Update README.md

README.md

@@ -74,7 +74,10 @@ Educational tools, tutoring platforms, and self-learning systems need a way to *
 
 ### Bonus: Retrieval-Augmented Generation (RAG)
 
-A **custom RAG function** is also provided. This enables question generation from larger content sources like textbooks:
+A **custom RAG function** is provided in this github link 
+https://github.com/Alla-Avinash/NLP-Question-Generation-with-RAG/blob/main/T5base_question_generation.py
+
+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.
@@ -117,6 +120,14 @@ def format_prompt(qtype, difficulty, context, answer=""):
 
 ---
 
+## Code & Fine-tuning Guide
+
+If you want to see how the T5 base model is Finetuned, you can check out the below github link
+
+https://github.com/Alla-Avinash/NLP-Question-Generation-with-RAG/blob/main/Finetune.ipynb
+
+---
+
 ## How to Use the Model
 
 ```python
@@ -132,7 +143,9 @@ 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}"
 
+# You can put any text here to create a question based on this context
 context = "The sun is the center of our solar system."
+
 qtype = "short answer"     # qtype: ("short answer", "multiple choice question", "true or false question")
 difficulty = "easy"        # difficulty: ("easy", "medium", "hard")
 prompt = format_prompt("short answer", "easy", context)
@@ -146,4 +159,9 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 ```
 
+---
+
+### Try it out in the Huggingface Spaces (without the RAG implementation)
+
+https://huggingface.co/spaces/Avinash250325/Question_Generation_with_RAG