Token Classification OOM
I am using the Token Classification Example on my dataset. It has around 20k lines for train and around 2k lines for validation and 2k for the test dataset. I have used the following example for my dataset.

https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb

After training the model, when eval is called it eats up all the GPU memory even on batch size 1.

Here is the output.
```shell
Some weights of the model checkpoint at /home/irfan/Downloads/bert-base-uncased were not used when initializing BertForTokenClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']
- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForTokenClassification were not initialized from the model checkpoint at /home/irfan/Downloads/bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Reusing dataset gector (/home/irfan/.cache/huggingface/datasets/gector/gector config/1.0.0/410838f652242763258c1912a4316a2d985c51d27bbb94a3301ad15cde38de06)
100%|██████████| 29/29 [00:01<00:00, 24.00ba/s]
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 25%|██▍       | 7000/28347 [10:22<31:18, 11.36it/s]{'loss': 0.6917, 'learning_rate': 1.5061205771333829e-05, 'epoch': 0.25}
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 67%|██████▋   | 19000/28347 [27:53<15:01, 10.37it/s]{'loss': 0.7156, 'learning_rate': 6.594701379334675e-06, 'epoch': 0.67}
 69%|██████▉   | 19500/28347 [28:41<13:53, 10.62it/s]{'loss': 0.6574, 'learning_rate': 6.241930363001376e-06, 'epoch': 0.69}
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 85%|████████▍ | 24000/28347 [35:09<06:01, 12.03it/s]{'loss': 0.6603, 'learning_rate': 3.0669912160016936e-06, 'epoch': 0.85}
 86%|████████▋ | 24500/28347 [35:50<05:11, 12.36it/s]{'loss': 0.5817, 'learning_rate': 2.7142201996683953e-06, 'epoch': 0.86}
 88%|████████▊ | 25000/28347 [36:32<04:34, 12.20it/s]{'loss': 0.6164, 'learning_rate': 2.3614491833350974e-06, 'epoch': 0.88}
 90%|████████▉ | 25500/28347 [37:13<03:57, 11.98it/s]{'loss': 0.6458, 'learning_rate': 2.0086781670017996e-06, 'epoch': 0.9}
 92%|█████████▏| 26000/28347 [37:55<03:19, 11.77it/s]{'loss': 0.6093, 'learning_rate': 1.6559071506685013e-06, 'epoch': 0.92}
 93%|█████████▎| 26500/28347 [38:36<02:29, 12.33it/s]{'loss': 0.6713, 'learning_rate': 1.3031361343352032e-06, 'epoch': 0.93}
 95%|█████████▌| 27000/28347 [39:18<01:52, 11.97it/s]{'loss': 0.6891, 'learning_rate': 9.503651180019051e-07, 'epoch': 0.95}
 97%|█████████▋| 27500/28347 [39:59<01:09, 12.19it/s]{'loss': 0.7025, 'learning_rate': 5.975941016686069e-07, 'epoch': 0.97}
 99%|█████████▉| 28000/28347 [40:43<00:29, 11.58it/s]{'loss': 0.6524, 'learning_rate': 2.4482308533530886e-07, 'epoch': 0.99}
100%|██████████| 28347/28347 [41:11<00:00, 11.47it/s]
{'train_runtime': 2471.9347, 'train_samples_per_second': 11.468, 'epoch': 1.0}
 35%|███▍      | 2276/6574 [01:49<06:12, 11.55it/s]Traceback (most recent call last):
  File "/home/irfan/PycharmProjects/GecPytorch/token_classification.py", line 62, in <module>
    trainer.evaluate()
  File "/home/irfan/environments/Allen/lib/python3.6/site-packages/transformers/trainer.py", line 1764, in evaluate
    metric_key_prefix=metric_key_prefix,
  File "/home/irfan/environments/Allen/lib/python3.6/site-packages/transformers/trainer.py", line 1900, in prediction_loop
    preds_host = logits if preds_host is None else nested_concat(preds_host, logits, padding_index=-100)
  File "/home/irfan/environments/Allen/lib/python3.6/site-packages/transformers/trainer_pt_utils.py", line 98, in nested_concat
    return torch_pad_and_concatenate(tensors, new_tensors, padding_index=padding_index)
  File "/home/irfan/environments/Allen/lib/python3.6/site-packages/transformers/trainer_pt_utils.py", line 66, in torch_pad_and_concatenate
    result = tensor1.new_full(new_shape, padding_index)
RuntimeError: CUDA out of memory. Tried to allocate 2.12 GiB (GPU 0; 7.80 GiB total capacity; 3.81 GiB already allocated; 2.18 GiB free; 3.97 GiB reserved in total by PyTorch)
 35%|███▍      | 2276/6574 [01:49<03:26, 20.77it/s]
```

I'm using Python 3.6, GPU RTX 2060 super, OS Ubuntu 18.04

@sgugger 
You should use `eval_accumulation_steps=n` (for instance 20) to have the predictions be moved to the CPU every n steps  during evaluation (n should be lower than the step you get an OOM error)<|||||>Thanks, it worked perfectly.<|||||>> You should use `eval_accumulation_steps=n` (for instance 20) to have the predictions be moved to the CPU every n steps during evaluation (n should be lower than the step you get an OOM error)

Thank you so much. I have been stuck at the point for several days until I landed here.