{"id":476003,"date":"2023-08-09T07:25:33","date_gmt":"2023-08-09T07:25:33","guid":{"rendered":""},"modified":"2023-09-05T11:11:49","modified_gmt":"2023-09-05T11:11:49","slug":"bertology","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/jp\/wiki\/bertology\/","title":{"rendered":"BERTology"},"content":{"rendered":"

BERTology \u306f\u3001\u81ea\u7136\u8a00\u8a9e\u51e6\u7406 (NLP) \u306e\u5206\u91ce\u306b\u304a\u3051\u308b\u9769\u65b0\u7684\u306a\u30e2\u30c7\u30eb\u3067\u3042\u308b BERT (Bidirectional Encoder Representations from Transformers) \u306e\u8907\u96d1\u3055\u3068\u5185\u90e8\u306e\u4ed5\u7d44\u307f\u3092\u7814\u7a76\u3059\u308b\u5206\u91ce\u3067\u3059\u3002\u3053\u306e\u5206\u91ce\u3067\u306f\u3001BERT \u3068\u305d\u306e\u3055\u307e\u3056\u307e\u306a\u30d0\u30ea\u30a8\u30fc\u30b7\u30e7\u30f3\u306e\u8907\u96d1\u306a\u30e1\u30ab\u30cb\u30ba\u30e0\u3001\u6a5f\u80fd\u5c5e\u6027\u3001\u52d5\u4f5c\u3001\u304a\u3088\u3073\u6f5c\u5728\u7684\u306a\u7528\u9014\u306b\u3064\u3044\u3066\u8abf\u67fb\u3057\u307e\u3059\u3002<\/p>\n

BERTology\u306e\u51fa\u73fe\u3068\u305d\u306e\u6700\u521d\u306e\u8a00\u53ca<\/h2>\n

BERT \u306f\u30012018 \u5e74\u306b\u767a\u8868\u3055\u308c\u305f\u300cBERT: \u8a00\u8a9e\u7406\u89e3\u306e\u305f\u3081\u306e\u30c7\u30a3\u30fc\u30d7\u53cc\u65b9\u5411\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u306e\u4e8b\u524d\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u300d\u3068\u3044\u3046\u8ad6\u6587\u3067\u3001Google AI Language \u306e\u7814\u7a76\u8005\u306b\u3088\u3063\u3066\u5c0e\u5165\u3055\u308c\u307e\u3057\u305f\u3002\u3057\u304b\u3057\u3001\u300cBERTology\u300d\u3068\u3044\u3046\u7528\u8a9e\u306f\u3001BERT \u304c\u5c0e\u5165\u3055\u308c\u5e83\u304f\u63a1\u7528\u3055\u308c\u305f\u5f8c\u306b\u6ce8\u76ee\u3092\u96c6\u3081\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u3053\u306e\u7528\u8a9e\u306b\u306f\u660e\u78ba\u306a\u8d77\u6e90\u306f\u3042\u308a\u307e\u305b\u3093\u304c\u3001\u5c02\u9580\u5bb6\u304c BERT \u306e\u6a5f\u80fd\u3068\u7279\u6027\u3092\u6df1\u304f\u6398\u308a\u4e0b\u3052\u3088\u3046\u3068\u3057\u305f\u305f\u3081\u3001\u7814\u7a76\u30b3\u30df\u30e5\u30cb\u30c6\u30a3\u3067\u305d\u306e\u4f7f\u7528\u304c\u5e83\u307e\u308a\u59cb\u3081\u307e\u3057\u305f\u3002<\/p>\n

