Update README.md

README.md

@@ -9,7 +9,6 @@ tags:
 - hr-tech
 license: apache-2.0
 library_name: transformers
-pipeline_tag: text2text-generation
 base_model: t5-base
 ---
 
@@ -89,4 +88,199 @@ Job Title: <job title>
 Job Description: <job description>
 Required Experience: <experience>
 Skills: <comma-separated skills>
-Question Type: <question type>
+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:**
+
+```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.