{"id":31005,"date":"2023-08-16T17:02:08","date_gmt":"2023-08-16T08:02:08","guid":{"rendered":"https:\/\/gri.jp\/media\/?p=31005"},"modified":"2023-09-05T16:10:39","modified_gmt":"2023-09-05T07:10:39","slug":"%e8%87%aa%e7%84%b6%e8%a8%80%e8%aa%9e%e3%83%a2%e3%83%87%e3%83%ab%e3%81%ab%e3%82%88%e3%82%8b%e6%a7%98%e3%80%85%e3%81%aa%e3%82%bf%e3%82%b9%e3%82%af%e3%82%92%e8%a9%95%e4%be%a1%e3%81%99%e3%82%8b%e3%83%99","status":"publish","type":"post","link":"https:\/\/gri.jp\/media\/entry\/31005","title":{"rendered":"\u81ea\u7136\u8a00\u8a9e\u30e2\u30c7\u30eb\u306b\u3088\u308b\u69d8\u3005\u306a\u30bf\u30b9\u30af\u3092\u8a55\u4fa1\u3059\u308b\u30d9\u30f3\u30c1\u30de\u30fc\u30af"},"content":{"rendered":"<p>\u5e74\u3005\u958b\u767a\u3055\u308c\u3066\u3044\u308b<strong>\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u30e2\u30c7\u30eb\u306e\u6027\u80fd\u3092\u5ba2\u89b3\u7684\u306b\u8a55\u4fa1\u3057\u3001\u30e2\u30c7\u30eb\u9593\u3067\u6bd4\u8f03<\/strong>\u3059\u308b\u305f\u3081\u306b\u3001<strong>\u30d9\u30f3\u30c1\u30de\u30fc\u30af\uff08\u8a55\u4fa1\u57fa\u6e96\uff09<\/strong>\u304c\u7528\u3044\u3089\u308c\u307e\u3059\u3002\u3053\u3053\u3067\u3044\u3046\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u3068\u306f\u3001\u8a00\u8a9e\u30e2\u30c7\u30eb\u306b\u51fa\u3059\u300c\u30c6\u30b9\u30c8\u300d\u3001\u3042\u308b\u3044\u306f\u305d\u308c\u306b\u4f7f\u308f\u308c\u308b\u3001\u516c\u958b\u3055\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u6307\u3057\u307e\u3059\u3002<\/p>\n<p>\u6c4e\u7528\u578b\u306e\u8a00\u8a9e\u30e2\u30c7\u30eb\u306f\u591a\u7528\u591a\u7a2e\u306e\u30bf\u30b9\u30af\u306b\u9069\u7528\u53ef\u80fd\u306a\u306e\u3067\u3001\u5358\u4e00\u306e\u30bf\u30b9\u30af\u3067\u306f\u306a\u304f\u3001\u4e00\u5b9a\u7bc4\u56f2\u306b\u53ca\u3076\u8907\u6570\u306e\u30bf\u30b9\u30af\u3092\u30e2\u30c7\u30eb\u306b\u5b9f\u884c\u3055\u305b\u3001\u305d\u306e\u7d50\u679c\u306b\u57fa\u3065\u304f\u7dcf\u5408\u7684\u306a\u8a55\u4fa1\u304c\u5fc5\u8981\u3067\u3059\u3002<\/p>\n<p>\u30c6\u30b9\u30c8\u306e\u7d50\u679c\u304b\u3089\u30e2\u30c7\u30eb\u306e\u5f31\u70b9\u3092\u7279\u5b9a\u3057\u3001\u6539\u5584\u306e\u305f\u3081\u306e\u65b9\u5411\u6027\u304c\u793a\u3055\u308c\u307e\u3059\u3002\u307e\u305f\u3001\u65b0\u3057\u3044\u30e2\u30c7\u30eb\u304c\u65e2\u5b58\u30e2\u30c7\u30eb\u3092\u8d85\u3048\u3066\u3069\u3093\u3069\u3093\u9032\u6b69\u3057\u3066\u3044\u304f\u3068\u3001\u3088\u308a\u96e3\u3057\u3044\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u304c\u63d0\u6848\u3055\u308c\u3001\u305d\u306e\u96e3\u3057\u3044\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u3092\u89e3\u6c7a\u3067\u304d\u308b\u3088\u3046\u306b\u3001\u958b\u767a\u8005\u306b\u3088\u308a\u30e2\u30c7\u30eb\u306e\u6539\u5584\u304c\u884c\u308f\u308c\u2026\u2026\u3068\u3044\u3046\u30b5\u30a4\u30af\u30eb\u306b\u3088\u308a\u6280\u8853\u304c\u9032\u6b69\u3057\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n<p>\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u3068\u3057\u3066\u3088\u304f\u4f7f\u7528\u3055\u308c\u308b\u8a00\u8a9e\u30bf\u30b9\u30af\u306e\u4ee3\u8868\u4f8b\u3068\u3057\u3066\u3001\u4ee5\u4e0b\u306e\u8868\u306b\u3044\u304f\u3064\u304b\u306e\u4f8b\u3092\u6319\u3052\u307e\u3059\u3002<\/p>\n<table style=\"height: 1356px;\" width=\"567\">\n<tbody>\n<tr style=\"height: 60px;\">\n<td style=\"height: 60px; width: 183.879px;\">\u8a00\u8a9e\u30bf\u30b9\u30af\u306e\u7a2e\u985e<\/td>\n<td style=\"height: 60px; width: 470.402px;\">\u6982\u8981<\/td>\n<\/tr>\n<tr style=\"height: 73px;\">\n<td style=\"height: 73px; width: 183.879px;\"><strong>\u6a5f\u68b0\u7ffb\u8a33 (Machine Translation)<\/strong><\/td>\n<td style=\"height: 73px; width: 470.402px;\">Source Language\u306e\u5165\u529b\u6587\u3092Target Language\u306b\u7ffb\u8a33\u3057\u3066\u51fa\u529b\u3059\u308b<\/td>\n<\/tr>\n<tr style=\"height: 179px;\">\n<td style=\"height: 179px; width: 183.879px;\"><strong>\u611f\u60c5\u5206\u6790(Sentiment Analysis)<\/strong><\/td>\n<td style=\"height: 179px; width: 470.402px;\">\u30fb\u30c6\u30ad\u30b9\u30c8\u304b\u3089\u30dd\u30b8\u30c6\u30a3\u30d6\u307e\u305f\u306f\u30cd\u30ac\u30c6\u30a3\u30d6\u306a\u611f\u60c5\u3092\u7279\u5b9a\u3059\u308b\uff08\u300c\u30cd\u30ac\u30dd\u30b8\u5224\u5b9a\u300d\u3068\u3082\u547c\u3070\u308c\u308b\uff09<\/p>\n<p>\u30fb\uff08\u7528\u9014\u4f8b\uff09\u30ec\u30d3\u30e5\u30fc\u306e\u611f\u60c5\u5206\u6790\u3001SNS\u3001\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u3001\u3000\u65bd\u7b56\u306b\u5f79\u306b\u7acb\u3064<\/td>\n<\/tr>\n<tr style=\"height: 179px;\">\n<td style=\"height: 179px; width: 183.879px;\"><strong>\u6587\u7ae0\u306e\u8981\u7d04\uff08Summarization\uff09<\/strong><\/p>\n<p>&nbsp;<\/td>\n<td style=\"height: 179px; width: 470.402px;\">\u30fb\u4e0e\u3048\u3089\u308c\u305f\u6587\u66f8\u3084\u30c6\u30ad\u30b9\u30c8\u306e\u4e3b\u8981\u306a\u30dd\u30a4\u30f3\u30c8\u3092\u62bd\u51fa\u3057\u3001\u305d\u308c\u3092\u77ed\u3044\u8981\u7d04\u306b\u307e\u3068\u3081\u308b<\/p>\n<p>\u30fb\uff08\u7528\u9014\u4f8b\uff09\u8a18\u4e8b\u306e\u5185\u5bb9\u304b\u3089\u30bf\u30a4\u30c8\u30eb\u306e\u81ea\u52d5\u751f\u6210\u3001\u539f\u6587\u306e\u610f\u5473\u3092\u5fe0\u5b9f\u306b\u4fdd\u6301\u3057\u3064\u3064\u7c21\u6f54\u306b\u3059\u308b<\/td>\n<\/tr>\n<tr