BERTology \u306e\u5c55\u958b: \u8a73\u7d30\u306a\u6982\u8981<\/h2>\n

BERTology \u306f\u3001\u8a00\u8a9e\u5b66\u3001\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u30fc \u30b5\u30a4\u30a8\u30f3\u30b9\u3001\u4eba\u5de5\u77e5\u80fd\u306e\u5074\u9762\u3092\u7d44\u307f\u5408\u308f\u305b\u305f\u5b66\u969b\u7684\u306a\u5206\u91ce\u3067\u3059\u3002BERTology \u3067\u306f\u3001\u8a00\u8a9e\u306e\u610f\u5473\u3068\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3092\u7406\u89e3\u3057\u3001\u3055\u307e\u3056\u307e\u306a NLP \u30bf\u30b9\u30af\u3067\u3088\u308a\u6b63\u78ba\u306a\u7d50\u679c\u3092\u63d0\u4f9b\u3059\u308b\u305f\u3081\u306e BERT \u306e\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0 \u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u7814\u7a76\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n

BERT \u306f\u3001\u4ee5\u524d\u306e\u30e2\u30c7\u30eb\u3068\u306f\u7570\u306a\u308a\u3001\u53cc\u65b9\u5411\u3067\u8a00\u8a9e\u3092\u5206\u6790\u3059\u308b\u3088\u3046\u306b\u8a2d\u8a08\u3055\u308c\u3066\u304a\u308a\u3001\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3092\u3088\u308a\u5305\u62ec\u7684\u306b\u7406\u89e3\u3067\u304d\u307e\u3059\u3002BERTology \u3067\u306f\u3001\u3053\u306e\u30e2\u30c7\u30eb\u3092\u3055\u3089\u306b\u5206\u6790\u3057\u3066\u3001\u8cea\u554f\u5fdc\u7b54\u30b7\u30b9\u30c6\u30e0\u3001\u611f\u60c5\u5206\u6790\u3001\u30c6\u30ad\u30b9\u30c8\u5206\u985e\u306a\u3069\u3001\u305d\u306e\u5f37\u529b\u3067\u591a\u7528\u9014\u306a\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3092\u7406\u89e3\u3057\u307e\u3059\u3002<\/p>\n

BERTology \u306e\u5185\u90e8\u69cb\u9020: BERT \u306e\u5206\u6790<\/h2>\n

BERT \u306e\u4e2d\u6838\u306f\u3001\u8a00\u8a9e\u7406\u89e3\u306e\u305f\u3081\u306b\u9806\u6b21\u51e6\u7406\u3067\u306f\u306a\u304f\u6ce8\u610f\u30e1\u30ab\u30cb\u30ba\u30e0\u3092\u4f7f\u7528\u3059\u308b Transformer \u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u306b\u3042\u308a\u307e\u3059\u3002\u91cd\u8981\u306a\u30b3\u30f3\u30dd\u30fc\u30cd\u30f3\u30c8\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/p>\n

    \n
  1. \u57cb\u3081\u8fbc\u307f\u30ec\u30a4\u30e4\u30fc<\/strong>: \u5165\u529b\u3055\u308c\u305f\u5358\u8a9e\u3092\u3001\u30e2\u30c7\u30eb\u304c\u7406\u89e3\u3067\u304d\u308b\u9ad8\u6b21\u5143\u30d9\u30af\u30c8\u30eb\u7a7a\u9593\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3057\u307e\u3059\u3002<\/li>\n
  2. \u5909\u5727\u5668\u30d6\u30ed\u30c3\u30af<\/strong>BERT \u306f\u3001\u7a4d\u307f\u91cd\u306d\u3089\u308c\u305f\u8907\u6570\u306e\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc \u30d6\u30ed\u30c3\u30af\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u5404\u30d6\u30ed\u30c3\u30af\u306f\u3001\u81ea\u5df1\u6ce8\u610f\u30e1\u30ab\u30cb\u30ba\u30e0\u3068\u30d5\u30a3\u30fc\u30c9\u30d5\u30a9\u30ef\u30fc\u30c9 \u30cb\u30e5\u30fc\u30e9\u30eb \u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3067\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/li>\n
  3. \u81ea\u5df1\u6ce8\u610f\u30e1\u30ab\u30cb\u30ba\u30e0<\/strong>: \u3053\u308c\u306b\u3088\u308a\u3001\u30e2\u30c7\u30eb\u306f\u6587\u8108\u3092\u8003\u616e\u3057\u3066\u3001\u6587\u4e2d\u306e\u5358\u8a9e\u306e\u76f8\u5bfe\u7684\u306a\u91cd\u8981\u5ea6\u3092\u8a55\u4fa1\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/li>\n
  4. \u30d5\u30a3\u30fc\u30c9\u30d5\u30a9\u30ef\u30fc\u30c9\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af<\/strong>\u3053\u306e\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306f\u3059\u3079\u3066\u306e\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc \u30d6\u30ed\u30c3\u30af\u5185\u306b\u5b58\u5728\u3057\u3001\u81ea\u5df1\u6ce8\u610f\u30e1\u30ab\u30cb\u30ba\u30e0\u306e\u51fa\u529b\u3092\u5909\u63db\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/li>\n<\/ol>\n

