Zeeshan506/echohire-qgen-distilgpt2

Model Card for EchoHire Question Generator (DistilGPT-2 Fine-tuned)

This model is a fine-tuned DistilGPT-2 designed to generate interview questions based on a job title and associated skills. It was trained on a curated dataset of ~100 jobs, each with 17–19 questions, to produce relevant, structured interview questions for recruiters or learning platforms.

Model Details

Model Description

This model generates questions automatically given a prompt with the job title and skills. The outputs are intended to help recruiters, HR teams, or training platforms quickly generate relevant interview questions without manually writing each one.

• Developed by: Syed Zeeshan Shah
• Model type: Causal Language Model (GPT-2)
• Language(s) (NLP): English
• License: MIT
• Finetuned from model: distilgpt2
• Shared by: Zeeshan506

Model Sources

• Repository: Hugging Face Model Hub
• Demo: N/A

Uses

Direct Use

• Generate interview questions automatically by providing a job title and skills.
• Can be used for recruitment platforms, HR automation, or interview prep content.

Downstream Use

• Could be fine-tuned further on domain-specific roles (e.g., data science, embedded systems).
• Can be integrated into apps, bots, or SaaS platforms for HR automation.

Out-of-Scope Use

• Not intended for generating biased or discriminatory questions.
• Should not be used as a sole source of assessment or evaluation for candidates.
• Outputs may not always reflect up-to-date technology trends or best practices.

Bias, Risks, and Limitations

• Trained on a limited dataset of ~100 jobs, so may not generalize to rare or niche roles.
• Model can produce long or merged questions; post-processing may be needed.
• Users should verify all generated questions for correctness and appropriateness.

Recommendations

• Review outputs before using in real interviews.
• Consider further fine-tuning for specific industries or technical domains.
• Post-process outputs for clean question formatting.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "Zeeshan506/echohire-qgen-distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Job Title: Backend Developer\nSkills: Python, FastAPI, PostgreSQL\nQuestions:"

inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"]
attention_mask = (input_ids != tokenizer.pad_token_id).long()

output_ids = model.generate(
    input_ids=input_ids,
    attention_mask=attention_mask,
    max_new_tokens=300,
    num_beams=3,
    no_repeat_ngram_size=2
)

generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(generated_text)

Training Details

Training Data

• Dataset of ~100 jobs with 17–19 interview questions each.
• Input: Job title + skills
• Output: Structured questions

Training Procedure

• Fine-tuned using Hugging Face Trainer API
• Epochs: 2
• Batch size: 2 per GPU
• Learning rate: 5e-5
• Tokenizer: distilgpt2 tokenizer, max input length 512
• Output: Structured questions

Metrics

• Training Loss: Cross-entropy loss tracked during fine-tuning.

• Human Review: Qualitative evaluation for relevance, coherence, and completeness of generated questions.

• Automated Evaluation (BERTScore):
  We performed BERTScore evaluation on a subset of the dataset (first 10 examples) to measure semantic similarity between the generated questions and reference completions using contextual embeddings.

  BERTScore Results (first 10 examples):

  • Precision: 0.8691
  • Recall: 0.8870
  • F1 Score: 0.8780

These results indicate that the model generates questions that are highly aligned with reference completions, confirming the quality and effectiveness of the fine-tuned model.

Results

• Model successfully generates relevant interview questions for multiple technical domains.
• Some outputs may merge multiple questions; minor formatting post-processing recommended.

Environmental Impact

• Hardware Type: NVIDIA Tesla T4 (Google Colab)
• Hours used: ~3–4 for training
• Compute Region: Colab USA/Europe
• Carbon Emitted: Minimal (single Colab GPU session)

Technical Specifications

• Architecture: DistilGPT-2, causal language model
• Objective: Generate text conditioned on job title + skills
• Software: Transformers library, PyTorch, Hugging Face Hub

Citation

If you use this model, please cite as:

APA:

Shah, S. Z. (2025). EchoHire Question Generator (DistilGPT-2 Fine-tuned). Hugging Face.

(https://huggingface.co/Zeeshan506/echohire-qgen-distilgpt2)

BibTeX:

@misc{shah2025echohire,
  title={EchoHire Question Generator (DistilGPT-2 Fine-tuned)},
  author={Shah, Syed Zeeshan},
  year={2025},
  howpublished={\url{https://huggingface.co/Zeeshan506/echohire-qgen-distilgpt2}}
}