{"id":15897,"date":"2024-01-18T10:14:13","date_gmt":"2024-01-18T06:44:13","guid":{"rendered":"https:\/\/rasanegar.com\/blog\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%d8%a8%d8%a7-bert-tokenizer-%d9%88-tf-2-0-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86\/"},"modified":"2024-01-18T10:14:13","modified_gmt":"2024-01-18T06:44:13","slug":"%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%d8%a8%d8%a7-bert-tokenizer-%d9%88-tf-2-0-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86","status":"publish","type":"post","link":"https:\/\/rasanegaar.com\/blog\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%d8%a8%d8%a7-bert-tokenizer-%d9%88-tf-2-0-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86\/","title":{"rendered":"\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0628\u0627 BERT Tokenizer \u0648 TF 2.0 \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646"},"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\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%d8%a8%d8%a7-bert-tokenizer-%d9%88-tf-2-0-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86\/#%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-2\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%d8%a8%d8%a7-bert-tokenizer-%d9%88-tf-2-0-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86\/#%d9%86%d8%b5%d8%a8_%d9%88_%d9%88%d8%a7%d8%b1%d8%af_%da%a9%d8%b1%d8%af%d9%86_%da%a9%d8%aa%d8%a7%d8%a8%d8%ae%d8%a7%d9%86%d9%87_%d9%87%d8%a7%db%8c_%d9%85%d9%88%d8%b1%d8%af_%d9%86%db%8c%d8%a7%d8%b2\" >\u0646\u0635\u0628 \u0648 \u0648\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632<\/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\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%d8%a8%d8%a7-bert-tokenizer-%d9%88-tf-2-0-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86\/#%d9%88%d8%a7%d8%b1%d8%af_%da%a9%d8%b1%d8%af%d9%86_%d9%88_%d9%be%db%8c%d8%b4_%d9%be%d8%b1%d8%af%d8%a7%d8%b2%d8%b4_%d9%85%d8%ac%d9%85%d9%88%d8%b9%d9%87_%d8%af%d8%a7%d8%af%d9%87\" >\u0648\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u0648 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \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-4\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%d8%a8%d8%a7-bert-tokenizer-%d9%88-tf-2-0-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86\/#%d8%a7%db%8c%d8%ac%d8%a7%d8%af_%d8%aa%d9%88%da%a9%d9%86%d8%a7%db%8c%d8%b2%d8%b1_bert\" >\u0627\u06cc\u062c\u0627\u062f \u062a\u0648\u06a9\u0646\u0627\u06cc\u0632\u0631 BERT<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%d8%a8%d8%a7-bert-tokenizer-%d9%88-tf-2-0-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86\/#%d8%a2%d9%85%d8%a7%d8%af%d9%87_%d8%b3%d8%a7%d8%b2%db%8c_%d8%af%d8%a7%d8%af%d9%87_%d9%87%d8%a7_%d8%a8%d8%b1%d8%a7%db%8c_%d8%a2%d9%85%d9%88%d8%b2%d8%b4\" >\u0622\u0645\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634<\/a><\/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\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%d8%a8%d8%a7-bert-tokenizer-%d9%88-tf-2-0-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86\/#%d8%a7%db%8c%d8%ac%d8%a7%d8%af_%d9%85%d8%af%d9%84\" >\u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/rasanegaar.com\/blog\/%d8%b7%d8%a8%d9%82%d9%87-%d8%a8%d9%86%d8%af%db%8c-%d9%85%d8%aa%d9%86-%d8%a8%d8%a7-bert-tokenizer-%d9%88-tf-2-0-%d8%af%d8%b1-%d9%be%d8%a7%db%8c%d8%aa%d9%88%d9%86\/#%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<p>\u0627\u06cc\u0646 \u0628\u06cc\u0633\u062a \u0648 \u0633\u0648\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.  \u062f\u0631 \u0645\u0642\u0627\u0644\u0647 \u0642\u0628\u0644\u06cc \u0627\u06cc\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u0631\u0648\u0634 \u0627\u0646\u062c\u0627\u0645 \u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646 \u0639\u0635\u0628\u06cc \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0622\u0646 \u0631\u0627 \u062a\u0648\u0636\u06cc\u062d \u062f\u0627\u062f\u0645 <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/google.github.io\/seq2seq\/\">\u0645\u0639\u0645\u0627\u0631\u06cc seq2seq<\/a> \u0628\u0627 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 Keras \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0645\u0637\u0627\u0644\u0639\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u067e\u0631\u062f\u0627\u062e\u062a <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/en.wikipedia.org\/wiki\/BERT_(language_model)\">\u0628\u0631\u062a<\/a>\u060c \u06a9\u0647 \u0645\u062e\u0641\u0641 \u0622\u0646 \u0627\u0633\u062a <em>\u0646\u0645\u0627\u06cc\u0634 \u0631\u0645\u0632\u06af\u0630\u0627\u0631 \u062f\u0648 \u0637\u0631\u0641\u0647 \u0627\u0632 \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631\u0647\u0627<\/em> \u0648 \u06a9\u0627\u0631\u0628\u0631\u062f \u0622\u0646 \u062f\u0631 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646.  BERT \u06cc\u06a9 \u062a\u06a9\u0646\u06cc\u06a9 \u0646\u0645\u0627\u06cc\u0634 \u0645\u062a\u0646 \u0645\u0627\u0646\u0646\u062f \u062c\u0627\u0633\u0627\u0632\u06cc \u0648\u0631\u062f \u0627\u0633\u062a.  \u0627\u06af\u0631 \u0647\u06cc\u0686 \u0627\u06cc\u062f\u0647 \u0627\u06cc \u0627\u0632 \u0631\u0648\u0634 \u06a9\u0627\u0631 \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0644\u0645\u0627\u062a \u0646\u062f\u0627\u0631\u06cc\u062f\u060c \u0628\u0647 \u0645\u0642\u0627\u0644\u0647 \u0645\u0646 \u0646\u06af\u0627\u0647\u06cc \u0628\u06cc\u0646\u062f\u0627\u0632\u06cc\u062f \u0631\u0648\u06cc \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0644\u0645\u0627\u062a<\/p>\n<p>\u0645\u0627\u0646\u0646\u062f \u062c\u0627\u0633\u0627\u0632\u06cc \u06a9\u0644\u0645\u0627\u062a\u060c BERT \u0646\u06cc\u0632 \u06cc\u06a9 \u062a\u06a9\u0646\u06cc\u06a9 \u0646\u0645\u0627\u06cc\u0634 \u0645\u062a\u0646 \u0627\u0633\u062a \u06a9\u0647 \u062a\u0644\u0641\u06cc\u0642\u06cc \u0627\u0632 \u0627\u0646\u0648\u0627\u0639 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642\u060c \u0645\u0627\u0646\u0646\u062f \u0631\u0645\u0632\u06af\u0630\u0627\u0631 \u062f\u0648 \u0637\u0631\u0641\u0647 LSTM \u0648 Transformers \u0627\u0633\u062a.  BERT \u062a\u0648\u0633\u0637 \u0645\u062d\u0642\u0642\u0627\u0646 Google \u062f\u0631 \u0633\u0627\u0644 2018 \u062a\u0648\u0633\u0639\u0647 \u062f\u0627\u062f\u0647 \u0634\u062f \u0648 \u062b\u0627\u0628\u062a \u0634\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0631\u0627\u06cc \u0627\u0646\u0648\u0627\u0639 \u0648\u0638\u0627\u06cc\u0641 \u067e\u0631\u062f\u0627\u0632\u0634 \u0632\u0628\u0627\u0646 \u0637\u0628\u06cc\u0639\u06cc \u0645\u0627\u0646\u0646\u062f \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646\u060c \u062e\u0644\u0627\u0635\u0647 \u0633\u0627\u0632\u06cc \u0645\u062a\u0646\u060c \u062a\u0648\u0644\u06cc\u062f \u0645\u062a\u0646 \u0648 \u063a\u06cc\u0631\u0647 \u067e\u06cc\u0634\u0631\u0641\u062a\u0647 \u0627\u0633\u062a. <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.blog.google\/products\/search\/search-language-understanding-bert\/\">\u06af\u0648\u06af\u0644 \u0627\u0639\u0644\u0627\u0645 \u06a9\u0631\u062f<\/a> \u06a9\u0647 BERT \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0628\u062e\u0634 \u0627\u0635\u0644\u06cc \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u062c\u0633\u062a\u062c\u0648\u06cc \u0622\u0646\u0647\u0627 \u0628\u0631\u0627\u06cc \u062f\u0631\u06a9 \u0628\u0647\u062a\u0631 \u067e\u0631\u0633 \u0648 \u062c\u0648\u0647\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u062c\u0632\u0626\u06cc\u0627\u062a \u0631\u06cc\u0627\u0636\u06cc \u0631\u0648\u0634 \u067e\u06cc\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc BERT \u0646\u0645\u06cc \u067e\u0631\u062f\u0627\u0632\u06cc\u0645\u060c \u0632\u06cc\u0631\u0627 \u0645\u0646\u0627\u0628\u0639 \u0632\u06cc\u0627\u062f\u06cc \u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 \u0628\u0647 \u0635\u0648\u0631\u062a \u0622\u0646\u0644\u0627\u06cc\u0646 \u062f\u0631 \u062f\u0633\u062a\u0631\u0633 \u0647\u0633\u062a\u0646\u062f.  \u062f\u0631 \u0639\u0648\u0636 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u06cc\u062f \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0648\u06a9\u0646\u0627\u06cc\u0632\u0631 BERT \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f.  \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 BERT Tokenizer \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f.  \u062f\u0631 \u0645\u0642\u0627\u0644\u0647 \u0628\u0639\u062f\u06cc \u062a\u0648\u0636\u06cc\u062d \u062e\u0648\u0627\u0647\u0645 \u062f\u0627\u062f \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 BERT Tokenizer \u0628\u0647 \u0647\u0645\u0631\u0627\u0647 \u0644\u0627\u06cc\u0647 \u062a\u0639\u0628\u06cc\u0647 \u0634\u062f\u0647 BERT \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644 \u0647\u0627\u06cc NLP \u062d\u062a\u06cc \u06a9\u0627\u0631\u0622\u0645\u062f\u062a\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f.