{"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\/it\/blog\/news\/vypusk-sistemy-mashinnogo-perevoda-opennmt-2-28-0","title":{"rendered":"Uscita del sistema di traduzione automatica OpenNMT 2.28.0","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

\u00c8 stata pubblicata la versione 0.28.0 del sistema di traduzione automatica OpenNMT (Open Neural Machine Translation), che utilizza metodi di apprendimento automatico. Per costruire la rete neurale, il progetto sfrutta le capacit\u00e0 della libreria di deep learning TensorFlow. Il codice dei moduli sviluppati dal progetto OpenNMT \u00e8 scritto in Python ed \u00e8 distribuito con licenza MIT. I modelli pronti sono disponibili per le lingue inglese, tedesca e catalana; per le altre lingue, \u00e8 possibile creare un modello autonomamente basato sul set di dati del progetto OPUS (la formazione del sistema avviene tramite due file \u2014 uno con le frasi nella lingua sorgente e l'altro con la traduzione di qualit\u00e0 di queste frasi nella lingua target). <\/p>\n

Il progetto si sviluppa con la partecipazione della societ\u00e0 SYSTRAN, specializzata nella creazione di strumenti di traduzione automatica, e di un gruppo di ricercatori di Harvard che sviluppano modelli linguistici per i sistemi di apprendimento automatico. L'interfaccia utente \u00e8 massimamente semplificata e richiede solo l'indicazione del file di input con il testo e del file per salvare il risultato della traduzione. Il sistema di estensioni consente di implementare funzionalit\u00e0 aggiuntive basate su OpenNMT, come l'autoreferenziazione, la classificazione dei testi e la generazione di sottotitoli. <\/p>\n

L'uso di TensorFlow consente di sfruttare le capacit\u00e0 della GPU (per accelerare il processo di formazione della rete neurale). Per semplificare la distribuzione del prodotto, il progetto sta sviluppando anche una versione autonoma del traduttore in C++ \u2014 CTranslate2, che utilizza modelli pre-addestrati senza dipendenze aggiuntive. <\/p>\n

Nella nuova versione \u00e8 stato aggiunto il parametro initial_learning_rate e sono stati implementati diversi nuovi argomenti (mha_bias e output_layer_bias) per configurare il generatore di modelli Transformer. Inoltre, sono stati apportati dei correttivi bug.
\n
Fonte: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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