Update README.md

README.md

@@ -47,17 +47,29 @@ The model achieved the following results:
 To use this model for question-answering tasks, load it with the `transformers` library:
 
 ```python
-from transformers import RobertaForQuestionAnswering, RobertaTokenizer
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
 
-model_name = "hosseinhimself/tara-roberta-base-fa-qa"
-tokenizer = RobertaTokenizer.from_pretrained(model_name)
-model = RobertaForQuestionAnswering.from_pretrained(model_name)
+model = "hosseinhimself/tara-roberta-base-fa-qa"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model)
+model = AutoModelForQuestionAnswering.from_pretrained(model)
+
+# Create a QA pipeline
+qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
 
 # Example usage
-inputs = tokenizer("چه زمانی شرکت فولاد مبارکه تأسیس شد؟", "شرکت فولاد مبارکه در سال 1371 تأسیس شد.", return_tensors='pt')
-outputs = model(**inputs)
-start_logits = outputs.start_logits
-end_logits = outputs.end_logits
+context = "CONTEXT"
+
+question = "QUESTION"
+
+# Modify the pipeline to return more answers
+results = qa_pipeline(question=question, context=context, top_k=5)  # top_k specifies the number of answers
+
+# Display the answers
+for idx, result in enumerate(results):
+    print(f"Answer {idx+1}: {result['answer']}")
+
 ```
 
 ## Datasets