Quiz Part III
Answers Key
Multiple Choice:
- (c)
- (d)
- (d)
- (b)
- (c)
True/False:
6. False
7. True
8. True
9. True
10. False
Fill-in-the-Blank:
11. token IDs
12. bidirectional
13. softmax
14. predefined categories
15. task-specific
Short Answer:
16. Text tokenization, truncation, padding, converting text into token IDs.
17. F1-score balances precision and recall, making it better suited for imbalanced datasets.
18. Sentiment analysis identifies sentiment polarity; news categorization assigns topics to articles.
19. Loading pre-trained BERT, preprocessing data, fine-tuning, and evaluation.
20. Monitoring customer satisfaction, analyzing social media sentiment.
This quiz is designed to reinforce your understanding of the material covered in Part III. You’ll gain clarity on key concepts and practical applications by answering these questions. Keep practicing and applying these ideas in real-world scenarios to solidify your knowledge!
Answers Key
Multiple Choice:
- (c)
- (d)
- (d)
- (b)
- (c)
True/False:
6. False
7. True
8. True
9. True
10. False
Fill-in-the-Blank:
11. token IDs
12. bidirectional
13. softmax
14. predefined categories
15. task-specific
Short Answer:
16. Text tokenization, truncation, padding, converting text into token IDs.
17. F1-score balances precision and recall, making it better suited for imbalanced datasets.
18. Sentiment analysis identifies sentiment polarity; news categorization assigns topics to articles.
19. Loading pre-trained BERT, preprocessing data, fine-tuning, and evaluation.
20. Monitoring customer satisfaction, analyzing social media sentiment.
This quiz is designed to reinforce your understanding of the material covered in Part III. You’ll gain clarity on key concepts and practical applications by answering these questions. Keep practicing and applying these ideas in real-world scenarios to solidify your knowledge!
Answers Key
Multiple Choice:
- (c)
- (d)
- (d)
- (b)
- (c)
True/False:
6. False
7. True
8. True
9. True
10. False
Fill-in-the-Blank:
11. token IDs
12. bidirectional
13. softmax
14. predefined categories
15. task-specific
Short Answer:
16. Text tokenization, truncation, padding, converting text into token IDs.
17. F1-score balances precision and recall, making it better suited for imbalanced datasets.
18. Sentiment analysis identifies sentiment polarity; news categorization assigns topics to articles.
19. Loading pre-trained BERT, preprocessing data, fine-tuning, and evaluation.
20. Monitoring customer satisfaction, analyzing social media sentiment.
This quiz is designed to reinforce your understanding of the material covered in Part III. You’ll gain clarity on key concepts and practical applications by answering these questions. Keep practicing and applying these ideas in real-world scenarios to solidify your knowledge!
Answers Key
Multiple Choice:
- (c)
- (d)
- (d)
- (b)
- (c)
True/False:
6. False
7. True
8. True
9. True
10. False
Fill-in-the-Blank:
11. token IDs
12. bidirectional
13. softmax
14. predefined categories
15. task-specific
Short Answer:
16. Text tokenization, truncation, padding, converting text into token IDs.
17. F1-score balances precision and recall, making it better suited for imbalanced datasets.
18. Sentiment analysis identifies sentiment polarity; news categorization assigns topics to articles.
19. Loading pre-trained BERT, preprocessing data, fine-tuning, and evaluation.
20. Monitoring customer satisfaction, analyzing social media sentiment.
This quiz is designed to reinforce your understanding of the material covered in Part III. You’ll gain clarity on key concepts and practical applications by answering these questions. Keep practicing and applying these ideas in real-world scenarios to solidify your knowledge!