diff --git a/spacy/ml/models/tok2vec.py b/spacy/ml/models/tok2vec.py
index 8d78e418f..44ab50e85 100644
--- a/spacy/ml/models/tok2vec.py
+++ b/spacy/ml/models/tok2vec.py
@@ -53,7 +53,7 @@ def build_hash_embed_cnn_tok2vec(
     window_size (int): The number of tokens on either side to concatenate during
         the convolutions. The receptive field of the CNN will be
         depth * (window_size * 2 + 1), so a 4-layer network with window_size of
-        2 will be sensitive to 17 words at a time. Recommended value is 1.
+        2 will be sensitive to 20 words at a time. Recommended value is 1.
     embed_size (int): The number of rows in the hash embedding tables. This can
         be surprisingly small, due to the use of the hash embeddings. Recommended
         values are between 2000 and 10000.
diff --git a/website/docs/api/architectures.md b/website/docs/api/architectures.md
index 7044a7d02..01ca4540b 100644
--- a/website/docs/api/architectures.md
+++ b/website/docs/api/architectures.md
@@ -82,7 +82,7 @@ consisting of a CNN and a layer-normalized maxout activation function.
 | `width`              | The width of the input and output. These are required to be the same, so that residual connections can be used. Recommended values are `96`, `128` or `300`. ~~int~~                                                                                                          |
 | `depth`              | The number of convolutional layers to use. Recommended values are between `2` and `8`. ~~int~~                                                                                                                                                                                |
 | `embed_size`         | The number of rows in the hash embedding tables. This can be surprisingly small, due to the use of the hash embeddings. Recommended values are between `2000` and `10000`. ~~int~~                                                                                            |
-| `window_size`        | The number of tokens on either side to concatenate during the convolutions. The receptive field of the CNN will be `depth * (window_size * 2 + 1)`, so a 4-layer network with a window size of `2` will be sensitive to 17 words at a time. Recommended value is `1`. ~~int~~ |
+| `window_size`        | The number of tokens on either side to concatenate during the convolutions. The receptive field of the CNN will be `depth * (window_size * 2 + 1)`, so a 4-layer network with a window size of `2` will be sensitive to 20 words at a time. Recommended value is `1`. ~~int~~ |
 | `maxout_pieces`      | The number of pieces to use in the maxout non-linearity. If `1`, the [`Mish`](https://thinc.ai/docs/api-layers#mish) non-linearity is used instead. Recommended values are `1`-`3`. ~~int~~                                                                                   |
 | `subword_features`   | Whether to also embed subword features, specifically the prefix, suffix and word shape. This is recommended for alphabetic languages like English, but not if single-character tokens are used for a language such as Chinese. ~~bool~~                                       |
 | `pretrained_vectors` | Whether to also use static vectors. ~~bool~~                                                                                                                                                                                                                                  |