{"id":476002,"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":"bert","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/bert\/","title":{"rendered":"BERT"},"content":{"rendered":"

BERT veya Transformers'tan \u00c7ift Y\u00f6nl\u00fc Kodlay\u0131c\u0131 G\u00f6sterimleri, daha \u00f6nceki teknolojilerle m\u00fcmk\u00fcn olmayan bir \u015fekilde dili anlamak i\u00e7in Transformer modellerini kullanan, do\u011fal dil i\u015fleme (NLP) alan\u0131nda devrim niteli\u011finde bir y\u00f6ntemdir.<\/p>\n

BERT'in K\u00f6keni ve Tarih\u00e7esi<\/h2>\n

BERT, 2018 y\u0131l\u0131nda Google AI Language'daki ara\u015ft\u0131rmac\u0131lar taraf\u0131ndan tan\u0131t\u0131ld\u0131. BERT'i olu\u015fturman\u0131n ard\u0131ndaki ama\u00e7, \u00f6nceki dil temsil modellerinin s\u0131n\u0131rlamalar\u0131n\u0131n \u00fcstesinden gelebilecek bir \u00e7\u00f6z\u00fcm sa\u011flamakt\u0131. BERT'ten ilk kez arXiv'de yay\u0131nlanan \u201cBERT: Dil Anlay\u0131\u015f\u0131 i\u00e7in Derin \u00c7ift Y\u00f6nl\u00fc Transformat\u00f6rlerin \u00d6n E\u011fitimi\u201d makalesinde bahsedilmi\u015ftir.<\/p>\n

BERT'i Anlamak<\/h2>\n

BERT, dil temsillerinin \u00f6n e\u011fitimine y\u00f6nelik bir y\u00f6ntemdir; bu, b\u00fcy\u00fck miktarda metin verisi \u00fczerinde genel ama\u00e7l\u0131 bir "dil anlama" modelinin e\u011fitilmesi ve ard\u0131ndan bu modelin belirli g\u00f6revler i\u00e7in ince ayarlanmas\u0131 anlam\u0131na gelir. BERT, dillerin karma\u015f\u0131kl\u0131klar\u0131n\u0131 daha do\u011fru bir \u015fekilde modellemek ve anlamak i\u00e7in tasarland\u0131\u011f\u0131 i\u00e7in NLP alan\u0131nda devrim yaratt\u0131.<\/p>\n

BERT'in en \u00f6nemli yenili\u011fi Transformat\u00f6rlerin \u00e7ift y\u00f6nl\u00fc e\u011fitimidir. Metin verilerini tek y\u00f6nde (soldan sa\u011fa veya sa\u011fdan sola) i\u015fleyen \u00f6nceki modellerin aksine, BERT t\u00fcm kelime dizisini ayn\u0131 anda okur. Bu, modelin bir kelimenin ba\u011flam\u0131n\u0131 t\u00fcm \u00e7evresine (kelimenin soluna ve sa\u011f\u0131na) dayal\u0131 olarak \u00f6\u011frenmesine olanak tan\u0131r.<\/p>\n

BERT'in \u0130\u00e7 Yap\u0131s\u0131 ve \u0130\u015fleyi\u015fi<\/h2>\n

BERT, Transformer ad\u0131 verilen bir mimariden yararlan\u0131r. Transformat\u00f6r bir kodlay\u0131c\u0131 ve kod \u00e7\u00f6z\u00fcc\u00fc i\u00e7erir, ancak BERT yaln\u0131zca kodlay\u0131c\u0131 k\u0131sm\u0131n\u0131 kullan\u0131r. Her Transformer kodlay\u0131c\u0131n\u0131n iki par\u00e7as\u0131 vard\u0131r:<\/p>\n

    \n
  1. \u00d6z-dikkat mekanizmas\u0131: Bir c\u00fcmledeki hangi kelimelerin birbiriyle alakal\u0131 oldu\u011funu belirler. Bunu, her kelimenin alaka d\u00fczeyini puanlayarak ve bu puanlar\u0131 kelimelerin birbirleri \u00fczerindeki etkisini tartmak i\u00e7in kullanarak yapar.<\/li>\n
  2. \u0130leri beslemeli sinir a\u011f\u0131: Dikkat mekanizmas\u0131ndan sonra kelimeler ileri beslemeli sinir a\u011f\u0131na iletilir.<\/li>\n<\/ol>\n

    BERT'teki bilgi ak\u0131\u015f\u0131 \u00e7ift y\u00f6nl\u00fcd\u00fcr, bu da mevcut kelimenin \u00f6nceki ve sonraki kelimeleri g\u00f6rmesine olanak tan\u0131yarak daha do\u011fru bir ba\u011flamsal anlay\u0131\u015f sa\u011flar.<\/p>\n

