  \u062f\u0631 \u0631\u0648\u06cc\u06a9\u0631\u062f \u0627\u0648\u0644 \u0645\u0627 \u0627\u0632 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0645\u062a\u0631\u0627\u06a9\u0645 \u0628\u0627 \u0686\u0646\u062f\u06cc\u0646 \u0646\u0648\u0631\u0648\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f\u06cc\u0645 \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0647\u0631 \u0646\u0648\u0631\u0648\u0646 \u06cc\u06a9 \u0628\u0631\u0686\u0633\u0628 \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc\u200c\u062f\u0627\u062f.<\/p>\n<p>\u062f\u0631 \u0631\u0648\u06cc\u06a9\u0631\u062f \u062f\u0648\u0645\u060c \u0644\u0627\u06cc\u0647\u200c\u0647\u0627\u06cc \u0645\u062a\u0631\u0627\u06a9\u0645 \u062c\u062f\u0627\u06af\u0627\u0646\u0647 \u0628\u0631\u0627\u06cc \u0647\u0631 \u0628\u0631\u0686\u0633\u0628 \u0628\u0627 \u06cc\u06a9 \u0646\u0648\u0631\u0648\u0646 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0631\u062f\u06cc\u0645.  \u0646\u062a\u0627\u06cc\u062c \u0646\u0634\u0627\u0646 \u0645\u06cc\u200c\u062f\u0647\u062f \u06a9\u0647 \u062f\u0631 \u0645\u0648\u0631\u062f \u0645\u0627\u060c \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u0645\u0646\u0641\u0631\u062f \u0628\u0627 \u0686\u0646\u062f \u0646\u0648\u0631\u0648\u0646 \u0628\u0647\u062a\u0631 \u0627\u0632 \u0686\u0646\u062f\u06cc\u0646 \u0644\u0627\u06cc\u0647 \u062e\u0631\u0648\u062c\u06cc \u06a9\u0627\u0631 \u0645\u06cc\u200c\u06a9\u0646\u062f.<\/p>\n<p>\u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u06af\u0627\u0645 \u0628\u0639\u062f\u06cc\u060c \u0645\u0646 \u0628\u0647 \u0634\u0645\u0627 \u062a\u0648\u0635\u06cc\u0647 \u0645\u06cc \u06a9\u0646\u0645 \u06a9\u0647 \u0639\u0645\u0644\u06a9\u0631\u062f \u0641\u0639\u0627\u0644 \u0633\u0627\u0632\u06cc \u0648 \u062a\u0642\u0633\u06cc\u0645 \u062a\u0633\u062a \u0642\u0637\u0627\u0631 \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u062f\u0647\u06cc\u062f \u062a\u0627 \u0628\u0628\u06cc\u0646\u06cc\u062f \u0622\u06cc\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0646\u062a\u0627\u06cc\u062c \u0628\u0647\u062a\u0631\u06cc \u0646\u0633\u0628\u062a \u0628\u0647 \u0622\u0646\u0686\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0631\u0627\u0626\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a \u0628\u06af\u06cc\u0631\u06cc\u062f.<\/p>\n<\/div>\n<p><script>\n                        !function(f,b,e,v,n,t,s)\n                        {if(f.fbq)return;n=f.fbq=function(){n.callMethod?\n                        n.callMethod.apply(n,arguments):n.queue.push(arguments)};\n                        if(!f._fbq)f._fbq=n;n.push=n;n.loaded=!0;n.version='2.0';\n                        n.queue=();t=b.createElement(e);t.async=!0;\n                        t.src=v;s=b.getElementsByTagName(e)(0);\n                        s.parentNode.insertBefore(t,s)}(window, document,'script',\n                        'https:\/\/connect.facebook.net\/en_US\/fbevents.js');\n                        fbq('init', '525232124909042');\n                        fbq('track', 'PageView');\n                    <\/script>    (\u0628\u0631\u0686\u0633\u0628\u200c\u0647\u0627 \u0628\u0647 \u062a\u0631\u062c\u0645\u0647)# python<br \/>\n<br \/><br \/>\n<br \/>\u0645\u0646\u062a\u0634\u0631 \u0634\u062f\u0647 \u062f\u0631 1403-01-20 18:47:03<br \/>\n<\/p>\n\n\n<div class=\"kk-star-ratings kksr-auto kksr-align-center kksr-valign-bottom\"\n    data-payload='{&quot;align&quot;:&quot;center&quot;,&quot;id&quot;:&quot;16096&quot;,&quot;slug&quot;:&quot;default&quot;,&quot;valign&quot;:&quot;bottom&quot;,&quot;ignore&quot;:&quot;&quot;,&quot;reference&quot;:&quot;auto&quot;,&quot;class&quot;:&quot;&quot;,&quot;count&quot;:&quot;0&quot;,&quot;legendonly&quot;:&quot;&quot;,&quot;readonly&quot;:&quot;&quot;,&quot;score&quot;:&quot;0&quot;,&quot;starsonly&quot;:&quot;&quot;,&quot;best&quot;:&quot;5&quot;,&quot;gap&quot;:&quot;5&quot;,&quot;greet&quot;:&quot;\u0627\u0645\u062a\u06cc\u0627\u0632 