sudaisdev/Question_Generation_model

Interview Question Generation Model (T5-base)

A fine-tuned T5-base model that generates interview questions from job details such as job title, description, required experience, and skills.

Developed for HR platforms, hiring tools, and interview preparation apps.

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Model Description

This model is based on T5-base (Text-to-Text Transfer Transformer). It was fine-tuned on a custom dataset of job descriptions paired with interview questions. The model takes a structured prompt as input and returns a single interview question as output.

Model type: Encoder-decoder (T5) Language: English License: Apache 2.0 Fine-tuned from: t5-base

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Highlights

• Text-to-text generation: One input prompt → one interview question.
• Multi-role support: Works for ML, backend, frontend, data, DevOps roles.
• Question-type control: You can specify Technical, Scenario, Behavioral, or Problem-Solving.
• Lightweight: ~220M parameters, runs on CPU and GPU.
• Hugging Face ready: Works with transformers, pipeline, and the Hugging Face inference widget.

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Model Details

Attribute	Value
Base model	T5-base
Architecture	T5ForConditionalGeneration
Parameters	~220M
Model size	~967 MB (float32)
Context length	512 tokens
Vocab size	32128
Fine-tuned on	Custom interview Q&A dataset
Task	Text-to-text generation

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Intended Use

Direct use:

• HR platforms generating screening questions
• Interview preparation apps
• Recruitment automation tools
• Educational content creation

Out-of-scope use:

• Non-English languages
• Medical, legal, or financial advice
• Long-form interview scripts
• Automated hiring decisions without human review

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Input Format

The model expects one plain text prompt with the following structure:

Generate an interview question.
Job Title: <job title>
Job Description: <job description>
Required Experience: <experience>
Skills: <comma-separated skills>
Question Type: <question type>

Rules:

• Each field must be on its own line.
• Question Type is optional. Default: Scenario.
• Total prompt must be under 512 tokens.

Example input:

Generate an interview question.
Job Title: Machine Learning Engineer
Job Description: Build and deploy ML models for production.
Required Experience: 2 years
Skills: Python, PyTorch, NLP, Docker
Question Type: Scenario

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Output Format

The model returns a single interview question as plain text.

Example output:

Can you describe a situation where you had to improve a machine learning model's performance under a tight deadline?

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How to Use

Installation

pip install transformers torch

Basic usage with Python

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "sudaisdev/Question_Generation_model"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

input_text = """Generate an interview question.
Job Title: Machine Learning Engineer
Job Description: Build and deploy ML models for production.
Required Experience: 2 years
Skills: Python, PyTorch, NLP, Docker
Question Type: Scenario"""

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

outputs = model.generate(
    **inputs,
    max_length=128,
    do_sample=True,
    temperature=0.8,
    top_p=0.9,
)

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

Using the Hugging Face pipeline

from transformers import pipeline

generator = pipeline(
    "text2text-generation",
    model="sudaisdev/Question_Generation_model",
)

result = generator(
    "Generate an interview question.\n"
    "Job Title: Backend Developer\n"
    "Job Description: Build REST APIs.\n"
    "Required Experience: 3 years\n"
    "Skills: Python, Django, SQL\n"
    "Question Type: Technical",
    max_length=128,
    do_sample=True,
    temperature=0.8,
    top_p=0.9,
)

print(result[0]["generated_text"])

Test on Hugging Face

You can also test the model directly on this page using the inference widget on the right side.

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Generation Parameters

Recommended defaults:

Parameter	Value	Meaning
max_length	128	Max output tokens
do_sample	True	Enable sampling
temperature	0.8	Higher = more creative
top_p	0.9	Nucleus sampling

Tuning tips:

• Lower temperature (0.5) → focused, deterministic questions.
• Raise temperature (1.0) → more variety, sometimes off-topic.

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Question Types

The model recognizes these question types:

• Scenario — situational questions
• Technical — technical skill questions
• Behavioral — past experience questions
• Problem-Solving — analytical questions

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Training Data

The model was fine-tuned on a custom dataset containing:

• Job titles from technical roles (ML, backend, frontend, data, DevOps)
• Job descriptions in English
• Skill lists in comma-separated format
• Required experience in years
• Corresponding interview questions

No personally identifiable information was included.

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Limitations

• English only. Non-English inputs will produce poor results.
• Prompt-sensitive. Wrong prompt format → poor output.
• Vague descriptions produce generic questions.
• No bias evaluation. May reflect biases in training data.
• No fact-checking. Generated questions should be reviewed before use.
• Not evaluated for safety, toxicity, or harmful content.
• Small model. T5-base is smaller than modern LLMs; quality reflects that.

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Ethical Considerations

This model generates interview questions automatically. Always review output before using it in real hiring processes.

Do not use this model for:

• Automated rejection of candidates
• Discriminatory screening
• Legal or medical advice

The developers are not responsible for misuse.

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Citation

If you use this model, please cite:

@misc{sudaisdev_question_generation_model,
  title={Interview Question Generation Model (T5-base)},
  author={Sudais},
  year={2026},
  howpublished={\url{https://huggingface.co/sudaisdev/Question_Generation_model}}
}

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Contact

For questions or issues, open an issue on the model repository page.