Hi, I made this post to see if anyone knows how can I save in the logs the results of my training and validation loss.

I’m using this code:
*training_args = TrainingArguments(*
*    output_dir='./results',          # output directory*
*    num_train_epochs=3,              # total number 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*
*    warmup_steps=50,                 # number of warmup steps for learning rate scheduler*
*    weight_decay=0.01,               # strength of weight decay*
*    logging_dir='./logs',            # directory for storing logs*
*    logging_steps=20,*
*    evaluation_strategy="steps"*
*)*

*trainer = Trainer(*
*    model=model,                         # the instantiated 🤗 Transformers model to be trained*
*    args=training_args,                  # training arguments, defined above*
*    train_dataset=train_dataset,         # training dataset*
*    eval_dataset=val_dataset             # evaluation dataset*
*)*

And I thought that using logging_dir and logging_steps would achieve that but in such logs all I see is this:
*output_dir ^A"^X*
*^Toverwrite_output_dir ^B"^L*
*^Hdo_train ^B"^K*
*^Gdo_eval ^A"^N*

*do_predict ^B"^\*
*^Xevaluate_during_training ^B"^W*
*^Sevaluation_strategy ^A"^X*
*^Tprediction_loss_only ^B"^_*
*^[per_device_train_batch_size ^C"^^*
*^Zper_device_eval_batch_size ^C"^\*
*^Xper_gpu_train_batch_size ^A"^[*
*^Wper_gpu_eval_batch_size ^A"^_*
*^[gradient_accumulation_steps ^C"^[*
*^Weval_accumulation_steps ^A"^Q*
*^Mlearning_rate ^C"^P*
*^Lweight_decay ^C"^N*

And it goes on like that.
Any ideas will be welcome. 
My system instalation:
- transformers version: 3.4.0
- Platform: Linux-3.10.0-1127.13.1.el7.x86_64-x86_64-with-centos-7.8.2003-Core
- Python version: 3.6.8
- PyTorch version (GPU?): 1.6.0 (False)
- Tensorflow version (GPU?): not installed (NA)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No