diff --git a/website/docs/api/cli.md b/website/docs/api/cli.md
index e99166af8..3fd6b7a76 100644
--- a/website/docs/api/cli.md
+++ b/website/docs/api/cli.md
@@ -790,6 +790,12 @@ in the section `[paths]`.
 
 </Infobox>
 
+> #### Example
+>
+> ```cli
+> $ python -m spacy train config.cfg --output ./output --paths.train ./train --paths.dev ./dev
+> ```
+
 ```cli
 $ python -m spacy train [config_path] [--output] [--code] [--verbose] [--gpu-id] [overrides]
 ```
@@ -808,15 +814,16 @@ $ python -m spacy train [config_path] [--output] [--code] [--verbose] [--gpu-id]
 ## pretrain {#pretrain new="2.1" tag="command,experimental"}
 
 Pretrain the "token to vector" ([`Tok2vec`](/api/tok2vec)) layer of pipeline
-components on [raw text](/api/data-formats#pretrain), using an approximate
-language-modeling objective. Specifically, we load pretrained vectors, and train
-a component like a CNN, BiLSTM, etc to predict vectors which match the
-pretrained ones. The weights are saved to a directory after each epoch. You can
-then include a **path to one of these pretrained weights files** in your
+components on raw text, using an approximate language-modeling objective.
+Specifically, we load pretrained vectors, and train a component like a CNN,
+BiLSTM, etc to predict vectors which match the pretrained ones. The weights are
+saved to a directory after each epoch. You can then include a **path to one of
+these pretrained weights files** in your
 [training config](/usage/training#config) as the `init_tok2vec` setting when you
 train your pipeline. This technique may be especially helpful if you have little
 labelled data. See the usage docs on
-[pretraining](/usage/embeddings-transformers#pretraining) for more info.
+[pretraining](/usage/embeddings-transformers#pretraining) for more info. To read
+the raw text, a [`JsonlCorpus`](/api/top-level#jsonlcorpus) is typically used.
 
 <Infobox title="Changed in v3.0" variant="warning">
 
@@ -830,6 +837,12 @@ auto-generated by setting `--pretraining` on
 
 </Infobox>
 
+> #### Example
+>
+> ```cli
+> $ python -m spacy pretrain config.cfg ./output_pretrain --paths.raw_text ./data.jsonl
+> ```
+
 ```cli
 $ python -m spacy pretrain [config_path] [output_dir] [--code] [--resume-path] [--epoch-resume] [--gpu-id] [overrides]
 ```
diff --git a/website/docs/api/data-formats.md b/website/docs/api/data-formats.md
index 3df9a7ba4..fc2cda547 100644
--- a/website/docs/api/data-formats.md
+++ b/website/docs/api/data-formats.md
@@ -148,7 +148,7 @@ This section defines a **dictionary** mapping of string keys to functions. Each
 function takes an `nlp` object and yields [`Example`](/api/example) objects. By
 default, the two keys `train` and `dev` are specified and each refer to a
 [`Corpus`](/api/top-level#Corpus). When pretraining, an additional `pretrain`
-section is added that defaults to a [`JsonlCorpus`](/api/top-level#JsonlCorpus).
+section is added that defaults to a [`JsonlCorpus`](/api/top-level#jsonlcorpus).
 You can also register custom functions that return a callable.
 
 | Name       | Description                                                                                                                                                                 |