{"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":"Rilascio 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 tecniche 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 linguaggio Python ed \u00e8 distribuito sotto licenza MIT. I modelli pronti sono disponibili per le lingue inglese, tedesca e catalana; per le altre lingue, \u00e8 possibile creare autonomamente un modello basato su un insieme di dati fornito dal progetto OPUS (per addestrare il sistema vengono forniti 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 \u00e8 sviluppato con la partecipazione della societ\u00e0 SYSTRAN, specializzata nella creazione di strumenti di traduzione automatica, e da un gruppo di ricercatori di Harvard che sviluppa modelli linguistici per sistemi di apprendimento automatico. L'interfaccia utente \u00e8 stata semplificata al massimo e richiede solo di specificare il file di input con il testo e il file per salvare il risultato della traduzione. Il sistema di estensioni consente di implementare funzionalit\u00e0 aggiuntive basate su OpenNMT, come l'autoriferimento, 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 addestramento della rete neurale). Per semplificare la distribuzione del prodotto, il progetto sviluppa anche una versione autonoma del traduttore in linguaggio C++ \u2014 CTranslate2, che utilizza modelli precedentemente addestrati senza legami a dipendenze aggiuntive. <\/p>\n

Nella nuova versione \u00e8 stato aggiunto il parametro initial_learning_rate ed \u00e8 stata implementata una serie di nuovi argomenti (mha_bias e output_layer_bias) per la configurazione del generatore di modelli Transformer. Inoltre, sono stati corretti alcuni errori.
\n
Fonte: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

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