Update README.md

README.md

@@ -57,13 +57,56 @@ The model's predictions may be biased or overly reliant on the training dataset,
 Use the code below to get started with the model.
 
 ```python
-from transformers import pipeline  
-qa_pipeline = pipeline('question-answering', model='bert_squad')  
+import torch
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
 
-context = "BERT is a transformers model for natural language processing."  
-question = "What is BERT used for?"  
-result = qa_pipeline(question=question, context=context)  
-print(result)  
+# Load the model and tokenizer
+model_name = "Sadat07/bert_squad"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+
+context = """
+The person who invented light was 
+Thomas Edison.He was born in 1879.
+"""
+question = "When did Thomas Edison invent?"
+
+
+inputs = tokenizer(question, context, return_tensors="pt", truncation=True, max_length=512)
+input_ids = inputs["input_ids"].to(device)
+attention_mask = inputs["attention_mask"].to(device)
+
+
+print("Tokenized Input:", tokenizer.decode(input_ids[0]))
+
+# Perform inference
+with torch.no_grad():
+    outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+    start_scores = outputs.start_logits
+    end_scores = outputs.end_logits
+
+# Logits
+print("Start logits:", start_scores)
+print("End logits:", end_scores)
+
+# Get start and end indices
+start_idx = torch.argmax(start_scores)
+end_idx = torch.argmax(end_scores) + 1
+
+# Decode the answer
+if start_idx >= end_idx:
+    print("Model did not predict a valid answer. Please check context and question.")
+else:
+    answer = tokenizer.convert_tokens_to_string(
+        tokenizer.convert_ids_to_tokens(input_ids[0][start_idx:end_idx])
+    )
+    print(f"Question: {question}")
+    print(f"Answer: {answer}")
+  
 ```
 
 ## Training Details