README.md

---

language: en
license: apache-2.0
tags:
- t5
- question-generation
- text2text-generation
- mcq
pretty_name: T5 for Technical MCQ Generation
---


# T5 for Technical MCQ Generation

## Model Description

This is a `t5-base` model fine-tuned for the specific task of generating technical multiple-choice questions (MCQs). Given a context paragraph and a correct answer, the model generates a relevant question.

This model is part of a larger pipeline that also generates distractors for the MCQ. It was developed to assist in creating educational content and assessments for technical topics.

The model was fine-tuned by [Ayush472](https://huggingface.co/Ayush472).

## Intended Uses & Limitations

### How to Use

This model is designed to be used within a larger MCQ generation pipeline but can be used as a standalone question generator. You can use it with the `transformers` library `pipeline` function for text-to-text generation.

First, install the necessary library:
```bash

pip install transformers sentencepiece

```

Then, you can use the following Python code to generate a question:

```python

from transformers import T5ForConditionalGeneration, T5Tokenizer



model_name = "Ayush472/Technical_mcq_model"

tokenizer = T5Tokenizer.from_pretrained(model_name)

model = T5ForConditionalGeneration.from_pretrained(model_name)



# The context from which the question should be generated

context = "The `await` keyword pauses the execution of an async function until a Promise is settled, making asynchronous code look synchronous."

# The desired answer to the question

answer = "It pauses the execution of an async function until a Promise is settled"



# Prepare the input for the model

input_text = f"generate question: context: {context} answer: {answer}"



inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)



# Generate the output

outputs = model.generate(

    inputs.input_ids, 

    attention_mask=inputs.attention_mask,

    max_length=64,

    num_beams=4,

    early_stopping=True

)



# Decode the generated question

generated_question = tokenizer.decode(outputs[0], skip_special_tokens=True)



print(f"Context: {context}")

print(f"Answer: {answer}")

print(f"Generated Question: {generated_question}")



# Expected Output:

# Generated Question: What does the `await` keyword do in JavaScript?

```

### Limitations and Bias

*   The model's knowledge is limited to the data it was trained on. It may not be able to generate questions for highly niche or very new technical topics.
*   The quality of the generated question is highly dependent on the quality and clarity of the input context and answer.
*   While the model is designed to generate factually consistent questions, it may occasionally produce questions that are awkwardly phrased or not perfectly aligned with the provided answer.
*   There is no inherent mechanism to prevent the generation of biased or unfair questions if the training data contained such biases.

## Training Data

The model was fine-tuned on a private, custom-built dataset of technical articles and their corresponding multiple-choice questions. The dataset covered various topics in software development, including programming languages (Python, JavaScript), data structures, algorithms, and machine learning concepts.

## Training Procedure

The model was fine-tuned using the `transformers` library's `Trainer` API on a single NVIDIA T4 GPU. The `t5-base` model was used as the starting checkpoint. The training process involved formatting the dataset into `context: {context} answer: {answer}` inputs and the corresponding question as the target label.

## Citation

If you use this model in your work, please consider citing it:

```bibtex

@misc{ayush472_t5_mcq_2025,

  author = {Ayush},

  title = {T5 for Technical MCQ Generation},

  year = {2025},

  publisher = {Hugging Face},

  journal = {Hugging Face repository},

  howpublished = {\\url{https://huggingface.co/Ayush472/Technical_mcq_model}}

}

```