Create question_answering.py

question_answering.py

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+import torch
+from transformers import BertTokenizer, BertForQuestionAnswering
+
+# Load the pre-trained model and tokenizer
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertForQuestionAnswering.from_pretrained("bert-base-uncased")
+
+def answer_query(question, context):
+  # Preprocess the question and context using the tokenizer
+  inputs = tokenizer(question, context, return_tensors="pt")
+
+  # Use the model for question answering
+  with torch.no_grad():
+    outputs = model(**inputs)
+
+  # Get start and end logits directly from model outputs
+  start_logits = outputs.start_logits
+  end_logits = outputs.end_logits
+
+  # Find the most likely answer span
+  answer_start = torch.argmax(start_logits)
+  answer_end = torch.argmax(end_logits) + 1
+
+  # Extract the answer from the context
+  answer = tokenizer.convert_tokens_to_string(context)[answer_start:answer_end]
+
+  return answer