style=\"height: 150px;\">\n<td style=\"height: 150px; width: 183.879px;\"><strong>\u30c6\u30ad\u30b9\u30c8\u5206\u985e (Text Classification)<\/strong><\/td>\n<td style=\"height: 150px; width: 470.402px;\">\u30fb\u6587\u7ae0\u3092\u4e00\u3064\u307e\u305f\u306f\u8907\u6570\u306e\u30ab\u30c6\u30b4\u30ea\u306b\u5206\u985e\u3057\u3001\u691c\u7d22\u53ef\u80fd\u306b\u3059\u308b<\/p>\n<p>\u30fb\uff08\u7528\u9014\u4f8b\uff09\u30b9\u30d1\u30e0\u30e1\u30fc\u30eb\u306e\u632f\u308a\u5206\u3051\u3001\u30a6\u30a7\u30d6\u30cb\u30e5\u30fc\u30b9\u8a18\u4e8b\u306e\u30ab\u30c6\u30b4\u30ea\u5316\u3084\u30ec\u30b3\u30e1\u30f3\u30c9<\/td>\n<\/tr>\n<tr style=\"height: 179px;\">\n<td style=\"height: 179px; width: 183.879px;\"><strong>\u56fa\u6709\u8868\u73fe\u62bd\u51fa\uff08NER; Named Entity Recognition\uff09<\/strong><\/td>\n<td style=\"height: 179px; width: 470.402px;\">\u30fb\u6587\u7ae0\u304b\u3089\u3001\u4eba\u540d\u3001\u7d44\u7e54\u540d\u3001\u5730\u540d\u3001\u65e5\u6642\u8868\u73fe\u3001\u91d1\u92ad\u8868\u73fe\u306a\u3069\u306e\u56fa\u6709\u8868\u73fe\u3092\u7279\u5b9a\u3059\u308b<\/p>\n<p>\u30fb\uff08\u7528\u9014\u4f8b\uff09\u30d7\u30e9\u30a4\u30d0\u30b7\u30fc\u4fdd\u8b77\u306e\u305f\u3081\u306b\u3001\u30c7\u30fc\u30bf\u304b\u3089\u4eba\u500b\u4eba\u60c5\u5831\u306b\u3042\u305f\u308b\u6587\u5b57\u5217\u3092\u8b58\u5225\u3057\u305d\u306e\u60c5\u5831\u3092\u9069\u5207\u306b\u51e6\u7406\u3059\u308b<\/td>\n<\/tr>\n<tr style=\"height: 239px;\">\n<td style=\"height: 239px; width: 183.879px;\"><strong>\u8cea\u554f\u5fdc\u7b54 (Question Answering)<\/strong><\/td>\n<td style=\"height: 239px; width: 470.402px;\">\u30fb\u7279\u5b9a\u306e\u8cea\u554f\u306b\u5bfe\u3059\u308b\u6b63\u78ba\u306a\u7b54\u3048\u3092\u51fa\u529b\u3059\u308b<\/p>\n<p>\u9078\u629e\u554f\u984c\u3084\uff0c\u6587\u7ae0\u304b\u3089\u554f\u984c\u6587\u306e\u7b54\u3048\u3092\u629c\u304d\u51fa\u3059\u6a5f\u68b0\u8aad\u89e3 (Reading Comprehension)\uff0c\u5bfe\u8a71\u5f62\u5f0f\u306e\u8cea\u554f\u5fdc\u7b54\u306a\u3069\u304c\u3042\u308b<\/p>\n<p>\u30fb\uff08\u7528\u9014\u4f8b\uff09\u30c1\u30e3\u30c3\u30c8\u578b\u30b5\u30fc\u30d3\u30b9\u306a\u3069<\/td>\n<\/tr>\n<tr style=\"height: 118px;\">\n<td style=\"height: 118px; width: 