{"id":38448,"date":"2019-10-31T22:23:47","date_gmt":"2019-10-31T19:23:47","guid":{"rendered":"https:\/\/prohoster.info\/blog\/github-otkryl-narabotki-po-primeneniyu-mashinnogo-obucheniya-dlya-poiska-i-analiza-koda\/"},"modified":"2019-10-31T22:23:47","modified_gmt":"2019-10-31T19:23:47","slug":"github-otkryl-narabotki-po-primeneniyu-mashinnogo-obucheniya-dlya-poiska-i-analiza-koda","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sv\/blog\/news\/github-otkryl-narabotki-po-primeneniyu-mashinnogo-obucheniya-dlya-poiska-i-analiza-koda","title":{"rendered":"GitHub har \u00f6ppnat arbeten kring anv\u00e4ndningen av maskininl\u00e4rning f\u00f6r att s\u00f6ka och analysera kod","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

GitHub \u00e5r<\/a><\/noindex> projektet CodeSearchNet<\/a><\/noindex>, d\u00e4r modeller f\u00f6r maskininl\u00e4rning och dataset har f\u00f6rberetts f\u00f6r att analysera, klassificera och analysera kod p\u00e5 olika programmeringsspr\u00e5k. CodeSearchNet, i likhet med ImageNet<\/a><\/noindex>, inkluderar en stor samling kodavsnitt uppbackade av anteckningar som formaliserar de handlingar som utf\u00f6rs av koden. Komponenter f\u00f6r att tr\u00e4na modeller och exempel p\u00e5 anv\u00e4ndning av CodeSearchNet \u00e4r skrivna i Python med hj\u00e4lp av Tensorflow-ramverket och sprids<\/a><\/noindex> licensieras under MIT.<\/p>\n

Vid skapandet av CodeSearchNet har tekniker f\u00f6r naturlig spr\u00e5kbehandling anv\u00e4nts, vilket g\u00f6r det m\u00f6jligt f\u00f6r maskininl\u00e4rningssystem att ta h\u00e4nsyn till inte bara syntaktiska egenskaper utan \u00e4ven betydelsen av de handlingar som koden utf\u00f6r. P\u00e5 GitHub har systemet anv\u00e4nder<\/a><\/noindex> i experiment f\u00f6r semantisk kods\u00f6kning med hj\u00e4lp av fr\u00e5gor p\u00e5 naturligt spr\u00e5k<\/a><\/noindex> (till exempel, n\u00e4r man beg\u00e4r «sortering av en lista med str\u00e4ngar» visas koden med implementeringen av motsvarande algoritmer).<\/p>\n

Det f\u00f6reslagna datasetset inneh\u00e5ller mer \u00e4n 2 miljoner par «kod-kommentar» som har f\u00f6rberetts baserat p\u00e5 k\u00e4lltexter fr\u00e5n existerande \u00f6ppna bibliotek. Koden omfattar hela k\u00e4lltexten f\u00f6r enskilda funktioner eller metoder, medan kommentaren beskriver vad funktionen g\u00f6r (detaljerad dokumentation ges). F\u00f6r n\u00e4rvarande har datasets f\u00f6rberetts f\u00f6r spr\u00e5ken Python, JavaScript, Ruby, Go, Java och PHP. Exempel p\u00e5 anv\u00e4ndning av de f\u00f6reslagna datasets f\u00f6r att tr\u00e4na olika typer av neurala n\u00e4tverk har tillhandah\u00e5llits. Neural-Bag-Of-Words<\/a><\/noindex>, RNN<\/a><\/noindex>, Self-Attention<\/a><\/noindex> (BERT) och 1D-CNN+Self-Attention Hybrid<\/a><\/noindex>.<\/p>\n

F\u00f6r att utveckla mekanismer f\u00f6r s\u00f6kningar p\u00e5 naturligt spr\u00e5k har ett ytterligare dataset, CodeSearchNet Challenge, f\u00f6rberetts, vilket inkluderar
\n99 typiska<\/a><\/noindex> fr\u00e5gor med cirka 4 000 expertnoteringar som beskriver de mest sannolika kopplingarna till kod i datasetet CodeSearchNet Corpus, som omfattar cirka 6 miljoner metoder och funktioner (datasetstorlek<\/a><\/noindex> \u00e4r cirka 20 GB). CodeSearchNet Challenge kan fungera som en referens f\u00f6r att utv\u00e4rdera effektiviteten av olika metoder f\u00f6r kods\u00f6kning p\u00e5 naturligt spr\u00e5k. Med hj\u00e4lp av verktygen Kubeflow<\/a><\/noindex> f\u00f6rberedd
\nexempel<\/a><\/noindex> en motor f\u00f6r kods\u00f6kning.<\/p>\n

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

K\u00e4lla: opennet.ru<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

GitHub \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b \u043f\u0440\u043e\u0435\u043a\u0442 CodeSearchNet, \u0432 \u0440\u0430\u043c\u043a\u0430\u0445 \u043a\u043e\u0442\u043e\u0440\u043e\u0433\u043e \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u044b \u043c\u043e\u0434\u0435\u043b\u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0438 \u043d\u0430\u0431\u043e\u0440\u044b \u0434\u0430\u043d\u043d\u044b\u0445, \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0434\u043b\u044f \u0440\u0430\u0437\u0431\u043e\u0440\u0430, \u043a\u043b\u0430\u0441\u0441\u0438\u0444\u0438\u043a\u0430\u0446\u0438\u0438 \u0438 \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u043a\u043e\u0434\u0430 \u043d\u0430 \u0440\u0430\u0437\u043b\u0438\u0447\u043d\u044b\u0445 \u044f\u0437\u044b\u043a\u0430\u0445 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f. CodeSearchNet, \u043f\u043e \u0430\u043d\u0430\u043b\u043e\u0433\u0438\u0438 \u0441 ImageNet, \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 \u0431\u043e\u043b\u044c\u0448\u0443\u044e \u043a\u043e\u043b\u043b\u0435\u043a\u0446\u0438\u044e \u043e\u0442\u0440\u044b\u0432\u043a\u043e\u0432 \u043a\u043e\u0434\u0430, \u0441\u043d\u0430\u0431\u0436\u0451\u043d\u043d\u044b\u0445 \u0430\u043d\u043d\u043e\u0442\u0430\u0446\u0438\u044f\u043c\u0438, \u0444\u043e\u0440\u043c\u0430\u043b\u0438\u0437\u0443\u044e\u0449\u0438\u043c\u0438 \u0432\u044b\u043f\u043e\u043b\u043d\u044f\u0435\u043c\u044b\u0435 \u043a\u043e\u0434\u043e\u043c \u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f. \u041a\u043e\u043c\u043f\u043e\u043d\u0435\u043d\u0442\u044b \u0434\u043b\u044f \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u0438 \u043f\u0440\u0438\u043c\u0435\u0440\u044b \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f CodeSearchNet \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u044b \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \u0441 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u0435\u043c […]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-38448","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\n","aioseo_head_json":{"title":"\ud83e\udd47GitHub har \u00f6ppnat sin forskning kring till\u00e4mpningen av maskininl\u00e4rning f\u00f6r s\u00f6kning och analys av kod | ProHoster","description":"GitHub presenterade 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