diff --git a/spacy/ml/models/coref_util.py b/spacy/ml/models/coref_util.py
index d45cdc810..c75314fa6 100644
--- a/spacy/ml/models/coref_util.py
+++ b/spacy/ml/models/coref_util.py
@@ -56,21 +56,6 @@ def doc2clusters(doc: Doc, prefix=DEFAULT_CLUSTER_PREFIX) -> MentionClusters:
     return out
 
 
-def topk(xp, arr, k, axis=1):
-    """Given an array and a k value, give the top values and idxs for each row."""
-
-    part = xp.argpartition(arr, -k, axis=axis)
-    idxs = xp.flip(part)[:, :k]
-
-    vals = xp.take_along_axis(arr, idxs, axis=axis)
-
-    sidxs = xp.argsort(-vals, axis=axis)
-    # map these idxs back to the original
-    oidxs = xp.take_along_axis(idxs, sidxs, axis=axis)
-    svals = xp.take_along_axis(vals, sidxs, axis=axis)
-    return svals, oidxs
-
-
 # from model.py, refactored to be non-member
 def get_predicted_antecedents(xp, antecedent_idx, antecedent_scores):
     """Get the ID of the antecedent for each span. -1 if no antecedent."""
@@ -124,55 +109,6 @@ def get_predicted_clusters(
     return predicted_clusters
 
 
-def get_sentence_map(doc: Doc):
-    """For the given span, return a list of sentence indexes."""
-    if doc.has_annotation("SENT_START"):
-        si = 0
-        out = []
-        for sent in doc.sents:
-            for _ in sent:
-                out.append(si)
-            si += 1
-        return out
-    else:
-        # If there are no sents then just return dummy values.
-        # Shouldn't happen in general training, but typical in init.
-        return [0] * len(doc)
-
-
-def get_candidate_mentions(
-    doc: Doc, max_span_width: int = 20
-) -> Tuple[List[int], List[int]]:
-    """Given a Doc, return candidate mentions.
-
-    This isn't a trainable layer, it just returns raw candidates.
-    """
-    # XXX Note that in coref-hoi the indexes are designed so you actually want [i:j+1], but here
-    # we're using [i:j], which is more natural.
-
-    sentence_map = get_sentence_map(doc)
-
-    begins = []
-    ends = []
-    for tok in doc:
-        si = sentence_map[tok.i]  # sentence index
-        for ii in range(1, max_span_width):
-            ei = tok.i + ii  # end index
-
-            # Note: this matches slice syntax, so the token index is one less
-            if ei > len(doc) or sentence_map[ei - 1] != si:
-                break
-
-            begins.append(tok.i)
-            ends.append(ei)
-
-    return (begins, ends)
-
-
-@registry.misc("spacy.CorefCandidateGenerator.v1")
-def create_mention_generator() -> Any:
-    return get_candidate_mentions
-
 
 def select_non_crossing_spans(
     idxs: List[int], starts: List[int], ends: List[int], limit: int