diff --git a/.circleci/config.yml b/.circleci/config.yml
index 4d76bb6ae2..100109539b 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -56,7 +56,7 @@ jobs:
             RUN_CUSTOM_TOKENIZERS: yes
         steps:
             - checkout
-            - run: sudo pip install .[mecab,testing]
+            - run: sudo pip install .[ja,testing]
             - run: python -m pytest -s ./tests/test_tokenization_bert_japanese.py | tee output.txt
             - store_artifacts:
                 path: ~/transformers/output.txt
diff --git a/docs/source/pretrained_models.rst b/docs/source/pretrained_models.rst
index 27f048dc25..0ef6c976f1 100644
--- a/docs/source/pretrained_models.rst
+++ b/docs/source/pretrained_models.rst
@@ -74,14 +74,16 @@ For a list that includes community-uploaded models, refer to `https://huggingfac
 |                   |                                                            | (see `details on dbmdz repository <https://github.com/dbmdz/german-bert>`__).                                                         |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``cl-tohoku/bert-base-japanese``                           | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece.                                                               |
-|                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
+|                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
+|                   |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
+|                   |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
 |                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
 |                   | ``cl-tohoku/bert-base-japanese-whole-word-masking``        | | 12-layer, 768-hidden, 12-heads, 110M parameters.                                                                                    |
-|                   |                                                            | | Trained on Japanese text using Whole-Word-Masking. Text is tokenized with MeCab and WordPiece.                                      |
-|                   |                                                            | | `MeCab <https://taku910.github.io/mecab/>`__ is required for tokenization.                                                          |
+|                   |                                                            | | Trained on Japanese text. Text is tokenized with MeCab and WordPiece and this requires some extra dependencies,                     |
+|                   |                                                            | | `fugashi <https://github.com/polm/fugashi>`__ which is a wrapper around `MeCab <https://taku910.github.io/mecab/>`__.               |
+|                   |                                                            | | Use ``pip install transformers["ja"]`` (or ``pip install -e .["ja"]`` if you install from source) to install them.                  |
 |                   |                                                            |                                                                                                                                       |
 |                   |                                                            | (see `details on cl-tohoku repository <https://github.com/cl-tohoku/bert-japanese>`__).                                               |
 |                   +------------------------------------------------------------+---------------------------------------------------------------------------------------------------------------------------------------+
diff --git a/setup.cfg b/setup.cfg
index d630f0b3a9..e5467ab623 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -10,10 +10,10 @@ known_third_party =
     faiss
     fastprogress
     fire
+    fugashi
     git
     h5py
     matplotlib
-    MeCab
     nlp
     nltk
     numpy
diff --git a/setup.py b/setup.py
index c4fc91ab31..206c3e3540 100644
--- a/setup.py
+++ b/setup.py
@@ -65,7 +65,7 @@ if stale_egg_info.exists():
 
 extras = {}
 
-extras["mecab"] = ["mecab-python3<1"]
+extras["ja"] = ["fugashi>=1.0", "ipadic>=1.0,<2.0"]
 extras["sklearn"] = ["scikit-learn"]
 
 # keras2onnx and onnxconverter-common version is specific through a commit until 1.7.0 lands on pypi
@@ -97,7 +97,7 @@ extras["quality"] = [
     "isort @ git+git://github.com/timothycrosley/isort.git@e63ae06ec7d70b06df9e528357650281a3d3ec22#egg=isort",
     "flake8",
 ]
-extras["dev"] = extras["testing"] + extras["quality"] + ["mecab-python3<1", "scikit-learn", "tensorflow", "torch"]
+extras["dev"] = extras["testing"] + extras["quality"] + extras["ja"] + ["scikit-learn", "tensorflow", "torch"]
 
 setup(
     name="transformers",
diff --git a/src/transformers/tokenization_bert_japanese.py b/src/transformers/tokenization_bert_japanese.py
index 43ae8e1894..c3ede2c47e 100644
--- a/src/transformers/tokenization_bert_japanese.py
+++ b/src/transformers/tokenization_bert_japanese.py
@@ -185,9 +185,14 @@ class MecabTokenizer:
         self.never_split = never_split if never_split is not None else []
         self.normalize_text = normalize_text
 
-        import MeCab
+        import fugashi
+        import ipadic
 
-        self.mecab = MeCab.Tagger(mecab_option) if mecab_option is not None else MeCab.Tagger()
+        # Use ipadic by default (later options can override it)
+        mecab_option = mecab_option or ""
+        mecab_option = ipadic.MECAB_ARGS + " " + mecab_option
+
+        self.mecab = fugashi.GenericTagger(mecab_option)
 
     def tokenize(self, text, never_split=None, **kwargs):
         """Tokenizes a piece of text."""
@@ -197,21 +202,13 @@ class MecabTokenizer:
         never_split = self.never_split + (never_split if never_split is not None else [])
         tokens = []
 
-        mecab_output = self.mecab.parse(text)
+        for word in self.mecab(text):
+            token = word.surface
 
-        cursor = 0
-        for line in mecab_output.split("\n"):
-            if line == "EOS":
-                break
-
-            token, _ = line.split("\t")
-            token_start = text.index(token, cursor)
-            token_end = token_start + len(token)
             if self.do_lower_case and token not in never_split:
                 token = token.lower()
 
             tokens.append(token)
-            cursor = token_end
 
         return tokens