"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference" when I am finetuning on distilert pretrained model, After printing this it is taking a lot of time and using only one CPU, how can we parallelized to all the cores in the system ( even I hve 8 GPU's but it is not using tht)
# ❓ Questions & Help

<!-- The GitHub issue tracker is primarly intended for bugs, feature requests,
     new models and benchmarks, and migration questions. For all other questions,
     we direct you to the Hugging Face forum: https://discuss.huggingface.co/ .
     You can also try Stack Overflow (SO) where a whole community of PyTorch and
     Tensorflow enthusiast can help you out. In this case, make sure to tag your
     question with the right deep learning framework as well as the
     huggingface-transformers tag: 
     https://stackoverflow.com/questions/tagged/huggingface-transformers 
     -->

## Details
<!-- Description of your issue -->

<!-- You should first ask your question on the forum or SO, and only if
     you didn't get an answer ask it here on GitHub. -->
**A link to original question on the forum/Stack Overflow**:
Hi, could you fill in the issue template rather than the issue title?<|||||>python ./examples/text-classification/run_glue.py \
    --model_name_or_path ./examples/language-modeling/output/SIA_Ontopof_RISK/DistilBERT/Trained_on_iRisk/ \
    --do_train  \
    --do_predict \
    --task_name=mrpc \
    --data_dir=/mnt/share/app/proj/ph/com/gbl/irisk/project_ph_com_irisk/Udita/Code_evaluation/Data//Train_Test_Data_BERTformat_Biz13Aug/BusinessRules_more_0s/ \
    --output_dir=./proc_data/mrpc/BusinessRules_more_0s   \
    --max_seq_length=512   \
    --per_device_train_batch_size=24   \
    --per_device_eval_batch_size=32 \
    --gradient_accumulation_steps=1 \
    --max_steps=16323 \
    --save_steps=10000 \


This is the command i am executing (finetuning a distilbert on a customer pre trained on a specific dataset)

Train.tsv file is of 1GB.

Feature creation is taking longer time(more than 24 hours and it is still running)

![image](https://user-images.githubusercontent.com/36273175/92735019-abbaa000-f396-11ea-86c4-bc40fe6bc921.png)

 <|||||>![image](https://user-images.githubusercontent.com/36273175/92735247-e15f8900-f396-11ea-92cf-dcbcc72caa6f.png)
<|||||>This warning means that the weights of `pre_classifier` and `classifier` have not been initialized by your checkpoint. This is normal since you seem to be loading a checkpoint right after a language modeling training. These layers will now be trained for sequence classification using the command you've shown.<|||||>Hi, Can we use multi core CPU's here, if yes, where to change and in which function?

If i look at all the cores are not utilized. <|||||>![image](https://user-images.githubusercontent.com/36273175/92739146-35b83800-f39a-11ea-9166-f68df50b528a.png)
<|||||>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.
