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Hamzasajjad38/
t5-small-qg

Name: t5-small-qg
Availability: InStock

automatic-question-generation

fine-tuned

rajpurkar/squad

apache-2.0

text-generation-inference

endpoints_compatible

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🧠 T5-Small · Automatic Question Generator

Answer-aware question generation fine-tuned on SQuAD 1.1

📌 Overview

This is a fully fine-tuned T5-small (60M parameters) model for automatic question generation (AQG) from educational text passages. Given a passage and a highlighted answer span, the model generates a grammatically correct, context-aware question.

This model was developed as part of an MPhil Data Science semester research project at Punjab University, Lahore, Pakistan, focusing on NLP applications in educational technology.

🏗️ Model Architecture

Base Model      →  google-t5/t5-small  (60M parameters)
Task            →  Text-to-Text Generation (Seq2Seq)
Input format    →  "generate question: {passage with <hl> answer <hl>}"
Output format   →  "{generated question}"
Max input len   →  512 tokens
Max output len  →  64 tokens
Decoding        →  Beam search (num_beams=4)

📊 Training Details

Hyperparameter	Value
Base model	`google-t5/t5-small`
Dataset	SQuAD 1.1
Training samples	20,000
Validation samples	2,000
Epochs	3
Batch size	4
Gradient accumulation steps	2 (effective batch = 8)
Learning rate	3e-4
Weight decay	0.01
Warmup steps	500
Optimizer	AdamW
Precision	FP16 (mixed precision)
Hardware	Google Colab T4 GPU (16GB)
Framework	HuggingFace Transformers + PyTorch
Training time	~75 minutes

📈 Evaluation Results

Evaluated on 200 samples from the SQuAD 1.1 validation split:

Metric	Score	Description
ROUGE-1	0.47	Unigram overlap with reference questions
ROUGE-2	0.22	Bigram overlap — phrase-level similarity
ROUGE-L	0.44	Longest common subsequence — fluency
BLEU	0.45	N-gram precision score

🚀 Quick Start

Installation

pip install transformers torch sentencepiece

Basic Usage

python

from transformers import T5ForConditionalGeneration, AutoTokenizer

model_id  = "Hamzasajjad38/t5-small-qg"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model     = T5ForConditionalGeneration.from_pretrained(model_id)

def generate_question(passage: str, answer: str) -> str:
    """Generate a question given a passage and answer span."""
    highlighted = passage.replace(answer, f"<hl> {answer} <hl>", 1)
    input_text  = f"generate question: {highlighted}"
    inputs  = tokenizer(
        input_text,
        return_tensors="pt",
        max_length=512,
        truncation=True
    )
    outputs = model.generate(
        **inputs,
        max_new_tokens=64,
        num_beams=4,
        early_stopping=True
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

Example

python

passage = """
Photosynthesis is a process used by plants to convert light energy 
into chemical energy stored in glucose. It occurs mainly in the 
chloroplasts using chlorophyll pigment to absorb sunlight.
"""

examples = [
    ("plants",       "What organism uses photosynthesis?"),
    ("chloroplasts", "Where does photosynthesis take place?"),
    ("chlorophyll",  "What pigment absorbs sunlight in photosynthesis?"),
]

for answer, expected in examples:
    generated = generate_question(passage.strip(), answer)
    print(f"Answer   : {answer}")
    print(f"Generated: {generated}")
    print(f"Expected : {expected}")
    print("─" * 50)

Batch Generation with spaCy

python

import spacy
from transformers import T5ForConditionalGeneration, AutoTokenizer

nlp       = spacy.load("en_core_web_sm")
model_id  = "Hamzasajjad38/t5-small-qg"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model     = T5ForConditionalGeneration.from_pretrained(model_id)

def generate_all_questions(passage: str, num_questions: int = 3):
    doc        = nlp(passage)
    candidates = [e.text for e in doc.ents]
    candidates += [c.text for c in doc.noun_chunks]

    # Deduplicate
    seen, unique = set(), []
    for c in candidates:
        if c.lower() not in seen and 1 < len(c.split()) <= 5:
            seen.add(c.lower())
            unique.append(c)

    results = []
    for answer in unique[:num_questions]:
        q = generate_question(passage, answer)
        results.append({"question": q, "answer": answer})

    return results

passage = "The Amazon River is the largest river by discharge in the world, \
flowing through Brazil, Peru, and Colombia."

for item in generate_all_questions(passage, num_questions=3):
    print(f"Q: {item['question']}")
    print(f"A: {item['answer']}\n")

📁 Repository Structure

Hamzasajjad38/t5-small-qg/
├── config.json              # Model architecture config
├── generation_config.json   # Generation hyperparameters
├── model.safetensors        # Fine-tuned model weights (242 MB)
├── tokenizer.json           # Fast tokenizer vocab
├── tokenizer_config.json    # Tokenizer configuration
├── spiece.model             # SentencePiece vocabulary
└── training_args.bin        # Saved training arguments

🎯 Intended Use

✅ Suitable for:

Generating comprehension questions from textbook passages
Educational assessment and quiz creation
E-learning platforms requiring automated question banks
Research in NLP and educational technology
Prototype development for EdTech applications

❌ Not suitable for:

Questions requiring external knowledge beyond the passage
Non-English text (use mT5 for multilingual support)
High-stakes exam generation without human review
Very short passages (< 30 words)

⚠️ Limitations

Language: English only — trained exclusively on English Wikipedia text
Domain: Best performance on factual, encyclopedic-style passages similar to Wikipedia
Size: 60M parameter T5-small limits generation quality vs larger variants (T5-base, T5-large)
Answer extraction: Model requires answer spans to be provided — it does not extract answers automatically
Repetition: May generate similar questions for passages with repetitive content
Hallucination: Occasionally generates questions not fully grounded in the passage

🔬 Research Context

This model is part of ongoing research into NLP-powered educational tools. Potential future extensions include:

🌐 Urdu language support using mT5 fine-tuning for Pakistani educational content
📚 Bloom's Taxonomy alignment — categorizing questions by cognitive level
🎯 MCQ distractor generation using WordNet and sense2vec
📊 Human evaluation beyond automated ROUGE/BLEU metrics

🖥️ Live Demo

Try the model without any code on Hugging Face Spaces:

👉 https://huggingface.co/spaces/Hamzasajjad38/Automatic-Question-Generator

Features:

Paste any passage and generate questions instantly
Adjustable number of questions (1–5)
Short answer and True/False question types
Download generated questions as .txt

📜 Citation

If you use this model in your research or project, please cite:

bibtex

@misc{sajjad2025t5qg,
  author       = {Muhammad Hamza Sajjad},
  title        = {T5-Small Fine-tuned for Automatic Question Generation on SQuAD 1.1},
  year         = {2025},
  publisher    = {Hugging Face},
  journal      = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/Hamzasajjad38/t5-small-qg}}
}

👤 Author

Muhammad Hamza Sajjad

🎓 MPhil Data Science — Punjab University, Lahore
🔬 Research interests: NLP, Computer Vision, Educational AI
🤗 Hugging Face

_{Fine-tuned with ❤️ using HuggingFace Transformers · PyTorch · Google Colab}