subtitles/en/tasks_05_🤗-tasks-translation.srt (76 lines of code) (raw):

1 00:00:04,569 --> 00:00:07,529 Welcome to the Hugging Face tasks series. 2 00:00:07,529 --> 00:00:11,840 In this video, we will take a look at the Translation task. 3 00:00:11,840 --> 00:00:19,420 Translation is the task of translating text from one language to another. 4 00:00:19,420 --> 00:00:24,420 These models take a text in the source language and output the translation of that text in 5 00:00:24,420 --> 00:00:28,609 the target language. 6 00:00:28,609 --> 00:00:31,619 The task is evaluated on the BLEU score. 7 00:00:31,619 --> 00:00:38,430 The score ranges from 0 to 1, in which 1 means the translation perfectly matched and 0 did 8 00:00:38,430 --> 00:00:40,110 not match at all. 9 00:00:40,110 --> 00:00:45,320 BLEU is calculated over subsequent tokens called n-grams. 10 00:00:45,320 --> 00:00:51,629 Unigram refers to a single token while bi-gram refers to token pairs and n-grams refer to 11 00:00:51,629 --> 00:00:56,219 n subsequent tokens. 12 00:00:56,219 --> 00:01:01,859 Machine translation datasets contain pairs of text in a language and translation of the 13 00:01:01,859 --> 00:01:05,910 text in another language. 14 00:01:05,910 --> 00:01:11,290 These models can help you build conversational agents across different languages. 15 00:01:11,290 --> 00:01:16,110 One option is to translate the training data used for the chatbot and train a separate 16 00:01:16,110 --> 00:01:19,970 chatbot. 17 00:01:19,970 --> 00:01:24,950 You can put one translation model from your user’s language to the language your chatbot 18 00:01:24,950 --> 00:01:31,360 is trained on, translate the user inputs and do intent classification, take the output 19 00:01:31,360 --> 00:01:39,399 of the chatbot and translate it from the language your chatbot was trained on to the user’s 20 00:01:39,399 --> 00:01:40,850 language. 21 00:01:40,850 --> 00:01:49,720 For more information about the Translation task, check out the Hugging Face course.