Update docs [ci skip]

website/docs/usage/layers-architectures.md

@@ -103,7 +103,7 @@ bit of validation goes a long way, especially if you
 tools to highlight these errors early. The config file is also validated at the
 beginning of training, to verify that all the types match correctly.
 
-<Accordion title="Tip: Static type checking in your editor" emoji="💡">
+<Accordion title="Tip: Static type checking in your editor">
 
 If you're using a modern editor like Visual Studio Code, you can
 [set up `mypy`](https://thinc.ai/docs/usage-type-checking#install) with the
@@ -143,11 +143,11 @@ nO = null
 
 spaCy has two additional built-in `textcat` architectures, and you can easily
 use those by swapping out the definition of the textcat's model. For instance,
-to use the simpel and fast [bag-of-words model](/api/architectures#TextCatBOW),
-you can change the config to:
+to use the simple and fast bag-of-words model
+[TextCatBOW](/api/architectures#TextCatBOW), you can change the config to:
 
 ```ini
-### config.cfg (excerpt)
+### config.cfg (excerpt) {highlight="6-10"}
 [components.textcat]
 factory = "textcat"
 labels = []
@@ -160,8 +160,9 @@ no_output_layer = false
 nO = null
 ```
 
-The details of all prebuilt architectures and their parameters, can be consulted
-on the [API page for model architectures](/api/architectures).
+For details on all pre-defined architectures shipped with spaCy and how to
+configure them, check out the [model architectures](/api/architectures)
+documentation.
 
 ### Defining sublayers {#sublayers}
 

website/docs/usage/training.md

@@ -669,10 +669,9 @@ def custom_logger(log_path):
 
 #### Example: Custom batch size schedule {#custom-code-schedule}
 
-You can also implement your own batch size schedule to use
-during training. The `@spacy.registry.schedules` decorator lets you register
-that function in the `schedules` [registry](/api/top-level#registry) and assign
-it a string name:
+You can also implement your own batch size schedule to use during training. The
+`@spacy.registry.schedules` decorator lets you register that function in the
+`schedules` [registry](/api/top-level#registry) and assign it a string name:
 
 > #### Why the version in the name?
 >
@@ -806,14 +805,22 @@ def filter_batch(size: int) -> Callable[[Iterable[Example]], Iterator[List[Examp
 
 ### Defining custom architectures {#custom-architectures}
 
-Built-in pipeline components such as the tagger or named entity recognizer are 
-constructed with default neural network [models](/api/architectures). 
-You can change the model architecture 
-entirely by implementing your own custom models and providing those in the config 
-when creating the pipeline component. See the
-documentation on
-[layers and model architectures](/usage/layers-architectures) for more details.
+Built-in pipeline components such as the tagger or named entity recognizer are
+constructed with default neural network [models](/api/architectures). You can
+change the model architecture entirely by implementing your own custom models
+and providing those in the config when creating the pipeline component. See the
+documentation on [layers and model architectures](/usage/layers-architectures)
+for more details.
 
+> ```ini
+> ### config.cfg
+> [components.tagger]
+> factory = "tagger"
+>
+> [components.tagger.model]
+> @architectures = "custom_neural_network.v1"
+> output_width = 512
+> ```
 
 ```python
 ### functions.py
@@ -828,16 +835,6 @@ def MyModel(output_width: int) -> Model[List[Doc], List[Floats2d]]:
     return create_model(output_width)
 ```
 
-```ini
-### config.cfg (excerpt)
-[components.tagger]
-factory = "tagger"
-
-[components.tagger.model]
-@architectures = "custom_neural_network.v1"
-output_width = 512
-```
-
 ## Internal training API {#api}
 
 <Infobox variant="warning">