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İlk bölümde kütüphanenin temel özellikleri ve GitHub'daki avantajları açıklanırken, ikinci bölümde algoritmanın çalışma prensibi dört adımlı bir süreç olarak detaylandırılmaktadır. Ayrıca, kütüphanenin kurulumu, SKLM'den fetch20 news group veri seti üzerinde uygulaması ve metinlerdeki konuları tespit etme, frekanslarını görme, kelime bulutları oluşturma ve konuları arama gibi fonksiyonlar gösterilmektedir.","Video, metin analizi ve NLP alanında çalışanlar için Top2Wec'in nasıl kullanılabileceğini gösteren kapsamlı bir rehber niteliğindedir ve çok fazla konu elde edildiğinde bu konuları nasıl azaltabileceğimiz de gösterilmektedir."]},"endTime":1411,"title":"Top2Wec Kütüphanesi ile Metin Analizi Eğitimi","beginTime":0}],"fullResult":[{"index":0,"title":"Top2Wec Kütüphanesi Tanıtımı","list":{"type":"unordered","items":["Top2Wec, topic modeling ve semantic search için kullanılan bir kütüphane ve algoritmadır.","Bu kütüphane, metinlerdeki konuları otomatik olarak tespit edebilir, embeddings oluşturabilir ve belgeleri vektörize edebilir.","Top2Wec, belirli bir metindeki konuları belirleyebilir ve bu sayıları istenen seviyeye 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algoritması dört temel adımdan oluşur: önce metinlerden word embeddings'ler oluşturulur.","Oluşturulan embeddings'ler, doc2vec, BERT, Word2vec veya diğer transformer modelleri gibi farklı modellerle oluşturulabilir.","İkinci adım olarak, dimensionality reduction teknikleri kullanılarak embeddings'lerin boyutları azaltılır.","Üçüncü adım olarak, HDB scan gibi bir clustering algoritması kullanılarak benzer belgeler ve konular kümelere ayrılır."]},"beginTime":199,"endTime":435,"href":"/video/preview/9257840699280560964?parent-reqid=1773479793023457-3175422176622154582-balancer-l7leveler-kubr-yp-vla-73-BAL&text=Blert+-+Topic&t=199&ask_summarization=1"},{"index":3,"title":"Top2Wec'in Kurulumu ve Kullanımı","list":{"type":"unordered","items":["Top2Wec, GitHub'dan çeşitli formatlarda (BERT, Sentence Transformer, Sentence Encoder) kurulabilir.","Kurulum sonrası, Pandas ve NumPy gibi temel kütüphaneler ile birlikte Top2Wec kütüphanesi import edilir.","Örnek olarak, Skiml'den fetch20 news group veri seti kullanılarak 18.84 makale açıklaması işlenir.","Top2Wec fonksiyonu, veri kümesini liste formatında alarak çalıştırılır ve model, word embeddings'ler oluşturur, dimensionality reduction yapar ve HDB scan ile clustering gerçekleştirir.","Sonuç olarak, 98 farklı konu oluşturulur ve get_topic_size fonksiyonu ile konuların sıklığı ve indeksleri görüntülenebilir."]},"beginTime":435,"endTime":743,"href":"/video/preview/9257840699280560964?parent-reqid=1773479793023457-3175422176622154582-balancer-l7leveler-kubr-yp-vla-73-BAL&text=Blert+-+Topic&t=435&ask_summarization=1"},{"index":4,"title":"Top2Web ile Konu Analizi","list":{"type":"unordered","items":["Top2Web, metinlerdeki konuları indeksler şeklinde sunar ve her konunun frekansını gösterir.","Sistem, 98 farklı konu üretir ve her konunun frekansını belirtir.","Her konu için kullanılan kelimelerin listesi görüntülenebilir ve konu vektörleri 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Supervised approaches to the problem have proven...","preview":{"posterSrc":"//avatars.mds.yandex.net/get-vthumb/1657920/fe2be5da45f5eb8ba8b42479ad8b1c36/564x318_1","videoSrc":"https://video-preview.s3.yandex.net/Drf62gEAAAA.mp4","videoType":"video/mp4"},"target":"_self","position":"6","reqid":"1773479793023457-3175422176622154582-balancer-l7leveler-kubr-yp-vla-73-BAL","summary":{"isFull":true,"fullTextUrl":"/video/result?ask_summarization=1&numdoc=1&noreask=1&nomisspell=1&parent-reqid=1773479793023457-3175422176622154582-balancer-l7leveler-kubr-yp-vla-73-BAL&text=videoid:15841316795663824249","teaser":[{"list":{"type":"unordered","items":["Bu video, Triple IT Bangalore'da master öğrencisi olan Prakhar tarafından sunulan bir araştırma sunumudur. Prakhar, makine öğrenimi alanında araştırma kağıtlarını açıklamayı amaçlayan bir kanalda içerik üretmektedir.","Videoda, Facebook'un \"Unsupervised Topic Segmentation of Meetings with Bert Embeddings\" başlıklı preprint'i ele alınmaktadır. Sunum, toplantı transkriptlerini farklı konulara ayırma problemi ve bu problemi çözmek için kullanılan yöntemleri detaylı bir şekilde açıklamaktadır. Prakhar, önce problem tanımını yaparak, ardından Bert ve Sentence Bert gibi modellerin nasıl kullanıldığını, blokların nasıl oluşturulduğunu ve benzerlik eşleştirme yöntemlerinin nasıl uygulandığını adım adım anlatmaktadır. Video, modelin performansını gösteren örneklerle sonlanmaktadır."]},"endTime":540,"title":"Facebook'un Unsupervised Topic Segmentation of Meetings ile Bert Embeddings Üzerine Araştırma Sunumu","beginTime":0}],"fullResult":[{"index":0,"title":"Giriş ve Kağıt Hakkında Genel Bilgi","list":{"type":"unordered","items":["Video, Facebook'tan gelen \"Unsupervised Topic Segmentation of Meetings with Bert Embeddings\" başlıklı preprint'i ele alıyor.","Konuşmacı Prakhar, Triple IT Bangalore'da master öğrencisi olup, bu kanalda makine öğrenmesi alanındaki araştırma kağıtlarını açıklıyor.","Kağıt, metinleri farklı konulara göre bölmeyi amaçlıyor ve özellikle pandemi nedeniyle Zoom ve Google Meet gibi uygulamaların yaygınlaşmasıyla toplantılar için bir kullanım alanı 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