{"id":104737,"date":"2022-07-30T09:37:00","date_gmt":"2022-07-30T07:37:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/vypusk-sistemy-mashinnogo-perevoda-opennmt-2-28-0"},"modified":"2022-07-30T09:37:00","modified_gmt":"2022-07-30T07:37:00","slug":"vypusk-sistemy-mashinnogo-perevoda-opennmt-2-28-0","status":"publish","type":"post","link":"https:\/\/prohoster.info\/cs\/blog\/news\/vypusk-sistemy-mashinnogo-perevoda-opennmt-2-28-0","title":{"rendered":"Bylo vyd\u00e1no OpenNMT 2.28.0, syst\u00e9m strojov\u00e9ho p\u0159ekladu.","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Byla zve\u0159ejn\u011bna verze syst\u00e9mu strojov\u00e9ho p\u0159ekladu OpenNMT 0.28.0 (Open Neural Machine Translation), kter\u00e1 vyu\u017e\u00edv\u00e1 metody strojov\u00e9ho u\u010den\u00ed. Pro vybudov\u00e1n\u00ed neuronov\u00e9 s\u00edt\u011b projekt vyu\u017e\u00edv\u00e1 mo\u017enost\u00ed knihovny pro hlubok\u00e9 u\u010den\u00ed TensorFlow. K\u00f3d modul\u016f vyv\u00edjen\u00fdch projektem OpenNMT je naps\u00e1n v jazyce Python a je distribuov\u00e1n pod licenc\u00ed MIT. Hotov\u00e9 modely jsou p\u0159ipraveny pro angli\u010dtinu, n\u011bm\u010dinu a katal\u00e1n\u0161tinu, pro ostatn\u00ed jazyky si m\u016f\u017eete sami vytvo\u0159it model na z\u00e1klad\u011b datov\u00e9ho souboru z projektu OPUS (pro u\u010den\u00ed syst\u00e9mu se p\u0159ed\u00e1v\u00e1 dva soubory \u2014 jeden se v\u011btami v p\u016fvodn\u00edm jazyce a druh\u00fd s kvalitn\u00edm p\u0159ekladem t\u011bchto v\u011bt do c\u00edlov\u00e9ho jazyka). <\/p>\n

Projekt se rozv\u00edj\u00ed za \u00fa\u010dasti spole\u010dnosti SYSTRAN, specializuj\u00edc\u00ed se na vytv\u00e1\u0159en\u00ed n\u00e1stroj\u016f pro strojov\u00fd p\u0159eklad, a skupiny v\u00fdzkumn\u00edk\u016f z Harvardu, kte\u0159\u00ed vyv\u00edjej\u00ed modely lidsk\u00e9ho jazyka pro syst\u00e9my strojov\u00e9ho u\u010den\u00ed. U\u017eivatelsk\u00e9 rozhran\u00ed je maxim\u00e1ln\u011b zjednodu\u0161en\u00e9 a vy\u017eaduje pouze zad\u00e1n\u00ed vstupn\u00edho souboru s textem a souboru pro ulo\u017een\u00ed v\u00fdsledku p\u0159ekladu. Syst\u00e9m roz\u0161\u00ed\u0159en\u00ed umo\u017e\u0148uje realizovat na z\u00e1klad\u011b OpenNMT dodate\u010dn\u00e9 funkce, jako je auto-refer\u00e1t, klasifikaci text\u016f a generov\u00e1n\u00ed titulk\u016f. <\/p>\n

Pou\u017eit\u00ed TensorFlow umo\u017e\u0148uje vyu\u017eit\u00ed mo\u017enost\u00ed GPU (pro urychlen\u00ed procesu u\u010den\u00ed neuronov\u00e9 s\u00edt\u011b). Pro zjednodu\u0161en\u00ed distribuce produktu projekt rovn\u011b\u017e vyv\u00edj\u00ed samostatnou variantu p\u0159eklada\u010de v jazyce C++ \u2014 CTranslate2, kter\u00fd pou\u017e\u00edv\u00e1 p\u0159edem vy\u0161kolen\u00e9 modely bez vazby na dal\u0161\u00ed z\u00e1vislosti. <\/p>\n

V nov\u00e9 verzi byl p\u0159id\u00e1n parametr initial_learning_rate a implementov\u00e1no n\u011bkolik nov\u00fdch argument\u016f (mha_bias a output_layer_bias) pro nastaven\u00ed gener\u00e1toru model\u016f Transformer. Jinak jsou uvedena opravy chyb.
\n
Zdroj: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\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 0.28.0 (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. \u0414\u043b\u044f \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u0438\u044f \u043d\u0435\u0439\u0440\u043e\u043d\u043d\u043e\u0439 \u0441\u0435\u0442\u0438 \u043f\u0440\u043e\u0435\u043a\u0442 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e\u0441\u0442\u0438 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 \u0433\u043b\u0443\u0431\u0438\u043d\u043d\u043e\u0433\u043e \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f TensorFlow. \u041a\u043e\u0434 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u043c\u044b\u0445 \u043f\u0440\u043e\u0435\u043a\u0442\u043e\u043c OpenNMT \u043c\u043e\u0434\u0443\u043b\u0435\u0439 \u043d\u0430\u043f\u0438\u0441\u0430\u043d \u043d\u0430 \u044f\u0437\u044b\u043a\u0435 Python \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. \u0413\u043e\u0442\u043e\u0432\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u044b \u0434\u043b\u044f \u0430\u043d\u0433\u043b\u0438\u0439\u0441\u043a\u043e\u0433\u043e, \u043d\u0435\u043c\u0435\u0446\u043a\u043e\u0433\u043e \u0438 \u043a\u0430\u0442\u0430\u043b\u0430\u043d\u0441\u043a\u043e\u0433\u043e \u044f\u0437\u044b\u043a\u043e\u0432, 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