Welcome to the world of question generation! In this blog, we will explore how to use T5 Transformers to create question-answer pairs along with distractors, all while understanding the nuances of the process. This method serves as a great entry point into artificial intelligence, allowing you to see meaningful results with a simple and modular approach.

Understanding the General Idea

Before diving into the code, let’s break down the process of generating questions from text. Think of this as creating a quiz from a book. You want to take important information and frame it as questions, while also providing some enticing but incorrect answers (distractors) to test knowledge effectively. Here’s how we approach this:

• Identify Keywords: Extract significant words from the text that will serve as correct answers.
• Replace the Answer: Substitute the answer with a blank space in its corresponding sentence.
• Transform the Sentence: Alter the sentence with the blank space into a question-like format.
• Generate Distractors: Create similar-sounding yet incorrect answers to challenge the user.

To visualize this process, you can imagine a chef crafting a gourmet dish. They source high-quality ingredients (keywords), transform them (replace the answer), and then plate them appealingly (generate the question). To complete the dish, they might sprinkle some similar but less desirable ingredients (distractors) to intrigue the diners.

Installation Process

Before you can start generating questions, you’ll need to properly set up your environment. Follow these easy steps:

Creating a Virtual Environment (Optional)

This step helps to maintain package integrity by isolating your project dependencies.

python -m venv venv

Activate the virtual environment:

• Windows:

  . .venv\Scripts\activate

• Linux or MacOS:

  source .venv/Scripts/activate

Now install Jupyter Lab for running notebooks:

pip install jupyterlab

Installing Packages

Once your virtual environment is activated, install the necessary packages:

pip install -r requirements.txt

Run Jupyter

jupyter lab

Execution Steps

Data Exploration

Start by exploring datasets to understand how questions are formulated. The SQuAD 1.0 dataset is a great resource, featuring 100,000 questions derived from Wikipedia.

Identifying Answers

Utilize libraries like spaCy for word tagging. Extract non-stop words and classify them using features such as:

• Part of Speech
• Named Entity Status
• Character Composition
• Word Count

Model Training

Here, you’ll employ the Gaussian Naive Bayes algorithm from scikit-learn to classify potential answers.

Creating Questions

A simple way to convert a sentence into a question is by replacing the answer with a blank space, transforming it into a cloze question.

Answer: Oxygen
Question: _____ is a chemical element with symbol O and atomic number 8.

Generating Incorrect Answers

Provide similar-sounding words as distractors. Employ word embeddings and cosine similarity for this step, ensuring that incorrect answers maintain the same part of speech.

Results Outcomes

While the generated questions may not be classroom-ready, they provide great insights into the functionality and usability of your model. The modular design allows easy identification of problem areas, similar to refining recipe techniques over time.

Troubleshooting

If you encounter issues, such as problems with installations or executing the code, consider the following troubleshooting tips:

• Ensure that your virtual environment is activated.
• Double-check package installations for any missing dependencies.
• Review logs for error messages which may indicate the source of the issue.
• Consult the [fxis.ai](https://fxis.ai) community for support and updates.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Future Work

As I continue to delve into this fascinating field, updates will certainly follow. I aspire to craft a comprehensive tutorial to guide newcomers into AI development, all while expanding upon the original framework.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.