    BERT'in Temel \u00d6zellikleri<\/h2>\n
      \n
    1. \n

      \u00c7ift y\u00f6nl\u00fcl\u00fck<\/strong>: \u00d6nceki modellerden farkl\u0131 olarak BERT, bir kelimenin tam ba\u011flam\u0131n\u0131 ondan \u00f6nce ve sonra g\u00f6r\u00fcnen kelimelere bakarak de\u011ferlendirir.<\/p>\n<\/li>\n

    2. \n

      Transformat\u00f6rler<\/strong>: BERT, uzun kelime dizilerini daha etkili ve verimli bir \u015fekilde i\u015flemesine olanak tan\u0131yan Transformer mimarisini kullan\u0131r.<\/p>\n<\/li>\n

    3. \n

      \u00d6n E\u011fitim ve \u0130nce Ayar<\/strong>: BERT, geni\u015f bir etiketsiz metin verisi toplulu\u011fu \u00fczerinde \u00f6nceden e\u011fitilmi\u015ftir ve ard\u0131ndan belirli bir g\u00f6reve g\u00f6re ince ayar yap\u0131lm\u0131\u015ft\u0131r.<\/p>\n<\/li>\n<\/ol>\n

      BERT T\u00fcrleri<\/h2>\n

      BERT iki boyutta gelir:<\/p>\n

        \n
      1. BERT-Taban<\/strong>: 12 katman (transformat\u00f6r bloklar\u0131), 12 dikkat ba\u015fl\u0131\u011f\u0131 ve 110 milyon parametre.<\/li>\n
      2. BERT-B\u00fcy\u00fck<\/strong>: 24 katman (transformat\u00f6r bloklar\u0131), 16 dikkat ba\u015fl\u0131\u011f\u0131 ve 340 milyon parametre.<\/li>\n<\/ol>\n\n\n\n\n\n\n\n
        <\/th>\nBERT-Taban<\/th>\nBERT-B\u00fcy\u00fck<\/th>\n<\/tr>\n<\/thead>\n
        Katmanlar (Transformat\u00f6r Bloklar\u0131)<\/td>\n12<\/td>\n24<\/td>\n<\/tr>\n
        Dikkat Kafalar\u0131<\/td>\n12<\/td>\n16<\/td>\n<\/tr>\n
        Parametreler<\/td>\n110 milyon<\/td>\n340 milyon<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        BERT ile Kullan\u0131m, Zorluklar ve \u00c7\u00f6z\u00fcmler<\/h2>\n

        BERT, soru cevaplama sistemleri, c\u00fcmle s\u0131n\u0131fland\u0131rmas\u0131 ve varl\u0131k tan\u0131ma gibi bir\u00e7ok NLP g\u00f6revinde yayg\u0131n olarak kullan\u0131lmaktad\u0131r.<\/p>\n

        BERT ile ilgili zorluklar \u015funlar\u0131 i\u00e7erir:<\/p>\n

          \n
        1. \n

          Hesaplamal\u0131 kaynaklar<\/strong>: BERT, \u00e7ok say\u0131da parametresi ve derin mimarisi nedeniyle e\u011fitim i\u00e7in \u00f6nemli hesaplama kaynaklar\u0131 gerektirir.<\/p>\n<\/li>\n

        2. \n

          \u015eeffafl\u0131k eksikli\u011fi<\/strong>: Bir\u00e7ok derin \u00f6\u011frenme modeli gibi BERT de bir "kara kutu" g\u00f6revi g\u00f6rebilir ve belirli bir karara nas\u0131l ula\u015ft\u0131\u011f\u0131n\u0131n anla\u015f\u0131lmas\u0131n\u0131 zorla\u015ft\u0131r\u0131r.<\/p>\n<\/li>\n<\/ol>\n

          Bu sorunlar\u0131n \u00e7\u00f6z\u00fcmleri \u015funlar\u0131 i\u00e7erir:<\/p>\n

            \n
          1. \n

            \u00d6nceden e\u011fitilmi\u015f modelleri kullanma<\/strong>: S\u0131f\u0131rdan e\u011fitim vermek yerine, \u00f6nceden e\u011fitilmi\u015f BERT modelleri kullan\u0131labilir ve daha az hesaplama kayna\u011f\u0131 gerektiren belirli g\u00f6revlerde bunlara ince ayar yap\u0131labilir.<\/p>\n<\/li>\n

          2. \n

            A\u00e7\u0131klay\u0131c\u0131 ara\u00e7lar<\/strong>: LIME ve SHAP gibi ara\u00e7lar BERT modelinin kararlar\u0131n\u0131n daha yorumlanabilir olmas\u0131na yard\u0131mc\u0131 olabilir.<\/p>\n<\/li>\n<\/ol>\n

            BERT ve Benzeri Teknolojiler<\/h2>\n\n\n\n\n\n\n\n
            <\/th>\nBERT<\/th>\nLSTM<\/th>\n<\/tr>\n<\/thead>\n
            Y\u00f6n<\/td>\n\u00c7ift y\u00f6nl\u00fc<\/td>\nTek y\u00f6nl\u00fc<\/td>\n<\/tr>\n
            Mimari<\/td>\nTrafo<\/td>\nTekrarlayan<\/td>\n<\/tr>\n
            Ba\u011flamsal Anlama<\/td>\nDaha iyi<\/td>\nS\u0131n\u0131rl\u0131<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            BERT ile ilgili Gelecek Perspektifleri ve Teknolojiler<\/h2>\n

