Add documentation for BertJapanese (#11219)

* Start writing BERT-Japanese doc

* Fix typo, Update toctree

* Modify model file to use comment for document, Add examples

* Clean bert_japanese by make style

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Split a big code block into two

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Add prefix >>> to all lines in code blocks

* Clean bert_japanese by make fixup

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/model_doc/bert_japanese.rst

@@ -0,0 +1,78 @@
+.. 
+    Copyright 2020 The HuggingFace Team. All rights reserved.
+
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+    the License. You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+    an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+    specific language governing permissions and limitations under the License.
+
+BertJapanese
+-----------------------------------------------------------------------------------------------------------------------
+
+Overview
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The BERT models trained on Japanese text.
+
+There are models with two different tokenization methods:
+
+- Tokenize with MeCab and WordPiece. This requires some extra dependencies, `fugashi
+  <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.
+- Tokenize into characters.
+
+To use `MecabTokenizer`, you should ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install
+from source) to install dependencies.
+
+See `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__.
+
+Example of using a model with MeCab and WordPiece tokenization:
+
+.. code-block::
+
+  >>> import torch
+  >>> from transformers import AutoModel, AutoTokenizer 
+
+  >>> bertjapanese = AutoModel.from_pretrained("cl-tohoku/bert-base-japanese")
+  >>> tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese")
+
+  >>> ## Input Japanese Text
+  >>> line = "吾輩は猫である。"
+
+  >>> inputs = tokenizer(line, return_tensors="pt")
+
+  >>> print(tokenizer.decode(inputs['input_ids'][0]))
+  [CLS] 吾輩 は 猫 で ある 。 [SEP]
+
+  >>> outputs = bertjapanese(**inputs)
+
+Example of using a model with Character tokenization:
+
+.. code-block::
+
+  >>> bertjapanese = AutoModel.from_pretrained("cl-tohoku/bert-base-japanese-char")
+  >>> tokenizer = AutoTokenizer.from_pretrained("cl-tohoku/bert-base-japanese-char")
+
+  >>> ## Input Japanese Text
+  >>> line = "吾輩は猫である。"
+
+  >>> inputs = tokenizer(line, return_tensors="pt")
+
+  >>> print(tokenizer.decode(inputs['input_ids'][0]))
+  [CLS] 吾 輩 は 猫 で あ る 。 [SEP]
+
+  >>> outputs = bertjapanese(**inputs)
+
+Tips:
+
+- This implementation is the same as BERT, except for tokenization method. Refer to the :doc:`documentation of BERT
+  <bert>` for more usage examples.
+
+BertJapaneseTokenizer
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: transformers.BertJapaneseTokenizer
+    :members: