Update README.md

README.md

@@ -34,7 +34,7 @@ language:
 
 
 
-# T5-Based Question Generator Model
+# Finetuned T5-Base 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.
 
@@ -123,8 +123,9 @@ def format_prompt(qtype, difficulty, context, answer=""):
 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")
+model_name = "Avinash250325/T5BaseQuestionGeneration"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
 
 # Format input prompt
 def format_prompt(qtype, difficulty, context, answer=""):
@@ -132,6 +133,8 @@ def format_prompt(qtype, difficulty, context, answer=""):
     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."
+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)
 
 # Tokenize and generate