Fix some typos in the docs (#14126)

* Fix some typos in the docs

* Fix a styling issue

* Fix code quality check error

docs/README.md

@@ -166,7 +166,7 @@ Values that should be put in `code` should either be surrounded by double backti
 an object using the :obj: syntax: :obj:\`like so\`. Note that argument names and objects like True, None or any strings
 should usually be put in `code`.
 
-When mentionning a class, it is recommended to use the :class: syntax as the mentioned class will be automatically
+When mentioning a class, it is recommended to use the :class: syntax as the mentioned class will be automatically
 linked by Sphinx: :class:\`~transformers.XXXClass\`
 
 When mentioning a function, it is recommended to use the :func: syntax as the mentioned function will be automatically

docs/source/installation.md

@@ -79,9 +79,9 @@ Here is how to quickly install `transformers` from source:
 pip install git+https://github.com/huggingface/transformers
 ```
 
-Note that this will install not the latest released version, but the bleeding edge `master` version, which you may want to use in case a bug has been fixed since the last official release and a new release hasn't  been yet rolled out.
+Note that this will install not the latest released version, but the bleeding edge `master` version, which you may want to use in case a bug has been fixed since the last official release and a new release hasn't been yet rolled out.
 
-While we strive to keep `master` operational at all times, if you notice some issues, they usually get fixed within a few hours or a day and and you're more than welcome to help us detect any problems by opening an [Issue](https://github.com/huggingface/transformers/issues) and this way, things will get fixed even sooner.
+While we strive to keep `master` operational at all times, if you notice some issues, they usually get fixed within a few hours or a day and you're more than welcome to help us detect any problems by opening an [Issue](https://github.com/huggingface/transformers/issues) and this way, things will get fixed even sooner.
 
 Again, you can run:
 

docs/source/model_sharing.rst

@@ -76,7 +76,7 @@ Transformers:
 It will store your access token in the Hugging Face cache folder (by default :obj:`~/.cache/`).
 
 If you don't have an easy access to a terminal (for instance in a Colab session), you can find a token linked to your
-acount by going on `huggingface.co <https://huggingface.co/>`, click on your avatar on the top left corner, then on
+account by going on `huggingface.co <https://huggingface.co/>`, click on your avatar on the top left corner, then on
 `Edit profile` on the left, just beneath your profile picture. In the submenu `API Tokens`, you will find your API
 token that you can just copy.
 

docs/source/tokenizer_summary.rst

@@ -182,9 +182,10 @@ base vocabulary, we obtain:
 
 BPE then counts the frequency of each possible symbol pair and picks the symbol pair that occurs most frequently. In
 the example above ``"h"`` followed by ``"u"`` is present `10 + 5 = 15` times (10 times in the 10 occurrences of
-``"hug"``, 5 times in the 5 occurrences of "hugs"). However, the most frequent symbol pair is ``"u"`` followed by "g",
-occurring `10 + 5 + 5 = 20` times in total. Thus, the first merge rule the tokenizer learns is to group all ``"u"``
-symbols followed by a ``"g"`` symbol together. Next, "ug" is added to the vocabulary. The set of words then becomes
+``"hug"``, 5 times in the 5 occurrences of ``"hugs"``). However, the most frequent symbol pair is ``"u"`` followed by
+``"g"``, occurring `10 + 5 + 5 = 20` times in total. Thus, the first merge rule the tokenizer learns is to group all
+``"u"`` symbols followed by a ``"g"`` symbol together. Next, ``"ug"`` is added to the vocabulary. The set of words then
+becomes
 
 .. code-block::
 

docs/source/training.rst

@@ -335,7 +335,7 @@ scheduler. The default optimizer used by the :class:`~transformers.Trainer` is :
 
     optimizer = AdamW(model.parameters(), lr=5e-5)
 
-Finally, the learning rate scheduler used by default it just a linear decay form the maximum value (5e-5 here) to 0:
+Finally, the learning rate scheduler used by default is just a linear decay from the maximum value (5e-5 here) to 0:
 
 .. code-block:: python