    BERTology\u306e\u4e3b\u306a\u7279\u5fb4<\/h2>\n

    BERTology \u3092\u7814\u7a76\u3059\u308b\u3068\u3001BERT \u3092\u5091\u51fa\u3057\u305f\u30e2\u30c7\u30eb\u306b\u3059\u308b\u4e00\u9023\u306e\u91cd\u8981\u306a\u5c5e\u6027\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n

      \n
    1. \u53cc\u65b9\u5411\u7406\u89e3<\/strong>BERT \u306f\u53cc\u65b9\u5411\u3067\u30c6\u30ad\u30b9\u30c8\u3092\u8aad\u307f\u53d6\u308a\u3001\u5b8c\u5168\u306a\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3092\u7406\u89e3\u3057\u307e\u3059\u3002<\/li>\n
    2. \u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3<\/strong>BERT \u306f\u3001LSTM \u3084 GRU \u306a\u3069\u306e\u5148\u884c\u6280\u8853\u3088\u308a\u3082\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3092\u628a\u63e1\u3059\u308b\u305f\u3081\u306b\u30a2\u30c6\u30f3\u30b7\u30e7\u30f3 \u30e1\u30ab\u30cb\u30ba\u30e0\u3092\u4f7f\u7528\u3059\u308b\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u3092\u6d3b\u7528\u3057\u307e\u3059\u3002<\/li>\n
    3. \u4e8b\u524d\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3068\u5fae\u8abf\u6574<\/strong>: BERT \u306f 2 \u6bb5\u968e\u306e\u30d7\u30ed\u30bb\u30b9\u306b\u5f93\u3044\u307e\u3059\u3002\u307e\u305a\u3001\u5927\u898f\u6a21\u306a\u30c6\u30ad\u30b9\u30c8 \u30b3\u30fc\u30d1\u30b9\u3067\u4e8b\u524d\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3055\u308c\u3001\u6b21\u306b\u7279\u5b9a\u306e\u30bf\u30b9\u30af\u306b\u5408\u308f\u305b\u3066\u5fae\u8abf\u6574\u3055\u308c\u307e\u3059\u3002<\/li>\n<\/ol>\n

