docs(pretrained_models): fix num parameters
Author: amineabdaoui
Pull request: yes

# What does this PR do?

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This PR corrects the number of parameters of pretrained BERT based models presented in the documentation.

Sometimes the difference between a given model and its pairs is important. For instance:
`bert-base-uncased` has **110M parameters** but `bert-base-multilingual-cased` has more than **178M parameters**, even if both models share the same architecture (12-layers, 768-hidden, 12-heads).

The difference is due to the vocabulary size:
`bert-base-uncased` uses a vocabulary of **30k** entries while `bert-base-multilingual-cased` uses a vocabulary of **119k** entries.

To compute the number of parameters:
``` python
from transformers import AutoModelForMaskedLM

bert_base = AutoModelForMaskedLM.from_pretrained('bert-base-uncased')
print(bert_base.num_parameters())

bert_multiling = AutoModelForMaskedLM.from_pretrained('bert-base-multilingual-cased')
print(bert_multiling.num_parameters())
```


## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dimiss the other checks if that's the case).
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      Pull Request section?



## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
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Particularly: @LysandreJik and @sgugger

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