Squad stands for Stanford Question Answering Dataset, which is essentially a bunch of articles with questions and answers attached to them. The goal is to train our model to read through these articles and find the correct answer to each question.
Now, here’s how it works in more detail:
1. First, we load up the pre-trained DistilBERT model (which has already been trained on a bunch of text data) into memory using Python code like this:
# Importing the pipeline function from the transformers library
from transformers import pipeline
# Creating a pipeline for question-answering using the pre-trained DistilBERT model
question_answerer = pipeline("question-answering", model='distilbert-base-uncased-distilled-squad')
# The pipeline function allows us to easily use pre-trained models for specific tasks, in this case, question-answering
# The "question-answering" parameter specifies the task we want to perform
# The "model" parameter specifies the pre-trained model we want to use, in this case, the DistilBERT model trained on the SQuAD dataset
2. Next, we feed our question and article into the model using a function called `forward()`. This will return us an answer (if it exists) or None if no match was found:
# First, we define the context as a raw string using triple quotes:
context = r"""Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a question answering dataset is the SQuAD dataset, which is entirely based on that task."""
# Next, we define the question as a string:
question = "What is Extractive Question Answering?"
# Then, we call the function `question_answerer` with the arguments `question` and `context`:
answer = question_answerer(question=question, context=context)['text']
# The function `question_answerer` takes in a question and a context and returns an answer (if it exists) or None if no match was found.
# The `question` argument is set to the value of the `question` variable we defined earlier, and the `context` argument is set to the value of the `context` variable we defined earlier.
# The returned answer is then assigned to the `answer` variable.
3. If an answer was found (i.e., `answer` is not None), we can print it out:
# This line prints out a statement with the value of the variable "answer" at the end
print("The answer to your question is:", answer)
And that’s pretty much all there is to it! The model will use its pre-trained knowledge of language and context to find the best match for our question in the given article. Pretty cool, huh?
Now some examples:
Example 1:
Question: What is a good example of a question answering dataset?
Context: The Stanford Question Answering Dataset (SQuAD) is a collection of programmatically generated questions and answers that are designed to test the ability of systems to understand natural language. It consists of over 50,000 examples in total, with each example consisting of a question and an answer extracted from a Wikipedia article.
The Stanford Question Answering Dataset (SQuAD) is a good example of a question answering dataset.