      BERT \u30e2\u30c7\u30eb\u306e\u7a2e\u985e<\/h2>\n

      BERTology \u306b\u306f\u3001\u7279\u5b9a\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3084\u8a00\u8a9e\u5411\u3051\u306b\u958b\u767a\u3055\u308c\u305f\u3055\u307e\u3056\u307e\u306a BERT \u30d0\u30ea\u30a2\u30f3\u30c8\u306e\u7814\u7a76\u304c\u542b\u307e\u308c\u307e\u3059\u3002\u6ce8\u76ee\u3059\u3079\u304d\u30d0\u30ea\u30a2\u30f3\u30c8\u306b\u306f\u6b21\u306e\u3088\u3046\u306a\u3082\u306e\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n\n\n\n\n\n\n\n\n
      \u30e2\u30c7\u30eb<\/th>\n\u8aac\u660e<\/th>\n<\/tr>\n<\/thead>\n
      \u30ed\u30d9\u30eb\u30bf<\/td>\nBERT \u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30a2\u30d7\u30ed\u30fc\u30c1\u3092\u6700\u9069\u5316\u3057\u3066\u3001\u3088\u308a\u5805\u7262\u306a\u7d50\u679c\u3092\u5b9f\u73fe\u3057\u307e\u3059\u3002<\/td>\n<\/tr>\n
      \u30c7\u30a3\u30b9\u30c6\u30a3\u30eb\u30d0\u30fc\u30c8<\/td>\nBERT \u306e\u3088\u308a\u5c0f\u578b\u3001\u9ad8\u901f\u3001\u8efd\u91cf\u306a\u30d0\u30fc\u30b8\u30e7\u30f3\u3067\u3059\u3002<\/td>\n<\/tr>\n
      \u30a2\u30eb\u30d0\u30fc\u30c8<\/td>\n\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5411\u4e0a\u3055\u305b\u308b\u30d1\u30e9\u30e1\u30fc\u30bf\u524a\u6e1b\u6280\u8853\u3092\u5099\u3048\u305f\u9ad8\u5ea6\u306a BERT\u3002<\/td>\n<\/tr>\n
      \u591a\u8a00\u8a9eBERT<\/td>\nBERT \u306f\u591a\u8a00\u8a9e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u5411\u3051\u306b 104 \u306e\u8a00\u8a9e\u3067\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3055\u308c\u307e\u3057\u305f\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u5b9f\u8df5\u7684\u306aBERTology: \u4f7f\u7528\u6cd5\u3001\u8ab2\u984c\u3001\u89e3\u6c7a\u7b56<\/h2>\n

      BERT \u3068\u305d\u306e\u6d3e\u751f\u6280\u8853\u306f\u3001\u611f\u60c5\u5206\u6790\u3001\u56fa\u6709\u8868\u73fe\u62bd\u51fa\u3001\u8cea\u554f\u5fdc\u7b54\u30b7\u30b9\u30c6\u30e0\u306a\u3069\u3001\u3055\u307e\u3056\u307e\u306a\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306b\u5927\u304d\u304f\u8ca2\u732e\u3057\u3066\u304d\u307e\u3057\u305f\u3002BERTology \u306f\u512a\u308c\u305f\u6280\u8853\u3067\u3042\u308b\u306b\u3082\u304b\u304b\u308f\u3089\u305a\u3001\u9ad8\u3044\u8a08\u7b97\u8981\u4ef6\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u7528\u306e\u5927\u898f\u6a21\u306a\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u5fc5\u8981\u6027\u3001\u30d6\u30e9\u30c3\u30af \u30dc\u30c3\u30af\u30b9\u306e\u6027\u8cea\u306a\u3069\u3001\u3044\u304f\u3064\u304b\u306e\u8ab2\u984c\u3082\u62b1\u3048\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u3089\u306e\u554f\u984c\u3092\u8efd\u6e1b\u3059\u308b\u305f\u3081\u306b\u3001\u30e2\u30c7\u30eb\u306e\u30d7\u30eb\u30fc\u30cb\u30f3\u30b0\u3001\u77e5\u8b58\u306e\u84b8\u7559\u3001\u89e3\u91c8\u53ef\u80fd\u6027\u306e\u7814\u7a76\u306a\u3069\u306e\u6226\u7565\u304c\u4f7f\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n

      BERTology \u306e\u6bd4\u8f03: \u7279\u5fb4\u3068\u985e\u4f3c\u30e2\u30c7\u30eb<\/h2>\n

