{"id":106018,"date":"2022-12-13T09:36:49","date_gmt":"2022-12-13T07:36:49","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/vypusk-sistemy-mashinnogo-perevoda-opennmt-tf-2-30"},"modified":"2022-12-13T09:36:49","modified_gmt":"2022-12-13T07:36:49","slug":"vypusk-sistemy-mashinnogo-perevoda-opennmt-tf-2-30","status":"publish","type":"post","link":"https:\/\/prohoster.info\/hr\/blog\/news\/vypusk-sistemy-mashinnogo-perevoda-opennmt-tf-2-30","title":{"rendered":"Izdanje sustava za strojno prevo\u0111enje OpenNMT-tf 2.30","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Objavljeno je izdanje sustava za strojno prevo\u0111enje OpenNMT-tf 2.30.0 (Open Neural Machine Translation), koji koristi metode strojno u\u010denja. Kod modula koji se razvijaju kroz projekt OpenNMT-tf napisan je na jeziku Python, koristi TensorFlow biblioteku i distribuira se pod MIT licencom.     <\/p>\n<p>Paralelno se razvija varijanta OpenNMT temeljena na biblioteci PyTorch, koja se razlikuje po razini podr\u017eanih mogu\u0107nosti. Pored toga, OpenNMT na bazi PyTorch dolazi kao lak\u0161a za kori\u0161tenje i multimodalna, dok se varijanta na bazi TensorFlow isti\u010de kao modularna, stabilna i omogu\u0107ava kori\u0161tenje GPU za ubrzavanje procesa u\u010denja neuronske mre\u017ee. Za pojednostavljenje distribucije proizvoda, projekat tako\u0111er razvija samodostatnu varijantu prevoditelja na C++ jeziku \u2014 CTranslate2, koja koristi prethodno uvje\u017ebane modele bez ovisnosti o dodatnim paketima.         <\/p>\n<p>Modeli su pripremljeni za engleski, njema\u010dki i katalonski jezik, dok se za ostale jezike mo\u017ee samostalno formirati model na osnovu skupa podataka iz projekta OPUS (za obuku sustava potrebna su dva datoteka \u2014 jedna sa re\u010denicama na izvorni jezik, a druga s kvalitetnim prijevodom tih re\u010denica na ciljni jezik).    <\/p>\n<p>Projekt se razvija uz sudjelovanje tvrtke SYSTRAN, specijalizirane za izradu alata za strojno prevo\u0111enje, i grupe istra\u017eiva\u010da s Harvarda koji razvijaju modele ljudskog jezika za sustave strojnog u\u010denja. Korisni\u010dko su\u010delje je maksimalno pojednostavljeno i zahtijeva samo navo\u0111enje ulazne datoteke s tekstom i datoteke za pohranu rezultata prevo\u0111enja. Sustav pro\u0161irenja omogu\u0107uje realizaciju dodatne funkcionalnosti na bazi OpenNMT, poput automatskog referiranja, klasifikacije tekstova i generiranja titlova.     <\/p>\n<p>U novoj verziji:  <\/p>\n<ul>\n<li class=\"l\"> Dodana je podr\u0161ka za TensorFlow biblioteku 2.11, no novi Keras optimizatori jo\u0161 uvijek nisu podr\u017eani (potrebno je koristiti na\u010din tf.keras.optimizers.legacy).\n<li class=\"l\"> Dodana je podr\u0161ka za novu verziju motora CTranslate2 3.x, namijenjenu za u\u010dinkovito izvo\u0111enje modela s arhitekturom \u201etransformer\u201c.\n<li class=\"l\"> Dodana je opcija za obuku modela pad_to_bucket_boundary za uklju\u010divanje dodatnog punjenja koje izravnava veli\u010dinu bloka na vrijednosti vi\u0161ekratne od length_bucket_width.\n<li class=\"l\"> Integrirana je podr\u0161ka za metrikama chrf i chrf++ iz projekta SacreBLEU, koje uspore\u0111uju strojno prevo\u0111enje s referentnim ljudskim prijevodom.\n<li class=\"l\"> Uklonjen je atribut modela ctranslate2_spec, koji se vi\u0161e ne koristi u CTranslate2.  <\/ul>\n<p>Izvor: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=58316\">opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043d \u0432\u044b\u043f\u0443\u0441\u043a \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u0430 OpenNMT-tf 2.30.0 (Open Neural Machine Translation), \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0449\u0435\u0439 \u043c\u0435\u0442\u043e\u0434\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f. \u041a\u043e\u0434 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u043c\u044b\u0445 \u043f\u0440\u043e\u0435\u043a\u0442\u043e\u043c OpenNMT-tf \u043c\u043e\u0434\u0443\u043b\u0435\u0439 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0443 TensorFlow \u0438 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u0435\u0442\u0441\u044f \u043f\u043e\u0434 \u043b\u0438\u0446\u0435\u043d\u0437\u0438\u0435\u0439 MIT. \u041f\u0430\u0440\u0430\u043b\u043b\u0435\u043b\u044c\u043d\u043e \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u0432\u0430\u0440\u0438\u0430\u043d\u0442 OpenNMT \u043d\u0430 \u0431\u0430\u0437\u0435 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 PyTorch, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043e\u0442\u043b\u0438\u0447\u0430\u0435\u0442\u0441\u044f \u043d\u0430 \u0443\u0440\u043e\u0432\u043d\u0435 \u043f\u043e\u0434\u0434\u0435\u0440\u0436\u0438\u0432\u0430\u0435\u043c\u044b\u0445 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u0435\u0439. \u041a\u0440\u043e\u043c\u0435 \u0442\u043e\u0433\u043e, OpenNMT \u043d\u0430 \u0431\u0430\u0437\u0435 PyTorch \u043f\u0440\u0435\u043f\u043e\u0434\u043d\u043e\u0441\u0438\u0442\u0441\u044f \u043a\u0430\u043a \u0431\u043e\u043b\u0435\u0435 [&hellip;]<\/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-106018","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.4 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041e\u043f\u0443\u0431\u043b\u0438\u043a\u043e\u0432\u0430\u043d \u0432\u044b\u043f\u0443\u0441\u043a \u0441\u0438\u0441\u0442\u0435\u043c\u044b 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