<\/p>\n<p><strong>\u062a\u0648\u062c\u0647 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u06cc\u062f<\/strong>: \u062a\u0645\u0627\u0645\u06cc \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0647\u0627\u06cc \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0633\u062a \u0634\u062f\u0647 \u0627\u0646\u062f <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/colab.research.google.com\/\" class=\"broken_link\">\u06af\u0648\u06af\u0644 \u06a9\u0648\u0644\u0628<\/a> \u0645\u062d\u06cc\u0637\u060c \u0628\u0627 \u0632\u0645\u0627\u0646 \u0627\u062c\u0631\u0627 \u067e\u0627\u06cc\u062a\u0648\u0646 \u0631\u0648\u06cc GPU \u062a\u0646\u0638\u06cc\u0645 \u0634\u062f\u0647 \u0627\u0633\u062a.<\/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 \u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 \u0627\u06cc\u0646\u062c\u0627 \u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u0631\u062f <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/www.kaggle.com\/lakshmi25npathi\/imdb-dataset-of-50k-movie-reviews\">\u0627\u06cc\u0646 \u0644\u06cc\u0646\u06a9 \u06a9\u0627\u06af\u0644<\/a>.<\/p>\n<p>\u0627\u06af\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u062f\u0627\u0646\u0644\u0648\u062f \u06a9\u0646\u06cc\u062f \u0648 \u0641\u0627\u06cc\u0644 \u0641\u0634\u0631\u062f\u0647 \u0631\u0627 \u0627\u0633\u062a\u062e\u0631\u0627\u062c \u06a9\u0646\u06cc\u062f\u060c \u06cc\u06a9 \u0641\u0627\u06cc\u0644 CSV \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f.  \u0627\u06cc\u0646 \u0641\u0627\u06cc\u0644 \u0634\u0627\u0645\u0644 50000 \u0631\u06a9\u0648\u0631\u062f \u0648 \u062f\u0648 \u0633\u062a\u0648\u0646 \u0627\u0633\u062a: \u0628\u0631\u0631\u0633\u06cc \u0648 \u0627\u062d\u0633\u0627\u0633.  \u0633\u062a\u0648\u0646 \u0628\u0631\u0631\u0633\u06cc \u062d\u0627\u0648\u06cc \u0645\u062a\u0646\u06cc \u0628\u0631\u0627\u06cc \u0628\u0631\u0631\u0633\u06cc \u0648 \u0633\u062a\u0648\u0646 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u062d\u0627\u0648\u06cc \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0628\u0631\u0627\u06cc \u0628\u0631\u0631\u0633\u06cc \u0627\u0633\u062a.  \u0633\u062a\u0648\u0646 \u0627\u062d\u0633\u0627\u0633 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u062f\u0648 \u0645\u0642\u062f\u0627\u0631 \u062f\u0627\u0634\u062a\u0647 \u0628\u0627\u0634\u062f \u06cc\u0639\u0646\u06cc &#8220;\u0645\u062b\u0628\u062a&#8221; \u0648 &#8220;\u0645\u0646\u0641\u06cc&#8221; \u06a9\u0647 \u0645\u0634\u06a9\u0644 \u0645\u0627 \u0631\u0627 \u0628\u0647 \u06cc\u06a9 \u0645\u0634\u06a9\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0628\u0627\u06cc\u0646\u0631\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u06cc \u06a9\u0646\u062f.<\/p>\n<p>\u0645\u0627 \u0642\u0628\u0644\u0627\u064b \u062f\u0631 \u0645\u0642\u0627\u0644\u0647 \u0642\u0628\u0644\u06cc \u062a\u062c\u0632\u06cc\u0647 \u0648 \u062a\u062d\u0644\u06cc\u0644 \u0627\u062d\u0633\u0627\u0633\u0627\u062a\u06cc \u0627\u06cc\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f\u0647 \u0627\u06cc\u0645 \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0628\u0647 \u062d\u062f\u0627\u06a9\u062b\u0631 \u062f\u0642\u062a 92\u066a \u062f\u0633\u062a \u06cc\u0627\u0641\u062a\u0647 \u0627\u06cc\u0645. \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0627\u0632 \u0637\u0631\u06cc\u0642 word \u06cc\u06a9 \u062a\u06a9\u0646\u06cc\u06a9 \u062c\u0627\u0633\u0627\u0632\u06cc \u0648 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u0627\u0644.  \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a \u062d\u062f\u0627\u06a9\u062b\u0631 \u062f\u0642\u062a \u0628\u0647 \u062f\u0633\u062a \u0622\u0645\u062f\u0647 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u06a9\u0644\u0645\u0647 embedding \u0648 LSTM \u0645\u0646\u0641\u0631\u062f \u0628\u0627 128 \u06af\u0631\u0647 85.40\u066a \u0628\u0648\u062f.  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0628\u0628\u06cc\u0646\u06cc\u0645 \u0622\u06cc\u0627 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0646\u0645\u0627\u06cc\u0634 BERT \u0628\u0647 \u062f\u0642\u062a \u0628\u0647\u062a\u0631\u06cc \u062f\u0633\u062a \u067e\u06cc\u062f\u0627 \u06a9\u0646\u06cc\u0645.<\/p>\n<h2 id=\"installingandimportingrequiredlibraries\"><span class=\"ez-toc-section\" id=\"%d9%86%d8%b5%d8%a8_%d9%88_%d9%88%d8%a7%d8%b1%d8%af_%da%a9%d8%b1%d8%af%d9%86_%da%a9%d8%aa%d8%a7%d8%a8%d8%ae%d8%a7%d9%86%d9%87_%d9%87%d8%a7%db%8c_%d9%85%d9%88%d8%b1%d8%af_%d9%86%db%8c%d8%a7%d8%b2\"><\/span>\u0646\u0635\u0628 \u0648 \u0648\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 \u0647\u0627\u06cc \u0645\u0648\u0631\u062f \u0646\u06cc\u0627\u0632<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0642\u0628\u0644 \u0627\u0632 \u0627\u06cc\u0646\u06a9\u0647 \u0628\u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0631\u0648\u06cc\u062f \u0648 \u0627\u0632 \u0646\u0645\u0627\u06cc\u0634 \u0645\u062a\u0646 BERT \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f\u060c \u0628\u0627\u06cc\u062f BERT \u0631\u0627 \u0628\u0631\u0627\u06cc TensorFlow 2.0 \u0646\u0635\u0628 \u06a9\u0646\u06cc\u062f.  \u0645\u0648\u0627\u0631\u062f \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f pip \u062f\u0633\u062a\u0648\u0631\u0627\u062a \u0631\u0648\u06cc \u0634\u0645\u0627 terminal \u0628\u0631\u0627\u06cc \u0646\u0635\u0628 BERT \u0628\u0631\u0627\u06cc TensorFlow 2.0.<\/p>\n<pre><code class=\"hljs\">!pip install bert-for-tf2\n!pip install sentencepiece\n<\/code><\/pre>\n<p>\u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0628\u0639\u062f\u060c \u0628\u0627\u06cc\u062f \u0645\u0637\u0645\u0626\u0646 \u0634\u0648\u06cc\u062f \u06a9\u0647 TensorFlow 2.0 \u0631\u0627 \u0627\u062c\u0631\u0627 \u0645\u06cc \u06a9\u0646\u06cc\u062f.  Google Colab\u060c \u0628\u0647 \u0637\u0648\u0631 \u067e\u06cc\u0634 \u0641\u0631\u0636\u060c \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0634\u0645\u0627 \u0631\u0627 \u0627\u062c\u0631\u0627 \u0646\u0645\u06cc \u06a9\u0646\u062f \u0631\u0648\u06cc TensorFlow 2.0.  \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646\u060c \u0628\u0631\u0627\u06cc \u0627\u0637\u0645\u06cc\u0646\u0627\u0646 \u0627\u0632 \u0627\u06cc\u0646\u06a9\u0647 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u062e\u0648\u062f \u0631\u0627 \u0627\u0632 \u0637\u0631\u06cc\u0642 TensorFlow 2.0 \u0627\u062c\u0631\u0627 \u0645\u06cc \u06a9\u0646\u06cc\u062f\u060c \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">try<\/span>:\n    %tensorflow_version <span class=\"hljs-number\">2.<\/span>x\n<span class=\"hljs-keyword\">except<\/span> Exception:\n    <span class=\"hljs-keyword\">pass<\/span>\n<span class=\"hljs-keyword\">import<\/span> tensorflow <span class=\"hljs-keyword\">as<\/span> tf\n\n<span class=\"hljs-keyword\">import<\/span> tensorflow_hub <span class=\"hljs-keyword\">as<\/span> hub\n\n<span class=\"hljs-keyword\">from<\/span> tensorflow.keras <span class=\"hljs-keyword\">import<\/span> layers\n<span class=\"hljs-keyword\">import<\/span> bert\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0641\u0648\u0642\u060c \u0639\u0644\u0627\u0648\u0647 \u0628\u0631 TensorFlow 2.0\u060c \u0645\u0627 \u0646\u06cc\u0632 import tensorflow_hub\u060c \u06a9\u0647 \u0627\u0633\u0627\u0633\u0627\u064b \u0645\u06a9\u0627\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u062a\u0645\u0627\u0645 \u0645\u062f\u0644 \u0647\u0627\u06cc \u0627\u0632 \u067e\u06cc\u0634 \u0633\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0648 \u0627\u0632 \u067e\u06cc\u0634 \u0622\u0645\u0648\u0632\u0634 \u062f\u0627\u062f\u0647 \u0634\u062f\u0647 \u062a\u0648\u0633\u0639\u0647 \u06cc\u0627\u0641\u062a\u0647 \u062f\u0631 TensorFlow \u0631\u0627 \u067e\u06cc\u062f\u0627 \u06a9\u0646\u06cc\u062f.  \u0645\u0627 \u06cc\u06a9 \u0645\u062f\u0644 BERT \u062f\u0627\u062e\u0644\u06cc \u0631\u0627 \u0627\u0632 \u0647\u0627\u0628 TF \u0648\u0627\u0631\u062f \u0648 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.  \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u0627\u06af\u0631 \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u062e\u0631\u0648\u062c\u06cc \u0632\u06cc\u0631 \u0631\u0627 \u0645\u0634\u0627\u0647\u062f\u0647 \u06a9\u0631\u062f\u06cc\u062f\u060c \u0628\u0647\u062a\u0631 \u0627\u0633\u062a \u0628\u0631\u0648\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">TensorFlow 2.x selected.