[Docs] Fix typos (#814)

* Update model.md

* Update model.md

* Update model.md

* Update evaluation.md

* Update param_scheduler.md

* Update hook.md

* Fix lint issue

* fix lint issues

Co-authored-by: shanmo <shanmo1412@gmail.com>

docs/en/tutorials/hook.md

@@ -73,7 +73,7 @@ The four features mentioned above are described below.
 
 - Save checkpoints by interval, and support saving them by epoch or iteration
 
-  Suppose we train a total of 20 epochs and want to save the checkpoints every 5 epochs, the following configuration will help us to achieve this requirement.
+  Suppose we train a total of 20 epochs and want to save the checkpoints every 5 epochs, the following configuration will help us achieve this requirement.
 
   ```python
   # the default value of by_epoch is True

docs/en/tutorials/model.md

@@ -113,7 +113,7 @@ class MMResNet50(BaseModel):
         elif mode == 'predict':
             return x, labels
 
-    # train_step, val_step and test_step have been implemented in BaseModel. we
+    # train_step, val_step and test_step have been implemented in BaseModel.
     # We list the equivalent code here for better understanding
     def train_step(self, data, optim_wrapper):
         data = self.data_preprocessor(data)
@@ -135,7 +135,7 @@ class MMResNet50(BaseModel):
 
 Now, you may have a deeper understanding of dataflow, and can answer the first question in [Runner and model](#runner-and-model).
 
-`BaseModel.train_step` implements the standard optimization standard, and if we want to customize a new optimization process, we can override it in the subclass. However, it is important to note that we need to make sure that `train_step` returns a loss dict.
+`BaseModel.train_step` implements the standard optimization, and if we want to customize a new optimization process, we can override it in the subclass. However, it is important to note that we need to make sure that `train_step` returns a loss dict.
 
 ## DataPreprocessor
 
@@ -155,7 +155,7 @@ The answer to the first question is that: `MMResNet50` inherit from `BaseModel`,
 class BaseDataPreprocessor(nn.Module):
     def forward(self, data, training=True):  # ignore the training parameter here
         # suppose data given by CIFAR10 is a tuple. Actually
-        # BaseDataPreprocessor could move varies type of data
+        # BaseDataPreprocessor could move various type of data
         # to target device.
         return tuple(_data.cuda() for _data in data)
 ```

docs/en/tutorials/param_scheduler.md

@@ -11,8 +11,8 @@ We first introduce how to use PyTorch's `torch.optim.lr_scheduler` to adjust lea
 
 <details>
 <summary>How to use PyTorch's builtin learning rate scheduler?</summary>
-Here is an example which refers from [PyTorch official documentation](https://pytorch.org/docs/stable/optim.html):
 
+Here is an example which refers from [PyTorch official documentation](https://pytorch.org/docs/stable/optim.html):
 Initialize an ExponentialLR object, and call the `step` method after each training epoch.
 
 ```python

docs/zh_cn/tutorials/evaluation.md

@@ -71,7 +71,7 @@ class SimpleAccuracy(BaseMetric):
     def process(self, data_batch: Sequence[dict], data_samples: Sequence[dict]):
         """Process one batch of data and predictions. The processed
         Results should be stored in `self.results`, which will be used
-        to computed the metrics when all batches have been processed.
+        to compute the metrics when all batches have been processed.
 
         Args:
             data_batch (Sequence[Tuple[Any, dict]]): A batch of data