diff --git a/spacy/ml/models/tok2vec.py b/spacy/ml/models/tok2vec.py
index 4c4bd0d22..448f9d1d0 100644
--- a/spacy/ml/models/tok2vec.py
+++ b/spacy/ml/models/tok2vec.py
@@ -1,13 +1,15 @@
 from typing import Optional, List
 from thinc.api import chain, clone, concatenate, with_array, with_padded
-from thinc.api import Model, noop
-from thinc.api import FeatureExtractor, HashEmbed, StaticVectors
-from thincapi import expand_window, residual, Maxout, Mish
+from thinc.api import Model, noop, list2ragged, ragged2list
+from thinc.api import FeatureExtractor, HashEmbed
+from thinc.api import expand_window, residual, Maxout, Mish
 from thinc.types import Floats2d
 
+from ...tokens import Doc
 from ... import util
 from ...util import registry
 from ...ml import _character_embed
+from ..staticvectors import StaticVectors
 from ...pipeline.tok2vec import Tok2VecListener
 from ...attrs import ID, ORTH, NORM, PREFIX, SUFFIX, SHAPE
 
@@ -21,20 +23,19 @@ def tok2vec_listener_v1(width, upstream="*"):
 @registry.architectures.register("spacy.Tok2Vec.v1")
 def Tok2Vec(
     embed: Model[List[Doc], List[Floats2d]],
-    encode: Model[List[Floats2d], List[Floats2d]
+    encode: Model[List[Floats2d], List[Floats2d]]
 ) -> Model[List[Doc], List[Floats2d]]:
-    tok2vec = with_array(
-        chain(embed, encode),
-        pad=encode.attrs.get("receptive_field", 0)
-    )
+
+    receptive_field = encode.attrs.get("receptive_field", 0)
+    tok2vec = chain(embed, with_array(encode, pad=receptive_field))
     tok2vec.set_dim("nO", encode.get_dim("nO"))
     tok2vec.set_ref("embed", embed)
     tok2vec.set_ref("encode", encode)
     return tok2vec
 
 
-@registry.architectures.register("spacy.HashEmbed.v1")
-def HashEmbed(
+@registry.architectures.register("spacy.MultiHashEmbed.v1")
+def MultiHashEmbed(
     width: int,
     rows: int,
     also_embed_subwords: bool,
@@ -56,9 +57,9 @@ def HashEmbed(
     
     if also_embed_subwords:
         embeddings = [
-            make_hash_embed(NORM)
-            make_hash_embed(PREFIX)
-            make_hash_embed(SUFFIX)
+            make_hash_embed(NORM),
+            make_hash_embed(PREFIX),
+            make_hash_embed(SUFFIX),
             make_hash_embed(SHAPE)
         ]
     else:
@@ -67,15 +68,25 @@ def HashEmbed(
     if also_use_static_vectors:
         model = chain(
             concatenate(
-                chain(FeatureExtractor(cols), concatenate(*embeddings)),
+                chain(
+                    FeatureExtractor(cols),
+                    list2ragged(),
+                    with_array(concatenate(*embeddings))
+                ),
                 StaticVectors(width, dropout=0.0)
             ),
-            Maxout(width, pieces=3, dropout=0.0, normalize=True)
+            with_array(Maxout(width, nP=3, dropout=0.0, normalize=True)),
+            ragged2list()
         )
     else:
         model = chain(
-            chain(FeatureExtractor(cols), concatenate(*embeddings)),
-            Maxout(width, pieces=3, dropout=0.0, normalize=True)
+            chain(
+                FeatureExtractor(cols),
+                list2ragged(),
+                with_array(concatenate(*embeddings))
+            ),
+            with_array(Maxout(width, nP=3, dropout=0.0, normalize=True)),
+            ragged2list()
         )
     return model