Quiz Part I
True or False
6. T5 can handle multiple NLP tasks like summarization, translation, and question-answering.
True / False
7. Beam search with a higher num_beams value often produces better results, but it can increase computational costs.
True / False
8. MarianMT is a proprietary model for machine translation that requires licensing fees to use.
True / False
9. Text summarization with T5 requires input text to be prefixed with a task-specific keyword.
True / False
10. T5 can only be used for extractive summarization, not abstractive summarization.
True / False
True or False
6. T5 can handle multiple NLP tasks like summarization, translation, and question-answering.
True / False
7. Beam search with a higher num_beams value often produces better results, but it can increase computational costs.
True / False
8. MarianMT is a proprietary model for machine translation that requires licensing fees to use.
True / False
9. Text summarization with T5 requires input text to be prefixed with a task-specific keyword.
True / False
10. T5 can only be used for extractive summarization, not abstractive summarization.
True / False
True or False
6. T5 can handle multiple NLP tasks like summarization, translation, and question-answering.
True / False
7. Beam search with a higher num_beams value often produces better results, but it can increase computational costs.
True / False
8. MarianMT is a proprietary model for machine translation that requires licensing fees to use.
True / False
9. Text summarization with T5 requires input text to be prefixed with a task-specific keyword.
True / False
10. T5 can only be used for extractive summarization, not abstractive summarization.
True / False
True or False
6. T5 can handle multiple NLP tasks like summarization, translation, and question-answering.
True / False
7. Beam search with a higher num_beams value often produces better results, but it can increase computational costs.
True / False
8. MarianMT is a proprietary model for machine translation that requires licensing fees to use.
True / False
9. Text summarization with T5 requires input text to be prefixed with a task-specific keyword.
True / False
10. T5 can only be used for extractive summarization, not abstractive summarization.
True / False