training_args = TrainingArguments(
  output_dir='./results',          # output directory
  num_train_epochs=3,              # total # of training epochs
  per_device_train_batch_size=16,  # batch size per device during training
  per_device_eval_batch_size=16,   # batch size for evaluation
  logging_dir='./logs',            # directory for storing logs
)

trainer = Trainer(
  model=model,
  args=training_args,
  train_dataset=tokenized_datasets["train"],
  eval_dataset=tokenized_datasets["test"],
  tokenizer=tokenizer
)

Error Log:
/usr/local/lib/python3.6/dist-packages/transformers/trainer.py in train(self, model_path, trial)
745                         tr_loss += self.training_step(model, inputs)
746                 else:
–> 747                     tr_loss += self.training_step(model, inputs)
748                 self._total_flos += self.floating_point_ops(inputs)
749
/usr/local/lib/python3.6/dist-packages/transformers/trainer.py in training_step(self, model, inputs)
1073                 loss = self.compute_loss(model, inputs)
1074         else:
-> 1075             loss = self.compute_loss(model, inputs)
1076
1077         if self.args.n_gpu > 1:
/usr/local/lib/python3.6/dist-packages/transformers/trainer.py in compute_loss(self, model, inputs)
1103             self._past = outputs[self.args.past_index]
1104         # We don’t use .loss here since the model may return tuples instead of ModelOutput.
-> 1105         return outputs[“loss”] if isinstance(outputs, dict) else outputs[0]
1106
1107     def is_local_process_zero(self) -> bool:
/usr/local/lib/python3.6/dist-packages/transformers/file_utils.py in getitem(self, k)
1356         if isinstance(k, str):
1357             inner_dict = {k: v for (k, v) in self.items()}
-> 1358             return inner_dict[k]
1359         else:
1360             return self.to_tuple()[k]
KeyError: ‘loss’