finalmaybe

app.py

@@ -11,22 +11,27 @@ model = AutoModelForQuestionAnswering.from_pretrained(model_name)
 
 
 def answer_question(context, question):
-  # Encode the context and question
-  inputs = tokenizer(context, question, return_tensors="pt")
+  """
+  This function takes a context and question as input,
+  performs question answering using the loaded model,
+  and returns the predicted answer.
+  """
+  # Encode the context and question with special character handling
+  inputs = tokenizer(context, question, return_tensors="pt", truncation=True)
 
   # Perform question answering
   outputs = model(**inputs)
 
   # Get the predicted start and end token positions
-  start_scores, end_scores = outputs.start_logits, outputs.end_logits
+  start_scores, end_scores = outputs.start_logits, outputs.end_scores
 
   # Decode the answer based on predicted positions
   answer_start = torch.argmax(start_scores)
   answer_end = torch.argmax(end_scores) + 1
 
-  # Get answer tokens and convert them to string
+  # Get answer tokens and convert them to string, removing special tokens
   answer = tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][answer_start:answer_end])
-  answer = "".join(answer)
+  answer = "".join(answer[2:-2])  # Remove special tokens ([CLS] and [SEP])
 
   return answer