diff --git a/spacy/ml/models/span_predictor.py b/spacy/ml/models/span_predictor.py
index d44e632bd..f11ecb5d5 100644
--- a/spacy/ml/models/span_predictor.py
+++ b/spacy/ml/models/span_predictor.py
@@ -35,7 +35,6 @@ def build_span_predictor(
             ),
             convert_inputs=convert_span_predictor_inputs,
         )
-        # TODO use proper parameter for prefix
         head_info = build_get_head_metadata(prefix)
         model = (tok2vec & head_info) >> span_predictor
 
@@ -96,7 +95,6 @@ def predict_span_clusters(
 
 
 def build_get_head_metadata(prefix):
-    # TODO this name is awful, fix it
     model = Model(
         "HeadDataProvider", attrs={"prefix": prefix}, forward=head_data_forward
     )
@@ -142,7 +140,6 @@ class SpanPredictor(torch.nn.Module):
             raise ValueError("max_distance has to be an even number")
         # input size = single token size
         # 64 = probably distance emb size
-        # TODO check that dist_emb_size use is correct
         self.ffnn = torch.nn.Sequential(
             torch.nn.Linear(input_size * 2 + dist_emb_size, hidden_size),
             torch.nn.ReLU(),
@@ -159,7 +156,6 @@ class SpanPredictor(torch.nn.Module):
             torch.nn.Conv1d(dist_emb_size, conv_channels, kernel_size, 1, 1),
             torch.nn.Conv1d(conv_channels, 2, kernel_size, 1, 1),
         )
-        # TODO make embeddings size a parameter
         self.max_distance = max_distance
         # handle distances between +-(max_distance - 2 / 2)
         self.emb = torch.nn.Embedding(max_distance, dist_emb_size)
diff --git a/spacy/pipeline/coref.py b/spacy/pipeline/coref.py
index cd07f80e8..9fd3c9472 100644
--- a/spacy/pipeline/coref.py
+++ b/spacy/pipeline/coref.py
@@ -95,7 +95,7 @@ def make_coref(
 class CoreferenceResolver(TrainablePipe):
     """Pipeline component for coreference resolution.
 
-    DOCS: https://spacy.io/api/coref (TODO)
+    DOCS: https://spacy.io/api/coref
     """
 
     def __init__(
@@ -118,8 +118,10 @@ class CoreferenceResolver(TrainablePipe):
             are stored in.
         span_cluster_prefix (str): Prefix for the key in doc.spans to store the
             coref clusters in.
+        scorer (Optional[Callable]): The scoring method. Defaults to 
+            Scorer.score_coref_clusters.
 
-        DOCS: https://spacy.io/api/coref#init (TODO)
+        DOCS: https://spacy.io/api/coref#init
         """
         self.vocab = vocab
         self.model = model
@@ -133,11 +135,12 @@ class CoreferenceResolver(TrainablePipe):
 
     def predict(self, docs: Iterable[Doc]) -> List[MentionClusters]:
         """Apply the pipeline's model to a batch of docs, without modifying them.
+        Return the list of predicted clusters.
 
         docs (Iterable[Doc]): The documents to predict.
-        RETURNS: The models prediction for each document.
+        RETURNS (List[MentionClusters]): The model's prediction for each document.
 
-        DOCS: https://spacy.io/api/coref#predict (TODO)
+        DOCS: https://spacy.io/api/coref#predict
         """
         out = []
         for doc in docs:
@@ -163,7 +166,7 @@ class CoreferenceResolver(TrainablePipe):
         docs (Iterable[Doc]): The documents to modify.
         clusters: The span clusters, produced by CoreferenceResolver.predict.
 
-        DOCS: https://spacy.io/api/coref#set_annotations (TODO)
+        DOCS: https://spacy.io/api/coref#set_annotations
         """
         docs = list(docs)
         if len(docs) != len(clusters_by_doc):
@@ -204,7 +207,7 @@ class CoreferenceResolver(TrainablePipe):
             Updated using the component name as the key.
         RETURNS (Dict[str, float]): The updated losses dictionary.
 
-        DOCS: https://spacy.io/api/coref#update (TODO)
+        DOCS: https://spacy.io/api/coref#update
         """
         if losses is None:
             losses = {}
@@ -218,12 +221,10 @@ class CoreferenceResolver(TrainablePipe):
         total_loss = 0
 
         for eg in examples:
-            # TODO check this causes no issues (in practice it runs)
             preds, backprop = self.model.begin_update([eg.predicted])
             score_matrix, mention_idx = preds
             loss, d_scores = self.get_loss([eg], score_matrix, mention_idx)
             total_loss += loss
-            # TODO check shape here
             backprop((d_scores, mention_idx))
 
         if sgd is not None:
@@ -257,7 +258,7 @@ class CoreferenceResolver(TrainablePipe):
         scores: Scores representing the model's predictions.
         RETURNS (Tuple[float, float]): The loss and the gradient.
 
-        DOCS: https://spacy.io/api/coref#get_loss (TODO)
+        DOCS: https://spacy.io/api/coref#get_loss
         """
         ops = self.model.ops
         xp = ops.xp
@@ -270,9 +271,8 @@ class CoreferenceResolver(TrainablePipe):
         clusters = get_clusters_from_doc(example.reference)
         span_idxs = create_head_span_idxs(ops, len(example.predicted))
         gscores = create_gold_scores(span_idxs, clusters)
-        # TODO fix type here. This is bools but asarray2f wants ints.
+        # Note on type here. This is bools but asarray2f wants ints.
         gscores = ops.asarray2f(gscores)  # type: ignore
-        # top_gscores = xp.take_along_axis(gscores, cidx, axis=1)
         top_gscores = xp.take_along_axis(gscores, mention_idx, axis=1)
         # now add the placeholder
         gold_placeholder = ~top_gscores.any(axis=1).T
@@ -304,7 +304,7 @@ class CoreferenceResolver(TrainablePipe):
             returns a representative sample of gold-standard Example objects.
         nlp (Language): The current nlp object the component is part of.
 
-        DOCS: https://spacy.io/api/coref#initialize (TODO)
+        DOCS: https://spacy.io/api/coref#initialize
         """
         validate_get_examples(get_examples, "CoreferenceResolver.initialize")
 
diff --git a/spacy/pipeline/entity_linker.py b/spacy/pipeline/entity_linker.py
index aa7985a9c..36a291a88 100644
--- a/spacy/pipeline/entity_linker.py
+++ b/spacy/pipeline/entity_linker.py
@@ -383,7 +383,7 @@ class EntityLinker(TrainablePipe):
         no prediction.
 
         docs (Iterable[Doc]): The documents to predict.
-        RETURNS (List[str]): The models prediction for each document.
+        RETURNS (List[str]): The model's prediction for each document.
 
         DOCS: https://spacy.io/api/entitylinker#predict
         """