diff --git a/spacy/ml/models/entity_linker.py b/spacy/ml/models/entity_linker.py
index f37203b1b..21e1c53b9 100644
--- a/spacy/ml/models/entity_linker.py
+++ b/spacy/ml/models/entity_linker.py
@@ -8,7 +8,7 @@ from ...kb import KnowledgeBase, Candidate, get_candidates
 from ...vocab import Vocab
 
 
-@registry.architectures.register("spacy.EntityLinker.v1")
+@registry.architectures("spacy.EntityLinker.v1")
 def build_nel_encoder(tok2vec: Model, nO: Optional[int] = None) -> Model:
     with Model.define_operators({">>": chain, "**": clone}):
         token_width = tok2vec.get_dim("nO")
@@ -25,7 +25,7 @@ def build_nel_encoder(tok2vec: Model, nO: Optional[int] = None) -> Model:
     return model
 
 
-@registry.misc.register("spacy.KBFromFile.v1")
+@registry.misc("spacy.KBFromFile.v1")
 def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
     def kb_from_file(vocab):
         kb = KnowledgeBase(vocab, entity_vector_length=1)
@@ -35,7 +35,7 @@ def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
     return kb_from_file
 
 
-@registry.misc.register("spacy.EmptyKB.v1")
+@registry.misc("spacy.EmptyKB.v1")
 def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
     def empty_kb_factory(vocab):
         return KnowledgeBase(vocab=vocab, entity_vector_length=entity_vector_length)
@@ -43,6 +43,6 @@ def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
     return empty_kb_factory
 
 
-@registry.misc.register("spacy.CandidateGenerator.v1")
+@registry.misc("spacy.CandidateGenerator.v1")
 def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
     return get_candidates
diff --git a/spacy/ml/models/multi_task.py b/spacy/ml/models/multi_task.py
index 7c0589bff..8aa0f3c2b 100644
--- a/spacy/ml/models/multi_task.py
+++ b/spacy/ml/models/multi_task.py
@@ -16,7 +16,7 @@ if TYPE_CHECKING:
     from ...tokens import Doc  # noqa: F401
 
 
-@registry.architectures.register("spacy.PretrainVectors.v1")
+@registry.architectures("spacy.PretrainVectors.v1")
 def create_pretrain_vectors(
     maxout_pieces: int, hidden_size: int, loss: str
 ) -> Callable[["Vocab", Model], Model]:
@@ -40,7 +40,7 @@ def create_pretrain_vectors(
     return create_vectors_objective
 
 
-@registry.architectures.register("spacy.PretrainCharacters.v1")
+@registry.architectures("spacy.PretrainCharacters.v1")
 def create_pretrain_characters(
     maxout_pieces: int, hidden_size: int, n_characters: int
 ) -> Callable[["Vocab", Model], Model]:
diff --git a/spacy/ml/models/parser.py b/spacy/ml/models/parser.py
index da53f562e..861094209 100644
--- a/spacy/ml/models/parser.py
+++ b/spacy/ml/models/parser.py
@@ -10,7 +10,7 @@ from ..tb_framework import TransitionModel
 from ...tokens import Doc
 
 
-@registry.architectures.register("spacy.TransitionBasedParser.v1")
+@registry.architectures("spacy.TransitionBasedParser.v1")
 def transition_parser_v1(
     tok2vec: Model[List[Doc], List[Floats2d]],
     state_type: Literal["parser", "ner"],
@@ -31,7 +31,7 @@ def transition_parser_v1(
     )
 
 
-@registry.architectures.register("spacy.TransitionBasedParser.v2")
+@registry.architectures("spacy.TransitionBasedParser.v2")
 def transition_parser_v2(
     tok2vec: Model[List[Doc], List[Floats2d]],
     state_type: Literal["parser", "ner"],
diff --git a/spacy/ml/models/tagger.py b/spacy/ml/models/tagger.py
index 09405214c..87944e305 100644
--- a/spacy/ml/models/tagger.py
+++ b/spacy/ml/models/tagger.py
@@ -6,7 +6,7 @@ from ...util import registry
 from ...tokens import Doc
 
 
-@registry.architectures.register("spacy.Tagger.v1")
+@registry.architectures("spacy.Tagger.v1")
 def build_tagger_model(
     tok2vec: Model[List[Doc], List[Floats2d]], nO: Optional[int] = None
 ) -> Model[List[Doc], List[Floats2d]]:
diff --git a/spacy/ml/models/textcat.py b/spacy/ml/models/textcat.py
index 0234530e6..a1855c5a0 100644
--- a/spacy/ml/models/textcat.py
+++ b/spacy/ml/models/textcat.py
@@ -15,7 +15,7 @@ from ...tokens import Doc
 from .tok2vec import get_tok2vec_width
 
 
-@registry.architectures.register("spacy.TextCatCNN.v1")
+@registry.architectures("spacy.TextCatCNN.v1")
 def build_simple_cnn_text_classifier(
     tok2vec: Model, exclusive_classes: bool, nO: Optional[int] = None
 ) -> Model[List[Doc], Floats2d]:
@@ -41,7 +41,7 @@ def build_simple_cnn_text_classifier(
     return model
 
 
-@registry.architectures.register("spacy.TextCatBOW.v1")
+@registry.architectures("spacy.TextCatBOW.v1")
 def build_bow_text_classifier(
     exclusive_classes: bool,
     ngram_size: int,
@@ -60,7 +60,7 @@ def build_bow_text_classifier(
     return model
 
 
-@registry.architectures.register("spacy.TextCatEnsemble.v2")
+@registry.architectures("spacy.TextCatEnsemble.v2")
 def build_text_classifier_v2(
     tok2vec: Model[List[Doc], List[Floats2d]],
     linear_model: Model[List[Doc], Floats2d],
@@ -112,7 +112,7 @@ def init_ensemble_textcat(model, X, Y) -> Model:
     return model
 
 
-@registry.architectures.register("spacy.TextCatLowData.v1")
+@registry.architectures("spacy.TextCatLowData.v1")
 def build_text_classifier_lowdata(
     width: int, dropout: Optional[float], nO: Optional[int] = None
 ) -> Model[List[Doc], Floats2d]:
diff --git a/spacy/ml/models/tok2vec.py b/spacy/ml/models/tok2vec.py
index c4bd6b0d7..5790af631 100644
--- a/spacy/ml/models/tok2vec.py
+++ b/spacy/ml/models/tok2vec.py
@@ -14,7 +14,7 @@ from ...pipeline.tok2vec import Tok2VecListener
 from ...attrs import intify_attr
 
 
-@registry.architectures.register("spacy.Tok2VecListener.v1")
+@registry.architectures("spacy.Tok2VecListener.v1")
 def tok2vec_listener_v1(width: int, upstream: str = "*"):
     tok2vec = Tok2VecListener(upstream_name=upstream, width=width)
     return tok2vec
@@ -31,7 +31,7 @@ def get_tok2vec_width(model: Model):
     return nO
 
 
-@registry.architectures.register("spacy.HashEmbedCNN.v1")
+@registry.architectures("spacy.HashEmbedCNN.v1")
 def build_hash_embed_cnn_tok2vec(
     *,
     width: int,
@@ -87,7 +87,7 @@ def build_hash_embed_cnn_tok2vec(
     )
 
 
-@registry.architectures.register("spacy.Tok2Vec.v2")
+@registry.architectures("spacy.Tok2Vec.v2")
 def build_Tok2Vec_model(
     embed: Model[List[Doc], List[Floats2d]],
     encode: Model[List[Floats2d], List[Floats2d]],
@@ -108,7 +108,7 @@ def build_Tok2Vec_model(
     return tok2vec
 
