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Avinash250325/
T5BaseQuestionGeneration

Name: T5BaseQuestionGeneration
Rating: 2.5 (31 reviews)

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Finetuned T5-Base Question Generator Model

This model is a fine-tuned T5 model designed specifically for automatic question generation from any given context or passage. It supports different types of questions like short answer, multiple choice question, and true or false quesiton, while also allowing customization by difficulty level — easy, medium or hard.

Why is this Project Important?

Educational tools, tutoring platforms, and self-learning systems need a way to generate relevant questions automatically from content. Our model bridges that gap by providing a flexible and robust question generation system using a structured prompt format and powered by a fine-tuned T5-base model.

Key Features

Supports multiple question types:
- Short answer
- Multiple choice
- True/false
Questions are generated based on:
- The provided context
- The type of question
- The difficulty level
Difficulty reflects the reasoning depth required (multi-hop inference).
Uses a structured prompt format with clearly defined tags, making it easy to use or integrate into other systems.
Fine-tuned from the t5-base model:
- Lightweight and fast
- Easy to run on CPU
- Ideal for customization by teachers or Educational platforms

Ideal For

Teachers creating quizzes or exam material
EdTech apps generating practice questions
Developers building interactive learning tools
Automated assessment and content enrichment

Bonus: Retrieval-Augmented Generation (RAG)

A custom RAG function is provided in this github link https://github.com/Alla-Avinash/NLP-Question-Generation-with-RAG/blob/main/T5base_question_generation.py

This enables question generation from larger content sources like textbooks:

Input can be a subheading or small excerpt from a textbook.
The model fetches relevant supporting context form the textbook using a retirever.
Generates questions grounded in the fetched material.

This extends the model beyond single-passage generation into more dynamic, scalable educational use cases.

Prompt Format

To generate good quality questions, the model uses a structured input prompt format with special tokens. This helps the model understand the intent and expected output type.

Prompt Fields:

<extra_id_97> – followed by the question type
- short answer, multiple choice question, or true or false question
<extra_id_98> – followed by the difficulty
- easy, medium, or hard
<extra_id_99> – followed by [optional answer] context
- optional answer – for targeted question generation, or you can leave it as blank
- context – the main passage/content from which questions are generated

Helper Function to Create the Prompt

To simplify prompt construction, use this Python function:

python

def format_prompt(qtype, difficulty, context, answer=""):
    """
    Format input prompt for question generation
    """
    answer_part = f"[{answer}]" if answer else ""
    return f"<extra_id_97>{qtype} <extra_id_98>{difficulty} <extra_id_99>{answer_part} {context}"

Code & Fine-tuning Guide

If you want to see how the T5 base model is Finetuned, you can check out the below github link

https://github.com/Alla-Avinash/NLP-Question-Generation-with-RAG/blob/main/Finetune.ipynb

How to Use the Model

python

from transformers import T5Tokenizer, T5ForConditionalGeneration

# Load model from Hugging Face Hub
model_name = "Avinash250325/T5BaseQuestionGeneration"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

# Format input prompt
def format_prompt(qtype, difficulty, context, answer=""):
    answer_part = f"[{answer}]" if answer else ""
    return f"<extra_id_97>{qtype} <extra_id_98>{difficulty} <extra_id_99>{answer_part} {context}"

# You can put any text here to create a question based on this context
context = "The sun is the center of our solar system."

qtype = "short answer"     # qtype: ("short answer", "multiple choice question", "true or false question")
difficulty = "easy"        # difficulty: ("easy", "medium", "hard")
prompt = format_prompt("short answer", "easy", context)

# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=150)

# Decode output
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Try it out in the Huggingface Spaces (without the RAG implementation)

https://huggingface.co/spaces/Avinash250325/Question_Generation_with_RAG