Update README.md

README.md

@@ -1,3 +1,9 @@
+---
+pipeline_tag: text2text-generation
+widget:
+  - text: "<extra_id_97>short answer <extra_id_98>easy <extra_id_99> The sun is the center of our solar system."
+---
+
 ---
 license: mit
 datasets:
@@ -27,4 +33,114 @@ tags:
 - physics
 language:
 - en
----
+---
+# T5-Based Question Generator Model
+
+This model is a fine-tuned T5 model designed specifically for **automatic question generation** from any given context or passage. It supports different types of questions like **short answer**, **multiple choice question**, and **true or false quesiton**, while also allowing customization by **difficulty level** — easy, medium or hard.
+
+---
+
+## Why is this Project Important?
+
+Educational tools, tutoring platforms, and self-learning systems need a way to **generate relevant questions** automatically from content. Our model bridges that gap by providing a flexible and robust question generation system using a **structured prompt** format and powered by a **fine-tuned `T5-base` model**.
+  
+### Key Features
+
+- Supports **multiple question types**:  
+  - Short answer  
+  - Multiple choice  
+  - True/false  
+
+- Questions are generated based on:  
+  - The **provided context**  
+  - The **type of question**  
+  - The **difficulty level**  
+
+- Difficulty reflects the **reasoning depth** required (multi-hop inference).
+
+- Uses a **structured prompt format** with clearly defined tags, making it easy to use or integrate into other systems.
+
+- Fine-tuned from the `t5-base` model:  
+  - Lightweight and fast  
+  - Easy to run on CPU  
+  - Ideal for customization by teachers or Educational platforms
+
+### Ideal For
+
+- Teachers creating quizzes or exam material
+- EdTech apps generating practice questions  
+- Developers building interactive learning tools  
+- Automated assessment and content enrichment
+
+### Bonus: Retrieval-Augmented Generation (RAG)
+
+A **custom RAG function** is also provided. This enables question generation from larger content sources like textbooks:
+
+- Input can be a **subheading** or **small excerpt** from a textbook.
+- The model fetches relevant supporting context form the textbook using a retirever.
+- Generates questions grounded in the fetched material.
+
+This extends the model beyond single-passage generation into more dynamic, scalable educational use cases.
+
+
+---
+
+## Prompt Format
+
+To generate good quality questions, the model uses a **structured input prompt** format with special tokens. This helps the model understand the intent and expected output type.
+
+
+### Prompt Fields:
+- `<extra_id_97>` – followed by the **question type**  
+  - `short answer`, `multiple choice question`, or `true or false question`
+- `<extra_id_98>` – followed by the **difficulty**  
+  - `easy`, `medium`, or `hard`
+- `<extra_id_99>` – followed by **[optional answer] context** 
+  - `optional answer` – for targeted question generation, or you can leave it as blank
+  - `context` – the main passage/content from which questions are generated
+
+
+
+### Helper Function to Create the Prompt
+
+To simplify prompt construction, use this Python function:
+
+```python
+def format_prompt(qtype, difficulty, context, answer=""):
+    """
+    Format input prompt for question generation
+    """
+    answer_part = f"[{answer}]" if answer else ""
+    return f"<extra_id_97>{qtype} <extra_id_98>{difficulty} <extra_id_99>{answer_part} {context}"
+
+```
+
+---
+
+## How to Use the Model
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Load model from Hugging Face Hub
+tokenizer = T5Tokenizer.from_pretrained("your-username/t5-question-gen")
+model = T5ForConditionalGeneration.from_pretrained("your-username/t5-question-gen")
+
+# Format input prompt
+def format_prompt(qtype, difficulty, context, answer=""):
+    answer_part = f"[{answer}]" if answer else ""
+    return f"<extra_id_97>{qtype} <extra_id_98>{difficulty} <extra_id_99>{answer_part} {context}"
+
+context = "The sun is the center of our solar system."
+prompt = format_prompt("short answer", "easy", context)
+
+# Tokenize and generate
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=150)
+
+# Decode output
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+```
+
+