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In recent years, natural language processing (NLP) has made significant strides in understanding and generating human language. One of the most important applications of NLP is question answering (QA), which involves using a machine to understand a question and generate an appropriate answer.
One of the most popular models for QA is BERT, which stands for “Bidirectional Encoder Representations from Transformers.” BERT is a transformer-based model that has been trained on a large corpus of text data and fine-tuned on specific QA tasks.
In this article, we will be using the bert-large-uncased-whole-word-masking-finetuned-squad
version of BERT to perform QA. This version of BERT has been pre-trained on a large corpus of text data and fine-tuned on the Squad dataset, which is a popular QA dataset.
To use the BERT model for QA, we will be using the transformers
library, which is a popular library for working with pre-trained transformer models. We will be using the pipeline
function from the transformers
library to initialize the QA pipeline.
from transformers import pipeline# Initialize the QA pipeline
qa_pipeline = pipeline('question-answering', model='bert-large-uncased-whole-word-masking-finetuned-squad')
Now that we have initialized the QA pipeline, we can use it to perform QA on any question and context. In the following example, we will be using the question “What is the capital of France” and the context “The capital of France is Paris. My name is Gaurav and I am a software engineer.”
# Define the question and context
question = "What is the capital of France."
context = "The capital of France is Paris. My name is Gaurav and I am a software engineer."# Get the answer
answer = qa_pipeline({
'question': question,
'context': context
})
print(answer)
When we run the above code, we will get the following output:
{'answer': 'Paris', 'score': 0.8361075103282928, 'start': 16, 'end': 21}
As you can see, the QA pipeline was able to correctly identify the capital of France as “Paris” with a high confidence score of 0.836.
In conclusion, the transformers
library and the bert-large-uncased-whole-word-masking-finetuned-squad
version of BERT makes it easy to perform QA tasks. With just a few lines of code, we were able to get accurate answers to a question within a given context. This demonstrates the power and ease of use of pre-trained transformer models for NLP tasks.