183.879px;\"><strong>\u610f\u5473\u7684\u985e\u4f3c\u5ea6\uff08Semantic Similarity\uff09<\/strong><\/td>\n<td style=\"height: 118px; width: 470.402px;\">\uff12\u3064\u306e\u6587\u304c\u540c\u3058\u610f\u5473\u304b\u3069\u3046\u304b\u3092\u5224\u5b9a\u3059\u308b<\/td>\n<\/tr>\n<tr style=\"height: 179px;\">\n<td style=\"height: 179px; width: 183.879px;\"><strong>\u81ea\u7136\u8a00\u8a9e\u63a8\u8ad6\uff08Natural Language Inference, NLI\uff09<\/strong><\/td>\n<td style=\"height: 179px; width: 470.402px;\">2\u3064\u306e\u6587\u306e\u9593\u306e\u8ad6\u7406\u7684\u306a\u95a2\u4fc2\u3092\u63a8\u8ad6\u3059\u308b\u30012\u3064\u306e\u6587\u306e\u5185\u5bb9\u306b\u77db\u76fe\u304c\u3042\u308b\u306e\u304b\u3001\u4e00\u65b9\u304c\u4ed6\u65b9\u3092\u542b\u610f\u3059\u308b\u306e\u304b\u306a\u3069\u3092\u5224\u5b9a\u3059\u308b<\/p>\n<p>\u30e2\u30c7\u30eb\u304c\u6587\u306e\u5185\u5bb9\u3092\u7406\u89e3\u3057\u3001\u305d\u308c\u3089\u306e\u9593\u306e\u8907\u96d1\u306a\u95a2\u4fc2\u6027\u3092\u6349\u3048\u308b\u80fd\u529b\u3092\u8a55\u4fa1\u3059\u308b\u306e\u306b\u6709\u7528<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><span style=\"color: #0000ff;\"><strong>GLUE\uff08General language Understanding Evaluation\uff09<\/strong><\/span>\u306f\u3001\u4ee3\u8868\u7684\u306a\u8a00\u8a9e\u30bf\u30b9\u30af\u306e\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u306e1\u3064\u3067\u3059\u3002\u65b0\u3057\u3044\u8a00\u8a9e\u30e2\u30c7\u30eb\u3092\u8ad6\u6587\u3067\u767a\u8868\u3059\u308b\u969b\u306b\u306f\u3001\u300cGLUE\u30b9\u30b3\u30a2\u300d\u3092\u63b2\u8f09\u3059\u308b\u3053\u3068\u304c\u6697\u9ed9\u306e\u4e86\u89e3\u306b\u306a\u308b\u304f\u3089\u3044\u3067\u3059\u30022022\u5e74\u306b\u306f\u65e5\u672c\u8a9e\u7248\u306eJGLUE\u3082\u958b\u767a\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<p>\u4ee5\u4e0b\u306e\u516c\u5f0f\u30b5\u30a4\u30c8\u3084\u8ad6\u6587\u3092\u8aad\u3080\u3068\u7406\u89e3\u304c\u6df1\u307e\u308a\u307e\u3059\u3002<\/p>\n<p>\u516c\u5f0f<\/p>\n<div class=\"linkcard\"><table border=\"1\" cellspacing=\"0\" cellpadding=\"4\"><tbody><\/tr><tr><td>The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems<br><a class=\"lkc-link no_icon\" href=\"https:\/\/gluebenchmark.com\" target=\"_blank\" rel=\"external noopenner\">GLUE Benchmark<\/a> - gluebenchmark.com<\/td><\/tr><\/tbody><\/table><\/div>\n<p>\u8ad6\u6587\uff1a<a