Quiz Part II
Multiple-Choice Questions
The following quiz will test your understanding of the tools and techniques for working with transformers. It covers topics from Hugging Face libraries to training, fine-tuning, and deployment strategies. The quiz includes multiple-choice, true/false, and short-answer questions. Answers are provided at the end.
1. Which Hugging Face library is used for efficiently loading and processing datasets for NLP tasks?
a) Tokenizers
b) Transformers
c) Datasets
d) PyTorch
2. What is the primary purpose of LoRA in fine-tuning transformer models?
a) Reducing model size
b) Minimizing the number of trainable parameters
c) Increasing inference speed
d) Optimizing memory usage during training
3. Which of the following metrics is recall-oriented and best suited for evaluating text summarization tasks?
a) BLEU
b) ROUGE
c) BERTScore
d) Perplexity
4. What is the main benefit of converting models to ONNX format?
a) Enables compatibility with multiple frameworks
b) Reduces model accuracy
c) Makes models larger for cloud deployment
d) Ensures models run only on GPU
5. When deploying a transformer model on Hugging Face Spaces, which library is commonly used to create interactive apps?
a) TensorFlow Lite
b) Gradio
c) FastAPI
d) Flask
Multiple-Choice Questions
The following quiz will test your understanding of the tools and techniques for working with transformers. It covers topics from Hugging Face libraries to training, fine-tuning, and deployment strategies. The quiz includes multiple-choice, true/false, and short-answer questions. Answers are provided at the end.
1. Which Hugging Face library is used for efficiently loading and processing datasets for NLP tasks?
a) Tokenizers
b) Transformers
c) Datasets
d) PyTorch
2. What is the primary purpose of LoRA in fine-tuning transformer models?
a) Reducing model size
b) Minimizing the number of trainable parameters
c) Increasing inference speed
d) Optimizing memory usage during training
3. Which of the following metrics is recall-oriented and best suited for evaluating text summarization tasks?
a) BLEU
b) ROUGE
c) BERTScore
d) Perplexity
4. What is the main benefit of converting models to ONNX format?
a) Enables compatibility with multiple frameworks
b) Reduces model accuracy
c) Makes models larger for cloud deployment
d) Ensures models run only on GPU
5. When deploying a transformer model on Hugging Face Spaces, which library is commonly used to create interactive apps?
a) TensorFlow Lite
b) Gradio
c) FastAPI
d) Flask
Multiple-Choice Questions
The following quiz will test your understanding of the tools and techniques for working with transformers. It covers topics from Hugging Face libraries to training, fine-tuning, and deployment strategies. The quiz includes multiple-choice, true/false, and short-answer questions. Answers are provided at the end.
1. Which Hugging Face library is used for efficiently loading and processing datasets for NLP tasks?
a) Tokenizers
b) Transformers
c) Datasets
d) PyTorch
2. What is the primary purpose of LoRA in fine-tuning transformer models?
a) Reducing model size
b) Minimizing the number of trainable parameters
c) Increasing inference speed
d) Optimizing memory usage during training
3. Which of the following metrics is recall-oriented and best suited for evaluating text summarization tasks?
a) BLEU
b) ROUGE
c) BERTScore
d) Perplexity
4. What is the main benefit of converting models to ONNX format?
a) Enables compatibility with multiple frameworks
b) Reduces model accuracy
c) Makes models larger for cloud deployment
d) Ensures models run only on GPU
5. When deploying a transformer model on Hugging Face Spaces, which library is commonly used to create interactive apps?
a) TensorFlow Lite
b) Gradio
c) FastAPI
d) Flask
Multiple-Choice Questions
The following quiz will test your understanding of the tools and techniques for working with transformers. It covers topics from Hugging Face libraries to training, fine-tuning, and deployment strategies. The quiz includes multiple-choice, true/false, and short-answer questions. Answers are provided at the end.
1. Which Hugging Face library is used for efficiently loading and processing datasets for NLP tasks?
a) Tokenizers
b) Transformers
c) Datasets
d) PyTorch
2. What is the primary purpose of LoRA in fine-tuning transformer models?
a) Reducing model size
b) Minimizing the number of trainable parameters
c) Increasing inference speed
d) Optimizing memory usage during training
3. Which of the following metrics is recall-oriented and best suited for evaluating text summarization tasks?
a) BLEU
b) ROUGE
c) BERTScore
d) Perplexity
4. What is the main benefit of converting models to ONNX format?
a) Enables compatibility with multiple frameworks
b) Reduces model accuracy
c) Makes models larger for cloud deployment
d) Ensures models run only on GPU
5. When deploying a transformer model on Hugging Face Spaces, which library is commonly used to create interactive apps?
a) TensorFlow Lite
b) Gradio
c) FastAPI
d) Flask