returned value from parse method of MeCab >= 1.0.0 was changed
Author: gorogoroyasu
Pull request: yes

As shown below, returned value from MeCab's parse method was changed, 
we need to fix MecabTokenizer in BertJapaneseTokenizer.

# mecab-python3==1.0.0 (latest version)

```
root@713173e4bace:/# pip freeze | grep mecab
mecab-python3==1.0.0
root@713173e4bace:/# python
Python 3.8.3 (default, Jul  7 2020, 11:33:46)
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import MeCab
>>> m = MeCab.Tagger()
>>> m.parse('こんにちは')
'こんにちは\tコンニチワ\tコンニチハ\t今日は\t感動詞-一般\t\t\t5\nEOS\n'
```

# mecab-python3==0.996.5 (previous version)

```
root@713173e4bace:/# pip install mecab-python3==0.996.5 --upgrade
Collecting mecab-python3==0.996.5
  Downloading mecab_python3-0.996.5-cp38-cp38-manylinux2010_x86_64.whl (17.1 MB)
     |████████████████████████████████| 17.1 MB 1.2 MB/s
Installing collected packages: mecab-python3
  Attempting uninstall: mecab-python3
    Found existing installation: mecab-python3 1.0.0
    Uninstalling mecab-python3-1.0.0:
      Successfully uninstalled mecab-python3-1.0.0
Successfully installed mecab-python3-0.996.5
root@713173e4bace:/# pip freeze | grep mecab
mecab-python3==0.996.5
root@713173e4bace:/# python
Python 3.8.3 (default, Jul  7 2020, 11:33:46)
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import MeCab
>>> m = MeCab.Tagger()
>>> m.parse('こんにちは')
'こんにちは\t感動詞,*,*,*,*,*,こんにちは,コンニチハ,コンニチワ\nEOS\n'
```


I found other issues related to MeCab>=1.0.0 ( like #5392 ) and understood the latest version of MeCab will not be supported soon. So if this PR is too early to merge, do not hesitate to close.
Thanks.

Comments:
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/5598?src=pr&el=h1) Report
> Merging [#5598](https://codecov.io/gh/huggingface/transformers/pull/5598?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/f82a2a5e8e6827343322a4a9831924c5bb9bd2b2&el=desc) will **increase** coverage by `0.18%`.
> The diff coverage is `0.00%`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/5598/graphs/tree.svg?width=650&height=150&src=pr&token=9qOlN6Hb1c)](https://codecov.io/gh/huggingface/transformers/pull/5598?src=pr&el=tree)

```diff
@@            Coverage Diff             @@
##           master    #5598      +/-   ##
==========================================
+ Coverage   76.69%   76.88%   +0.18%     
==========================================
  Files         145      145              
  Lines       25351    25351              
==========================================
+ Hits        19444    19491      +47     
+ Misses       5907     5860      -47     
```


| [Impacted Files](https://codecov.io/gh/huggingface/transformers/pull/5598?src=pr&el=tree) | Coverage Δ | |
|---|---|---|
| [src/transformers/tokenization\_bert\_japanese.py](https://codecov.io/gh/huggingface/transformers/pull/5598/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy90b2tlbml6YXRpb25fYmVydF9qYXBhbmVzZS5weQ==) | `30.48% <0.00%> (ø)` | |
| [src/transformers/modeling\_tf\_distilbert.py](https://codecov.io/gh/huggingface/transformers/pull/5598/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9kaXN0aWxiZXJ0LnB5) | `64.90% <0.00%> (-33.90%)` | :arrow_down: |
| [src/transformers/generation\_tf\_utils.py](https://codecov.io/gh/huggingface/transformers/pull/5598/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9nZW5lcmF0aW9uX3RmX3V0aWxzLnB5) | `86.46% <0.00%> (+0.25%)` | :arrow_up: |
| [src/transformers/modeling\_tf\_electra.py](https://codecov.io/gh/huggingface/transformers/pull/5598/diff?src=pr&el=tree#diff-c3JjL3RyYW5zZm9ybWVycy9tb2RlbGluZ190Zl9lbGVjdHJhLnB5) | `95.53% <0.00%> (+69.51%)` | :arrow_up: |

------

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Thanks for your PR. The problem is that, even after doing this, the tokenization returned is not the same, which is the main reason we pinned mecab to <1 (with no plan to support v1 for now): since it changes the tokenization, it will break models on the Hub that were pretrained with the old tokenization.

Thanks for your reply and summary of the problem.
I didn't realize the breaking changes of tokenizer...
For now, I'll keep using <1 versions.