            BERT, NLP'de yeni modellere ilham vermeye devam ediyor. BERT'in daha k\u00fc\u00e7\u00fck, daha h\u0131zl\u0131 ve daha hafif bir versiyonu olan DistilBERT ve BERT'in bir sonraki c\u00fcmle \u00f6n e\u011fitim hedefini ortadan kald\u0131ran bir versiyonu olan RoBERTa, son geli\u015fmelerin \u00f6rnekleridir.<\/p>\n

            BERT'te gelecekteki ara\u015ft\u0131rmalar, modeli daha verimli, daha yorumlanabilir ve daha uzun dizileri daha iyi i\u015flemeye y\u00f6nelik hale getirmeye odaklanabilir.<\/p>\n

            BERT ve Proxy Sunucular\u0131<\/h2>\n

            BERT bir NLP modeli oldu\u011fundan ve proxy sunucular\u0131 a\u011f olu\u015fturma ara\u00e7lar\u0131 oldu\u011fundan, BERT'in proxy sunucularla b\u00fcy\u00fck \u00f6l\u00e7\u00fcde ilgisi yoktur. Ancak \u00f6nceden e\u011fitilmi\u015f BERT modellerini indirirken veya API'ler arac\u0131l\u0131\u011f\u0131yla kullan\u0131rken OneProxy gibi g\u00fcvenilir, h\u0131zl\u0131 ve g\u00fcvenli bir proxy sunucusu istikrarl\u0131 ve g\u00fcvenli veri iletimi sa\u011flayabilir.<\/p>\n

            \u0130lgili Ba\u011flant\u0131lar<\/h2>\n
              \n
            1. \n

              BERT: Dil Anlamak i\u00e7in Derin \u00c7ift Y\u00f6nl\u00fc Transformat\u00f6rlerin \u00d6n E\u011fitimi<\/a><\/p>\n<\/li>\n

            2. \n

              Google AI Blogu: A\u00e7\u0131k Kaynak Kullan\u0131m\u0131 BERT<\/a><\/p>\n<\/li>\n

            3. \n

              BERT A\u00e7\u0131kland\u0131: Teori ve \u00d6\u011fretici \u0130\u00e7eren Eksiksiz Bir K\u0131lavuz<\/a><\/p>\n<\/li>\n<\/ol>","protected":false},"featured_media":467710,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476002","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Bidirectional Encoder Representations from Transformers (BERT)<\/mark>","faq_items":[{"question":"What is BERT?","answer":"

              BERT, or Bidirectional Encoder Representations from Transformers, is a cutting-edge method in the field of natural language processing (NLP) that leverages Transformer models to understand language in a way that surpasses earlier technologies.<\/p>"},{"question":"Who introduced BERT and when?","answer":"

              BERT was introduced by researchers at Google AI Language in 2018. The paper titled \"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,\" published on arXiv, was the first to mention BERT.<\/p>"},{"question":"What is the key innovation of BERT?","answer":"

              The key innovation of BERT is its bidirectional training of Transformers. This is a departure from previous models that processed text data in one direction only. BERT reads the entire sequence of words at once, learning the context of a word based on all its surroundings.<\/p>"},{"question":"How does BERT work internally?","answer":"

              BERT uses an architecture known as Transformer, specifically its encoder part. Each Transformer encoder comprises a self-attention mechanism, which determines the relevance of words to each other, and a feed-forward neural network, which the words pass through after the attention mechanism. BERT's bidirectional information flow gives it a richer contextual understanding of language.<\/p>"},{"question":"What are the main types of BERT?","answer":"

              BERT primarily comes in two sizes: BERT-Base and BERT-Large. BERT-Base has 12 layers, 12 attention heads, and 110 million parameters. BERT-Large, on the other hand, has 24 layers, 16 attention heads, and 340 million parameters.<\/p>"},{"question":"What challenges might one face when using BERT?","answer":"

              BERT requires substantial computational resources for training due to its large number of parameters and deep architecture. Furthermore, like many deep learning models, BERT can be a \"black box,\" making it challenging to understand how it makes a particular decision.<\/p>"},{"question":"How do BERT and proxy servers relate?","answer":"

              While BERT and proxy servers operate in different spheres (NLP and networking, respectively), a proxy server can be crucial when downloading pre-trained BERT models or using them via APIs. A reliable proxy server like OneProxy ensures secure and stable data transmission.<\/p>"},{"question":"What are the future prospects related to BERT?","answer":"

              BERT continues to inspire new models in NLP like DistilBERT and RoBERTa. Future research in BERT may focus on making the model more efficient, more interpretable, and better at handling longer sequences.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476002","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/476002\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/467710"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=476002"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}