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Welcome to the Hugging Face tasks series.  

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In this video, we will take a look 
at the Question Answering task. 

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Question answering is the task of 
extracting an answer in a given document. 

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Question answering models take a context, 
which is the document you want to search in,  

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and a question and return an answer. 
Note that the answer is not generated,  

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but extracted from the context. This type 
of question answering is called extractive. 

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The task is evaluated on two 
metrics, exact match and F1-Score. 

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As the name implies, exact match looks for an  

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exact match between the predicted 
answer and the correct answer. 

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A common metric used is the F1-Score, which 
is calculated over tokens that are predicted  

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correctly and incorrectly. It is calculated 
over the average of two metrics called  

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precision and recall which are metrics that 
are used widely in classification problems. 

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An example dataset used for this task is called 
SQuAD. This dataset contains contexts, questions  

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and the answers that are obtained 
from English Wikipedia articles. 

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You can use question answering models to 
automatically answer the questions asked  

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by your customers. You simply need a document 
containing information about your business  

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and query through that document with 
the questions asked by your customers. 

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For more information about the Question Answering 
task, check out the Hugging Face course.