\n<\/code><\/pre>\n<h2 id=\"importingandpreprocessingthedataset\"><span class=\"ez-toc-section\" id=\"%d9%88%d8%a7%d8%b1%d8%af_%da%a9%d8%b1%d8%af%d9%86_%d9%88_%d9%be%db%8c%d8%b4_%d9%be%d8%b1%d8%af%d8%a7%d8%b2%d8%b4_%d9%85%d8%ac%d9%85%d9%88%d8%b9%d9%87_%d8%af%d8%a7%d8%af%d9%87\"><\/span>\u0648\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u0648 \u067e\u06cc\u0634 \u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 <code>read_csv()<\/code> \u0631\u0648\u0634 \u0686\u0627\u0631\u0686\u0648\u0628 \u062f\u0627\u062f\u0647 \u067e\u0627\u0646\u062f\u0627\u0647\u0627  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0647\u0645\u0686\u0646\u06cc\u0646 \u0634\u06a9\u0644 \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\">movie_reviews = pd.read_csv(<span class=\"hljs-string\">\"\/content\/drive\/My Drive\/Colab Datasets\/IMDB Dataset.csv\"<\/span>)\n\nmovie_reviews.isnull().values.<span class=\"hljs-built_in\">any<\/span>()\n\nmovie_reviews.shape\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc<\/strong><\/p>\n<pre><code class=\"hljs\">(50000, 2)\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f \u06a9\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627 50000 \u0633\u0637\u0631 \u0648 2 \u0633\u062a\u0648\u0646 \u062f\u0627\u0631\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\u0631\u0627\u06cc \u062d\u0630\u0641 \u0647\u0631 \u06af\u0648\u0646\u0647 \u0639\u0644\u0627\u0645\u062a \u06af\u0630\u0627\u0631\u06cc \u0648 \u06a9\u0627\u0631\u0627\u06a9\u062a\u0631\u0647\u0627\u06cc \u062e\u0627\u0635 \u0627\u0632 \u0642\u0628\u0644 \u067e\u0631\u062f\u0627\u0632\u0634 \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631\u060c \u062a\u0627\u0628\u0639\u06cc \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u06cc\u06a9 \u0628\u0631\u0631\u0633\u06cc \u0645\u062a\u0646 \u062e\u0627\u0645 \u0631\u0627 \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0631\u0648\u062f\u06cc \u0645\u06cc \u06af\u06cc\u0631\u062f \u0648 \u0628\u0631\u0631\u0633\u06cc \u0645\u062a\u0646 \u067e\u0627\u06a9 \u0634\u062f\u0647 \u0645\u0631\u0628\u0648\u0637\u0647 \u0631\u0627 \u0628\u0631\u0645\u06cc \u06af\u0631\u062f\u0627\u0646\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 = remove_tags(sen)\n\n    \n    sentence = re.sub(<span class=\"hljs-string\">'(^a-zA-Z)'<\/span>, <span class=\"hljs-string\">' '<\/span>, sentence)\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<pre><code class=\"hljs\">TAG_RE = re.<span class=\"hljs-built_in\">compile<\/span>(<span class=\"hljs-string\">r'&lt;(^&gt;)+&gt;'<\/span>)\n\n<span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">remove_tags<\/span>(<span class=\"hljs-params\">text<\/span>):<\/span>\n    <span class=\"hljs-keyword\">return<\/span> TAG_RE.sub(<span class=\"hljs-string\">''<\/span>, text)\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u062a\u0645\u0627\u0645 \u0628\u0631\u0631\u0633\u06cc \u0647\u0627\u06cc \u0645\u062a\u0646 \u0631\u0627 \u067e\u0627\u06a9 \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">reviews = ()\nsentences = <span class=\"hljs-built_in\">list<\/span>(movie_reviews(<span class=\"hljs-string\">'review'<\/span>))\n<span class=\"hljs-keyword\">for<\/span> sen <span class=\"hljs-keyword\">in<\/span> sentences:\n    reviews.append(preprocess_text(sen))\n<\/code><\/pre>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627 \u0634\u0627\u0645\u0644 \u062f\u0648 \u0633\u062a\u0648\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0627\u0632 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0642\u0627\u0628\u0644 \u062a\u0623\u06cc\u06cc\u062f \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(movie_reviews.columns.values)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">('review' 'sentiment')\n<\/code><\/pre>\n<p>\u0631\u0627 <code>review<\/code> \u0633\u062a\u0648\u0646 \u062d\u0627\u0648\u06cc \u0645\u062a\u0646 \u0627\u0633\u062a \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 <code>sentiment<\/code> \u0633\u062a\u0648\u0646 \u062d\u0627\u0648\u06cc \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0627\u0633\u062a.  \u0633\u062a\u0648\u0646 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u062d\u0627\u0648\u06cc \u0645\u0642\u0627\u062f\u06cc\u0631\u06cc \u0628\u0647 \u0634\u06a9\u0644 \u0645\u062a\u0646 \u0627\u0633\u062a.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0645\u0646\u062d\u0635\u0631 \u0628\u0647 \u0641\u0631\u062f \u0631\u0627 \u062f\u0631 <code>sentiment<\/code> \u0633\u062a\u0648\u0646:<\/p>\n<pre><code class=\"hljs\">movie_reviews.sentiment.unique()\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">array(('positive', 'negative'), dtype=object)\n<\/code><\/pre>\n<p>\u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0633\u062a\u0648\u0646 \u0627\u062d\u0633\u0627\u0633 \u0634\u0627\u0645\u0644 \u062f\u0648 \u0645\u0642\u062f\u0627\u0631 \u0645\u0646\u062d\u0635\u0631 \u0628\u0647 \u0641\u0631\u062f \u0627\u0633\u062a <code>positive<\/code> \u0648 <code>negative<\/code>.  \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642 \u0628\u0627 \u0627\u0639\u062f\u0627\u062f \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u0646\u062f.  \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u0627 \u0641\u0642\u0637 \u062f\u0648 \u0645\u0642\u062f\u0627\u0631 \u0645\u0646\u062d\u0635\u0631 \u0628\u0647 \u0641\u0631\u062f \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u062f\u0627\u0631\u06cc\u0645\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0622\u0646\u0647\u0627 \u0631\u0627 \u0628\u0647 1 \u0648 0 \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u0645. \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646 \u0645\u06cc \u0634\u0648\u062f. <code>positive<\/code> \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u062a\u0648\u0633\u0637 <code>1<\/code> \u0648 \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u0645\u0646\u0641\u06cc \u062a\u0648\u0633\u0637 <code>0<\/code>.<\/p>\n<pre><code class=\"hljs\">y = movie_reviews(<span class=\"hljs-string\">'sentiment'<\/span>)\n\ny = np.array(<span class=\"hljs-built_in\">list<\/span>(<span class=\"hljs-built_in\">map<\/span>(<span class=\"hljs-keyword\">lambda<\/span> x: <span class=\"hljs-number\">1<\/span> <span class=\"hljs-keyword\">if<\/span> x==<span class=\"hljs-string\">\"positive\"<\/span> <span class=\"hljs-keyword\">else<\/span> <span class=\"hljs-number\">0<\/span>, y)))\n<\/code><\/pre>\n<p>\u062f\u0631 \u062d\u0627\u0644 \u062d\u0627\u0636\u0631 <code>reviews<\/code> \u0645\u062a\u063a\u06cc\u0631 \u0634\u0627\u0645\u0644 \u0628\u0631\u0631\u0633\u06cc \u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u0627\u0633\u062a \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 <code>y<\/code> \u0645\u062a\u063a\u06cc\u0631 \u062d\u0627\u0648\u06cc \u0628\u0631\u0686\u0633\u0628 \u0647\u0627\u06cc \u0645\u0631\u0628\u0648\u0637\u0647 \u0627\u0633\u062a.  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0628\u0647 \u0637\u0648\u0631 \u062a\u0635\u0627\u062f\u0641\u06cc print \u0628\u0627\u0632\u0646\u06af\u0631\u06cc.<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(reviews(<span class=\"hljs-number\">10<\/span>))\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">Phil the Alien is one of those quirky films where the humour is based around the oddness of everything rather than actual punchlines At first it was very odd and pretty funny but as the movie progressed didn find the jokes or oddness funny anymore Its low budget film thats never problem in itself there were some pretty interesting characters but eventually just lost interest imagine this film would appeal to stoner who is currently partaking For something similar but better try Brother from another planet \n<\/code><\/pre>\n<p>\u0628\u0647 \u0648\u0636\u0648\u062d \u06cc\u06a9 \u0628\u0631\u0631\u0633\u06cc \u0645\u0646\u0641\u06cc \u0628\u0647 \u0646\u0638\u0631 \u0645\u06cc \u0631\u0633\u062f.  \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0628\u0627 \u0686\u0627\u067e \u0645\u0642\u062f\u0627\u0631 \u0628\u0631\u0686\u0633\u0628 \u0645\u0631\u0628\u0648\u0637\u0647 \u0622\u0646 \u0631\u0627 \u062a\u0623\u06cc\u06cc\u062f \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">print<\/span>(y(<span class=\"hljs-number\">10<\/span>))\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">0\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc 0 \u062a\u0627\u06cc\u06cc\u062f \u0645\u06cc \u06a9\u0646\u062f \u06a9\u0647 \u06cc\u06a9 \u0628\u0631\u0631\u0633\u06cc \u0645\u0646\u0641\u06cc \u0627\u0633\u062a.  \u0645\u0627 \u0627\u06a9\u0646\u0648\u0646 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0627\u0632 \u0642\u0628\u0644 \u067e\u0631\u062f\u0627\u0632\u0634 \u06a9\u0631\u062f\u0647\u200c\u0627\u06cc\u0645 \u0648 \u0627\u06a9\u0646\u0648\u0646 \u0622\u0645\u0627\u062f\u0647 \u0647\u0633\u062a\u06cc\u0645 \u062a\u0627 \u0627\u0632 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u0645\u062a\u0646\u06cc \u062e\u0648\u062f\u060c \u0646\u0645\u0627\u06cc\u0634 BERT \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645.