diff --git a/spacy/pipeline/span_predictor.py b/spacy/pipeline/span_predictor.py
index d7e96a4b2..99a1f7ef6 100644
--- a/spacy/pipeline/span_predictor.py
+++ b/spacy/pipeline/span_predictor.py
@@ -29,7 +29,7 @@ distance_embedding_size = 64
 conv_channels = 4
 window_size = 1
 max_distance = 128
-prefix = coref_head_clusters
+prefix = "coref_head_clusters"
 
 [model.tok2vec]
 @architectures = "spacy.Tok2Vec.v2"
diff --git a/website/docs/api/architectures.md b/website/docs/api/architectures.md
index 4e70eee87..e881864a9 100644
--- a/website/docs/api/architectures.md
+++ b/website/docs/api/architectures.md
@@ -587,8 +587,8 @@ consists of either two or three subnetworks:
   run once for each batch.
 - **lower**: Construct a feature-specific vector for each `(token, feature)`
   pair. This is also run once for each batch. Constructing the state
-  representation is then a matter of summing the component features and
-  applying the non-linearity.
+  representation is then a matter of summing the component features and applying
+  the non-linearity.
 - **upper** (optional): A feed-forward network that predicts scores from the
   state representation. If not present, the output from the lower model is used
   as action scores directly.
@@ -628,8 +628,8 @@ same signature, but the `use_upper` argument was `True` by default.
 > ```
 
 Build a tagger model, using a provided token-to-vector component. The tagger
-model adds a linear layer with softmax activation to predict scores given
-the token vectors.
+model adds a linear layer with softmax activation to predict scores given the
+token vectors.
 
 | Name        | Description                                                                                |
 | ----------- | ------------------------------------------------------------------------------------------ |
@@ -920,8 +920,8 @@ A function that reads an existing `KnowledgeBase` from file.
 A function that takes as input a [`KnowledgeBase`](/api/kb) and a
 [`Span`](/api/span) object denoting a named entity, and returns a list of
 plausible [`Candidate`](/api/kb/#candidate) objects. The default
-`CandidateGenerator` uses the text of a mention to find its potential
-aliases in the `KnowledgeBase`. Note that this function is case-dependent.
+`CandidateGenerator` uses the text of a mention to find its potential aliases in
+the `KnowledgeBase`. Note that this function is case-dependent.
 
 ## Coreference Architectures
 
@@ -975,7 +975,11 @@ The `Coref` model architecture is a Thinc `Model`.
 > [model]
 > @architectures = "spacy.SpanPredictor.v1"
 > hidden_size = 1024
-> dist_emb_size = 64
+> distance_embedding_size = 64
+> conv_channels = 4
+> window_size = 1
+> max_distance = 128
+> prefix = "coref_head_clusters"
 >
 > [model.tok2vec]
 > @architectures = "spacy-transformers.TransformerListener.v1"
@@ -986,13 +990,14 @@ The `Coref` model architecture is a Thinc `Model`.
 
 The `SpanPredictor` model architecture is a Thinc `Model`.
 
-| Name                      | Description                                                                                                                                                                              |
-| ------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `tok2vec`                 | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~                                                                                                                                  |
-| `distance_embedding_size` | A representation of the distance between two candidates. ~~int~~                                                                                                                         |
-| `dropout`                 | The dropout to use internally. Unlike some Thinc models, this has separate dropout for the internal PyTorch layers. ~~float~~                                                            |
-| `hidden_size`             | Size of the main internal layers. ~~int~~                                                                                                                                                |
-| `depth`                   | Depth of the internal network. ~~int~~                                                                                                                                                   |
-| `antecedent_limit`        | How many candidate antecedents to keep after rough scoring. This has a significant effect on memory usage. Typical values would be 50 to 200, or higher for very long documents. ~~int~~ |
-| `antecedent_batch_size`   | Internal batch size. ~~int~~                                                                                                                                                             |
-| **CREATES**               | The model using the architecture. ~~Model[List[Doc], TupleFloats2d]~~                                                                                                                    |
+| Name                      | Description                                                                                                                   |
+| ------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
+| `tok2vec`                 | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~                                                                       |
+| `distance_embedding_size` | A representation of the distance between two candidates. ~~int~~                                                              |
+| `dropout`                 | The dropout to use internally. Unlike some Thinc models, this has separate dropout for the internal PyTorch layers. ~~float~~ |
+| `hidden_size`             | Size of the main internal layers. ~~int~~                                                                                     |
+| `conv_channels`           | The number of channels in the internal CNN. ~~int~~                                                                           |
+| `window_size`             | The number of neighboring tokens to consider in the internal CNN. `1` means consider one token on each side. ~~int~~          |
+| `max_distance`            | The longest possible length of a predicted span. ~~int~~                                                                      |
+| `prefix`                  | The prefix that indicates spans to use for input data. ~~string~~                                                             |
+| **CREATES**               | The model using the architecture. ~~Model[List[Doc], TupleFloats2d]~~                                                         |