      BERT \u306f\u3001\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc \u30d9\u30fc\u30b9\u306e\u30e2\u30c7\u30eb\u306e\u4e00\u90e8\u3067\u3042\u308a\u3001\u4ed6\u306e\u30e2\u30c7\u30eb\u3068\u306e\u985e\u4f3c\u70b9\u3068\u76f8\u9055\u70b9\u3092\u5171\u6709\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n\n\n\n\n\n\n\n
      \u30e2\u30c7\u30eb<\/th>\n\u8aac\u660e<\/th>\n\u985e\u4f3c\u70b9<\/th>\n\u9055\u3044<\/th>\n<\/tr>\n<\/thead>\n
      GPT-2\/3<\/td>\n\u81ea\u5df1\u56de\u5e30\u8a00\u8a9e\u30e2\u30c7\u30eb<\/td>\nTransformer\u30d9\u30fc\u30b9\u3001\u5927\u898f\u6a21\u30b3\u30fc\u30d1\u30b9\u3067\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f<\/td>\n\u4e00\u65b9\u5411\u3001\u3055\u307e\u3056\u307e\u306aNLP\u30bf\u30b9\u30af\u3092\u6700\u9069\u5316<\/td>\n<\/tr>\n
      \u30a8\u30eb\u30e2<\/td>\n\u6587\u8108\u7684\u5358\u8a9e\u57cb\u3081\u8fbc\u307f<\/td>\n\u5927\u898f\u6a21\u306a\u30b3\u30fc\u30d1\u30b9\u3067\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f\u3001\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u8a8d\u8b58<\/td>\n\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u30d9\u30fc\u30b9\u3067\u306f\u306a\u304f\u3001bi-LSTM \u3092\u4f7f\u7528\u3057\u307e\u3059<\/td>\n<\/tr>\n
      \u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fcXL<\/td>\n\u5909\u5727\u5668\u30e2\u30c7\u30eb\u306e\u62e1\u5f35<\/td>\nTransformer\u30d9\u30fc\u30b9\u3001\u5927\u898f\u6a21\u30b3\u30fc\u30d1\u30b9\u3067\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f<\/td>\n\u7570\u306a\u308b\u6ce8\u610f\u30e1\u30ab\u30cb\u30ba\u30e0\u3092\u4f7f\u7528\u3059\u308b<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      BERTology\u306e\u5c06\u6765\u5c55\u671b<\/h2>\n

      BERTology \u306f\u3001NLP \u306e\u9769\u65b0\u3092\u63a8\u9032\u3057\u7d9a\u3051\u307e\u3059\u3002\u30e2\u30c7\u30eb\u306e\u52b9\u7387\u6027\u3001\u65b0\u3057\u3044\u8a00\u8a9e\u3084\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3078\u306e\u9069\u5fdc\u3001\u89e3\u91c8\u53ef\u80fd\u6027\u306e\u3055\u3089\u306a\u308b\u5411\u4e0a\u304c\u671f\u5f85\u3055\u308c\u307e\u3059\u3002BERT \u306e\u5f37\u307f\u3068\u4ed6\u306e AI \u624b\u6cd5\u3092\u7d44\u307f\u5408\u308f\u305b\u305f\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9 \u30e2\u30c7\u30eb\u3082\u9593\u3082\u306a\u304f\u767b\u5834\u3057\u307e\u3059\u3002<\/p>\n

      BERTology \u3068\u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc<\/h2>\n

      \u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001BERT \u30d9\u30fc\u30b9\u306e\u30e2\u30c7\u30eb\u306e\u8a08\u7b97\u8ca0\u8377\u3092\u8907\u6570\u306e\u30b5\u30fc\u30d0\u30fc\u306b\u5206\u6563\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u3001\u30ea\u30bd\u30fc\u30b9\u3092\u5927\u91cf\u306b\u6d88\u8cbb\u3059\u308b\u3053\u308c\u3089\u306e\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306e\u901f\u5ea6\u3068\u52b9\u7387\u3092\u5411\u4e0a\u3055\u305b\u307e\u3059\u3002\u3055\u3089\u306b\u3001\u30d7\u30ed\u30ad\u30b7\u306f\u3001\u3053\u308c\u3089\u306e\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306b\u4f7f\u7528\u3055\u308c\u308b\u30c7\u30fc\u30bf\u306e\u53ce\u96c6\u3068\u533f\u540d\u5316\u306b\u304a\u3044\u3066\u91cd\u8981\u306a\u5f79\u5272\u3092\u679c\u305f\u3057\u307e\u3059\u3002<\/p>\n

