Chapter 2, Fine-Tuning BERT Models

1. BERT stands for Bidirectional Encoder Representations from Transformers. (True/False)

   True.

2. BERT is a two-step framework. Step 1 is pretraining. Step 2 is fine-tuning. (True/False)

   True.

3. Fine-tuning a BERT model implies training parameters from scratch. (True/False)

   False. BERT fine-tuning is initialized with the trained parameters of pretraining.

4. BERT only pretrains using all downstream tasks. (True/False)

   False.

5. BERT pretrains with Masked Language Modeling (MLM). (True/False)

   True.

6. BERT pretrains with Next Sentence Predictions (NSP). (True/False)

   True.

7. BERT pretrains mathematical functions. (True/False)

   False.

8. A question-answer task is a downstream task. (True/False)

   True.

9. A BERT pretraining model does not require tokenization. (True/False)

   False.

10. Fine-tuning a BERT model takes less time than pretraining. (True/False)

    True.

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