Cannot reproduce example token classification GermEval 2014 (German NER) dataset
## Environment info
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- `transformers` version: 3.2.0
- Platform:  Linux-4.4.0-131-generic-x86_64-with-Ubuntu-18.04-bionic
- Python version: 3.6.8
- PyTorch version (GPU?): 1.3.1 (True)
- Tensorflow version (GPU?): 2.2.0 (False)
- Using GPU in script?: True
- Using distributed or parallel set-up in script?: False

### Who can help

@stefan-it Please help.
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 albert, bert, GPT2, XLM: @LysandreJik 
 tokenizers: @mfuntowicz
 Trainer: @sgugger
 Speed and Memory Benchmarks: @patrickvonplaten
 Model Cards: @julien-c
 Translation: @sshleifer
 Summarization: @sshleifer
 TextGeneration: @TevenLeScao 
 examples/distillation: @VictorSanh
 nlp datasets: [different repo](https://github.com/huggingface/nlp)
 rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
 Text Generation: @TevenLeScao
 blenderbot: @mariamabarham
 Bart: @sshleifer
 Marian: @sshleifer
 T5: @patrickvonplaten
 Longformer/Reformer: @patrickvonplaten
 TransfoXL/XLNet: @TevenLeScao 
 examples/seq2seq: @sshleifer
 examples/bert-loses-patience: @JetRunner
 tensorflow: @jplu
 examples/token-classification: @stefan-it
 documentation: @sgugger
 -->

## Information

Model I am using (Bert, XLNet ...): bert-base-multilingual-cased

The problem arises when using:

* [x] the official example scripts: (give details below)

I am running the pytorch version:
transformers/examples/token-classification/run_ner.py


The tasks I am working on is:
* [x] an official GLUE/SQUaD task: (give the name)

GermEval 2014 (German NER) dataset

## To reproduce

Steps to reproduce the behavior:

1. download dataset: https://drive.google.com/drive/folders/1kC0I2UGl2ltrluI9NqDjaQJGw5iliw_J?usp=sharing
2. Because our accese for training model can not access Internet, I download the pretrained model here: [https://huggingface.co/bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased), and put all the downloaded files at transformers/examples/token-classification/bert-base-multilingual-cased
3. 
```
cat NER-de-train.tsv | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > train.txt.tmp
cat NER-de-dev.tsv | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > dev.txt.tmp
cat NER-de-test.tsv | grep -v "^#" | cut -f 2,3 | tr '\t' ' ' > test.txt.tmp

export MAX_LENGTH=128
export BERT_MODEL=./bert-base-multilingual-cased

python3 scripts/preprocess.py train.txt.tmp $BERT_MODEL $MAX_LENGTH > train.txt
python3 scripts/preprocess.py dev.txt.tmp $BERT_MODEL $MAX_LENGTH > dev.txt
python3 scripts/preprocess.py test.txt.tmp $BERT_MODEL $MAX_LENGTH > test.txt

cat train.txt dev.txt test.txt | cut -d " " -f 2 | grep -v "^$"| sort | uniq > labels.txt

export OUTPUT_DIR=germeval-model
export BATCH_SIZE=32
export NUM_EPOCHS=3
export SAVE_STEPS=750
export SEED=1

python3 run_ner.py --data_dir ./ \
--labels ./labels.txt \
--model_name_or_path $BERT_MODEL \
--output_dir $OUTPUT_DIR \
--max_seq_length  $MAX_LENGTH \
--num_train_epochs $NUM_EPOCHS \
--per_device_train_batch_size $BATCH_SIZE \
--save_steps $SAVE_STEPS \
--seed $SEED \
--do_train \
--do_eval \
--do_predict

```

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## Expected behavior

The f1 score on evaluation and test should be `0.8784592370979806` and `0.8624150210424085` as the README write. However, by runing the script above, on one V100 GPU, I get `0.83919` on evaluation, and `0.81673` on test, much lower than expected.

<!-- A clear and concise description of what you would expect to happen. -->

I find that after deleting the cache, the results can be reproduced. I guess that is because in my first attempt, I used the wrong arguments setting, and something is cached. Then although I fixed the setting later, the code alway loads from the wrong cache. 