Code icon

The App is Under a Quick Maintenance

We apologize for the inconvenience. Please come back later

Cuantum Technologies logo
Dashboard
Profile
Billing
Full Access
Log out
Menu iconMenu iconNLP with Transformers: Fundamentals and Core Applications
NLP with Transformers: Fundamentals and Core Applications
Arrow Icon
Quiz Part II
Questions
Answers
Buy this book
You've learned this already. ✅
Click here to view the next lesson.

Quiz Part II

Answers

Multiple Choice Questions

  1. c) It processes sequences in parallel.
  2. b) To encode the order of tokens in a sequence.
  3. b) GPT
  4. c) By maximizing similarity between paired image-text embeddings.
  5. a) It is pre-trained on biomedical corpora like PubMed.

True/False Questions

  1. False - GPT uses a unidirectional (autoregressive) context, not bidirectional.
  2. True - DistilBERT uses knowledge distillation to achieve a smaller and faster model.
  3. True - DALL-E generates images based on textual prompts.

Short Answer Questions

9. BERT processes context bidirectionally, capturing relationships between preceding and succeeding tokens. In contrast, GPT processes text unidirectionally (left-to-right), focusing on generating the next token in a sequence.

10. BioBERT would excel in a task like extracting chemical-disease relationships from biomedical research articles, as it is pre-trained on domain-specific texts that include terminology and structure not present in general-purpose datasets.

Code-Based Question

Solution:

from transformers import pipeline

def classify_text(model_name, text, labels):
    """
    Classify text using a pre-trained BERT or its variant.
    model_name: Hugging Face model name (e.g., 'bert-base-uncased').
    text: Text to classify.
    labels: List of labels to map predictions.
    """
    classifier = pipeline("text-classification", model=model_name)
    result = classifier(text)
    label_id = int(result[0]['label'].split('_')[-1])  # Extract label index
    return labels[label_id]

# Example usage
model_name = "bert-base-uncased"
text = "The patient shows symptoms of severe dehydration."
labels = ["Healthy", "Dehydrated"]
predicted_label = classify_text(model_name, text, labels)
print("Predicted Label:", predicted_label)

Expected Output:

Predicted Label: Dehydrated

Congratulations!

Completing this quiz demonstrates your understanding of the Transformer architecture and its key models. You’ve covered foundational concepts, applications of multimodal models, and specialized adaptations like BioBERT and LegalBERT.

Answers

Multiple Choice Questions

  1. c) It processes sequences in parallel.
  2. b) To encode the order of tokens in a sequence.
  3. b) GPT
  4. c) By maximizing similarity between paired image-text embeddings.
  5. a) It is pre-trained on biomedical corpora like PubMed.

True/False Questions

  1. False - GPT uses a unidirectional (autoregressive) context, not bidirectional.
  2. True - DistilBERT uses knowledge distillation to achieve a smaller and faster model.
  3. True - DALL-E generates images based on textual prompts.

Short Answer Questions

9. BERT processes context bidirectionally, capturing relationships between preceding and succeeding tokens. In contrast, GPT processes text unidirectionally (left-to-right), focusing on generating the next token in a sequence.

10. BioBERT would excel in a task like extracting chemical-disease relationships from biomedical research articles, as it is pre-trained on domain-specific texts that include terminology and structure not present in general-purpose datasets.

Code-Based Question

Solution:

from transformers import pipeline

def classify_text(model_name, text, labels):
    """
    Classify text using a pre-trained BERT or its variant.
    model_name: Hugging Face model name (e.g., 'bert-base-uncased').
    text: Text to classify.
    labels: List of labels to map predictions.
    """
    classifier = pipeline("text-classification", model=model_name)
    result = classifier(text)
    label_id = int(result[0]['label'].split('_')[-1])  # Extract label index
    return labels[label_id]

# Example usage
model_name = "bert-base-uncased"
text = "The patient shows symptoms of severe dehydration."
labels = ["Healthy", "Dehydrated"]
predicted_label = classify_text(model_name, text, labels)
print("Predicted Label:", predicted_label)

Expected Output:

Predicted Label: Dehydrated

Congratulations!

