Output training losses over iterations/epochs to file from trainer.py in HuggingFace Transfrormers - huggingface-transformers

Question

In the Transformer's library framework, by HuggingFace only the evaluation step metrics are outputted to a file named eval_resuls_{dataset}.txt in the "output_dir" when running run_glue.py. In the eval_resuls file, there are the metrics associated with the dataset. e.g., accuracy for MNLI and the evaluation loss.
Can a parameter be passed to run_glue.py to generate a training_results_{dataset}.txt file that tracks the training loss? Or would I have to build the functionality myself?
My file named run_python_script_glue.bash:
GLUE_DIR=../../huggingface/GLUE_SMALL/
TASK_NAME=MNLI
ID=OT
python3 run_glue.py \
--local_rank -1 \
--seed 42 \
--model_type albert \
--model_name_or_path albert-base-v2 \
--task_name $TASK_NAME \
--do_train \
--do_eval \
--data_dir $GLUE_DIR/$TASK_NAME \
--max_seq_length 128 \
--per_gpu_train_batch_size 8 \
--per_gpu_eval_batch_size 8 \
--gradient_accumulation_steps 2\
--learning_rate 3e-5 \
--max_steps -1 \
--warmup_steps 1000\
--doc_stride 128 \
--num_train_epochs 3.0 \
--save_steps 9999\
--output_dir ./results/GLUE_SMALL/$TASK_NAME/ALBERT/$ID/ \
--do_lower_case \
--overwrite_output_dir \
--label_noise 0.2\
--att_kl 0.01\
--att_se_hid_size 16\
--att_se_nonlinear relu\
--att_type soft_attention \
--adver_type ot \
--rho 0.5 \
--model_type whai \
--prior_gamma 2.70 \
--three_initial 0.0
In the trainer.py file in the transformer library, the training loss variable during the training step is called tr_loss.
tr_loss = self._training_step(model, inputs, optimizer, global_step)
loss_scalar = (tr_loss - logging_loss) / self.args.logging_steps
logs["loss"] = loss_scalar
logging_loss = tr_loss
In the code, the training loss is first scaled by the logging steps and later passed to a logs dictionary. The logs['loss'] is later printed to the terminal but not to a file. Is there a way to upgrade this to include an update to a txt file?