<\/p>\n<h2 id=\"creatingaberttokenizer\"><span class=\"ez-toc-section\" id=\"%d8%a7%db%8c%d8%ac%d8%a7%d8%af_%d8%aa%d9%88%da%a9%d9%86%d8%a7%db%8c%d8%b2%d8%b1_bert\"><\/span>\u0627\u06cc\u062c\u0627\u062f \u062a\u0648\u06a9\u0646\u0627\u06cc\u0632\u0631 BERT<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0628\u0631\u0627\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u0639\u0628\u06cc\u0647\u200c\u0647\u0627\u06cc \u0645\u062a\u0646 BERT \u0628\u0647 \u0639\u0646\u0648\u0627\u0646 \u0648\u0631\u0648\u062f\u06cc \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0645\u062a\u0646\u060c \u0628\u0627\u06cc\u062f \u0645\u0631\u0648\u0631\u0647\u0627\u06cc \u0645\u062a\u0646 \u062e\u0648\u062f \u0631\u0627 \u0646\u0634\u0627\u0646\u0647\u200c\u06af\u0630\u0627\u0631\u06cc \u06a9\u0646\u06cc\u0645.  Tokenization \u0628\u0647 \u062a\u0642\u0633\u06cc\u0645 \u06cc\u06a9 \u062c\u0645\u0644\u0647 \u0628\u0647 \u06a9\u0644\u0645\u0627\u062a \u062c\u062f\u0627\u06af\u0627\u0646\u0647 \u0627\u0634\u0627\u0631\u0647 \u062f\u0627\u0631\u062f.  \u0628\u0631\u0627\u06cc \u062a\u0648\u06a9\u0646 \u06a9\u0631\u062f\u0646 \u0645\u062a\u0646 \u062e\u0648\u062f\u060c \u0627\u0632 \u062a\u0648\u06a9\u0646\u0627\u06cc\u0632\u0631 BERT \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.  \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\">BertTokenizer = bert.bert_tokenization.FullTokenizer\nbert_layer = hub.KerasLayer(<span class=\"hljs-string\">\"https:\/\/tfhub.dev\/tensorflow\/bert_en_uncased_L-12_H-768_A-12\/1\"<\/span>,\n                            trainable=<span class=\"hljs-literal\">False<\/span>)\nvocabulary_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()\nto_lower_case = bert_layer.resolved_object.do_lower_case.numpy()\ntokenizer = BertTokenizer(vocabulary_file, to_lower_case)\n<\/code><\/pre>\n<p>\u062f\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0628\u0627\u0644\u0627 \u0627\u0628\u062a\u062f\u0627 \u06cc\u06a9 \u0634\u06cc \u0627\u0632 the \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>FullTokenizer<\/code> \u06a9\u0644\u0627\u0633 \u0627\u0632 <code>bert.bert_tokenization<\/code> \u0645\u062f\u0648\u0644.  \u0633\u067e\u0633\u060c \u0628\u0627 \u0648\u0627\u0631\u062f \u06a9\u0631\u062f\u0646 \u0645\u062f\u0644 BERT \u0627\u0632 \u06cc\u06a9 \u0644\u0627\u06cc\u0647 \u062a\u0639\u0628\u06cc\u0647\u200c\u06a9\u0646\u0646\u062f\u0647 BERT \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc\u200c\u06a9\u0646\u06cc\u0645 <code>hub.KerasLayer<\/code>.  \u0631\u0627 <code>trainable<\/code> \u067e\u0627\u0631\u0627\u0645\u062a\u0631 \u062a\u0646\u0638\u06cc\u0645 \u0634\u062f\u0647 \u0627\u0633\u062a <code>False<\/code>\u060c \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u06a9\u0647 \u0645\u0627 \u062a\u0639\u0628\u06cc\u0647 BERT \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u0646\u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f.  \u062f\u0631 \u062e\u0637 \u0628\u0639\u062f\u06cc\u060c \u06cc\u06a9 \u0641\u0627\u06cc\u0644 \u0648\u0627\u0698\u06af\u0627\u0646 BERT \u0628\u0647 \u0634\u06a9\u0644 \u06cc\u06a9 \u0622\u0631\u0627\u06cc\u0647 numpy \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0633\u067e\u0633 \u0645\u062a\u0646 \u0631\u0627 \u0628\u0627 \u062d\u0631\u0648\u0641 \u06a9\u0648\u0686\u06a9 \u062a\u0646\u0638\u06cc\u0645 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u0648 \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u0645\u0627 \u0631\u0627 \u067e\u0627\u0633 \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>vocabulary_file<\/code> \u0648 <code>to_lower_case<\/code> \u0645\u062a\u063a\u06cc\u0631\u0647\u0627 \u0628\u0647 <code>BertTokenizer<\/code> \u0647\u062f\u0641 &#8211; \u0634\u06cc.<\/p>\n<p>\u0644\u0627\u0632\u0645 \u0628\u0647 \u0630\u06a9\u0631 \u0627\u0633\u062a \u06a9\u0647 \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u0627\u0632 BERT Tokenizer \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.  \u062f\u0631 \u0645\u0642\u0627\u0644\u0647 \u0628\u0639\u062f\u06cc \u0627\u0632 BERT Embeddings \u0628\u0647 \u0647\u0645\u0631\u0627\u0647 \u062a\u0648\u06a9\u0646\u0627\u06cc\u0632\u0631 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u06a9\u0631\u062f.<\/p>\n<p>\u0627\u06a9\u0646\u0648\u0646 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0628\u0628\u06cc\u0646\u06cc\u0645 \u06a9\u0647 \u0622\u06cc\u0627 \u062a\u0648\u06a9\u0646\u0627\u06cc\u0632\u0631 BERT \u0645\u0627 \u0648\u0627\u0642\u0639\u0627\u064b \u06a9\u0627\u0631 \u0645\u06cc \u06a9\u0646\u062f \u06cc\u0627 \u062e\u06cc\u0631.  \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631\u060c \u0645\u0627\u0646\u0646\u062f \u0634\u06a9\u0644 \u0632\u06cc\u0631\u060c \u06cc\u06a9 \u062c\u0645\u0644\u0647 \u062a\u0635\u0627\u062f\u0641\u06cc \u0631\u0627 \u0646\u0634\u0627\u0646\u0647 \u06af\u0630\u0627\u0631\u06cc \u0645\u06cc \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">tokenizer.tokenize(<span class=\"hljs-string\">\"don't be so judgmental\"<\/span>)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">('don', \"'\", 't', 'be', 'so', 'judgment', '##al')\n<\/code><\/pre>\n<p>\u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0645\u062a\u0646 \u0628\u0627 \u0645\u0648\u0641\u0642\u06cc\u062a \u062a\u0648\u06a9\u0646 \u0634\u062f\u0647 \u0627\u0633\u062a.  \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0634\u0646\u0627\u0633\u0647 \u062a\u0648\u06a9\u0646 \u0647\u0627 \u0631\u0627 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 <code>convert_tokens_to_ids()<\/code> \u0627\u0632 \u0634\u06cc \u062a\u0648\u06a9\u0646 \u0633\u0627\u0632.  \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\">tokenizer.convert_tokens_to_ids(tokenizer.tokenize(<span class=\"hljs-string\">\"dont be so judgmental\"<\/span>))\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">(2123, 2102, 2022, 2061, 8689, 2389)\n<\/code><\/pre>\n<p>\u0627\u06a9\u0646\u0648\u0646 \u062a\u0627\u0628\u0639\u06cc \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc\u200c\u0634\u0648\u062f \u06a9\u0647 \u06cc\u06a9 \u0628\u0631\u0631\u0633\u06cc \u0645\u062a\u0646\u06cc \u0631\u0627 \u0645\u06cc\u200c\u067e\u0630\u06cc\u0631\u062f \u0648 \u0634\u0646\u0627\u0633\u0647\u200c\u0647\u0627\u06cc \u06a9\u0644\u0645\u0627\u062a \u0631\u0645\u0632\u06af\u0630\u0627\u0631\u06cc\u200c\u0634\u062f\u0647 \u062f\u0631 \u0628\u0631\u0631\u0633\u06cc \u0631\u0627 \u0628\u0631\u0645\u06cc\u200c\u06af\u0631\u062f\u0627\u0646\u062f.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">tokenize_reviews<\/span>(<span class=\"hljs-params\">text_reviews<\/span>):<\/span>\n    <span class=\"hljs-keyword\">return<\/span> tokenizer.convert_tokens_to_ids(tokenizer.tokenize(text_reviews))\n<\/code><\/pre>\n<p>\u0648 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f \u062a\u0627 \u062f\u0631 \u0648\u0627\u0642\u0639 \u062a\u0645\u0627\u0645 \u0628\u0631\u0631\u0633\u06cc \u0647\u0627 \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0648\u0631\u0648\u062f\u06cc \u0631\u0627 \u0646\u0634\u0627\u0646\u0647 \u06af\u0630\u0627\u0631\u06cc \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">tokenized_reviews = (tokenize_reviews(review) <span class=\"hljs-keyword\">for<\/span> review <span class=\"hljs-keyword\">in<\/span> reviews)\n<\/code><\/pre>\n<h2 id=\"prerparingdatafortraining\"><span class=\"ez-toc-section\" id=\"%d8%a2%d9%85%d8%a7%d8%af%d9%87_%d8%b3%d8%a7%d8%b2%db%8c_%d8%af%d8%a7%d8%af%d9%87_%d9%87%d8%a7_%d8%a8%d8%b1%d8%a7%db%8c_%d8%a2%d9%85%d9%88%d8%b2%d8%b4\"><\/span>\u0622\u0645\u0627\u062f\u0647 \u0633\u0627\u0632\u06cc \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0628\u0631\u0631\u0633\u06cc\u200c\u0647\u0627\u06cc \u0645\u0648\u062c\u0648\u062f \u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627 \u0637\u0648\u0644\u200c\u0647\u0627\u06cc \u0645\u062a\u0641\u0627\u0648\u062a\u06cc \u062f\u0627\u0631\u0646\u062f.  \u0628\u0631\u062e\u06cc \u0627\u0632 \u0628\u0631\u0631\u0633\u06cc \u0647\u0627 \u0628\u0633\u06cc\u0627\u0631 \u06a9\u0648\u0686\u06a9 \u0647\u0633\u062a\u0646\u062f \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u0628\u0631\u062e\u06cc \u062f\u06cc\u06af\u0631 \u0628\u0633\u06cc\u0627\u0631 \u0637\u0648\u0644\u0627\u0646\u06cc \u0647\u0633\u062a\u0646\u062f.  \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644\u060c \u062c\u0645\u0644\u0627\u062a \u0648\u0631\u0648\u062f\u06cc \u0628\u0627\u06cc\u062f \u062f\u0627\u0631\u0627\u06cc \u0637\u0648\u0644 \u0645\u0633\u0627\u0648\u06cc \u0628\u0627\u0634\u0646\u062f.  \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u062c\u0645\u0644\u0627\u062a \u0628\u0627 \u0637\u0648\u0644 \u0645\u0633\u0627\u0648\u06cc\u060c \u06cc\u06a9\u06cc \u0627\u0632 \u0631\u0627\u0647\u200c\u0647\u0627 \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u062c\u0645\u0644\u0627\u062a \u06a9\u0648\u062a\u0627\u0647\u200c\u062a\u0631 \u0631\u0627 \u0628\u0627 \u06f0 \u062b\u0627\u0646\u06cc\u0647 \u0627\u0636\u0627\u0641\u0647 \u06a9\u0646\u06cc\u062f.  \u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644\u060c \u0627\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u062f \u0645\u0646\u062c\u0631 \u0628\u0647 \u06cc\u06a9 \u0645\u0627\u062a\u0631\u06cc\u0633 \u067e\u0631\u0627\u06a9\u0646\u062f\u0647 \u062d\u0627\u0648\u06cc \u062a\u0639\u062f\u0627\u062f \u0632\u06cc\u0627\u062f\u06cc 0 \u0634\u0648\u062f.  \u0631\u0627\u0647 \u062f\u06cc\u06af\u0631 \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u062c\u0645\u0644\u0627\u062a \u0631\u0627 \u062f\u0631 \u0647\u0631 \u062f\u0633\u062a\u0647 \u0627\u0636\u0627\u0641\u0647 \u06a9\u0646\u06cc\u062f.  \u0627\u0632 \u0622\u0646\u062c\u0627\u06cc\u06cc \u06a9\u0647 \u0645\u0627 \u0645\u062f\u0644 \u0631\u0627 \u0628\u0647 \u0635\u0648\u0631\u062a \u062f\u0633\u062a\u0647\u200c\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u062e\u0648\u0627\u0647\u06cc\u0645 \u062f\u0627\u062f\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0628\u0633\u062a\u0647 \u0628\u0647 \u0637\u0648\u0644 \u0637\u0648\u0644\u0627\u0646\u06cc\u200c\u062a\u0631\u06cc\u0646 \u062c\u0645\u0644\u0647\u060c \u062c\u0645\u0644\u0627\u062a \u062f\u0631\u0648\u0646 \u062f\u0633\u062a\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc \u0631\u0627 \u0628\u0647 \u0635\u0648\u0631\u062a \u0645\u062d\u0644\u06cc \u0627\u0636\u0627\u0641\u0647 \u06a9\u0646\u06cc\u0645.  \u0628\u0631\u0627\u06cc \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0627\u0628\u062a\u062f\u0627 \u0628\u0627\u06cc\u062f \u0637\u0648\u0644 \u0647\u0631 \u062c\u0645\u0644\u0647 \u0631\u0627 \u067e\u06cc\u062f\u0627 \u06a9\u0646\u06cc\u0645.<\/p>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0641\u0647\u0631\u0633\u062a\u06cc \u0627\u0632 \u0644\u06cc\u0633\u062a\u200c\u0647\u0627 \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc\u200c\u06a9\u0646\u062f \u06a9\u0647 \u062f\u0631 \u0622\u0646 \u0647\u0631 \u0641\u0647\u0631\u0633\u062a \u0641\u0631\u0639\u06cc \u0634\u0627\u0645\u0644 \u0628\u0631\u0631\u0633\u06cc \u0646\u0634\u0627\u0646\u0647\u200c\u06af\u0630\u0627\u0631\u06cc \u0634\u062f\u0647\u060c \u0628\u0631\u0686\u0633\u0628 \u0628\u0631\u0631\u0633\u06cc \u0648 \u0637\u0648\u0644 \u0628\u0631\u0631\u0633\u06cc \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\">reviews_with_len = ((review, y(i), <span class=\"hljs-built_in\">len<\/span>(review))\n                 <span class=\"hljs-keyword\">for<\/span> i, review <span class=\"hljs-keyword\">in<\/span> <span class=\"hljs-built_in\">enumerate<\/span>(tokenized_reviews))\n<\/code><\/pre>\n<p>\u062f\u0631 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0627\u060c \u0646\u06cc\u0645\u0647 \u0627\u0648\u0644 \u0628\u0631\u0631\u0633\u06cc \u0647\u0627 \u0645\u062b\u0628\u062a \u0647\u0633\u062a\u0646\u062f \u062f\u0631 \u062d\u0627\u0644\u06cc \u06a9\u0647 \u0646\u06cc\u0645\u0647 \u0622\u062e\u0631 \u062d\u0627\u0648\u06cc \u0646\u0638\u0631\u0627\u062a \u0645\u0646\u0641\u06cc \u0647\u0633\u062a\u0646\u062f.  \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646\u060c \u0628\u0631\u0627\u06cc \u062f\u0627\u0634\u062a\u0646 \u0646\u0638\u0631\u0627\u062a \u0645\u062b\u0628\u062a \u0648 \u0645\u0646\u0641\u06cc \u062f\u0631 \u062f\u0633\u062a\u0647 \u0647\u0627\u06cc \u0622\u0645\u0648\u0632\u0634\u06cc\u060c \u0628\u0627\u06cc\u062f \u0646\u0638\u0631\u0627\u062a \u0631\u0627 \u0628\u0647 \u0647\u0645 \u0628\u0632\u0646\u06cc\u0645.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0628\u0647 \u0637\u0648\u0631 \u062a\u0635\u0627\u062f\u0641\u06cc \u0628\u0647 \u0647\u0645 \u0645\u06cc \u0632\u0646\u062f:<\/p>\n<pre><code class=\"hljs\">random.shuffle(reviews_with_len)\n<\/code><\/pre>\n<p>\u0647\u0646\u06af\u0627\u0645\u06cc \u06a9\u0647 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0647 \u0647\u0645 \u0631\u06cc\u062e\u062a\u0647 \u0634\u062f\u0646\u062f\u060c \u062f\u0627\u062f\u0647 \u0647\u0627 \u0631\u0627 \u0628\u0631 \u0627\u0633\u0627\u0633 \u0637\u0648\u0644 \u0628\u0631\u0631\u0633\u06cc \u0647\u0627 \u0645\u0631\u062a\u0628 \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631\u060c \u0645\u0627 \u0627\u0632 <code>sort()<\/code> \u062a\u0627\u0628\u0639 \u0644\u06cc\u0633\u062a \u0627\u0633\u062a \u0648 \u0628\u0647 \u0622\u0646 \u0645\u06cc \u06af\u0648\u06cc\u062f \u06a9\u0647 \u0645\u06cc \u062e\u0648\u0627\u0647\u06cc\u0645 \u0644\u06cc\u0633\u062a \u0631\u0627 \u0628\u0627 \u062a\u0648\u062c\u0647 \u0628\u0647 \u0633\u0648\u0645\u06cc\u0646 \u0645\u0648\u0631\u062f \u062f\u0631 \u0641\u0647\u0631\u0633\u062a \u0641\u0631\u0639\u06cc \u06cc\u0639\u0646\u06cc \u0637\u0648\u0644 \u0628\u0631\u0631\u0633\u06cc \u0645\u0631\u062a\u0628 \u06a9\u0646\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\">reviews_with_len.sort(key=<span class=\"hljs-keyword\">lambda<\/span> x: x(<span class=\"hljs-number\">2<\/span>))\n<\/code><\/pre>\n<p>\u0648\u0642\u062a\u06cc \u0645\u0631\u0648\u0631\u0647\u0627 \u0628\u0631 \u0627\u0633\u0627\u0633 \u0637\u0648\u0644 \u0645\u0631\u062a\u0628 \u0634\u062f\u0646\u062f\u060c \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0648\u06cc\u0698\u06af\u06cc length \u0631\u0627 \u0627\u0632 \u0647\u0645\u0647 \u0645\u0631\u0648\u0631\u0647\u0627 \u062d\u0630\u0641 \u06a9\u0646\u06cc\u0645.  \u0628\u0631\u0627\u06cc \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">sorted_reviews_labels = ((review_lab(<span class=\"hljs-number\">0<\/span>), review_lab(<span class=\"hljs-number\">1<\/span>)) <span class=\"hljs-keyword\">for<\/span> review_lab <span class=\"hljs-keyword\">in<\/span> reviews_with_len)\n<\/code><\/pre>\n<p>\u0647\u0646\u06af\u0627\u0645\u06cc \u06a9\u0647 \u0628\u0631\u0631\u0633\u06cc \u0647\u0627 \u0645\u0631\u062a\u0628 \u0634\u062f\u0646\u062f\u060c \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0628\u0647 \u06af\u0648\u0646\u0647 \u0627\u06cc \u062a\u0628\u062f\u06cc\u0644 \u0645\u06cc \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0628\u062a\u0648\u0627\u0646 \u0627\u0632 \u0622\u0646 \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u0647\u0627\u06cc TensorFlow 2.0 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f.  \u06a9\u062f \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f \u062a\u0627 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0645\u0631\u062a\u0628 \u0634\u062f\u0647 \u0631\u0627 \u0628\u0647 \u0634\u06a9\u0644 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0648\u0631\u0648\u062f\u06cc \u0645\u0637\u0627\u0628\u0642 \u0628\u0627 TensorFlow 2.0 \u062a\u0628\u062f\u06cc\u0644 \u06a9\u0646\u06cc\u062f.<\/p>\n<pre><code class=\"hljs\">processed_dataset = tf.data.Dataset.from_generator(<span class=\"hljs-keyword\">lambda<\/span>: sorted_reviews_labels, output_types=(tf.int32, tf.int32))\n<\/code><\/pre>\n<p>\u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u0627\u06a9\u0646\u0648\u0646 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647\u200c\u0647\u0627\u06cc \u062e\u0648\u062f \u0631\u0627 \u0628\u0631\u0627\u06cc \u0647\u0631 \u062f\u0633\u062a\u0647 \u0627\u0636\u0627\u0641\u0647 \u06a9\u0646\u06cc\u0645.  \u0627\u0646\u062f\u0627\u0632\u0647 \u062f\u0633\u062a\u0647 \u0627\u06cc \u06a9\u0647 \u0642\u0631\u0627\u0631 \u0627\u0633\u062a \u0627\u0632 \u0622\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645 32 \u0627\u0633\u062a \u06a9\u0647 \u0628\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0646\u06cc \u0627\u0633\u062a \u06a9\u0647 \u067e\u0633 \u0627\u0632 \u067e\u0631\u062f\u0627\u0632\u0634 32 \u0628\u0631\u0631\u0633\u06cc\u060c \u0648\u0632\u0646 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0628\u0647 \u0631\u0648\u0632 \u0645\u06cc \u0634\u0648\u062f.  \u0628\u0631\u0627\u06cc \u062a\u06a9\u0645\u06cc\u0644 \u0646\u0638\u0631\u0627\u062a \u0628\u0647 \u0635\u0648\u0631\u062a \u0645\u062d\u0644\u06cc \u0628\u0627 \u062a\u0648\u062c\u0647 \u0628\u0647 \u062f\u0633\u062a\u0647\u200c\u0647\u0627\u060c \u0645\u0648\u0627\u0631\u062f \u0632\u06cc\u0631 \u0631\u0627 \u0627\u062c\u0631\u0627 \u06a9\u0646\u06cc\u062f:<\/p>\n<pre><code class=\"hljs\">BATCH_SIZE = <span class=\"hljs-number\">32<\/span>\nbatched_dataset = processed_dataset.padded_batch(BATCH_SIZE, padded_shapes=((<span class=\"hljs-literal\">None<\/span>, ), ()))\n<\/code><\/pre>\n<p>\u0627\u062c\u0627\u0632\u0647 \u062f\u0647\u06cc\u062f print \u062f\u0633\u062a\u0647 \u0627\u0648\u0644 \u0648 \u0628\u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 padding \u0631\u0648\u06cc \u0622\u0646 \u0627\u0639\u0645\u0627\u0644 \u0634\u062f\u0647 \u0627\u0633\u062a:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-built_in\">next<\/span>(<span class=\"hljs-built_in\">iter<\/span>(batched_dataset))\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">(&lt;tf.Tensor: shape=(32, 21), dtype=int32, numpy=\n array((( 2054,  5896,  2054,  2466,  2054,  6752,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0),\n        ( 3078,  5436,  3078,  3257,  3532,  7613,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0),\n        ( 3191,  1996,  2338,  5293,  1996,  3185,     0,     0,     0,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0),\n        ( 2062, 23873,  3993,  2062, 11259,  2172,  2172,  2062, 14888,\n             0,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0),\n        ( 1045,  2876,  9278,  1402,  2028,  2130,  2006,  7922, 12635,\n          2305,     0,     0,     0,     0,     0,     0,     0,     0,\n             0,     0,     0),\n      ......\n      \n        ( 7244,  2092,  2856, 10828,  1997, 10904,  2402,  2472,  3135,\n          2293,  2466,  2007, 10958,  8428, 10102,  1999,  1996,  4281,\n          4276,  3773,     0),\n        ( 2005,  5760,  7788,  4393,  8808,  2498,  2064, 12826,  2000,\n          1996, 11056,  3152,  3811, 16755,  2169,  1998,  2296,  2028,\n          1997,  2068,     0),\n        ( 2307,  3185,  2926,  1996,  2189,  3802,  2696,  2508,  2012,\n          2197,  1402,  8847,  6702,  2043,  2017,  2031,  2633,  2179,\n          2008,  2569,  2619),\n        ( 2028,  1997,  1996,  4569, 15580,  2102,  5691,  2081,  1999,\n          3522,  2086,  2204, 23191,  5436,  1998, 11813,  6370,  2191,\n          1402,  2028,  4438),\n        ( 1402,  3185,  2097,  2467,  2022,  5934,  1998,  3185,  4438,\n          2004,  2146,  2004,  2045,  1403,  2145,  2111,  2040,  6170,\n          3153,  1998,  2552)), dtype=int32)&gt;,\n &lt;tf.Tensor: shape=(32,), dtype=int32, numpy=\n array((0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1,\n        1, 1, 0, 1, 0, 1, 1, 0, 1, 1), dtype=int32)&gt;)\n<\/code><\/pre>\n<p>\u062e\u0631\u0648\u062c\u06cc \u0628\u0627\u0644\u0627 \u067e\u0646\u062c \u0628\u0631\u0631\u0633\u06cc \u0627\u0648\u0644 \u0648 \u067e\u0646\u062c \u0628\u0631\u0631\u0633\u06cc \u0622\u062e\u0631 \u0631\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f.  \u0627\u0632 \u067e\u0646\u062c \u0645\u0631\u0648\u0631 \u0622\u062e\u0631\u060c \u0645\u06cc \u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u062a\u0639\u062f\u0627\u062f \u06a9\u0644 \u06a9\u0644\u0645\u0627\u062a \u062f\u0631 \u0628\u0632\u0631\u06af\u062a\u0631\u06cc\u0646 \u062c\u0645\u0644\u0647 21 \u0628\u0648\u062f\u0647 \u0627\u0633\u062a. \u0628\u0646\u0627\u0628\u0631\u0627\u06cc\u0646\u060c \u062f\u0631 \u067e\u0646\u062c \u0645\u0631\u0648\u0631 \u0627\u0648\u0644\u060c 0 \u0647\u0627 \u062f\u0631 \u0627\u0646\u062a\u0647\u0627\u06cc \u062c\u0645\u0644\u0647 \u0647\u0627 \u0627\u0636\u0627\u0641\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f \u062a\u0627 \u0637\u0648\u0644 \u06a9\u0644 \u0622\u0646\u0647\u0627 \u0646\u06cc\u0632 21 \u0628\u0627\u0634\u062f. \u0628\u0631\u0627\u06cc \u062f\u0633\u062a\u0647 \u0628\u0639\u062f\u06cc \u0628\u0633\u062a\u0647 \u0628\u0647 \u0627\u0646\u062f\u0627\u0632\u0647 \u0628\u0632\u0631\u06af\u062a\u0631\u06cc\u0646 \u062c\u0645\u0644\u0647 \u062f\u0631 \u062f\u0633\u062a\u0647 \u0645\u062a\u0641\u0627\u0648\u062a \u062e\u0648\u0627\u0647\u062f \u0628\u0648\u062f.<\/p>\n<p>\u0647\u0646\u06af\u0627\u0645\u06cc \u06a9\u0647 \u0645\u0627 padding \u0631\u0627 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u062e\u0648\u062f \u0627\u0639\u0645\u0627\u0644 \u06a9\u0631\u062f\u06cc\u0645\u060c \u06af\u0627\u0645 \u0628\u0639\u062f\u06cc \u0627\u06cc\u0646 \u0627\u0633\u062a \u06a9\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0631\u0627 \u0628\u0647 \u0645\u062c\u0645\u0648\u0639\u0647 \u0647\u0627\u06cc \u0622\u0632\u0645\u0627\u06cc\u0634\u06cc \u0648 \u0622\u0645\u0648\u0632\u0634\u06cc \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0646\u06cc\u0645.  \u0628\u0627 \u06a9\u0645\u06a9 \u06a9\u062f \u0632\u06cc\u0631 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0647\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\">TOTAL_BATCHES = math.ceil(<span class=\"hljs-built_in\">len<\/span>(sorted_reviews_labels) \/ BATCH_SIZE)\nTEST_BATCHES = TOTAL_BATCHES \/\/ <span class=\"hljs-number\">10<\/span>\nbatched_dataset.shuffle(TOTAL_BATCHES)\ntest_data = batched_dataset.take(TEST_BATCHES)\ntrain_data = batched_dataset.skip(TEST_BATCHES)\n<\/code><\/pre>\n<p>\u062f\u0631 \u06a9\u062f \u0628\u0627\u0644\u0627 \u0627\u0628\u062a\u062f\u0627 \u062a\u0639\u062f\u0627\u062f \u06a9\u0644 \u062f\u0633\u062a\u0647 \u0647\u0627 \u0631\u0627 \u0628\u0627 \u062a\u0642\u0633\u06cc\u0645 \u06a9\u0644 \u0631\u06a9\u0648\u0631\u062f\u0647\u0627 \u0628\u0631 32 \u0645\u06cc \u06cc\u0627\u0628\u06cc\u0645. \u0633\u067e\u0633 10% \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627 \u0628\u0631\u0627\u06cc \u0622\u0632\u0645\u0627\u06cc\u0634 \u06a9\u0646\u0627\u0631 \u06af\u0630\u0627\u0634\u062a\u0647 \u0645\u06cc \u0634\u0648\u0646\u062f.  \u0628\u0631\u0627\u06cc \u0627\u06cc\u0646 \u06a9\u0627\u0631 \u0627\u0632 <code>take()<\/code> \u0631\u0648\u0634 \u0627\u0632 <code>batched_dataset()<\/code> \u0628\u0631\u0627\u06cc \u0630\u062e\u06cc\u0631\u0647 10 \u062f\u0631\u0635\u062f \u0627\u0632 \u062f\u0627\u062f\u0647 \u0647\u0627 \u062f\u0631 <code>test_data<\/code> \u0645\u062a\u063a\u06cc\u0631.  \u062f\u0627\u062f\u0647 \u0647\u0627\u06cc \u0628\u0627\u0642\u06cc \u0645\u0627\u0646\u062f\u0647 \u062f\u0631 \u0630\u062e\u06cc\u0631\u0647 \u0645\u06cc \u0634\u0648\u062f <code>train_data<\/code> \u0634\u06cc \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 <code>skip()<\/code> \u0631\u0648\u0634.<\/p>\n<p>\u0645\u062c\u0645\u0648\u0639\u0647 \u062f\u0627\u062f\u0647 \u0622\u0645\u0627\u062f\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a \u0648 \u0627\u06a9\u0646\u0648\u0646 \u0645\u0627 \u0622\u0645\u0627\u062f\u0647 \u0647\u0633\u062a\u06cc\u0645 \u062a\u0627 \u0645\u062f\u0644 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u062e\u0648\u062f \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645.<\/p>\n<h2 id=\"creatingthemodel\"><span class=\"ez-toc-section\" id=\"%d8%a7%db%8c%d8%ac%d8%a7%d8%af_%d9%85%d8%af%d9%84\"><\/span>\u0627\u06cc\u062c\u0627\u062f \u0645\u062f\u0644<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>\u0627\u06a9\u0646\u0648\u0646 \u0647\u0645\u0647 \u0645\u0627 \u0622\u0645\u0627\u062f\u0647 \u0627\u06cc\u0645 \u062a\u0627 \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u0628\u0633\u0627\u0632\u06cc\u0645.  \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0627\u06cc\u0646 \u06a9\u0627\u0631\u060c \u06a9\u0644\u0627\u0633\u06cc \u0628\u0647 \u0646\u0627\u0645 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u06a9\u0646\u06cc\u0645 <code>TEXT_MODEL<\/code> \u06a9\u0647 \u0627\u0632 \u0627\u0631\u062b \u0645\u06cc \u0628\u0631\u062f <code>tf.keras.Model<\/code> \u06a9\u0644\u0627\u0633  \u062f\u0631 \u062f\u0627\u062e\u0644 \u06a9\u0644\u0627\u0633 \u0645\u0627 \u0644\u0627\u06cc\u0647 \u0647\u0627\u06cc \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0645\u062f\u0644 \u0645\u0627 \u0627\u0632 \u0633\u0647 \u0644\u0627\u06cc\u0647 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646 \u062a\u0634\u06a9\u06cc\u0644 \u0634\u062f\u0647 \u0627\u0633\u062a.  \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0647 \u062c\u0627\u06cc \u0622\u0646 \u0627\u0632 \u0644\u0627\u06cc\u0647 \u0647\u0627\u06cc LSTM \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u062f \u0648 \u0647\u0645\u0686\u0646\u06cc\u0646 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u062a\u0639\u062f\u0627\u062f \u0644\u0627\u06cc\u0647 \u0647\u0627 \u0631\u0627 \u06a9\u0645 \u06cc\u0627 \u0632\u06cc\u0627\u062f \u06a9\u0646\u06cc\u062f.  \u0645\u0646 \u062a\u0639\u062f\u0627\u062f \u0648 \u0627\u0646\u0648\u0627\u0639 \u0644\u0627\u06cc\u0647 \u0647\u0627 \u0631\u0627 \u0627\u0632 \u0622\u0646 \u06a9\u067e\u06cc \u06a9\u0631\u062f\u0647 \u0627\u0645 <a rel=\"nofollow noopener\" target=\"_blank\" href=\"https:\/\/colab.research.google.com\/drive\/12noBxRkrZnIkHqvmdfFW2TGdOXFtNePM\" class=\"broken_link\">\u0647\u0645\u06a9\u0627\u0631\u06cc \u06af\u0648\u06af\u0644 SuperDataScience notebook<\/a>  \u0648 \u0628\u0647 \u0646\u0638\u0631 \u0645\u06cc\u200c\u0631\u0633\u062f \u06a9\u0647 \u0627\u06cc\u0646 \u0645\u0639\u0645\u0627\u0631\u06cc \u0628\u0631\u0627\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0628\u0631\u0631\u0633\u06cc\u200c\u0647\u0627\u06cc \u0641\u06cc\u0644\u0645 IMDB \u0646\u06cc\u0632 \u0628\u0647 \u062e\u0648\u0628\u06cc \u06a9\u0627\u0631 \u0645\u06cc\u200c\u06a9\u0646\u062f.