qa_agent.py

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