{"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\/pl\/blog\/news\/vypusk-sistemy-mashinnogo-perevoda-opennmt-2-28-0","title":{"rendered":"Wydanie systemu t\u0142umaczenia maszynowego OpenNMT 2.28.0","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Opublikowano wersj\u0119 systemu t\u0142umaczenia maszynowego OpenNMT 0.28.0 (Open Neural Machine Translation), wykorzystuj\u0105cego metody uczenia maszynowego. Do budowy sieci neuronowej projekt wykorzystuje mo\u017cliwo\u015bci biblioteki g\u0142\u0119bokiego uczenia maszynowego TensorFlow. Kod rozwijanych modu\u0142\u00f3w projektu OpenNMT napisany jest w j\u0119zyku Python i rozpowszechniany na licencji MIT. Gotowe modele przygotowane s\u0105 dla j\u0119zyka angielskiego, niemieckiego i katalo\u0144skiego, dla pozosta\u0142ych j\u0119zyk\u00f3w mo\u017cna samodzielnie stworzy\u0107 model na podstawie zbioru danych z projektu OPUS (do uczenia systemu przekazywane s\u0105 dwa pliki \u2014 jeden z zdaniami w j\u0119zyku \u017ar\u00f3d\u0142owym, a drugi z jako\u015bciowym t\u0142umaczeniem tych zda\u0144 na j\u0119zyk docelowy). <\/p>\n

Projekt rozwija si\u0119 przy udziale firmy SYSTRAN, specjalizuj\u0105cej si\u0119 w tworzeniu narz\u0119dzi do t\u0142umaczenia maszynowego, oraz grupy badaczy z Harvardu, opracowuj\u0105cych modele ludzkiego j\u0119zyka dla system\u00f3w uczenia maszynowego. Interfejs u\u017cytkownika zosta\u0142 maksymalnie uproszczony i wymaga jedynie podania pliku wej\u015bciowego z tekstem oraz pliku do zapisania wyniku t\u0142umaczenia. System rozszerze\u0144 umo\u017cliwia wdra\u017canie dodatkowej funkcjonalno\u015bci na bazie OpenNMT, takiej jak autorefereowanie, klasyfikacja tekst\u00f3w i generowanie napis\u00f3w. <\/p>\n

Zastosowanie TensorFlow pozwala na wykorzystanie mo\u017cliwo\u015bci GPU (do przyspieszenia procesu uczenia sieci neuronowej). W celu uproszczenia dystrybucji produktu, projektem rozwijany jest r\u00f3wnie\u017c samowystarczalny wariant t\u0142umacza w j\u0119zyku C++ \u2014 CTranslate2, kt\u00f3ry wykorzystuje wst\u0119pnie wytrenowane modele bez zwi\u0105zania z dodatkowymi zale\u017cno\u015bciami. <\/p>\n

W nowej wersji dodano parametr initial_learning_rate i wdro\u017cono kilka nowych argument\u00f3w (mha_bias i output_layer_bias) do konfiguracji generatora modeli Transformer. Poza tym wprowadzono poprawki b\u0142\u0119d\u00f3w.
\n
\u0179r\u00f3d\u0142o: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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