QA with Hugging Face pipeline in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the Hugging Face pipeline for QA?

It is a simple tool that lets you ask questions about a text and get answers using a pre-trained model, without needing to write complex code.

beginner

Which model type is commonly used in Hugging Face QA pipelines?

Models like BERT or RoBERTa fine-tuned on question answering tasks are commonly used because they understand context well.

beginner

What inputs does the Hugging Face QA pipeline require?

It needs two inputs: the question you want to ask and the context text where the answer might be found.

intermediate

How does the QA pipeline return answers?

It returns the answer text, the position of the answer in the context, and a confidence score showing how sure the model is.

beginner

Why is using a pipeline helpful for beginners in QA tasks?

Because it hides complex steps like tokenization and model loading, letting beginners get answers quickly with just a few lines of code.

What two pieces of information do you need to provide to the Hugging Face QA pipeline?

A Only the question

B Question and context text

C Only the context text

D Model name and question

The pipeline requires both the question and the context text to find the answer.

Which of these is a common model used in Hugging Face QA pipelines?

A BERT

B ResNet

C GAN

D KMeans

BERT is a popular model fine-tuned for question answering tasks.

What does the confidence score in the QA pipeline output represent?

A The number of words in the question

B The length of the answer

C How sure the model is about the answer

D The time taken to answer

The confidence score shows how confident the model is that the answer is correct.

What is the main benefit of using the Hugging Face pipeline for QA?

A Simplifies using complex models with minimal code

B Requires manual tokenization

C Needs training from scratch

D Only works with images

The pipeline simplifies using pre-trained models with just a few lines of code.

If you want to find an answer inside a paragraph, which pipeline should you use?

A Translation pipeline

B Text Generation pipeline

C Image Classification pipeline

D Question Answering pipeline

The Question Answering pipeline is designed to find answers inside text paragraphs.

Explain how the Hugging Face QA pipeline works from input to output.

Think about what you give the pipeline and what it gives back.

Describe why using a pre-trained model with the Hugging Face pipeline is helpful for beginners.

Focus on ease and speed of use.