custom-architectures section

website/docs/usage/training.md

@@ -669,7 +669,7 @@ def custom_logger(log_path):
 
 #### Example: Custom batch size schedule {#custom-code-schedule}
 
-For example, let's say you've implemented your own batch size schedule to use
+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:
@@ -806,7 +806,37 @@ def filter_batch(size: int) -> Callable[[Iterable[Example]], Iterator[List[Examp
 
 ### Defining custom architectures {#custom-architectures}
 
-<!-- TODO: this should probably move to new section on models -->
+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.
+
+
+```python
+### functions.py
+from typing import List
+from thinc.types import Floats2d
+from thinc.api import Model
+import spacy
+from spacy.tokens import Doc
+
+@spacy.registry.architectures("custom_neural_network.v1")
+def MyModel(output_width: int) -> Model[List[Doc], List[Floats2d]]:
+    # ...
+```
+
+```ini
+### config.cfg (excerpt)
+[components.tagger]
+factory = "tagger"
+
+[components.tagger.model]
+@architectures = "custom_neural_network.v1"
+output_width = 512
+```
 
 ## Internal training API {#api}