Update README.md

README.md

@@ -29,15 +29,17 @@ This is a fine-tuned BERT model for question answering tasks, trained on a custo
 ```python
 from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
 
-# Load a pretrained tokenizer and model from Hugging Face
-tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased-distilled-squad")
-model = AutoModelForQuestionAnswering.from_pretrained("distilbert-base-uncased-distilled-squad")
+# Load the tokenizer and model from Hugging Face
+tokenizer = AutoTokenizer.from_pretrained("prabinpanta0/ZenGQ")
+model = AutoModelForQuestionAnswering.from_pretrained("prabinpanta0/ZenGQ")
 
 # Create a pipeline for question answering
 qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
 
 # Define your context and questions
-context = "Berlin is the capital of Germany. Paris is the capital of France. Madrid is the capital of Spain."
+contexts = ["Berlin is the capital of Germany.",
+          "Paris is the capital of France.",
+          "Madrid is the capital of Spain."]
 questions = [
     "What is the capital of Germany?",
     "Which city is the capital of France?",
@@ -45,7 +47,7 @@ questions = [
 ]
 
 # Get answers
-for question in questions:
+for context, question in zip(contexts, questions):
     result = qa_pipeline(question=question, context=context)
     print(f"Question: {question}")
     print(f"Answer: {result['answer']}\n")