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628&quot;,&quot;legend&quot;:&quot;0\\\/5 (0 \u0631\u0627\u06cc)&quot;,&quot;size&quot;:&quot;30&quot;,&quot;title&quot;:&quot;\u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc NLP: \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0686\u0646\u062f \u0628\u0631\u0686\u0633\u0628\u06cc \u0628\u0627 Keras&quot;,&quot;width&quot;:&quot;0&quot;,&quot;_legend&quot;:&quot;{score}\\\/{best} ({count} \u0631\u0627\u06cc)&quot;,&quot;font_factor&quot;:&quot;1.25&quot;}'>\n            \n<div class=\"kksr-stars\">\n    \n<div class=\"kksr-stars-inactive\">\n            <div class=\"kksr-star\" data-star=\"1\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"2\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"3\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"4\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" data-star=\"5\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n    <\/div>\n    \n<div class=\"kksr-stars-active\" style=\"width: 0px;\">\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n            <div class=\"kksr-star\" style=\"padding-left: 5px\">\n            \n\n<div class=\"kksr-icon\" style=\"width: 30px; height: 30px;\"><\/div>\n        <\/div>\n    <\/div>\n<\/div>\n                \n\n<div class=\"kksr-legend\" style=\"font-size: 24px;\">\n            <span class=\"kksr-muted\">\u0627\u0645\u062a\u06cc\u0627\u0632 \u0634\u0645\u0627 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0637\u0644\u0628<\/span>\n    <\/div>\n    <\/div>\n","protected":false},"excerpt":{"rendered":"<p><span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">\u0632\u0645\u0627\u0646 \u0644\u0627\u0632\u0645 \u0628\u0631\u0627\u06cc \u0645\u0637\u0627\u0644\u0639\u0647: <\/span> <span class=\"rt-time\"> 11<\/span> <span class=\"rt-label rt-postfix\">\u062f\u0642\u06cc\u0642\u0647<\/span><\/span>\u0645\u0639\u0631\u0641\u06cc \u0627\u06cc\u0646 \u0646\u0648\u0632\u062f\u0647\u0645\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0632 \u0633\u0631\u06cc \u0645\u0642\u0627\u0644\u0627\u062a \u0645\u0646 \u0627\u0633\u062a \u0631\u0648\u06cc \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc NLP. \u0627\u0632 \u0686\u0646\u062f \u0645\u0642\u0627\u0644\u0647 \u0627\u062e\u06cc\u0631\u060c \u0645\u0627 \u0645\u0641\u0627\u0647\u06cc\u0645 \u0646\u0633\u0628\u062a\u0627\u064b \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 NLP \u0631\u0627 \u0628\u0631\u0631\u0633\u06cc \u06a9\u0631\u062f\u0647 \u0627\u06cc\u0645 \u0631\u0648\u06cc \u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u062f\u0631 \u0622\u062e\u0631\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647\u060c \u0631\u0648\u0634 \u0627\u06cc\u062c\u0627\u062f \u06cc\u06a9 \u0645\u062f\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0622\u0645\u0648\u0632\u0634 \u062f\u06cc\u062f\u0647 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0648\u0631\u0648\u062f\u06cc \u0647\u0627\u06cc \u0645\u062a\u0639\u062f\u062f \u0627\u0632 \u0627\u0646\u0648\u0627\u0639 \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u0631\u0627 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":16097,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1743,620],"tags":[],"class_list":["post-16096","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python","category-programming"],"acf":[],"_links":{"self":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/16096","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/comments?post=16096"}],"version-history":[{"count":0,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/16096\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media\/16097"}],"wp:attachment":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media?parent=16096"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/categories?post=16096"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/tags?post=16096"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}