Update README.md

README.md

@@ -29,18 +29,34 @@ This is a fine-tuned BERT model for question answering tasks, trained on a custo
 ```python
 from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("prabinpanta0/ZenGQ")
-model = AutoModelForQuestionAnswering.from_pretrained("prabinpanta0/ZenGQ")
+# 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")
 
+# Create a pipeline for question answering
 qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
 
-context = "Berlin is the capital of Germany."
-question = "What is the capital of Germany?"
-
-result = qa_pipeline(question=question, context=context)
-print(f"Answer: {result['answer']}")
+# Define your context and questions
+context = "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?",
+    "What is the capital of Spain?"
+]
+
+# Get answers
+for question in questions:
+    result = qa_pipeline(question=question, context=context)
+    print(f"Question: {question}")
+    print(f"Answer: {result['answer']}\n")
 ```
 
 ### Training Details
 - Epochs: 3
-- Training Loss: 2.050335, 1.345047, 1.204442
+- Training Loss: 2.050335, 1.345047, 1.204442
+
+### Dataset
+The model was trained on the [Rep00Zon](https://huggingface.co/datasets/prabinpanta0/Rep00Zon) dataset.
+
+### License
+This model is licensed under the MIT License.