text: 
  
  TITLE
  Bug SqueezeBERT stops with no error 
  
  COMMENTS
  2
  
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  ## 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: 
  - Platform: Ubuntu
  - Python version: anaconda python 3.7
  - PyTorch version (GPU?): 
  - Tensorflow version (GPU?):
  - Using GPU in script?:
  - Using distributed or parallel set-up in the script?:
  
  The GPUs available were : 
  ```
  Geforce GTX 980 4gb
  Geforce GTX Titan 12gb
  ```
  ```
  transformers == 4.1.1
  torch==1.7.0
  torchvision == 0.8.1
  ```
  
  ### 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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   albert, bert, GPT2, XLM: @LysandreJik 
   tokenizers: @mfuntowicz
   Trainer: @sgugger
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   TextGeneration: @TevenLeScao 
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   nlp datasets: [different repo](https://github.com/huggingface/nlp)
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   tensorflow: @jplu
   examples/token-classification: @stefan-it
   documentation: @sgugger
   -->
  
  ## Information
  
  Model I am using (Bert, XLNet ...): SqueezeBERT
  
  The problem arises when using:
  * [ ] the official example scripts: (give details below)
  using [Sequence Classification with IMDb Reviews
  ](https://huggingface.co/transformers/custom_datasets.html#seq-imdb) example I have made my own script
  * [x] my own modified scripts: (give details below)
  The only changes I have made in this script are
  
  1.  to use Yelp dataset,
  2. use SqueezeBERT instead of DistilBERT, 
  3. also do a 5label sentiment...
  
  ```python
  training_args = TrainingArguments(
      output_dir='./SqueezeBERT_10ep_result',          # output directory
      per_device_train_batch_size=3,  # batch size per device during training
      per_device_eval_batch_size=3,   # batch size for evaluation
      warmup_steps=500,                # number of warmup steps for learning rate scheduler
      weight_decay=0.01,               # strength of weight decay
      logging_dir='./SqueezeBERT_10ep_log',            # directory for storing logs
      logging_steps=500,
      num_train_epochs=10,              # total number of training epochs
      evaluation_strategy="epoch",
      do_train=True,
      do_eval=True,
  )
  
  model = SqueezeBertForSequenceClassification.from_pretrained('squeezebert/squeezebert-mnli-headless', return_dict=True)
  model.num_labels = 5
  model.classifier = nn.Linear(768,5)
  
  
  
  from sklearn.metrics import accuracy_score, precision_recall_fscore_support
  
  
  def compute_metrics(pred):    
      
      labels = pred.label_ids
      preds = pred.predictions.argmax(-1)
      precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='macro')
      acc = accuracy_score(labels, preds)
  
      return {
          'accuracy': acc,
          'f1': f1,
          'precision': precision,
          'recall': recall
      }
  
  
  print("Displaying model architecture... !\n")
  print(model)
  print("Training model starting...!\n")
         
  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
      compute_metrics=compute_metrics,
  )
  
  trainer.train()
  
  
  ```
  The tasks I am working on is:
  * [ ] an official GLUE/SQUaD task: (give the name)
  * [x] my own task or dataset: (give details below) 
  Using the yelp full dataset
  
  ## To reproduce
  
  Steps to reproduce the behavior:
  
  1. Run the scripts as mentioned
  2. Reaching Epoch 3, it will suddenly stop using the GPU and although no error is showing up, nothing changes...
  3. the Last checkpoint that saves is `checkpoint-310000` 
  
  
  <!-- If you have code snippets, error messages, stack traces please provide them here as well.
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  ## Expected behavior
  
  <!-- A clear and concise description of what you would expect to happen. -->
  it should have just go on and finished the training process.
  