train/eval step results log not shown in terminal for tf_trainer.py
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 3.1.0
- Platform: Linux-5.4.0-42-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.9
- PyTorch version (GPU?): 1.6.0 (False)
- Tensorflow version (GPU?): 2.2.0 (False)
- Using GPU in script?: No
- Using distributed or parallel set-up in script?: No

### Who can help
<!-- Your issue will be replied to more quickly if you can figure out the right person to tag with @
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 Please tag fewer than 3 people.
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 Trainer: @sgugger
 tensorflow: @jplu
@LysandreJik 


## Information

In the current code, which is without setting `logger.setLevel(logging.INFO)` in `trainer_tf.py`:

	09/12/2020 03:42:41 - INFO - absl -   Load dataset info from /home/imo/tensorflow_datasets/glue/sst2/1.0.0
	09/12/2020 03:42:41 - INFO - absl -   Reusing dataset glue (/home/imo/tensorflow_datasets/glue/sst2/1.0.0)
	09/12/2020 03:42:41 - INFO - absl -   Constructing tf.data.Dataset for split validation, from /home/imo/tensorflow_datasets/glue/sst2/1.0.0
	2020-09-12 03:42:57.010229: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:184] Filling up shuffle buffer (this may take a while): 41707 of 67349
	2020-09-12 03:43:03.412045: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:233] Shuffle buffer filled.
	2020-09-12 03:43:56.636791: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:184] Filling up shuffle buffer (this may take a while): 36279 of 67349
	2020-09-12 03:44:04.474751: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:233] Shuffle buffer filled.
	09/12/2020 03:44:51 - INFO - __main__ -   *** Evaluate ***
	09/12/2020 03:45:02 - INFO - __main__ -   ***** Eval results *****
	09/12/2020 03:45:02 - INFO - __main__ -     eval_loss = 0.712074209790711
	09/12/2020 03:45:02 - INFO - __main__ -     eval_acc = 0.48977272727272725

You can see that the train/eval step logs are not shown.

If I specify, manually, `logger.setLevel(logging.INFO)` in `trainer_tf.py`:


	09/12/2020 06:04:39 - INFO - absl -   Load dataset info from /home/imo/tensorflow_datasets/glue/sst2/1.0.0
	09/12/2020 06:04:39 - INFO - absl -   Reusing dataset glue (/home/imo/tensorflow_datasets/glue/sst2/1.0.0)
	09/12/2020 06:04:39 - INFO - absl -   Constructing tf.data.Dataset for split validation, from /home/imo/tensorflow_datasets/glue/sst2/1.0.0
	You are instantiating a Trainer but W&B is not installed. To use wandb logging, run `pip install wandb; wandb login` see https://docs.wandb.com/huggingface.
	To use comet_ml logging, run `pip/conda install comet_ml` see https://www.comet.ml/docs/python-sdk/huggingface/
	***** Running training *****
	  Num examples = 67349
	  Num Epochs = 1
	  Instantaneous batch size per device = 4
	  Total train batch size (w. parallel, distributed & accumulation) = 4
	  Gradient Accumulation steps = 1
	  Steps per epoch = 4
	  Total optimization steps = 4
	2020-09-12 06:04:49.637373: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:184] Filling up shuffle buffer (this may take a while): 39626 of 67349
	2020-09-12 06:04:56.805687: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:233] Shuffle buffer filled.
	{'loss': 0.6994307, 'learning_rate': 3.7499998e-05, 'epoch': 0.5, 'step': 1}
	{'loss': 0.6897122, 'learning_rate': 2.5e-05, 'epoch': 0.75, 'step': 2}
	Saving checkpoint for step 2 at ./sst-2/checkpoint/ckpt-1
	{'loss': 0.683386, 'learning_rate': 1.25e-05, 'epoch': 1.0, 'step': 3}
	{'loss': 0.68290234, 'learning_rate': 0.0, 'epoch': 1.25, 'step': 4}
	Saving checkpoint for step 4 at ./sst-2/checkpoint/ckpt-2
	Training took: 0:00:43.099437
	Saving model in ./sst-2/
	09/12/2020 06:05:26 - INFO - __main__ -   *** Evaluate ***
	***** Running Evaluation *****
	  Num examples = 872
	  Batch size = 8
	{'eval_loss': 0.6990196158032899, 'eval_acc': 0.49204545454545456, 'epoch': 1.25, 'step': 4}
	09/12/2020 06:05:35 - INFO - __main__ -   ***** Eval results *****
	09/12/2020 06:05:35 - INFO - __main__ -     eval_loss = 0.6990196158032899
	09/12/2020 06:05:35 - INFO - __main__ -     eval_acc = 0.49204545454545456

We see more information like

    {'loss': 0.6994307, 'learning_rate': 3.7499998e-05, 'epoch': 0.5, 'step': 1}


More importantly, we also see this message

	You are instantiating a Trainer but W&B is not installed. To use wandb logging, run `pip install wandb; wandb login` see https://docs.wandb.com/huggingface.
	To use comet_ml logging, run `pip/conda install comet_ml` see https://www.comet.ml/docs/python-sdk/huggingface/

, which won't be shown if logging level is not set to INFO.

## Related
In the PR #6097, @LysandreJik changed `logger.info(output)` to `print(output)` in `trainer.py` in order to show logs on the screen.
Maybe we should do the same thing for `tf_trainer.py`. If not, could we set logging level to INFO in `tf_trainer.py` - however this would become different from `trainer.py` where the logging level is not set (at least, not in the trainer script).

## To reproduce

    python3 run_tf_glue.py \
    --task_name sst-2 \
    --model_name_or_path distilbert-base-uncased \
    --output_dir ./sst-2/ \
    --max_seq_length  16 \
    --num_train_epochs 2 \
    --per_device_train_batch_size 4 \
    --gradient_accumulation_steps 1 \
    --max_steps 4 \
    --logging_steps 1 \
    --save_steps 2 \
    --seed 1 \
    --do_train \
    --do_eval \
    --do_predict \
    --overwrite_output_dir


## Expected behavior

I expect the train/eval step logs will be shown on the screen.

## Remark

I can make a PR once a decision is made by the team.
You are user the wrong logger I think. Transformers now uses a centralized logger so you should go:
```
import transformers
transformers.logging.set_verbosity_info()
```
to set the verbosity to the INFO level. Not sure it there is an additional issue in tf_trainer or not.<|||||>> 
> 
> You are user the wrong logger I think. Transformers now uses a centralized logger so you should go:
> 
> ```
> import transformers
> transformers.logging.set_verbosity_info()
> ```
> 
> to set the verbosity to the INFO level. Not sure it there is an additional issue in tf_trainer or not.

@sgugger , but I am testing `run_tf_glue.py`, not my own script. I assume it should work directly, no? Other scripts like `run_tf_ner.py` also have the same issue. And it seems there is no logging level command-line argument to specify.<|||||>As @sgugger said, all the examples uses the Python logging lib instead of the HF wrapper.

I will do a fix early next week.<|||||>@sgugger 

While I worked on #7125, I found that in `trainer.py`, I found that

                logger.info("  Continuing training from checkpoint, will skip to saved global_step")
                logger.info("  Continuing training from epoch %d", epochs_trained)
                logger.info("  Continuing training from global step %d", self.global_step)
                logger.info("  Continuing training from %d non-embedding floating-point operations", self.total_flos)
                logger.info("  Will skip the first %d steps in the first epoch", steps_trained_in_current_epoch)

are not shown while I launched example scripts like `run_glue.py`.<|||||>Yes the default log level is `warning`, you have to change it in the script to info if you want, by adding the line:
```
logging.set_verbosity_info()
```
Or if you always want info, there is an env variable you can set called `TRANSFORMERS_VERBOSITY` (set it to 20 for info level).<|||||>There is a small issue with the HF logger. I'm currently working on it and checking with @LysandreJik <|||||>No problem. If this is the default behavior expected (at least for pytorch trainer), I am fine.<|||||>@jplu As you might know, I open this issue, but I don't necessary have the whole context. So I leave you to decide the desired behavior for tf_trainer.<|||||>alternatively you can simply modify the following line in transormers/utils/logging.py to change the default behaviour:

from `_default_log_level = logging.WARNING ` to  `_default_log_level = logging.INFO`<|||||>A fix has been pushed, you just have to write `transformers.logging.set_verbosity_info()`<|||||>This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