Completing this quiz demonstrates your understanding of the Transformer architecture and its key models. You’ve covered foundational concepts, applications of multimodal models, and specialized adaptations like BioBERT and LegalBERT.

Answers

Multiple Choice Questions

  1. c) It processes sequences in parallel.
  2. b) To encode the order of tokens in a sequence.
  3. b) GPT
  4. c) By maximizing similarity between paired image-text embeddings.
  5. a) It is pre-trained on biomedical corpora like PubMed.

True/False Questions

  1. False - GPT uses a unidirectional (autoregressive) context, not bidirectional.
  2. True - DistilBERT uses knowledge distillation to achieve a smaller and faster model.
  3. True - DALL-E generates images based on textual prompts.

Short Answer Questions

9. BERT processes context bidirectionally, capturing relationships between preceding and succeeding tokens. In contrast, GPT processes text unidirectionally (left-to-right), focusing on generating the next token in a sequence.

10. BioBERT would excel in a task like extracting chemical-disease relationships from biomedical research articles, as it is pre-trained on domain-specific texts that include terminology and structure not present in general-purpose datasets.

Code-Based Question

Solution:

from transformers import pipeline

def classify_text(model_name, text, labels):
    """
    Classify text using a pre-trained BERT or its variant.
    model_name: Hugging Face model name (e.g., 'bert-base-uncased').
    text: Text to classify.
    labels: List of labels to map predictions.
    """
    classifier = pipeline("text-classification", model=model_name)
    result = classifier(text)
    label_id = int(result[0]['label'].split('_')[-1])  # Extract label index
    return labels[label_id]

# Example usage
model_name = "bert-base-uncased"
text = "The patient shows symptoms of severe dehydration."
labels = ["Healthy", "Dehydrated"]
predicted_label = classify_text(model_name, text, labels)
print("Predicted Label:", predicted_label)

Expected Output:

Predicted Label: Dehydrated

Congratulations!

Completing this quiz demonstrates your understanding of the Transformer architecture and its key models. You’ve covered foundational concepts, applications of multimodal models, and specialized adaptations like BioBERT and LegalBERT.

Answers

Multiple Choice Questions

  1. c) It processes sequences in parallel.
  2. b) To encode the order of tokens in a sequence.
  3. b) GPT
  4. c) By maximizing similarity between paired image-text embeddings.
  5. a) It is pre-trained on biomedical corpora like PubMed.

True/False Questions

  1. False - GPT uses a unidirectional (autoregressive) context, not bidirectional.
  2. True - DistilBERT uses knowledge distillation to achieve a smaller and faster model.
  3. True - DALL-E generates images based on textual prompts.

Short Answer Questions

9. BERT processes context bidirectionally, capturing relationships between preceding and succeeding tokens. In contrast, GPT processes text unidirectionally (left-to-right), focusing on generating the next token in a sequence.

10. BioBERT would excel in a task like extracting chemical-disease relationships from biomedical research articles, as it is pre-trained on domain-specific texts that include terminology and structure not present in general-purpose datasets.

Code-Based Question

Solution:

from transformers import pipeline

def classify_text(model_name, text, labels):
    """
    Classify text using a pre-trained BERT or its variant.
    model_name: Hugging Face model name (e.g., 'bert-base-uncased').
    text: Text to classify.
    labels: List of labels to map predictions.
    """
    classifier = pipeline("text-classification", model=model_name)
    result = classifier(text)
    label_id = int(result[0]['label'].split('_')[-1])  # Extract label index
    return labels[label_id]

# Example usage
model_name = "bert-base-uncased"
text = "The patient shows symptoms of severe dehydration."
labels = ["Healthy", "Dehydrated"]
predicted_label = classify_text(model_name, text, labels)
print("Predicted Label:", predicted_label)

Expected Output:

Predicted Label: Dehydrated

Congratulations!

Completing this quiz demonstrates your understanding of the Transformer architecture and its key models. You’ve covered foundational concepts, applications of multimodal models, and specialized adaptations like BioBERT and LegalBERT.

Purchase this book