trained the model and modify the law_ai_qna.py file

backend/law_ai_qna.py

@@ -28,4 +28,23 @@ class QnADataset(Dataset):
     def __len__(self):
         return len(self.question_encoding)
 
-dataset = QnADataset(question_encoding, answer_encoding)
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+dataset = QnADataset(question_encoding, answer_encoding)
+
+training_args = TrainingArguments(
+                                output_dir='Question n Answering',          # output directory
+                                num_train_epochs=1,                                 # total # of training epochs
+                                per_device_train_batch_size=100,                      # batch size per device during training
+                                per_device_eval_batch_size=100,                       # batch size for evaluation
+                                warmup_steps=500,                                   # number of warmup steps for learning rate scheduler
+                                weight_decay=0.01,                                  # strength of weight decay
+                                logging_dir='Question n Answering/logs',            # directory for storing logs
+                                logging_steps=10
+                            )
+
+trainer = Trainer(
+                model=model,                         # the instantiated 🤗 Transformers model to be trained
+                args=training_args,                  # training arguments, defined above
+                train_dataset=dataset                # evaluation dataset
+            )
+
+trainer.train()
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