<\/p>\n<p>\u062d\u0627\u0644 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u06a9\u0644\u0627\u0633 \u0645\u062f\u0644 \u0631\u0627 \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-class\"><span class=\"hljs-keyword\">class<\/span> <span class=\"hljs-title\">TEXT_MODEL<\/span>(<span class=\"hljs-params\">tf.keras.Model<\/span>):<\/span>\n    \n    <span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">__init__<\/span>(<span class=\"hljs-params\">self,\n                 vocabulary_size,\n                 embedding_dimensions=<span class=\"hljs-number\">128<\/span>,\n                 cnn_filters=<span class=\"hljs-number\">50<\/span>,\n                 dnn_units=<span class=\"hljs-number\">512<\/span>,\n                 model_output_classes=<span class=\"hljs-number\">2<\/span>,\n                 dropout_rate=<span class=\"hljs-number\">0.1<\/span>,\n                 training=<span class=\"hljs-literal\">False<\/span>,\n                 name=<span class=\"hljs-string\">\"text_model\"<\/span><\/span>):<\/span>\n        <span class=\"hljs-built_in\">super<\/span>(TEXT_MODEL, self).__init__(name=name)\n        \n        self.embedding = layers.Embedding(vocabulary_size,\n                                          embedding_dimensions)\n        self.cnn_layer1 = layers.Conv1D(filters=cnn_filters,\n                                        kernel_size=<span class=\"hljs-number\">2<\/span>,\n                                        padding=<span class=\"hljs-string\">\"valid\"<\/span>,\n                                        activation=<span class=\"hljs-string\">\"relu\"<\/span>)\n        self.cnn_layer2 = layers.Conv1D(filters=cnn_filters,\n                                        kernel_size=<span class=\"hljs-number\">3<\/span>,\n                                        padding=<span class=\"hljs-string\">\"valid\"<\/span>,\n                                        activation=<span class=\"hljs-string\">\"relu\"<\/span>)\n        self.cnn_layer3 = layers.Conv1D(filters=cnn_filters,\n                                        kernel_size=<span class=\"hljs-number\">4<\/span>,\n                                        padding=<span class=\"hljs-string\">\"valid\"<\/span>,\n                                        activation=<span class=\"hljs-string\">\"relu\"<\/span>)\n        self.pool = layers.GlobalMaxPool1D()\n        \n        self.dense_1 = layers.Dense(units=dnn_units, activation=<span class=\"hljs-string\">\"relu\"<\/span>)\n        self.dropout = layers.Dropout(rate=dropout_rate)\n        <span class=\"hljs-keyword\">if<\/span> model_output_classes == <span class=\"hljs-number\">2<\/span>:\n            self.last_dense = layers.Dense(units=<span class=\"hljs-number\">1<\/span>,\n                                           activation=<span class=\"hljs-string\">\"sigmoid\"<\/span>)\n        <span class=\"hljs-keyword\">else<\/span>:\n            self.last_dense = layers.Dense(units=model_output_classes,\n                                           activation=<span class=\"hljs-string\">\"softmax\"<\/span>)\n    \n    <span class=\"hljs-function\"><span class=\"hljs-keyword\">def<\/span> <span class=\"hljs-title\">call<\/span>(<span class=\"hljs-params\">self, inputs, training<\/span>):<\/span>\n        l = self.embedding(inputs)\n        l_1 = self.cnn_layer1(l) \n        l_1 = self.pool(l_1) \n        l_2 = self.cnn_layer2(l) \n        l_2 = self.pool(l_2)\n        l_3 = self.cnn_layer3(l)\n        l_3 = self.pool(l_3) \n        \n        concatenated = tf.concat((l_1, l_2, l_3), axis=-<span class=\"hljs-number\">1<\/span>) \n        concatenated = self.dense_1(concatenated)\n        concatenated = self.dropout(concatenated, training)\n        model_output = self.last_dense(concatenated)\n        \n        <span class=\"hljs-keyword\">return<\/span> model_output\n<\/code><\/pre>\n<p>\u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0641\u0648\u0642 \u0628\u0633\u06cc\u0627\u0631 \u0633\u0627\u062f\u0647 \u0627\u0633\u062a.  \u062f\u0631 \u0633\u0627\u0632\u0646\u062f\u0647 \u06a9\u0644\u0627\u0633\u060c \u0628\u0631\u062e\u06cc \u0627\u0632 \u0648\u06cc\u0698\u06af\u06cc \u0647\u0627 \u0631\u0627 \u0628\u0627 \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u06cc\u0634 \u0641\u0631\u0636 \u0645\u0642\u062f\u0627\u0631\u062f\u0647\u06cc \u0627\u0648\u0644\u06cc\u0647 \u0645\u06cc \u06a9\u0646\u06cc\u0645.  \u0627\u06cc\u0646 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0628\u0639\u062f\u0627 \u062c\u0627\u06cc\u06af\u0632\u06cc\u0646 \u062e\u0648\u0627\u0647\u0646\u062f \u0634\u062f \u0631\u0648\u06cc \u062a\u0648\u0633\u0637 \u0645\u0642\u0627\u062f\u06cc\u0631 \u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 \u0632\u0645\u0627\u0646\u06cc \u06a9\u0647 \u0634\u06cc\u0621 \u0627\u0632 <code>TEXT_MODEL<\/code> \u06a9\u0644\u0627\u0633 \u0627\u06cc\u062c\u0627\u062f \u0645\u06cc \u0634\u0648\u062f.<\/p>\n<p>\u0633\u067e\u0633\u060c \u0633\u0647 \u0644\u0627\u06cc\u0647 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u0627\u0644 \u0628\u0627 \u0645\u0642\u0627\u062f\u06cc\u0631 \u06a9\u0631\u0646\u0644 \u06cc\u0627 \u0641\u06cc\u0644\u062a\u0631 \u0628\u0647 \u062a\u0631\u062a\u06cc\u0628 2\u060c 3 \u0648 4 \u0645\u0642\u062f\u0627\u0631\u062f\u0647\u06cc \u0627\u0648\u0644\u06cc\u0647 \u0634\u062f\u0647 \u0627\u0646\u062f.  \u062f\u0631 \u0635\u0648\u0631\u062a \u062a\u0645\u0627\u06cc\u0644 \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0627\u0646\u062f\u0627\u0632\u0647 \u0641\u06cc\u0644\u062a\u0631\u0647\u0627 \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u062f\u0647\u06cc\u062f.<\/p>\n<p>\u0628\u0639\u062f\u060c \u062f\u0631 \u062f\u0627\u062e\u0644 <code>call()<\/code> \u062a\u0627\u0628\u0639\u060c \u0627\u062f\u063a\u0627\u0645 \u062d\u062f\u0627\u06a9\u062b\u0631 \u062c\u0647\u0627\u0646\u06cc \u0628\u0631\u0627\u06cc \u062e\u0631\u0648\u062c\u06cc \u0647\u0631 \u06cc\u06a9 \u0627\u0632 \u0644\u0627\u06cc\u0647 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646 \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc \u0634\u0648\u062f.  \u062f\u0631 \u0646\u0647\u0627\u06cc\u062a\u060c \u0633\u0647 \u0644\u0627\u06cc\u0647 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u06a9\u0627\u0646\u0648\u0644\u0648\u0634\u0646\u0627\u0644 \u0628\u0647 \u0647\u0645 \u0645\u062a\u0635\u0644 \u0634\u062f\u0647 \u0648 \u062e\u0631\u0648\u062c\u06cc \u0622\u0646\u0647\u0627 \u0628\u0647 \u0627\u0648\u0644\u06cc\u0646 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0645\u062a\u0635\u0644 \u0645\u062a\u0631\u0627\u06a9\u0645 \u062a\u063a\u0630\u06cc\u0647 \u0645\u06cc \u0634\u0648\u062f.  \u062f\u0648\u0645\u06cc\u0646 \u0634\u0628\u06a9\u0647 \u0639\u0635\u0628\u06cc \u0645\u062a\u0635\u0644 \u0645\u062a\u0631\u0627\u06a9\u0645 \u0628\u0631\u0627\u06cc \u067e\u06cc\u0634\u200c\u0628\u06cc\u0646\u06cc \u0627\u062d\u0633\u0627\u0633\u0627\u062a \u062e\u0631\u0648\u062c\u06cc \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f\u060c \u0632\u06cc\u0631\u0627 \u0641\u0642\u0637 \u0634\u0627\u0645\u0644 2 \u06a9\u0644\u0627\u0633 \u0627\u0633\u062a.  \u0627\u06af\u0631 \u06a9\u0644\u0627\u0633 \u0647\u0627\u06cc \u0628\u06cc\u0634\u062a\u0631\u06cc \u062f\u0631 \u062e\u0631\u0648\u062c\u06cc \u062f\u0627\u0631\u06cc\u062f\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0622\u0646 \u0631\u0627 \u0628\u0647 \u0631\u0648\u0632 \u06a9\u0646\u06cc\u062f <code>output_classes<\/code> \u0628\u0631 \u0627\u06cc\u0646 \u0627\u0633\u0627\u0633 \u0645\u062a\u063a\u06cc\u0631 \u0627\u0633\u062a.<\/p>\n<p>\u062d\u0627\u0644 \u0628\u06cc\u0627\u06cc\u06cc\u062f \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc hyper \u0645\u062f\u0644 \u062e\u0648\u062f \u0631\u0627 \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0646\u06cc\u0645.