href=\"https:\/\/arxiv.org\/abs\/1804.07461\">[1804.07461] GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding<\/a><\/p>\n<p>GLUE\u306f9\u3064\u306e\u516c\u958b\u3055\u308c\u3066\u3044\u308b\u8a00\u8a9e\u7406\u89e3\u30bf\u30b9\u30af\u304b\u3089\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u5e83\u305d\u308c\u305e\u308c\u306b\u5bfe\u3059\u308b\u6027\u80fd\u3092\u8a55\u4fa1\u3057\u3001\u305d\u308c\u3089\u306e\u7dcf\u5408\u5024\u306b\u3088\u3063\u3066\u30e2\u30c7\u30eb\u306e\u300c\u4e00\u822c\u7684\u306a\u6027\u80fd\u300d\u304c\u8868\u73fe\u3055\u308c\u307e\u3059\u3002\u305d\u306e\u4e2d\u306b\u306f\u3001\u300c\u610f\u5473\u985e\u4f3c\u5ea6\u306e\u5224\u65ad\u300d\u3001\u300c\u30cd\u30ac\u30dd\u30b8\u5224\u5b9a\u300d\u3001\u300c\u8cea\u554f\u5fdc\u7b54\u300d\u306a\u3069\u3001\u4e0a\u306e\u8868\u306b\u542b\u307e\u308c\u308b\u30bf\u30b9\u30af\u3068\u5171\u901a\u306e\u3082\u306e\u304c\u591a\u3044\u3067\u3059\u3002<\/p>\n<p>GLUE\u306e\u30bf\u30b9\u30af\u306b\u4f7f\u308f\u308c\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u4f8b\u3068\u3057\u3066\u3044\u304f\u3064\u304b\u6319\u3052\u3066\u304a\u304d\u307e\u3059\u3002<\/p>\n<ul>\n<li>CoLA\uff08The Corpus of Linguistic Acceptability\uff09 : \u6587\u304c\u82f1\u8a9e\u6587\u6cd5\u3068\u3057\u3066\u6b63\u3057\u3044\u304b\u3069\u3046\u304b\uff08\u8a00\u8a9e\u5b66\u7684\u8a31\u5bb9\u6027\uff09\u3092\u5224\u5b9a\u3002\u4e8c\u5024\u6587\u5206\u985e\u306b\u8a72\u5f53\u3002\u30c7\u30fc\u30bf\u306f23\u306e\u66f8\u7c4d\u3084\u96d1\u8a8c\u8a18\u4e8b\u3092\u5143\u306b\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n<li>SST-2\uff08The Stanford Sentiment Treebank\uff09 : \u6620\u753b\u30ec\u30d3\u30e5\u30fc\u306e\u611f\u60c5\u89e3\u6790\uff08\u6587\u7ae0\u5206\u985e\uff09QNLI (Question Natural Language Inference)\uff1a\u8cea\u554f\u3068\u305d\u306e\u7b54\u3048\u306e\u30da\u30a2\u304c\u4e0e\u3048\u3089\u308c\u3001\u7b54\u3048\u304c\u8cea\u554f\u304b\u3089\u8ad6\u7406\u7684\u306b\u5c0e\u304b\u308c\u308b\u304b\u3069\u3046\u304b\u3092\u5224\u65ad\u3059\u308b\uff08\u30c6\u30ad\u30b9\u30c8\u5206\u985e\uff09<\/li>\n<li>MRPC\uff08Microsoft Research Paraphrase Corpus\uff09 : \u30aa\u30f3\u30e9\u30a4\u30f3\u30cb\u30e5\u30fc\u30b9\u304b\u3089\u306e2\u3064\u306e\u6587\u306e\u30da\u30a2\u304c\u540c\u3058\u610f\u5473\u304b\u3069\u3046\u304b\u3092\u5224\u5b9a\u3002\uff08\u610f\u5473\u7684\u985e\u4f3c\u5ea6\u306e\u305f\u3081\u306e\u6587\u7ae0\u5206\u985e\uff09<\/li>\n<li>SQuAD\uff08Stanford Question Answering Dataset\uff09: \u30a6\u30a3\u30ad\u30da\u30c7\u30a3\u30a2\u304b\u3089\u8cea\u554f\u306e\u7b54\u3048\u3068\u306a\u308b\u30c6\u30ad\u30b9\u30c8\u3092\u898b\u3064\u3051\u308b\uff08\u8cea\u554f\u5fdc\u7b54\uff09<\/li>\n<\/ul>\n<p>GLUE\u306e\u30bf\u30b9\u30af\u306b\u4f7f\u308f\u308c\u3066\u3044\u308b\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306f\u4ee5\u4e0b\u3067\u5165\u624b\u3067\u304d\u307e\u3059\u3002<\/p>\n<div class=\"linkcard\"><table border=\"1\" cellspacing=\"0\" cellpadding=\"4\"><tbody><\/tr><tr><td>The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems<br><a class=\"lkc-link no_icon\" href=\"https:\/\/gluebenchmark.com\/tasks\" target=\"_blank\" rel=\"external noopenner\">GLUE Benchmark<\/a> - gluebenchmark.com<\/td><\/tr><\/tbody><\/table><\/div>\n<p>\u3068\u3053\u308d\u3067\u3001\u8a00\u8a9e\u30e2\u30c7\u30eb\u306e\u6025\u901f\u306a\u767a\u9054\u306b\u3088\u308a\u3001\u672c\u6765\u306eGLUE\u3067\u306f\u3084\u3084\u7269\u8db3\u308a\u306a\u3044\u5834\u5408\u304c\u51fa\u3066\u304d\u3066\u3044\u308b\u304f\u3089\u3044\u3067\u3059\u3002\u4eca\u306f\u3055\u3089\u306b\u96e3\u6613\u5ea6\u306e\u9ad8\u3044<strong><span style=\"color: #0000ff;\">SuperGLUE<\/span><\/strong>\u304c\u5c0e\u5165\u3055\u308c\u306f\u3058\u3081\u3066\u3044\u307e\u3059\u3002WiC (Words in Context)\u3084ROPES (Reasoning Over Paragraph Effects in Situations)\u306a\u3069\u3001\u8108\u306b\u57fa\u3065\u3044\u305f\u7406\u89e3\u3084\u3001\u6587\u306e\u9593\u306e\u8ad6\u7406\u7684\u306a\u95a2\u4fc2\u3092\u63a8\u8ad6\u3059\u308b\u306a\u3069\u3001\u3088\u308a\u9ad8\u5ea6\u306a\u8a00\u8a9e\u7406\u89e3\u80fd\u529b\u3092\u5fc5\u8981\u3068\u3059\u308b\u30bf\u30b9\u30af\u304c\u8ffd\u52a0\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u5e74\u3005\u958b\u767a\u3055\u308c\u3066\u3044\u308b\u81ea\u7136\u8a00\u8a9e\u51e6\u7406\u30e2\u30c7\u30eb\u306e\u6027\u80fd\u3092\u5ba2\u89b3\u7684\u306b\u8a55\u4fa1\u3057\u3001\u30e2\u30c7\u30eb\u9593\u3067\u6bd4\u8f03\u3059\u308b\u305f\u3081\u306b\u3001\u30d9\u30f3\u30c1\u30de\u30fc\u30af\uff08\u8a55\u4fa1\u57fa\u6e96\uff09\u304c\u7528\u3044\u3089\u308c\u307e\u3059\u3002\u3053\u3053\u3067\u3044\u3046\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u3068\u306f\u3001\u8a00\u8a9e\u30e2\u30c7\u30eb\u306b\u51fa\u3059\u300c\u30c6\u30b9\u30c8\u300d\u3001\u3042\u308b\u3044\u306f\u305d\u308c\u306b\u4f7f\u308f\u308c\u308b\u3001\u516c\u958b\u3055\u308c\u3066\u3044\u308b<\/p>\n","protected":false},"author":8,"featured_media":31371,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[149,150,83,4,125],"tags":[],"class_list":["post-31005","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-gkentei","category-dskentei","category-ai","category-datascience","category-deeplearning"],"acf":[],"meta_field":{"_edit_lock":["1705968736:1"],"_edit_last":["6"],"_thumbnail_id":["31371"],"hidden_toppage":["0"],"_hidden_toppage":["field_61933136630d2"],"note_url":[""],"_note_url":["field_61243c8278b90"],"_oembed_71b00310fb816d18ca46402eeb568dd6":["{{unknown}}"],"_oembed_8622a91b10689cfcd6c8285eb5f11e4b":["{{unknown}}"],"_oembed_b233ac3e393c801105962869ec932e92":["{{unknown}}"],"_oembed_6025389faea2fc3e1caa82c20dec5c5f":["{{unknown}}"],"_oembed_4cee422773349835f739499e215db426":["{{unknown}}"],"_oembed_bdd8a7fb684b06e038320691686d2343":["{{unknown}}"],"_oembed_6bf3de7bb0bf17265e4650727b4e7ab2":["{{unknown}}"],"_oembed_2d6e068d259c28808d29923e51718b8d":["{{unknown}}"],"_oembed_5a182508b56613eb99a122f80120cf08":["{{unknown}}"],"_oembed_fed8d9047e4fbd0fc7af7be810954ce8":["{{unknown}}"],"_pv_count":["a:24:{i:17;i:175;i:18;i:150;i:5;i:48;i:9;i:92;i:10;i:151;i:11;i:162;i:0;i:98;i:14;i:218;i:20;i:97;i:22;i:110;i:16;i:229;i:19;i:117;i:4;i:31;i:12;i:129;i:8;i:71;i:21;i:93;i:13;i:214;i:1;i:61;i:2;i:52;i:23;i:80;i:3;i:29;i:7;i:61;i:15;i:165;i:6;i:47;}"],"pv_count":["2680"],"_oembed_eed68e64f174639b7f250ba2b29b94bb":["{{unknown}}"],"_oembed_d476dbf4ef0fafcc5c66a6fe52e697c3":["{{unknown}}"],"_oembed_02d08b48f8501f76e9e7caf1cab12935":["{{unknown}}"],"_oembed_ada744b298874f165a9c6c0489353dd5":["{{unknown}}"]},"_links":{"self":[{"href":"https:\/\/gri.jp\/media\/wp-json\/wp\/v2\/posts\/31005","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/gri.jp\/media\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/gri.jp\/media\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/gri.jp\/media\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/gri.jp\/media\/wp-json\/wp\/v2\/comments?post=31005"}],"version-history":[{"count":4,"href":"https:\/\/gri.jp\/media\/wp-json\/wp\/v2\/posts\/31005\/revisions"}],"predecessor-version":[{"id":31155,"href":"https:\/\/gri.jp\/media\/wp-json\/wp\/v2\/posts\/31005\/revisions\/31155"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/gri.jp\/media\/wp-json\/wp\/v2\/media\/31371"}],"wp:attachment":[{"href":"https:\/\/gri.jp\/media\/wp-json\/wp\/v2\/media?parent=31005"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/gri.jp\/media\/wp-json\/wp\/v2\/categories?post=31005"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/gri.jp\/media\/wp-json\/wp\/v2\/tags?post=31005"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}