      \u95a2\u9023\u30ea\u30f3\u30af<\/h2>\n
        \n
      1. BERT: \u8a00\u8a9e\u7406\u89e3\u306e\u305f\u3081\u306e\u30c7\u30a3\u30fc\u30d7\u53cc\u65b9\u5411\u30c8\u30e9\u30f3\u30b9\u30d5\u30a9\u30fc\u30de\u30fc\u306e\u4e8b\u524d\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0<\/a><\/li>\n
      2. BERTology \u2013 BERT \u306e\u89e3\u91c8\u53ef\u80fd\u6027\u3068\u5206\u6790<\/a><\/li>\n
      3. BERT \u306e\u8aac\u660e: \u7406\u8ad6\u3068\u30c1\u30e5\u30fc\u30c8\u30ea\u30a2\u30eb\u3092\u542b\u3080\u5b8c\u5168\u30ac\u30a4\u30c9<\/a><\/li>\n
      4. RoBERTa: \u5805\u7262\u306b\u6700\u9069\u5316\u3055\u308c\u305f BERT \u4e8b\u524d\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30a2\u30d7\u30ed\u30fc\u30c1<\/a><\/li>\n
      5. DistilBERT\u3001BERT\u306e\u7cbe\u88fd\u30d0\u30fc\u30b8\u30e7\u30f3<\/a><\/li>\n<\/ol>","protected":false},"featured_media":467712,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476003","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about BERTology: A Deeper Understanding of BERT-Based Models in Natural Language Processing<\/mark>","faq_items":[{"question":"What is BERTology?","answer":"

        BERTology is the study of the intricacies and inner workings of BERT (Bidirectional Encoder Representations from Transformers), a revolutionary model in the field of Natural Language Processing (NLP). It explores the complex mechanisms, feature attributes, behaviors, and potential applications of BERT and its many variants.<\/p>"},{"question":"When did BERTology originate?","answer":"

        BERT was introduced in 2018 by Google AI Language. The term \"BERTology\" came into prominence after the introduction and wide adoption of BERT. It's used to describe the deep study of BERT's functionalities and peculiarities.<\/p>"},{"question":"What does BERTology entail?","answer":"

        BERTology involves the study of BERT\u2019s deep learning approach to understanding language semantics and context to provide more accurate results in various NLP tasks. This includes areas such as question answering systems, sentiment analysis, and text classification.<\/p>"},{"question":"How does BERT work?","answer":"

        BERT relies on the Transformer architecture, using attention mechanisms instead of sequential processing for language understanding. It employs bidirectional training, which means it understands the context from both left and right of a word in a sentence. This approach makes BERT powerful for understanding the context of language.<\/p>"},{"question":"What are the key features of BERT?","answer":"

        BERT's key features include bidirectional understanding of text, the use of transformer architecture, and a two-step process involving pretraining on a large corpus of text and then fine-tuning on specific tasks.<\/p>"},{"question":"What are some variants of BERT?","answer":"

        Several BERT variants have been developed for specific applications or languages. Some notable variants are RoBERTa, DistilBERT, ALBERT, and Multilingual BERT.<\/p>"},{"question":"What are the uses and challenges of BERT?","answer":"

        BERT has been applied to various NLP tasks like sentiment analysis, named entity recognition, and question-answering systems. However, it presents challenges such as high computational requirements, the necessity for large datasets for training, and its \"black-box\" nature.<\/p>"},{"question":"How does BERT compare with similar models?","answer":"

        BERT, as part of transformer-based models, shares similarities and differences with other models like GPT-2\/3, ELMo, and Transformer-XL. Key similarities include being transformer-based and pretrained on large corpora. Differences lie in the directionality of understanding and the types of NLP tasks optimized.<\/p>"},{"question":"What is the future of BERTology?","answer":"

        BERTology is expected to drive innovations in NLP. Further improvements in model efficiency, adaptation to new languages and contexts, and advancements in interpretability are anticipated.<\/p>"},{"question":"How can proxy servers be associated with BERTology?","answer":"

        Proxy servers can distribute the computational load in a BERT-based model across multiple servers, aiding in the speed and efficiency of training these resource-intensive models. Proxies can also play a vital role in collecting and anonymizing data used for training these models.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/476003","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/476003\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media\/467712"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media?parent=476003"}],"curies":[{"name":"\u3046\u30fc\u3093","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}