Minor documentation revisions from copyediting (#9266)

* typo: Revise "checkout" to "check out"

* typo: Change "seemlessly" to "seamlessly"

* typo: Close parentheses in "Using the tokenizer"

* typo: Add closing parenthesis to supported models aside

* docs: Treat ``position_ids`` as plural

Alternatively, the word "argument" could be added to make the subject singular.

* docs: Remove comma, making subordinate clause

* docs: Remove comma separating verb and direct object

* docs: Fix typo ("next" -> "text")

* docs: Reverse phrase order to simplify sentence

* docs: "quicktour" -> "quick tour"

* docs: "to throw" -> "from throwing"

* docs: Remove disruptive newline in padding/truncation section

* docs: "show exemplary" -> "show examples of"

* docs: "much harder as" -> "much harder than"

* docs: Fix typo "seach" -> "search"

* docs: Fix subject-verb disagreement in WordPiece description

* docs: Fix style in preprocessing.rst

docs/source/task_summary.rst

@@ -327,7 +327,7 @@ Masked Language Modeling
 Masked language modeling is the task of masking tokens in a sequence with a masking token, and prompting the model to
 fill that mask with an appropriate token. This allows the model to attend to both the right context (tokens on the
 right of the mask) and the left context (tokens on the left of the mask). Such a training creates a strong basis for
-downstream tasks, requiring bi-directional context such as SQuAD (question answering, see `Lewis, Lui, Goyal et al.
+downstream tasks requiring bi-directional context, such as SQuAD (question answering, see `Lewis, Lui, Goyal et al.
 <https://arxiv.org/abs/1910.13461>`__, part 4.2).
 
 Here is an example of using pipelines to replace a mask from a sequence:
@@ -657,7 +657,7 @@ Here are the expected results:
         {'word': 'Bridge', 'score': 0.990249514579773, 'entity': 'I-LOC'}
     ]
 
-Note, how the tokens of the sequence "Hugging Face" have been identified as an organisation, and "New York City",
+Note how the tokens of the sequence "Hugging Face" have been identified as an organisation, and "New York City",
 "DUMBO" and "Manhattan Bridge" have been identified as locations.
 
 Here is an example of doing named entity recognition, using a model and a tokenizer. The process is the following: