trainer process bar can't move while training
Author: jongjyh
Pull request: no

## Environment info
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- `transformers` version:4.12.5
- Platform:pytorch
- Python version:3.8
- PyTorch version (GPU?):1.12.0-cuda11.3
- Tensorflow version (GPU?):
- Using GPU in script?:yes
- Using distributed or parallel set-up in script?:no

### Who can help
@sgugger
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- Benchmarks: @patrickvonplaten
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## Information

Model I am using (Bert, XLNet ...):XLNET

The problem arises when using:
* [ ] the official example scripts: (give details below)
I use the official notebook:how to fine-tuning a tokenClassification model
* [ ] my own modified scripts: (give details below)
my code is alomost the same as the official notebook.
link:https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb#scrollTo=h9lcmFol-_Le

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
token classification 
* [ ] my own task or dataset: (give details below)
I used my own dataset ,and use the datasets to load my own datasets.


when the training start, the process just stop at 2/66672 (steps),while the training process seems to continue, because after a while, the validation began, and the validation process bar still didn't show up, the training bar didn't move. 
I test my code in colab, there is no problem when running in colab environment, I uninstall all packages to ensure all the package version is correct ,but It didn't work. could anyone can help me
the matrix I use is seqeval. 
<img width="388" alt="WX20211126-001218@2x" src="https://user-images.githubusercontent.com/37979232/143475094-f21b7600-3446-4721-8156-9e210c00494f.png">
## To reproduce

Steps to reproduce the behavior:

1.run my code

here is my code snippets
`
model_name = model_checkpoint.split("/")[-1]

args = TrainingArguments(
    f"{model_name}-finetuned-NER",
    fp16=True,
    evaluation_strategy = "steps",
    learning_rate=2e-5,
    gradient_accumulation_steps= 8,
    per_device_train_batch_size=batch_size,
    per_device_eval_batch_size=batch_size,
    num_train_epochs=3,
    weight_decay=0.01,
    push_to_hub=False,
    logging_steps=100
)
`

<!-- If you have code snippets, error messages, stack traces please provide them here as well.
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the matrix I use is several
## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->
I hope the bar can run normally



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