Quiz Part II
Questions
This quiz is designed to test your understanding of the concepts and models covered in Part II of the book. Answer the following questions based on the material from Chapters 4 and 5.
Multiple Choice Questions
1. What is the primary advantage of the Transformer architecture over RNNs?
a) It eliminates the vanishing gradient problem.
b) It uses fewer parameters than RNNs.
c) It processes sequences in parallel.
d) It is limited to short sequence tasks.
2. What is the purpose of positional encoding in Transformers?
a) To normalize input data.
b) To encode the order of tokens in a sequence.
c) To increase the Transformer’s training speed.
d) To reduce the number of parameters in the model.
3. Which of the following models is based on the autoregressive Transformer architecture?
a) BERT
b) GPT
c) RoBERTa
d) DistilBERT
4. How does CLIP align text and images?
a) By training separate models for text and image tasks.
b) By generating images from text descriptions.
c) By maximizing similarity between paired image-text embeddings.
d) By pre-training exclusively on textual data.
5. What distinguishes BioBERT from the original BERT model?
a) It is pre-trained on biomedical corpora like PubMed.
b) It uses a decoder-only architecture.
c) It is designed for multimodal tasks.
d) It relies on a unidirectional context.
True/False Questions
6. The decoder-only architecture of GPT focuses on bidirectional context.
True / False
7. DistilBERT reduces the size and speed of BERT by using knowledge distillation.
True / False
8. DALL-E generates images based on textual descriptions.
True / False
Short Answer Questions
9. Explain the main difference between BERT and GPT in terms of their architecture and context processing.
10. Describe a use case where BioBERT would outperform general-purpose models like BERT.
Code-Based Question
11. Write a Python function to classify text using a pre-trained BERT or its variant. Use the Hugging Face Transformers library.
Questions
This quiz is designed to test your understanding of the concepts and models covered in Part II of the book. Answer the following questions based on the material from Chapters 4 and 5.
Multiple Choice Questions
1. What is the primary advantage of the Transformer architecture over RNNs?
a) It eliminates the vanishing gradient problem.
b) It uses fewer parameters than RNNs.
c) It processes sequences in parallel.
d) It is limited to short sequence tasks.
2. What is the purpose of positional encoding in Transformers?
a) To normalize input data.
b) To encode the order of tokens in a sequence.
c) To increase the Transformer’s training speed.
d) To reduce the number of parameters in the model.
3. Which of the following models is based on the autoregressive Transformer architecture?
a) BERT
b) GPT
c) RoBERTa
d) DistilBERT
4. How does CLIP align text and images?
a) By training separate models for text and image tasks.
b) By generating images from text descriptions.
c) By maximizing similarity between paired image-text embeddings.
d) By pre-training exclusively on textual data.
5. What distinguishes BioBERT from the original BERT model?
a) It is pre-trained on biomedical corpora like PubMed.
b) It uses a decoder-only architecture.
c) It is designed for multimodal tasks.
d) It relies on a unidirectional context.
True/False Questions
6. The decoder-only architecture of GPT focuses on bidirectional context.
True / False
7. DistilBERT reduces the size and speed of BERT by using knowledge distillation.
True / False
8. DALL-E generates images based on textual descriptions.
True / False
Short Answer Questions
9. Explain the main difference between BERT and GPT in terms of their architecture and context processing.
10. Describe a use case where BioBERT would outperform general-purpose models like BERT.
Code-Based Question
11. Write a Python function to classify text using a pre-trained BERT or its variant. Use the Hugging Face Transformers library.
Questions
This quiz is designed to test your understanding of the concepts and models covered in Part II of the book. Answer the following questions based on the material from Chapters 4 and 5.
Multiple Choice Questions
1. What is the primary advantage of the Transformer architecture over RNNs?
a) It eliminates the vanishing gradient problem.
b) It uses fewer parameters than RNNs.
c) It processes sequences in parallel.
d) It is limited to short sequence tasks.
2. What is the purpose of positional encoding in Transformers?
a) To normalize input data.
b) To encode the order of tokens in a sequence.
c) To increase the Transformer’s training speed.
d) To reduce the number of parameters in the model.
3. Which of the following models is based on the autoregressive Transformer architecture?
a) BERT
b) GPT
c) RoBERTa
d) DistilBERT
4. How does CLIP align text and images?
a) By training separate models for text and image tasks.
b) By generating images from text descriptions.
c) By maximizing similarity between paired image-text embeddings.
d) By pre-training exclusively on textual data.
5. What distinguishes BioBERT from the original BERT model?
a) It is pre-trained on biomedical corpora like PubMed.
b) It uses a decoder-only architecture.
c) It is designed for multimodal tasks.
d) It relies on a unidirectional context.
True/False Questions
6. The decoder-only architecture of GPT focuses on bidirectional context.
True / False
7. DistilBERT reduces the size and speed of BERT by using knowledge distillation.
True / False
8. DALL-E generates images based on textual descriptions.
True / False
Short Answer Questions
9. Explain the main difference between BERT and GPT in terms of their architecture and context processing.
10. Describe a use case where BioBERT would outperform general-purpose models like BERT.
Code-Based Question
11. Write a Python function to classify text using a pre-trained BERT or its variant. Use the Hugging Face Transformers library.
Questions
This quiz is designed to test your understanding of the concepts and models covered in Part II of the book. Answer the following questions based on the material from Chapters 4 and 5.
Multiple Choice Questions
1. What is the primary advantage of the Transformer architecture over RNNs?
a) It eliminates the vanishing gradient problem.
b) It uses fewer parameters than RNNs.
c) It processes sequences in parallel.
d) It is limited to short sequence tasks.
2. What is the purpose of positional encoding in Transformers?
a) To normalize input data.
b) To encode the order of tokens in a sequence.
c) To increase the Transformer’s training speed.
d) To reduce the number of parameters in the model.
3. Which of the following models is based on the autoregressive Transformer architecture?
a) BERT
b) GPT
c) RoBERTa
d) DistilBERT
4. How does CLIP align text and images?
a) By training separate models for text and image tasks.
b) By generating images from text descriptions.
c) By maximizing similarity between paired image-text embeddings.
d) By pre-training exclusively on textual data.
5. What distinguishes BioBERT from the original BERT model?
a) It is pre-trained on biomedical corpora like PubMed.
b) It uses a decoder-only architecture.
c) It is designed for multimodal tasks.
d) It relies on a unidirectional context.
True/False Questions
6. The decoder-only architecture of GPT focuses on bidirectional context.
True / False
7. DistilBERT reduces the size and speed of BERT by using knowledge distillation.
True / False
8. DALL-E generates images based on textual descriptions.
True / False
Short Answer Questions
9. Explain the main difference between BERT and GPT in terms of their architecture and context processing.
10. Describe a use case where BioBERT would outperform general-purpose models like BERT.
Code-Based Question
11. Write a Python function to classify text using a pre-trained BERT or its variant. Use the Hugging Face Transformers library.