Fix doc errors and typos across the board (#8139)

* Fix doc errors and typos across the board

* Fix a typo

* Fix the CI

* Fix more typos

* Fix CI

* More fixes

* Fix CI

* More fixes

* More fixes

docs/source/installation.md

@@ -80,9 +80,9 @@ cache home followed by ``/transformers/`` (even if you don't have PyTorch instal
 So if you don't have any specific environment variable set, the cache directory will be at
 ``~/.cache/torch/transformers/``.
 
-**Note:** If you have set a shell enviromnent variable for one of the predecessors of this library
+**Note:** If you have set a shell environment variable for one of the predecessors of this library
 (``PYTORCH_TRANSFORMERS_CACHE`` or ``PYTORCH_PRETRAINED_BERT_CACHE``), those will be used if there is no shell
-enviromnent variable for ``TRANSFORMERS_CACHE``.
+environment variable for ``TRANSFORMERS_CACHE``.
 
 ### Note on model downloads (Continuous Integration or large-scale deployments)
 

docs/source/migration.md

@@ -20,7 +20,7 @@ Here is a quick summary of what you should take care of when migrating from `pyt
 
 The main breaking change when migrating from `pytorch-pretrained-bert` to 🤗 Transformers is that the models forward method always outputs a `tuple` with various elements depending on the model and the configuration parameters.
 
-The exact content of the tuples for each model are detailled in the models' docstrings and the [documentation](https://huggingface.co/transformers/).
+The exact content of the tuples for each model are detailed in the models' docstrings and the [documentation](https://huggingface.co/transformers/).
 
 In pretty much every case, you will be fine by taking the first element of the output as the output you previously used in `pytorch-pretrained-bert`.
 
@@ -109,7 +109,7 @@ for batch in train_data:
     loss.backward()
     optimizer.step()
 
-### In 🤗 Transformers, optimizer and schedules are splitted and instantiated like this:
+### In 🤗 Transformers, optimizer and schedules are split and instantiated like this:
 optimizer = AdamW(model.parameters(), lr=lr, correct_bias=False)  # To reproduce BertAdam specific behavior set correct_bias=False
 scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)  # PyTorch scheduler
 ### and used like this:

docs/source/model_sharing.rst

@@ -119,7 +119,7 @@ Other files can safely be deleted.
 Upload your model with the CLI
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Now go in a terminal and run the following command. It should be in the virtual enviromnent where you installed 🤗
+Now go in a terminal and run the following command. It should be in the virtual environment where you installed 🤗
 Transformers, since that command :obj:`transformers-cli` comes from the library.
 
 .. code-block::

docs/source/task_summary.rst

@@ -510,8 +510,8 @@ As a default all models apply *Top-K* sampling when used in pipelines, as config
 
 
 Here, the model generates a random text with a total maximal length of *50* tokens from context *"As far as I am
-concerned, I will"*. The default arguments of ``PreTrainedModel.generate()`` can be directly overriden in the pipeline,
-as is shown above for the argument ``max_length``.
+concerned, I will"*. The default arguments of ``PreTrainedModel.generate()`` can be directly overridden in the
+pipeline, as is shown above for the argument ``max_length``.
 
 Here is an example of text generation using ``XLNet`` and its tokenzier.