 
-@registry.architectures.register("spacy.MultiHashEmbed.v1")
+@registry.architectures("spacy.MultiHashEmbed.v1")
 def MultiHashEmbed(
     width: int,
     attrs: List[Union[str, int]],
@@ -182,7 +182,7 @@ def MultiHashEmbed(
     return model
 
 
-@registry.architectures.register("spacy.CharacterEmbed.v1")
+@registry.architectures("spacy.CharacterEmbed.v1")
 def CharacterEmbed(
     width: int,
     rows: int,
@@ -255,7 +255,7 @@ def CharacterEmbed(
     return model
 
 
-@registry.architectures.register("spacy.MaxoutWindowEncoder.v2")
+@registry.architectures("spacy.MaxoutWindowEncoder.v2")
 def MaxoutWindowEncoder(
     width: int, window_size: int, maxout_pieces: int, depth: int
 ) -> Model[List[Floats2d], List[Floats2d]]:
@@ -287,7 +287,7 @@ def MaxoutWindowEncoder(
     return with_array(model, pad=receptive_field)
 
 
-@registry.architectures.register("spacy.MishWindowEncoder.v2")
+@registry.architectures("spacy.MishWindowEncoder.v2")
 def MishWindowEncoder(
     width: int, window_size: int, depth: int
 ) -> Model[List[Floats2d], List[Floats2d]]:
@@ -310,7 +310,7 @@ def MishWindowEncoder(
     return with_array(model)
 
 
-@registry.architectures.register("spacy.TorchBiLSTMEncoder.v1")
+@registry.architectures("spacy.TorchBiLSTMEncoder.v1")
 def BiLSTMEncoder(
     width: int, depth: int, dropout: float
 ) -> Model[List[Floats2d], List[Floats2d]]:
diff --git a/spacy/tests/pipeline/test_entity_linker.py b/spacy/tests/pipeline/test_entity_linker.py
index 348298e06..4883cceb8 100644
--- a/spacy/tests/pipeline/test_entity_linker.py
+++ b/spacy/tests/pipeline/test_entity_linker.py
@@ -230,7 +230,7 @@ def test_el_pipe_configuration(nlp):
     def get_lowercased_candidates(kb, span):
         return kb.get_alias_candidates(span.text.lower())
 
-    @registry.misc.register("spacy.LowercaseCandidateGenerator.v1")
+    @registry.misc("spacy.LowercaseCandidateGenerator.v1")
     def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
         return get_lowercased_candidates
 
diff --git a/spacy/tests/serialize/test_serialize_config.py b/spacy/tests/serialize/test_serialize_config.py
index 6709defb8..86f726c43 100644
--- a/spacy/tests/serialize/test_serialize_config.py
+++ b/spacy/tests/serialize/test_serialize_config.py
@@ -160,7 +160,7 @@ subword_features = false
 """
 
 
-@registry.architectures.register("my_test_parser")
+@registry.architectures("my_test_parser")
 def my_parser():
     tok2vec = build_Tok2Vec_model(
         MultiHashEmbed(
diff --git a/spacy/tests/serialize/test_serialize_kb.py b/spacy/tests/serialize/test_serialize_kb.py
index 352c335ea..fb04d31a3 100644
--- a/spacy/tests/serialize/test_serialize_kb.py
+++ b/spacy/tests/serialize/test_serialize_kb.py
@@ -108,7 +108,7 @@ def test_serialize_subclassed_kb():
             super().__init__(vocab, entity_vector_length)
             self.custom_field = custom_field
 
-    @registry.misc.register("spacy.CustomKB.v1")
+    @registry.misc("spacy.CustomKB.v1")
     def custom_kb(
         entity_vector_length: int, custom_field: int
     ) -> Callable[["Vocab"], KnowledgeBase]:
diff --git a/spacy/tests/test_architectures.py b/spacy/tests/test_architectures.py
index 31b2a2d2f..c9e451471 100644
--- a/spacy/tests/test_architectures.py
+++ b/spacy/tests/test_architectures.py
@@ -4,12 +4,12 @@ from thinc.api import Linear
 from catalogue import RegistryError
 
