Update span predictor docstrings

spacy/pipeline/span_predictor.py

@@ -95,6 +95,8 @@ class SpanPredictor(TrainablePipe):
     """Pipeline component to resolve one-token spans to full spans.
 
     Used in coreference resolution.
+
+    DOCS: https://spacy.io/api/span_predictor
     """
 
     def __init__(
@@ -119,6 +121,14 @@ class SpanPredictor(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 span clusters.
+
+        docs (Iterable[Doc]): The documents to predict.
+        RETURNS (List[MentionClusters]): The model's prediction for each document.
+
+        DOCS: https://spacy.io/api/span_predictor#predict
+        """
         # for now pretend there's just one doc
 
         out = []
@@ -151,6 +161,13 @@ class SpanPredictor(TrainablePipe):
         return out
 
     def set_annotations(self, docs: Iterable[Doc], clusters_by_doc) -> None:
+        """Modify a batch of Doc objects, using pre-computed scores.
+
+        docs (Iterable[Doc]): The documents to modify.
+        clusters: The span clusters, produced by SpanPredictor.predict.
+
+        DOCS: https://spacy.io/api/span_predictor#set_annotations
+        """
         for doc, clusters in zip(docs, clusters_by_doc):
             for ii, cluster in enumerate(clusters):
                 spans = [doc[mm[0] : mm[1]] for mm in cluster]
@@ -166,6 +183,15 @@ class SpanPredictor(TrainablePipe):
     ) -> Dict[str, float]:
         """Learn from a batch of documents and gold-standard information,
         updating the pipe's model. Delegates to predict and get_loss.
+
+        examples (Iterable[Example]): A batch of Example objects.
+        drop (float): The dropout rate.
+        sgd (thinc.api.Optimizer): The optimizer.
+        losses (Dict[str, float]): Optional record of the loss during training.
+            Updated using the component name as the key.
+        RETURNS (Dict[str, float]): The updated losses dictionary.
+
+        DOCS: https://spacy.io/api/span_predictor#update
         """
         if losses is None:
             losses = {}
@@ -222,6 +248,15 @@ class SpanPredictor(TrainablePipe):
         examples: Iterable[Example],
         span_scores: Floats3d,
     ):
+        """Find the loss and gradient of loss for the batch of documents and
+        their predicted scores.
+
+        examples (Iterable[Examples]): The batch of examples.
+        scores: Scores representing the model's predictions.
+        RETURNS (Tuple[float, float]): The loss and the gradient.
+
+        DOCS: https://spacy.io/api/span_predictor#get_loss
+        """
         ops = self.model.ops
 
         # NOTE This is doing fake batching, and should always get a list of one example
@@ -258,6 +293,15 @@ class SpanPredictor(TrainablePipe):
         *,
         nlp: Optional[Language] = None,
     ) -> None:
+        """Initialize the pipe for training, using a representative set
+        of data examples.
+
+        get_examples (Callable[[], Iterable[Example]]): Function that
+            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/span_predictor#initialize
+        """
         validate_get_examples(get_examples, "SpanPredictor.initialize")
 
         X = []