QA with Hugging Face pipeline in NLP - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a question answering pipeline using Hugging Face.

NLP

from transformers import pipeline
qa_pipeline = pipeline([1])

Drag options to blanks, or click blank then click option'

A"question-answering"

B"translation"

C"sentiment-analysis"

D"text-generation"

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong pipeline type like "text-generation" or "sentiment-analysis".

The pipeline type for question answering is "question-answering".

2fill in blank

medium

Complete the code to provide the context and question to the QA pipeline.

NLP

result = qa_pipeline({"question": [1], "context": "Hugging Face makes machine learning easy."})

Drag options to blanks, or click blank then click option'

A'Where is the moon?'

B'How to train a model?'

C'What is Python?'

D'What is Hugging Face?'

Attempts:

3 left

💡 Hint

Common Mistakes

Asking questions unrelated to the context, which leads to wrong answers.

The question should relate to the context. "What is Hugging Face?" fits the context about Hugging Face.

3fill in blank

hard

Fix the error in accessing the answer from the result dictionary.

NLP

print(result[[1]])

Drag options to blanks, or click blank then click option'

A"text"

B"answer"

C"output"

D"result"

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect keys like "result" or "text" which do not exist in the output.

The QA pipeline returns a dictionary with the key "answer" containing the predicted answer.

4fill in blank

hard

Fill both blanks to create a QA pipeline with a specific model and tokenizer.

NLP

qa_pipeline = pipeline("question-answering", model=[1], tokenizer=[2])

Drag options to blanks, or click blank then click option'

A"distilbert-base-uncased-distilled-squad"

B"bert-base-uncased"

C"gpt2"

D"roberta-base"

Attempts:

3 left

💡 Hint

Common Mistakes

Using different names for model and tokenizer causing errors.

Choosing models not trained for QA like "gpt2".

The model and tokenizer names must match. "distilbert-base-uncased-distilled-squad" is a common QA model and tokenizer.

5fill in blank

hard

Fill all three blanks to extract the answer, score, and start position from the QA result.

NLP

answer = result[[1]]
score = result[[2]]
start = result[[3]]

Drag options to blanks, or click blank then click option'

A"answer"

B"score"

C"start"

D"end"

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong keys like "end" instead of "start" for the start position.

The result dictionary keys are "answer" for the text, "score" for confidence, and "start" for the answer start index.

Practice

1. What does the Hugging Face QA pipeline do when given a question and a context?

easy

A. It translates the question into another language.

B. It summarizes the context without answering the question.

C. It finds the answer to the question from the given context.

D. It generates a new question based on the context.

Solution

1. Step 1: Understand the QA pipeline purpose

   The QA pipeline is designed to find answers from a given text based on a question.

2. Step 2: Match function to options

   Only It finds the answer to the question from the given context. describes finding an answer from the context, which is the pipeline's main job.

3. Final Answer:

   It finds the answer to the question from the given context. -> Option C

4. Quick Check:

   QA pipeline = find answer from context [OK]

Hint: QA pipeline = question + context -> answer [OK]

Common Mistakes:

• Confusing QA with translation or summarization
• Thinking it generates new questions
• Assuming it works without context

2. Which of the following is the correct way to create a QA pipeline using Hugging Face Transformers in Python?

easy

A. import pipeline from transformers
qa = pipeline('qa')

B. from transformers import QA
qa = QA('pipeline')

C. from transformers import question_answering
qa = question_answering()

D. from transformers import pipeline
qa = pipeline('question-answering')

Solution

1. Step 1: Recall correct import and pipeline creation

   The correct import is from transformers import pipeline, then call pipeline('question-answering').

2. Step 2: Check each option syntax

   Only from transformers import pipeline qa = pipeline('question-answering') matches the correct syntax and function call.

3. Final Answer:

   from transformers import pipeline qa = pipeline('question-answering') -> Option D

4. Quick Check:

   Correct import and pipeline call = from transformers import pipeline qa = pipeline('question-answering') [OK]

Hint: Use pipeline('question-answering') from transformers [OK]

Common Mistakes:

• Wrong import statement
• Incorrect pipeline argument
• Using non-existent classes or functions

3. What will be the output of this code snippet?

from transformers import pipeline
qa = pipeline('question-answering')
result = qa(question='Where is the Eiffel Tower?', context='The Eiffel Tower is in Paris.')
print(result['answer'])

medium

A. In Paris

B. Paris

C. The Eiffel Tower

D. Eiffel Tower

Solution

1. Step 1: Understand the question and context

   The question asks for the location of the Eiffel Tower, and the context states it is in Paris.

2. Step 2: Predict the pipeline answer output

   The pipeline extracts the answer span from the context, which is 'Paris'.

3. Final Answer:

   Paris -> Option B

4. Quick Check:

   Answer extracted = Paris [OK]

Hint: Answer is the location mentioned in context [OK]

Common Mistakes:

• Choosing the full phrase instead of the exact answer
• Confusing question with context text
• Expecting the pipeline to generate new text

4. Identify the error in this code snippet that uses the Hugging Face QA pipeline:

from transformers import pipeline
qa = pipeline('question-answering')
result = qa(question='Who wrote Hamlet?', text='Hamlet was written by Shakespeare.')
print(result['answer'])

medium

A. The argument 'text' should be 'context'.

B. The pipeline name should be 'qa' instead of 'question-answering'.

C. The print statement should use result.answer instead of result['answer'].

D. The import statement is incorrect.

Solution

1. Step 1: Check pipeline argument names

   The QA pipeline expects 'question' and 'context' as arguments, not 'text'.

2. Step 2: Verify other parts of the code

   Pipeline name and import are correct; accessing result['answer'] is valid.

3. Final Answer:

   The argument 'text' should be 'context'. -> Option A

4. Quick Check:

   Use 'context' argument for QA pipeline [OK]

Hint: Use 'context' not 'text' for QA input [OK]

Common Mistakes:

• Using 'text' instead of 'context'
• Changing pipeline name incorrectly
• Wrong result access syntax

5. You want to build a QA system that answers questions from multiple documents. Which approach using Hugging Face pipelines is best?

hard

A. Run the QA pipeline separately on each document and pick the answer with highest score.

B. Concatenate all documents into one string and run the QA pipeline once.

C. Use the QA pipeline only on the first document and ignore others.

D. Train a new model from scratch for multiple documents.

Solution

1. Step 1: Understand pipeline input limits

   QA pipelines work best on one context at a time; long concatenated text may reduce accuracy.

2. Step 2: Evaluate options for multiple documents

   Running QA on each document separately and selecting the best answer is effective and practical.

3. Final Answer:

   Run the QA pipeline separately on each document and pick the answer with highest score. -> Option A

4. Quick Check:

   Separate runs + best score = best multi-doc QA [OK]

Hint: Run QA on each doc, choose best answer [OK]

Common Mistakes:

• Concatenating all documents causing context overflow
• Ignoring documents except first
• Unnecessarily retraining models