 
-@registry.architectures.register("my_test_function")
-def create_model(nr_in, nr_out):
-    return Linear(nr_in, nr_out)
-
-
 def test_get_architecture():
+
+    @registry.architectures("my_test_function")
+    def create_model(nr_in, nr_out):
+        return Linear(nr_in, nr_out)
+
     arch = registry.architectures.get("my_test_function")
     assert arch is create_model
     with pytest.raises(RegistryError):
diff --git a/spacy/tests/training/test_readers.py b/spacy/tests/training/test_readers.py
index 1c698abcc..f53660818 100644
--- a/spacy/tests/training/test_readers.py
+++ b/spacy/tests/training/test_readers.py
@@ -27,7 +27,7 @@ def test_readers():
     factory = "textcat"
     """
 
-    @registry.readers.register("myreader.v1")
+    @registry.readers("myreader.v1")
     def myreader() -> Dict[str, Callable[[Language, str], Iterable[Example]]]:
         annots = {"cats": {"POS": 1.0, "NEG": 0.0}}
 
diff --git a/website/docs/usage/layers-architectures.md b/website/docs/usage/layers-architectures.md
index d7a7d3ce8..0bc935d51 100644
--- a/website/docs/usage/layers-architectures.md
+++ b/website/docs/usage/layers-architectures.md
@@ -15,7 +15,7 @@ next: /usage/projects
 > ```python
 > from thinc.api import Model, chain
 >
-> @spacy.registry.architectures.register("model.v1")
+> @spacy.registry.architectures("model.v1")
 > def build_model(width: int, classes: int) -> Model:
 >     tok2vec = build_tok2vec(width)
 >     output_layer = build_output_layer(width, classes)
@@ -563,7 +563,7 @@ matrix** (~~Floats2d~~) of predictions:
 
 ```python
 ### The model architecture
-@spacy.registry.architectures.register("rel_model.v1")
+@spacy.registry.architectures("rel_model.v1")
 def create_relation_model(...) -> Model[List[Doc], Floats2d]:
     model = ...  # 👈 model will go here
     return model
@@ -589,7 +589,7 @@ transforms the instance tensor into a final tensor holding the predictions:
 
 ```python
 ### The model architecture {highlight="6"}
-@spacy.registry.architectures.register("rel_model.v1")
+@spacy.registry.architectures("rel_model.v1")
 def create_relation_model(
     create_instance_tensor: Model[List[Doc], Floats2d],
     classification_layer: Model[Floats2d, Floats2d],
@@ -613,7 +613,7 @@ The `classification_layer` could be something like a
 
 ```python
 ### The classification layer
-@spacy.registry.architectures.register("rel_classification_layer.v1")
+@spacy.registry.architectures("rel_classification_layer.v1")
 def create_classification_layer(
     nO: int = None, nI: int = None
 ) -> Model[Floats2d, Floats2d]:
@@ -650,7 +650,7 @@ that has the full implementation.
 
 ```python
 ### The layer that creates the instance tensor
-@spacy.registry.architectures.register("rel_instance_tensor.v1")
+@spacy.registry.architectures("rel_instance_tensor.v1")
 def create_tensors(
     tok2vec: Model[List[Doc], List[Floats2d]],
     pooling: Model[Ragged, Floats2d],
@@ -731,7 +731,7 @@ are within a **maximum distance** (in number of tokens) of each other:
 
 ```python
 ### Candidate generation
-@spacy.registry.misc.register("rel_instance_generator.v1")
+@spacy.registry.misc("rel_instance_generator.v1")
 def create_instances(max_length: int) -> Callable[[Doc], List[Tuple[Span, Span]]]:
     def get_candidates(doc: "Doc") -> List[Tuple[Span, Span]]:
         candidates = []