diff --git a/website/docs/usage/layers-architectures.md b/website/docs/usage/layers-architectures.md
index c4b3fb9dc..7e563cb5c 100644
--- a/website/docs/usage/layers-architectures.md
+++ b/website/docs/usage/layers-architectures.md
@@ -613,7 +613,7 @@ get_candidates = model.attrs["get_candidates"]
 
 #### Step 2: Implementing the pipeline component {#component-rel-pipe}
 
-To use our new relation extraction model as part of a custom component, we 
+To use our new relation extraction model as part of a custom component, we
 create a subclass of [`Pipe`](/api/pipe) that will hold the model:
 
 ```python
@@ -635,15 +635,57 @@ def make_relation_extractor(nlp, name, model, labels):
     return RelationExtractor(nlp.vocab, model, name, labels=labels)
 ```
 
-The [`predict`](/api/pipe#predict ) function needs to be implemented for each subclass. 
-In our case, we can simply delegate to the internal model's 
+The [`predict`](/api/pipe#predict) function needs to be implemented for each
+subclass. In our case, we can simply delegate to the internal model's
 [predict](https://thinc.ai/docs/api-model#predict) function:
+
 ```python
 def predict(self, docs: Iterable[Doc]) -> Floats2d:
     scores = self.model.predict(docs)
     return self.model.ops.asarray(scores)
 ```
 
+The other method that needs to be implemented, is
+[`set_annotations`](/api/pipe#set_annotations). It takes the predicted scores,
+and modifies the given `Doc` object in place to hold the predictions. For our
+relation extraction component, we'll store the data as a dictionary in a custom
+extension attribute `doc._.rel`. As keys, we represent the candidate pair by the
+start offsets of each entity, as this defines an entity uniquely within one
+document.
+
+To interpret the scores predicted by the REL model correctly, we need to 
+refer to the model's `get_candidates` function that originally defined which 
+pairs of entities would be run through the model, so that the scores can be 
+related to those exact entities:
+
+> #### Example output
+>
+> ```python
+> doc = nlp("Amsterdam is the capital of the Netherlands.")
+> print(f"spans: {[(e.start, e.text, e.label_) for e in doc.ents]}")
+> for value, rel_dict in doc._.rel.items():
+>     print(f"{value}: {rel_dict}")
+> ```
+
+> ```
+> spans [(0, 'Amsterdam', 'LOC'), (6, 'Netherlands', 'LOC')]
+> (0, 6): {'CAPITAL_OF': 0.89, 'LOCATED_IN': 0.75, 'UNRELATED': 0.002}
+> (6, 0): {'CAPITAL_OF': 0.01, 'LOCATED_IN': 0.13, 'UNRELATED': 0.017}
+> ```
+
+```python
+def set_annotations(self, docs: Iterable[Doc], rel_scores: Floats2d):
+    c = 0
+    get_candidates = self.model.attrs["get_candidates"]
+    for doc in docs:
+        for (e1, e2) in get_candidates(doc):
+            offset = (e1.start, e2.start)
+            if offset not in doc._.rel:
+                doc._.rel[offset] = {}
+            for j, label in enumerate(self.labels):
+                doc._.rel[offset][label] = rel_scores[c, j]
+            c += 1
+```