diff --git a/spacy/ml/models/tok2vec.py b/spacy/ml/models/tok2vec.py
index b9c9b8766..2e5f8a802 100644
--- a/spacy/ml/models/tok2vec.py
+++ b/spacy/ml/models/tok2vec.py
@@ -176,8 +176,8 @@ def CharacterEmbed(width: int, rows: int, nM: int, nC: int):
     ensures that the final character is always in the last position, instead
     of being in an arbitrary position depending on the word length.
 
-    The characters are embedded in a embedding table with 256 rows, and the
-    vectors concatenated. A hash-embedded vector of the NORM of the word is
+    The characters are embedded in a embedding table with a given number of rows,
+    and the vectors concatenated. A hash-embedded vector of the NORM of the word is
     also concatenated on, and the result is then passed through a feed-forward
     network to construct a single vector to represent the information.
 
diff --git a/website/docs/api/architectures.md b/website/docs/api/architectures.md
index ee844d961..bcca97324 100644
--- a/website/docs/api/architectures.md
+++ b/website/docs/api/architectures.md
@@ -181,10 +181,10 @@ characters would be `"jumpping"`: 4 from the start, 4 from the end. This ensures
 that the final character is always in the last position, instead of being in an
 arbitrary position depending on the word length.
 
-The characters are embedded in a embedding table with 256 rows, and the vectors
-concatenated. A hash-embedded vector of the `NORM` of the word is also
-concatenated on, and the result is then passed through a feed-forward network to
-construct a single vector to represent the information.
+The characters are embedded in a embedding table with a given number of rows,
+and the vectors concatenated. A hash-embedded vector of the `NORM` of the word
+is also concatenated on, and the result is then passed through a feed-forward
+network to construct a single vector to represent the information.
 
 | Name        | Description                                                                                                                                                     |
 | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- |
diff --git a/website/docs/usage/training.md b/website/docs/usage/training.md
index 9c18e4606..3f0141d72 100644
--- a/website/docs/usage/training.md
+++ b/website/docs/usage/training.md
@@ -683,7 +683,7 @@ You can also implement your own batch size schedule to use during training. The
 import spacy
 
 @spacy.registry.schedules("my_custom_schedule.v1")
-def my_custom_schedule(start: int = 1, factor: int = 1.001):
+def my_custom_schedule(start: int = 1, factor: float = 1.001):
    while True:
       yield start
       start = start * factor