diff --git a/spacy/ml/models/tok2vec.py b/spacy/ml/models/tok2vec.py
index 23cfe883b..1a78cf75e 100644
--- a/spacy/ml/models/tok2vec.py
+++ b/spacy/ml/models/tok2vec.py
@@ -110,7 +110,7 @@ def MultiHashEmbed(
 
     The features used can be configured with the 'attrs' argument. The suggested
     attributes are NORM, PREFIX, SUFFIX and SHAPE. This lets the model take into
-    account some subword information, without construction a fully character-based
+    account some subword information, without constructing a fully character-based
     representation. If pretrained vectors are available, they can be included in
     the representation as well, with the vectors table will be kept static
     (i.e. it's not updated).
diff --git a/website/docs/usage/embeddings-transformers.md b/website/docs/usage/embeddings-transformers.md
index 73540b3d3..856685dad 100644
--- a/website/docs/usage/embeddings-transformers.md
+++ b/website/docs/usage/embeddings-transformers.md
@@ -516,16 +516,14 @@ Many neural network models are able to use word vector tables as additional
 features, which sometimes results in significant improvements in accuracy.
 spaCy's built-in embedding layer,
 [MultiHashEmbed](/api/architectures#MultiHashEmbed), can be configured to use
-word vector tables using the `also_use_static_vectors` flag. This setting is
-also available on the [MultiHashEmbedCNN](/api/architectures#MultiHashEmbedCNN)
-layer, which builds the default token-to-vector encoding architecture.
+word vector tables using the `include_static_vectors` flag. 
 
 ```ini
 [tagger.model.tok2vec.embed]
 @architectures = "spacy.MultiHashEmbed.v1"
 width = 128
-rows = 7000
-also_embed_subwords = true
+attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
+rows = [7000, 3500, 3500, 3500]
 also_use_static_vectors = true
 ```