updated readme, completed model card

README.md

@@ -1,3 +1,163 @@
 ---
 license: mit
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- jobs
+- HR
+- Interview
+- Question
+- job
+metrics:
+- bertscore
+base_model:
+- openai-community/gpt2
+library_name: transformers
 ---
+
+# 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](https://huggingface.co/Zeeshan506/echohire-qgen-distilgpt2)  
+- **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
+
+```python
+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}}
+}
+```