Add 2 models for qa task

app.py

@@ -120,9 +120,21 @@ m_bert_sa.load_state_dict(torch.load('bert_model_sentiment_analysis.pth', map_lo
 m_bert_sa.to(device)
 
 # Load Q&A model
-roberta_qa = AutoModelForQuestionAnswering.from_pretrained("HungLV2512/Vietnamese-QA-fine-tuned")
-roberta_qa_tokenizer = AutoTokenizer.from_pretrained("HungLV2512/Vietnamese-QA-fine-tuned")
-roberta_qa.to(device)
+
+## XLM-RoBERTa-Large
+roberta_large_qa = AutoModelForQuestionAnswering.from_pretrained("HungLV2512/Vietnamese-QA-fine-tuned")
+roberta_large_qa_tokenizer = AutoTokenizer.from_pretrained("HungLV2512/Vietnamese-QA-fine-tuned")
+roberta_large_qa.to(device)
+
+## XLM-RoBERTa-Base
+roberta_base_qa = AutoModelForQuestionAnswering.from_pretrained("HungLV2512/xlm-roberta-base-fine-tuned-qa-vietnamese", output_hidden_states=True)
+roberta_base_qa_tokenizer = AutoTokenizer.from_pretrained("HungLV2512/xlm-roberta-base-fine-tuned-qa-vietnamese")
+roberta_base_qa.to(device)
+
+## Multilingual BERT
+m_bert_qa = AutoModelForQuestionAnswering.from_pretrained("HungLV2512/bert-base-multilingual-cased-fine-tuned-qa-vietnamese")
+m_bert_qa_tokenizer = AutoTokenizer.from_pretrained("HungLV2512/bert-base-multilingual-cased-fine-tuned-qa-vietnamese")
+m_bert_qa.to(device)
 
 # Load NER model
 label_map = {
@@ -244,7 +256,9 @@ def process_input(input_text, context, task):
         results["BARTPho Base"] = multitask_inference(bartpho_mt_base, bartpho_mt_base_tokenizer, input_text, "mt-vi-en", device)
         results["BARTPho Large"] = multitask_inference(bartpho_mt, bartpho_mt_tokenizer, input_text, "mt-vi-en", device)
     elif task == "Question Answering":
-        results["RoBERTa"] = qa_inference(roberta_qa, roberta_qa_tokenizer, input_text, context, device)
+        results["RoBERTa Base"] = qa_inference(roberta_base_qa, roberta_base_qa_tokenizer, input_text, context, device)
+        results["RoBERTa Large"] = qa_inference(roberta_large_qa, roberta_large_qa_tokenizer, input_text, context, device)
+        results["Multilingual BERT"] = qa_inference(m_bert_qa, m_bert_qa_tokenizer, input_text, context, device)
     elif task == "Named Entity Recognition":
         results["PhoBERT"] = ner_inference(phobert_ner, phobert_ner_tokenizer, input_text, device)
         results["PhoBERTv2"] = ner_inference(phobertv2_ner, phobertv2_ner_tokenizer, input_text, device)