This is like a fancy way of saying that we can use these cool tools to answer questions using text data.

So, let’s say you have this chunk of text: “Junk food refers to items like pizza,chips ,oily snacks which have high fat content. They are the main reason for obesity and heart diseases. You need to contol and reduce the amount.You should eat healthy food like fruits and vegetables.”

And you want to know something about junk food. Maybe: “What is considered as junk food according to this text?”

Well, that’s where our QA pipeline comes in! We can use it to extract information from the given context (the text) based on a specific question. Here’s how we do it:

1. First, you need to install transformers and other necessary libraries using pip or conda.
2. Then, create an instance of the QuestionAnsweringPipeline class by passing ‘question-answering’ as input for task. This will load a pretrained model that can handle question answering tasks.
3. Next, pass your question (as a string) and context (also as a string or list of strings) to the pipeline object.
4. The QA pipeline will then use its fancy algorithms to find an answer in the given text based on the provided question. It’s like having a super smart assistant that can read and understand text!
5. Finally, you can print out the result or do whatever else you want with it.

So, let’s see this in action:

# Importing the pipeline module from the transformers library
from transformers import pipeline

# Creating a pipeline object for the question answering task
qa_pipeline = pipeline(task="question-answering")

# Defining the context and question variables
context = "Junk food refers to items like pizza, chips, oily snacks which have high fat content. They are the main reason for obesity and heart diseases. You need to control and reduce the amount. You should eat healthy food like fruits and vegetables."
question = "What is considered as junk food according to this text?"

# Using the pipeline object to find the answer to the question in the given context
result = qa_pipeline(question=question, context=context)

# Printing out the answer from the result dictionary
print("The answer is:", result['answer'])

You’ve just used transformers for NLP to extract information from text using a QA pipeline. It’s like having your very own personal assistant that can read and understand text, but without the expensive price tag or annoying personality quirks.

Hope this helps clarify things in simpler terms!