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Fix some typos in the docs (#14126)

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* Fix some typos in the docs

* Fix a styling issue

* Fix code quality check error
This commit is contained in:
Reza Gharibi
2021-10-25 15:10:44 +03:30
committed by GitHub
parent 95bab53868
commit 6b83090e80
5 changed files with 9 additions and 8 deletions
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2
docs/README.md
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@@ -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

4
docs/source/installation.md
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@@ -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:

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docs/source/model_sharing.rst
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@@ -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.

7
docs/source/tokenizer_summary.rst
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@@ -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::

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docs/source/training.rst
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@@ -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