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language:
  - en
tags:
  - text2text-generation
  - t5
  - interview-questions
  - question-generation
  - hr-tech
license: apache-2.0
library_name: transformers
base_model: t5-base

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.

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

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.

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

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

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

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?

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.

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.

Question Types

The model recognizes these question types:

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

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.

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.

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.

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}}
}

Contact

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