Create qa_agent.py

qa_agent.py

@@ -0,0 +1,26 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+
+tokenizer = AutoTokenizer.from_pretrained("Intel/dynamic_tinybert")
+model = AutoModelForQuestionAnswering.from_pretrained("Intel/dynamic_tinybert")
+
+def QA_Agent(context,question):
+    
+
+    #Tokenize the context and question
+    tokens = tokenizer.encode_plus(question, context, return_tensors="pt", truncation=True)
+
+    #Get the input IDs and attention mask
+    input_ids = tokens["input_ids"]
+    attention_mask = tokens["attention_mask"]
+
+    #Perform question answering
+    outputs = model(input_ids, attention_mask=attention_mask)
+    start_scores = outputs.start_logits
+    end_scores = outputs.end_logits
+
+    #Find the start and end positions of the answer
+    answer_start = torch.argmax(start_scores)
+    answer_end = torch.argmax(end_scores) + 1
+    answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[0][answer_start:answer_end]))
+    return answer