--- 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: ```text Generate an interview question. Job Title: Job Description: Required Experience: Skills: 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:** ```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 ``` --- ## Output Format The model returns a **single interview question** as plain text. **Example output:** ```text Can you describe a situation where you had to improve a machine learning model's performance under a tight deadline? ``` --- ## How to Use ### Installation ```bash pip install transformers torch ``` ### Basic usage with Python ```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 ```python 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: ```bibtex @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.