{"id":33865,"date":"2019-10-31T21:55:06","date_gmt":"2019-10-31T18:55:06","guid":{"rendered":"https:\/\/prohoster.info\/blog\/microsoft-otkryl-kod-biblioteki-vektornogo-poiska-ispolzuemoj-v-bing\/"},"modified":"2019-10-31T21:55:06","modified_gmt":"2019-10-31T18:55:06","slug":"microsoft-otkryl-kod-biblioteki-vektornogo-poiska-ispolzuemoj-v-bing","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sq\/blog\/news\/microsoft-otkryl-kod-biblioteki-vektornogo-poiska-ispolzuemoj-v-bing","title":{"rendered":"Microsoft ka hapur kodi i bibliotek\u00ebs s\u00eb k\u00ebrkimit vektorial q\u00eb p\u00ebrdoret n\u00eb Bing","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Kompania Microsoft publikoi<\/a><\/noindex> tekstet burimore t\u00eb bibliotek\u00ebs s\u00eb m\u00ebsimit t\u00eb makinerive SPTAG<\/a><\/noindex> (Tree Ne Nd\u00ebrprerje Hap\u00ebsinore Dhe Graf) me implementimin e algoritmit t\u00eb k\u00ebrkimit af\u00ebrsi t\u00eb p\u00ebraf\u00ebrt p\u00ebr k\u00ebrkimin e fqinjit m\u00eb t\u00eb af\u00ebrt<\/a><\/noindex>. Biblioteka u zhvillua<\/a><\/noindex> n\u00eb nj\u00ebsin\u00eb k\u00ebrkimore Microsoft Research dhe qendr\u00ebn e zhvillimit t\u00eb teknologjive t\u00eb k\u00ebrkimit (Microsoft Search Technology Center). N\u00eb praktik\u00eb, SPTAG p\u00ebrdoret n\u00eb sistemin e k\u00ebrkimit Bing p\u00ebr t\u00eb p\u00ebrcaktuar rezultatet m\u00eb relevante duke marr\u00eb parasysh kontekstin e k\u00ebrkesave t\u00eb p\u00ebrdoruesve. Kodi \u00ebsht\u00eb shkruar n\u00eb gjuh\u00ebn C++ dhe shp\u00ebrndahet<\/a><\/noindex> \u00ebsht\u00eb n\u00ebn licenc\u00ebn MIT. Mb\u00ebshtetet nd\u00ebrtimi p\u00ebr Linux dhe Windows. Ka nj\u00eb lidhje p\u00ebr gjuh\u00ebn Python.<\/p>\n

Pavar\u00ebsisht se idet\u00eb e aplikimit t\u00eb magazinave vektor n\u00eb sistemet e k\u00ebrkimit kan\u00eb qarkulluar p\u00ebr nj\u00eb koh\u00eb t\u00eb gjat\u00eb, implementimi i tyre praktik pengohet nga k\u00ebrkesat e larta t\u00eb burimeve p\u00ebr operacionet me vektor\u00eb dhe kufizimet n\u00eb shkall\u00ebzim. Kombinimi i metodave t\u00eb m\u00ebsimit t\u00eb thell\u00eb me algoritmet e k\u00ebrkimit af\u00ebrsi t\u00eb p\u00ebraf\u00ebrt ka mund\u00ebsuar q\u00eb performanca dhe shkall\u00ebzimi i sistemeve vektor t\u00eb arrij\u00eb nj\u00eb nivel t\u00eb pranuesh\u00ebm p\u00ebr sistemet e m\u00ebdha t\u00eb k\u00ebrkimit. P\u00ebr shembull, n\u00eb Bing p\u00ebr indisin vektor me m\u00eb shum\u00eb se 150 miliard vektor\u00eb, koha e marrjes s\u00eb rezultateve m\u00eb relevante \u00ebsht\u00eb brenda 8 ms.<\/p>\n

Biblioteka p\u00ebrfshin mjete p\u00ebr nd\u00ebrtimin e indisit dhe organizimin e k\u00ebrkimeve t\u00eb vektor\u00ebve, si edhe nj\u00eb grup mjetesh p\u00ebr mb\u00ebshtetje t\u00eb nj\u00eb sistemi t\u00eb shp\u00ebrndar\u00eb t\u00eb k\u00ebrkimeve online, q\u00eb mbulon koleksione shum\u00eb t\u00eb m\u00ebdha vektor\u00ebsh. Ofrohen<\/a><\/noindex> module t\u00eb ndryshme: nd\u00ebrtuesi i indeksit p\u00ebr indeksimin, k\u00ebrkuesi p\u00ebr k\u00ebrkimin me p\u00ebrdorimin e indeksit, i shp\u00ebrndar\u00eb n\u00eb nj\u00eb klaster me disa nyje, serveri p\u00ebr t\u00eb nisur p\u00ebrpunuesit n\u00eb nyje, Aggregator p\u00ebr t\u00eb kombinuar disa server\u00eb n\u00eb nj\u00eb t\u00eb vetme dhe klienti p\u00ebr t\u00eb d\u00ebrguar k\u00ebrkesat. Mb\u00ebshtetet inkorporimi i vektor\u00ebve t\u00eb rinj n\u00eb indeks dhe fshirja e vektor\u00ebve n\u00eb koh\u00eb reale.<\/p>\n

