From 3449c45fd905393d6e94866d66a00ec8d62f6880 Mon Sep 17 00:00:00 2001
From: Ines Montani <ines@ines.io>
Date: Wed, 29 Jul 2020 19:48:26 +0200
Subject: [PATCH] Update docs [ci skip]

---
 website/docs/usage/training.md     | 13 ++++++++++++-
 website/docs/usage/transformers.md |  7 ++++---
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/website/docs/usage/training.md b/website/docs/usage/training.md
index 948a13086..12785b6de 100644
--- a/website/docs/usage/training.md
+++ b/website/docs/usage/training.md
@@ -243,7 +243,14 @@ compound = 1.001
 
 ### Using transformer models like BERT {#transformers}
 
-<!-- TODO: document usage of spacy-transformers, refer to example config/project -->
+spaCy v3.0 lets you use almost any statistical model to power your pipeline. You
+can use models implemented in a variety of frameworks. A transformer model is
+just a statistical model, so the
+[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
+actually has very little work to do: it just has to provide a few functions that
+do the required plumbing. It also provides a pipeline component,
+[`Transformer`](/api/transformer), that lets you do multi-task learning and lets
+you save the transformer outputs for later use.
 
 <Project id="en_core_bert">
 
@@ -253,6 +260,10 @@ visualize your model.
 
 </Project>
 
+For more details on how to integrate transformer models into your training
+config and customize the implementations, see the usage guide on
+[training transformers](/usage/transformers#training).
+
 ### Pretraining with spaCy {#pretraining}
 
 <!-- TODO: document spacy pretrain -->
diff --git a/website/docs/usage/transformers.md b/website/docs/usage/transformers.md
index a7fd83ac6..bab1b82d3 100644
--- a/website/docs/usage/transformers.md
+++ b/website/docs/usage/transformers.md
@@ -18,8 +18,8 @@ frameworks to be wrapped with a common interface, using our machine learning
 library [Thinc](https://thinc.ai). A transformer model is just a statistical
 model, so the
 [`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
-actually has very little work to do: we just have to provide a few functions
-that do the required plumbing. We also provide a pipeline component,
+actually has very little work to do: it just has to provide a few functions that
+do the required plumbing. It also provides a pipeline component,
 [`Transformer`](/api/transformer), that lets you do multi-task learning and lets
 you save the transformer outputs for later use.
 
@@ -201,7 +201,8 @@ def configure_custom_sent_spans():
 
 To resolve the config during training, spaCy needs to know about your custom
 function. You can make it available via the `--code` argument that can point to
-a Python file:
+a Python file. For more details on training with custom code, see the
+[training documentation](/usage/training#custom-code).
 
 ```bash
 $ python -m spacy train ./train.spacy ./dev.spacy ./config.cfg --code ./code.py