<\/p>\n<pre><code class=\"hljs\">VOCAB_LENGTH = <span class=\"hljs-built_in\">len<\/span>(tokenizer.vocab)\nEMB_DIM = <span class=\"hljs-number\">200<\/span>\nCNN_FILTERS = <span class=\"hljs-number\">100<\/span>\nDNN_UNITS = <span class=\"hljs-number\">256<\/span>\nOUTPUT_CLASSES = <span class=\"hljs-number\">2<\/span>\n\nDROPOUT_RATE = <span class=\"hljs-number\">0.2<\/span>\n\nNB_EPOCHS = <span class=\"hljs-number\">5<\/span>\n<\/code><\/pre>\n<p>\u0628\u0639\u062f\u060c \u0628\u0627\u06cc\u062f \u06cc\u06a9 \u0634\u06cc \u0627\u0632 the \u0627\u06cc\u062c\u0627\u062f \u06a9\u0646\u06cc\u0645 <code>TEXT_MODEL<\/code> \u06a9\u0644\u0627\u0633 \u0648 \u0645\u0642\u0627\u062f\u06cc\u0631 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627\u06cc hyper \u0631\u0627 \u06a9\u0647 \u062f\u0631 \u0645\u0631\u062d\u0644\u0647 \u0622\u062e\u0631 \u062a\u0639\u0631\u06cc\u0641 \u06a9\u0631\u062f\u06cc\u0645 \u0628\u0647 \u0633\u0627\u0632\u0646\u062f\u0647 the \u0627\u0631\u0633\u0627\u0644 \u06a9\u0646\u06cc\u062f <code>TEXT_MODEL<\/code> \u06a9\u0644\u0627\u0633<\/p>\n<pre><code class=\"hljs\">text_model = TEXT_MODEL(vocabulary_size=VOCAB_LENGTH,\n                        embedding_dimensions=EMB_DIM,\n                        cnn_filters=CNN_FILTERS,\n                        dnn_units=DNN_UNITS,\n                        model_output_classes=OUTPUT_CLASSES,\n                        dropout_rate=DROPOUT_RATE)\n<\/code><\/pre>\n<p>\u0642\u0628\u0644 \u0627\u0632 \u0627\u06cc\u0646\u06a9\u0647 \u0648\u0627\u0642\u0639\u0627\u064b \u0628\u062a\u0648\u0627\u0646\u06cc\u0645 \u0645\u062f\u0644 \u0631\u0627 \u0622\u0645\u0648\u0632\u0634 \u062f\u0647\u06cc\u0645\u060c \u0628\u0627\u06cc\u062f \u0622\u0646 \u0631\u0627 \u06a9\u0627\u0645\u067e\u0627\u06cc\u0644 \u06a9\u0646\u06cc\u0645.  \u0627\u0633\u06a9\u0631\u06cc\u067e\u062a \u0632\u06cc\u0631 \u0645\u062f\u0644 \u0631\u0627 \u06a9\u0627\u0645\u067e\u0627\u06cc\u0644 \u0645\u06cc \u06a9\u0646\u062f:<\/p>\n<pre><code class=\"hljs\"><span class=\"hljs-keyword\">if<\/span> OUTPUT_CLASSES == <span class=\"hljs-number\">2<\/span>:\n    text_model.<span class=\"hljs-built_in\">compile<\/span>(loss=<span class=\"hljs-string\">\"binary_crossentropy\"<\/span>,\n                       optimizer=<span class=\"hljs-string\">\"adam\"<\/span>,\n                       metrics=(<span class=\"hljs-string\">\"accuracy\"<\/span>))\n<span class=\"hljs-keyword\">else<\/span>:\n    text_model.<span class=\"hljs-built_in\">compile<\/span>(loss=<span class=\"hljs-string\">\"sparse_categorical_crossentropy\"<\/span>,\n                       optimizer=<span class=\"hljs-string\">\"adam\"<\/span>,\n                       metrics=(<span class=\"hljs-string\">\"sparse_categorical_accuracy\"<\/span>))\n<\/code><\/pre>\n<p>\u062f\u0631 \u0646\u0647\u0627\u06cc\u062a \u0628\u0631\u0627\u06cc \u0622\u0645\u0648\u0632\u0634 \u0645\u062f\u0644 \u062e\u0648\u062f\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0632 \u0622\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645 <code>fit<\/code> \u0631\u0648\u0634 \u06a9\u0644\u0627\u0633 \u0645\u062f\u0644<\/p>\n<pre><code class=\"hljs\">text_model.fit(train_data, epochs=NB_EPOCHS)\n<\/code><\/pre>\n<p>\u0627\u06cc\u0646 \u0647\u0645 \u0646\u062a\u06cc\u062c\u0647 \u0628\u0639\u062f \u0627\u0632 5 \u062f\u0648\u0631\u0647:<\/p>\n<pre><code class=\"hljs\">Epoch 1\/5\n1407\/1407 (==============================) - 381s 271ms\/step - loss: 0.3037 - accuracy: 0.8661\nEpoch 2\/5\n1407\/1407 (==============================) - 381s 271ms\/step - loss: 0.1341 - accuracy: 0.9521\nEpoch 3\/5\n1407\/1407 (==============================) - 383s 272ms\/step - loss: 0.0732 - accuracy: 0.9742\nEpoch 4\/5\n1407\/1407 (==============================) - 381s 271ms\/step - loss: 0.0376 - accuracy: 0.9865\nEpoch 5\/5\n1407\/1407 (==============================) - 383s 272ms\/step - loss: 0.0193 - accuracy: 0.9931\n&lt;tensorflow.python.keras.callbacks.History at 0x7f5f65690048&gt;\n<\/code><\/pre>\n<p>\u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u062f \u0628\u0628\u06cc\u0646\u06cc\u062f \u06a9\u0647 \u0645\u0627 \u062f\u0642\u062a 99.31\u066a \u0631\u0627 \u062f\u0631\u06cc\u0627\u0641\u062a \u06a9\u0631\u062f\u06cc\u0645 \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u0622\u0645\u0648\u0632\u0634\u06cc<\/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\">results = text_model.evaluate(test_dataset)\n<span class=\"hljs-built_in\">print<\/span>(results)\n<\/code><\/pre>\n<p><strong>\u062e\u0631\u0648\u062c\u06cc:<\/strong><\/p>\n<pre><code class=\"hljs\">156\/Unknown - 4s 28ms\/step - loss: 0.4428 - accuracy: 0.8926(0.442786190037926, 0.8926282)\n<\/code><\/pre>\n<p>\u0627\u0632 \u062e\u0631\u0648\u062c\u06cc\u060c \u0645\u06cc \u062a\u0648\u0627\u0646\u06cc\u0645 \u0645\u062a\u0648\u062c\u0647 \u0634\u0648\u06cc\u0645 \u06a9\u0647 \u062f\u0642\u062a 89.26\u066a \u0631\u0627 \u0628\u0647 \u062f\u0633\u062a \u0622\u0648\u0631\u062f\u0647 \u0627\u06cc\u0645. \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a<\/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>\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062f\u06cc\u062f\u06cc\u062f \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646\u06cc\u0645 \u0627\u0632 BERT Tokenizer \u0628\u0631\u0627\u06cc \u0627\u06cc\u062c\u0627\u062f \u062c\u0627\u0633\u0627\u0632\u06cc\u200c\u0647\u0627\u06cc \u06a9\u0644\u0645\u0647 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0646\u06cc\u0645 \u06a9\u0647 \u0645\u06cc\u200c\u062a\u0648\u0627\u0646 \u0627\u0632 \u0622\u0646\u0647\u0627 \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0637\u0628\u0642\u0647\u200c\u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f.  \u0645\u0627 \u0622\u0646\u0627\u0644\u06cc\u0632 \u0627\u062d\u0633\u0627\u0633\u0627\u062a\u06cc \u0646\u0642\u062f\u0647\u0627\u06cc \u0641\u06cc\u0644\u0645 IMDB \u0631\u0627 \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f\u06cc\u0645 \u0648 \u0628\u0647 \u062f\u0642\u062a 89.26\u066a \u0631\u0633\u06cc\u062f\u06cc\u0645. \u0631\u0648\u06cc \u0645\u062c\u0645\u0648\u0639\u0647 \u062a\u0633\u062a  \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0632 \u062a\u0639\u0628\u06cc\u0647\u200c\u0647\u0627\u06cc BERT \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0646\u06a9\u0631\u062f\u06cc\u0645\u060c \u0645\u0627 \u0641\u0642\u0637 \u0627\u0632 BERT Tokenizer \u0628\u0631\u0627\u06cc \u062a\u0648\u06a9\u0646 \u06a9\u0631\u062f\u0646 \u06a9\u0644\u0645\u0627\u062a \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\u062f\u06cc\u0645.  \u062f\u0631 \u0645\u0642\u0627\u0644\u0647 \u0628\u0639\u062f\u06cc \u062e\u0648\u0627\u0647\u06cc\u062f \u062f\u06cc\u062f \u06a9\u0647 \u0686\u06af\u0648\u0646\u0647 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0627\u0632 BERT Tokenizer \u0628\u0647 \u0647\u0645\u0631\u0627\u0647 BERT Embeddings \u0628\u0631\u0627\u06cc \u0627\u0646\u062c\u0627\u0645 \u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u06a9\u0631\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 1403-01-18 10:14:03<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;15897&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;\u0637\u0628\u0642\u0647 \u0628\u0646\u062f\u06cc \u0645\u062a\u0646 \u0628\u0627 BERT Tokenizer \u0648 TF 2.0 \u062f\u0631 \u067e\u0627\u06cc\u062a\u0648\u0646&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>\u0627\u06cc\u0646 \u0628\u06cc\u0633\u062a \u0648 \u0633\u0648\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. \u062f\u0631 \u0645\u0642\u0627\u0644\u0647 \u0642\u0628\u0644\u06cc \u0627\u06cc\u0646 \u0645\u062c\u0645\u0648\u0639\u0647 \u0631\u0648\u0634 \u0627\u0646\u062c\u0627\u0645 \u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646 \u0639\u0635\u0628\u06cc \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u0622\u0646 \u0631\u0627 \u062a\u0648\u0636\u06cc\u062d \u062f\u0627\u062f\u0645 \u0645\u0639\u0645\u0627\u0631\u06cc seq2seq \u0628\u0627 \u06a9\u062a\u0627\u0628\u062e\u0627\u0646\u0647 Keras \u067e\u0627\u06cc\u062a\u0648\u0646 \u0628\u0631\u0627\u06cc \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0639\u0645\u06cc\u0642. \u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0645\u0637\u0627\u0644\u0639\u0647 \u062e\u0648\u0627\u0647\u06cc\u0645 \u067e\u0631\u062f\u0627\u062e\u062a \u0628\u0631\u062a\u060c \u06a9\u0647 \u0645\u062e\u0641\u0641 \u0622\u0646 \u0627\u0633\u062a \u0646\u0645\u0627\u06cc\u0634 \u0631\u0645\u0632\u06af\u0630\u0627\u0631 \u062f\u0648 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":9398,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1743,620],"tags":[],"class_list":["post-15897","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\/15897","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=15897"}],"version-history":[{"count":0,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/posts\/15897\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media\/9398"}],"wp:attachment":[{"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/media?parent=15897"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/categories?post=15897"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rasanegaar.com\/blog\/wp-json\/wp\/v2\/tags?post=15897"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}