Biblioteka n\u00ebnkupton se t\u00eb dh\u00ebnat e p\u00ebrpunuara dhe t\u00eb paraqitura n\u00eb koleksion jan\u00eb t\u00eb organizuara si vektor\u00eb t\u00eb lidhur q\u00eb mund t\u00eb krahasohen bazuar n\u00eb distancat e Euclidit<\/a><\/noindex> (L2) ose distancat kosinore.<\/a><\/noindex> Kur b\u00ebhet nj\u00eb k\u00ebrkes\u00eb, kthehen vektor\u00ebt, distanca midis t\u00eb cil\u00ebve dhe vektorit origjinal \u00ebsht\u00eb minimale. N\u00eb SPTAG ofrohen dy metoda p\u00ebr organizimin e hap\u00ebsir\u00ebs vektor: SPTAG-KDT (Pema K-dimensionale (kd-tree<\/a><\/noindex>) dhe grafi i fqinj\u00ebve t\u00eb lidhur<\/a><\/noindex>) dhe SPTAG-BKT (pema e k-mesme (k-means tree<\/a><\/noindex> dhe grafiku i fqinj\u00ebsive relative). Metoda e par\u00eb k\u00ebrkon m\u00eb pak burime gjat\u00eb pun\u00ebs me indeksin, nd\u00ebrsa e dyta tregon sakt\u00ebsi m\u00eb t\u00eb lart\u00eb t\u00eb rezultateve t\u00eb k\u00ebrkimit n\u00eb koleksione shum\u00eb t\u00eb m\u00ebdha vektor\u00ebsh.<\/p>\n

N\u00eb k\u00ebt\u00eb m\u00ebnyr\u00eb, k\u00ebrkimi me vektor\u00eb nuk kufizohet vet\u00ebm n\u00eb tekst dhe mund t\u00eb aplikohet n\u00eb informacionin multimedial dhe imazhe, si dhe n\u00eb sistemet e formimit automatik t\u00eb rekomandimeve. P\u00ebr shembull, n\u00eb nj\u00eb nga prototipat e nd\u00ebrtuar mbi framework-un PyTorch u realizua nj\u00eb sistem vektor\u00ebsh p\u00ebr k\u00ebrkimin duke marr\u00eb parasysh ngjashm\u00ebrin\u00eb e objekteve n\u00eb imazhe, e nd\u00ebrtuar me t\u00eb dh\u00ebnat nga disa koleksione referuese me imazhe t\u00eb kafsh\u00ebve, maceve dhe qenve, t\u00eb cilat u transformuan n\u00eb grupe vektor\u00ebsh. Kur nj\u00eb imazh hyres vjen p\u00ebr k\u00ebrkimin, ai transformohet duke p\u00ebrdorur nj\u00eb model t\u00eb m\u00ebsimit t\u00eb makineris\u00eb n\u00eb nj\u00eb vektor, mbi t\u00eb cilin me algoritmin SPTAG zgjidhen vektor\u00ebt m\u00eb t\u00eb ngjash\u00ebm nga indeksi dhe si rezultat kthehen imazhet e lidhura me ta.<\/p>\n

<\/a><\/noindex><\/p>\n

Burimi: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u041a\u043e\u043c\u043f\u0430\u043d\u0438\u044f Microsoft \u043e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043b\u0430 \u0438\u0441\u0445\u043e\u0434\u043d\u044b\u0435 \u0442\u0435\u043a\u0441\u0442\u044b \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f SPTAG (Space Partition Tree And Graph) \u0441 \u0440\u0435\u0430\u043b\u0438\u0437\u0430\u0446\u0438\u0435\u0439 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u0430 \u043f\u0440\u0438\u0431\u043b\u0438\u0437\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0433\u043e \u043f\u043e\u0438\u0441\u043a\u0430 \u0431\u043b\u0438\u0436\u0430\u0439\u0448\u0435\u0433\u043e \u0441\u043e\u0441\u0435\u0434\u0430. \u0411\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0430 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u0430 \u0432 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u0441\u043a\u043e\u043c \u043f\u043e\u0434\u0440\u0430\u0437\u0434\u0435\u043b\u0435\u043d\u0438\u0438 Microsoft Research \u0438 \u0446\u0435\u043d\u0442\u0440\u0435 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u043a\u0438 \u043f\u043e\u0438\u0441\u043a\u043e\u0432\u044b\u0445 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0439 (Microsoft Search Technology Center). \u041d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435 SPTAG \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u0435\u0442\u0441\u044f \u0432 \u043f\u043e\u0438\u0441\u043a\u043e\u0432\u043e\u0439 \u0441\u0438\u0441\u0442\u0435\u043c\u0435 Bing \u0434\u043b\u044f \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0435\u043d\u0438\u044f \u043d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u0440\u0435\u043b\u0435\u0432\u0430\u043d\u0442\u043d\u044b\u0445 \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u0432 \u0441 \u0443\u0447\u0451\u0442\u043e\u043c \u043a\u043e\u043d\u0442\u0435\u043a\u0441\u0442\u0430 \u043f\u043e\u0438\u0441\u043a\u043e\u0432\u044b\u0445 \u0437\u0430\u043f\u0440\u043e\u0441\u043e\u0432. 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