From 99d0412b6ebf1af8e5bd4755f2ec8d1947d8cef5 Mon Sep 17 00:00:00 2001
From: svlandeg <sofie.vanlandeghem@gmail.com>
Date: Sun, 15 Nov 2020 18:35:56 +0100
Subject: [PATCH] add link to REL project

---
 website/docs/usage/layers-architectures.md | 22 ++++++++++++++++------
 1 file changed, 16 insertions(+), 6 deletions(-)

diff --git a/website/docs/usage/layers-architectures.md b/website/docs/usage/layers-architectures.md
index 641db02f5..481a60574 100644
--- a/website/docs/usage/layers-architectures.md
+++ b/website/docs/usage/layers-architectures.md
@@ -534,9 +534,14 @@ steps required:
    machine learning model that sets annotations on the [`Doc`](/api/doc) passing
    through the pipeline.
 
-<!-- TODO: <Project id="tutorials/ner-relations">
-
-</Project> -->
+<Project id="tutorials/rel_component">
+Run this example use-case by using our project template. It includes all the 
+code to create the ML model and the pipeline component from scratch.
+It contains two config files to train the model: 
+one to run on CPU with a Tok2Vec layer, and one for the GPU using a transformer.
+The project applies the relation extraction component to identify biomolecular 
+interactions, but you can easily swap in your own dataset for your experiments.
+</Project>
 
 #### Step 1: Implementing the Model {#component-rel-model}
 
@@ -924,6 +929,11 @@ def make_relation_extractor(nlp, name, model):
     return RelationExtractor(nlp.vocab, model, name)
 ```
 
-<!-- TODO: <Project id="tutorials/ner-relations">
-
-</Project> -->
+<Project id="tutorials/rel_component">
+Run this example use-case by using our project template. It includes all the 
+code to create the ML model and the pipeline component from scratch.
+It contains two config files to train the model: 
+one to run on CPU with a Tok2Vec layer, and one for the GPU using a transformer.
+The project applies the relation extraction component to identify biomolecular 
+interactions, but you can easily swap in your own dataset for your experiments.
+</Project>