diff --git a/website/docs/api/language.md b/website/docs/api/language.md
index d65b217a4..7799f103b 100644
--- a/website/docs/api/language.md
+++ b/website/docs/api/language.md
@@ -828,8 +828,10 @@ subclass of the built-in `dict`. It supports the additional methods `to_disk`
 
 ## Language.to_disk {#to_disk tag="method" new="2"}
 
-Save the current state to a directory. If a trained pipeline is loaded, this
-will **include all model data**.
+Save the current state to a directory. Under the hood, this method delegates to
+the `to_disk` methods of the individual pipeline components, if available. This
+means that if a trained pipeline is loaded, all components and their weights
+will be saved to disk.
 
 > #### Example
 >
diff --git a/website/docs/usage/linguistic-features.md b/website/docs/usage/linguistic-features.md
index 7d3613cf5..ff08d547c 100644
--- a/website/docs/usage/linguistic-features.md
+++ b/website/docs/usage/linguistic-features.md
@@ -1222,7 +1222,7 @@ print(doc.text, [token.text for token in doc])
 
 Keep in mind that your models' results may be less accurate if the tokenization
 during training differs from the tokenization at runtime. So if you modify a
-trained pipeline' tokenization afterwards, it may produce very different
+trained pipeline's tokenization afterwards, it may produce very different
 predictions. You should therefore train your pipeline with the **same
 tokenizer** it will be using at runtime. See the docs on
 [training with custom tokenization](#custom-tokenizer-training) for details.